Document 2881682

The aging brain and changes in cognitive performance
Findings from morphometry and
quantitative susceptibility mapping of iron
Thesis for doctoral degree (Ph.D.)
By
Ninni Persson
Stockholm University, 2015
©Ninni Persson, Stockholm University 2015
Cover illustration by Prof. M. Hausser, Sarah Rieubland & Arnd Roth,
UCL/Wellcome Images
Purkinje cell and dendritic tree, rat cerebellar cortex
Imaged by focused ion beam scanning electron microscopy,
110 micrometres (0.11 mm).
ISBN 978-91-7649-295-6
Printed in Sweden by Holmbergs, Malmö 2015
Distributor: The Department of Psychology, Stockholm University
The aging brain and changes in cognitive performance
Findings from morphometry and quantitative susceptibility mapping of iron
Ninni Persson
To all the women in academia who resist the patriarchy.
List of studies
I.
Persson N., Ghisletta, P., Dahle, C. L., Bender, A. R., Yang, Y.,
Yuan, P., ... Raz, N. (2014). Regional brain shrinkage over two
years: Individual differences and effects of pro-inflammatory genetic polymorphisms. NeuroImage, 103, 334-348.*
II.
Persson N., Wu, J., Zhang, Q., Liu, T., Shen, J., Bao, R., Ni, M.,
Liu, T, Wang, Y. & Spincemaille, P. (2015). Age and sex related
differences in subcortical brain iron concentrations among healthy
adults. NeuroImage, 122, 385-398.*
III.
Persson N., Ghisletta, P., Dahle, C. L., Bender, A. R., Yang, Y.,
Yuan, P., ... Raz, N. (2016). Regional brain shrinkage and change
in cognitive performance over two years: The bidirectional influences of the brain and cognitive reserve factors. NeuroImage, 126,
15-26.*
* reprinted with permission from Elsevier.
Other contributions
I.
Persson, N., Lavebratt, C., & Wahlin, Å. (2013). Synergy effects
of HbA1c and variants of APOE and BDNFVal66Met explains individual differences in memory performance.
Neurobiology of Learning and Memory, 106, 274–282.
II.
Persson, N., Viitanen, M., Almkvist, O., & Wahlin, Å. (2013). A
principal component model of medical health: implications for
cognitive deficits and decline among adults in a population-based
sample. Journal of Health Psychology, 18, 1268–87.
III.
Persson, N., Lavebratt, C., Sundström, A., & Fischer, H. (2015).
Pulse pressure magnifies the effect of COMTVal158Met on 15
year episodic memory trajectories. Submitted. Frontiers in Aging
Neuroscience.
Abstract
Brain aging is a heterogeneous phenomenon, and this thesis illustrates
how the course of aging can vary within individuals over time and between individuals as a function of age, sex, and genetic variability. We
used two contrasts from magnetic resonance imaging (MRI), namely spinlattice T1-weighted imaging, and quantitative susceptibility mapping
(QSM) from gradient-echo images, to picture the aging brain, by means of
morphometric measures and brain-iron concentrations. Within each study,
the same rigorous imaging acquisitioning protocols were used over large
samples sizes of 167-183 individuals, which contribute to the uniqueness
of the studies. Most of the current knowledge about the aging brain rests
on the foundation of cross-sectional age-related differences, and studies I
and III contribute to current knowledge with longitudinal designs to investigate individual rates of change. The importance of genetic variation
in relation to regional brain changes was addressed with a specific emphasis on functional polymorphisms involved in pro-inflammatory responses.
These studies further shed light on the importance of bi-directional relations between structural integrity and maintained cognitive abilities over
time. Study II is the largest study to date to have quantitative susceptibility estimates examined in healthy adults, and the first in-vivo report to
show a lowering in overall subcortical brain iron estimates in women from
midlife to old age. Studies I and III are unique by examining longitudinal
differences in anatomical brain regions using high resolution images from
a 4 Tesla scanner. Peripheral vascular risk factors were not strong determinants of either brain- or cognitive changes in the studied samples. The
results are discussed in the context of cognitive reserve, the brain maintenance hypothesis, and potential influences of hormones, inflammation and
oxidative stress.
Keywords: brain aging; volumes; individual differences; QSM; cognitive
aging; iron; episodic memory; fluid-; crystalized abilities; sex differences;
gender differences.
Contents
1
Introduction ............................................................................................. 13
1.1
Brain aging ............................................................................................................... 14
1.1.1
Neuroanatomical regions ............................................................................ 15
1.1.2
Brain iron ....................................................................................................... 17
1.1.3
Theories of Brain Aging............................................................................... 20
1.1.3.1 Inflammation ........................................................................................... 20
1.1.3.2 Oxidative stress ...................................................................................... 20
1.1.3.3 Brain reserve ........................................................................................... 21
1.1.3.4 Brain maintenance ................................................................................. 21
1.1.3.5 Frontal aging hypothesis ....................................................................... 21
1.1.4
Modifiers of brain aging .............................................................................. 22
1.1.4.1 Sex differences ........................................................................................ 22
1.1.4.2 Genetics .................................................................................................... 23
1.1.4.3 Cardiovascular risk factors ................................................................... 24
1.1.4.4 Socioeconomic factors ........................................................................... 25
1.2
Cognitive aging ........................................................................................................ 26
1.3
Brain–cognition relationships ............................................................................... 27
1.4
Theories of cognitive aging ................................................................................... 28
1.4.1
Successful aging ........................................................................................... 28
1.4.2
Cognitive reserve ......................................................................................... 29
2
Goals .......................................................................................................... 30
3
Method ...................................................................................................... 31
3.1
Participants ............................................................................................................... 31
3.2
MRI methods ............................................................................................................ 33
3.2.1
T1-weighted imaging (spin lattice) .......................................................... 33
3.2.2
Quantitative susceptibility mapping ......................................................... 34
3.3
Design ........................................................................................................................ 36
3.4
Statistical methods ................................................................................................. 36
3.4.1
Structural equation modeling .................................................................... 37
3.4.1.1 Measurement model............................................................................... 37
3.4.1.2 SEM with covariates ............................................................................... 39
3.4.1.3 Multiple group analysis .......................................................................... 39
3.4.1.4 Model fit .................................................................................................... 39
3.4.2
4
Summary of studies ............................................................................... 42
4.1
Study I ....................................................................................................................... 43
4.1.1
Background.................................................................................................... 43
4.1.2
Objective ........................................................................................................ 43
4.1.3
Method ............................................................................................................ 44
4.1.4
Results ............................................................................................................ 45
4.1.5
Conclusion...................................................................................................... 47
4.2
Study II ..................................................................................................................... 48
4.2.1
Background.................................................................................................... 48
4.2.2
Objective ........................................................................................................ 49
4.2.3
Method ............................................................................................................ 49
4.2.4
Results ............................................................................................................ 50
4.2.5
Conclusion...................................................................................................... 51
4.3
5
Latent change score modeling .................................................................. 40
Study III.................................................................................................................... 53
4.3.1
Background.................................................................................................... 53
4.3.2
Objective ........................................................................................................ 54
4.3.3
Method ............................................................................................................ 54
4.3.4
Results ............................................................................................................ 55
4.3.5
Conclusion...................................................................................................... 56
Overall discussion ................................................................................... 57
5.1
Mean change and individual differences ............................................................ 57
5.2
The influence of age ............................................................................................... 59
5.3
Determinants of age-related changes ................................................................ 61
5.4
Sex related differences in brain iron................................................................... 62
5.5
Bidirectional brain-cognition relationships ........................................................ 62
5.6
Methodological considerations ............................................................................. 64
5.7
Future directions ..................................................................................................... 67
5.8
Concluding remarks ................................................................................................ 68
6
Summary in Swedish ............................................................................. 71
7
Acknowledgements ................................................................................. 73
8
References ................................................................................................ 74
Abbreviations
AD
CFA
EM
Fe
Gc
Gf
ICC
LCSM
MGA
MRI
NMR
PD
PM
QSM
Relaxation
ROI
SEM
SNP
T1w
V
1.5 T
4T
Alzheimer’s disease
Confirmatory factor analysis
Episodic memory
Iron
Crystalized intelligence
Fluid intelligence
Intra-class coefficient
Latent change score model
Multiple group analysis
Magnetic resonance imaging
Nuclear magnetic resonance
Parkinson’s disease
Post-mortem
Quantitative susceptibility mapping
Signals change with time
Region of interest
Structural equation modeling
Single nucleotide polymorphism
T1 weighted (T1 = longitudinal relaxation)
Vocabulary
1.5 Tesla (strength of the magnetic field)
4 Tesla (strength of the magnetic field)
“It is never too late to turn on the light. Your ability to break an unhealthy habit or turn off an old tape doesn't depend on how long it has
been running; a shift in perspective doesn't depend on how long
you've held on to the old view.
When you flip the switch in that attic, it doesn't matter whether its
been dark for ten minutes, ten years or ten decades.
The light still illuminates the room and banishes the murkiness, letting
you see the things you couldn't see before.
Its never too late to take a moment to look.”
—Sharon Salzberg
12
1 Introduction
“The future is not some place we are going to, but one we are creating. The
paths are not to be found, but made, and the activity of making them, changes both the maker and the destination”.
—John Schaar, futurist
Brain changes following aging can have implications for mental abilities and our functioning in everyday life. The aging brain undergoes
changes in neuroanatomical regions as well as neurotransmitter systems, and brain iron levels. It is well known that in many aspects of
development, each person ages at an individual rate. The phenomenon
is well studied in the research area of cognitive aging, but less is
known about the heterogeneity of brain aging. It is important to know
more about differential age-related brain changes to prolong cognitive
health, but also to learn more about differences between normal agerelated versus pathology-related neurodegeneration. Aging is a dynamic phenomenon that cannot be studied in a snapshot frozen in
time. Yet, most of the existing reports focus selectively on crosssectional age-related differences in neural correlates of aging, hence
ignoring individual differences in change. Another important issue is
unraveling the determinants of such individual differences. Both genetic variants, health factors, and social and demographic factors may
act as a reserve for maintaining sufficient structural integrity of the
brain for optimal function on one hand, and to offset accelerated
shrinkage on the other hand.
Most of the current knowledge in the field of brain aging has emerged
from cross-sectional age-differences as a function of chronological
age at a single time point. Also, most of our current knowledge about
brain aging from structural magnetic resonance imaging is based on
the contrast between gray and white matter or white matter integrity.
Brain iron estimates from iron-sensitive contrasts provide new possibilities for describing age-related changes in the human brain, and
perhaps even illustrate the mechanisms underlying aging, by influencing processes such as oxidative stress. Further, little is known about
other determinants of iron accumulation beyond chronological age,
and herein we focused on sex-related variations, and how iron elevation occurs in conjunction over several of the subcortical nuclei,
which is a less studied phenomenon.
13
Brain aging is also accompanied by age-related changes in cognitive
functions. Most of the current work reflects the contribution of neuronal variations to individual differences in cognitive performance,
while substantially less work has focused on the bi-directional influences between cognition and brain. As science reveals how behavior
also potentially shapes the brain, the relevance of further assessment
of such bi-directional brain-cognition relationships becomes more
obvious; people who maintain their cognitive abilities may have “vital
brains.” The overall aim of this thesis is to fill the outlined gaps in
knowledge.
1.1 Brain aging
Although behavior alters the brain, and virtually any experience may
leave traces, certain age-related changes of the parenchyma are to be
expected, although varying in degree across individuals. The brain
undergoes age-related changes and declines in volume and weight
after the sixth decade of life at a more gross level. This is manifested
in shrinking gyri and widening sulci, as well as ventricular enlargements of the cortex (Anderton, 1997), and alterations in the vasculature (Trollor & Valenzuela, 2001). Many individual brain regions exhibit shrinkage in volume in both gray and white matter (Fjell &
Walhovd, 2010; Hedman, van Haren, Schnack, Kahn, & Hulshoff Pol,
2012; Persson et al., 2014; Raz et al., 2005a; Sullivan & Pfefferbaum,
2006). In post mortem (PM) can neurochemical and more microscopic
assessment of the brain be conducted; however important aspects of
developmental changes are then lost. PM findings reveal morphological and molecular markers of tissue degeneration in the prefrontal- and
medial temporal lobes as well as the anterior cingulum in specimens
from participants free of dementia (Henstridge et al., 2015). Further,
neurofibrillary tangles, amyloid plaques, and neuropil threads are present in the gray matter, primarily in the medial temporal lobe and
amygdala, also in non-pathologic aging, although to a considerably
lesser extent than in Alzheimer’s disease (AD) (Anderton, 1997;
Mrak, Griffin, & Graham, 1997; Price, Davis, Morris, & White;
Whalley, Deary, Appleton, & Starr, 2004).
Axons also demyelinate during aging (Flechsig Of Leipsic, 1901),
which influences action potentials traveling along the axon (Waxman,
2006), potentially slowing cognitive processing (Ylikoski et al., 1993).
Aging is also associated with alterations in several neurotransmitter
systems that signal via acetylcholine and monoamines (serotonin and
dopamine) (Trollor & Valenzuela, 2001), which may have implica14
tions for behavioral changes. The dopamine neurotransmitter system
(Seeman et al., 1987; Severson, Marcusson, Winblad, & Finch, 1982;
Volkow et al., 1998), which can alter both cognitive and motor functions (Volkow et al., 1998) is of particular interest in aging. For reasons that are poorly understood, heavy metals may accumulate in the
aging brain. Both iron and copper increases are reported (Zecca et al.,
2004), but only iron has sufficiently high paramagnetic influence on
the magnetic resonance (MR) signal, to manifest itself as contrast enhancement in MR images (Schenck, 2003). Age-related brain iron
accumulation is primarily observed in the subcortical nuclei, but also
to a somewhat lesser extent in the cortex according to PM findings
(Hallgren & Sourander, 1958; Ward, Zucca, Duyn, Crichton, & Zecca,
2014).
In summary, PM techniques allow for thorough assessment of the
brain at a molecular level, but fail to capture age-related changes over
time, which is only plausible via study of the live human brain. Noninvasive in vivo magnetic resonance imaging (MRI) provides a unique
window into the aging human brain.
1.1.1 Neuroanatomical regions
Structural MRI is a powerful tool for non-invasive study of the aging
brain in vivo. Shrinkage of regional brain volumes is often reported to
reflect neuronal cell death or de-generation, but precise counts of synapses or neurons are not possible through in vivo MRI. One of the
great advantages of MRI is that the brain can be studied in the context
of development, which PM studies do not allow. Findings from structural MR contrast imaging reveal that the magnitude of shrinkage varies across brain regions (Persson et al., 2014; Raz, Ghisletta,
Rodrigue, Kennedy, & Lindenberger, 2010). The literature often emphasizes that effects are greater for the prefrontal areas of the brain
when measured at a point in time or over short repeated measurement
intervals (Fjell et al., 2009; Raz et al., 2004), but other studies contradict this (Persson et al., 2014; Raz et al., 2010). White matter integrity
is hypothesized, primarily on the basis of cross-sectional reports, to
have a curvilinear relationship with age, with increasing structural
integrity until early adulthood followed by a brief plateau, after which
there is a decline from approximately the seventh decade of life
(Westlye et al., 2010). This trend is more pronounced in regions of the
frontal and the parietal lobes (Sowell et al., 2003). Recent longitudinal
15
work over shorter measurement intervals supports the presence of agerelated decline from the fifth decade of life (Sexton et al., 2014). It
would be necessary to collect repeated measurements at several time
points to assess the true functional form of white matter aging, e.g.,
whether it is non-linear or has some other relationship. Late postadolescent brain maturation, expressed as a reduction of cortical gray
matter of the dorsal frontal cortex, has been observed up until young
adulthood. Thinner cortex in cross-section has been interpreted as denoting synaptic pruning (Sowell, Thompson, Tessner, & Toga, 2001).
Smaller volumes as a function of age are also observed in the structures of the limbic cortices. Significant age-related alterations in structure and function have been observed in the hippocampus, entorhinal
cortices, and parahippocampal gyrus (Rodrigue & Raz, 2004;
Thomann et al., 2013), as well as the amygdala formation (St Jacques,
Dolcos, & Cabeza, 2009). Volume and microstructural degradation
with advanced age have also been observed in phylogenetically older
structures in the basal ganglia and the thalamic nuclei (Fama &
Sullivan, 2015; Wenjing Li et al., 2014). Finally, significant shrinkage
is observed in the cerebellar cortices over two years in healthy adults
(Persson et al., 2014). Longitudinal studies suggest that the posterior
regions of the brain are in general spared until advanced age in healthy
adults (Fjell et al., 2009; Raz et al., 2005a).
In addition to general trends toward brain shrinkage there exist individual differences in the course of brain aging (Persson et al., 2014;
Raz, Ghisletta, Rodrigue, Kennedy, & Lindenberger, 2010). See Figure 1 for an illustration of individual differences at two measurement
points (adopted from Persson et al., 2014).
16
Figure 1. Longitudinal spaghetti plots illustrate each individual’s values
over measurement occasions as a function of age at each time point. The
figure is reprinted with permission from Elsevier.
1.1.2 Brain iron
Iron serves as an important cofactor in myelination and dopamine
transmission (Berg & Youdim, 2006; Mills, Dong, Wang, & Xu,
2010) but can become neurotoxic when there is excessive accumula17
tion intracellularly (Andersen, Johnsen, & Moos, 2014a; Dixon &
Stockwell, 2014) (see Figure 2 for an illustration). In vivo MRI shows
that iron levels are elevated in numerous age-dependent neurodegenerative diseases (Bartzokis, Tishler, Shin, Lu, & Cummings, 2004;
Langkammer, Ropele, Pirpamer, Fazekas, & Schmidt, 2013). Iron is
believed to enter the brain via the blood-brain barrier by transferrin
receptor mediated endocytosis (active cell transport of molecules) in
the brain capillaries and released back to circulation via absorption of
cerebrospinal fluid (Andersen et al., 2014a; Andersen, Johnsen, &
Moos, 2014b; Madsen & Gitlin, 2007; Moos, Skjoerringe, Gosk, &
Morgan, 2006). Iron is present in various cell types in the central
nervous system (CNS), but is abundant in the astrocytes (star-shaped
glial cells), which has given rise to the idea that glial cells are involved in iron storage and regulation (Madsen & Gitlin, 2007).
Iron can be studied directly in the PM brain. PM findings of several
decades ago show that iron accumulates as a function of chronological
age in the brain (Hallgren & Sourander, 1958). Brains from individuals suffering from cerebrovascular and neuropsychiatric disorders
were carefully excluded from the aforementioned study (Hallgren &
Sourander, 1958). This study provides a unique contribution to the
body of research, including the remarkably high number of 98 specimens.
More iron is present in the subcortical nuclei, and interestingly, iron
deposits are particularly prevalent in the extrapyramidal system,
which supports motor functions. Iron deposits also occur in the cortex,
and to a somewhat greater degree in the motor cortex than the rest of
the cortices. Prefrontal regions, the medial temporal lobes, and sensory cortices show an exponential increase in iron until early midlife, but
lower total concentrations than the subcortical nuclei (Hallgren &
Sourander, 1958).
With the development of iron-sensitive MRI contrasts, it has become
possible to study the presence of iron in the in vivo human brain. In
vivo MRI can approximate iron deposits based on enhanced contrast
in the MR image, caused by interplay between iron’s paramagnetic
properties and the proton relaxation resonance behavior of tissue water (Schenck, 2003). Most of the non-heme iron in the brain, which
affects the MR signal, consists of ferritin or hemosiderin (Schenck &
Zimmerman, 2004; Schenck, 2003).
Novel techniques in post-mortem MRI have made it possible to more
carefully evaluate the association between PM brain iron levels and
18
MRI estimates of iron concentrations, and a novel group of iron sensitive contrasts in quantitative susceptibility mapping (QSM) show particular promise (Langkammer et al., 2012). MRI findings reveal both
linear and non-linear age-related trends in iron distribution (Haacke et
al., 2010; Hagemeier et al., 2013; W Li et al., 2014; Xu, Wang, &
Zhang, 2008). Age-dependency of MRI estimates of iron have been
studied to the greatest extent in the basal ganglia (Bartzokis et al.,
1997a, 2011; Bilgic, Pfefferbaum, Rohlfing, Sullivan, &
Adalsteinsson, 2012; Hagemeier et al., 2013; W Li et al., 2014; A.
Pfefferbaum, Adalsteinsson, Rohlfing, & Sullivan, 2009; Xu et al.,
2008), as opposed to other subcortical nuclei such as the sub-thalamic
nuclei (Haacke et al., 2010; Hagemeier et al., 2013), cerebellar dentate
nuclei (W Li et al., 2014), the red nucleus and substantia nigra (Bilgic
et al., 2012; Haacke et al., 2010; W Li et al., 2014). Few reports focus
on age dependency of iron estimates in cortical white matter and the
motor cortex, but existing findings reveal that iron levels are much
lower in the cortex than in the subcortical nuclei (W Li et al., 2014).
Figure 2. Iron (Fe) cycles in the brain. Iron crosses the blood brain barrier
via the transferrin receptor pathway on the endothelium. The brain iron cycle
consists of glia and neurons. Astrocytes extend long processes that enclose
the brain capillaries and help to form the brain blood barrier (BBB). The
divalent metal transporter 1 (DMT1) transports Fe. Near the ends of these
processes a special form of the Fe oxidizing enzyme, ceruloplasmin (Cp), is
expressed. The protein Cp is transported to the plasma membrane by the
glycophosphatidylinositol anchor (a glycolipid). Adopted from Madsen and
Gitlin (2007), with the permission from Annual Reviews.
19
1.1.3 Theories of Brain Aging
Two important theories focus on specific mechanisms underlying neurodegeneration in normative aging (i.e., aging free from neurologic
diseases) have attracted significant attention in the research community: inflammation and oxidative stress. After a description of these theories, the hypotheses of brain maintenance and neuronal reserve will
be introduced, followed by discussion of the frontal aging hypothesis.
1.1.3.1
Inflammation
Individuals vary in length of their lifespan, and various factors may
accelerate or mitigate aging. The immune system is genetically controlled, differs among individuals, and is thought to account for the
observed differences in life expectancy (Franceschi et al., 2000). Importantly, lifestyle factors, including improvement of dietary habits
(increased fiber intake), can mediate the inflammatory response
(Kantor, Lampe, Kratz, & White, 2013). Increased levels of inflammatory plasma markers have been associated with individual differences
in brain structure. Elevated circulatory pro-inflammatory cytokines
(e.g., interleukin (IL)-6, and tumor necrosis factor α), C-reactive protein (CRP), and homocysteine (Hcy) have been linked to reduced volumes in the hippocampus, cerebral cortex, and white matter, as well as
increased burden of white matter hyperintensities (Bettcher et al.,
2012; Taki, Thyreau, Kinomura, Sato, Goto, Wu, Kakizaki, et al.,
2013; van Dijk et al., 2005).
1.1.3.2
Oxidative stress
Production of free radicals, as generated by reactive oxygen and nitrogen species (antimicrobial molecules) occurs in the normal cellular
metabolism, and these free radicals are hypothesized to influence cellular degeneration in normal brains over time. These free radicals
cause damage to the cellular DNA (deoxyribonucleic acid), lipids, and
proteins causing cellular dysfunction and ultimately cell death (Trollor
& Valenzuela, 2001). Degree of oxidative damage can vary across
individuals with some people being less protected against oxygen radicals than others; such individuals are at greater risk of experiencing
significant oxidative damage (Franceschi et al., 2000). Inflammation
20
has been proposed to play a key role in mediating cellular death and
destruction via poorly liganded iron (Kell, 2009). Increased brain iron
levels is one factor that can influence oxygen species (ROS), favoring
oxidative stress, and cell death if levels become excessive (Andersen
et al., 2014b; Dixon & Stockwell, 2014)
1.1.3.3
Brain reserve
The concept of brain reserve stems from the need to explain individual
differences in resilience to trauma and neurodegeneration. Manifest
neuropathology does not necessarily correspond with clinical symptoms (Snowdon, 2003). Larger brain volumes and more synaptic connections may slow brain pathology, and a certain threshold of damage
is needed to manifest itself in symptoms. Greater resilience has been
explained on a neuronal level as arising from a greater initial number
of neurons and synapses (Katzman et al., 1988), as well as greater
gross brain volume, and higher overall brain weight (Katzman et al.,
1988; Satz, 1993).
1.1.3.4
Brain maintenance
The brain maintenance hypothesis posits that older people vary in agerelated changes, and those whose brains resemble those of younger
individuals maintain better cognitive performance (Lindenberger,
2014; Nyberg, Lövdén, Riklund, Lindenberger, & Bäckman, 2012).
Brain maintenance or lack of brain pathology constitutes the foundation of maintaining cognitive abilities with increasing age
(Lindenberger, 2014; Nyberg et al., 2012).
Some support for this hypothesis has been gained from associative
studies investigating individual differences in morphometry and white
matter integrity (Charlton, Schiavone, Barrick, Morris, & Markus,
2010; Nyberg et al., 2012; Persson et al., 2014; Raz et al., 2010), but
the vast majority of studies in this field are yet cross-sectional.
1.1.3.5
Frontal aging hypothesis
The view of the prefrontal cortex as the most age-sensitive brain region is dominant in the research community and in recent years has
21
gained a renaissance in the “last in, first out" hypothesis in aging research. This hypothesis suggests the inverse of development: latematuring regions of the brain are preferentially vulnerable to agerelated loss of structural integrity in gray and white-matter (Fjell et al.,
2009; Raz, 2000; Westlye et al., 2010). Alterations of the frontal lobes
have further implications for cognitive aging. Cognitive domains, such
as executive and memory functions, that depend on the prefrontal lobe
are affected in aging, while cognitive functions relying on other brain
regions are relatively spared (West, 1996). This localization-focused
approach has been criticized in favor of a more network-oriented approach involving several brain regions (Greenwood, 2000).
1.1.4 Modifiers of brain aging
Multiple factors and their interplay may underlie the course of brain
aging, and explain why some individual’s brains are more resistant to
age-related changes than others. Apart from chronological age, as discussed in the previous section, other factors may include the interplay
of genetic, environmental, and lifestyle factors. I will next discuss the
potential influence of sex, genetics, and socio-economic factors as
modifiers of the course of brain development in adulthood.
1.1.4.1
Sex differences
Sex-specific differences are sporadically reported in the literature in
relation to regional brain volumes. Recent studies suggest that most of
the reported sex-specific variations in regional brain volumes may be
attributed to different ways of accounting for the intracranial volume
(ICV) between studies (Pintzka, Hansen, Evensmoen, & Håberg,
2015; Voevodskaya et al., 2014). Sex differences may, however, be of
greater importance in relation to neurotransmission (Davis, Ward,
Selmanoff, Herbison, & McCarthy, 1999; Vries, 1990), and brain iron
levels.
Sex differences have been reported in prevalence, age of onset, and
symptom severity of neurodegenerative diseases that are related to
increased iron deposits (e.g., multiple sclerosis, Parkinson's and
Alzheimer's disease; Bartzokis et al., 2007; de Rijk et al., 2000;
Langkammer et al., 2013; Taylor, Cook, & Counsell, 2007). Women
tend to exhibit lower peripheral iron levels (e.g. Bartzokis et al., 2007;
Fleming et al., 2001; Whitfield, Treloar, Zhu, Powell, & Martin,
22
2003), while men tend to have higher iron concentrations in the cortical white matter and subcortical nuclei according to changes in MRI
contrast (Bartzokis et al., 2007, 2011; Hagemeier et al., 2013; Tishler,
Raven, Lu, Altshuler, & Bartzokis, 2012, but see Xu, Wang, and
Zhang 2008). Peripheral iron levels may influence brain iron deposits,
according to early PM findings showing that a history of anemia can
predict reduced brain iron (Hallgren & Sourander, 1958). Findings
from in vivo MRI suggest that menstrual blood loss in women may
contribute to sex differences in brain iron accumulation (Tishler et al.,
2012). Interestingly, recent PM work indicates that women have lower
levels of total brain iron than men from midlife to old age (Ramos et
al., 2014). Sex steroids, whose levels change post menopause (AlAzzawi & Palacios, 2009), could account for some of the sex-related
variations in brain iron levels (Gu, Xi, Liu, Keep, & Hua, 2010). Taken together, such findings render sex of interest when studying agerelated brain iron accumulation.
1.1.4.2
Genetics
The most prominent genetic polymorphism investigated in relation to
cerebral aging is the APOE ε4 allelic variant in the apolipoprotein E
gene. The ε4 allelic variant in this single nucleotide polymorphism
(SNP) increases the risk for late-onset Alzheimer's disease (AD)
(Farlow, 1997; Roses, 1996), and the ε2 variant is considered protective of longevity, while ε4 being deleterious (Brooks-Wilson, 2013).
The APOE ε4 allele controls availability of the apolipoprotein E,
which is a vital factor in lipid transport. The APOE ε4 allelic variant
has also been linked to elevated risk of developing cardiovascular disease (Mahley & Rall, 2000; Rall & Mahley, 1992) and hyperlipidemia
(Davignon, Gregg, & Sing; Rall & Mahley, 1992). However, the extant literature concerning the effect of APOE ε4 on healthy brain aging
and cognition is contradictory (Reiman, 2007; Reinvang, Espeseth, &
Westlye, 2013), and some studies suggest that positive findings may
reflect inclusion of individuals in the prodromal stages of dementia
(Cherbuin, Leach, Christensen, & Anstey, 2007). Variation in inflammatory response may serve as an important factor in explaining
variability across individuals in cerebral aging, considering its importance for longevity. Several SNPs regulating pro-inflammatory
responses may be of importance in individual brain aging. MTHFR
C677T or rs1801133 is a polymorphism in the methylenetetrahydrofolate reductase gene (MTHFR) that controls production of the enzyme necessary for metabolizing homocysteine (Hcy) (Bathum et al.,
23
2007; de Lau et al., 2010), which is related to the inflammatory response (Li et al., 2015). Variants of the IL-1β gene (e.g., rs16944) are
associated with release of cytokines in response to infection
(Yarlagadda, Alfson, & Clayton, 2009).
The current knowledge regarding inflammation and brain integrity has
all emerged from cross-sectional research studies. Studies have associated several SNPs that promote inflammation with structural brain
differences. For example, the MTHFR -677 T allele has been linked to
smaller white matter volumes and accelerated shrinkage in
periventricular fronto-parietal and parieto-occipital brain regions
(Rajagopalan et al., 2012). G homozygotes of the polymorphism of
the IL-6 gene (IL-6A-174G, rs1800795) have greater hippocampal
gray matter volumes than heterozygotes and A homozygotes (Baune
et al., 2012). In healthy adults (Raz, Yang, Dahle, & Land, 2012), the
T allelic variant of the IL-1β C-511T and CRP-286 (rs3091244) polymorphisms are associated with greater white matter hyperintensities
(WMH).
1.1.4.3
Cardiovascular risk factors
Factors influencing cardiovascular health are also important for the
brain. Changes in endocrine, vascular, and inflammatory systems with
advancing age render the older individual at risk of numerous cardiovascular diseases (Perry, 1999; Wu, Xia, Kalionis, Wan, & Sun,
2014). Cardiovascular risk factors are important determinants of structural brain changes and are also important for maintenance of cognitive functions over time. The substantial relevance of many cardiovascular risk factors in determining cognitive individual differences, as
well as cerebral alterations, are well documented (see e.g., Amenta, Di
Tullio, & Tomassoni, 2003; Waldstein, Manuck, Ryan, & Muldoon,
1991, for reviews). Treatment and exercise interventions targeting
such modifiable risk factors may slow brain aging. Yet such factors
are often ignored in the study of aging and in the development of theories about reserve factors. Cardiovascular risk, when addressed, is
often on an individual basis. A multivariate approach taking into account shared variance among factors may be of greater interest because cardiovascular risk factors often accompany each other.
Poorly regulated blood pressure is linked to decreased white matter
integrity (Foster-Dingley et al., 2015; Mortamais, Artero, & Ritchie,
2013; Salat et al., 2012), as well as reduced volumes of prefrontal gray
24
and white matter (Raz et al., 2005a; Raz, Rodrigue, & Acker, 2003),
and appears to accelerate age-related shrinkage of the hippocampus
(Hc) and the striatum (Elcombe et al., 2015; Foster-Dingley et al.,
2015; Fotuhi, Do, & Jack, 2012). Higher blood glucose levels in the
normal range have further been associated with smaller gray and white
matter volumes in the frontal cortices (Mortby et al., 2014), Hc
(Cherbuin et al., 2007; Cherbuin, Sachdev, & Anstey, 2012; Convit,
Wolf, Tarshish, & De Leon, 2003) and amygdala (Cherbuin et al.,
2012). Further, higher plasma homocysteine levels have been linked
to greater Hc atrophy in non-demented adults aged 60-90 years (den
Heijer et al., 2003). Some controversy exists regarding more gross
measures of regional brain volumes and higher body mass index
(BMI), with studies both supporting and refuting the potential influence of higher-range BMI on whole brain and total gray matter volume (Willette & Kapogiannis, 2015; Gunstad et al., 2008). Smaller
volumes of gray matter in the frontal, parietal, and temporal lobes as a
function of higher BMI have been noted in middle-aged and older
adults (Walther, Birdsill, Glisky, & Ryan, 2010; Willette &
Kapogiannis, 2015). However, higher levels of serum lipids have been
further linked to larger Hc size (Wolf et al., 2004), and higher low
density lipoprotein levels (LDL) may be associated with decreased
WM integrity in frontal and temporal regions (Williams et al., 2013).
Studies have also failed to find associations between cardiovascular
risk factors, such as hypertension, low-density lipoprotein levels, and
BMI, global and regional brain volumes, and white matter integrity
(Persson et al., 2014; Raz et al., 2012; Willette & Kapogiannis, 2015).
Differences in sampling strategies and participants’ ages may in part
mediate such variations in results.
1.1.4.4
Socioeconomic factors
It is known that experience can alter the brain. Experiments have elucidated experience-dependent neuroanatomical changes following
training in motor tasks (Sampaio-Baptista et al., 2014, 2015), repeated
cognitive test administration (Bäckman et al., 2011; Bäckman &
Nyberg, 2013; Dahlin, Neely, Larsson, Bäckman, & Nyberg, 2008;
Engvig et al., 2014), and skill training in early life (music training)
(White-Schwoch, Carr, Anderson, Strait, & Kraus, 2013). As socioeconomic disparities increase, it is important to address potential concomitants at the level of the individual. Specifically, does socioeconomic adversity contribute to inequalities concerning education,
25
health, stress, economy, and cognitive abilities (Cochrane, Leslie, &
O’Hara, 1982; D’Angiulli, Lipina, & Olesinska, 2012; Lupien et al.,
2005). Early socio-economic status (SES) disparities may indeed influence the risk of disease and limitations on activities of daily living
in adulthood (Ziol-Guest, Duncan, Kalil, & Boyce, 2012). Numerous
factors are thought to be of importance in defining the construct of
SES, and a common proxy for SES is educational achievement, which
may be a consequence of early influences of SES. Recent work stresses the importance of addressing early factors beyond an individual’s
SES in adulthood, such as the individual’s parent’s education (Noble,
2014). Socioeconomic factors have been linked to thickness of the
prefrontal cortex in children, white-matter integrity in adolescence
(Noble, Korgaonkar, Grieve, & Brickman, 2013), and gross surface
area (Noble et al., 2015). Further, hippocampal size may be altered in
both children and adults (Brito & Noble, 2014; Noble et al., 2012) as a
function of SES. Recent longitudinal work shows that childhood SES
predicts hippocampal size, even after accounting for childhood cognitive ability, adult SES, and educational attainment (Staff et al., 2012),
suggesting the possibility of long-term effects of early life socioeconomic inequalities.
1.2 Cognitive aging
Human aging is accompanied by average declines in various cognitive
abilities (Cattell, 1943; Horn & Cattell, 1967; Salthouse, 2010), but
more importantly the trajectories of age-related change also vary
among individuals (Hultsch, Hertzog, Small, McDonald-Miszczak, &
Dixon, 1992; Lindenberger, 2014; Rabbitt, 1993) and age-related
changes vary in magnitude over different cognitive domains
(Ghisletta, Rabbitt, Lunn, & Lindenberger, 2012; Rabbitt et al., 2004;
Rabbitt, 1993). Cattell (1943) further differentiated Spearman’s
(1904) concept of general intelligence to include two aspects of cognitive abilities: fluid (Gf) and crystallized intelligence (Gc). The former
relates to problem solving, logical reasoning, and speed of processing,
while the latter is based on acquired knowledge. The development of
fluid abilities was viewed as continuing until young adulthood, followed by a brief plateau, with a subsequent decline from the seventh
decade of life into senescence (Cattell, 1943). In contrast, crystallized
abilities improve throughout child- and adulthood, with gradual declines from the upper part of the lifespan (Cattell, 1943). Cattell’s
landmark theories and early empirical work have gained extensive
support from the research community (Finkel, Reynolds, McArdle,
26
Gatz, & Pedersen, 2003; Flicker, Ferris, & Reisberg, 1993; Ghisletta
et al., 2012; Horn & Cattell, 1967; McArdle, Ferrer-Caja, Hamagami,
& Woodcock, 2002). Research conducted after Cattell’s initial proposal suggests more elaborated sets of age-sensitive cognitive domains, with the rates of change varying across different cognitive constructs. Working memory, episodic memory, processing speed, and
spatial reasoning scores all exhibit pronounced age-effects, while vocabulary and verbal comprehension are spared until late life (Hultsch,
Hertzog, Small, McDonald-Miszczak, & Dixon, 1992; Persson,
Lavebratt, & Wahlin, 2013; Persson, Viitanen, Almkvist, & Wahlin,
2013; Rabbitt et al., 2004; Rabbitt, 1993).
1.3 Brain–cognition relationships
The vast majority of studies investigating relationships between brain
and cognition are based on cross-sectional study designs. Crosssectional designs focus on age-related differences, rather than changes
within individuals. Thus, cross-sectional designs are ineffectual for
generating hypotheses about changes in brain-cognition relationships
over time (Ghisletta & Lindenberger, 2003; Hofer & Sliwinski, 2001;
Lindenberger, 2014; Sliwinski, Hoffman, & Hofer, 2010). Further, the
existing longitudinal studies focus primarily on average effects, without accounting for individual differences in changes.
Cross-sectional differences in prefrontal volume, and increased prefrontal white matter connectivity (i.e., increased fractional anisotropy,
interpreted as increased fiber density and decreased diffusivity as cell
growth) are related to age differences in fluid reasoning and in more
specific cognitive functions, such as strategic control of episodic
memory, processing speed, and executive functions (Bennett &
Madden, 2014; Euston, Gruber, & McNaughton, 2012; Kane & Engle,
2002; Madden, Bennett, & Song, 2009; Rajah & D’Esposito, 2005).
Additionally, both functional and structural imaging studies performed
over the last decade have revealed the importance cerebellar involvement in multiple cognitive processes (Buckner, 2013; Stoodley, 2012).
Longitudinal studies of non-demented adults show that global deterioration of the brain, expressed in ventricular expansion (Grimm, An,
McArdle, Zonderman, & Resnick, 2012; McArdle et al., 2004) as well
as regional shrinkage of the hippocampus (Kramer et al., 2007;
Rusinek et al., 2003), leads to declines in episodic memory. Changes
in episodic memory are also related to changes in resting-state connec27
tivity, beyond the influence of structural shrinkage in healthy adults
(Fjell et al., 2015).
Empirical findings illuminate the bi-directional relationship between
brain and cognition during the adult lifespan. Smaller gross brain volume has been linked to declines in fluid intelligence among older
adults (Rabbitt et al., 2008). Longitudinal evidence further links higher fluid intelligence and episodic memory scores to less shrinkage of
the medial temporal lobes (MTL) (Borghesani et al., 2012; Raz et al.,
2008; Rodrigue & Raz, 2004). Further, greater general cognitive ability measured in youth predicts larger brain volumes in old age (Royle
et al., 2013).
1.4 Theories of cognitive aging
The distinction between non-normative and normative or nonpathological versus pathological aging is often very arbitrary. Different criteria are applied in studies to exclude participants with cardiovascular disorders and neurodegenerative diseases. One complicating
factor in this context is that the prenominal phase of AD is very extensive (Small, Mobly, Laukka, Jones, & Bäckman, 2003). Recent findings suggest that there might be little age-related cognitive decline in
old people relative to those who will die within approximately 42
month (Wilson, Beckett, Bienias, Evans, & Bennett, 2003; Wilson,
Beck, Bienias, & Bennett, 2007), and studies also reveal that elderly
individuals can actually improve or maintain their cognitive abilities
(Dahlin et al., 2008; Persson et al., 2016; Rönnlund, Nyberg,
Bäckman, & Nilsson, 2005). Such findings make it interesting to further investigate the possibility of successful cognitive aging and cognitive reserve factors.
1.4.1 Successful aging
Rowe and Kahn (1987) made a distinction between successful and
unsuccessful normal aging, and challenged earlier more deterministic
traditions focusing selectively on decline in aging. Successful aging
was characterized by maintenance of high physical and cognitive
function, and sustained engagement in social and productive activities
(Rowe & Kahn, 1997). These extrinsic factors were hypothesized to
play a neutral or positive role in successful aging. Their early claim
28
that the negative aspects of the aging processes was exaggerated,
while mitigating effects of diet, exercise, personal habits, and psychosocial factors were underestimated has been very influential in the
development of different notions of reserve. The successful aging paradigm has received substantial criticisms in recent years for empirical
and methodological limitations that ignore social inequality, health
disparities, and age relations (Katz & Calasanti, 2015; Rubinstein &
de Medeiros, 2015). There is also great inconsistency among studies
in conceptualizing and operationalizing the notion of successful aging
(Katz & Calasanti, 2015). The theories of successful aging as conceptualized by Rowe and Kahn (1987, 1997) also lack a clear neurobiological foundation in terms of neural underpinnings.
1.4.2 Cognitive reserve
The influential notion of cognitive reserve holds that some individuals
cope better than others with maintaining cognitive performance over
time (Stern, 2002, 2003), and individuals vary in how well they can
make use of available brain reserve (Stern, 2002). Moreover, there is a
distinction between passive and active reserve that can be summarized
as “having” more versus “doing more”(Staff, 2012; Stern, 2002). The
already available brain reserve can mitigate the expression of neurodegeneration. The active component of cognitive reserve refers to the
person’s ability to compensate for age-related changes through maintaining well-functioning cognitive abilities, acquisition of alternative
coping skills, and the tendency to adhere to lifestyles and practices
that promote maintenance of neural circuit function (Stern, 2002;
Tucker & Stern, 2011). The theory includes better efficiency in general cognitive ability and knowledge, and causal variables associated
with the level of cognitive functioning. Standard proxies for cognitive
reserve are education, intelligence, literacy, occupational attainment,
engagement in leisure activities, and integrity of social networks
(Tucker & Stern, 2011). Recent work suggests that aspects of personality (conscientiousness) contribute to cognitive reserve (Tucker &
Stern, 2011; Wilson, Schneider, Arnold, Bienias, & Bennett, 2007).
Often cognitive reserve factors are operationalized at a single level,
although more multivariate approaches exist (Satz, Cole, Hardy, &
Rassovsky, 2011; Scarmeas et al., 2003). Recent work casts doubt on
the link between various factors included under the cognitive reserve
umbrella of causal variables, but cognitive activity or engagement
exhibits a more direct mitigating influence on neurodegeneration
(Bennett, Arnold, Valenzuela, Brayne, & Schneider, 2014).
29
2 Goals
Previous work has primarily focused on cross-sectional agedifferences in describing the cognitive neuroscience of aging. The
existing longitudinal structural MRI studies focus predominately on
average effects and fail to take into account individual differences in
changes. Further, the vast majority of studies consider the structural
integrity of gray and white matter. Modern iron-sensitive contrasts
provide new opportunities and may illuminate novel mechanisms underpinning neuronal aging. Moreover, the existing work focuses on
how brain integrity predicts cognitive functions, rather than their bidirectional relationship over time. The goal of this thesis is to reduce
these outlined gaps in knowledge.
The specific goals were the following:
Study I: examined changes on average, as well as individual differences in change in neuroanatomical brain volumes, and the influence
of determinants of individual rates of change were examined. Genetic
and cardiovascular risk factors were considered. A specific focus was
placed on genetic polymorphisms influencing pro-inflammatory responses.
Study II: examined the functional form of age-related differences in
brain iron distributions. Further, we assessed whether brain iron increased in synchrony across the brain structures of interest. A final
important goal was to unravel potential sex differences in subcortical
brain iron concentrations.
Study III: assessed whether brain changes could be coupled with cognitive changes to unravel the bi-directional relationships between anatomical brain regions and three cognitive domains: episodic memory
(EM), fluid intelligence (Gf), and vocabulary (V). The effects of socioeconomic status, and genetic and cardiovascular risk, were accounted
for.
30
3 Method
3.1 Participants
For study I and III, volunteers were recruited through media advertisement and flyers in a major metropolitan area in the mid-western
United States. People who reported a history of cardiovascular, neurologic or psychiatric diseases, head trauma with lost consciousness in
excess of five minutes, thyroid dysfunction, or history of treatment for
drug and/or alcohol abuse or habitual consumption of alcohol (three or
more drinks per day), as well as individuals taking anti-seizure medication, anxiolytics or antidepressants were excluded from the study
sample. Claustrophobic individuals were advised not to participate.
People with a reported diagnosis of hypertension were included, if
they were taking anti-hypertensive medications, such as beta-blockers,
calcium channel blockers, angiotensin-converting enzyme inhibitors
or potassium-sparing diuretics. All participants were screened for dementia and depression using the Mini Mental State Examination
(MMSE; Folstein, Folstein, & McHugh, 1975), using a cut-off of 26;
Center for Epidemiologic Studies Depression Scale (CES-D; Radloff,
1977), with a cut-off of 15. All participants were right-handed. The
magnetic resonance (MR) scans were examined by a neuroradiologist
for suspected space-occupying lesions and signs of pathology. Out of
1055 respondents, 360 were eligible to participate in the study. See
Figure 3 for details.
31
Figure 3. The flow chart illustrates the process of recruitment, selection, and
attrition of participants for study I and III. The figure is reprinted with permission from Elsevier.
For study III, participants were recruited from hospital personnel in
the city of Dalian, China. Participants were free of neurological, psychiatric, or cardiovascular diseases (diabetes, stroke, and hypertension), drug-or alcohol abuse, and history of concussion or brain surgery. In all studies, MRI images were screened by an experienced radiologist for brain abnormalities. Absence of brain resection, infarction, focal lesions, and large hyperintensities were further confirmed
by a radiologist. All participants were right-handed.
32
Participants in studies I, II, and III gave their informed consent, and
the studies were subject to ethical approval by the regional ethics
committees.
3.2 MRI methods
Structural magnetic resonance imaging was used in the studies, specifically spin-lattice relaxation rates and quantitative susceptibility mapping.
3.2.1 T1-weighted imaging (spin lattice)
In T1-weighted (or spin lattice) imaging, the T1 parameter reflects
how the MR signal changes over time according to an exponential
relaxation curve that describes how the magnetized spinning proton
returns to its equilibrium state and realigns with the main magnetic
field, after excitement by a radiofrequency pulse (that is turned on and
off). The energy that is generated when the protons return to the location determined by the external magnetic field of the scanner is detected via the head coil of the scanner. T1 images, which contrast solid
and fluid properties as well as white versus gray matter, are used for
morphometric analyses of neuroanatomical brain volumes. Differences in contrast are caused by differential density of protons
(Hornak, 1997; Lipton, 2008).
For study I and III, all image acquisition was performed at the Magnetic Resonance Research Facility at Wayne State University, USA,
on a 4-Tesla scanner (Bruker Biospin, Ettlingen, Germany) using an
8-channel radio frequency coil. Magnetization-prepared rapid gradient
echo (MPRAGE) T1-weighted images in the coronal plane were acquired for volume measurements. The following acquisition parameters were used: echo time (TE) = 4.38 ms, repetition time (TR) = 1600
ms, inversion time (TI) = 800 ms, field of view (FOV) = 256 × 256
mm2, resolution = 0.67 × 0.67 × 1.34 mm3, matrix size = 384 × 384
and flip angle (FA) = 8°. The regions of interest were manually outlined on the T1-weighted images, based on contrast differences and
neuroanatomical boundaries defined by a previously described manual
tracing protocol (Persson et al., 2014; Raz et al., 2010).
33
Figure 4. T1 recovery (spin-lattice relaxation), involving recovery of the
longitudinal magnetization (yellow) because of the energy release (green)
into the environment. The lattice is indicated in tan. Adopted from Bitar et
al. (2006). Reprinted with permission from RSNA.
3.2.2 Quantitative susceptibility mapping
Quantitative susceptibility mapping (QSM) is a novel technique that
incorporates both phase and magnitude information from flowcompensated gradient echo (GRE) images (Wang & Liu, 2015). A
gradient-recalled echo (GRE) pulse sequence is generated to acquire
images. This method exploits the susceptibility differences between
tissues and uses the phase image (phase shifts of the protons in response to the gradient), in addition to signal magnitude (protons fall
out of phase, transverse magnetization decreases). QSM allows for
short scan times, and provides a better solution to non-local field effects that are prominent in phase-contrast imaging (J. Li et al., 2012).
QSM is better at visualizing the iron distribution in the brain than other iron-sensitive contrasts (Langkammer et al., 2012). 𝑇2∗ and 𝑅2∗ (𝑅2∗
= 1/ 𝑇2∗ ) from magnitude data are more sensitive to changes in imaging parameters such as echo time (TE) (Brown, Cheng, & Haacke,
2014). Morphology-enabled dipole inversion (MEDI) (de Rochefort et
al., 2010; Wang & Liu, 2015), a QSM algorithm incorporating both
phase and magnitude information, holds much promise with its sensitivity to both para-magnetism (iron) (Langkammer et al., 2012; J. Li et
al., 2012) and diamagnetism (e.g., calcium, white-matter) (Schweser,
Deistung, Lehr, & Reichenbach, 2010), and shows good reproducibility over different scanner manufacturers and magnet strengths (Deh et
al., 2015). For study II, we used a modified version of the MEDI Laplacian (1) algorithm (Persson et al., 2015). An example of the reconstruction of MEDI QSM is illustrated in Figure 4.
34
The MRI examination for study II was performed on a 1.5-T scanner
(GE Signa EXCITE 14.0) using an 8-channel head coil, via a 3D
spoiled gradient echo sequence with flow compensation. The following imaging parameters were used for the gradient echo sequence: true
axial plane, echo time (TE) = 40 ms, repetition time (TR) = 53 ms, flip
angle (FA) = 20º, slice thickness = 3 mm, bandwidth = +/31.25Hz/pixel, field of view (FOV) = 24 cm, matrix size = 512 × 512
× 40.
The regions of interest were manually outlined on the QSM images,
based on contrast differences as described in Persson et al. (2015).
Figure 5. Illustrating an outline of the MEDI QSM reconstruction using
edge information from the magnitude image, and the tissue field from the
local field map, using morphology-enabled dipole inversion (MEDI), here
illustrated in 3-T images of the transverse plane. Reprinted with permission.
35
3.3 Design
Both cross-sectional and longitudinal study designs were used in this
thesis. A longitudinal design was applied in Studies I and III (Persson
et al., 2014; Persson et al., 2016), while study II was cross-sectional
(Persson et al., 2015).
A cross-sectional study design can only account for age-related differences among individuals, while a longitudinal design also captures
individual differences in change trajectories (Hofer & Sliwinski,
2001). Studies addressing individual variation show that individuals
do indeed evidence substantial heterogeneity in brain development
and cognitive functions (McArdle et al., 2004; Rabbitt, 1993; Rabbitt
et al., 2008), and some studies suggest that such heterogeneity increases particularly in older age (Rabbitt, 1993, but see Salthouse,
2011). Nonetheless, many longitudinal studies that focus on brain aging and its relation to cognitive aging often solely examine average
change rates. In the context of latent variable modeling, variance in
change can be specified as latent or random effects that reflect a random probability distribution around the fixed effect (average) (Curran,
Obeidat, & Losardo, 2010).
In the longitudinal study of cognitive aging, retest effects are important because healthy people tend to learn from repeated task exposure. To more accurately determine the true effects of chronological
age, it is important to account for bias due to retest effects (Ferrer,
Salthouse, McArdle, Stewart, & Schwartz, 2005). The assessment of
retest effects requires several measurement intervals, and such effects
could be underestimated over just two test occasions. This issue will
be discussed in greater detail in relation to study III in the General
discussion.
3.4 Statistical methods
Structural equation modeling (SEM) was used in all the studies of this
thesis. Confirmatory and second-order factor models and structural
equation models with covariate effects were estimated in study II,
while univariate and bivariate latent change score models (LCSMs)
were fitted to the data in studies I and III. The SEM algorithms for the
specified models can be viewed in Persson et al. (2015, 2016). All
36
models were estimated using the Mplus software (Muthén, & Muthén,
1998, 2015).
3.4.1 Structural equation modeling
In structural equation modeling (SEM) latent variables can be specified to make the measurement error less influential (Jöreskog, 1970;
McArdle, 1996). The basic model assumption is that the latent variables and the residuals are normally distributed (Jöreskog, 1970).
Analysis of variance (ANOVA) and traditional regression techniques
are more sensitive to bias from measurement error, and estimates of
effect size may be underestimated. Measurement error can attenuate
correlations and regression slopes, and increase standard errors of the
parameter estimates (Rigdon, 1994).
Other important advantages of SEM include a greater ability to model
complex multivariate relations with multiple dependent and independent variables, and assessment of moderation and mediation. SEM with
latent variables can focus on a measurement model, which is analogous to common factor or a confirmatory factor model, where the
common factors (also called latent variables) are represented by various measured (or manifest or observed) variables, and a structural
model in which variance in several measures can be explained by a set
of predictors/covariates.
3.4.1.1
Measurement model
In specifying models for study II, I first fit a multivariate measurement
model, or confirmatory factor analysis model (CFA) to the data. The
model was comprised of seven latent variables, each represented by
average susceptibility over left and right hemispheres. The left hemisphere was set to the value of one across the latent variables for model
identification. The common factors were further interrelated by twenty-one covariance relationships between them. Dentate nucleus images
were available for 88 participants, so this ROI was estimated as a single latent variable. The residuals were constrained to be equal for local
identification, and the model was evaluated by the strength of the factor loadings; values ≥ .852 were considered strong (cut-off, .70, Kline,
1994). In study III, two measurement models were specified to reflect
socioeconomic status and cardiovascular risk, based on two and four
37
manifest or observed variables, which were included as predictors in
the longitudinal models described in section 3.5.2.
A second order latent variable can be added to the model to represent
another hierarchical level for conceptualizing a further construct level.
In study II, such a hierarchical factor model was specified to represent
total brain iron via a second-order latent variable that was represented
by the susceptibility measurements of iron estimates in the seven subordinate latent variables (see Figure 6).
Figure 6. The path diagram shows the second-order factor model. The first
order factors (η1,…,η7) reflect the susceptibilities of the seven ROIs. λ are the
factor loadings of susceptibilities in left and right hemispheres on the latent
variable η, and ε is the residual error term of each manifest indicator (e.g.,
Cdl = left caudate nucleus). ξ is the second-order latent variable reflecting
total estimated iron concentration across the subcortical nuclei. Γ denotes the
regression coefficient between the second-order latent variable ξ and the
subordinate factors η1,…,η7. The figure is reprinted with permission from
Elsevier.
38
3.4.1.2
SEM with covariates
As described in the previous sections, SEMs with age and sex as predictors of the level of susceptibility were estimated in a second step in
study II.
3.4.1.3
Multiple group analysis
In study II, we performed a multiple group analysis (MGA) to simultaneously derive parameter estimates for men and women, to compare
parameter estimates between the groups, and to evaluate the effects of
the sex by age interaction on susceptibility counts. Measurement invariance was first established to make certain that the same measurement model held across the two groups, by comparing a model with
all parameters estimated freely across groups, with two models with
gradually imposed equality constraints on the parameters. The models
were estimated under the assumption of strict factorial invariance, and
the parameter constraints did not indicate substantial loss of fit when
the difference in χ2 (Δχ2 = χ2restricted − χ2 unrestricted with df = dfrestricted −
dfunrestricted) (see e.g., Bentler & Bonett, 1980) was calculated upon parameter constraints.
3.4.1.4
Model fit
Model fit statistics were used to evaluate the mathematical models.
Through all evaluations of model fit, we recognize that all models are
approximations, rather than the final truth: all models are wrong, but
some are useful. Joint criteria based on several model-fit indices were
used, as follows: comparative fit index (CFI) > 0.95, standardized root
mean square residual (SRMR) < 0.08, and root-mean-square error of
approximation (RMSEA) < 0.08 (Browne, Cudeck, & Bollen, 1993;
Hu & Bentler, 1998; Hu & Bentler, 1999).
39
3.4.2 Latent change score modeling
Latent-change score models (LCSMs) were specified in study I and
III. Based on the extant literature, we expected to find heterogeneity in
change, in addition to average trends in change (Rabbitt et al., 2004;
Rabbitt, 1993). More specifically, a latent change score was used to
examine change in sample averages as well as variance across individuals, after accounting for baseline level mean differences and variance. For instance, the latent score at time point two of one participant
was constructed as the unit-weighted sum of the latent scores at baseline and a latent score representing change between baseline and follow-up. Further, the covariance between initial levels and rates of
change were specified. In study III, the multivariate construct episodic
memory (EM) was specified in a second order latent variable represented by two subordinate latent constructs: episodic recall and recognition at time point 1 (T1) and T2. Specification at the latent level
assures that the change estimation is reliable and not contaminated by
measurement error, unlike, for example, simple difference scores between measured indicators (Cronbach & Furby, 1970). See Figure 7
for an illustration of the LCSM for EM from study III.
When creating common factors from repeated measures, it is important to ascertain that the same common factors are measured at
each measurement occasion, so that changes in the observed mean can
be represented by change in factor means, and to avoid bias in the
estimation of mean change (Grimm & Ram, 2009; McArdle &
Grimm, 2010; McArdle & Nesselroade). Tests of factorial invariance
can be applied by freeing and adding parameter constraints, ranging
from weak to strict factorial invariance (Gregorich, 2006; Meredith &
Horn, 2001; Meredith, 1993; Persson et al., 2015). Once the presence
of invariance is established, various covariates can be added as determinants of individual differences in change of regional brain volumes
and cognitive performance scores, as in the analyses performed in
studies I and III. Specific covariances of the measurements over time
were further specified to address the possibility of additional confounds from measurement error (McArdle, 2009), and initial level
versus change correlations were added to the model.
Lead-lag relationships can be established to evaluate dynamic expressions in bivariate LCSMs (Grimm, McArdle, Zonderman, & Resnick,
2012; McArdle, & Grimm, 2010). In study III, such bivariate models
were specified with initial levels in test scores or regional brain volumes predicting change in the other. Further, coupled changes were
40
specified if both cognitive scores and volumetric measures exhibited
significant variance in change.
Figure 7. A latent change score model for the estimation of a two-occasion
change in episodic memory (EM), with first-order factors recall and recognition. Squares represent observed variables, circles are latent variables. The
triangle indicates that the model has means. All free parameters are marked
by an asterisk. Constrained equality is denoted by an equals sign and the
same subscript. Change (Δ) is specified in a second order latent variable
(EM), represented by two subordinate latent variables: recall and recognition. Notations: δ = mean at baseline, α = variance at baseline, β = mean
change, γ = variance in change, ε = covariance between individual differences in EM at baseline and individual differences in changes between baseline and follow-up. The model has 21 degrees of freedom and contains eight
observed variables. The figure is reprinted with permission from Elsevier.
41
4 Summary of studies
The following section summarizes the three empirical studies included in
this thesis. All reports are reprinted with permission from Elsevier.
Table 1. Overview of the studies included in the thesis.
Study Design
MRI
I*
Longitudinal T1w
N
167
Age
19-79
II
183
20-69
167
19-79
III*
CrossT1w
sectional
Longitudinal QSM
Variables
10 ROIs, 4 SNPs, age,
HBP
8 ROIs, age, sex
6 ROIs, 1 SNP, age,
SES, VR
Note. MRI = magnetic resonance imaging, T1w = T1-weighted (spinlattice), N = number, QSM = Quantitative susceptibility mapping, Age
= age in years at baseline, ROI = region of interest, HBP = diagnosis
of hypertension at baseline (0 = no, 1 = yes), SNPs = single nucleotide
polymorphism, SES = socio-economic status (parental), VR = Vascular risk (pulse pressure, body mass index, low density lipoprotein,
fasting blood glucose). * Sex was omitted from the equation due to
lack of significance.
42
4.1 Study I
Persson, N., Ghisletta, P., Dahle, C. L., Bender, A. R., Yang, Y.,
Yuan, P., … Raz, N. (2014). Regional brain shrinkage over
two years: Individual differences and effects of proinflammatory genetic polymorphisms. NeuroImage, 103, 334348.
4.1.1 Background
Brain volumes decrease with age and significant reductions are observed in several brain regions. The vast majority of studies are crosssectional and many existing studies focus predominately on average
rates of change (but see Raz et al., 2005b, 2010). The prefrontal cortices, medial temporal lobes, limbic cortices, and cerebellum all exhibit
average declines (Fjell, McEvoy, Holland, Dale, & Walhovd, 2013;
Resnick, Pham, Kraut, Zonderman, & Davatzikos, 2003; Scahill et al.,
2003), and the subcortical basal ganglia shrink to a somewhat lesser
extent (Fjell et al., 2009; Raz et al., 2005b). Interestingly, individuals
also vary in rates of change across many neuroanatomical regions
(Raz et al., 2005b, 2010). Several brain biomarkers, including circulatory plasma markers and single nucleotide polymorphisms (SNPs)
influencing pro-inflammatory responses, have been linked to individual differences in hippocampal volume and cerebral white and gray
matter (Bettcher & Kramer, 2014; Taki, Thyreau, Kinomura, Sato,
Goto, Wu, Kawashima, et al., 2013; van Dijk et al., 2005).
4.1.2 Objective
The first aim was to assess regional brain changes occurring over two
years in healthy adults and to replicate previous findings obtained using weaker systems (1.5T). Based on the extant literature, we expected
to observe mean shrinkage of the cerebellum, hippocampus, striatum,
and prefrontal cortices, but no substantial changes in the volume of the
primary visual cortex, because this region is known to be more resistant to age-related changes. We further examined individual rates of
43
change across regional brain volumes. Based on previous studies, we
hypothesized that individuals would show variance in changes across
all regions of interest (ROIs). Finally, we assessed determinants of
brain-aging trajectories in regional brain volumes. Specifically, we
considered the effects of vascular risk factors such as arterial hypertension, and genetic variants associated with increased pro-inflammatory response, namely IL-1βC-511T, rs16944; CRP-286C>A>T,
rs3091244; and MTHFR C677T, rs1801133. In addition, we accounted for confounding effects by considering the effect of a common genetic risk factor for AD (APOE ε4) on rates of change in regional brain volumes.
4.1.3 Method
Data were analyzed from 167 healthy adults over approximately two
years (average interval between assessments: two years and 24 days).
Participants suffering from dementia, psychiatric illnesses, cardiovascular diseases, or exhibiting brain abnormalities were excluded. Individuals with medically controlled hypertension were included. The
regions of interest (ROIs) were segmented using a manual tracing protocol on images from a 4-Tesla scanner (Bruker Biospin, Ettlingen,
Germany). Raters were blind to participants’ demographic characteristics. The intra-class coefficient (ICC) between the raters exceeded
.90.
A series of latent change score models (LCSMs) were fitted to the
data with parameters for both average changes and individual variance
in changes in the following regions of interest (ROIs): lateral prefrontal cortex (LPFC), orbital frontal cortex (OF), prefrontal white matter
(PFw), hippocampus (Hc), parahippocampal gyrus (PhG), caudate
nucleus (Cd), putamen (Pt), insula (In), cerebellar hemispheres (CbH),
and primary visual cortex (VC). LCSMs allow for estimation of longitudinal age-related changes, simultaneously taking into account
cross-sectional effects. Second, predictors of change were added to
explain individual rates of change across the ROIs. We accounted for
the influence of pro-inflammatory genetic polymorphisms by considering the influence of chronological age, arterial hypertension, and the
APOE ε4 polymorphisms.
44
4.1.4 Results
Greatest magnitudes of change were noted in In and CbH, followed by
PhG, Hc, and OF; very small average effects were observed in LPFC,
PFw, VC, Cd, and Pt. Larger proportions of individual variance
emerged in the In, and CbH, followed by PhG and VC. More moderate levels of individual variance were observed in Hc, Pt, PFw, LPFC
and Cd. A smaller degree of variance was evident in OF. Larger baseline volumes of the CbH and In were associated with greater shrinkage. No other baseline versus change relationships emerged.
Age was a strong predictor of cross-sectional age-related differences
in volumes, whereby advanced age was associated with smaller neuroanatomical brain regions. However, baseline age showed no significant relationship with the course of individual rates of change over
two years. None of the covariates showed significant relationships
with neuroanatomical ROIs at baseline. Possession of two copies of
the T allele in the IL-1β-511 SNP predicted greater shrinkage over two
years in the PhG, and the CbH (see Figures 8A and 9). At least one
copy of the T allelic variant in the MTHFR-677 SNP indicated increased decline in volume in the PhG (see Figure 8B).
45
Figure 8. The bar charts illustrate the effect of two pro-inflammatory singlenucleotide polymorphisms IL-1β C-511T (A) and MTHFR C677T (B) on
regional shrinkage in the parahippocampal gyrus. The T allelic variant
marked in dark gray in A is associated with higher circulatory interleukin1β, contrasted with carriers of any C allele. B shows carriers of the MTHFR
T allele are linked with higher plasma homocysteine levels compared to
homozygote C carriers. Shrinkage is indexed by the mean expected value of
latent change scores computed from the estimated means of the models
while taking into account the effects of covariates. The error bars represent
95% confidence intervals around the means. Reprinted with permission from
Elsevier.
Figure 9. The figure shows the effect of a pro-inflammatory SNP, IL-1β C511T, on shrinkage of the cerebellar hemispheres in normotensive participants. Shrinkage is indexed by the mean expected value of latent change
scores computed from the estimated means of the models while taking into
account the effects of covariates. The error bars represent 95% confidence
intervals around the means. Reprinted with permission from Elsevier.
46
4.1.5 Conclusion
These results confirm previous findings of average and differential
rates of shrinkage in brain aging, but counteract previous reports in
one important respect: we found no average shrinkage in the volume
of the lateral prefrontal cortex. These findings cast doubt on the
unique role of the prefrontal cortex in aging. Further, in conflict with
the brain reserve hypothesis, we observed that larger volumes at baseline predicted greater shrinkage of the cerebellar hemispheres and the
insula. Unlike other reports, age and hypertension did not predict
shrinkage in brain volumes. This may be related to our selection of
individuals in optimal health. Our findings highlight the importance of
investigating the role of inflammation in brain aging, but warrant replication in future studies.
47
4.2 Study II
Persson N., Wu, J., Zhang, Q., Liu, T., Shen, J., Bao, R., Ni, M., Liu,
T., Wang, Y. & Spincemaille, P. (2015). Age and sex related
differences in subcortical brain iron concentrations among
healthy adults. NeuroImage, 122, 385-398.
4.2.1 Background
Age-related brain iron accumulations accelerate across the lifespan
according to post mortem (PM) findings by Hallgren and Sourander
(1958). Substantially more brain iron is observed in the subcortical
nuclei than in the cortex (Gelman, 1995). Age-related iron distributions assessed by in vivo MRI have been studied to a greater degree in
the striatum and pallidus (Bartzokis et al., 1997b; Bilgic et al., 2012;
Cherubini, Peran, Caltagirone, Sabatini, & Spalletta, 2009; Haacke et
al., 2005) as compared with the substantia nigra, red nucleus, subthalamic nucleus, and cerebellar dentate nucleus (Bilgic et al., 2012;
W Li et al., 2014). Further, sex-related variations in sub-cortical brain
iron estimates have also been studied to a lesser degree. Recent PM
work provides clarity by revealing lower brain iron levels in women
than men from midlife (Ramos et al., 2014). Various contrasts exist
for generating brain iron estimates in vivo, but there are several limitations to be noted. Both field-dependent relaxation rates (FDRI)
(Bartzokis, Aravagiri, Oldendorf, Mintz, & Marder, 1993), magnitude
(𝑅2∗ ) (Gorell et al., 1995; Haacke et al., 2005), and phase signal
(Haacke et al., 2007) have been used. FDRI requires two field
strengths and is time consuming. Both phase and 𝑇2∗ / 𝑅2∗ (𝑅2∗ = 1/ 𝑇2∗ )
suffer from non-local field effects. 𝑅2∗ is also sensitive to echo time,
field strength, and object orientation (Brown et al., 2014). Quantitative
susceptibility mapping (QSM) is a novel method that can address nonlocal field effects in the phase information and retain the tissue magnetic susceptibility. It also preserves magnitude information in the
MEDI(morphology enabled dipole inversion) algorithm (de Rochefort
et al., 2010; Wang & Liu, 2015).
48
4.2.2 Objective
The main objective was to study age- and sex-related distributions of
subcortical brain iron concentrations. We aimed to replicate previous
findings of age-related differences in brain iron of the striatum, but
also to add to the current state of knowledge by studying smaller, less
studied subcortical nuclei, such as the sub-thalamic pulvinar complex,
substantia nigra and red nucleus, and dentate nucleus in the cerebellum. We examined intercorrelations among subcortical nuclei to discover if a uniform pattern of correlations would emerge, potentially
reflecting an age-related mechanism. We further assessed sex-related
differences in the subcortical nuclei. We also investigated potential
lowering of brain iron levels from expected menopause age, because
recent postmortem work reveals smaller iron deposits in women compared to men from midlife.
4.2.3 Method
One hundred and eighty-three healthy participants were included in
the study (20-69 years, 49% women). Phase and magnitude information were derived from a three dimensional flow-compensated gradient echo sequence on a 1.5-Tesla scanner (GE Signa EXCITE 14.0).
QSM images were constructed using a modified version of MEDI
QSM (de Rochefort et al., 2010). The gradient mask was based on
filtering the first echo magnitude image. QSM in CSF was set to zero
to allow comparison across participants (Dong et al., 2015; Persson et
al., 2015). The following ROIs: were segmented manually on the
QSM image by experienced raters: caudate nucleus (Cd), putamen
(Pt), globus pallidus (Gp), substantia nigra (Sn), red nuclei (Rn), thalamus (Th), pulvinar (Pul), and dentate nucleus (Dn). To minimize
inter-subject variability, each ROI was segmented by a single radiologist. Average susceptibility was measured within each ROI. A series
of structural equation models (SEMs) were fitted to the data. Both
multivariate confirmatory factor models (CFA) and hierarchical factor
models were evaluated. Sex differences were evaluated in the context
of multiple group analysis (MGA). Age and sex were added as covariates and both linear and curvilinear effects of age were evaluated. Total subcortical brain iron levels were represented by a second order
factor to examine the influence of post-menopause age (women at the
average age of menopause onset 51 years were coded as 1). The
dummy variable was added as a predictor to the model.
49
Figure 10. Distribution of iron in the subcortical nuclei reported by Hallgren
and Sourander (1958), in relation to the average sample distribution of magnetic susceptibility values of the present study. The QSM data reflect mean
susceptibility values in parts per billion (ppb) from 183 subjects (20–69
years). The values on the x-axis reflect mg iron per 100 g wet tissue, as reported in Table 1a, p. 43 in the work by Hallgren and Sourander (1958),
from subjects aged 30–100 years (r = .970, p = .0001). The error bars represent standard deviations. ©Elsevier 2015, reprinted with permission.
4.2.4 Results
The distributions of iron varied across structures, consistent with previous post-mortem findings (see Figure 10). All models showed good
fit to the data according to conventional thresholds for joint criteria of
fit indices (described in greater detail in the Method section). Strict
factorial invariance was established by careful evaluation of model
constraints so that parameter estimates and constraints could be evaluated across men and women. Brain iron levels were correlated across
many ROIs: twenty out of twenty-eight correlations were significant
and positive. The strongest linear effects of age on susceptibility increase were observed in the striatum (Figure 11). Curvilinear effects
of age on susceptibility increase was particularly strong in the RN
(Δf212 = 0.344, proportion of variance accounted for linear age), moderate for the Pul (0.131), and small for the other curves (<0.15) (Co50
hen, 1992). Men showed higher overall brain iron concentrations as a
function of age according to mean proportions of explained variance,
and sex-specific effects emerged in Sn and the Pul whereby men had
higher levels of susceptibility. A trend in the same direction was present for susceptibility levels in the Rn (p =.059). The second-order
factor analysis revealed that women expected to be post-menopause
had a lower total subcortical susceptibility level than men and younger
women, after accounting for individual age (Figure 12). Women expected to be post menopause were 169 parts per billion (ppb) lower in
susceptibility than the rest of the subjects, according to the unstandardized parameter estimates.
Figure 11. Scatter plots illustrating linear increment of susceptibility values
as a function of chronological age. High values are indicative of high iron
load. The outer gray lines represent the 95% prediction limits. Susceptibility
is presented in parts per billion (ppb). ©Elsevier 2015, reprinted with permission.
4.2.5 Conclusion
The findings relating to age-dependent brain iron accumulation are
consistent with previous reports, but also add to the current state of
knowledge by reporting age-related changes in less studied, smaller
subcortical nuclei. We suggest that the many positive correlations over
the subcortical nuclei may reflect an age-related mechanism. A potential explanation is that excessive accumulation of intracellular non51
heme iron promotes reactive oxygen species (ROS), oxidative stress,
and cell death, resulting in neurodegeneration (Andersen et al., 2014b;
Dixon & Stockwell, 2014; Floyd & Carney, 1993). This report is, to
the best of our knowledge, the first to show in vivo that women have
lower total subcortical brain iron levels after expected menopause onset. Age-related changes in estrogen levels may be a mediating factor
in such associations. The current study illustrates that age and sex are
important co-factors to take into account when establishing a baseline
level to differentiate pathologic neurodegeneration from healthy aging. Longitudinal evaluation is necessary to determine how the agerelated differences reported herein evolve over time.
Figure 12. Decrease in susceptibility in women, after average age of menopause onset (51 years). Total susceptibility is reflected in a second order
latent variable representing the susceptibility of seven ROIs: caudate nucleus
(Cd), putamen (Pt), globus pallidus (Gp), thalamus (Th), pulvinar (Pul), red
nucleus (Rn), and the substantia nigra (Sn). Susceptibility is presented in
parts per billion (ppb). ©Elsevier 2015, reprinted with permission
52
4.3 Study III
Persson, N., Ghisletta, P., Dahle, C. L., Bender, A. R., Yang, Y.,
Yuan, P., … Raz, N. (2016). Regional brain shrinkage and
change in cognitive performance over two years: The bidirectional influences of the brain and cognitive reserve factors.
NeuroImage, 126, 15-26
4.3.1 Background
Aging is characterized by shrinkage in multiple brain regions, although individuals also exhibit substantial heterogeneity in change
rates. Aging is further characterized by growth, decline, and stability
of cognitive functions, depending on the measured domain. Fluid abilities tend to decline on average, while crystalized abilities tend to remain stable until late in the lifespan (Rabbitt, 1993). As the brain ages,
it is likely that cognitive functions are affected. Most studies focusing
on the neural correlates of cognitive changes rely on cross-sectional
designs, which are insufficient for generating hypothesis about braincognition relationships over time. Longitudinal studies have related
ventricular expansion to declines in episodic memory, and higher fluid
intelligence and episodic memory scores to slower shrinkage of the
medial temporal lobes (Borghesani et al., 2012; McArdle et al., 2004;
Raz et al., 2008). Further, various structural and functional MRI studies shed light on the important role of the cerebellum in various cognitive functions (Stoodley, 2012), and variations in prefrontal volumes
are related to age differences in fluid reasoning and in strategic control
of episodic memory (Kane & Engle, 2002). Further, higher intelligence scores in childhood predict greater brain volumes later in life
(Royle et al., 2013). Hence the relationship may be bi-directional rather than unidirectional, and studies investigating reciprocal longitudinal relationships between brain and cognition are warranted.
53
4.3.2 Objective
The main objective of this study was to examine the relationship between regional heterogeneity of brain shrinkage and change in performance in age-sensitive cognitive domains. Brain regions of interest
(ROIs) were chosen based on their theoretical and empirical relevance
to the studied cognitive domains. ROIs consisted of the prefrontal cortices, the medial temporal lobes, and the cerebellar hemispheres. The
visual cortex was used as control region, as this ROI generally shows
slower age-related changes. Two age-sensitive cognitive constructs,
episodic memory (EM) and fluid ability (Gf), and one age-resilient
construct, vocabulary (crystallized ability), were chosen. Parameters
representing both mean change and variance in change were specified
in latent change score models (LCSMs). We further evaluated bidirectional lags between baseline levels and subsequent changes in brain
volumes and cognitive performance scores. Finally, we tested the role
of putative modifiers of change in the brain and cognition by adding
the following variables: cardiovascular risk, a common genetic variant
associated with dementia risk (APOE ε4), and socioeconomic status,
in addition to chronological age as predictors of individual differences
in neuroanatomical and cognitive changes.
4.3.3 Method
Data were analyzed from 167 healthy adults over approximately two
years (average interval between assessments: two years and 24 days).
Participants suffering from dementia, psychiatric illnesses, cardiovascular diseases or exhibiting brain abnormalities were excluded. Individuals with medically controlled hypertension were included. The
following regions of interest (ROIs) were manually segmented following a previously described protocol (Persson et al., 2014): lateral prefrontal cortex (LPFC), prefrontal white matter (PFw), hippocampus
(Hc), parahippocampal gyrus (PhG), the cerebellar hemispheres
(CbH), and primary visual cortex (VC; calcarine fissure). Cognitive
tasks were used from the Culture Fair Intelligence Test (CFIT; Cattell
& Cattell, 1960), the Memory for Names subtest of the WoodcockJohnson Psychoeducational Battery – Revised (Woodcock & Mather,
1989), and the Logical Memory (LM) subtest from the Wechsler
Memory Scale – Revised (Wechsler, 1987). Word knowledge was
measured by vocabulary tests generated from the ETS Kit of factor54
referenced cognitive tests (Ekstrom, French, Harman, & Dermen,
1976). The memory and CFIT tasks were identical over the test occasions, while list one of the vocabulary (V) lists was identical and list 2
was replaced by list 3 (the correlation of stability was perfect, r =
1.000). LCSMs were fitted to the cognitive tasks and the ROI data.
Separate confirmatory factor analyses (CFAs) were applied to the data
for latent representation of vascular risk (BMI, fasting glucose, pulse
pressure, and LDL cholesterol) and socioeconomic status (maternal
and paternal education). Age in years was treated as a time-invariant
interval-scaled variable, and APOE ε4 was coded as 1.
4.3.4 Results
The univariate LCSMs revealed individual differences in change
across all ROIs. Individuals with larger brain volumes did not exhibit
slower rates of shrinkage over two years. Moreover, higher Gf baseline scores were associated with smaller gains. The participants exhibited improvement in episodic memory (EM) and vocabulary (V) performance, but not fluid ability (Gf). Of the cognitive scores, variance
in change only occurred for Gf, while EM and V scores showed only
changes in average trends. Hence baseline EM and V predicted change
in ROIs but not vice versa, because there was no variance in EM and
V. Bidirectional lags could only be specified for the ROIs and Gf because this cognitive domain showed significant variance in change.
After accounting for the covariates, larger baseline volumes of four
regions (PFw, Hc, PhG, and CbH) predicted positive change in Gf.
Only one region (PFw) had less shrinkage and was associated with
greater gains in Gf (r = .346). Higher EM scores at inception predicted
slower shrinkage of LPFC (p = .013, α’= .017), with Gf showing a
trend (p = .051) and V a non-significant relationship (p = .13) in the
same direction.
Sex was not associated with any of the measures (all p’s > .05), and
was excluded from further analyses on the basis of model parsimony.
The bivariate models revealed that older baseline age was associated
with smaller volumes of all ROIs, independently of socioeconomic
status (SES), vascular risk (VR), and APOE ε4 status. Older age was
further related to lower memory, lower Gf scores, and better V performance. Higher SES status indicated greater initial V scores and
fluid intelligence, but no statistically reliable relationship was established with EM. Of all ROIs measured at baseline, only Hc volume
55
was associated with SES, and the effect rendered significant after statistical correction selectively in one of the three models were the ROI
was included. Neither vascular risk nor APOE ε4 variant displayed
significant associations with any of the baseline volumes or cognitive
factors. The analyses of bivariate models for Gf and regional volumes
revealed that in the model that included CbH, younger age and higher
SES were associated with greater gains in Gf. No independent significant effects of age, SES, VR, or APOE ε4 variant on the rate of
shrinkage of any ROI were observed, although trends for VR and APOE ε4 were noted. However, in at least one model (with CbH and Gf
as target variables), the combined influence of baseline age, SES, VR,
and APOE ε4 was significant (R2 = .551), with the direction of change
suggesting faster shrinkage for older participants, carriers of the APOE ε4 allele, and individuals with lower SES.
4.3.5 Conclusion
The results suggest that although larger brain volumes were not associated with slower rates of shrinkage, they may be important for maintaining high performance in important age-sensitive cognitive domains. Notably, brain and cognition exert a bi-directional influence on
each other: better cognitive performance predicts better maintenance
of structural integrity in an important age-sensitive region, and larger
brain volumes at inception predict greater gains in fluid intelligence
scores. Our findings contribute to better understanding of the neural
underpinnings of cognitive aging and highlight the importance of
maintaining cognitive functioning in older age. The specific mechanisms of the observed effects of cognitive and brain reserve remain to
be elucidated.
56
5 Overall discussion
Brain aging is a heterogeneous phenomenon, and this thesis illustrates
how the course of aging can vary within individuals over time and between individuals as a function of age, sex, and genetic variability. We
used two contrasts from magnetic resonance imaging (MRI), namely
spin-lattice T1-weighted imaging, and quantitative susceptibility mapping (QSM) from gradient-echo images, to picture the aging brain, by
means of morphometric measures and brain-iron concentrations.
Within each study, the same rigorous imaging acquisition protocols
were used with large samples sizes of 167-183 individuals, which contribute to the uniqueness of the studies. Most of the current knowledge
about the aging brain rests on a foundation of cross-sectional agerelated differences, and studies I and III contribute to current
knowledge by using longitudinal designs to investigate individual
rates of change. The importance of genetic variation in relation to regional brain changes was addressed with specific emphasis on functional polymorphisms involved in pro-inflammatory responses. These
studies further illuminate the importance of bi-directional relationships between structural integrity and preserved cognitive abilities
over time. Study II is the largest study to date to obtain quantitative
susceptibility estimates from healthy adults, and the first in vivo report
to show a lowering in overall subcortical brain-iron estimates in women from midlife to old age. Studies I and III are unique in their examination of longitudinal differences in anatomical brain regions using
high resolution images from a 4-Tesla scanner. Peripheral vascular
risk factors were not strong determinants of either brain or cognitive
changes in the studied samples. The results are discussed here in the
context of cognitive reserve, the brain maintenance hypothesis, and
potential influences of hormones, inflammation, and oxidative stress.
5.1 Mean change and individual differences
The latent change score models (LCSMs) of study I and III allow us to
make inferences about change, and parameters reflecting sample average trends as well as heterogeneity in change rates were specified,
based on empirical findings of age-related heterogeneity (Hertzog,
Lindenberger, Ghisletta, & Oertzen, 2006; Rabbitt, 1993; Raz et al.,
2010; Raz et al., 2008). Both mean differences and variance in change
were present across many of the neuroanatomical regions. The observed shrinkage on the images from the 4-Tesla scanner concurred
reasonably well with previous reports using weaker magnets (1.5 T)
57
(Raz et al., 2005a, 2010). Importantly, the average shrinkages varied
in magnitude over the neuroanatomical regions of interest (ROIs). The
pronounced shrinkage of the cerebellar hemispheres, shown in study I
and III, replicates previous findings (Raz et al., 2005a, 2010). Declines in the volumes of the medial temporal lobes described herein
(hippocampus and the parahippocampal gyrus) have been previously
reported in longitudinal studies involving repeated scans after six
months to five years (Pfefferbaum, 1998; Raz et al., 2005a, 2013;
Resnick et al., 2003). In addition, the observed stability in average
trends of the visual cortices (calcarine fissure) replicates previous
studies (Fjell et al., 2009; Raz et al., 2005a, 2010). Relative stability of
the striatum (putamen and caudate nucleus) both confirms (Raz et al.,
2010) and refutes (Fjell et al., 2009) previous longitudinal findings.
In contrast, the insula showed a much greater magnitude of average
shrinkage than previously reported (Raz et al., 2010). The lack of
mean shrinkage in the lateral prefrontal cortices and the subsequent
prefrontal white matter observed in studies I and III contrasts with
previous longitudinal work using weaker magnets (Driscoll et al.,
2009; Fjell et al., 2009; Raz et al., 2013; Resnick et al., 2003), with
one exception (Raz et al., 2010). These findings challenge the “last in,
first out” view of aging, namely that the regions developing later in
life are the first to show age-related deteriorations, and further contradict the prefrontal-aging hypothesis that has dominated the field over
the last two decades (West, 1996). This result needs further replication
using high-field MRI, and shrinkage may be more prominent over
longer periods of time.
In addition to shrinkage on average, the analyses also revealed a
prominent pattern of significant individual differences in the rates of
change of the ROIs, with all ROIs but the orbitofrontal cortex exhibiting substantial variance. The heterogeneity in developmental trends
replicates previous work on similar study populations, with individual
differences in change being most pronounced in the insula and cerebellum, followed by parahippocampal gyrus, hippocampus, striatum,
lateral prefrontal cortex, prefrontal white matter, and the visual cortices (Raz et al., 2005a, 2010).
The mechanisms underpinning the observed regional brain shrinkages
remain unclear because there is no well-established neurobiological
basis of volume differences and changes observed on MRI. Volume
variation in some regions has been linked to neuronal attrition in the
brains of patients suffering from neurodegenerative disease (Bobinski
et al., 2000). Experimental evidence suggests that MRI-derived vol58
ume differences may reflect changes in neuropil (unmyelinated axons,
dendrites, and glial cells); in rodents volume changes closely track
expansion and loss of neuropil during the estrous cycle (Qiu et al.,
2013). Volume loss is further associated with loss of neuropil following chronic treatment with antipsychotic drugs (Vernon et al., 2014) or
cardiac arrest (Suzuki et al., 2013). Loss of Purkinje cells in a mouse
model of autoimmune encephalomyelitis has been related to smaller
cerebellar volume (MacKenzie-Graham et al., 2009), as well as a decrease in numbers of layer V pyramidal neurons, and a decrease in
length of the apical dendrites (of single trunk, which branches in the
upper layers of the neocortex), whereas the remaining neurons have
been related to atrophy in MRI studies (Spence et al., 2014).
5.2 The influence of age
Replicating previous work (Raz & Kennedy, 2009), chronological age
was a strong determinant of cross-sectional age-related differences
across all ROIs (studies I and III), with older individuals having
smaller brain regions at baseline. The cross-sectional component of
the analyses further revealed that older adults had lower fluid intelligence and episodic memory scores, but better vocabulary performance
than younger participants, which also replicates earlier work (Horn &
Cattell, 1967). The lack of correspondence between the reported crosssectional age differences and longitudinal age-effects suggests that the
latter cannot always be inferred from the former. Age-heterogeneous
cross-sectional designs have high commonality between age and multiple measures of interest, thereby increasing the likelihood of confounds of between-individual age trends (Hertzog et al., 2006; Hofer
& Sliwinski, 2001; Lindenberger & Pötter, 1998; Robitaille et al.,
2013; Sliwinski et al., 2010), which can lead to spurious inferences
concerning interdependencies among age-related functions (Hofer,
Berg, & Era, 2003).
In study II, we showed that the distributions of brain iron varied over
structures, consistent with earlier postmortem findings (Hallgren &
Sourander, 1958). The shape of the age-function in the striatum over
the ages 20-69 years was linear for the susceptibility distributions,
which replicates previous reports using various iron-sensitive contrasts (Bartzokis et al., 1997a; Cherubini et al., 2009; Haacke et al.,
2010; Westlye et al., 2010). All other structures showed non-linearity
in the age-trends that varied in degree across the ROIs. The strongest
curvilinear fit in susceptibility rise was observed in the red nucleus,
followed by the sub-thalamic nuclei and substantia nigra. Less marked
59
nonlinear trends were observed in susceptibility counts for the thalamus, dentate nucleus, and putamen. Non-linear susceptibility trends in
those structures have been reported previously, yet studies also exist
that suggest more linear trends in thalamic iron distributions (Haacke
et al., 2010; Hagemeier et al., 2013). These differences may arise from
the use of high-pass filtered gradient-echo phase images in the aforementioned studies. Different settings for filter kernel size can change
the sensitivity of the measure to detect iron, and the impact of this
depends on the size and shape of the structure (Haacke, Xu, Cheng, &
Reichenbach, 2004; Pfefferbaum et al., 2009). A recent paper further
demonstrates that when using high-pass filtering, anatomical changes
due to gray matter atrophy may introduce a phase shift seemingly indicative of increased iron concentration, even though the biophysical
tissue composition has not changed (Schweser, Dwyer, Deistung,
Reichenbach, & Zivadinov, 2013). These results further highlight the
need for longitudinal evaluations due to the issues of commonality in
age-heterogeneous cross-sectional designs discussed earlier. As reproducibility of iron-sensitive contrasts becomes more consistent (Deh et
al., 2015), the investigation of age-related changes and variance therein becomes plausible.
High iron concentrations were positively correlated among several
subcortical structures, which may reflect an underlying age-related
process. A potential mechanism could be that excessive accumulation
of intracellular non-heme iron promotes reactive oxygen species
(ROS), oxidative stress, and cell death, resulting in neurodegeneration
(Andersen et al., 2014a; Dixon & Stockwell, 2014; Floyd & Carney,
1993). Iron distributions in several of the structures assessed followed
the same inverted U-shape pattern as myelination in brain aging
(Westlye et al., 2010). Changes in ferritin may follow the same curvilinear shape as myelination through life. Iron is a cofactor in the synthesis of myelin (Piñero & Connor, 2000) and there is evidence that
iron undergoes translocation between brain regions (Barkai, Durkin,
Dwork, & Nelson, 1991; Dwork et al., 1990). Myelin breakdown in
the surrounding regions may interact with the release of ferritin in the
subcortical gray matter. Iron is stored in oligodendrocytes (Madsen &
Gitlin, 2007) and many of the subcortical gray matter structures both
contain and border deep white matter tracts (see e.g., Bartzokis et al.,
1997a; Mitrofanis & Guillery, 1993). Myelin breakdown has been
associated with ferritin in Alzheimer's disease (Quintana et al., 2006),
and late-life myelination has also been encountered in phylogenetically older structures (Benes, 1994).
60
5.3 Determinants of age-related changes
The strongest predictors of longitudinal brain changes were proinflammatory genetic variants that mediated greater shrinkage in the
parahippocampal gyrus (PhG) and the cerebellar cortices (CbH) over
two years. This is indeed interesting given that the examined allelic
variants (IL-1β C-511T: PhG, CbH; MTHFR C677T, PhG) promote a
greater systemic pro-inflammatory response (IL-1β 511T: greater circulatory interleukin-1β; MTHFR T allele: higher plasma homocysteine
levels). It is unclear why shrinkage in the parahippocampal gyrus and
cerebellum is particularly pronounced in carriers of T alleles in two of
the examined pro-inflammatory genetic variants. The literature on
regional effects of pro-inflammatory cytokines on the brain is still
scarce. Response to neuroinflammation has been linked to shrinkage
of the medial temporal lobes in rats (Hauss-Wegrzyniak, Galons, &
Wenk, 2000), and IL-1β may affect glutaminergic and GABAergic
transmission in the cerebellum (Mandolesi et al., 2013).
Further, recent cross-sectional studies highlight the relationship between higher levels of plasma IL-1beta (Sudheimer et al., 2014), genetic variants regulating pro-inflammatory responses, and smaller volumes in the medial temporal lobe structures (Raz et al., 2015). The
MTHFR-677 T allele has been associated with decreased density of
the parahippocampal gyrus in schizophrenia (Zhang et al., 2013).
Those, however, were cross-sectional studies, and their findings are
not directly comparable with the longitudinal observations reported
herein. Our findings are consistent with both the possibility that the
major alleles of IL-1β and MTHFR polymorphisms have neuroprotective influence on the parahippocampal cortex, and that variant alleles
promote shrinkage. However, all the mentioned studies are crosssectional, and the observational nature and limited statistical power
concerning the allelic variants of the current report preclude a more
definitive conclusion about causality.
In study I and III, the covariates socio-economic status (SES), the
APOE ε4 allelic variant and vascular risk had limited importance regarding changes in both brain and cognition. The effects of age and
SES on regional brain volumes (e.g., hippocampus), and cognition
(fluid intelligence) were sporadic, observed in few models, and we
found no specific effects of vascular risk or APOE ε4. Further, only
individuals with controlled hypertension were included in the studies,
and those suffering from cardiovascular pathologies were excluded.
Elevated cardiovascular plasma markers may be more important for
61
brain and cognition, if the former reach clinical levels. The APOE ε4
variant may have greater influence amongst individuals with Alzheimer’s disease (AD). However, in combination, risk-relevant covariates (advanced age, lower SES, APOE ε4 allele, and vascular risk)
were associated with significantly greater shrinkage of at least one
region, namely the lateral prefrontal cortex. Such cumulative influence
of multiple interrelated risk factors, rather than independent effects of
such factors, may be plausible in a sample selected for better than typical health. Unfortunately, the sample size precludes us from the investigating interactive effects, given the relatively small number of
individuals possessing variants of the risk allele.
5.4 Sex related differences in brain iron
Sex was a poor predictor of regional brain volumes and was therefore
eliminated from analyses in studies I and III. Sporadically reported
sex differences in regional brain volumes may have arisen due to differences in accounting for the intracranial volume, as suggested by
recent studies (Pintzka et al., 2015; Voevodskaya et al., 2014). Sex
differences may be less influential in brain morphology than in molecular or neurochemical functions. Sex-specific variations are present
in neurodegenerative diseases (Bartzokis et al., 2011; Taylor et al.,
2007), often accompanied by an accumulation of iron deposits
(Bartzokis et al., 2007; Langkammer et al., 2013), which makes sex
differences important in relation to iron accumulation. Study II
showed that greater proportions of variance in susceptibility can be
attributed to age in men than women. Further, women post expected
menopause age had lower total sub-cortical susceptibility counts,
which replicates recent post mortem findings of lower tissue brain iron
levels in women than men from midlife to old age (Ramos et al.,
2014). These findings require replication, but are of importance: as
various MRI studies consider brain iron elevations in various neurodegenerative diseases, the potential effect of sex must be considered
(Bartzokis et al., 1997a; Langkammer et al., 2013).
5.5 Bidirectional brain-cognition relationships
The results from study III demonstrate that larger brain volumes predict lesser decline in fluid abilities (Gf) and that better cognitive attainment early in life may mitigate short term shrinkage of an important age-sensitive brain region: the lateral prefrontal cortex. Showing such reciprocal relationships between brain and cognition in a lon62
gitudinal study is important as such a design circumvents problems
with interpreting changes in cognition alone.
These results can be interpreted in the context of reserve. Our findings
support recent more complex and nuanced interpretations of braincognition reserve than initially proposed by Satz (1993), and are more
consistent with Stern’s (2009) recent conceptualization of the phenomenon. The former brain reserve hypothesis posits that larger brains
containing more neurons and synaptic connections among them
(Katzman et al., 1988; Satz, 1993) are better prepared for compensating for acute and chronic insults. Stern (2009) incorporated both neuronal aspects and cognitive aspects into the concept of reserve. This
reconceived cognitive reserve refers to the advantage conveyed by
higher cognitive ability and education in coping with cognitive expressions of brain pathology, presumably through efficient use of cognitive processes to resist cognitive decline, regardless of their specific
connection to identifiable neural changes (Stern, 2009; Tucker &
Stern, 2011). Cognitive reserve can arise from multiple sources and
multiple processes can contribute to expression of cognitive reserve,
including compensation based on existing cognitive skills, acquisition
of alternative coping strategies, life or propensity to adhere to lifestyles, as well as socioeconomic status and practices that benefit
maintenance of healthy neural circuits (Hertzog & Kramer, 2008;
Tucker & Stern, 2011).
Evidence for the cognitive reserve hypothesis has been generated primarily from cross-sectional observational and epidemiological studies,
and as mentioned, study III provides some support for this hypothesis
via our finding of the mitigating effect of better episodic memory
(EM) performance at baseline. The effect generalized to all agesensitive cognitive domains to some extent. Baseline Gf scores had a
similar positive trend on change in the tertiary cortices as EM (p =
.051, α' = .017). One explanation for the lack of significance may be
that the relationship between Gf and tertiary volumes was attenuated
by the underlying correlation between socio-economic status (SES)
and Gf, essentially reflecting different aspects of cognitive reserve.
Similar attenuations emerge from the commonality between intelligence and SES (Deary & Batty, 2007).
Study III illustrated that the rate of cognitive change may depend on
initial brain volume, which can aid in characterizing bias problems
when cognitive decline is diagnosed using neuropsychological assessments. Some studies suggest that cognitive reserve may simply
reflect initial levels of performance, with highly educated individuals
63
approaching the diagnostic threshold later than those with poorer educational attainment and lower baseline cognitive performance scores
(Tuokko, Garrett, McDowell, Silverberg, & Kristjansson, 2003).
Moreover, in our sample, as in some of the extant studies (e.g.,
McArdle, 2009), those with higher baseline fluid abilities actually
exhibited smaller gains after the second administration of the tests.
Thus, the advantage of higher cognitive endowment in slowing cognitive decline is likely to be a real, albeit complex, phenomenon, based
on reciprocal influences between the brain and behavior. This is consistent with the “brain maintenance” hypothesis, which stresses the
importance of structural integrity for preservation of high cognitive
function in aging (Nyberg et al., 2012).
5.6 Methodological considerations
One of the major strengths of the current reports is the large sample
sizes in the context of imaging studies. Low statistical power and subsequent risk of failure in detecting effects is often a major issue for
most MR studies that use smaller sample sizes. Further, all participants in each study were scanned according to the same image acquisition protocol. Larger studies often comprise several pooled samples
combining different acquisition protocols, which may potentially induce between scan variability to the measures. Study II is one of the
largest samples of healthy adults to have in vivo MRI estimates of
iron, and the largest study applying QSM maps on adults of a wide
age range.
QSM is a relatively new technique, and researchers may prefer more
traditional measures. However, QSM has advantages over more traditional methods, as will be discussed. Recent work further supports the
validity and reproducibility of quantitative susceptibility maps. The
association between magnetic susceptibility and its estimation using
QSM has been validated by phantom experiments (de Rochefort et al.,
2010; Liu, Xu, Spincemaille, Avestimehr, & Wang, 2012). Recent
evidence from postmortem evaluation of QSM (via mass spectrometry) supports the validity of the measure by showing that iron is the
dominant source of magnetic susceptibility (Langkammer et al.,
2012). Further QSM maps show good reproducibility between scans,
and among scanner manufacturers and magnet strengths (Deh et al.,
2015). Compared with gradient-echo phase images, QSM provides a
more accurate spatial depiction and a clearer image reconstruction,
64
and suppresses the blooming artifacts observed in phase (J. Li et al.,
2012).
Additionally, latent variable modeling holds many advantages over,
for example, regression and other general linear model approaches. By
fitting the observed scores to a theoretical model comprised of latent
variables, using likelihood estimation, we gain better insight into
many of the artifacts observed in traditional linear regression models,
such as measurement error, residual error, and regression to the mean,
by accounting for the covariance between initial levels and changes
(McArdle, Nesselroade, 2003).
In study I and II, a rigorous segmentation protocol was applied. Manual segmentation is often considered a gold standard in the clinical context, and it is used for quantitative evaluation of automatic segmentation (see e.g., Destrieux, Fischl, Dale, & Halgren, 2010 for FreeSurfer). Semi-automated methods may sacrifice anatomically valid
manual measurements (Kennedy et al., 2009) and for some brain regions, introduce age-related bias (Wenger et al., 2014). However, with
manual segmentation techniques there is also the risk of introducing
subjective error. We addressed this by using tracing protocols in
which the operators were blind to participant age and sex. Further,
intra class correlations between raters were assessed in studies I and
III. To counteract potential inter-subject variability in study II, each
region was traced by a single tracer with training in neuroanatomy.
The neuroanatomical regions were segmented on the 3 mm slice that
was most prominent, and we cannot know if the results would be the
same if the structure was examined in its entirety. The rank order of
iron distributions followed previously reported iron concentrations
from post-mortem work, suggesting that this limitation is unlikely to
be a major issue (Hallgren & Sourander, 1958).
The results of these studies should be interpreted in the context of several limitations. For all studies, we relied on a convenience sample.
Hence, the results suffer from limited generalizability due to the nonrandom recruitment procedures. For instance, prevalence of hypertension in American adults (age 20 and above) is 33% compared to 20%
in this sample, and prevalence of diabetes is 8% compared to 0%, respectively (Go et al., 2014).
However, we make no claims regarding generalization of the results to
the general population. The findings may further be contaminated by
selection bias. Rigorous exclusion was applied in the sampling procedure for studies I and III, and only people free of cardiovascular, psy65
chiatric, or neurodegenerative diseases, or substance abuse were recruited for study II. The purpose, however, was to study nonpathological aging, which is a necessity in this field.
Despite exclusion criteria, sub-clinical influence of disease may be an
issue. Even younger participants may harbor early precursors of neurological and vascular diseases but do not express them at a detectable
level. The prodromal stage of Alzheimer’s disease is known to be extensive, and we cannot completely exclude the possibility that some of
the participants were in early stages of the disease, despite our screening procedures. We know from a study of nuns (i.e., religious order
studies) that individuals with preserved mental functioning may also
have considerably progressed neuropathology post-mortem, despite
high cognitive achievement through senescence (Snowdon, 2003).
The higher prevalence of cardiovascular pathology in the older populations of studies I and III may have led to even more selective recruiting of optimally aged older adults. However, we also included individuals with medically controlled hypertension to counteract such
bias. Selection for good health could have different effects on the
younger and older segments of the age continuum, and reduce possible age-related differences in the variables of interest. Thus, a sampling strategy aimed at minimizing confounding effects of low education and age-related diseases could have introduced more selection
bias and hence further reduced generalizability of the findings.
A few limitations should be mentioned regarding imaging procedures.
One single gradient echo was acquired in study II, and 𝑅2∗ maps could
not be calculated. A comparison of QSM with 𝑅2∗ would have been
useful for assessing age-related dependency in ROIs with high iron
concentration. QSM is still a primarily research methodology, although recent work has shown good reproducibility (Deh et al., 2015)
and validity (Langkammer et al., 2012), and its clinical use is growing
in popularity (Haacke et al., 2015; Wang & Liu, 2015). Previous work
has shown that 𝑅2∗ and susceptibility yield similar results regarding
age dependency in estimates of iron deposits in the subcortical gray
matter (W Li et al., 2014), and the unavailability of 𝑅2∗ maps is unlikely to be a major limitation.
A potential weakness of study III is the lack of assessment of practice
or retest effects. The single-sample study design with only two measurement occasions precluded estimation of retest effects. This could
confound the results by masking declines and exaggerating gains. Because of the influence of one prior test experience (Salthouse, 2013),
66
and re-test effects (Salthouse, 2014) on subsequent cognitive performance, changes in scores on all cognitive measures observed in study
III might be underestimated. Future studies with three or more repeated measures, or a control group comparison, should address the issue
of retest-effects in the context of neural correlates of cognitive aging.
In study II, we approximated the onset of menopause by using the
reported average age of menopause onset in Chinese women (Cheung
et al., 2011). Although studies have shown relatively small standard
deviations in the age of menopause onset in Asians (Cheung et al.,
2011; Gold et al., 2001), we do not know the exact variance of menopause age among the particular cohort of Chinese women included in
the study. Information about hormone replacement therapy was not
available. This should be of minor concern because large studies report that few Chinese women use hormone replacement therapy (0.8%
or less) (Lundberg, Tolonen, Stegmayr, Kuulasmaa, & Asplund, 2004;
Yang et al., 2008).
5.7 Future directions
The cross-sectional work in study II should be further evaluated in the
context of age-related changes to evaluate the true iron accumulation
in aging, in addition to the age-related differences reported herein. The
longitudinal relationship between susceptibility and cognitive change
is important, given recent cross-sectional findings of associations between cognitive functions and iron accumulation (Ghadery et al.,
2015; Pinter et al., 2015). Such studies are still scarce (but see Penke
et al., 2012). The brief time window of two occasions precludes us
from drawing conclusions about change over longer periods of time.
Long-term assessments over several test occasions are uncommon (but
see Raz et al., 2010 for a 3 occasion design), and several repeated
imaging occasions would allow for the testing of different trajectories
over time. Phylogenetically motivated models, as the “last in, first
out” hypothesis, could be tested appropriately, rather than in current
approaches, i.e., those primarily assessing age-related differences between individuals. The bi-directional effects reported in study III
needs replication, and it would be interesting to see if high cognitive
performers maintain structural integrity over longer periods of time
than the brief time window of two years studied herein. The influence
of, and neuronal underpinnings of, test-retest effects need further investigation using several repeated measurement intervals.
67
The relationship between structure shrinkage and regional brain iron
levels needs further investigation and quantitative susceptibility mapping holds much promise as a more reliable measure for determining
adequate biophysical tissue composition of iron in the presence of
atrophy in the gray matter (Schweser et al., 2013). Little is known
about the specific mechanisms of iron accumulation in the brain. The
age-related iron distributions of many of the structures followed the
same curvilinear shape as white matter development, and as oligodendrocytes store iron, iron release following myelin breakdown may be
an important co-factor in the study of myelination (Piñero & Connor,
2000). Future work incorporating iron-sensitive contrasts with whitematter imaging could aid in quantifying such relationships, and highresolution diffusion spectrum imaging may allow more detailed investigation of brain white matter.
5.8 Concluding remarks
The thesis assessed age-related changes in the healthy human brain by
means of structural MRI correlates from spin-lattice images and quantitative susceptibility mapping, as well as changes in age sensitive and
age-resilient cognitive domains. The contributions of demographic-,
genetic-, and health-related factors were further evaluated. The results
may aid in establishing a baseline distinguishing pathological neurodegeneration from healthy aging, and emphasize the importance of
addressing both risk promoting and mitigating factors in healthy aging. The following points are highlighted as concluding remarks:
68

Average shrinkage and individual differences in change were
observed in regional brain volumes over a brief time window
of two years. The findings from studies I and III are unique,
and the existent longitudinal literature looking at individual
differences in change are vast, but the findings would benefit
from further replication over longer periods of time, to examine different trajectories (e.g., various non-linear relationships).

No mean shrinkage was noted in the lateral prefrontal cortex,
in a sample of adults in good health, which challenges the “last
in, first out” hypothesis of brain aging. We may have to reevaluate the current dominant role of the frontal aging hypothesis in healthy aging, and intact prefrontal functions may be a
key factor in successful aging.

Pro-inflammatory single nucleotide polymorphisms predicted
greater shrinkage in the cerebellum and the parahippocampal
gyrus (PhG). These findings from study I are novel, and need
further replication. It is uncertain why the hippocampus was
spared, while other medial temporal lobe structures were not.
Future work focusing on hippocampal subfields segmented via
ultra-high resolution in vivo MRI may be of importance since
effects might be specific to certain bordering subfields communicating with the PhG rather than the structure in its entirety.

The distributions of susceptibility varied across the subcortical
nuclei, consistent with previous post-mortem findings which
provide validation of our results in the absence of PM specimens. A gradual linear rise with age was observed in the striatum and varying degrees of curvilinearity were observed
among the other nuclei. The results from study II were consistent with previous work using QSM and R*2 maps; when
deviations emerged these were predominately related to the
high-pass filtering phase, which is known to show greater variability at different kernel sizes.

A novel finding of study II was that a positive manifold of
correlations of susceptibility were observed across several of
the subcortical nuclei supporting the dopaminergic system.
Iron serves as a co-factor in dopamine signaling and it would
be interesting to investigate whether the synchronicity among
regions maintains over time, and how iron relates to agerelated dopamine alterations, by applying a multimodal imaging protocol.

Sex could influence degree of susceptibility, and the most
prominent difference emerged in total susceptibility across the
regions of interest in women post menopause relative to men
and younger women. This is an interesting new finding, which
replicated previous PM work showing that women have lower
brain iron levels from midlife to old age relative to their male
counterparts. Changing estrogen levels may mitigate brain-iron
decrease in women post menopause. Experimental work shows
that estrogen can influence cellular iron and mitigate brain
damage, but the associative nature of the current report precludes definite conclusions about causality. The results would
benefit from further replication and the neurobiological mech-
69
anisms underlying lower brain iron levels in humans post
menopause need to be addressed.
70

Gains were noted in memory, vocabulary, and individual differences in change in Gf scores in study III, showing that repeated cognitive tests indeed improve measured cognitive performance over brief measurement intervals. Because of the influence of prior test experience and test-retest effects on subsequent cognitive performance, changes in scores on all
cognitive measures observed in this study are positively biased
and should be viewed as underestimation of the true change
over the two-year period. The two-period design used herein
precludes separating re-test from time effects. Future studies
with three or more repeated measures should address the issue
of retest practice effects in the context cognitive aging and its
neural correlates.

Study III showed that better baseline cognitive performance
mitigated individual prefrontal gray matter shrinkage, highlighting the need to maintain good cognitive performance to
better maintain structural integrity in an important agesensitive region. The mechanisms by which higher cognitive
performance at baseline may mitigate individual prefrontal
shrinkage or even promote volume growth and thus serve as a
neuroprotective factor are unclear.

Larger brain volumes at baseline predicted greater gains in Gf.
Unfortunately; we did not find similar results for episodic
memory and vocabulary scores. Could we have found additional brain-cognition and cognition-brain associations? This
question cannot be answered empirically, and study III benefitted from a relatively large sample size. We estimated latent
variance in change to better account for measurement error and
regression to the mean effects; the findings are thus likely robust. Designs incorporating several measurement occasions
(3+) may be helpful in elucidating individual differences in episodic memory and vocabulary scores, as individuals were
closer to the sample mean effects in the current work.

The findings from study III contribute to better understanding
of the neural substrates of cognitive aging and the importance
of maintaining cognitive functioning in older age. The specific
mechanisms of the observed effects of cognitive and brain reserve remain to be elucidated.
6 Summary in Swedish
Hjärnans åldrande är en heterogen företeelse, och denna avhandling
illustrerar hur åldrandets förlopp varierar inom individer över tid, men
också mellan individer beroende på ålder, kön, och genetisk variation.
Det mesta av det som är känt om hjärnans åldrande idag baseras på
studier som gjorts vid ett enda mättillfälle. Longitudinella studier med
flera mättillfällen är dock nödvändiga för att ta reda på hur åldrandet
tar sig uttryck över tid. Två magnetic resonance imaging (MRI) kontraster, T1 weighted imaging och quantitative susceptibility mapping
(QSM), användes för att avbilda den åldrande hjärnan och kvantifiera
volymer och koncentrationer av järn i vävnaden. Magnet styrkan i
MRI scannern varierade från 1.5 Tesla till 4 Tesla. De inkluderade
stick proven på 167-183 individer är stora urval i hjärnavbildnings
sammanhang. Den här avhandlingen bidrar med longitudinella undersökningar i studie I och III avseende dels hjärnans morfometri, men
också kognitiva förmågor. I studie II undersöktes med hjälp av tvärsnitts data hur koncentrationen av järn i hjärnans subcortikala strukturer förändras som en funktion av ålder och kön. Studie I visar att
flertalet strukturer i mediala temporal loben, orbitofrontala cortex,
samt cerebellum (lill-hjärnan) krymper över tid, medan striatum och
den visuella cortex är mer stabila. Prefrontala cortex däremot krymper
inte signifikant över tid, vare sig avseende vit eller grå substans. Detta
fynd motsäger dominerande idé strömningar, om att just denna region
är särskilt känslig för åldersmässiga förändringar. Det är viktigt att
poängtera att även individuella skillnader förekom så tillvida att det
fanns signifikanta avvikelser från medelvärdes trender i alla de mätta
stukturerna bortsett orbitofrontala cortex. Vidare visade innehavare av
genetisk risk variant som ökar pro-inflammatorisk respons mer omfattande förändringar i delar av mediala temporal loberna samt lillhjärnan, jämfört med personer som bar en skyddande genetisk variant. I
studie II där effekter av ålder och kön på MRI-estimat av järn mättes
framkom att halterna av järn i striatum steg linjärt per levnadsår från
20 till 69 års ålder. I övriga strukturer var denna utveckling icke linjär,
med platå-liknande förhållande från cirka 30 års ålder, för att sedan
avta från det sextionde levnadsåret. Dessa icke linjära samband var
starkast i red nucleus i hjärnstammen, följt av det subthalamiska komplexet och substantia nigra, även denna senare region lokaliserad i
hjärnstammen. Marginella icke-linjära effekter återfanns i sambandet
mellan järn nivåer och ålder avseende thalamus, dentate nucleus i cerebellum (lill-hjärnan), samt globus pallidus. Kvinnor hade överlag
lägre nivåer av järn i hjärnan än män, och kvinnor från 51 års ålder
och äldre hade lägre total subcortikal järn-nivå jämfört med både män
71
och yngre kvinnor. I studie III observerades ömsesidig påverkan mellan neuroanatomiska regioner och kognitiva variabler. Deltagarna förbättrades genomsnittligen i episodiskt minne (EM), samt ordförråd
som är ett mått på s.k. kristalliserad intelligens, men inte avseende
fluid ability (Gf) (ungefär flytande intelligens). Individuell variation
återfanns på Gf testen men inte EM eller ordförråd, där deltagarna låg
närmare medelvärdena. Större hjärnvolym vid baslinjen predicerade
bättre bibehållen kognitiv förmåga. Bättre EM vid baslinjemätningen
motverkade reduktion av den laterala prefrontala cortex. Genomgående hade kardiovaskulära riskfaktorer ingen större betydelse för förändring i hjärna eller kognition. Däremot var genetiska varianter som
påverkar inflammations nivåer i kroppen av betydelse för hur mycket
åldersrelaterade förändringar i hjärnans struktur som personerna uppvisade. Resultaten diskuteras i sammanhanget kognitiv reserv och vikten av att behålla god strukturell integritet i hjärnan under åldrandet,
samt potentiellt inflytande av hormoner, inflammation och oxidativ
stress. Resultaten kan ha betydelse för att etablera en baslinje mellan
patologisk neurodegeneration och icke patologiskt åldrande.
72
7 Acknowledgements
I thank the faculty of social sciences for honoring me with a four-year
full-time thematic PhD student position in cognitive aging. I want to
express my gratitude to the following funding bodies for letting me
pursue my research, enabling research placements in the USA, and
presentation of my work at various international conferences. Additional information about specific projects grants are presented in the
acknowledgements for each study.
American-Scandinavian Foundation (Thord-Gray Memorial Award);
The Solstickan Foundation (The Rolf Zetterström Award); the Royal
Swedish Academy of Sciences; Swedish Council for Working Life and
Social Research; the Swedish Medical Association; Stiftelsen för
Gamla Tjänarinnor; the Stockholm Brain Institute; Elisabeth and
Herman Rhodins minnesfond; Jubileumsdonationen Knut och Alice
Wallenbergs Stiftelse; the Lars Hierta Memorial Foundation and the
Adlerbertska Stipendiestiftelse.
I want to extend my gratitude to my main-advisor Dr Håkan Fischer
and co-advisor Dr Timo Mäntylä for proof reading, and general encouragement throughout the process of thesis writing. Håkan Fischer
has always been positive about my research, including in difficult
moments. I am thankful to Dr Naftali Raz for sharing the data for
studies I and III. Dr Paolo Ghisletta has been incredibly helpful in
providing statistical advice for studies I and III. I also want to thank
Dr Yi Wang, Dr Pascal Spincemaille, and Dr Tian Liu for opening up
my eyes to the wonderful world of MRI physics. Dr Pascal Spincemaille has been very helpful and supportive through the work of study
II, and I have benefited from the Socratic questioning. I extend my
appreciation and gratitude to everyone in Dr Wang’s research group at
Weill Cornell Medical College at Cornell University for being kind
and helpful throughout my stay. I thank Dr Jianlin Wu for sharing the
data for study II of the thesis, and Dr Jing Shen for providing your
expertise throughout the work with the study. I thank Drs Eric Westman and Laura Ferrer-Wreder for reviewing the final version of the
thesis. Last I thank my friends and family for love and for life.
Ninni Persson,
Stockholm, December 8, 2015
73
8 References
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