Brain characteristics of memory decline and stability in aging Sara Pudas

Brain characteristics of memory
decline and stability in aging
Contributions from longitudinal observations
Sara Pudas
©Sara Pudas, Stockholm University 2013
ISBN 978-91-7447-734-4
Printed in Sweden by US-AB, Stockholm 2013
Distributor: Department of Psychology, Stockholm University
To my grandmother, Viola
Abstract
Aging is typically associated with declining mental abilities, most prominent
for some forms of memory. There are, however, large inter-individual differences within the older population. Some people experience rapid decline
whereas others seem almost spared from any adverse effects of aging. This
thesis examined the neural underpinnings of such individual differences by
using longitudinal observations of episodic memory change across 15-20
years, combined with structural and functional magnetic resonance imaging
of the brain. Study I found significant correlations between volume and activity of the hippocampus (HC), and memory change over a 6-year period.
That is, individuals with decline in HC function also had declining memory.
In contrast, Study II showed that successfully aged individuals, who maintained high memory scores over 15-20 years, had preserved HC function
compared to age-matched elderly with average memory change. The successful agers had HC activity levels comparable to those of young individuals, as well as higher frontal activity. Study III revealed that individual differences in memory ability and brain activity of elderly reflect both differential age-related changes, and individual differences in memory ability that
are present already in midlife, when age effects are minimal. Specifically,
memory scores obtained 15-20 years earlier reliably predicted brain activity
in memory-relevant regions such as the frontal cortex and HC. This observation challenges results from previous cross-sectional aging studies that did
not consider individual differences in cognitive ability from youth. Collectively the three studies implicate HC and frontal cortex function behind heterogeneity in cognitive aging, both substantiating and qualifying previous
results from cross-sectional studies. More generally, the findings highlight
the importance of longitudinal estimates of cognitive change for fully understanding the mechanisms of neurocognitive aging.
Keywords: aging, episodic memory, individual differences, longitudinal
assessment, magnetic resonance imaging, hippocampus, frontal cortex
List of studies
This doctoral thesis is based on the following studies:
I.
Persson, J., Pudas, S., Lind, J., Kauppi, K., Nilsson, L-G., &
Nyberg, L. (2012). Longitudinal structure-function correlates in
elderly reveal MTL dysfunction with cognitive decline. Cerebral Cortex, 22(10), 2297-2304.
II.
Pudas, S., Persson, J., Josefsson, M., de Luna, X., Nilsson, L-G.,
& Nyberg, L. (2013). Brain characteristics of individuals resisting age-related cognitive decline over two decades. Journal of
Neuroscience, 33(20), 8668-8677.
III.
Pudas, S., Persson, J., Nilsson, L-G., & Nyberg, L. (2013). Midlife memory ability accounts for brain activity differences in
healthy aging. Manuscript submitted for publication.
Contents
Introduction ................................................................................................ 13
The study of human memory ................................................................. 15
Overview of memory systems...............................................................................15
Magnetic resonance-based neuroimaging...........................................................17
Episodic memory in the brain ...............................................................................19
Medial temporal lobe contributions to memory ............................................20
Frontal cortex contributions to episodic memory .........................................21
Frontal – medial temporal interactions in memory processing .................24
Other memory-relevant brain regions ...........................................................25
Aging and memory .................................................................................... 26
Individual differences and their determinants ...................................................29
Brain aging .................................................................................................. 33
Structural brain changes in aging .......................................................................33
Structure-cognition relations ...........................................................................35
Functional brain changes in aging ........................................................................37
Frontal cortex .....................................................................................................38
Medial temporal lobe .........................................................................................41
Synthesis and summary of MTL and PFC function in aging........................43
Aims of the thesis ...................................................................................... 45
Methods ....................................................................................................... 46
The Betula study......................................................................................................46
Study samples and selection procedures ............................................................47
The ImAGen cohort............................................................................................47
Study I participants ...........................................................................................48
Study II participants..........................................................................................49
Study III participants ........................................................................................51
Assessing selection effects ....................................................................................51
Longitudinal memory measure .............................................................................53
Statistical classification for Study II ....................................................................55
Scanner tasks...........................................................................................................56
Study I: Incidental encoding of words ...........................................................56
Study II and Study III: Face-name paired associates ................................56
Brain imaging ...........................................................................................................57
Preprocessing and analysis of imaging data ......................................................58
Functional data ...................................................................................................58
Structural data....................................................................................................60
Diffusion Tensor Imaging .................................................................................61
Overview of empirical studies ................................................................. 63
Study I.......................................................................................................................63
Study II .....................................................................................................................65
Study III....................................................................................................................67
Discussion ................................................................................................... 70
The hippocampus and the medial temporal lobe ...............................................70
Structural findings .............................................................................................73
Incidental findings .............................................................................................74
Frontal cortex contributions to memory in aging ..............................................76
Left inferior frontal cortex ................................................................................77
Right inferior frontal cortex ..............................................................................80
Left superior frontal cortex...............................................................................82
Synthesis and summary of frontal cortex findings ......................................82
Contributions of longitudinal data ........................................................................84
Limitations and future directions ..........................................................................85
Concluding remarks ................................................................................................87
Acknowledgements ................................................................................... 89
References .................................................................................................. 90
Abbreviations
APOE
apolipoprotein E
BOLD
blood-oxygen-level-dependent
BPM
biological parametric mapping
DTI
diffusion tensor imaging
fMRI
functional magnetic resonance imaging
HC
hippocampus
LIFC
left inferior frontal cortex
MCI
mild cognitive impairment
MNI
Montreal Neurological Institute
MRI
magnetic resonance imaging
MTL
medial temporal lobe
PFC
prefrontal cortex
PHG
parahippocampal gyrus
ROI
region of interest
SPM
statistical parametric mapping
Introduction
Aging will affect us all. The common notion is that it entails a decline in
both physical and mental abilities. However, this does not need to be the
case. The degree of decline varies substantially in the older population, and
some individuals seem to be almost spared from any negative effects of aging. This thesis will deal with the neural underpinnings of these large individual differences in mental abilities in aging. The specific focus will be on
our memory ability, since memory failure is one of the most common complaints in the older population, and its effects on daily activities of elderly
individuals can be substantial.
Today there is already a wealth of knowledge of how memory functions
change in aging, and also about the neural underpinnings of such changes.
However, as will be become clear in the following sections of this thesis,
controversies still persist. What is the reason for this? One reason could be
that most of what is known about the effects of aging on cognition and the
brain is derived from studying age-differences between young and elderly
individuals, neglecting the variability within the older population. More recently this variability has become acknowledged and studied, but most investigations have still only studied individuals at one point in time, with socalled cross-sectional designs. These designs only provide a snapshot of the
age-related processes affecting individuals, and are at risks of confusing
variability in memory ability from youth with age-related changes. That is,
one can fail to detect age-related memory decline in an initially highperforming individual who still performs above average in older years. Or
mistakenly infer memory decline in an elderly individual who has had a low
memory ability from youth. Therefore, in this thesis, I have used longitudinal data on memory performance change measured in the same group of
individuals during 15-20 years. This approach offers improved sensitivity
and specificity when addressing the neural underpinnings of age effects on
memory ability.
Throughout the empirical studies in this thesis I have used magnetic resonance imaging (MRI) to study the brain characteristics of elderly individuals.
To the larger part I have focused on functional MRI (fMRI) to study agerelated changes in brain function, while the research participants are performing memory tasks. fMRI assesses brain function by measuring changes
13
in blood flow in response to task requirements. I have also looked at agerelated change in brain structure, for instance shrinkage of brain structures
that are relevant for memory. MRI as a brain imaging method has several
advantages. MRI scanners are commonly available at most large hospitals,
and do not expose research participants to radiation, hence making them
suitable in longitudinal studies of brain function.
The scope of this thesis is restricted to age-related changes within the normal
range. That is, aging in the absence of pathological conditions, such as Alzheimer’s disease, that compromise memory and other mental abilities. Getting a better understanding of the processes that dictate normal aging will
make it easier to differentiate and diagnose pathological conditions early in
their progression. Also, by better understanding normal aging processes one
can devise interventions to attempt to mitigate their negative effects on
memory function in elderly individuals. On the other hand, learning what
characterizes the brains of those who are successfully aged could guide the
search for ways to preserve such brain structures and functions in a larger
proportion of the aging population. Ultimately, I hope that the work presented in this thesis will take us one step (no matter how small) towards alleviating the societal burden of an aging population, and increasing the quality of
life for older individuals.
14
The study of human memory
It is generally agreed that human memory is not a unitary concept, but comprises several interrelated subsystems or functions. This thesis will deal with
age-related decline and stability of a specific subsystem referred to as episodic memory. Episodic memory is a consciously accessible long-term
memory system that involves personally experienced events, tied to a specific place and time (Tulving, 1972). Although the definition might seem cumbersome at first glance, episodic memory encompasses much of what we
think of as everyday memories. This can include things like the name of
your first grade teacher, the birthday presents you received for your last
birthday, and items from a word list you were given as a part of a psychology experiment 10 minutes ago. In the following sections I will first provide
an overview of the memory systems humans are thought to possess, in order
to illustrate what distinguishes episodic memory from the other types of
memory. Then, a brief overview of neuroimaging methods will be provided,
before turning to the subject of how episodic memory is represented in the
brain and affected by aging.
Overview of memory systems
Memory can be defined in many different ways. A straightforward definition
is that it involves the retention of information that is no longer available to
the senses. More poetically, some forms of memory have also been referred
to as “mental time travel” (Tulving, 2002). A long line of memory research
has shown that human memory is not a unitary concept (Gazzaniga, Ivry, &
Mangun, 2009; Squire, 2004). Distinctions can be made on several grounds.
First, and perhaps most fundamentally, there is usually a distinction based on
the temporal interval at which information is retained. Short-term memory is
usually thought to last over seconds or minutes, unless conscious effort is
made to rehearse the retained information. This type of memory has been
shown to be distinct from long-term memory since some patients with brain
injuries seem to have selective deficits in short term retention (Warrington &
Shallice, 1969). Nowadays the concept of short-term memory has been extended, and is usually referred to as working memory (Baddeley & Hitch,
1974). This is to allow for the fact that information held in short-term
memory can also be manipulated, or “worked” on.
15
Long-term memory is thought to operate on the time span of days to years,
and decades, although tasks employed in memory research can have a much
shorter time scale than that. Long-term memory is considered to be distinct
from short-term memory, but there is also interaction between these two
memory systems. For instance, one can intentionally rehearse information in
working memory to attempt to encode it into long-term memory. Commonly, long-term memory is subdivided based on different the types of
knowledge that can be stored. Firstly, there is usually a separation between
explicit and implicit long-term memories, also referred to as declarative and
non-declarative (Squire, 1992a). This distinction is based on whether one has
conscious access to, and can account for the stored knowledge. Implicit
memory covers a wide variety of memory abilities that include, for instance,
procedural memory and priming. The former involves various types of skill
learning, such as learning to ride a bike. Priming is a type of implicit
memory, which is observed when a response to a stimulus is facilitated by
prior experience, even in the absence of conscious recollection of that experience. Various types of implicit memories can be intact in otherwise amnesic patients (for reviews, see Squire, 1992, 2004).
Explicit long-term memory is conventionally thought to comprise two different types of knowledge, episodic and semantic (Tulving, 1972). Semantic
memory refers to decontextualized general world knowledge and facts, for
instance the meanings of words, the capital of France, or what a fork is used
for. Episodic memory on the other hand, involves personally experienced
events that can be tied to a spatial and temporal context. Episodic and semantic memory can be viewed as parallel and partially overlapping information processing systems (Tulving, 1972). These two types of memory
have been distinguished from each other based on reports of brain injured
patients with episodic, but not semantic memory deficits (Rosenbaum et al.,
2005; Vargha-Khadem et al., 1997), although the reverse pattern has not
been clearly established. Also, neuroimaging studies have shown partially
different brain regions engaged when participants are performing episodic
and semantic memory tasks (Cabeza & Nyberg, 2000). And, importantly for
this thesis, these two memory systems also display different trajectories of
age-related changes (e.g., Rönnlund, Nyberg, Bäckman, & Nilsson, 2005),
with episodic memory being more sensitive to the effects of age. Since episodic memory impairment is also one of the earliest signs of Alzheimer’s
disease (Bäckman, Small, & Fratiglioni, 2001) it is highly relevant to study
in relation to aging.
In research on declarative long-term memory there is typically a distinction
between three different stages of memory processing (Gazzaniga et al.,
2009). Memory formation begins with encoding, during which information
16
is acquired and consolidated into brain networks, in which it is stored. Finally, the last stage is retrieval, when the stored information is accessed and
used. In practice there is most likely interaction and partial overlap between
these phases (Fletcher & Henson, 2001). For instance, encoding of new information is likely to involve reactivation of previous knowledge related to
the information to be encoded. Also, retrieval of previously acquired information may act as a form of recoding of the stored memory trace, strengthening, weakening, or otherwise changing it. And, in principle, memory failure in aging and other situations could result from disturbance of any of
these phases or processes.
Although the characterization of different memory systems presented here is
commonly used, it should be viewed as a simplification since our understanding of the organization of human memory is still evolving. Some object
to the notion of there being neatly separable memory “systems” (see e.g.,
Roediger, Buckner, & McDermott, 1999), and it is commonly acknowledged
that the boundaries between the different types of memory are vague. Most
researchers would however agree that memory is not a unitary concept. The
categorization given above does capture some broad distinctions in the data
and is a useful way to frame discussions about human memory function.
Although I refer to different memory systems in this thesis, the interpretation
of system should not be taken too literally. I could equally well refer to different memory functions, processes or mechanisms, which are accomplished
in the brain by partially overlapping and interacting neural mechanisms.
Magnetic resonance-based neuroimaging
Before turning to the topic of how memory is implemented in brain, I will
review the basics of structural and functional MRI. Although a large share of
the knowledge on the neural bases of memory derives from lesion and animal studies, functional neuroimaging techniques have provided a unique
opportunity to study memory processes in the healthy human brain. But in a
strict sense MRI, like other neuroimaging methods, only allows observations
of neural events that co-occur, or accompany, for instance, changes in
memory ability. One can not necessarily conclude that the observed neural
events cause changes in memory. Luckily, there is often converging evidence from lesion, pharmacological, and other types of studies to support
conclusions from neuroimaging.
The principle behind structural MRI is to utilize magnetic properties of hydrogen atoms in organic tissue to create images. For a detailed description of
MRI principles, see Huettel, Song, and McCarthy (2009). In brief, the constant motion of protons in atomic nuclei (spinning around their principal
17
axis), generates small magnetic fields. An MRI scanner has a large magnet
that generates a static magnetic field, which orients the protons in organic
tissue so that their principle axis is parallel to that magnetic field. Thereafter,
radio frequency (RF) pulses are passed through the tissue, which causes the
protons to absorb energy and thereby change the direction of their orientation in a predictable manner. When the RF pulse is turned off, the protons
return to the orientation determined by the external magnetic field of the
scanner, at the same time emitting the energy absorbed from the RF pule.
This energy is picked up by detectors in the head coil of the scanner. The
information from the scanner is then sent to a computer, where complex
algorithms construct an image out of it. The fact that different classes of
brain tissue, such as gray and white matter, have different density of protons
makes the contrast between them appear on structural MR images.
Functional MRI operates on similar principles as structural MRI. But instead
of contrasting between tissue classes, it distinguishes between oxygenated
and deoxygenated blood, due to their different magnetic properties. When
neural tissue becomes active, blood flow is increased to that brain area in
order to accommodate metabolic demands. This results in an increase in
oxygenated blood, which exceeds the amount of oxygen that is consumed by
the active neural tissue. This excess forms the basis for the blood-oxygenlevel-dependent (BOLD) signal (Ogawa, Lee, Kay, & Tank, 1990). Although the BOLD signal does not measure neural activity directly, it is generally considered to be a reasonable proxy (Logothetis, Pauls, Augath,
Trinath, & Oeltermann, 2001; Mukamel et al., 2005), shown to reflect predominantly the input to and intracortical processing of a given brain area
(Logothetis et al., 2001). Therefore, throughout this thesis, the BOLD signal
will be considered equivalent to neural activation.
By contrasting BOLD-images of the brain while the participant is performing a cognitive task, with images acquired during some (typically) low-level
control condition, one can identify brain areas that are more engaged by the
task. During an fMRI experiment there are typically repeated acquisitions of
task and control conditions in an alternating manner. Experiments are commonly set up in either blocked or event-related designs, the former assessing
neural activation during extended blocks of time, while the latter measures
activation in response to short events, such as a single item in a memory
experiment. Event-related designs allow for more analytical flexibility, for
instance, sorting items of a memory experiment based on whether they were
subsequently remembered or forgotten (e.g., Wagner et al., 1998).
MRI can also be used to study the microstructural properties of white matter
in the brain. The integrity of white matter can be compromised by disease
and aging, which has consequences for memory and other cognitive func18
tions (Gunning-Dixon & Raz, 2000). Investigation of white matter integrity
can be performed by quantifying the diffusion of water molecules by application of controlled magnetic gradients in the RF pulse sequence, a technique known as diffusion tensor imaging (DTI). This technique takes advantage of the fact that thermal energy causes water molecules to move randomly (i.e., diffuse) at all temperatures above absolute zero. In brain white
matter this movement is constrained by white matter fibers so that diffusion
is anisotropic, that is, not equal in all directions. Since diffusion attenuates
the MR-signal, the magnitude and preferred direction of diffusion can be
quantified for each voxel (i.e., three-dimensional image element) in the
brain. For each scan, however, only diffusion in the direction of the specific
magnetic gradient can be detected. Therefore, repeated scans with systematically applied gradients in different directions are required to collect as many
directions of diffusion as possible (usually at least six). More anisotropic
diffusion at a given location in the brain generally reflects more intact white
matter at that location. There are, however, acknowledged problems with
some of the most common DTI-derived measures of anisotropy. Specifically,
they can produce unreliable results in brain regions with crossing white matter fibers, which are commonly occurring across the brain (Jeurissen,
Leemans, Tournier, Jones, & Sijbers, 2012). In such cases, increased anisotropy can result from age- or illness-related degeneration of crossing fibers
(Douaud et al., 2011). Hence, caution is needed when interpreting anisotropy
measures.
Episodic memory in the brain
The focus of this section will be on how our episodic memory ability is
thought to be implemented in the brain. Although a lot is known about the
cellular and molecular bases of memory, this overview will be held on the
more macroscopic level, covering phenomena that can be observed with
functional neuroimaging techniques such as fMRI.
Memory encoding, storage and retrieval have been shown to engage largescale brain networks (Cabeza & Nyberg, 2000; Nyberg et al., 2000), but
there are also some key regions in these networks that have been shown to
be specifically important for declarative memory processes. As will be described, these include the hippocampus (HC) and the surrounding medial
temporal lobe (MTL), including the entorhinal, perirhinal and parahippocampal cortices; as well as the frontal cortex. Importantly, these structures
have also consistently been shown to be among the ones that are the most
sensitive to the effects of age (e.g., Grady, 2008; Raz et al., 2005). Therefore
the MTL and the frontal cortex were chosen to be the main focus of this
19
thesis, and the discussion in the remaining sections will be restricted to these
brain regions.
Medial temporal lobe contributions to memory
The importance of the MTL for declarative memories has been acknowledged for a long time. The first convincing demonstrations came from braininjured patients who had lost portions of the medial temporal lobe, perhaps
most famously, the case of patient H.M. (Milner, Corkin, & Teuber, 1968;
Scoville & Milner, 1957). These patients generally lose their ability to form
new declarative long-term memories, while most aspects of short-term
memory and skill learning are usually spared. Some sparing of an ability to
form new semantic-like memories has been reported (Rosenbaum et al.,
2005), but this could reflect a perceptual type of learning occurring directly
in the neocortex (Squire, Stark, & Clark, 2004). These MTL patients usually
also display a retrograde amnesia extending backwards months or years prior
to the injury, with newer memories being more vulnerable than older ones.
These observations, as well as a vast literature on animal and functional neuroimaging studies, suggest that the MTL structures are crucial for initial
encoding, as well as consolidation of new declarative memories (e.g., Squire,
1992b). The consolidation aspect is supported by the retrograde amnesia in
these patients, which is thought to result from a consolidation failure. The
fact that older memories are not as affected by MTL lesions reflects, according to the same reasoning, that they have been successfully consolidated into
neocortical networks that are independent of the MTL for their reactivation.
The long-term storage of declarative memories is thus believed to be in the
neocortex, within the modality-specific areas responsible for processing the
information when it first enters the brain (Wheeler, Petersen, & Buckner,
2000).
There is also an alternative account, that states that some aspects of episodic,
but not semantic, memory continues to be dependent on the MTL even years
and decades after the memories were first acquired (reviewed by Winocur &
Moscovitch, 2011). According to this account some episodic memories are
transformed over time, so that they lose their contextual details and become
represented in an abstract, gist-like form, i.e., as semantic-like memories,
independent of the MTL. Other episodic memories retain their rich, contextual details, and are dependent on the MTL for their retrieval no matter how
old they are (Rekkas & Constable, 2005).
The MTL thus seems crucial for the acquisition of new episodic memories,
as well as for retrieval of recently, and perhaps also remotely acquired episodic memories. In contrast, the MTL does not appear to be crucial for retrieval of sematic memories. This view was formed predominantly on the
20
basis of evidence from lesion and animal studies, but functional neuroimaging studies have brought converging evidence, showing MTL activation
during episodic memory encoding and retrieval tasks (Squire et al., 1992;
Stern et al., 1996). Neuroimaging studies also provide unique opportunities
for studying aspects of episodic memory in healthy humans. As previously
mentioned, event-related designs make it possible to separate items during a
memory encoding task into those that were subsequently remembered, and
those that were later forgotten. These type of studies have confirmed the
importance of the MTL by showing more activation in the HC (Fernández et
al., 1998) and parahippocampal gyrus (Wagner et al., 1998) during encoding
of subsequently remembered compared to forgotten words.
There are indications that the different structures in the MTL are responsible
for somewhat different types of processing (Eichenbaum, 2004), although
they all contribute in some way to declarative memory (Squire et al., 2004).
The HC proper is for instance hypothesized to act by linking together information stored in different locations of the brain (Eichenbaum, 2004). Regarding the function of the extrahippocampal structures, neuroimaging studies have helped to clarify the distinction between two different processes
postulated to underlie recognition memory; familiarity and recollection
(Yonelinas, 2002). The former is thought to occur when one encounters a
previously seen stimulus, and it is recognized as familiar, but no contextual
details about its encounter can be retrieved. Recollection on the other hand
involves the full episodic memory traces, including where and when the
stimulus was previously seen. The general pattern seems to be that extrahippocampal structures in the MTL, specifically the perirhinal cortex, supports familiarity-based processing, while the hippocampus proper supports
recollection-based experiences (Eichenbaum, Yonelinas, & Ranganath,
2007), although contradicting evidence has also been found (e.g., see
summary by Squire et al., 2004). In animal studies it is difficult to separate
these processes, and lesion studies on humans often include damage to more
than one MTL region. Therefore neuroimaging evidence has been valuable,
making it possible to dissociate aspects of familiarity and recollection. For
instance, a study by Ranganath et al. (2004) showed that encoding-related
brain activity in the rhinal cortex predicted familiarity-based recognition,
while activity in the HC proper and parahippocampal cortex contributed
selectively to recollection.
Frontal cortex contributions to episodic memory
While restricted frontal lesions do not produce as severe amnesia as MTL
lesions, frontal lobe patients can display significant impairment on declarative memory tasks (Wheeler, Stuss, & Tulving, 1995). Frontal lobe patients
have been described as having particular problems with aspects of memory
21
that pertain to, for instance, contextual information and temporal order
(Buckner & Wheeler, 2001; Dickerson & Eichenbaum, 2010), and their deficits are often worse when assessed with tests requiring free recall than tests
based on recognition (M. A. Wheeler et al., 1995). This latter finding is
thought to reflect the differential processing demands of the two types of
tasks, with recognition tests being relatively easier since they entail representation of the previously encoded items during testing. A different line
of evidence for frontal cortex involvement in episodic memory comes from
functional neuroimaging studies, that consistently show frontal activity both
during episodic memory encoding and retrieval (Cabeza & Nyberg, 2000).
The exact role played by the frontal cortex in episodic memory processing is
still unclear, but it is commonly believed that it involves high-level cognitive
control processes that support and optimize encoding and retrieval in concert
with the structures in the MTL (Fletcher & Henson, 2001), as well as the
execution of more consciously driven memory strategies. It is important to
keep in mind that the frontal cortex is strongly implicated in a number of
cognitive control, attentional, and working memory functions (Cabeza &
Nyberg, 2000; Corbetta & Shulman, 2002; Duncan & Owen, 2000; Fletcher
& Henson, 2001), even in the absence of explicit long-term memory demands or task instructions. It is often suggested that the same frontallymediated cognitive processes that are engaged in, for example, working
memory tasks are recruited during long-term memory processing as well
(Fletcher & Henson, 2001). For instance, during episodic encoding, information likely needs to be maintained and perhaps also rehearsed in working
memory.
In addition to the functions discussed in the previous paragraph, many other,
more memory-specific functions have been ascribed to PFC activation during episodic memory tasks. For instance, during encoding it has been suggested to represent generation, maintenance, or selection of semantic associations to support encoding (Fletcher & Henson, 2001), which is commonly
hypothesized to occur in the left ventrolateral PFC. PFC involvement during
encoding could also be related to attentional processing, such as selecting
which features to attend to and which to ignore in the to-be-remembered
material (Blumenfeld & Ranganath, 2007). For instance, the right lateral
PFC is thought to be particularly important for inhibitory functions across
different cognitive tasks (Aron, Robbins, & Poldrack, 2004). Another suggestion is that organization and manipulation of the information to be remembered is a crucial aspect of the frontal involvement during memory encoding, which is thought to be handled by the dorsolateral regions of the
PFC (Blumenfeld & Ranganath, 2007). During episodic retrieval, similar
frontal cognitive control processes are thought to be responsible for search,
verification and monitoring of stored representations in memory (Fletcher &
Henson, 2001). In these processes, the ventrolateral PFC regions are be22
lieved to be more involved in search and temporary maintenance of retrieved
information, while dorsolateral regions are thought to perform verification
and monitoring.
Functional neuroimaging studies have suggested that frontal engagement
during episodic memory tasks is lateralized to some extent, so that encoding
tends to involve more left- than right-sided PFC activation, while retrieval
taxes the right PFC more than the left. This pattern of results has been referred to as the hemispheric encoding-retrieval asymmetry (HERA; Habib,
Nyberg, & Tulving, 2003; Tulving, Kapur, Craik, Moscovitch, & Houle,
1994). Exceptions to this generalization have been found, and HERA tends
to be most clearly observed for verbal materials, and more so during encoding than during retrieval (Fletcher & Henson, 2001). Other accounts have
focused more on lateralization in the PFC according to the type of material
involved in the memory task, with verbal material being more commonly
linked to left-lateralized activation, whereas non-verbal material such as
faces or visuo-spatial patterns are associated with right PFC activation
(Buckner, Kelley, & Petersen, 1999).
The strength of frontal recruitment during memory processing has also been
shown to be of relevance in functional neuroimaging studies. It has repeatedly been demonstrated that frontal regions are more strongly engaged during
encoding of stimuli that are subsequently remembered, compared to stimuli
that are later forgotten (e.g., Brewer, 1998; Wagner et al., 1998; for a review
see Kim, 2011), linking PFC involvement specifically to successful encoding. More frontal involvement during episodic memory tasks thus seems to
be beneficial. However, during other cognitive tasks such as working
memory, it is sometimes observed that individuals who perform the task
more proficiently have lower frontal activation (Neubauer & Fink, 2009).
Given the substantial overlap between frontal brain regions, and hypothesized cognitive processes, mediating working and episodic memory (Cabeza,
Dolcos, Graham, & Nyberg, 2002), this finding is somewhat puzzling. However, frontal involvement is sometimes thought to reflect higher effort,
which does not need to be synonymous to good performance. In this sense,
some believe that individuals with lower ability have lower neural efficiency
(Neubauer & Fink, 2009), so that they have to employ more frontal resources at a lower level of difficulty. Relatedly, the dissociation between
working and long-term memory tasks in terms of magnitude of frontal involvement could reflect the differential involvement of general cognitive
control process networks and memory-specific brain networks. Lower performing individuals might rely more heavily on prefrontal cognitive control
processes, when their memory-specific networks are inadequate (cf. Salami,
Eriksson, & Nyberg, 2012). Still, the conflict between “less is more” and
“more is more” of brain activity is a common issue in functional neuroimag23
ing studies, and is not restricted to the frontal cortex, which will be returned
to later on.
Frontal – medial temporal interactions in memory processing
Thus far, MTL and frontal contributions to memory have been considered
separately. However, it is generally agreed that these brain structures operate
in concert during the formation and retrieval of declarative long-term memories. This notion is based on both the extensive anatomical connectivity between these regions, as well as their common co-activation during memory
processing in functional imaging studies (Simons & Spiers, 2003). Although
functional interaction, or connectivity, will not be a major topic of this thesis, it deserves mentioning. Knowledge of MTL-PFC interaction is still
evolving, but one preliminary account was proposed by Simons and Spiers
(2003). It was inspired by previous work, including lesion studies, functional
neuroimaging and computer modeling. The model focuses on prefrontal topdown control during encoding, based on the current goals and demands. The
processes are thought to guide, modify, and elaborate on representations of
the to-be-remembered information that has been transmitted to the MTL
from the cortical areas responsible for processing and representing the information. PFC is also proposed to be responsible for ensuring that the representations that underlie memory traces are sufficiently distinct, i.e., separable from one another. Different PFC regions are thought to be involved
depending on the nature of the task, for instance the type of material or processing required, with dorsolateral PFC believed to be involved in organization and manipulation of representations.
Further, according to the framework of Simons and Spiers (2003), the interaction between PFC and MTL is even more important for retrieval, during
which the ventrolateral PFC is suggested to be responsible for generating
and specifying retrieval cues. The cue is then transmitted to the MTL where
a matching process seeks concordance between the cue and stored representations. The results of this matching process are believed to be maintained in
the ventrolateral PFC, while the dorsolateral PFC is responsible for verifying
or rejecting the retrieved representations. To the extent that the retrieval operations involve internally generated or self-relevant information, the anterior and medial aspects of PFC are thought to be involved, respectively.
More recent developments in the study of MTL-PFC interactions include the
application of functional connectivity techniques that investigate the functional coupling between brain regions during task performance or rest. For
instance, it has been shown that the strength of functional coupling in a brain
network encompassing the bilateral HC and left inferior frontal cortex during
memory encoding, can predict intra-individual differences in recall ability
24
(Dickerson, Miller, & Greve, 2007). That is, the functional coupling between
regions in this network was stronger during encoding of test items that were
subsequently remembered, compared to those that were later forgotten.
Other memory-relevant brain regions
Although the scope of this thesis is restricted to MTL and PFC due to their
well-established connections to cognitive aging, it should be stressed that
these are not the only regions relevant for memory processing. As already
mentioned, memory formation and retrieval has been shown to engage largescale brain networks (Cabeza & Nyberg, 2000; Nyberg et al., 2000), encompassing regions in all major lobes of the brain, as well as the cerebellum. In
addition to the MTL and PFC, there are several other prominent regions in
these networks. These include, for instance, midline diencephalic structures,
specifically the thalamus and mammillary bodies, which are tightly coupled
with the MTL memory circuits, and known to cause amnesia if injured
(Aggleton & Brown, 1999). The parietal cortex also appears to contribute
significantly to memory processing, particularly during retrieval, and its
function has been proposed to relate to, for instance, attentional aspects of
memory (Wagner, Shannon, Kahn, & Buckner, 2005). The lateral aspect of
the temporal lobe is also involved in memory processing, perhaps by virtue
of its role in stimulus perception, that is, the processing of the “content” of
memories (Dickerson & Eichenbaum, 2010). Several additional brain structures could be added to this list, but a full review of the field is beyond the
scope of this thesis.
25
Aging and memory
Before turning to the topic of brain aging and how it may affect memory
functions, I will address some important issues regarding the nature of agerelated memory decline, and some methodological disputes associated with
its study. I will briefly cover issues such as the relation between memory
decline and decline in other cognitive functions, the age of onset of agerelated cognitive decline, as well as individual differences in age-related
cognitive change. This brief review will mainly be concerned with findings
regarding normal aging, in the absence of dementia.
Firstly, although this thesis deals predominantly with episodic memory
change in aging, memory is just one of several interdependent mental abilities that tend to decline with age. It can be difficult to separate effects that
are memory-specific from impaired performance due to other failing processes. For instance, in addition to mnemonic processing, performing an
episodic memory task also requires sensory processing, holding information
online in working memory, as well as various cognitive control functions.
These are all functions with documented age-related decline (Braver &
West, 2008; Lindenberger & Baltes, 1994). One major topic in the study of
cognitive aging has therefore been to investigate whether certain failing abilities, such as general processing speed (Salthouse, 1996) or executive functions (Salthouse, Atkinson, & Berish, 2003) mediate decline in other cognitive abilities, such as memory. This can be demonstrated by showing that the
relationship between chronological age and a specific mental ability is diminished when covarying for the hypothesized mediating variable. Such
mediation analyses based on cross-sectional data have, however, been criticized on logical grounds (e.g., Lindenberger, von Oertzen, Ghisletta, &
Hertzog, 2011). While there continues to be some support for the processing
speed account, the estimated mediating effect has been shown to be weaker
in longitudinal than in cross-sectional studies (Lemke & Zimprich, 2005;
Sternäng, Wahlin, & Nilsson, 2008), and it has also been shown that processing speed differences in elderly might be more related to childhood intelligence than age-related changes (Deary, Johnson, & Starr, 2010). As for
executive functions, there is some support for mediating effects, but interpretations are compromised due to problems with construct validity of the concept of executive functions itself (Salthouse et al., 2003). Nevertheless, the
well-documented relationships between different cognitive variables are
26
important to keep in mind also when seeking neurocognitive explanations to
age-related decline in specific cognitive abilities such as memory.
Another, related, issue in cognitive aging research has been the extent of
covariation or independence of decline across different cognitive domains. Is
it the case that when one ability begins to decline, the rest of them follow?
On one hand it has been stated that “there is no uniform pattern of agerelated changes in adulthood across all intellectual abilities” (Schaie, 1994,
p. 306), on the other hand it has also been shown that up to two-thirds of the
variance in age-related change across cognitive domains is shared (Ghisletta,
Rabbitt, Lunn, & Lindenberger, 2012). One reason for this apparent discrepancy is whether one considers within-person or across-person trends. The
data implies that within individuals there likely is both a general, unitary
factor accounting for age-related cognitive decline across multiple domains,
and several (somewhat smaller) factors allowing for domain-specific declines (Tucker-Drob, 2011; Wilson et al., 2002). Across individuals, some
domains of cognition have typically been shown to be more resilient to the
effects of age, with semantic knowledge, such as vocabulary, being one of
the most prominent examples of relative sparing (Salthouse, 2004; Schaie,
1994).
Within the memory domain, there has been much focus on the differential
aging trajectories of different memory systems. Some types of implicit
memory measures, such as priming, have traditionally been thought to be
spared, or at least significantly less affected by age than other memory
measures, although the results are quite variable across studies (Fleischman
& Gabrieli, 1998; La Voie & Light, 1994; Nilsson, 2003). Working memory
ability has been shown to decline with age, although measures of short-term
memory, which do not require manipulation of information, seem somewhat
less affected (Nilsson, 2003; Park et al., 2002; Verhaeghen & Salthouse,
1997). As for the declarative memory systems, as already alluded to, semantic memory tends to be maintained longer in life than episodic memory
(Rönnlund et al., 2005; Salthouse, 2004; Schaie, 1994). The relative preservation of semantic knowledge was acknowledged already in relatively early
theories of human intellectual functioning (Horn & Cattell, 1967). Episodic
memory, on the other, hand has been one of the most studied memory
measures in relation to aging, and is held to be the one with the most consistent age-related decline (Nilsson, 2003). But there is also differentiation
within the episodic memory domain. It is well known that different encoding
and retrieval conditions can affect the magnitude of age-differences. For
instance, tests of recall tend to produce more pronounced age-differences
than tests of recognition (Craik & McDowd, 1987; Nyberg et al., 2003).
27
Figure 1. Difference between cross-sectional and longitudinal estimates of average
memory change. Estimates are based on a sample of 829 participants followed for 5
years. Reproduced with permission from the American Psychological Association,
from: Rönnlund et al., (2005). Stability, growth, and decline in adult life span development of declarative memory: Cross-sectional and longitudinal data from a population-based study. Psychology and Aging, 20(1), 3-18.
The average age of onset of episodic memory decline has also been an issue
of disagreement, which to a large extent reflects methodological considerations. Early cross-sectional studies showed steep linear declines in memory
and other cognitive functions, beginning as early as the twenties, whereas the
emergence of large-scale longitudinal studies has shifted this age of onset to
occur around age 60-65 (e.g., Rönnlund et al., 2005; Schaie, 1994). The discrepancy between cross-sectional and longitudinal estimates (see Figure 1)
has been a major issue in aging research. One reason behind the divergent
findings is that cross-sectional data may be contaminated by cohort effects,
i.e., that older and younger age-cohorts differ with respect to a number of
factors that are unrelated to aging per se, but associated with cognitive function, e.g., nutrition, sib-size and perhaps most importantly, education
(Rönnlund & Nilsson, 2008). These, and other factors, have been thought to
elevate cognitive levels in younger cohorts, making age effects in cognitive
ability appear larger than they actually are. Longitudinal estimates have generally been considered to be more accurate, since they assess true, withinperson change. However, longitudinal studies also face challenges, including
selective attrition and practice effects. Attrition bias occurs if individuals
who are initially lower-performing, or those who experience more than average cognitive decline, drop out of the study to a larger extent than average
and high-performing individuals. This has often been shown to be the case
(Josefsson, de Luna, Pudas, Nilsson, & Nyberg, 2012; Rönnlund et al.,
2005), and can lead to an underestimation of age-related cognitive changes.
Practice effects occur because individuals get better at performing cognitive
tests due to repeated exposure to them, which biases estimates of average
change in the same direction as attrition bias. Although measures have been
28
taken to control for these issues (Josefsson et al., 2012; Rönnlund & Nilsson,
2008), some claim that age-related changes begin much earlier than what is
suggested by longitudinal studies (Salthouse, 2009). Nevertheless, in this
thesis it will be assumed that, on average, age-related episodic memory
changes begin in later life. However the major focus will be on individual
differences in these changes, which will be described in the next section.
Individual differences and their determinants
While the foregoing paragraphs focused on cognitive declines in aging, it is
generally agreed that there are significant individual differences in cognitive
ability in late life, as well as in age-related trajectories of cognitive change
(Christensen et al., 1999; de Frias, Lövdén, Lindenberger, & Nilsson, 2007;
Mungas et al., 2010; Wilson et al., 2002). Individuals can differ substantially
both in age of onset and rate of change over time, as can be discerned from
Figure 2. Although aging research has traditionally focused more on agerelated declines, there are also older individuals who maintain very high
levels of cognitive performance into late life (e.g., Habib, Nyberg, &
Nilsson, 2007). Successful aging (Rowe & Kahn, 1987) has in recent years
become the topic of a growing research field. In this section I will briefly
summarize some risk and protective factors for age-related cognitive decline.
Figure 2. Illustration of individual differences in episodic memory change
across 15-20 years. Data is shown for 77 participants who completed the 5 th
test wave (T5) of the longitudinal Betula study (Nilsson et al., 2004) at the age
of 80. The memory score is a composite of five episodic memory test variables, described in the Methods section of this thesis.
29
Most of the factors covered below have been found to distinguish major
decline from normal decline, as well as successful cognitive aging from
normal decline. There have also been suggestions that successful cognitive
aging might be associated with a slightly different profile of predictors than
cognitive decline (Barnes et al., 2007; Yaffe et al., 2009), but further validation is needed for such claims. Hence, the factors addressed here should tentatively be considered equally predictive of decline or preservation of cognition in aging. That is, the absence of a risk factor should be viewed as protective.
The most commonly identified risk and protective factors comprise primarily
genetic, health and lifestyle factors, as well as educational or occupational
attainment. In the genetic domain, the best established association between a
genetic allele and cognitive function is that for the apolipoprotein E (APOE),
with elderly carriers of the ε4-allelle having lower cognitive function across
a number of domains (Wisdom, Callahan, & Hawkins, 2011). Although this
has been demonstrated also in healthy elderly, the association could be due
to the acknowledged link between Alzheimer’s disease and APOE ε4
(Corder et al., 1993). Several studies have also shown associations between
longitudinal cognitive decline rates and the ε4-allelle (Caselli et al., 2009;
Josefsson et al., 2012). Other genes, such as catechol-O-methyl transferase
(COMT) and brain-derived neurotrophic factor (BDNF) have also been
linked to cognitive function in aging (Payton, 2009), but the direction of
effects has been somewhat inconsistent, and less reliable effects have been
found in the few studies assessing actual cognitive change over time.
Health-related factors are also important for cognitive functions. Conditions
such as cardiovascular disease, hypertension, diabetes, and obesity (Barnes
et al., 2007; Yaffe et al., 2009) have all been associated with poorer cognitive outcomes. It seems safe to assume that physical health is related to brain
health and integrity in aging, which in turn translates to cognitive outcomes.
Related to this, a number of health-promoting lifestyle factors have also been
linked to better cognitive function, including physical exercise, being a nonsmoker, as well as better nutrition (Barnes et al., 2007; Josefsson et al.,
2012; Plassman & Williams, 2010; Yaffe et al., 2009). Another lifestyle
factor that has often been linked with better cognitive function in aging is
social engagement (Fratiglioni, Paillard-Borg, & Winblad, 2004; Plassman
& Williams, 2010), which includes factors such as the size of an individual’s
social network, marital status, and frequence of participation in social activities. Collectively, several genetic, health, and lifestyle factors could act to
preserve the brain’s grey and white matter integrity in aging, resulting in a
sparing of cognitive functions. This is captured in the concept of brain
maintenance (Nyberg, Lövdén, Riklund, Lindenberger, & Bäckman, 2012).
30
Another factor that has consistently been linked to better cognitive performance in elderly is educational attainment. However, although some studies
have found associations between educational attainment and preservation of
cognitive functions in aging (Habib et al., 2007; Yaffe et al., 2009), evidence
also suggests that it might be more linked to level of cognitive performance
rather than change in cognition over time (e.g., Lövdén et al., 2004; Zahodne
et al., 2011). Educational or occupational attainment has also been used as a
proxy for the hypothesized construct of cognitive reserve, which is thought
to moderate the impact of brain pathology on cognitive function (Y. Stern,
2009). According to the cognitive reserve account there are inter-individual
differences in the brain’s processing capacity that allow some individuals to
cope better with brain pathology. These differences are thought to be driven
by lifetime experiences such as physical and mental stimulation, which includes, but is not limited to, educational attainment. In essence, the reserve
concept captures some of the lifestyle factors that have previously been associated with cognitive outcomes in aging. But in contrast to the brain
maintenance hypothesis (Nyberg et al., 2012), the cognitive reserve account
instead focuses on how individuals might continue to be cognitively highfunctioning despite accumulating brain pathology. As reviewed by Stern
(2009), there is fairly ample support for the notion of cognitive reserve in the
epidemiological literature. There is also a similar notion of brain reserve
(Satz, 1993), which is a more passive account that focuses on individual
differences in brain characteristics such as neuronal count, as an explanation
to why some individuals have more resistance to brain pathology.
Both cognitive and brain reserves could be associated with (partially) heritable individual differences in general intelligence, which have also been suggested to buffer against cognitive decline. That is, age has been suggested to
be kinder to the initially more able (Thompson, 1954), so that individuals
with high intellectual abilities in youth are less vulnerable to age-related
cognitive decline. Some studies seem to support this notion (Richards,
Shipley, Fuhrer, & Wadsworth, 2004), whereas others fail to find such connections (Salthouse, 2012), and still others report different results for different study samples (Gow et al., 2012). Hence, as of now the results are inconclusive regarding the existence of a protective effect of high initial cognitive
ability level, and the mechanisms by which it would operate.
But there is also another, more methodological, reason why individual differences in cognitive ability in youth are important for research on cognitive
aging. It is well established that individual differences in cognitive ability,
including memory (Bors & MacLeod, 1996), are large also in early adulthood. In the past decade it has come to knowledge that that such individual
differences tend to be relatively stable across large portions of the human
lifespan (Deary, Whalley, Lemmon, Crawford, & Starr, 2000). Correlations
31
in the magnitude of r = 0.6-0.7 have been reported between cognitive ability
in childhood, and that in the eight decade of life (Deary, Whiteman, Starr,
Whalley, & Fox, 2004; Gow et al., 2011). These findings were obtained with
cognitive tests assessing general intelligence, which is likely to be a somewhat more stable trait than episodic memory. Still, similar stability estimates
cannot be ruled out for episodic memory measures as well. Since studies
with cross-sectional designs lack information on the individuals’ cognitive
ability prior to the onset of aging, the presence of such stability could cause
individual differences from early life to be confused for differential agerelated decline. For instance, one could erroneously conclude that a lowperforming individual has experienced aggravated cognitive decline, when in
fact he or she has always been low-performing. Thus, the need to consider
both initial level and change in cognition when studying cognitive aging
should be stressed.
In summary, the literature suggests that there are substantial individual differences in how cognitive abilities change in aging, and several genetic,
health, and lifestyle factors have been associated with better or worse cognitive outcomes. These factors could serve to protect cognitive functions either
through maintaining brain integrity or buffering brain function against pathology through a cognitive or brain reserve. On a methodological note, it
should be remembered that prior cognitive ability level could be a confound
in cross-sectional studies on individual differences in aging.
32
Brain aging
The aging brain undergoes many changes to its structure and function.
Sometimes these changes have been found to relate to decline in cognitive
functions, such as memory, but other times no observable links can be established. In this chapter, I will provide a brief overview of some important
findings regarding the aging brain. The main focus will be on brain structures associated with episodic memory function, i.e., the MTL and the
frontal cortex, which have also consistently been shown to be afflicted by
aging. Relatively more attention will be given to functional than structural
findings relating to brain aging. In the following paragraphs I will consider
structure and function separately, although there is much reason to believe
that functional age-related changes, at least in some instances, reflect underlying changes in brain structure. Both local gray and white matter reductions
have been linked to alterations in functional neuroimaging measures
(Kalpouzos, Persson, & Nyberg, 2012; Nordahl et al., 2006; Nyberg et al.,
2010). However, since the majority of studies thus far have not addressed
this possibility, it is difficult to consider them together. It should also be
mentioned that many important age-related brain changes will not be covered in this review. These include, for instance, changes in the brain’s neurotransmitter systems. A prominent example is the dopaminergic system, that
has been firmly linked to memory function in aging (Bäckman,
Lindenberger, Li, & Nyberg, 2010). Nor will this review go into detail regarding deposition of β-amyloid protein in the brain, which is strongly
linked to Alzheimer’s disease (Hardy & Selkoe, 2002), but also known to
affect cognitive function in normal aging (Rodrigue et al., 2012).
Structural brain changes in aging
Changes to brain structure in aging can occur on both macro- and microscopic levels. This review will focus on larger-scale volumetric changes that
are observable with MRI, as well as microstructural white matter changes
observable with DTI. On the global level, total brain volume has an average
annual decrease of about 0.2 - 0.5% across the adult lifespan, as assessed in a
recent review (Salthouse, 2011). Other salient features of the aging brain
include enlargement of the ventricles and the appearance of white matter
insults observable with DTI, or as so-called white matter hyperintensities on
33
structural MRI images (Raz, 2000). It is generally agreed that different brain
structures are differentially affected by age, with the frontal cortex (together
with the parietal cortex) consistently showing one of the steepest rates of
age-related change in both cross-sectional and longitudinal studies (e.g., Raz
et al., 2005; Resnick, Pham, Kraut, Zonderman, & Davatzikos, 2003).
Frontal lobe volume is estimated to decline 0.9-1.5% per year (Dennis &
Cabeza, 2008). It is important to note that longitudinal estimates of change
have generally been shown to be more sensitive to changes in brain structure, exceeding those from cross-sectional studies. This is thought to result
from the fact that cross-sectional studies contain noise from inter-individual
differences in brain volume from youth (Raz et al., 2005). Frontal volume
changes have been shown to affect both gray and white matter, with some
indications that white matter may be more sensitive (e.g., Salat, Kaye, &
Janowsky, 1999). Microscopic white matter changes observable with DTI
have also been shown to be more prominent in the frontal cortex, compared
with more posterior brain regions (Bennett, Madden, Vaidya, Howard, &
Howard, 2010; Head et al., 2004).
Volumetric reductions in the HC are also consistently demonstrated in aging,
although they are not as pronounced as for the frontal cortex, see Figure 3.
For instance, annual decrease in HC volume has been estimated to 0.79 0.84% in representative longitudinal studies (Fjell et al., 2009; Raz et al.,
2005; Scahill et al., 2003). Further, there is evidence that the relationship
between HC volume and age is non-linear, with atrophy rates increasing
with age (e.g., Du et al., 2006). There are some indications that the different
substructures within the MTL show different rates of volumetric change in
healthy aging, with the HC proper being more affected than the entorhinal
cortex (Raz et al., 2005). However, the entorhinal cortex does not seem to be
Figure 3. Longitudinal change in adjusted prefrontal and hippocampal volumes as a
function of baseline age. Reproduced with permission from Oxford University Press,
from Raz et al., (2005) Regional brain changes in aging healthy adults: General
trends, individual differences and modifiers. Cerebral Cortex, 15(11), 1676-1689.
34
altogether spared (e.g., an annual decrease of 0.55% was reported by Fjell et
al., 2009).
The above cited findings pertain to average rates of decline in structural
brain indices, but there are also substantial inter-individual differences in
rates of volumetric change over time. For instance, Raz et al. (2005) reported
significant individual differences in volumetric change in 11 out of 12 regions that they investigated (the only exception was the inferior parietal lobule). Further, Resnick et al. (2003) found attenuated rates of gray and white
matter volume change over a four-year interval in a subsample of elderly
individuals that were particularly healthy in terms of medical conditions and
cognitive impairment. But even within this group, significant reductions
were found across the frontal, parietal, temporal, and occipital lobes. A more
recent study specifically probed the neuroanatomical differences between
elderly individuals with maintained versus declining cognition during the
past 10 years, and found that spared gray matter volume in the MTL region,
as well as microstructural integrity of the cingulate cortex, were particularly
characteristic for those with maintained cognitive functions (Rosano et al.,
2012).
Structure-cognition relations
Although both brain structure and cognition correlate negatively with age,
the literature suggests that structure-cognition relationships are more mixed
than what would be expected (Raz & Rodrigue, 2006; Salthouse, 2011).
Most studies have reported positive correlations between regional or global
brain volume and cognition in aged individuals, that is, bigger is better, with
the assumption being that smaller volume implies more age-related atrophy
(Salthouse, 2011). However, the opposite pattern has also been found.
Memory performance in healthy older adults has for instance been shown to
correlate negatively with regional volume in the frontal cortex (Duarte et al.,
2006; Van Petten et al., 2004) and in the HC (Van Petten, 2004). That is,
smaller volume has been found to be associated with better cognitive performance. The reason for these discrepancies might be that in cross-sectional
studies of brain structure, one might confuse pre-existing inter-individuals
differences with age-related change. It has for instance been speculated that
in younger age, smaller regional brain volume might be associated with better cognitive function due to more efficient pruning processes during brain
development in youth (see discussion in Van Petten et al., 2004; Van Petten,
2004). Thus, negative associations between volume and cognition might be
remnants from younger years in older adults without pathological conditions.
On the other hand, correlations in samples of elderly with pathological conditions such as Alzheimer’s disease tend to show somewhat more consistent
35
positive correlation between regional volume and cognition (e.g., Duarte,
Henson, & Graham, 2008; Köhler et al., 1998).
When considering correlations between longitudinal changes in brain structure and changes in cognition, there have been some reports of HC volume
decline correlating with memory decline (Kramer et al., 2007; Murphy et al.,
2010). Smaller HC volume at baseline has also been found to predict subsequent memory decline (Golomb et al., 1996; Woodard et al., 2010). Furthermore, HC volume has been found to differ between individuals with
prior cognitive decline, compared to individuals with stable memory over the
same time period (Persson, Nyberg, et al., 2006). However, it should be noted that there are also longitudinal studies that have failed to observe correlations between MTL change and memory change in healthy aging (Ylikoski
et al., 2000). The discrepancy might reflect that the previously mentioned
study samples were more cognitively impaired, for instance, 30% of the
sample had converted to mild cognitive impairment, MCI (a transitional state
between normal aging and dementia) at follow-up in the study by Golomb et
al., (1996). Thus, it remains to be firmly established if the relationship between MTL decline and memory decline exists in healthy elderly samples
without individuals that may be in early stages of pathological conditions.
Although longitudinal studies that have specifically studied frontal volume
and cognition are hard to find, total cortical gray matter volume decline has
been associated with decline in executive functions (Kramer et al., 2007),
which might also mediate memory decline. Total brain volume change also
correlated with memory change in another study (Schmidt et al., 2005). Further, a longitudinal voxel-based morphometry study found associations between frontal volume and prior cognitive decline on at least two out of six
cognitive measures assessing memory, processing speed or executive function (Tisserand et al., 2004).
White matter integrity assessed with DTI or as white matter hyperintensities
on MR images has also been found to correlate with cognitive functions,
including memory, in several studies (Gunning-Dixon & Raz, 2000; Sullivan
& Pfefferbaum, 2006). More damage to the white matter is usually related to
lower cognitive function. Again, however, findings relating cross-sectional
measures of white matter integrity tend to be somewhat mixed (Salthouse,
2011), and longitudinal studies are rare. Changes in DTI measures over a
two-year period have been demonstrated to correlate with decline in working
memory scores in an elderly sample (Charlton, Schiavone, Barrick, Morris,
& Markus, 2010), but evidence regarding long-term memory is lacking. One
study found that individuals with prior decline in episodic memory had reduced white matter integrity in the anterior corpus callosum (Persson,
Nyberg, et al., 2006), but a recent large-scale cross-sectional study found
36
DTI measures to mediate age-differences in processing speed, but not in
episodic memory (Salami, Eriksson, Nilsson, & Nyberg, 2012). Thus, although the importance of white matter integrity for cognitive functions
should be acknowledged, it is still somewhat unclear how white matter integrity measures relate to episodic memory change over time.
Functional brain changes in aging
This section will attempt to briefly summarize the vast literature on agerelated changes that has been derived from functional neuroimaging studies,
predominantly employing fMRI and positron emission tomography (PET).
The current summary will mainly deal with changes that have been observed
while the study participants perform cognitive tasks, that is, changes in the
so-called task-positive network. The specific focus will be on memory tasks
and the memory-relevant structures in the frontal cortex and the MTL. Aging
also entails significant changes to the default mode network of the brain
(Hafkemeijer, van der Grond, & Rombouts, 2012), which is more engaged
during periods of inactivity or rest; as well as to the functional connectivity
between brain regions as they interact during task performance (Steffener,
Habeck, & Stern, 2012). These types of changes, however, will not be addressed further here.
Three general patterns of age-related changes have emerged from the many
studies conducted in this field. First, to a large extent older adults tend to
engage similar brain regions as young while performing memory-tasks (e.g.,
Duverne, Motamedinia, & Rugg, 2009). But elderly are also often reported
to significantly under- and overactivate certain brain regions compared to
young adults. Many of these differences between older and young adults
tend to occur across different task domains. Hence, they do not only pertain
to memory processing, although memory has been one of the most studied
topics in these studies. For instance, a common pattern is that elderly have
less activation than young in the occipital cortex together with increased
engagement of the frontal cortex. This pattern has been observed during
perceptual matching tasks (Grady et al., 1994), visual attention, working
memory, and episodic memory tasks (Cabeza et al., 2004), as well as across
a number of other task domains, as shown in a quantitative meta-analysis of
the literature (Spreng, Wojtowicz, & Grady, 2010). The pattern has been
denoted the Posterior to Anterior Shift in Aging; PASA (Davis, Dennis,
Daselaar, Fleck, & Cabeza, 2008). While the decrease in occipital cortex
activation has generally been attributed to deficient processing of sensory
stimuli (e.g., Cabeza et al., 2004; Davis et al., 2008), the nature of the increased frontal activation has been a subject of debate, as will be addressed
later.
37
Frontal cortex
As described, the frontal cortex is commonly observed to be more engaged
by elderly than young across many cognitive tasks, including episodic
memory ones. Another common pattern in functional neuroimaging studies
of episodic memory is underrecruitment of the PFC in elderly, most frequently during episodic encoding, but also during retrieval (Cabeza et al.,
1997; Grady et al., 1995; Logan, Sanders, Snyder, Morris, & Buckner, 2002;
Schacter, Savage, Alpert, Rauch, & Albert, 1996; Stebbins et al., 2002). In a
review of the literature, Dennis and Cabeza (2008) note that the most consistent finding during episodic memory encoding is an age-related reduction
in left-sided frontal activation. There is also longitudinal imaging evidence
for reduced memory-related frontal activation over a 6-year period in an
elderly sample (Nyberg et al., 2010). Such patters are typically interpreted as
impaired frontal function in the elderly, leading to poorer performance
(Persson & Nyberg, 2006). The observations of both increased and decreased frontal activation in elderly during episodic memory tasks do not
need to be incompatible, considering that the frontal cortex is a large structure, associated with many heterogeneous functions. One possibility is that
elderly underrecruit task-specific memory networks, while displaying increased activity levels in regions not significantly engaged, or less strongly
engaged, by young individuals. The alternative regions could include those
associated with general cognitive control processes (cf. Salami, Eriksson, &
Nyberg, 2012). Other possibilities for the discrepant findings will be discussed below, but first I will describe the various interpretations that have
been attributed to higher frontal activation in elderly compared to young
individuals.
There have been two common explanations for relatively increased frontal
activation in elderly individuals. According to one account, it reflects compensatory processes (Cabeza, Anderson, Locantore, & Mcintosh, 2002;
Reuter-Lorenz, Stanczak, & Miller, 1999). The alternative account states that
increased activation reflects an age-related impairment in engaging the most
efficient set of brain regions to perform a specific task, which is commonly
referred to as non-selective recruitment or dedifferentiation (Grady, 2008; Li
& Lindenberger, 1999; Logan et al., 2002). The compensation hypothesis
has often been evoked in association with bilateral frontal recruitment in
elderly while performing tasks associated with relatively more lateralized
activation in young participants, a pattern denoted as the Hemispheric
Asymmetry Reduction in OLDer adults; HAROLD (Cabeza, 2002). If the
contralateral frontal activation is found in high-performing elderly, and/or
correlates with successful task performance, it is usually interpreted as compensatory. Such patterns have been observed in many studies (e.g., Cabeza et
al., 2002; Grady, McIntosh, & Craik, 2005). However, in some cases in38
creased frontal activation has also been found in low-performing elderly
(Miller, Celone, et al., 2008) and negative performance-correlations have
also been observed (de Chastelaine, Wang, Minton, Muftuler, & Rugg,
2011). That is, increased frontal recruitment associated with poorer performance. Relatedly, increased frontal recruitment has also been found in participants with cognitive decline over the past decade, compared to elderly
with stable cognition during the same time (Persson, Nyberg, et al., 2006).
Such additional frontal activation in lower performing elderly has sometimes
been explained as unsuccessful or attempted compensation. Another term
that has been suggested is partial compensation (de Chastelaine et al., 2011),
indicating instances when the increased activation is beneficial for performance, but less effective than being able to perform the task by only recruiting the standard task-specific network.
The dedifferentiation account and related theories proposing that additional
frontal activation in elderly is detrimental or dysfunctional, have also received empirical support. For instance, sometimes increased frontal activation is seen in elderly when task performance is equivalent to that of young
individuals (Morcom, Li, & Rugg, 2007), which would imply less efficient
use of neural resources in elderly compared to young individuals. Also, some
studies have shown that older adults tend to engage the frontal cortex in a
more non-selective manner than young individuals (e.g., Logan et al., 2002),
which has been interpreted as a failure to use the most efficient set of brain
regions in performing the task at hand. A similar pattern has also been observed in a study that demonstrated right frontal activation in lowperforming elderly during memory encoding, but not in young individuals or
higher-performing elderly (Duverne et al., 2009). A more recent study also
favored a dedifferentiation, rather than a compensatory, account in explaining higher retrieval-related activation in elderly individuals, which was also
less sensitive to task conditions and did not contribute to task performance
(McDonough, Wong, & Gallo, 2013). So despite claims that more evidence
is consistent with the compensation hypothesis than the dedifferentiation
account (Dennis & Cabeza, 2008), the latter is still a viable candidate for
explaining neuroimaging patterns observed in elderly (Grady, 2012).
In addition to the compensation and dedifferentiation explanations of increased frontal activity in elderly, a third possibility is that it is an artifact
associated with the cross-sectional nature of most neuroimaging studies of
healthy aging. This was suggested by a study in which both cross-sectional
and longitudinal analyses were performed on the same data set (Nyberg et
al., 2010). Cross-sectional analyses showed apparent age-related increases in
frontal activation during episodic memory encoding, which were driven by a
subset of high-performing elderly who were not representative of the full
sample. When considering the longitudinal data, the same frontal region
39
showed decreased activation over time. However, another longitudinal imaging study demonstrated that longitudinal increases in frontal activity during
memory tasks are possible (Goh, Beason-Held, An, Kraut, & Resnick,
2013). Here, increased activity was found to be related to declining cognitive
functions, specifically those pertaining to executive control. In this study,
participants with stable or improved neuropsychological scores tended to
have decreased activity in several frontal regions, which the authors attributed to reduced processing needs owing to learning from repeated testing.
However, Nyberg et al., (2010) tested specifically for effects of repeated
testing, and did not observe any such. Hence, longitudinal evidence concerning frontal cortex function in aging is still inconclusive.
A number of other factors could be responsible for the discrepant findings
across studies. One such is the nature of the task. It has been shown that
older adults can overrecruit the frontal cortex compared to young individuals
during relatively easy task conditions, when performance is equivalent between the groups, while displaying less activation during difficult task conditions, coupled with a performance decrement. To put it another way, older
adults tend to reach their maximum level of neural activation faster than
young, as task difficulty increases. This pattern has been referred to as
CRUNCH, compensation-related utilization of neural circuits, and is consistent with many findings in the aging literature (Reuter-Lorenz & Cappell,
2008). Similar ideas have also been expressed as reductions in neural capacity and efficiency in older adults (Prvulovic, Van de Ven, Sack, Maurer, &
Linden, 2005; Y. Stern, 2009). More recent data also suggest that the previously mentioned HAROLD model, pertaining to functional asymmetry reductions in elderly, might be a special case of CRUNCH (Berlingeri,
Danelli, Bottini, Sberna, & Paulesu, 2013). While the original formulation of
CRUNCH referred to working memory tasks, in which it is easy to manipulate memory load, CRUNCH-like patterns have been observed during episodic memory tasks as well. In one study, young participants recruited bilateral frontal regions only during a difficult version of an episodic memory
task, while elderly had bilateral frontal activation during both easy and difficult conditions (Spaniol & Grady, 2012). In summary, the CRUNCH model
could thus explain some inconsistent findings regarding frontal under- versus
over-activation in elderly compared to young, if differences in task difficulty
are present between studies.
Another factor that can account for discrepant findings is the characteristics
of the sample. For instance, if convenience sampling is used, there is a risk
of recruiting individuals who are not representative of the elderly population
as a whole. This is more likely to involve individuals with higher cognitive
function than average (Dennis & Peterson, 2012), but inclusion of individuals in preclinical phases of dementia could also bias results (Buckner, 2004).
40
The composition of individuals in any given sample likely influences the
brain activity patterns that are observed since high- and low performing elderly would be expected to suffer different degrees of functional impairment
or dedifferentiation, as well as possessing differential capacities for compensation. However, it is not well understood how performance differences relate to frontal activation, since higher frontal activation has been demonstrated in both higher performing, or successfully aged, individuals (Cabeza,
Anderson, et al., 2002; Rosen, Prull, & O’Hara, 2002) as well as in individuals who have experienced cognitive decline (Persson, Nyberg, et al., 2006).
Due to possible differences in study design, task difficulty, and functional
localization it is not easy to reconcile these findings. Further discussion of
sample composition and other reasons for discrepant findings in neuroimaging studies of aging will follow after first reviewing evidence regarding
MTL function in aging.
Taken together, the picture of memory-related frontal cortex function in
aging is complex. Both decreased and increased activation patterns can
clearly be observed in elderly compared to young individuals. Decreased
activation is usually interpreted as a functional impairment, but as of yet,
there is no consensus regarding age-related increases in frontal activation in
aging (Grady, 2012). A number of factors, such as the functional heterogeneity of the frontal cortex, study design (cross-sectional or longitudinal), task
difficulty, and sample characteristics need to be considered when interpreting findings.
Medial temporal lobe
In contrast to the frontal cortex, which has been associated with numerous
cognitive functions, MTL activity can be assumed to reflect more memoryspecific processing. This somewhat facilitates interpretations of age-related
effects observed in the MTL, compared to the literature on frontal cortex
findings. For the MTL, one prevalent outcome is that elderly fail to activate
the MTL or HC proper during memory processing, or have significantly
lower MTL activation than younger individuals (Daselaar, Veltman,
Rombouts, Raaijmakers, & Jonker, 2003b; Dennis, Daselaar, & Cabeza,
2007; Grady et al., 1995; Gutchess et al., 2005). These reductions in MTL
activation have most consistently been observed during memory encoding,
leading to the suggestion that the age-related episodic memory deficit is
mainly an encoding failure (Daselaar, Veltman, Rombouts, Raaijmakers, &
Jonker, 2003a). However, age-related decline in MTL function, specifically
in the HC proper, has been observed during retrieval as well (Daselaar,
Fleck, Dobbins, Madden, & Cabeza, 2006; Grady et al., 2005). In several of
the reports of reduced MTL activation in elderly, it was coupled with increased frontal recruitment. Some studies also report significant negative
41
correlations between frontal and MTL activation, both during encoding
(Gutchess et al., 2005) and retrieval (Grady et al., 2005). This could support
the notion that increased frontal activation in elderly compensates for reduced MTL function.
Although the above cited studies reporting MTL impairment have found
age-related activity reductions in both the HC proper (e.g., Dennis et al.,
2007) and the parahippocampal gyrus (Gutchess et al., 2005), another line of
research has found a pattern of decreased HC activation coupled with increased activation in the parahippocampal gyrus during retrieval (e.g.,
Cabeza et al., 2004; Daselaar et al., 2006). These observations have been
attributed to an increased reliance on the familiarity-based retrieval processes governed by a substructure in the parahippocampal gyrus, the rhinal cortex, when the recollection-based mechanisms in the HC proper begin to fail.
These findings are in line with behavioral research indicating that elderly are
more impaired at recollection than familiarity-based memory retrieval (e.g.,
Mäntylä, 1993; Parkin & Walter, 1992), as already alluded to in the section
on aging and memory.
HC impairment does not, however, seem to be an inevitable consequence of
aging. There have also been several reports of preserved HC activation in
healthy elderly during episodic memory tasks (Düzel, Schütze, Yonelinas, &
Heinze, 2011; Miller, Celone, et al., 2008; Persson, Kalpouzos, Nilsson,
Ryberg, & Nyberg, 2011; Rand-Giovannetti et al., 2006; Schacter et al.,
1996). As discussed in relation to the frontal cortex, this finding could derive
from the sample composition of the specific studies. Perhaps the samples of
the studies that have not detected MTL impairment have been healthier and
more high-performing than average, that is, mainly comprised successfully
aged individuals. Another relevant factor in this context is the age of the
participants, which can vary substantially across studies. However, there
have also been suggestions that normal aging does not affect MTL function,
and that the memory impairment in normal aging derives from failures of
cortical processing (e.g., Rand-Giovannetti et al., 2006), such as the frontal
cortex alterations discussed in the previous section. According to this account, one reason why some studies show a decrement in HC function in
aging is that they fail to account for the lower task performance of the older
participants (Rand-Giovannetti et al., 2006), that is, lower HC recruitment is
seen as a consequence rather than a cause of low task performance. Relatedly, it has also been suggested that unintentional inclusion of individuals in
the early, undiagnosed, stages of Alzheimer’s disease could be responsible
for the observations of MTL impairment in some studies of healthy aging
(Buckner, 2004). Another possibility that has to be considered is that most
studies have used cross-sectional designs when investigating MTL function
in aging. Longitudinal estimates have been shown to be more sensitive to
42
true age effects with regard to brain structure (Raz et al., 2005), which could
also apply to functional aging studies, potentially explaining the failure of
some studies to observe functional decline in the HC in healthy aging.
In addition to preserved MTL activation in healthy elderly, a different picture has also emerged from studies of individuals with MCI. Several studies
have shown paradoxically increased HC activation in individuals in early
stages of MCI, compared to healthy elderly and individuals diagnosed with
Alzheimer’s disease (reviewed by Dickerson & Sperling, 2008). Elevated
HC activation has also been found to predict subsequent cognitive decline in
individuals who were diagnosed with MCI at baseline (O’Brien et al., 2010).
In these studies, the HC hyperactivation has been hypothesized to reflect a
time-limited compensatory mechanism, which later on fails, as the disease
progresses. It is unclear how these findings relate to the findings of impaired
HC function in healthy aging, but a possible explanation is that the mechanisms that underlie memory failure in normal aging are different from pathology that causes dementias such as Alzheimer’s disease (Buckner, 2004).
To summarize, while many studies report a functional MTL impairment in
healthy aging, there is still some disagreement over whether this is the norm,
or whether it is caused by performance confounds or samples contaminated
by preclinical Alzheimer’s disease. Some studies have also found spared HC
function in elderly samples, and yet others have observed paradoxically increased activity in the HC in persons who later experience cognitive decline.
Synthesis and summary of MTL and PFC function in aging
To summarize, the literatures on both MTL and PFC function in aging are
characterized by inconsistent findings. For the MTL, many studies report
decreased function with age, particularly in the HC, but there are also several
demonstrations of spared function, and a few studies that have found increased BOLD signal in individuals who later experience cognitive decline.
For the frontal cortex, both increases and decreases are commonly seen in
elderly relative to young, and it is not established whether it is good to have
a high frontal BOLD-signal (compensation) or whether it is dysfunctional
(decreased neural efficiency or dedifferentiation). Such inconsistencies can
stem from differences in task paradigms, task difficulty, choice of contrast,
or other methodological issues involved in neuroimaging research, and for
the PFC from the functional heterogeneity of the structure itself. Still, when
reviewing the literature a few shortcomings become evident. Firstly, many
studies have neglected the variability in cognitive status within the older
group, and when it has been taken into account, the elderly have oftentimes
been considered high- and low-performing relative to the mean performance
of the sample. This mean can be quite different from the mean of the aging
43
population as a whole, especially when small convenience samples are used.
Alternatively, performance is compared to that of young participants, in
which case it can be biased by cohort effects (as discussed in the section on
aging and memory). Thus, functional neuroimaging studies of aging have
generally had a quite poor characterization of their samples. Divergent results are unsurprising if some elderly samples are contaminated by participants in preclinical stages of dementia, while other samples are mainly characterized by individuals who are successfully aged. For this reason it is very
important to characterize what distinguishes successfully and less successfully aged individuals from average ones. While the most part of the aging
literature has focused on cognitive decline, relatively little systematic investigation has been done of successful aging, especially longitudinally defined
successful aging.
The relative scarcity of longitudinal data in general, both on the behavioral
and neural levels, is another shortcoming in the literature on neurocognitive
aging. On the behavioral level, the best way to avoid some of the problems
discussed in the previous paragraph is to characterize participants relative to
their own ability level prior to the onset of age-related changes, by using
longitudinal observations. Without this information, there is a risk of confusing variability associated with individual differences from youth with variability arising from age-related changes. That is, mistakenly inferring that a
low-performing older individual has experienced cognitive decline when he
or she has in fact been low-performing from youth, or vice-versa. Given the
relative stability of individual differences in cognition over the adult lifespan (Deary et al., 2000, 2004; Gow et al., 2011) such concerns are highly
relevant. A specific gap of knowledge in the neuroimaging literature is
whether cross-sectional observations of performance-related individual differences in brain activity of elderly also reflect differences that were already
present before the onset of aging.
On the neural level, it is clear that longitudinal and cross-sectional imaging
data can produce substantially different results (Nyberg et al., 2010). Also,
one of the few truly longitudinal studies showed a complex pattern of increasing and decreasing brain function across five scanning sessions during a
nine-year period (Beason-Held, Kraut, & Resnick, 2008), in a sample that
was cognitively stable during that time. By considering that cross-sectional
imaging data only provide a snapshot of brain function at one point in time,
it is easy to realize that this picture might appear quite different depending
on the specific time-point one looks at. Thus, more studies with longitudinal
imaging data are called for to confirm and qualify findings from previous
cross-sectional studies.
44
Aims of the thesis
The overarching aim of this thesis was to investigate how information on
individuals’ cognitive histories, that is, longitudinal behavioral data, can
advance knowledge of inter-individual differences in neurocognitive aging.
Focusing on memory-relevant structures in the medial temporal lobe and the
frontal cortex, the specific aims were to investigate:
i)
The relationship between cognitive decline and neural changes over
time (Study I).
ii)
The neural characteristics of longitudinally-defined successful cognitive aging (Study II).
iii)
The relative contributions of cognitive status in middle-age, and
age-related cognitive decline, on individual differences in memoryrelated brain activation in older age (Study III).
45
Methods
The Betula study
All data underlying the current thesis was derived from the Betula study
(Nilsson et al., 1997, 2004), which is a longitudinal population-based study
that started in 1988. The aims of the Betula study include investigating how
memory and health develop across the adult age-span, mapping early cognitive and biological markers of dementia, and finding determinants for successful aging. To date, there have been five data collection waves in Betula
(T1: 1988-90, T2: 1993-95, T3: 1998-00, T4: 2003-05, T5: 2008-10), with a
sixth beginning in the fall of 2013. At each wave, participants undergo extensive cognitive and health-related testing. Data is also collected on a number of social, medical, and life-style variables. In total, approximately 4500
participants, distributed across six samples, have been tested throughout the
years. However, all participants were not tested at each test wave, see Table
1. At each test occasion, a new sample was recruited to control for retest
effects on the cognitive test battery.
Several subsamples of Betula participants have undergone structural and
functional brain imaging with MRI. For the current thesis, data from three
brain imaging collections was used. The first took place before T4, in 20022003, and was followed up in connection with T5, in 2008-2009. The second
one was performed in parallel with T5, in 2009-2010. These samples will be
described in detail below.
Table 1. Recruitment and testing of samples in Betula. Bold fonts indicate samples
that were included in this thesis.
T1
Sample 1
T2
Sample 1
Sample 2
Sample 3
T3
Sample 1
Sample 2
Sample 3
Sample 4
T4
Sample 1
T5
Sample 1
Sample 3
Sample 3
Sample 5
Sample 6
46
Study samples and selection procedures
The Betula study is population-based, which means that participants are
recruited randomly from the population registry. This is valuable since it
increases the generalizability of the results derived from the study. However,
during the course of the study some participants drop out, and this is more
common among older participants, and participants with a lower performance at initial testing (Rönnlund et al., 2005). This compromises sample
representativeness, and can be an issue when interpreting the results, as will
be discussed later in this thesis. Therefore, sample characteristics and selection procedures for the samples used in this thesis will be thoroughly described in the following paragraphs.
Data from three samples of the Betula study were used in this thesis, namely
samples 1, 3 and 6. At the time of recruitment sample 1 comprised 1000
individuals evenly distributed across 10 age-cohorts, 35-80 years of age.
Sample 3 originally consisted of 966 individuals, who were age-matched to
sample 1, i.e., aged 40-85 at their first testing at T2. Betula has a narrow agecohort design, in which the cohorts are recruited with five year intervals; i.e.,
35, 40, 45 year olds, and so on. For samples 1 and 3, there were approximately 100 individuals in each age-cohort, except the oldest cohort in S3,
which comprised 70 individuals. Sample 6 consisted of 357 participants aged
25-80 at T5, with approximately 30 individuals per cohort. Gender proportions in the samples were roughly even across the age-cohorts, with overall
slightly more females in samples 1 and 3 (53 % and 57% female, respectively), while sample 6 contained slightly more males (51% males).
At the fifth test occasion in Betula, 366 individuals from sample 1 and 390
individuals from sample 3 completed cognitive testing, i.e., just over a third
of the original samples. At this time sample 1 had been part of the study for
20 years and had five measurement points, while sample 3 had participated
four times during 15 years. The remaining participants in these two samples,
as well as those from sample 6, who were recruited at T5, formed the basis
for recruitment into the brain imaging samples described next.
The ImAGen cohort
In connection with the fifth test occasion, a large brain imaging data collection was performed in the Betula study. We called this project ImAGen,
which is an acronym for Imaging Aging and Genetics. A total of 376 individuals were scanned with structural and functional MRI, and a follow-up of
this sample will begin in the fall of 2013. The ImAGen cohort formed the
basis for Study II and III of this thesis, as well as many other studies (e.g.,
Kauppi, Nilsson, Adolfsson, Eriksson, & Nyberg, 2011; Salami, Eriksson,
47
Nilsson, & Nyberg, 2012; Salami, Eriksson, & Nyberg, 2012). Participation
in the imaging study was offered to participants from samples 1, 3 and 6,
who had completed the cognitive testing in Betula at T5, and agreed to be
contacted for potential participation in an imaging study. Selection of participants was stratified by age and gender, but blind to their cognitive performance and other personal characteristics. An initial screening was made to
exclude participants who had MR contraindications, such as metal implants,
pacemakers, and/or were pregnant. Also, participants who had had strokes or
heart/brain surgery were excluded at this point, as were participants who
reported severe visual impairments or motoric problems that could interfere
with response collection.
The characteristics of the scanned participants can be seen in Table 2. As the
scanning took place on average 266 days (range: 35-552) after their memory
assessment in Betula, the participants were slightly older than what is suggested by their age-cohort.
Table 2. Description of the ImAGen cohort
Age
cohort
25
30
35
40
45
50
55
60
65
70
75
80
95
Total
n
10
9
10
8
9
10
55
54
54
67
61
28
1
376
Gender
(f/m)
5/5
5/4
5/5
4/4
4/5
6/4
27 / 28
27 / 27
27 / 27
37 / 30
34 / 27
15 / 13
1/197/179
Samples
(1/3/6)
- / - / 10
-/-/9
- / - / 10
-/-/8
-/-/9
- / - / 10
26 / 24 / 5
24 / 25 / 5
25 / 26 / 3
33 / 28 / 6
24 / 33 / 4
11 / 13 / 4
-/1/143/150/83
Education
(years)
15.0
16.0
15.5
16.2
14.2
13.3
14.5
14.2
13.9
11.5
10.2
8.9
7.0
12.8
Age at
MRI
25.9
31.0
35.9
41.0
45.7
50.9
56.4
61.1
66.0
71.1
75.9
80.5
96.9
63.5
Study I participants
The 26 participants that were included in Study I were a subsample of 60
Betula participants, originally recruited for a brain imaging study in 20022003 (Lind, Ingvar, et al., 2006; Lind, Larsson, et al., 2006; Lind, Persson, et
al., 2006; Persson et al., 2008; Persson, Lind, et al., 2006). A follow-up imaging study was conducted in 2008-2009, for which 41 participants returned.
48
Two of these were only scanned structurally, and out of the 39 with complete data, 13 were excluded for the purposes of Study I. Reasons for exclusion were: missing behavioral data (6 participants), misunderstanding or not
performing scanner task correctly (2 participants), corrupt MR-data (1 participant) and more than ± 10 points change in memory scores up to the baseline scanning session (4 participants). The requirement of stable memory
scores until the baseline scanning was a specific inclusion criterion, implemented to avoid inclusion individuals who had already experienced prior
cognitive decline. Since one of the objectives of the baseline study in 20022003 was to investigate the effects of the APOE gene on brain structure and
function, there was an overrepresentation of ε4-allele carriers in the study
sample. Since APOE ε4 is a known risk-factor for Alzheimer’s disease
(Corder et al., 1993), this factor was controlled for in the analyses of Study I
and I also report the number of ε4-carriers here.
The final sample of participants comprised 26 individuals, 18 females and 8
males. All were from Betula sample 1. They were aged 55-79 (mean: 69.7,
SD = 8.3) at the time of the follow-up scanning. The mean level of education
was 10.9 years (SD = 3.7). Seventeen were carriers of the APOE ε4 allele,
12 homozygotes and 5 heterozygotes. However, all participants were free
from signs of dementia at the time of the follow-up study, and scored ≥24 on
the Mini-Mental State Examination, or MMSE, (Folstein, Folstein, &
McHugh, 1975), which is a screening test for dementia.
Study II participants
All participants in Study II were part of the ImAGen cohort, described
above. For the purposes of this study, selection was based on a classification
of participants into groups of cognitive maintainers, decliners, and average
performers, according to statistical procedures that will be described below
(see also Josefsson, De Luna, Pudas, Nilsson, & Nyberg, 2012). The classification was based on prior cognitive change across 15-20 years. Since the aim
of Study II was to investigate the neural correlates of successful aging, the
main analysis contrasted a group of maintainers, who had a better cognitive
development over time than their peers, with a carefully selected control
group of average participants. All participants in the ImAGen cohort, who
were classified as maintainers and did not meet any of the exclusion criteria
(see below), were included in the successful aging group of Study II. The
final group consisted of 51 individuals, described in Table 3. A total of 24
maintainers were excluded for the following reasons: not meeting performance criteria on the scanner task (at least 10 correct responses out of 24;
and less than 50% missing responses, n = 9), problems with the structural
image preventing normalization procedures (e.g., missing data or outlier
status, n = 6), health-related issues or remarks from the radiologist screening
49
the structural scans for abnormalities (n = 5), and problems with visual acuity (n = 4)1. All exclusions were made prior to imaging analyses.
The control group of average performing participants in Study II was agematched, person by person, to the included successful agers. Also, to ensure
that the most representative of the average participants were selected into
this control group, we chose those individuals who were closest to the average baseline memory score, and slope of memory change, for each cohort in
the full Betula samples 1 and 3. Thus, we calculated the shortest Euclidian
distance to the mean baseline score and slope for each participant, and included those with the shortest distances in each age-cohort. Whenever an
average participant met an exclusion criterion, this person was omitted from
the control group, and the person with the next shortest Euclidian distance
was included instead. In total, 29 average participants were omitted for the
following reasons: not reaching performance criteria on the scanner task
(same as for maintainers, n = 26), problems with visual acuity (n = 2), and
misunderstanding the scanner task (n = 1). An additional participant was
excluded after preliminary imaging analyses, due to outlier status across all
voxels in the brain. The final selection of 51 average participants in the control group can be seen in Table 3. Note that the control group was not gender-matched to the maintainers. This was because female gender was found
to be one of the predictors of being classified as a maintainer (Josefsson et
al., 2012). Instead, we controlled whether the observed group differences
were driven by gender in retrospect.
Study II also had a young control group, to help interpret brain activation
differences between the successful and average older participants. This
group comprised all individuals in ImAGen who were 45 years or younger at
the time of scanning (Table 3). Only one participant was excluded due to
technical problems with response collection during scanning. All young participants were from Betula sample 6, which did not have longitudinal behavioral data. Therefore these individuals were not classified as maintainers,
average, or decliners.
1
Any one participant could have several reasons for exclusion, e.g., failing to reach performance criteria due to neurological illness. However, for simplicity, screening for health issues
was performed only in those participants who were not already excluded for other reasons.
50
Table 3. Sample description Study II
n
Age
Gender (f/m)
Education
MMSE
Successful
51
68.8 (7.1)
38 / 13
14.4 (4.4)
28.6 (1.4)
Average
51
68.8 (6.9)
23 / 28
12.1 (4.7)
28.1 (1.5)
Young
45
35.3 (7.1)
22 / 23
15.3 (2.6)
28.6 (1.2)
Standard deviations in parentheses.
Study III participants
A total of 203 ImAGen participants from Betula samples 1 and 3, who had
not met any of the exclusion criteria, were included in Study III. Mean age
was 65.7 years, and other sample characteristics are given in Table 4. In
total, 90 participants were excluded due to: not having performed, misunderstood, or not reached performance criteria (same as above) on the scanner
task (n = 67), corrupt functional data, severe movements and/or outlier status
(n = 3), missing structural data, or outlier status on structural scans (n = 8).
With these participants removed, the remaining ones were screened and 12
additional participants excluded after being found to have structural abnormalities in the brain, or self-reported health issues (e.g., stroke, multiple
sclerosis, or epilepsy).
Table 4. Sample description Study III
Age cohort
55
60
65
70
75-80*
Overall
n
43
42
45
39
34
203
Percent female
56%
43%
47%
59%
62%
53%
Education
15.0 (3.3)
14.5 (3.3)
14.4 (4.5)
12.4 (4.2)
10.4 (3.9)
13.5 (4.2)
* Four 80-year olds (two female) were included. Standard deviations
in parentheses.
Assessing selection effects
The representativeness of the brain imaging samples could be compromised
by selective attrition across the 20 years of the Betula study, as well as selection bias resulting from applying exclusion criteria in selecting which participants should be included in the final analyses, as described in the preceding
51
paragraphs. However, since the Betula study is population-based from outset, the representativeness of the included samples can be quantified. Calculating a measure of representativeness compared to the full population-based
samples can help interpretation and validity-assessments of the results derived from the studies in this thesis, as well as other studies using Betula and
ImAGen data. In the following paragraph I have attempted to quantify sample representativeness by computing a standardized measure of memory
performance at the first measurement occasion for each participant, compared to his/her full age-cohort in the Betula samples. This z-score was computed through the standard formula (x – μ)/ σ, where x is the raw memory
score for each participant, μ is the mean memory score for the age-cohort the
participant belongs to, and σ is the standard deviation in that age-cohort. A
selection/attrition effect is demonstrated if a subsample has a higher than
average memory already at the first measurement occasion in Betula. The
memory score will described in detail below.
First, I quantified the attrition effect for the remaining participants from
sample 1 and sample 3 at the latest measurement occasion in Betula (366 out
of 1000 from sample 1, and 390 out of 966 from sample 3). This subset of
participants had performed on average 0.13 standard deviations (SDs) above
the mean of the full population-based samples at recruitment to Betula. The
selection effect had a small, but positive correlation with age (r = 0.11, p <
0.01), indicating that attrition was more biased for older individuals. Regarding the imaging samples used in this thesis, the 26 participants included in
Study I had performed on average 0.28 SDs above the mean at first testing.
This can be compared to all 38 participants who completed follow-up in the
longitudinal imaging study, who were slightly more representative; 0.24, and
the full sample of 60 participants who were selected for the baseline study in
2002, who had a mean z-score of 0.16. For the 293 individuals from sample
1 and 3 that were scanned in the ImAGen cohort, the selection effect was
only slightly stronger than for all sample 1 and 3 participants tested at T5.
The ImAGen participants had performed on average 0.19 SDs above the
mean of their peers at first testing in Betula. Again the selection effect was
related to age, becoming apparent for participants aged 55 and older at first
testing, as can be seen in Figure 4. After applying exclusion criteria to this
sample, leaving the 203 participants who were included in Study III, the
mean z-score was 0.20. Thus, the exclusion criteria had only a marginal effect above the selection effects into the imaging cohort. As for the successfully aged and average participants who were selected for Study II, the successful had scored 0.92 SDs above the mean, while the participants in the
average group were indeed average and scored 0.07 SDs below the mean.
52
Figure 4. Selection effects for ImAGen participants compared to full samples 1 and 3 from the Betula study. The y-axis shows the raw memory
scores, and the x-axis shows the age at first testing. Actual age at scanning
was 20 years older for sample 1 participants and 15 years older for sample
3. Error bars represent 95% confidence intervals.
Longitudinal memory measure
The main measure of memory performance used in all three studies of this
thesis was a composite of five episodic memory test scores from the Betula
test battery. Hence, whenever I refer to memory performance, ability or
scores of the participants included in thesis, this is the measure that was
used. This measure was also used to assess memory change over time, as
change scores in Study I, and as a slope estimated by ordinary least squares
linear regression in Studies II and III. The composite score for each participant consisted of the raw number of items recalled in each task condition,
with a maximum of 76 points. The included test variables were the following:
53
1. Immediate free recall of 16 short verbal commands that were enacted by the participants using objects provided by the experimenter
(e.g., point at the book, lift the apple). Each command was presented
for 8 seconds, and the participants were given 2 minutes for recall.
2. Immediate free recall of 16 short verbal commands that were studied
without enactment. Instead, the experimenter read the commands
aloud while also showing them in written form. Presentation time
was 8 seconds per item, and 2 minutes was given for recall.
3. and 4. Delayed cued recall of nouns from the 16 enacted and 16
non-enacted commands, using 8 noun categories as recall cues (e.g.,
“fruits”). Three minutes was given for recall.
5. Immediate recall of 12 verbally presented, unrelated, nouns. Presentation rate was 2 seconds per word, and participants were given 45
seconds for recall.
For tasks 1 and 2, presentation order was counterbalanced across participants
and test occasions, so that half of the participants performed the non-enacted
condition first. There were two different lists of commands, with eight listorder variations each, which were also counterbalanced across participants.
Further, counterbalancing was done across test occasions, so that for each
participant, the list studied with enactment at first testing was studied without enactment at the next test occasion five years later, or vice versa. The
delayed cued recall test (tasks 3 and 4) was performed after completion of
the second immediate free recall condition. Task 5 was a part of a more extensive test which addressed learning and recall of words during divided
compared to focused attention conditions. Three conditions of this task had
concurrent card-sorting as a manipulation of attention, but only the fourth
condition without concurrent card sorting was used for the current purposes.
The presentation order of the different conditions of task 5 was counterbalanced across participants and across test occasions. In total there were two
sets of four word-lists for this test that varied across participants and test
occasions. By and large, all other testing procedures were kept as similar as
possible across test occasions in the Betula study. Counterbalancing and
switching of item-lists across test occasions served to reduce practice effects.
Finally, a few words should be mentioned about the reliability of the
memory composite score. The pairwise correlation coefficients between the
tests included in the composite ranged from r = 0.38 to r = 0.73, all significant at the p < 0.001 level (based on 1000 sample 1 subjects at T1 in Betula).
This yields a Cronbach’s alpha of 0.83, which can be considered a good
level of internal consistency, i.e., an indication that the different tests measure the same construct. Test-retest reliability of the memory composite was
estimated to 0.79 (Pearson correlation coefficient) between the first and sec-
54
ond Betula test occasion. This calculation was based on 838 participants
(age-range: 40-85 years) from sample 1 that returned for retest at T2.
Statistical classification for Study II
In Study II, successful and average agers were identified using a statistical
classification model including all Betula participants from samples 1 and 3,
with two or more memory assessments. Participants were classified as maintainers, average, or decliners based on how their memory performance over
time compared to the average of their age-cohort in Betula. The statistical
classification model has been described in full detail by Josefsson et al.
(2012). However, a slight modification of the model was implemented for
Study II, namely that separate models were estimated for sample 1 and 3
participants because of the different number of available measurement
points. A brief description of the classification procedures will be given here.
The classification model was based on 1954 participants’ baseline memory
scores, and 1561 participants’ linear slopes of memory change across 15-20
years of participation in the Betula study. The 1954 participants were the full
samples 1 and 3 from Betula, and 1561 of these had at least two measurement points, which was required for estimating a slope of change. The baseline score was from each participant’s first memory assessment in Betula.
The slopes were estimated with ordinary least squares regression of the episodic memory composite scores on time. Using these data, the average
memory development over time was estimated for each of the 10 age-cohorts
in the full samples. A key feature of the classification model was that it corrected for non-ignorable attrition (drop-out) by using random-effects patternmixture modeling (Little, 1995). This technique makes use of all available
data to factor in the scores of the participants who drop out during the course
of the study.
We wanted to define a cognitive maintainer as a person with a moderate to
high baseline memory, combined with a better than average slope for a given
baseline score. To obtain a measure that took into account both the baseline
score and the slope, we used the predicted score for the last measurement
point as an outcome measure. Each person’s predicted final score is a linear
combination of his/her baseline, score plus the rate of change multiplied by
the time in the study (i.e., 15 or 20 years). This final score was compared to
the average final score in each respective age-cohort, which was derived
from the pattern-mixture model. All participants with predicted final scores
higher than 1 SD from the average were classed as cognitive maintainers,
whereas all participants with predicted final scores lower than 1 SD from the
mean were classed as decliners. Everyone in-between was considered as
average. As previously mentioned, we only considered maintainers and av55
erage individuals in Study II. The decliners could hold potentially relevant
information, but were too few in the ImAGen cohort to form a group (n = 7,
after exclusions). However, the identification and exclusion of decliners
likely served to minimize the risk of inadvertently including individuals in
preclinical phases of dementia, which could bias the results.
Scanner tasks
During functional neuroimaging the participants usually perform a cognitive
task to elicit task-related brain activation. Two different episodic memory
tasks were used for the studies in this thesis.
Study I: Incidental encoding of words
During scanning participants performed a semantic categorization task in
which they indicated whether nouns that were presented to them were abstract (e.g., truth) or concrete (e.g., book). This task served as an incidental
encoding condition for a later recognition test. In total, 160 words were presented to the participants during the categorization task, one at a time. Half
of the words were familiar to the participants from having categorized them
twice before, once before scanning, and once in the scanner during collection
of structural scans (with shifted word order). However, for the purposes of
Study I, the data were collapsed across novel and previously presented
words. The categorization task was presented in a blocked manner, alternating between categorization blocks (30 seconds) and fixation blocks (21 seconds). The fixation blocks were used as a baseline, during which participants
merely focused on a fixation cross in the center of the screen. In total there
were four identical functional runs, each run starting and ending with a brief
fixation block (12 seconds), with four categorization blocks in between.
Each categorization block contained 10 words.
The recognition test was performed after the scanning at the baseline study
in 2002-2003, but during the follow-up study in 2008-2009 a portion of the
words was tested in the scanner. During the recognition tests, participants
made old/new judgments on a total of 240 words, of which 80 were new.
Study II and Study III: Face-name paired associates
The face-name task consisted of alternating blocks of encoding, retrieval,
and an active baseline task. During encoding, participants viewed photographs of unfamiliar faces, presented one by one, together with common first
names, see Figure 5. They were instructed to encode the face and indicate
with a button press that they had seen each face. The button press was in56
Figure 5. Sample stimuli from the face-name task, encoding on left-hand
side and retrieval on right-hand side.
cluded to equate the motoric components during encoding, retrieval, and
baseline. During the retrieval blocks, participants were presented with the
previously viewed faces, presented together with three letters (Figure 5).
One of the letters corresponded to the first letter of the name encoded for
that face. Participants indicated the correct letter with a button press. When
unsure, they were instructed to guess. During the baseline blocks, a simple
perceptual discrimination task was presented, in which participants were
instructed to indicate with a button press whenever a fixation cross was replaced by a circle.
Presentation time was 4 seconds per face in the encoding and retrieval
blocks, with 1.5 - 4.5 seconds randomized inter-stimulus intervals (allowing
for both event-related and blocked imaging analyses). Mean duration between the encoding and retrieval of a given face was 85.1 seconds (SD =
26.1). A total of 24 face-name pairs were presented throughout the task,
which lasted approximately 10 minutes. Block and stimulus order was pseudo-randomized and constant across all participants. Prior to scanning, participants were familiarized with the task by completing a short practice version.
In the same scanning session that the face-name task was given, the participants also performed a short visuo-motor task with the purpose of assessing
individual hemodynamic response functions for each participant, as well as a
working memory task. The face-name task was given after the visual task,
which lasted approximately 4 minutes, but before the working memory task.
Brain imaging
All brain image acquisition was performed at the Norrland’s University
Hospital in Umeå, Sweden. For Study I, a 1.5 tesla Philips Intra scanner was
used. The data for Studies II and III were collected on a 3 tesla Discover
MR750 scanner from General Electric. In all three studies of this thesis, both
functional and structural MRI data were used, as well as diffusion tensor
imaging (DTI) in Study II. For the functional MR-data, standard echo-planar
imaging pulse sequences were used, while the structural data were collected
57
with T1-weighted sequences. The DTI data were acquired with a single shot,
spin-echo planar T2-weighted pulse sequence. All details regarding the pulse
sequence parameters are found in each respective study’s methods section.
Preprocessing and analysis of imaging data
Functional data
Preprocessing
Before one can statistically analyze functional MRI data, a number of computational procedures need to be performed on it to remove uninteresting
variability inherent in the data. For the work presented in this thesis, the
preprocessing procedures and subsequent statistical analyses were performed
with versions 5 and 8 of the Statistical Parametric Mapping (SPM) software
(Wellcome Department of Imaging Science, Functional Imaging Laboratory). Some of the analysis steps, however, were performed in an in-house
developed software, DataZ, which is based on SPM.
The first step in the preprocessing is to correct for differences in slice acquisition times. Since the entire volume of the brain cannot be sampled at once,
MR-images are acquired in slices. For instance, 33 slices were acquired during 3 seconds to cover the entire brain in Study I, whereas 37 slices were
acquired in 2 seconds in Study II and III. Therefore there is a slight temporal
displacement between subsequent slices, which is corrected for using a temporal interpolation procedure known as slice timing. In the second step of the
preprocessing, a correction for head motion is performed by rigidly aligning
each image volume to the first volume of the series. An unwarping procedure is also used to correct for image distortions caused by interactions between head movement and inhomogeneities in the magnetic field.
In order to perform group analyses, the images of all participants need to be
spatially normalized to a common space. There is large individual variation
in the shape, size, and morphology of the human brain, and in group analyses one needs to assure that the same anatomical location is sampled for
each person. Therefore, all participants’ images are transformed into a common template in the third step of preprocessing. For Study I this was a standard template of the Montreal Neurological Institute (MNI), which is provided in SPM8. For Study II and Study III a slightly more sophisticated normalization procedure was used. The motivation behind this was that the MNI
temple is not optimally representative of the variation in brain morphology
caused by aging. Therefore, a sample-specific template was created from
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292 individuals (51% female, aged 25-81 years) from the ImAGen sample.
These were individuals selected for having performed the face-task according to performance criteria, and not being structurally deviating according to
the sample homogeneity function in the voxel-based morphometry (VBM)
toolbox in SPM8. The template was created using an algorithm called Diffeomorphic Anatomical Registration using Exponentiated Lie algebra, or
DARTEL (Ashburner, 2007). In brief, this was done by first segmenting
each individual’s structural T1-image into gray and white matter components, which were then imported into DARTEL space. Next, all subjectspecific gray/white-matter images were averaged into one initial template.
Each participant’s deformations from this template were subsequently computed, and the inverse of these applied to his/her segmented gray/whitematter image. Thereafter a new template was created from the mean of the
deformed subject-specific images. This procedure was iterated six times to
create the final template, which was then rigidly aligned to MNI standard
space. The functional MRI data, which had been co-registered to each person’s structural T1-image at the beginning of the template-creation process,
were then non-linearly normalized to the final template. This was done using
the subject-specific flow fields derived from the creation of the template.
The final step of the preprocessing is smoothing. This is done on the normalized images, by averaging data over adjacent voxels with a Gaussian spatial
filter. In the current studies, the size of this filter was 8 millimeters at fullwidth-half-maximum. There are several reasons for smoothing fMRI data. It
removes noise, and can also correct for remaining between-subject variability after normalization. Thereby the functional signal-to-noise ratio is improved. Smoothing also improves the validity of the statistical tests performed on the data, by making parameter errors more normally distributed.
Statistical analyses
There are several methods for analyzing fMRI data. Perhaps the simplest and
most commonly used is to treat the BOLD signal time-series from each
voxel in the brain as a separate dependent variable, and modeling the effects
of experimental manipulations with multiple regression. This approach is
known as a mass-univariate analysis (Friston et al., 1994), and was used in
all studies of this thesis. In this approach, one models the expected timeseries of each voxel by using a general linear model (GLM). Specifically,
one defines a set of regressors (i.e., predictors) that one believes explains
change in signal intensity during the experimental run. Each condition from
the experimental task is included as a regressor, and convolved with the hemodynamic response function (in order to account for the relatively sluggish
response of the vascular system, compared to neural activity). In blocked
tasks, for instance, the regressors take the form of a box-car waveform. One
59
can also add nuisance regressors, such as the realignment parameters derived
from the motion correction procedure during preprocessing, to remove uninteresting variance from the data. When the model is specified, the parameter
weights (βs) of each regressor are estimated to find the combination of parameter weights that minimizes residual unexplained variance (the error
term), compared to the measured time-course of the voxel. Subsequently,
one can statistically test the effects of the regressors at the voxel-level.
Individual-level contrasts are usually set up, for instance, contrasting an experimental condition (e.g., memory encoding) with a baseline condition
(e.g., visual fixation). Since fMRI does not assess absolute levels of activation, hypothesis testing usually involves contrasting two conditions in a subtractive logic. The subject-level contrast is represented in a contrast image
containing, for each voxel in the brain, an estimated difference in parameter
weights between the conditions. These contrast images can subsequently be
used to test hypothesis on the group-level, typically using random-effects
analyses that allow the effects of the experimental manipulation to vary
across participants. For instance, in Study II, a two-sample t-test was used to
compare successful and average agers. In Study III, a different form of second-level analyses was employed. Here, the effect of memory performance
was used as a covariate in a one-sample t-test to identify voxels in the brain
that correlated with memory performance. The results from second level
analyses are represented in statistical maps, containing a t-statistic for each
voxel in the brain. These maps are thresholded at a predetermined alphalevel (e.g., p < 0.001) to determine which, if any, voxels that express statistically significant effects (e.g., group differences or correlations with memory
performance).
Structural data
Both Study I and Study II investigated aspects of how the brain’s structural,
or anatomical, properties related to differences in cognition. In Study I, the
volumes of the hippocampi for each participant were assessed using Freesurfer software. This is an automated tool for anatomical labeling of voxels
in the brain, based both on signal intensities from the MR image, as well
information of brain anatomy contained in an anatomical atlas (Fischl et al.,
2002). This software generated raw hippocampal volumes for each participant, which were then adjusted for differences in head/body size using a
covariance approach. This adjustment removes variance associated with
individual differences in head size, that otherwise could confound volumetric comparisons. The following formula was used: adjusted volume = raw
volume – b × (height – mean height), where b is the slope of regression of
the raw volume on height in the sample. This procedure redefines the data
points as the difference between an individual’s volume, and that of others of
60
similar height in the sample. The choice of body height instead of a direct
measure of brain, or intracranial, volume in the formula, was motivated by
wanting to use the same procedure as in previous studies on the same sample
(Persson, Nyberg, et al., 2006). An alternative correction with intracranial
volume instead of height was run, but did not change the results substantially.
As mentioned in the section on brain aging, variation in BOLD-signal in
elderly samples could be driven by differences in gray matter volume, due
to, for instance, differential age-related atrophy. To control for such potential
confounding effects, the Biological Parametric Mapping (BPM) toolbox
(Casanova et al., 2007) was used to co-vary for gray matter volume in the
functional analyses in Study II and Study III. BPM uses a similar voxel-wise
general linear model approach as SPM, but allows adding an additional image as a regressor. In this way, each voxel gets a different design matrix
depending on the gray matter value of that voxel. If group differences in
BOLD signal that are observed in standard SPM analyses are replicated
when using the BPM approach, it is reasonable to conclude that differences
in gray matter volume were not driving the observed effects (Casanova et al.,
2007). To be able to use BPM on our data set, each individual’s structural
T1-weighted image was segmented, normalized, and coregistered to the
fMRI data. Thereafter, the gray matter image was used as a covariate of no
interest in an ANCOVA model in Study II, and in a multiple regression
model in Study III, with the functional data as the primary modality. Study II
investigated whether the differences in functional activation between successful and average older participants were driven by differences in brain
structure. In Study III, it was investigated whether the associations between
BOLD-signal and memory scores (or memory slopes) were driven by differential gray matter volume.
Diffusion Tensor Imaging
In Study II possible group differences in the integrity of the brain white matter was investigated using DTI. From diffusion weighted contrast images one
can calculate measures of fractional anisotropy (FA), which is a scalar quantity reflecting the tendency of water molecules to diffuse in a preferred direction. The FA value is bounded by 0 and 1, with values near 0 indicating that
the molecules are equally likely to diffuse in any direction and values near 1
indicating highly consistent diffusion orientation. High FA values are thus
indicative of a high integrity of the local white matter.
The DTI data used in Study II were obtained from another study on the
ImAGen cohort (Salami, Eriksson, Nilsson, et al., 2012), in which all details
regarding preprocessing and analyses are described. In brief, the analyses
61
were carried out using software from the University of Oxford’s Center for
Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software
Library (FSL), specifically the Tract-Based Spatial Statistics method (Smith
et al., 2006). Preprocessing steps included averaging data from the three
diffusion acquisitions that were collected during scanning, correcting for
head movement and eddy-current distortions, creation of three dimensional
FA maps and normalizing these to a template (the most typical participant of
the sample). Then, the individual FA images were averaged to a mean FA
image for the sample, from which a white matter skeleton was extracted.
Each participant’s aligned FA image was subsequently projected onto this
skeleton to account for residual misalignments from the normalization. Finally, individual-specific mean FA values were extracted from regions of
interest (ROIs) defined on the group skeleton. The ROIs were defined according to JHU ICBM-DTI-81 white matter labels which are part of the FSL
software package. FA values for each participant were averaged along the
length of each of 12 white matter tracts, and across hemispheres. The following tracts were included in the analyses: genu, body, and splenium of the
corpus callosum, cingulum, corona radiata, cortico-spinal tract, external and
internal capsules, superior/inferior fronto-occipital fasciculus, superior longitudinal fasciculus, sagittal striatum, and the uncinate fasciculus. The extracted FA values were entered into IBM SPSS statistics software and tested using a MANOVA to investigate possible differences in white matter integrity
between the successful and average older groups in Study II.
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Overview of empirical studies
Study I
The aim of Study I was to use longitudinal data to investigate relationships
between change in memory performance and changes in structural and functional brain characteristics. Most previous studies have used cross-sectional
designs for investigating the involvement of the HC and lateral PFC in cognitive decline in aging. As described in previous sections of this thesis,
cross-sectional and longitudinal data have been shown to generate diverging
findings on both the behavioral and neural level (Nyberg et al., 2010; Raz et
al., 2005; Rönnlund et al., 2005). Longitudinal data are commonly held to
have better sensitivity in detecting age-related changes, so it is well motivated to investigate whether longitudinal observations converge with the crosssectional literature.
By correlating memory change scores across a 6-year period with change in
brain activation during the same time, a significant association was found in
the left HC. Individuals with more functional decline in the HC also displayed more memory decline (Figure 6A). With regard to brain structure, an
analogous correlation between right HC volume and change in memory performance was also observed. This suggests that both structural and functional alterations of the HC contribute to age-related memory decline. However,
there was no significant correlation between HC volume change and change
HC activation, indicating that the observed functional decline was not directly related to atrophy of gray matter.
Figure 6. Relationship between memory change and HC activation change. Panel A
displays the continuous relationship between memory change and HC activation
change in the full sample (n = 26). Panel B displays HC signal change in stable and
declining subgroups, defined by a median split.
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An incidental observation was that the subsample of individuals with declining memory (n = 13, defined by a median split) had increased HC activation
at the baseline MR session compared to the group with stable or improved
memory over time (Figure 6B). In fact, higher HC activation at baseline
predicted more memory decline over the following 6 years (r = -0.41, p =
0.037)2. Although this was an incidental finding, it is in line with previous
observations (O’Brien et al., 2010), and suggests that cognitive decline could
be preceded by a period of paradoxically increased HC recruitment.
Brain activation change was also correlated with memory change in the bilateral parahippocampal gyrus (PHG). However, in contrast to the HC proper, the relationship in these regions was negative, indicating that individuals
with declining memory increased their activation over time. It has previously
been demonstrated that aging can affect substructures in the MTL differentially. For instance, during episodic retrieval, an increased reliance on the
PHG and/or the rhinal cortex has been demonstrated in combination with
functional reductions in the HC (Cabeza et al., 2004; Daselaar et al., 2006).
This has been related to the increased reliance on familiarity-based recall
processes in older individuals, since these processes seem less affected by
aging than recollection-based ones (Mäntylä, 1993; Parkin & Walter, 1992).
Since the current effects were found during episodic encoding this explanation is not directly applicable. However, half of the blocks in the incidental
encoding task contained previously seen words, which could have triggered
recognition-related processes.
It was further investigated how the reported findings were related to APOE
ε4 status. There were no statistically significant differences in MTL activation or memory performance change, but ε4 carriers had a numerically larger
memory decline than non-carriers (-4.9 points compared to +0.7 for the noncarriers). The ε4 carriers also had a significantly larger reduction of HC volume over time. Thus, it cannot be ruled out that APOE status was one of the
driving forces behind the observed results. However, there was a significant
correlation between hippocampus volume and memory change even after
controlling for APOE status, and age. As well, APOE status and age were
included as covariates of no interest in the functional whole brain analysis
that identified the MTL clusters. So although APOE status could have partially explained the findings, other underlying factors likely exist.
In conclusion, the results of Study I corroborated previous cross-sectional
results by showing a longitudinal association between memory change and
structural and functional decline in the HC. The study also contributed tentative new findings regarding increased recruitment of PHG regions with cog2
This finding was not reported in the published version of the paper.
64
nitive decline. However, contrary to expectations, no significant relationships were observed between memory change and activation change in the
lateral PFC in Study I.
Study II
The objective of Study II was to investigate structural and functional brain
characteristics that differentiate successfully aged individuals from those
with an average age-related cognitive change. In contrast to the extensive
literature on neural correlates of age-related cognitive decline (Buckner,
2004), relatively little is known about the brain characteristics of successful
agers and no study has thus far investigated functional brain characteristics
of longitudinally defined successful agers. In Study II, successful and average individuals were defined relative to the average attrition-corrected
memory development in a sample of 1561 participants from the Betula
study, as described in the Methods section. Figure 7 displays the longitudinal
memory change in the resulting groups, which differed in both initial
memory level and memory change over time. When comparing memoryrelated brain activation between the groups, it was found that successful
participants had higher encoding-related activation than average participants
in the bilateral PFC and the left HC. In order to better characterize these
findings, the activation levels were compared to those of a young reference
group. For the HC it was found that the activation of average, but not successful, older participants was significantly lower than for young individuals
(Figure 8). The activation pattern across the frontal clusters was more mixed.
In a left inferior frontal cluster (cluster a, Figure 8) successful older had
higher activation than young participants, while the activation levels in the
remaining frontal clusters were comparable between the groups (Figure 8).
Figure 7. Longitudinal memory scores for successful and average
groups.
65
The average older participants did not
differ significantly from young individuals in any frontal cluster, but a
trend was seen in the anterior cingulate cortex (cluster c).
The observed functional effects were
not directly driven by differences in
Figure 8. Activation levels of successgender proportions or educational at- ful and average elderly, compared
tainment between the groups. Neither young individuals. Error bars show +/could the differences be explained by 1 standard error of the mean.
differences in gray or white matter
integrity. The activation in the HC correlated with scanner task performance,
but no such relationship was observed for the frontal clusters.
As the successful and average older groups differed both in initial memory
level and slope of memory change, a number of control analyses were performed in order to try to tease apart the effects of level and slope. The results
were first replicated in subgroups matched on initial memory levels, indicating that the effects were not only driven by differences in initial level. Further, hierarchical regressions using initial level and slope as predictors of
BOLD-signal in the clusters from the main analysis showed that, in these
groups, slope was the only significant predictor of BOLD-signal in the HC
and anterior cingulate cortex, while both initial level and slope were significant predictors of the frontal effects.
The HC activation differences were interpreted as reflecting a relative sparing of HC function in the successful agers, which likely also contributed to
the sparing of memory functions over time. The decrease in HC function in
the average elderly is also noteworthy, since these individual’s memory per66
formance change was comparable to the average in the full population-based
sample. Regarding the frontal activation differences, they could not easily be
attributed to compensatory processes, since no correlations were observed
between activation and performance, nor activation and age. Rather they
were interpreted as mainly reflecting higher frontal functionality since youth,
and perhaps a higher neural capacity (Y. Stern, 2009), which is the capability
to recruit more neural resources in order to cope with a challenging cognitive
task. This brain characteristic in turn could be a manifestation of cognitive
reserve (Y. Stern, 2009), which could contribute to the preservation of cognitive abilities through the course of aging. In summary, the findings of
Study II illustrate that successful cognitive aging could be accomplished by
preservation of a youth-like HC recruitment in combination with a high
frontal function. Also, successful aging was found to be driven mainly by
functional, rather than structural brain characteristics.
Study III
Study III aimed at investigating the relative influences of prior level of
memory function on the one hand, and memory change over time, on the
other, on brain activation differences in elderly participants. Although there
is an average decline in cognitive functions with age, large-scale longitudinal
studies have reported that individual differences in cognitive ability are remarkably stable across the lifespan. Correlations in the magnitude of r = 0.60.7 have been reported between childhood cognitive performance and performance in the 8th decade of life (Deary et al., 2004). Further, it has also
been shown that childhood cognitive ability differences can account for associations between cognitive ability and brain structure in older age (Karama
et al., 2013). This prompts the question of whether individual differences in
cognitive ability, established in youth, also account for a significant portion
of variability in cross-sectional assessments of functional brain activation in
elderly.
First, we demonstrated that in our sample, midlife memory scores, assessed
15-20 years earlier, accounted for approximately three times as much variance (R2 = 0.37) in current memory performance, as did estimates of prior
memory change (R2 = 0.12). Then, current memory scores were correlated
with brain activation during the Face-Name task to identify brain regions
that were differentially engaged as a function of performance level. During
episodic encoding, the bilateral HC and four clusters in the left PFC were
found to correlate positively with memory performance (Figure 9A). Parameter estimates were extracted from these clusters and used as dependent variables in hierarchical regressions, with midlife memory performance and
memory slope as predictors. In clusters with significant age-correlations, age
67
Figure 9. Differential encoding-related activation as a function of
current memory performance is displayed in panel A. Panel B shows
effects of slope in green and midlife memory in blue. In panel C overlapping effects are shown for midlife and current memory as well as
slope (color scale is the same as in previous panels).
68
was controlled for by entering it first in the regression. It was found that both
midlife memory and slope were significant predictors of activation in the left
HC cluster, as well as the largest left inferior frontal cluster (cluster 4, Figure
9A). This was found regardless of order of entry into the regression, i.e.,
midlife memory before slope, or vice versa. Midlife memory explained numerically more variance than slope in these clusters. For a left superior cluster (no. 6 in Figure 9A), slope was the only significant predictor, while the
opposite was true for an inferior frontal cluster (no. 3), and for the right HC,
i.e., these effects reflected only differences in midlife memory. Finally, in
the last of the frontal clusters (no. 5), age was the only significant predictor,
whereas both midlife memory and slope failed to reach significance.
To assess the replicability of these findings, midlife memory scores and
memory slopes were regressed directly onto encoding-related brain activation. For midlife memory, a pattern similar to that of current memory performance was seen, with prominent overlap in the left inferior frontal cortex
and the bilateral hippocampus (Figure 9C, in blue). Thus, memory scores
assessed 15-20 years prior to scanning reliably predicted brain activation in
memory-relevant brain areas. Regressing memory slopes directly onto encoding-related activation identified clusters overlapping the bilateral HC and
left inferior frontal clusters from the current memory analysis (Figure 9B-C,
effects of slope in green). Additionally, the slope analysis identified a rightsided PFC cluster (Figure 9B, left), which was not found in the contrast using current memory as a covariate. The slope analysis also resulted in a substantially increased extent of the left superior frontal cluster from the current
memory contrast (Figure 9B, right-hand side). This points to the increased
sensitivity of longitudinal estimates in identifying brain regions indicative of
actual cognitive change. Hierarchical regressions confirmed that slope, but
not midlife memory ability predicted significant variance in these two frontal
clusters. Control analyses were also performed, showing that these functional results were not primarily driven by differences in gray matter volume.
In summary, the findings of Study III highlighted that individual differences in cognition, established early in life, can account for a substantial
share of variability in memory and brain activation of healthy elderly. In
the absence of longitudinal data, this variability can be misinterpreted as
differential age-related changes. These findings have implications for interpretations of results from cross-sectional studies of neurocognitive aging,
specifically for approaches that seek to use BOLD-signal variability in elderly as a neural marker for forthcoming cognitive decline. The results of Study
III also identified regions in the right inferior and left superior PFC that were
specifically indicative of that age-related cognitive change had occurred. The
importance of these regions would have been underestimated, or altogether
missed, if only cross-sectional memory performance data were available.
69
Discussion
The main aim of the work presented in this thesis was to explore how longitudinal observations of aging individuals can advance knowledge of neurocognitive aging.
The major contributions of this thesis to the cognitive neuroscience of aging
can be summarized as follows:
i)
Substantiating the role of hippocampal structure and function in agerelated memory decline
ii) Shedding light on the relatively neglected topic of neural underpinnings of successful cognitive aging
iii) Contributing new evidence for the role of the prefrontal cortex in
age-related memory decline and stability.
iv) Emphasizing the influence of prior memory ability behind crosssectional brain activation differences in elderly.
In the following paragraphs I will discuss the findings relating to the MTL
and the frontal cortex separately. Then I will summarize the contribution of
longitudinal assessments to the field of neurocognitive aging, before addressing some limitations of the current work, as well as some avenues for
future work.
The hippocampus and the medial temporal lobe
The fact that significant effects were found in the HC across all three studies
strongly underscores its importance for heterogeneity in age-related memory
change. Not only was encoding-related left HC activity found to decline in
parallel with episodic memory decline across 6 years (Study I), it was also
spared in a group of individuals who had maintained high levels of memory
performance across 15-20 years (Study II). Moreover, left HC activation
during memory encoding was found to correlate with memory decline over
the past decade in Study III. Collectively the findings from all three studies
converge to show that HC function is impaired in healthy aged individuals
with memory decline in the normal range, but spared in those who have
maintained memory function up to older age. Although these findings were
70
obtained with fMRI, which is a correlational method, when considered together with related findings from brain injured patients and animal studies,
they strongly suggest that HC function is an important factor in age-related
memory decline or preservation.
Although a low HC function has previously been found in elderly compared
to young participants (Daselaar et al., 2003b; Dennis et al., 2007; Grady et
al., 1995), and particularly for low performing elderly (Daselaar et al.,
2003a), longitudinal evidence has been scarce. Evidence has also been
somewhat inconsistent, with several studies on healthy aging failing to find
functional HC reductions in elderly (Miller, Celone, et al., 2008; Persson et
al., 2011). Some have also suggested that memory impairment in healthy
aging might predominantly be caused by frontal rather than MTL dysfunction (Buckner, 2004). Thus, the longitudinal finding of decreased HC function correlating with decline in memory performance in Study I is strong
evidence in establishing HC failure in healthy elderly with age-related
memory decline. Further, our findings are strikingly similar to a previous
longitudinal study that showed that decreased HC activation correlated with
change in a global dementia rating scale (O’Brien et al., 2010). Our study
extends these findings by demonstrating this longitudinal HC-cognition link
in individuals free of clinical cognitive impairment (MMSE > 24 at followup). Because in contrast to our study, several of the fastest declining individuals progressed to MCI or probable Alzheimer’s disease at the follow-up (8
out of 13 participants) in the previous study. The reason that we were able to
detect earlier signs of cognitive decline was likely the use of sensitive episodic memory measures instead of a global dementia rating scale.
Although Study II and III did not have longitudinal imaging data, the findings from both these studies are consistent with decreasing HC function in
healthy aging, as long as the participants are experiencing some degree of
memory decline. The finding of lower HC activation in the average older
group compared with the young participants in Study II speaks strongly for
this. This group had very close to average memory performance when recruited to the Betula study 15-20 years ago (0.07 SDs below the mean of
their population-based age-cohort), and also were the ones with the most
average rate of change compared to the population-based sample. Having
this information probably makes this one of the most well-characterized
cross-sectional imaging samples to date. Thus, observing a decrease in HC
function in these individuals strongly corroborates the notion of a functional
HC impairment also in age-related memory loss within the normal range.
Although one can never be completely certain that no individuals in preclinical stages of dementia were included in the samples, in this case it seems
unlikely. First of all, all individuals with more than average decline were
excluded in the statistical classification procedures (Josefsson et al., 2012),
71
and secondly, we applied rather strict exclusion criteria to the ImAGen sample (see Methods section). Thus, it is more likely that the observed effects
are underestimation of the true age effects, rather than caused by inclusion of
individuals with prodromal dementia.
The demonstration that longitudinally-defined successful aging is associated
with spared HC function is novel, although cross-sectional studies have provided such indications previously (e.g., Daselaar et al., 2003a; Düzel,
Schütze, Yonelinas, & Heinze, 2011). However, in the light of the findings
from Study III, cross-sectional definitions might produce results that are
mainly caused by differences in cognitive ability from youth. This was not
the case in Study II, however, since we carefully controlled for differences in
memory performance at the beginning of the Betula study and found that the
differences in brain activation between successful and average agers remained. A previous longitudinal PET study (Beason-Held et al., 2008), also
concluded that HC function was spared in healthy elderly with preserved
cognition over time (although here an increase in HC signal over time was
actually observed). In general, successful aging has been somewhat neglected topic in the functional neuroimaging literature, with very few studies explicitly aimed at investigating it. Several studies have, however, classified
elderly individuals as either high- or low-performing. In a review of this
literature (Eyler, Sherzai, Kaup, & Jeste, 2011), nine studies were identified
in which successful performers had more HC/MTL activation than normal
performers, whereas the opposite pattern was found in eight studies (26 studies reported no significant relationship, and one study provided mixed findings). Whereas the nine studies with successful > normal patterns are consistent with the results of Study II, it is unclear how the opposite pattern
could have emerged. Since no information is given regarding how normal
and successful were defined, or during which task conditions this pattern
was observed, it is difficult to speculate on this matter (but see discussion on
increased HC activation in cognitively impaired individuals below). Nevertheless, the findings in Study II were robust and suggest that one reason why
some studies fail to demonstrate age-related HC decline in healthy elderly
(e.g., Persson et al., 2011) could be that the study sample is biased towards
successfully aged individuals. Also, the finding that successful agers had
spared HC function is in line with the brain maintenance account (Nyberg et
al., 2012).
Another novel contribution regarding HC function in aging was provided by
Study III, in demonstrating that a low HC recruitment in elderly does not
necessarily imply age-related decline, but can also reflect a low memory
performance from midlife - when no substantial age-related memory decline
is expected to have occurred (Rönnlund et al., 2005). The finding that
memory scores assessed 15-20 years prior to scanning reliably predicted
72
activity in brain areas that have been implicated in age-related memory decline is quite striking. This finding further underscores the importance of
longitudinal data for investigating neurocognitive aging, and has implications for interpretation of results from cross-sectional studies, in which agerelated memory decline is inferred from low performance at one point in
time. The findings from Study III do not imply that cross-sectional studies
cannot detect true age-related changes in HC function, only that on an individual level it is not possible to conclude functional decline or risk for impending dementia merely from observing a low HC BOLD-signal at one
point in time. This mainly has implications for approaches that attempt to
use BOLD-signal as a stand-alone biomarker for cognitive decline. However, it is still possible that BOLD-signal reductions, in combination with other
biomarkers, such as HC volume and APOE-status, could be useful in predicting cognitive decline (Woodard et al., 2010).
The relative influences of midlife cognitive ability and age-related cognitive
change on HC activation in elderly are likely dependent on the sample characteristics. In the relatively healthy and high-performing sample in Study III,
midlife ability accounted for numerically more variance. It is, however, likely that age-related change would have more influence in older samples, and
in samples with clinically relevant cognitive impairment. Still, it is acknowledged that the majority of cross-sectional neuroimaging studies of aging
mainly include high-functioning and healthy individuals (e.g., Dennis &
Peterson, 2012), which highlights the widespread implications of the findings in Study III. Due to the cross-sectional design in this study, the exact
causal chain of events linking midlife memory ability and current brain activation patterns cannot be resolved. It could be the case that the brains of
initially high-and low performing individuals still function in the same manner as they did 15-20 years ago, i.e., that the brain processing differences
observed in Study III are a stable trait. Alternatively, they could reflect differential trajectories of age-related brain changes. The “age is kinder to the
initially more able”-hypothesis (Gow et al., 2012; Richards et al., 2004;
Thompson, 1954), as well as the cognitive and brain reserve hypotheses
(Satz, 1993; Y. Stern, 2009), would predict the latter, that is, differential
impact of detrimental age-related processes.
Structural findings
With regard to HC volume, both Study I and Study II provided complementary evidence to the already extensive literature on structural brain changes
in aging. First, in Study I, converging evidence was found for the notion that
HC volume loss is associated with memory decline over time in healthy elderly (Kramer et al., 2007). Interestingly, however, the functional findings in
Study I did not appear to be directly driven by HC volume reduction, indi73
cating that volumetric and functional loss might be at least partially independent contributors to memory decline in aging. Also, in Study II, the differences in HC activation between the successful and average agers were not
driven by more atrophy in the average individuals, as demonstrated in a
BPM analysis. Rather, it appeared as though successful agers had smaller
proportions of gray matter within the functional ROIs, which was partially
but not fully accounted for by the higher proportion of females in the successful group. This is in line with the notion that bigger is not always better
when it comes to HC volume, which seems to be the case in younger adults
(Foster et al., 1999; Van Petten, 2004). Thus, in the face of this evidence, the
smaller gray matter volumes of successful agers are likely remnants from
younger years, and it remains to be clarified with longitudinal imaging data
whether successful agers experience HC volume loss or whether they are
relatively spared of such losses. However, in the light of previous findings it
could be the case that functional, rather than structural, brain imaging
measures are more predictive of cognitive variability within the range of
healthy aging (Walhovd et al., 2010).
Incidental findings
A few incidental findings relating to the MTL also deserve mentioning. One
such is the observation of increased PHG activation over time in individuals
with declining memory in Study I. Although this was not an expected finding, similar observations have been reported in cross-sectional aging studies
(Cabeza et al., 2004; Daselaar et al., 2006), as well as in a longitudinal fMRI
study from our own lab (Nyberg et al., 2010). Both Cabeza et al. (2004) and
Daselaar et al. (2006) found older age to be related to decreased HC activation coupled with increased activation in the PHG during episodic retrieval,
and interpreted this shift as an increased reliance of familiarity-based processing when recollective processing in the HC proper begins to fail. The
findings in Study I were obtained during episodic encoding, and although I
am not aware of any studies reporting age-related increases in encodingrelated PHG activation, the distinction between familiarity and recollection
in the MTL has been found during memory encoding previously. For instance, Ranganath et al., (2004) reported that BOLD-signal in the rhinal cortex during encoding predicted familiarity-based recognition, while BOLDsignal in the HC predicted recollection in young participants. In our study
there is also a possibility that the study design, in which half of the items in
the encoding condition were repeated, could have elicited recognition processes in the PHG. Also, although no direct evidence was reported to support
it, the increased PHG activity could potentially have served a compensatory
role in our study. This is suggested by the fact that episodic memory scores
from the offline Betula test battery did not correlate with the change in
recognition scores from the scanner task (p = 0.76). Nor did the stable and
74
declining subgroups differ significantly in recognition performance on the
scanner task at follow-up (t = 0.46, p = 0.63), although the difference was
significant on the offline Betula composite score (t = 6.76, p < 0.001)3 which
mainly consisted of free recall measures. Thus, the declining individuals did
not show deficits in recognition memory performance comparable to their
impairment on the recall tasks that were used as outcome measures, much in
line with notion that recognition processes are relatively spared in aging
(Craik & McDowd, 1987). It is not impossible that the relative sparing of
recognition memory performance was due to elevated PHG processing, although more systematic investigation is certainly needed before any firm
conclusions can be drawn.
Another incidental finding in Study I was that HC activation at baseline was
increased in individuals who subsequently experienced memory decline.
These types of observations have been made previously, most commonly in
individuals with MCI (Dickerson et al., 2005; Miller, Fenstermacher, et al.,
2008; O’Brien et al., 2010), but also in low-performing healthy elderly
(Miller, Celone, et al., 2008). On the basis of these observations, it has been
suggested that HC hyperactivation is indicative of forthcoming dementia or
Alzheimer’s disease (Dickerson & Sperling, 2008). According to this account the increased HC activation is some form of attempted compensatory
response that is possible only as long as there is no severe structural pathology to the MTL structures. Once such pathology becomes manifest, HC hypoactivation is seen instead. This hypothesis has been qualified by a multimodal study that found increased HC activation to be associated with betaamyloid burden (i.e., Alzheimer’s-related pathology) in healthy elderly
(Mormino et al., 2012), but at the same time positively correlated with
memory performance. Conflicting evidence was however provided by a
study in which HC hyperactivation in MCI participants was reduced pharmacologically, resulting in better memory performance during scanning
(Bakker et al., 2012).
While a clear consensus cannot currently be reached regarding the nature of
HC hyperactivation preceding cognitive decline, in the light of the work
presented in this thesis it seems paradoxical to find higher HC activation in
both successfully aged individuals and individuals who will experience future cognitive decline. However, it needs to be considered that the mechanism behind higher BOLD-signal can be quite different in these two groups.
While the successfully aged individuals in Study II likely had preserved HC
activation levels since youth, the individuals who experienced decline in
Study I might have increased their activation levels in response to accumulating pathology. Also, the increased activation in the decliners in Study I
3
These statistics are from post hoc analyses not reported in the published paper.
75
was observed at a younger age than in the successful individuals in Study II,
i.e., the supposed hyperactivation preceded decline. It might therefore be the
case that these individuals have had elevated HC-activation levels since
youth, which could cause subsequent decline due to an increased metabolic
burden (cf. Jagust & Mormino, 2011). This would be consistent with the fact
that brain-behavior correlations are sometimes observed to be negative in
younger individuals (less activation is better), while they are positive in older individuals (Eyler et al., 2011). These and other possible explanations will
need further examination in larger scale longitudinal imaging studies, with
more cognitively diverse samples.
In summary, a few important points pertaining to the hippocampal findings
in this thesis should be highlighted. Firstly, the longitudinal assessments
used here confirmed prior cross-sectional findings suggesting that HC function and structure underlies individual differences in cognitive aging. Secondly, functional reductions were found also in well-characterized healthy
elderly samples, with little risk of inclusion of individuals in preclinical
phases of dementia. This speaks strongly for HC impairment also in normal
aging. However, successfully aged individuals were shown to be spared
from such detrimental changes. Hence, a third take-home message is that HC
impairment does not seem to be an inevitable consequence of aging, or at the
very least that the course of HC change with aging can vary markedly between individuals. Another noteworthy observation in the current work was
that the absolute level of HC activation at one point in time might not be a
reliable indicator of age-related changes in cognitive function, partially since
it might reflect individual differences unrelated to aging processes. In contrast, reduction in HC activation over time is thus far undisputed as a marker
of age-related cognitive decline.
Frontal cortex contributions to memory in aging
Frontal effects were found both in Study II and Study III. In Study II, successful agers had higher frontal recruitment during encoding than average
elderly, predominantly in the left hemisphere. The findings of Study III illustrated that encoding-related frontal activation is partially driven by preexisting memory differences from midlife, but that longitudinal data on actual memory change can identify different frontal brain regions that are
uniquely diagnostic of age-related decline in memory ability. Possible reasons for lack of lateral frontal findings in Study I will also be discussed further below.
In general, the findings from Study II and Study III converge on the notion
that more frontal activation is beneficial for elderly individuals, much in line
76
with a recent review on cross-sectional imaging markers of successful cognitive aging (Eyler et al., 2011). The current studies found no evidence for
higher brain activation in lower performing elderly, or in those displaying
cognitive decline (de Chastelaine et al., 2011; Miller, Celone, et al., 2008;
Persson, Nyberg, et al., 2006). Due to the lack of longitudinal data in these
two studies, it is not possible to conclusively determine whether the observed higher frontal activation is a result of a functional reorganization, so
that spared memory function goes together with increased frontal function
over time; or whether individuals who have maintained high levels of frontal
function have preserved memory function, while individuals with declining
memory have decreased their frontal function over time. The first scenario
would be predicted by the compensation hypothesis (Cabeza, Anderson, et
al., 2002; Reuter-Lorenz, 2002), whereas the second is in line with the brain
maintenance account of successful aging (Nyberg et al., 2012). In the following paragraphs I will discuss the possibility of these scenarios, and others, in relation to three prominent frontal regions in which findings from
Study II and Study III converged: i) the left inferior frontal cortex (LIFC), ii)
the right inferior frontal cortex, and iii) the left superior frontal cortex.
Left inferior frontal cortex
Largely overlapping clusters in the LIFC (around xyz = -42, 34, 4) were
identified in both Study II and Study III. This area was more engaged by the
successful than average older participants during encoding in Study II. But it
was also significantly related to memory performance when only considering
data for those with declining slopes in Study III (n = 106; r = 0.35, p <
0.001), indicating that it is also sensitive for memory performance differences in the lower ability range. Further, the results in both Study II and
Study III indicated that activity in this region was predicted by both midlife
memory scores, and slope of prior memory change. Thus, older individuals
with low activation in this area tend to have lower memory, and a low activation/memory could be a static trait since youth, or a result of age-related
decline.
The exact function performed by the LIFC during memory encoding is still
not fully established (cf. section ‘Frontal cortex contributions to memory’),
but it should be noted that it is the brain region that most strongly displays
subsequent memory effects, i.e., stronger activation for later remembered
compared to forgotten study items in neuroimaging studies (Kim, 2011).
However, another study that investigated both encoding effort and encoding
success, found that the LIFC activation was more predictive of effort than
success (Reber et al., 2002). Nevertheless, the LIFC region is known to be
one of the most consistently underrecruited regions in older individuals
compared to young, during memory encoding (Dennis & Cabeza, 2008).
77
The compensation account
The LIFC cluster is interesting because it is the only cluster in which a subgroup of elderly, namely the successful agers in Study II, had significantly
higher BOLD-signal than young individuals (cluster A in Figure 8). This
could be interpreted as a compensatory response in the elderly group. However, a few facts speak against this interpretation. Firstly, activation in this
cluster did not contribute directly to successful performance on the scanner
task, although the same inferior left PFC region did correlate with memory
performance on the offline Betula composite score in Study III. Secondly,
the observed pattern of findings goes against a central premise of the compensation hypothesis, namely that compensation should be most prominent
in those who need it the most (Cabeza & Dennis, 2012). Not only did the
successful agers have spared memory function relative to the average elderly, they also had spared HC function. Given that additional frontal recruitment is often thought to compensate for failing MTL processing (Grady et
al., 2005; Gutchess et al., 2005), the obtained pattern of results seems to
contradict the traditional view of compensation. There is of course a possibility that the activation in this cluster compensated for failing processing in
some other brain region, such as the occipital cortex. Since we did not directly compare young and older participants on whole-brain activation it cannot
be ruled out that even successful older had lower activity than young in some
other brain region. Also, since no longitudinal data were available for HC
activation there is no way of knowing whether the successful older had decreased their HC-activation relative their own levels in youth, and therefore
were in need of compensatory processes.
Another fact could possibly speak for a compensation account, namely that
the successful agers in Study II, like their average counterparts, did perform
the scanner task more slowly and less accurately than young participants,
indicating that some neural processes might be failing4 also in the successful
group. This entails the possibility that the task was perceived as more difficult by the older groups, and that their brains might need to “work harder” to
perform the task at an optimal level. So, could the increased activation of the
successfully aged reflect increased effort, or decreased neural efficiency,
relative to the young individuals? If this were the case, the most effortful
processing, and highest frontal activation, would have been expected in the
group of average older, with the lowest memory performance. But it could
also be the case that the average individuals lacked the resources, or neural
capacity (Y. Stern, 2009) to recruit additional neural populations in the
frontal cortex in order to cope with the task demands. In other words, it
4
This behavioral decrement could also reflect that the elderly in general were more distracted
or hindered by the scanner environment, the response mode, and/or the more speeded nature
of the task compared to the Betula episodic memory tasks.
78
could be that aging had caused reduced neural efficiency in both older
groups, but only the successful agers had the capacity to increase frontal
activation in response to the challenging cognitive task. Such individual
differences in capacity have been demonstrated for frontal cortex activation
during working memory tasks, in both younger and older individuals (Nagel
et al., 2011). This line of reasoning would also be consistent with a higher
cognitive or brain reserve in the successful agers (Y. Stern, 2009), which has
also been referred to as compensatory potential (Reuter-Lorenz & Cappell,
2008).
Are there any other grounds than lower scanner task performance for assuming reduced neural efficiency also in the successful elderly? Since we did not
quantify structural integrity of the older groups relative to young individuals
in Study II, we cannot determine whether this could be a cause for reduced
efficiency. We only noted that the functional activation differences between
successful and average older were not caused by differences in gray matter,
and that the two older groups did not differ in white matter integrity. Thus,
as far as can be discerned from these data, the successful and average older
participants seemed equally spared from, or equally afflicted by, age-related
structural degradation. This would entail an equal need for compensation,
but possibly different capacities to do so. The need to consider differing
efficiency, capacity, and reserve has previously been proposed when accounting for BOLD-signal patterns in elderly, and individuals with dementia
(Prvulovic et al., 2005). This account, however, assumed capacity to be limited by age-related neurodegeneration. In the light of Study III, individual
differences from youth are also an important determinant of brain activation
patterns in elderly. Specifically, individual differences in innate intellectual
ability, educational/occupational attainment, or other lifestyle factors could
potentially contribute to higher baseline neural capacity in the successfully
aged individuals (Y. Stern, 2009). So although the current findings on LIFC
activation do not clearly fit with a traditional account of compensation, a
compensatory explanation cannot be ruled out.
The case for brain maintenance
Perhaps the most parsimonious account of the higher left PFC activity of the
successful agers in Study II would be to assume that their activation levels
have not increased over time, but remained at a stable high level since youth.
This would be in line with a brain maintenance account of successful aging
(Nyberg et al., 2012). The reason that the successful agers had higher LIFC
activation than the young individuals could be that they, the successfully
aged, were such a highly select group. In fact, they performed on average
one SD above their peers on the memory composite score when they were
first recruited to the Betula study. The young individuals in the control group
in Study II likely had memory abilities closer to the average (cf. Table 1 in
79
Study II). Until longitudinal data exists that can confirm this pattern, the
plausibility of this account rests on whether it can be expected that individual differences in memory or general intellectual ability are reflected in a high
LIFC BOLD-signal in youth. To date, this has not been well established.
Subsequent memory studies do consistently show that more activation in the
left inferior frontal region during encoding of later remembered than forgotten items (Kim, 2011), but is unclear how this relates to individual differences in ability. And since the successful older performed worse than young
on the scanner task, the obtained pattern is not likely to be a subsequent
memory effect. As well, the literature on individual differences in general
intellectual ability (which is likely to also include memory) suggests that
higher-ability young individuals often display less brain activation, particularly in the frontal cortex (Neubauer & Fink, 2009). This pattern has, however, been less consistently demonstrated for long-term memory tasks.
In summary, activation in the LIFC distinguished between high and low
performing elderly. This was partially due to individual differences from
midlife. Also, high-performing successfully aged individuals appeared to
have higher LIFC BOLD-signal than young individuals, a pattern that could
be consistent with both brain maintenance and compensatory accounts.
However, the traditional compensatory account did not fit well with the data.
Right inferior frontal cortex
Both Study II and Study III observed effects in overlapping clusters in the
right inferior frontal region (around xyz = 54, 36, 12). In Study II, the successful agers had higher activation than the average individuals in this region. The successful older also had slightly, but not significantly, higher
activation than young in this right-sided frontal cluster. Further, in Study III
this right-sided effect was shown to be driven only by differences in prior
slope of memory change, as opposed to individual differences in midlife
memory performance, a pattern that also differs from the left inferior cluster.
Although the right PFC is not thought to be as strongly engaged by episodic
memory encoding as the left (Tulving et al., 1994), young individuals typically also engage this right frontal region when encoding face-name pairs
(Persson et al., 2011). This indicates that this region likely is a part of the
normal encoding-network, and one that begins to fail in elderly with memory
decline, as indicated in Study III. Successful older adults, however, seemed
to be spared from this decline, or at least significantly less affected by it,
according to the results from Study II. Although this pattern does not provide
strong evidence for the HAROLD model (Cabeza, Anderson, et al., 2002;
Cabeza, 2002), it is at least not inconsistent with it, considering that those
elderly with the most spared memory performance have the most involve80
ment of right PFC during encoding. But there is still no direct evidence for
functional reorganization, that is, increased right frontal involvement over
time, which is assumed in the HAROLD model.
On the other hand, the fact that lower performing individuals with a negative
memory slope over time tended to have lower right frontal activation in
Study III converges with the finding of decreased encoding-related right
frontal activation across a 6-year interval by Nyberg et al., (2010). So although a decrease in right frontal function over time cannot be concluded
based on the cross-sectional data in Study III, the findings from Nyberg et al.
(2010) suggest that this is a likely scenario. Interestingly, left frontal activation decreases during encoding is most commonly observed in crosssectional aging studies (Dennis & Cabeza, 2008), but as seen in Study III,
activation in the left frontal cortex can also reflect individual differences
from midlife. Thus, collectively, Study III and the findings in Nyberg et al.
(2010) suggest that a decreased right frontal encoding-related activation in
elderly is more diagnostic of actual memory decline.
The lack of lateral frontal findings in Study I seems to contradict this pattern,
however. Why wasn’t memory decline associated with longitudinal change
in right frontal activation, if cross-sectional imaging data (Study III) suggest
that this should be the case, and a main effect of time was observed in this
region in the full sample from which the individuals in Study I were drawn
(Nyberg et al., 2010)? The discrepancy between Study I and Study III could
be due to sample size. In Study I the sample comprised 26 participants, while
the results in Study III were based on 203 participants. Considering that the
PFC likely is a heterogeneous structure across participants, the relatively
small sample size could have made it difficult to detect effects that were
inconsistent across participants. Nyberg et al. (2010) found a frontal activation decrease in the full sample of participants (n = 38) that completed follow-up in the same longitudinal imaging study that Study I was based on. In
other words, the 26 participants included in Study I were a subsample of
these individuals (exclusions are listed in the Methods section, and mainly
comprised individuals with missing behavioral data and those with substantial memory decline before the baseline scanning session). The fact that
Study I failed to pick up the effect observed in Nyberg et al., (2010) could be
due to lack of power, or that the effect was driven by the excluded participants. Alternatively, the frontal activation decrease could be a general phenomenon, not restricted to individuals with declining memory. In this case
the contrast used in Study I might not have detected it due to the presence of
individuals with zero or positive slopes. Thus, the phenomenon of decreased
right frontal activation during encoding will need to be investigated further
in larger-scale longitudinal data sets.
81
Left superior frontal cortex
An effect in the left superior frontal cortex (peak xyz = -22, 20, 64) was only
observed in Study III, and this effect was driven selectively by differences in
slope. Thus, like the right inferior frontal cluster it was independent of individual differences from midlife. In contrast to the right inferior cluster, however, the left superior cluster was not found in the contrast between successful and average elderly in Study II, which could suggest that it might be
more sensitive to the more impaired end of the performance distribution,
specifically those individuals who have experienced cognitive decline. This
left-sided cluster was also the only one to show a trend (p = 0.056) towards
an interaction effect between slope and initial memory level in Study III, so
that a low initial level combined with a more negative slope tended to result
in the lowest activation levels. Many previous studies have also shown reduced activation in elderly compared to young in this region during memory
encoding, both according to a comprehensive literature review (Rajah &
D’Esposito, 2005), and prior findings from our own lab (Salami, Eriksson, &
Nyberg, 2012). In combination with the current findings, the left superior
frontal region appears to be a specifically diagnostic region for memory decline over time.
Synthesis and summary of frontal cortex findings
To summarize the findings regarding frontal cortex function, it should first
be noted that the studies in this thesis indicated that high frontal recruitment
during memory encoding is beneficial for older individuals. This was true
for all frontal regions that were identified. Another noteworthy observation
is that although the left inferior frontal region differentiated between high
and low performing elderly, activation differences in this region appeared to
offer less diagnostic value than those in the right inferior and left superior
regions. This was because midlife memory differences explained significant
BOLD-variability in the left inferior region. Although this pattern of results
needs to be replicated, this observation could be of potential value for imaging studies that lack longitudinal data. That is, knowing that low activation
levels in the left superior or right inferior frontal cortex during memory encoding, coupled with low performance, are more reliably indicative of agerelated decline than low activation in the left inferior frontal cortex.
Relatively higher frontal activation was found for higher performing and
successfully aged individuals, but this does not necessarily need to be a reflection of compensatory processes. The findings could also be consistent
with the notion of brain maintenance rather than age-related increases in
activation over time (Nyberg et al., 2012). Only longitudinal imaging data
can resolve this issue, and current longitudinal evidence points to both re82
ductions (Nyberg et al., 2010) and increases (Goh et al., 2013) in frontal
activation over time. Of course, successful cognitive aging could also be a
result of relative brain maintenance (compared to average elderly), in combination with some forms of compensatory neural processes. There are likely
multiple routes to successful aging, and the relative amount of maintenance
and/or compensation probably varies on the individual level. In line with this
idea, results from a recent study showed that preserving a youth-like brain
activation pattern during memory encoding was associated with successful
cognitive aging, but a subgroup of elderly with a high degree of deviation
from the youthful pattern were still high-performing, presumably thanks to
their higher recruitment in, for instance, the left frontal cortex (Düzel et al.,
2011).
Another factor that cannot be ignored in this context is pre-existing ability or
capacity differences between individuals, especially in the light of the results
from Study III. Individual differences in frontal cortex capacity, or responsiveness to cognitive demands, have been demonstrated in both young and
elderly individuals, and are reflected in individual differences in performance levels (Nagel et al., 2011). To a large degree, variability in frontal
cortex activation in aging is likely to reflect such stable individual capacity
differences from younger age. But it is also likely that individual capacities
are decreased by detrimental age-related changes (Prvulovic et al., 2005), of
which an individual could be more or less afflicted. Further, situations when
elderly, even high-performing ones, have higher frontal recruitment than
young individuals, possibly reflect increased effort in performing the task,
which is more or less equivalent to the concept of decreased neural efficiency (Y. Stern, 2009). However, elderly that for various reasons have lower
frontal capacity may not be able to modulate frontal activation to meet task
demands. Increased neural recruitment of this type could be considered
compensatory, as long as task performance is maintained at a relatively high,
or youthful, level. This type of compensation does not, however, need to be
unique to elderly individuals, but is likely an expression of the same mechanism as when young individuals increase frontal recruitment in response to
elevated task demands (Callicott et al., 1999).
In summary, it is tempting to conclude that both compensation and brain
maintenance can explain frontal cortex function in aging, and future studies
with longitudinal data will be able to better tease apart their relative influences. As the preceding discussion highlighted, the nature of frontal cortex
involvement in age-related changes is complex, and many factors need to be
considered in interpreting the results.
83
Contributions of longitudinal data
One methodological motivation behind the current thesis was to investigate
what is gained from knowing a person’s cognitive history, that is, having
access to longitudinal data. Firstly, as I have argued throughout this thesis,
longitudinal behavioral data provide a better characterization of samples.
The aging population is heterogeneous and as sampling rarely is random or
population-based, the risk for skewed and non-representative samples is
substantial. Longitudinal data eliminates the need for inferring age-related
decline or maintenance from a low or high cognitive performance at one
point in time. By extension, this allows for a better apprehension of the sample characteristics, that is, whether one is dealing with a successfully aged
sample or individuals who have experienced cognitive decline. Given that
such inter-individual differences are linked to specific brain characteristics,
it is not surprising that the findings from neuroimaging studies of normal
aging provide such divergent results. Better characterization of samples with
longitudinal data, or through other means, will be essential to get to the bottom with what normal neurocognitive aging is, and what characterizes successfully and less successfully aged individuals.
Longitudinal assessments might be even more important for neuroimaging
measures. Age-related change in brain function is likely a dynamic process,
with a continuous interplay of structural, functional, neurochemical, and
cognitive/strategic alterations causing increased and decreased BOLD-signal
over time. A glimpse of this complexity can be gained from one of the only
truly longitudinal functional imaging studies of aging, with multiple followups across 9 years (Beason-Held et al., 2008). This study demonstrated linear increases and decreases in brain function across the whole measurement
period, as well as step decreases and increases at various time-points in between. Further, the study demonstrated that increases and decreases over
time can co-occur within a single brain region, as was found for the middle
frontal gyrus. Considering that cross-sectional studies provide merely a
snap-shot of such dynamic processes, it is easy to come to erroneous conclusions. This was elegantly demonstrated in the study by Nyberg et al. (2010),
in which cross-sectional analyses alluded to age-related increases in frontal
activation over time, but longitudinal analyses of the same data set showed a
decrease in frontal activation. Another illuminating example is the longitudinal change in HC activation in Study I. Consider the left panel of Figure 6
(p. 63), which shows that the subgroups of declining and stable individuals
have equivalent HC activation at the follow-up scan. If the baseline measurement was lacking, it would have been easy to conclude that prior memory
change had no relation to HC activation.
84
Limitations and future directions
Two limitations of the work presented in this thesis were the small sample
size in Study I, as well as the lack of longitudinal imaging data in Study II
and Study III. Both of these limitations will be addressed with the upcoming
follow-up of the ImAGen sample. The resulting data set can be used to better
address remaining questions about, for instance, the nature of frontal activation change or stability over time, which has relevance for distinguishing
between compensation and maintenance accounts of frontal brain function in
aging. Another question that can be addressed is the paradox that higher HC
activation is both associated with successful aging, and predictive of subsequent cognitive decline. The current work is also limited in that there is, as
of yet, no follow-up data on the cognitive status of the participants. Such
data would be valuable since it would make it possible to, in retrospect, exclude participants who later go on to develop dementia or other pathological
conditions. Although all participants were thoroughly assessed with a comprehensive cognitive test battery in connection with the fifth measurement
point in Betula, there is always a risk that such individuals could have gone
undetected. Since brain pathology can be manifest before the onset of cognitive symptoms of dementia, the inclusion of individuals in preclinical stages
could influence the imaging results. However, as will be discussed below,
the samples used in this thesis are more likely to be healthier and more highfunctioning than average.
The studies in this thesis were also relatively modest in the use of structural
indices of brain aging, which are known to influence the BOLD-signal
(Kalpouzos et al., 2012). One major avenue for future research on neurocognitive aging will certainly be multi-modal imaging, not only combining
structural and functional MRI, but also considering PET-markers of, for
instance, dopaminergic function and amyloid burden. Such methods can
provide both converging and complementary information on the aging processes in the brain. This is also important because the BOLD-signal in itself
has some limitations that need to be considered, for instance that it only provides a correlative measure of neural activity. Converging evidence from
alternative imaging modalities and experimental procedures, such as transcranial magnetic stimulation and pharmacological studies, would therefore
be reassuring. There are also many known complications when attempting to
probe the aging brain using fMRI. These include age-related alterations in
the vascular response of the brain, differences in resting cerebral blood flow,
and a decreased signal-to-noise ratio (D’Esposito, Deouell, & Gazzaley,
2003; Kannurpatti, Motes, Rypma, & Biswal, 2010). Such confounds are
likely to have the largest influence when directly contrasting young and elderly individuals. For that reason, the main analyses throughout all three
studies of this thesis were comparisons within the elderly age-span (with the
85
exception of the comparisons with the young control group in Study II). This
should have served to lessen the impact of such concerns, although there are
likely individual differences in vascular factors within the older population
as well, which might influence the results.
While the merits of longitudinal data have been stressed throughout this
thesis, they are also associated with their own sets of challenges. One such is
that sample representativeness can be compromised by higher attrition of
lower-performing individuals, and those who experience accelerated decline
(Josefsson et al., 2012; Rönnlund et al., 2005). Although such effects are
hard to avoid altogether, measures were taken to correct for attrition in Study
II (cf. section ‘Statistical classification for Study II’), which helped to improve the representativeness of the control group of average elderly. In general, for Study II as well as Study I, an attrition effect would not compromise
the validity of the obtained results, but rather make it harder to detect effects
in the first place, i.e., increase the risk for type II errors (false negatives).
Possible attrition effects are, however, more troublesome for Study III since
having a more healthy and cognitively stable sample might overestimate
influences of midlife memory ability on brain activation patterns 15-20 years
later. This was acknowledged in the discussion of the paper, and I argue that
the findings of the study still are of relevance for the field, since our sample
likely is at least as representative as those used in the majority of crosssectional neuroimaging studies with convenience sampling. Also, I have
tried to be very thorough in describing the characteristics of the samples
used in this thesis, as well as the selection procedures applied to them. By
doing this, I hope that the possible impact of attrition on the presented results
can be better appraised.
Another issue in longitudinal research is the existence of practice effects on
cognitive scores over time, which results in positive slopes of memory
change over time. Positive slopes can also result from regression to the mean
effects, or rebound effects from temporary cognitive declines caused by factors such as depression or stress. Finding positive slopes in elderly samples
does not need to be undesirable in itself, since a practice effect can signal the
presence of an intact memory system. In Study II, for instance, finding improved memory scores over time further supports that a truly successfully
aged group of individuals has been identified. However, to the extent that
positive slopes result from rebound effects, they may interfere with detecting
effects in correlative approaches such as the ones in Study I and Study III.
Finally, I will briefly touch upon two future directions related to the findings
in this thesis, that I would find particularly interesting to pursue. Firstly, the
finding that midlife memory predicted brain activity 15-20 years later (Study
III), is worth investigating with longitudinal imaging data. Only by doing
86
this, one can uncover in what way midlife memory ability is related to brain
activation in older years. Is it because higher-ability individuals have high
activation levels and maintain them over time, while lower-ability individuals maintain low activation levels from youth? Or do high- and low ability
individuals show different rates of neural changes, so that age is kinder to
the initially more able? Or is it possible that high-ability individuals in fact
increase their activation over time in a compensatory manner? Another interesting path to pursue would be individual-level characterization of hippocampal function, especially with longitudinal imaging data. The major advantage of doing this characterization on the individual level is to avoid loss
of sensitivity when averaging data across participants, as is done in standard
imaging analyses (Vandenbroucke et al., 2004). Also, by only considering
group analyses there is a loss of some information, such as the opportunity to
assess the proportion of individuals who have spared HC function. For instance, by using individual characterization of hippocampal function at rest it
was found that 13 out of 30 healthy elderly from a longitudinal study had a
HC signal that was comparable to that of young individuals (Small, Tsai,
DeLaPaz, Mayeux, & Stern, 2002). Such a characterization would be interesting to implement in a larger sample, such as the ImAGen cohort, although
it would likely present some methodological challenges.
Concluding remarks
This thesis investigated how longitudinal observations of cognitive change
and neuroimaging measures can advance our knowledge of neurocognitive
aging. Based on the work presented in the preceding sections, I would like to
highlight the following points:
i)
Higher frontal cortex activation was found to be beneficial in aging,
but the pattern of frontal findings could not conclusively distinguish
between prevailing theories of frontal function in aging. Studies with
longitudinal imaging data will be needed to elucidate the dynamics
behind frontal cortex contributions to memory in aging.
ii) Tentatively, the results indicated that frontal cortex regions might be
differentially indicative of age-related cognitive decline. Activation
levels in the left superior and right inferior frontal cortices during
memory encoding were exclusively related to memory change over
time, while activation levels in other regions could reflect individual
differences unrelated to aging.
87
iii) Functional decline in the hippocampus was found to be present in
healthy elderly with memory decline in the normal range, but it is
not a necessary consequence of aging since successfully aged individuals were found to be spared from, or at least substantially less
afflicted by, such changes.
iv) Hippocampal activation levels at a given point in time might be unreliable indictors of age-related cognitive decline, but reduction in
hippocampal activation over time has consistently been associated
with declining cognitive functions.
v) Finally, longitudinal data is imperative in the study of neurocognitive aging since it makes it possible to better characterize elderly
samples, and eliminates the need to infer decline from low performance or brain activity at one point in time.
88
Acknowledgements
Firstly, I would like to thank my supervisors, Jonas Persson and Lars
Nyberg, for their excellent scientific guidance and all their patience and support during the past few years. I am also deeply grateful to Lars-Göran Nilsson for welcoming me to the Betula project, and for giving me access to such
an exceptional and high-quality data set. I have been truly privileged to be
working with such data.
Many people have made substantial contributions to the work presented in
this thesis. Maria Josefsson and Xavier de Luna at the statistics department
at Umeå University provided the statistical classification model for Study II,
and Maria has patiently helped me many times since. Micael Andersson
taught me all I know about fMRI data analyses, and has been immensely
helpful with all technical issues and questions I’ve had. Alireza Salami provided great assistance with methodological aspects of MRI analyses, and
Anders Lundquist was very helpful in answering all my statistical questions.
I sincerely thank all of you.
Thanks also to the lab group at UFBI for providing such an educational and
stimulating work environment, and particularly to my former roommates
Karolina, Urban, and Fredrik for all the discussions about work-related (and
not so work-related) matters. Special thanks to Karolina, my closest collaborator during these years, for all the thoughtful feedback, rewarding discussions, and for your ceaseless positivity.
Many thanks to my co-workers in the Betula project, especially to Mikael
Stiernstedt, for all the assistance during (and after) the data collection.
Thanks also to the nurses at MR for all their help during that time, and, of
course, to all the research participants!
I am also very grateful to my family for all the support you have given me
throughout the years.
Last but not least, my deepest gratitude to my grandmother, Viola, to whom
I have dedicated this thesis. Thank you for all your concern, encouragement,
and support throughout my life. And for being such a prime example of a
successful ager! Tack fammo!
89
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