BRAIN COACTIVITY: PROSPECTIVE MEMORY, APOE AND AGE
Candidate no. 87533
Differences in brain coativity in prospective memory associated with varying APOE allele
combinations and age: An event-related fMRI experiment.
Candidate Number: 87533
Supervisor: Jennifer Rusted
Word Count: 5, 956 (inc. footnotes)
Psychology BSc
School of Psychology, University of Sussex
May 2013
Acknowledgements
This study was funded by a BBSRC grant to Jennifer Rusted (BB/H000518/1) who
supervised this project. Thanks to Torsten Ruest for the data collection and Simon Evans for
study design and image preprocessing. The writer of this report was responsible for the PPI
analysis – with guidance from Simon Evans – and completion of this manuscript.
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Abstract
The apolipoprotein (APOE) ε4 allele is theorised to cause accelerated cognitive aging
because of its antagonistic pleiotropic properties: the allele correlates with increased
cognitive performance in young adulthood and earlier onset of AD in older adulthood. To
better understand how APOE affects cognitive aging, the current study investigated
differences in brain coactivation as a function of genotype (ε4+s vs. ε4-s) and age (middle
age vs. younger adult) in addition to trial-type (ongoing trials vs. prospective memory (PM)
trials). The study primarily focused on middle-aged participants who completed a
computerised-PM task whilst in the fMRI scanner. Secondarily, a younger cohort was added
to examine the genotype-specific effects of age on coactivation. The current study predicted
there would be an increase in bilateral frontal coactivation as a function of age in ε4+s based
on theories of normal cognitive aging and the antagonistic pleiotropic theory. The most
significant finding was the genotype-specific effect of age: increased bilateral coactivation
with the lInfFrontal (seed) region for middle-aged ε4+s compared to younger ε4+s when
masked by the contrast, middle-aged ε4-s > younger ε4-s. For future research, it would be
interesting to conduct a longitudinal study that focused on coactivation differences as a
function of age, genotype and AD-conversion to better understand how coactivation
differences predict AD.
Keywords: APOE ε4, antagonistic pleiotropy, prospective memory, coactivation
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1. Introduction
1.1 APOE ε4, The Brain and Alzheimer’s Disease
Since the early 1980s—when beta-amyloid (Aβ) plaques and neurofibrillary tangles
were first linked to the Alzheimer’s Disease (AD) pathology— there has been an ongoing
debate regarding what causes AD (Selkoe, 2011). The conversation continues because no
genetic factor, biomarker or environmental factor has been unequivocally linked to the cause
of AD.
In the mid-1990s, it was discovered that harbouring at least one apolipoprotein
(APOE) ε4 allele, located on chromosome 19, increased the risk of AD and negatively
correlated with the age of onset (Selkoe, 2011). In support of this discovery, Corder et al.
(1993) found that 64% of patients within a sporadic AD sample and 80% within a familial
sample had at least one ε4 allele, whilst only 14% of the general population have the allele
(Bertram, McQueen, Mullin, Blacker, & Tanzi, 2007). Therefore, though harbouring the ε4
allele is not the direct cause of AD, the allele’s presence is correlated with AD1.
The ε4 allele’s overrepresentation in the AD population has correlated with the
presence of amyloid beta (Aβ) plaques (Fillipinni, 2011; Mann, 1991), neuronal atrophy
(Buttini et al., 1999), and a loss of cortical choline acetyltransferase (ChAT) activity in key
areas of the brain (Poirier et al., 1995). In terms of ε4’s relation to Aβ plaques, Aβ deposition
is one of the first visible signs of the pathological process of AD, and APOE ε4 is known to
inhibit the normal clearing process of Aβ plaques in the brain (Fillipinni, 2011; Mann, 1991).
In addition, Buttini et al. (1999) discovered that mice harbouring at least one ε4 allele
experienced increased dendritic and synaptic loss with age (i.e., increased cerebral atrophy).
There are three types of APOE alleles: ε2, ε3, and ε4. Whilst ε4 is overrepresented
in the AD population, ε2 is underrepresented, ie., ε2 may protect against the onset of AD.
1
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1.2. APOE ε4 as an Antagonistic Pleiotropic
Curiously, on average, younger adults with at least one ε4 allele have higher IQ scores
and achieve higher levels of education than their ε3 and ε2 counterparts (Hubacek et al.,
2001; Yu, Lin, Chen, Hong, & Tsai, 2000). More specifically, young ε4+s (participants with
at least one ε4 allele) have been found to have a domain-specific advantage on frontal tasks
(decision making, prospective memory performance, and verbal fluency) (Marchant, King,
Tabet & Rusted, 2010). In turn, APOE ε4 is considered antagonistic pleiotropic (i.e., a gene
which has different effects on evolutionary fitness at different ages): APOE ε4 is correlated
with increased cognitive performance in younger adults and decreased cognitive performance
in older adults (Marchant et al.,2010).
Marchant et al. (2010) speculated that younger ε4+s’ increased cognitive performance
caused accelerated cognitive aging/earlier age-related decline in older ε4+s’, which may be
an additional explanation for the allele’s overrepresentation in the AD population. The
antagonistic pleiotropy theory is supported by the metabolic function of APOE ε4 discussed
earlier: high Aβ levels correlate with increased neuronal and synaptic activity in younger
adults and reduced functional brain connectivity in healthy older adults (Wei, 2010).
1.3. The Effects of APOE ε4 on Cognition and Cognitive Aging
Today, because APOE ε4 is the second leading risk factor for developing AD, after
age, there has been motivation to determine how ε4 affects specific cognitive processes
affected by AD and cognitive aging (i.e., prospective memory, visual attention, and memory
encoding) throughout the lifespan (Rocchi, Pellegrini, Siciliano and Murri, 2003).
Determining how the ε4 allele affects cognitive processes throughout the lifespan will help
researchers more accurately understand how the ε4 allele facilitates cognitive aging and the
development of AD in old age.
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The ε4 allele appears to impede visual attention in middle-age. Greenwood,
Sunderland, Friz and Prasuraman (2000) discovered that the effect of cue-validity (i.e., the
cost of having an invalid cue) on reaction time is greatest for middle-age ε4+ participants
compared to ε4-s (participants with homozygous ε3). Thus, the researchers concluded that the
possession of an ε4 allele in middle-age is associated with changes in attention processing
similar to that of someone with mild-AD (Greenwood et al., 2000).
In terms of how ε4 affects memory (i.e., encoding) with age, Filippini (2011)
discovered that aging was associated with decreased brain activity in ε4+s and increased
brain activity in ε4-s. Furthermore, the over-activity of brain function initially found in young
ε4+s was found to be disproportionately reduced with age even before the onset of
measurable memory impairment (Filippini, 2011; Mondadori et al., 2006). A caveat in
Filippini’s (2011) study is the broader age-range in the “older adults” group (50-78: 28 years)
compared to the “younger adults” group (20-35: 15 years). Thus, in light of current agerelated theories of compensation and dedifferentiation (to be discussed in the next section), it
is possible that that the large “older adult” age-range diluted a point of increased activation in
ε4+s that occurred earlier on in their lifespan (i.e., middle-age) than non-carriers (Marchant et
al., 2010).
1.4. Theories of Normal Cognitive Aging
The three most prominent theories of normal cognitive aging include the PosteriorAnterior Shift in Aging model (PASA), the dedifferentiation model of cognitive aging, and
the Hemispheric Asymmetry Reduction in Older Adults model (HAROLD). These all
involve forms of increased brain activation with age as compensation for neuronal loss and/or
an inability to recruit specialised brain regions (Cabeza, 2001; Davis, Dennis, Daselaar,
Fleck & Cabeza, 2008; Park & Reuter-Lorenz, 2009).
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1.4.1. The Posterior-Anterior Shift in Aging Model (PASA). The PASA model
contends that cognitive aging is associated with a reduction in posterior activity (e.g., in the
occipital lobe) and an increase in anterior activity (e.g., in the frontal lobe) as a compensatory
mechanism of cognitive aging (Davis et al., 2008; Grady et al., 1994). Parker and ReuterLorenz (2009) argued that this increase in frontal activation, as a function of age, is a
compensatory mechanism in response to neuronal changes caused by declining neural
structure and function.
1.4.2. The Dedifferentiation Model. In opposition to PASA—and all other
compensatory models— the dedifferentiation model suggests that when older adults carry out
certain cognitive processes, they engage more generalized neuronal processes as a
consequence of cognitive aging (Han, Bangen, & Bondi, 2009). In contrast to Han et al.
(2009), Cabeza (2001) argued that compensation and dedifferentiation theories are not
necessarily incompatible because dedifferentiation (combining neuronal pathways) can be
thought to compensate for cognitive decline associated with cognitive aging.
1.4.3. The Hemispheric Asymmetry Reduction in Older Adults Model
(HAROLD). An example of how compensation and dedifferentiation models can be
compatible is found in the HAROLD model (Cabeza, 2001). This model suggests that brain
activity tends to be less lateralized in older adults compared to younger adults during memory
tasks. Thus, the HAROLD effect can be explained by compensation models (i.e.,
bihemispheric involvement may help counteract age-related neurocognitive decline) and
dedifferentiation models alike (i.e., the loss of lateralisation reflects a difficulty in recruiting
specialized neural mechanisms).
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1.5. Prospective Memory
The current work specifically focused on how genotype and age affected prospective
memory (PM)—an individual’s ability to create, rehearse and carry out an intended action—
because of its relevance to everyday life and independent living (Burgess, Gonen-Yaacovi, &
Volle, 2011; Burgess, Scott & Frith, 2003; Marchant et al., 2010; Rusted, Ruest & Gray
2011). In fact, a decline in PM is one of the first major complaints older adults with memory
loss experience (Luo & Craik 2008). In addition, prospective memory is frontally mediated
and thus useful for determining the extent to which harbouring an APOE ε4 allele accelerates
age-related processes, which would be evidence for accelerated cognitive aging.
1.5.1. Prospective Memory and the Brain. Burgess et al. (2011) concluded that the
rostral prefrontal cortex (rPFC), especially the lateral rostral PFC, Brodmann Area 10 (BA
10), plays a superordinate role during the many stages of PM. Thus, the researcher proposed
the Gateway Hypothesis of Rostral PFC, which suggests that the main purpose of the rPFC is
to control differences between attending to independent thought (inner mental life) and
attending to the external world (stimulus-oriented attention). In addition, there has recently
been evidence for a fronto-parietal role in PM which links into Burgess’s Gateway
Hypothesis. More specifically, the fronto-parietal hypothesis suggests that parietal regions
project to frontal areas to complete the more executive tasks associated with PM (Rusted et
al., 2011; Simons et al., 2006).
1.5.2. Event-Based Prospective Memory (EBPM). There are two types of PM tasks
that can be tested in the laboratory and that are susceptible to age-related decline: The eventbased PM task (EBPM) and the time-based PM task (TBPM) (Luo & Craik, 2008; Henry,
MacLeod, Phillips &, Crawford, 2004). This study focused on the EBPM task—wherein
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participants are asked to perform an action in response to an environmental cue. The specific
EBPM task used in this study was embedded in a computer-based card-sort task (i.e., the
ongoing task) first developed by Rusted, Sawyer, Jones, Trawley and Marchant (2009).
Rusted et al. (2009) defined the task as attention-demanding and contended that PM detection
engages general attention processes in addition to those under the control of the central
executive system.
1.6. The Current Study
The current study looked at the effect of trial-type (PM trials vs. ongoing trials),
genotype (ε4+s vs. ε4-s) and age (younger adults vs. middle-age adults) on coactivation in the
brain (based on the results of an fMRI analysis) whilst participants completed Rusted et al.’s
(2009) card-sort task in the scanner. Differences in coactivation as a function of the
independent variables were measured by a Psychophysiological Interaction (PPI) Analysis.
Three seed regions were chosen which have been consistently implicated in PM: BA 10, the
Left Frontal Cortex and the Right Inferior Parietal Cortex (Burgess et al., 2011; Rusted et al.,
2011).
The two major goals of this study were to find out (1) how differences in coactivation
as a function of trial-type relate to current theories of PM and (2) how differences in
coactivation as a function of genotype and genotype-specific age differences relate to current
theories of cognitive aging.
In terms of cognitive aging theories, the results of our study will help us to better
understand how harboring an ε4 allele links to theories of normal cognitive aging and the
development of mild cognitive impairment (MCI) and/or mild AD. In contrast to Fillipinni
(2011), this study focuses on middle-age (50-65) because it is concerned with neuronal
changes as a function of age and genotype that specifically precede the onset of age-related
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memory decline. Additionally, the middle-age cohort is an under-researched group compared
to older adults.
Previous research has already suggested that for certain memory tasks, middle-aged
adults with an APOE ε4 allele demonstrated increased bilateral activation compared to noncarriers (Bondi et al., 2008; Bondi et al., 2006). This supports the HAROLD model and
Marchant et al.’s (2010) contention that harbouring an ε4 allele is related to accelerated
cognitive-aging (Cabeza, 2001).
1.6.1 Aims and Predictions. The primary aim of the current research was to look at
the effect of trial-type and genotype on brain coactivity as measured by the Blood Oxygen
Level Dependent (BOLD) response – an indirect measure of brain activity— in the middleaged cohort and secondarily to include the younger cohort in order to examine genotypespecific age differences. Data from the younger adults have also been analysed elsewhere.
Based on past research, this study predicted that there would be differences in
coactivation as a function of trial-type and genotype. In terms of trial-type, we predicted that
BA 10 would coactivate more with other frontal regions during the PM trials compared to the
ongoing trials. In support of the fronto-parietal hypothesis, we predicted that more frontoparietal coactivation would occur during the PM trials compared to the ongoing trials. In
terms of genotype, based on the PASA and HAROLD models of cognitive aging and the
possibility that harbouring an ε4 allele accelerates cognitive aging, we hypothesised that there
would be an increase in bilateral frontal coactivation in middle-aged ε4+s compared to
middle-aged ε4-s.
In addition, this study predicted that there would be coactivation differences as a
function of genotype-specific age differences. More specifically, we predicted that the
increase in bilateral frontal coactivation as a function of age would be more significant in
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ε4+s compared to ε4-s because of ε4s role as an antagonistic pleiotropic.
2. Methods
2.1 Participants
98 “younger” adults and 93 “middle-age” adults were recruited via SONA (The
University of Sussex’s online participant-recruitment service) or local adverts. Before
participating, all participants provided written consent in accordance with the ethics
procedures set out by the University of Sussex Schools of Psychology and the Life Sciences
Research Ethics Committee. All volunteers were informed that they could withdraw from the
study at any point and that all data would be kept anonymous and confidential. Volunteers
were excluded on the basis of untreated high blood pressure, cardiac pathology, a history of
psychiatric or neurological illness, pregnancy and presence of metallic implants including
bridges or braces, or tattoos above the shoulder – making them unsuitable for the fMRI
scanner. In addition, participants were excluded on the basis of smoking behaviour; this is
because the final two sessions (out of three) of the study involved receiving a nicotine or
placebo nasal spray. The sessions involving nicotine nasal spray are not analysed here.
Volunteers’ medical and psychiatric histories were assessed by self-report
questionnaires. Blood pressure and BMI were measured by the experimenter prior to the first
session. In order to determine participants’ APOE genotypes, cheek swab DNA-samples were
collected from every participant. KBiosciences then carried out the DNA analysis and
determined APOE genotypes. Participants with at least one APOE ε2 allele were excluded
from the study because research has shown that harbouring the ε2 allele might improve
cognition, which would therefore reduce the reliability of the results (Bloss, Delis, Salmon &
Bondi, 2010). After the selection procedure, 20 middle-age adults and 20 younger adults
were identified as “ε4-“ (ε3 homozygous) and 21 middle-age adults and 20 younger adults
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were identified as “ε4+” (either ε4 homozygous or ε4/ ε3 heterozygous). The final sample of
middle-age adults contained 18 males and 23 females between the ages of 43-57 (M = 49.95
years, SD = 4.2). The final sample of younger adults contained 13 males and 27 females
between the ages of 19 and 24 (M = 20.18 years, SD = 1.88). Participants that dropped out of
the study prior to completion were excluded from the analysis. The study was conducted
under double blind procedures (i.e., neither the participants nor the researchers knew the
genotype group allocation) – a triangulation process prevented participants and researchers
from determining participant genotypes.
2.2 Experimental task
The computerised PM card-sorting task was first developed by Rusted et al. (2009) as
a laboratory-based measure of event-related PM made of everyday stimuli. The task was
created using MATLAB and involved two parts: the ongoing task and the PM task, which is
not explained to the participant until after he/she was familiarised with the ongoing task. The
ongoing task involved sorting computerised images of 52 regular playing cards. Using a 4
button-box in their right hand, volunteers were instructed to press button 1 for HEART cards,
button 2 for SPADE cards (‘sort’ trials), and to make no response to CLUBS or DIAMONDS
(‘withhold’ trials). Volunteers were also instructed to respond as quickly as possible. Before
entering the fMRI scanner, participants were given the opportunity to practice the ongoing
task and are introduced to the PM task. For the PM task, participants were asked to press
button 3 for any occurrences of the number 7 card independent of suit. Whilst in the scanner,
participants performed the ongoing task as practiced outside of the scanner but with the
additional PM instruction.
During the card-sorting task, card faces were displayed for 1s, after which the card
back was displayed for 2s plus a variable jitter between .1 and 1s (M = .5 s, SD = .24 s).
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During each of the 3 scanning-sessions, volunteers sorted 8 decks of cards with the PM
intention over a total period of about 15 minutes. Per session, there was a total of 416 trials
(32 were PM trials, 192 were sort trials and 192 were withhold trials). In order to maximise
the estimate-ability of each event-type, ensure a delayed onset of PM-events, and ensure
minimum separation between PM-events, card order was pseudo-randomised (Friston,
Phillips, Chawla, &, Buchel, 1999). This involved randomising the task within the following
parameters: (1) never having a PM card occur in the first 7 cards of the entire sequence, (2)
always having at least 3 intervening cards between PM events and (3) having 1 PM trial (out
of 4 PM trials per deck) occur once per quarter-deck (13 cards). Accuracy and reaction times
for ongoing and PM trials were recorded for another behavioural analysis (not reported here).
2.3. Design
This study was a mixed-subject design. The first independent variable was trial-type
(two levels; repeated-measures): PM trials and ongoing card-sort trials. The second
independent variable was genotype (two levels; independent-measures): ε4-s and ε4+s.
Lastly, the third independent variable was age (two levels; independent-measures): middleage adults and younger adults. The dependent variable was activation (measured by the
BOLD response) that covaried with brain activity in the seed region during PM trials
(compared to ongoing card-sort trials) and as a function of the other independent variables.
Coactivation was measured by PPI analyses that included three main seed regions: lateral
rostral PFC (BA 10), the Left Frontal Cortex (which included both the Left Superior Frontal
Cortex (lSupFrontal) and the Left Inferior Frontal Cortex (lInfFrontal)) and the Right Inferior
Parietal Cortex (rInfParietal).
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2.4. Procedure
The participants that were selected to participate in this three-session study provided
informed consent in accordance with University of Sussex Schools of Psychology and the
Life Sciences Research Ethics Committee. During the first session, participants completed
various pen and paper tasks (e.g., the National Adult Reading Test) and provided their family
histories (i.e., prevalence of dementia and mental illness) for baseline measures that will not
be discussed in this paper. In addition, participants were given the opportunity to practice the
computer card-sort task outside of the scanner without the PM trials. During the second and
third sessions, participants were again given the opportunity to practice the card-sort task
outside of the scanner without the PM trials. Then, all of the participants self-administered
either a nicotinic nasal spray or placebo (NB half were given nicotinic nasal spray during
session 2 and the other half during session 3). In addition to genotype, the type of nasal spray
administered also followed double-blind procedures. 18-20 minutes after administering the
nasal spray, participants performed the card-sort task with the PM trials inside of the fMRI
scanner. Only data from the placebo sessions were analysed in this paper. It is important to
note, performance and reaction time did not differ significantly between the two final
sessions; thus, although half of the results discussed were from the third rather than the
second session, there was no evidence of practice effects. After the final session, participants
were debriefed and compensated for their time.
2.5 fMRI recording and analysis
fMRI datasets sensitive to BOLD contrast were acquired at 1.5 T (Siemens Avanto).
The BOLD responses acquired were from the overcompensation phase of the hemodynamic
response function— the point when blood flow increases to compensate for oxygen loss after
neuronal activity occurs (Ward, 2010). Thus, although the fMRI analysis provided high
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spatial accuracy, when neuronal activity actually occurred was a few seconds after the
BOLD. To minimise signal artefacts originating from the sinuses, axial slices were tilted 30
from inter-commissural plane. Thirty-six 3 mm slices (0.75 mm inter-slice gap) were
acquired with an in-plane resolution of 3 mm × 3 mm (TR = 3300 ms per volume, TE = 50
ms). Images were pre-processed using SPM8 (Ashburner et al. 2012). Raw T2 volumes were
spatially realigned and unwarped, spatially normalised to standard space and smoothed (8
mm kernel).
2.5.1 The PPI Analysis. A Psychophysiological Interaction Analysis (PPI) was used
to measure changes in coactivation (as measured by the BOLD response) between a “seed
region” and all other regions of the brain as a result of the study’s independent variables
(O’Reilly et al., 2012). A “seed region” was defined as a 5 mm (radius) sphere centred on the
coordinates of peak activation. Peak activations were taken from a previous event-related
fMRI analysis of the dataset. Three seed regions were chosen that have been consistently
implicated in PM: the lateral rostral PFC (BA 10), the Left Frontal Cortex (lSupFrontal and
the lInfFrontal) and the Right Inferior Parietal Cortex (rInfParietal) (Burgess et al., 2011;
Rusted et al., 2011). The first step of the PPI analysis involved calculating the PPI regressor.
The PPI regressor is a product of the BOLD response (the physiological factor) and task
condition of interest (the psychological factor; i.e., PM trials contrasted against ongoing
trials) at the seed region. The regressor therefore contains information from the time-course
of neuronal activity at the seed region specific to the task condition of interest. The second
step of the analysis involved including the PPI regressor in a new design matrix. This design
matrix also included regressors for the physiological and psychological factors. Including
these factors is important so as to account for the main effects of task and physiological
correlation. Thus, we could be confident that coactivation attributed to the PPI regressor was
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specific to the product of the physiological and psychological factors. Only trials where
correct responses were made were included in the PPI analysis. In order to determine the
effect of genotype on coactivity (for middle-age adults only), genotype (ε4+s and ε4-s) was
entered into a second-level full factorial model. In order to genotype-specific effect of age on
coactivity, genotype (ε4+s and ε4-s) and age (middle-age and younger adults) were entered
into a separate model.
The majority of analyses are presented at a threshold uncorrected for family-wise
error at p < .001. The exception was the computation of genotype-specific age differences: in
order to identify the genotype-specific effects of cognitive aging, exclusive masks were
applied at a threshold uncorrected for family-wise error at p < .05. The contrasts compared
young adults and middle-aged adults for each genotype. The contrasts were subsequently
masked against each other in order to identify age-related changes exclusive to each
genotype. A 50-voxel threshold was also applied. Regions of interest were defined using the
Wake Forest University PickAtlas. Anatomical localisation of clusters was performed using
the Talairach Daemon (University of Texas, USA), and the anatomy toolbox for SPM
(Eickhoff et al, 2005).
3. Results
3.1. Seed Region: BA 10
3.1.1. Average Effect of Condition in the Middle-age Cohort
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Figure 1. BA 10 (seed) average effect of condition indicated by increased coactivation with
the right and left caudate nucleus during PM trials.
Significantly increased brain coactivity was found in the right and left caudate
nucleus, when BA 10 was the seed region, during PM trials compared to ongoing trials
(cluster k = 506, Peak MNI: x = 12, y = 14, z = 3, p < .001 unc.). Thus, there was an average
effect of condition on BA 10 coactivity.
Figure 2. BA 10 (seed) average effect of condition indicated by a significant increase in
coactivation with the right cuneus during PM trials.
In addition, there was significantly increased brain coactivity in the right cuneus with
the BA 10 region (the seed region) during PM trials compared to ongoing trials (cluster k =
109, Peak MNI: x = 13, y = 78, z = 31, p < .001 unc.).
Figure 3. BA 10 (seed) average effect of condition indicated by a significant increase in
coactivation with the left anterior cingulate gyrus during PM trials.
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There was significantly increased brain coactivity in the left anterior cingulate with
the BA 10 region (the seed region) during PM trials compared to ongoing trials (see Figure 3)
(cluster k = 79, Peak MNI: x = 0, y = 32, z = 17, p < .001, unc.).
3.1.2. Main Effect Genotype in the Middle-age Cohort. Since there was no
significantly increased or decreased BA 10 coactivation in ε4+s compared to ε4-s, no main
effect of genotype on BA 10 coactivation was observed.
3.2. Seed Region: Left Superior Frontal Cortex (lSupFrontal)
3.2.1. Average Effect of Condition in the Middle-age Cohort
Figure 4. lSupFrontal (seed) average effect of condition indicated by a significant increase in
coactivation with the left postcentral gyrus during PM trials.
A significant difference in brain coactivity was found in the left postcentral gyrus
with the lSupFrontal region (seed region) during PM trials compared to ongoing trials (cluster
k = 71, Peak MNI: x = -50, y = -16, z = 52, p < .001, unc). Thus, there was a significant
average effect of condition on lSupFrontal (seed region) coactivity.
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3.2.2. Main Effect of Genotype in the Middle-age Cohort
Figure 5. lSupFrontal (seed) main effect of genotype indicated by a significant difference in
coactivation with the right superior frontal gyrus as a function of genotype.
There was a significant increase in brain coactivity in the right superior frontal gyrus
with the lSupFrontal region (the seed region) for ε4+s compared to ε4-s (see Figure 5)
(cluster k = 51, Peak MNI: x = 14, y = 54, z = 36, p < .001, unc.). Thus, there was a main
effect of genotype on lSupFrontal coactivity.
Contrast estimates at [14, 54, 36]
ε4-
Genotype
ε4+
Figure 6. Parameter estimates for the main effect of genotype on lSupFrontal (seed)
coactivation with the right superior frontal gyrus. The red bars represent 90% confidence
intervals.
The above contrast (Figure 6) indicates increased coactivation in the right superior
frontal region with the lSupFrontal region (the seed region) for ε4+s compared to ε4-s.
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Figure 7. lSupFrontal (seed) main effect of genotype (ε4+s vs. ε4-s) indicated by a significant
difference in coactivation with areas 3a and 4p as a function of genotype.
There was a significant increase in coactivation in areas 3a and 4p with the
lSupFrontal region (the seed region) for ε4+s compared to ε4-s (see Figure 7) (cluster k = 57,
Peak MNI = x= 43, y= -9, z= 34, p < .001, unc.).
Contrast estimates at [43, -9, 34]]
ε4-
ε4+
Genotype
Figure 8. Parameter estimates for the main effect of genotype on lSupFrontal (seed)
coactivation with areas 3a and 4p in ε4+s compared to ε4-s. The red bars represent 90%
confidence intervals.
The above contrast (Figure 8) indicates increased coactivation in area 3a and 4p with
the lSupFrontal region (the seed region) for ε4+s compared to ε4-s .
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3.3. Seed Region: Right Inferior Parietal Cortex (rInfParietal)
3.3.1. Average Effect of Condition and Main Effect of Genotype in the Middleage Cohort. No main effect of condition (PM vs. ongoing) or genotype was observed; thus,
there was no significant increase or decrease in rInfParietal coactivation during the PM trials
compared to the ongoing trials. Also, there was no significant increased or decreased
rInfParietal coactivation in ε4+s compared to ε4-s.
3.3.2. Genotype-specific Age Difference. In addition to no main effect of genotype,
there were no genotype-specific age differences. Thus, there was no significant increase or
decrease in rInfParietal coactivation as a function age (middle-age vs. younger adults) for a
specific genotype (ε4-s vs. ε4+s).
3.4. Seed Region: Left Inferior Frontal Cortex (lInfFrontal)
3.4.1. Age x Genotype Interaction (including the younger and middle-age cohort)
Figure 9. lInfFrontal (seed) genotype-specific coactivity difference as a function of age with
the right and left superior medial gyrus.
Figure 9 indicates that there was a significant genotype-specific age difference in
lInfFrontal (seed) coactivation with the right and left superior medial gyrus. (cluster k = 95,
Peak MNI = x= 8, y= 25, z= 46, p < .001).
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Contrast estimates at [8, 25, 46]
Young ε4-
Young ε4+
Mid ε4-
Mid ε4+
Age and Genotype
Figure 10. Parameter estimates for the genotype-specific effect of age on lInfFrontal (seed)
coactivity with the right and left superior medial gyrus. Red bars indicate 90% confidence
intervals.
The above contrast (Figure 10) indicates increased coactivation of the right and left
superior medial gyrus with the lInfFrontal (seed) region (the seed region) for middle-aged
ε4+s (“Mid ε4+”) compared to younger ε4+s (“Young ε4+”), when masked by the contrast,
middle-aged ε4-s (“Mid ε4-”) > younger ε4-s (“Young ε4-”).
Figure 11. lInfFrontal (seed) genotype-specific coactivity difference as a function of age
with the right superior orbital gyrus.
In addition, there was a significant genotype-specific age difference in lInfFrontal
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(seed) coactivation with the right superior orbital gyrus (see Figure 11) (Cluster k = 56, Peak
MNI = x= 25, y= 41, z= -9, p < .001, unc.).
Contrast estimates at [25,41,-9]
Young ε4-
Young ε4+
Mid ε4-
Mid ε4+
Age and Genotype
Figure 12. . Parameter estimates for the genotype-specific effect of age on lInfFrontal (seed)
coactivity with the right superior orbital gyrus. Red bars indicate 90% confidence intervals.
The above contrast (Figure 12) indicates increased coactivation in the right superior
orbital gyrus with the lInfFrontal region (the seed region) for middle-aged ε4+s (“Mid ε4+”)
compared to younger ε4+s (“Young ε4+”), when masked by the contrast, middle-aged ε4-s
(“Mid ε4-”) > younger ε4-s (“Young ε4-”).
4. Discussion
4.1. Summary of Findings
The majority of the results supported the researcher’s predictions. More specifically,
the BA 10 seed region coactivated significantly more with several frontal regions (right and
left caudate nucleus, right cuneus and left anterior cingulate gyrus) for PM vs. ongoing trials.
In addition, the lSupFrontal seed region coactivated significantly more with the left post
central gyrus and right superior frontal gyrus during PM trials compared to ongoing trials. In
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addition, there was decreased lateralisation in frontal region coactivation for ε4+s compared
to ε4-s within the middle-aged cohort (i.e. coactivity with the right frontal and parietal gyri).
More specifically, the lSupFrontal seed region coactivated significantly more with the
rSupFrontal gyrus and areas 3a and 4p. Finally, there was a significant increase in frontal
coactivity (when lInfFrontal was the seed region) with the right and left superior medial
gyrus and the right superior orbital gyrus as a function of genotype-specific age differences in
the middle-age ε4+ cohort compared to the younger ε4+s. The one finding that deviated from
the current study’s predictions was the rInfParietal lobe as the seed region: the rInfParietal
lobe did not significantly coactivate with any other brain areas as a function of trial-type,
genotype or a genotype-specific effect of age. Potential explanations for this absence of brain
coactivity will be discussed.
4.2 Analysis of Findings: in light of PM Theories
4.2.1. Seed Region: BA 10. Although there was no main effect of genotype when BA
10 was the seed region, there was a prominent main effect of trial-type. More specifically,
there was increased BA 10 coactivity during PM trials (compared to ongoing trials) in the
right and left caudate nucleus, the right cuneus and the left anterior cingulate. These results
support Burgess’s Gateway Hypothesis, which suggests that BA 10 plays an executive role
during PM trials. It is important to note that BA 10’s increased coactivity with the anterior
cingulate, specifically, is widely supported by past PM imaging research (Burgess et al.,
2011; Hashimoto, Umeda & Kojima, 2011). Hashimoto et al. (2011) found that increased
anterior cingulate activation (or coactivation) during PM tasks was associated with increased
attentiveness. With regards to Rusted et al.’s (2009) card-sorting task, increased vigilance is
reflected in the main effect found for condition.
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4.2.2. Seed Regions: lSupFrontal and rInfParietal. The increased brain coactivity
as a function of trial-type in the left post central gyrus (i.e., a notable parietal region which
makes up the primary somatosensory cortex) when lSupFrontal was the seed region suggests
that other frontal regions, in addition to BA 10, play a central role during PM trials. In
addition, the increased brain coactivity between frontal and parietal regions supports the
frontal-parietal hypothesis, which contends that during a PM task, parietal regions work with
frontal regions in order to carry out the more executive tasks associated with PM. The lack of
increased brain coactivity as a function of trial-type when rInfParietal was the seed region
does not discredit the fronto-parietal hypothesis; rather, the results suggest that frontalparietal coactivity during PM may only occur between specific frontal and parietal regions.
4.3. Analysis of Findings: in light of Cognitive aging Theories and AD
4.3.1. Seed Region: lSupFrontal. The left superior frontal cortex (lSupFrontal) was
the only seed region with increased brain coactivity as a function of genotype. More
specifically, there was increased coactivity with the rSupFrontal gyrus and areas 3a and 4p in
ε4+s compared to ε4-s in the middle-aged cohort. This result supports Greenwood et al.’s
(2000) finding that the effect of cue validity was greatest for middle-age ε4+s (compared to
ε4-s)—a function of weaker executive control in middle-age ε4+s. Even though middle-age
ε4+s and ε4-s performed similarly on the card-sorting task —a task that required executive
control to switch attention from ongoing to PM trials— this study supports Greenwood et
al.’s (2000) findings because the increased bilateral brain coactivity in middle-age ε4+s
suggests that they exerted more effort to perform as well as the ε4-s.
4.3.3. Seed Region: lInfFrontal. In order to determine the genotype-specific effect of
age on coactivity in the brain, a final analysis was completed that included younger adults.
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The analysis concluded that brain coactivity increased as a function of age only for ε4+s.
More specifically, there was increased lInfFrontal coactivation in the right and left superior
medial gyrus and the right superior orbital gyrus in middle-age ε4+s vs. younger ε4+s whilst
no difference between age groups was found for ε4-s. .
In the context of Filippini’s (2011) study, which found that cognitive aging was
associated with decreased brain activity in older ε4+s and increased activity in ε4-s, this
study found a more complex effect. The contrasting findings can be attributed to the fact that
Filippini (2011) included an older cohort that was comprised of middle-age and older adults
whilst the current study only focused on middle-age ε4+s and ε4-s. More specifically, our
findings suggest that after ε4+s period of enhanced cognition during younger adulthood and
before ε4+s significant decrease in cerebral activity during older adulthood, there is a short
period of increased coactivation during middle-age, which mimics the compensation and
dedifferentiation processes of older non-carriers (Parker & Reuter-Lorenz, 2009; Han et al.,
2009).
In the context of the PASA model of cognitive aging, the increased coactivation in
lInfFrontal—a form of compensation in response to neural structure and function—suggests
earlier cognitive aging (Parker & Reuter-Lorenz, 2009). In light of the HAROLD model of
cognitive aging, the decrease in lateralisation and the increase in activation clusters suggest
earlier cognitive aging in ε4+s (Cabeza, 2001). In terms of AD, the decrease in lateralisation
may also be a form of compensation for preclinical declines in PM (Bondi et al., 2006; Han et
al., 2009).
4.3.2. Seed Region: rInfParietal. The lack of increased brain coactivity in middle-age
ε4+s compared to ε4-s when rInfParietal was the seed region does not discredit the theory
that the ε4+ allele causes accelerated cognitive aging. Instead, the finding can attributed to
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past research that has found that atrophy in the inferior parietal lobe occurred alongside
cognitive decline rather than before it (Jacobs et al., 2011). Thus, increased recruitment is
more necessary in frontal regions as a form of cognitive age-related compensation before the
onset of cognitive-decline.
4.4. Limitations
It is important to note that our data was not corrected for family-wise error (FWE)
because doing so would have eliminated all significant results. Although conservative
parameters were applied (p < .001 and a 50-voxel minimum), Bennett, Wolford & Miller
(2009) have contended that the legitimacy of uncorrected data (i.e., the true likelihood of
false positives) cannot be determined until the results are replicated. In addition, Bennett al.
(2009) noted that within the current model of publication (where for the most part, only
significant results are published) false positives are not easily correctible (i.e., if a group of
researchers fail to reproduce the results of a published study, the null findings would be
difficult to share).
4.5. Future Research
Based on past research, it is possible that the significant increase in frontal
recruitment in middle-age ε4+s (compared to young ε4+s and mid ε4-s) may have been a
function of a reduction in cortical choline acetyltransferase (ChAT) activity in the frontal
cortex. More specifically, the possession of at least one APOE e4 allele has been linked to a
reduction in ChAT activity in the hippocampus (Poirier et al., 1995). In terms of the frontal
cortex and AD specifically, the loss of ChAT was recently found in the superior frontal
cortex of patients with AD (Ikonomovic et al., 2007). Thus, perhaps in future research, the
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relationship between a reduction in ChAT activity in frontal regions and increased brain
coactivity as a function of genotype and age should be looked at.
In regards to the current study’s focus on the relationship between brain coactivity,
age and genotype, it would be interesting to see whether ε4+s with the greatest coactivation
patterns in middle-age are more likely to experience significant clinical decline (e.g., to MCI
and/or AD) when followed longitudinally.
4.6. Conclusions
In terms of AD research, the most significant finding from this study was that bilateral coactivity in frontal regions increased in middle-age ε4+s compared to young ε4+s,
when masked by ε4-s mids > ε4-s youngs, which suggests that the antagonistic pleiotropic ε4
allele accelerates cognitive aging. Whilst this result verifies previous studies that have found
similar results, how this finding contributes to the AD literature remains unclear (Marchant et
al., 2010). In order to determine how this finding would correspond to the likelihood of
developing AD, researchers would need to conduct a longitudinal study that looked at
coactivation patterns as a function of age, genotype and AD conversion.
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6. Appendix I
Ethical approval screen-shot:
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