Cerebral Cortex

doi:10.1093/cercor/bhq210

Volumetric Correlates of Spatiotemporal Working and Recognition Memory Impairment inAged Rhesus Monkeys

Jul Lea Shamy1,2, Christian Habeck3, Patrick R. Hof1, David G. Amaral4, Sania G. Fong1,2, Michael H. Buonocore5, Yaakov Stern3,

Carol A. Barnes6 and Peter R. Rapp2

1Department of Neuroscience and Friedman Brain Institute, Mount Sinai School of Medicine, New York, NY 10029, USA, 2Laboratory

of Experimental Gerontology, National Institute on Aging, Baltimore, MD 21224, USA, 3Cognitive Neuroscience Division of the Taub

Institute, Columbia University, New York, NY 10032, USA, 4Medical Investigation of Neurodevelopmental Disorders (M.I.N.D.)

Institute, University of California, Davis, CA 95817, USA, 5Department of Radiology and Imaging Research Center, UC Davis School of

Medicine, Sacramento, CA 95817, USA and 6Evelyn F. McKnight Brain Institute and the ARL Division of Neural Systems, Memory and

Aging, University of Arizona, Tucson, AZ 85724, USA

Address correspondence to Peter R. Rapp, National Institutes of Health/National Institute on Aging, Laboratory of Experimental Gerontology, 251

Bayview Boulevard, Suite 100, Room 09C224, Baltimore, MD 21224, USA. Email: rappp@mail.nih.gov.

Spatiotemporal and recognition memory are affected by aging inhumans and macaque monkeys. To investigate whether thesedeficits are coupled with atrophy of memory-related brain regions,T1-weighted magnetic resonance images were acquired andvolumes of the cerebrum, ventricles, prefrontal cortex (PFC), cal-carine cortex, hippocampus, and striatum were quantified in youngand aged rhesus monkeys. Subjects were tested on a spatiotempo-ral memory procedure (delayed response [DR]) that requires theintegrity of the PFC and a medial temporal lobe-dependent recog-nition memory task (delayed nonmatching to sample [DNMS]). Regionof interest analyses revealed that age inversely correlated withstriatal, dorsolateral prefrontal cortex (dlPFC), and anterior cingulatecortex volumes. Hippocampal volume predicted acquisition of theDR task. Striatal volume correlated with DNMS acquisition, whereastotal prefrontal gray matter, prefrontal white matter, and dlPFCvolumes each predicted DNMS accuracy. A regional covariance anal-ysis revealed that age-related volumetric changes could be capturedin a distributed network that was coupled with declining performanceacross delays on the DNMS task. This volumetric analysis adds togrowing evidence that cognitive aging in primates arises from region-specific morphometric alterations distributed across multiple memory-related brain systems, including subdivisions of the PFC.

Keywords: age-related memory impairment, medial temporal lobe, MRI,prefrontal cortex, rhesus monkey

Introduction

Memory processing dependent on the prefrontal cortex (PFC)

and medial temporal lobe is particularly susceptible to the

effects of aging, although the onset and the degree of impair-

ment vary widely across individuals (Rapp 1988; Bachevalier

et al. 1991; Herndon et al. 1997). Neuron loss, as measured by

changes in cell density, was once considered a primary cause of

memory decline and an inevitable consequence of aging (Brody

1955). However, an age-related decrease in neuron density can

occur without actual cell loss due to reduced perfusion-related

shrinkage in aged brains compared with young ones (Haug

et al. 1981). The use of stereological methods circumvents this

confound by yielding estimates of the total number of neurons

that are unbiased by differences in regional brain volumes

(West 1993). Using stereology, it has been shown that neuron

number is largely preserved during normal aging in both the

PFC and medial temporal lobe structures in macaque monkeys

(Gazzaley et al. 1997; Merrill et al. 2000; Keuker et al. 2003,

2004; Smith et al. 2004). Nonetheless, substantial alterations of

neuronal structural components do occur, including decreased

dendritic branching, synapse number, and spine number (Jacobs

et al. 1997; Duan et al. 2003; Kabaso et al. 2009; Dumitriu et al.

2010). Such changes likely disrupt neuronal plasticity and in-

formation processing required for learning and memory.

In conjunction with age-related changes in gray matter (GM)

components, white matter (WM) degeneration has been widely

reported in humans and monkeys. Age-related structural abnor-

malities are present in oligodendrocytes, such as bulbous swellings

(Peters 1996), and in myelin, such as splitting and bulging of the

sheaths (Feldman and Peters 1998). Also, myelinated fiber density

and length decline from adulthood to old age (Tang et al. 1997;

Marner et al. 2003; Bowley et al. 2010). These WM alterations

may slow conduction velocity and ‘‘disconnect’’ GM regions

critical for normal memory function (Warrington and Weiskrantz

1982; O’Sullivan et al. 2001). Based on these morphological

changes, it has been hypothesized that normal cognitive aging

arises from changes in both GM and WM components.

Previously, we demonstrated that decreases in overall cerebral

volume predict age-related recognition memory impairment in

the rhesus monkey (Shamy et al. 2006). The current study

extended this analysis to a much larger number of subjects and

tested whether regionally selective alterations of memory-related

GM and WM components are coupled with multiple measures of

cognitive aging. Using an established nonhuman primate model,

young and aged rhesus monkeys were behaviorally characterized

on tests of spatiotemporal working memory (delayed response

[DR]) and recognition memory (delayed nonmatching to sample

[DNMS]). Using structural in vivo magnetic resonance imaging

(MRI) scans, region of interest (ROI) analyses were conducted

for the PFC, calcarine cortex, prefrontal WM, hippocampus, and

striatum. Both traditional univariate and principal component

(PC) analyses were performed to determine whether volumetric

changes across this network of brain regions or alterations within

individual brain regions better predict memory performance.

Materials and Methods

SubjectsSix young adult (9--12 years) and 14 aged (24--29 years) rhesus monkeys

(Macaca mulatta) served as subjects. Groups were comprised of both

females (3 young and 5 aged) and males (3 young and 9 aged). Six

young and 6 aged monkeys were included in a previous volumetric

study of the hippocampus (Shamy et al. 2006). All young females

� The Author 2010. Published by Oxford University Press. All rights reserved.

For permissions, please e-mail: journals.permissions@oxfordjournals.org

Cerebral Cortex Advance Access published December 1, 2010 at C

olumbia U

niversity Libraries on February 16, 2011

cercor.oxfordjournals.orgD

ownloaded from

exhibited regular menstrual activity. One aged female was amenorrheic

during behavioral testing and 3 had ceased cycling by the time of MRI

acquisition.

Standard rations of laboratory primate chowwere provided twice daily

without the use of dietary restriction regimens. The colony was main-

tained on a 12/12-h light/dark cycle, and water was available ad libitum

in the home cage. Environmental enrichment for all subjects was pro-

vided regularly consisting of behavioral testing, toys, fruits, and vege-

tables. Animals received regular veterinary clinical and physical exams

and were judged to be generally healthy. Notably, aged rhesus monkeys

do not develop the neuropathological hallmarks of Alzheimer’s disease

(AD) and instead provide a window on normal healthy aging, uncon-

taminated by neurodegenerative disease. Experimental procedures were

conducted in accordance with National Institutes of Health guidelines

following protocols approved by the UC Davis Institutional Animal Care

and Use Committee and by the California National Primate Research

Center affiliated with the University of California, Davis.

Behavioral CharacterizationMonkeys were tested on a DR test of spatiotemporal memory as

described in detail elsewhere (Rapp et al. 2003). This test is particularly

sensitive to aging (Bachevalier et al. 1991) and to dorsolateral

prefrontal cortex (dlPFC) lesions in young monkeys (Mishkin and

Pribram 1956). Briefly, while the monkey watched through a clear

Plexiglas screen of a manual Wisconsin General Testing Apparatus

(Harlow and Bromer 1939), the experimenter baited one of 2 wells and

then covered both with identical opaque plaques. The screen was then

raised and the monkey was allowed to displace one of the 2 plaques for

a reward. The rewarded location was randomized, and the left and right

wells were baited equally often across trials within a test session.

Subjects were tested for 30 trials a day, 5 days a week, until per-

formance reached at least a 90% correct criterion, defined as 9 errors or

less in 90 consecutive trials without a delay (0 s). Then, a 1-s delay was

implemented by lowering an opaque screen after the wells were

covered to hide the stimuli from view. Testing continued until monkeys

again reached the 90% criterion. Once the performance criterion was

achieved at both the 0- and 1-s delays, successively longer delays of 5,

10, 15, 30 and 60 s (90 trials each) were imposed. The primary memory

demand of the DR task was to remember over a delay period which

location was baited most recently, thus emphasizing spatiotemporal

working memory.

Monkeys were also tested on a DNMS recognition memory task

(Rapp and Amaral 1989, 1991; Rapp et al. 2003). This test is sensitive to

aging (Presty et al. 1987; Moss et al. 1988; Rapp and Amaral 1991; Moss

et al. 1997), and performance is impaired in young subjects with lesions

of various prefrontal and medial temporal regions (Mishkin 1978;

Squire and Zola-Morgan 1991; Zola et al. 2000). Briefly, DNMS trials

were initiated by presenting a sample object over the baited central

well of the test tray in a manual test apparatus. After a predetermined

interval, during which the stimuli were hidden from view, the sample

object was presented together with a novel stimulus that covered

a reward. Trial-unique pairs of objects were used and the rewarded

item appeared equally often over the left and right wells of the testing

apparatus. Subjects were tested for 20 trials a day, 5 days a week, using

a short delay of 10 s until they met a performance criterion of at least

90% correct across 100 trials. Once the nonmatching rule of the task

was learned, testing continued with successively more challenging

retention intervals of 15, 30, 60, 120 s (20 trials/day for 5 days each),

and 600 s (5 trials/day for 10 days). By this arrangement, DNMS

emphasizes visual object recognition memory.

MR Image AcquisitionMonkeys were anesthetized with ketamine (20 mg/kg, intra-muscular

[i.m.]) and atropine (0.04mg/kg, i.m.). The subject’s headwas then placed

inanMRI-compatible stereotaxic frameandpositionedwithin the standard

quadrature radiofrequency coil used for human imaging. Eighty contigu-

ous 1-mm coronal T1-weighted images of the whole brain were acquired

for each monkey on a 1.5 T GE Signa Horizon LX NV/i MRI system

(GE Medical Systems) with version 84M4 software, at the UC Davis

Imaging Research Center, Sacramento, CA. A radio frequency--spoiled

gradient-recalled echo sequence (3D SPGR) was used with the

following parameters: time repetition 21 ms, time echo 7.9 ms (full

echo); flip angle 30�, field of view 16 3 16 cm, acquisition matrix

256 3 256, NEX 4 (no phase wrap option), and bandwidth

15.63 kHz. The gradient subsystem of the MRI system was measured

prior to and throughout the study period. Local GE service personnel

calibrated the subsystem monthly. Geometric accuracy was docu-

mented as stable to within ±0.15% (i.e., ±0.15 mm per 10 cm).

Adjustments were not necessary to maintain that level of accuracy

and precision throughout the study period.

Image ProcessingPostacquisition processing was performed on an SGI Onyx 3400 system

using the Analyze 5.0 software package (BIR) by experimenters blind

with respect to the age and cognitive status of the subjects. Images

were reconstructed into multiple 3D files for each subject. One file,

used for hippocampal tracings, was reformatted to produce 0.3125-mm

isotropic voxels and was aligned perpendicular to the long axis of the

hippocampus. Another file, used for segmentation of the cerebrum,

WM, and ventricles, was reformatted to yield 0.3125 3 0.3125 3 1.0 mm

voxels and was aligned along the horizontal line connecting the anterior

and posterior commissures (AC--PC line). This file was manually edited in

the coronal view to exclude brainstem, cerebellum, and nonbrain struc-

tures such as muscle, connective tissue, and skull. Finally, this file was

reformatted to produce 0.3125-mm isotropic voxels and was used for the

segmentation of prefrontal WM and semi-automated tracings of the PFC,

calcarine cortex, and striatum.

ROI AnalysisAnatomical designations for the ROIs were guided by observations of

postmortem histological material and prominent landmarks identifiable

in MRI scans. Both semi-automated and manual tracing methods were

used to create the ROIs. Unless otherwise noted, ROI tracings were

completed in an anterior to posterior direction in coronal images and

lateral to medial in sagittal sections by raters blind to condition. In order

to maximize the accuracy of boundary placement (Geuze et al. 2005),

the images were compared in multiple orientations throughout manual

tracing. The Analyze ‘‘ROI module’’ was used to calculate the volume of

the delineated brain regions. In addition to the individual regional

measures described in the following sections, the ratio of prefrontal GM

to WM was calculated to control for individual variability of PFC volumes.

Intra- and interrater reliability measures were previously published

for the hippocampal (0.91--0.97; Shamy et al. 2006) and striatal (0.89--

0.96; Matochik et al. 2000, 2004) tracing methods used in this study. In

the development of the following methods, reliability was determined

from repeated tracing of the ROI’s on 8 hemispheres and was

calculated using Pearson r correlations. As expected based on variability

in the clarity of identifiable landmarks in the macaque brain, interrater

reliability was greatest for the dlPFC and anterior cingulate cortex

(ACC) regions (0.93 and 0.95, respectively) and somewhat lower for

the inferior frontal gyrus (IFG) and ventromedial prefrontal cortex

(vmPFC) (0.89 and 0.85, respectively). Intrarater reliability was similar

for the dlPFC and ACC (0.94 for both) and for the IFG and vmPFC (0.91

and 0.88, respectively).

Intracranial VolumeThe intracranial volume (ICV) was measured using semi-automated

tracing methods, as shown in Figure 1. Tracing was initiated at a mid-

sagittal section, and ICV borders were drawn dorsally along the inner

table of the skull to the posterior limit of the cerebellum and ventrally

along the base of the brain. Tracing continued laterally for each

hemisphere on 2-mm-thick sections. The region was completed using

the ‘‘propagate regions’’ tool in Analyze software program. Additional

manual editing was completed as needed to ensure the accuracy of ICV

boundary placement.

Cerebrum, Ventricules, and WMCerebral and ventricular volumes were acquired, as described by Shamy

et al. (2006) (Fig. 2A,B). A Gaussian cluster multispectral analysis was

Page 2 of 15 Regional Brain Volume and Memory Loss in Aged Monkeys d Shamy et al.

at Colum

bia University Libraries on F

ebruary 16, 2011cercor.oxfordjournals.org

Dow

nloaded from

applied to the edited images to obtain automated boundaries of the

cerebrum and non-cerebrum regions. This method segmented the

ventricular volume as a nonbrain region, which was then manually

reassigned as ventricular space. Segmentation of GM and WM was

completed by first smoothing the images using a kernel size of 3:3:3 to

decrease noise. A thresholding tool was then applied to segment the

WM from GM and nonbrain regions.

Prefrontal RegionsAlthough the functional organization of the PFC has been widely debated

(Fuster 1985, 2000, 2001; Barbas 2000; Miller and Cohen 2001), current

perspectives agree that there are functional and anatomical differences

between lateral, medial, and orbital PFC (Fuster 1985, 2000, 2001; Barbas

2000; Miller and Cohen 2001). Taking this into consideration, we

quantified total prefrontal GM and WM volume and also divided the PFC

into subregions. Regional boundary definitions were guided by an

approximation of architectonic boundaries (Carmichael and Price 1994,

1995), known anatomical connectivity of prefrontal regions of the rhesus

monkey (Goldman-Rakic et al. 1984; Petrides and Pandya 1999; Barbas

2000; Cavada et al. 2000), and what could be reliably segmented at the

resolution of the available MRI images.

The PFC was traced using semi-automated thresholding methods in

the coronal plane from the anterior limit of the frontal pole (FP) to the

arcuate sulcus. Premotor regions were removed in the sagittal plane,

with additional editing performed in the coronal plane. Regions

posterior to the termination of the principal sulcus and dorsal to the

anterior limb of the arcuate sulcus were also removed. As shown in

Figure 2C--L, the prefrontal ROI was segmented into 8 regions: the FP

(area 10), the superior frontal gyrus (SFG; areas 9 and 8B), the ACC

(areas 24 and 32), the dlPFC (area 46), the IFG (areas 12, 45, and 47),

the vmPFC (areas 11, 13, and 14), the periarcuate gyrus (8A; area 8A),

and the prefrontal WM. The sum of the 7 GM regions was defined as the

PFC GM volume. The ACC was manually outlined for both hemispheres

on a parasagittal section to guide tracing of the genu of the ACC in the

coronal plane. The boundaries were drawn dorsally along the cingulate

sulcus, ventrally along the rostral sulcus, and the anterior limit was

defined by connecting the 2 sulci. The ACC was then semi-

automatically traced from the appearance of the sagittal tracing in

the coronal plane along the dorsal portion of the ACC to the

disappearance of the principal sulcus and along the ventral portion of

the ACC to the end of the sagittal tracing. The dlPFC was traced in the

coronal plane, inclusive of the dorsal and ventral bank GM along

the entire extent of the principal sulcus. The ACC, the dlPFC, and the

prefrontal WM were then imported into the PFC ROI file. This file was

rendered in 3D and reoriented to the orbital view. The boundary of the

IFG was traced approximately 2 mm medial to the lateral edge of the

orbital surface from the anterior limit of the ACC to the posterior limit

of the prefrontal ROI, thereby reassigning all GM voxels ventral to the

dlPFC and lateral to the orbital surface boundary as the IFG. The ROI file

was viewed again in the coronal plane and edited to further refine the

dlPFC and IFG boundary. Area 8A was defined as the GM within

the arcuate sulcus, exclusive of the dlPFC. The FP was assigned from

the anterior tip of the brain until the appearance of the ACC, exclusive

of the dlPFC. The SFG was defined as the cortex dorsal to the dlPFC

from the FP to the disappearance of the principal sulcus. The vmPFC

was assigned as the orbital GM from the FP to the appearance of the

circular sulcus. Finally, WM regions were reassigned as prefrontal WM

from the anterior limit of the brain to the posterior limit of the

principal sulcus and the WM within the periarcuate sulcus.

Calcarine CortexThe volume of the primary visual cortex was measured using regional

definitions similar to those in postmortem analyses described by Hof

et al. (2000) (Fig. 3A--D). Human in vivo imaging studies and

postmortem nonhuman primate studies have found preservation of

occipital cortex with age (Hof et al. 2000; Raz et al. 2004). Therefore,

we considered the region as a negative control for assessing the

regional selectivity of any observed changes. The calcarine sulcus was

Figure 1. Measurement of ICV. The ICV was traced semi-automatically from the midline sagittal slice (A) progressing laterally on sections 2 mm apart to the right lateral edge ofthe skull (B--F). This was repeated for the left hemisphere. The propagate regions tool in the Analyze 5.0 software package was used to complete the ICV region.

Cerebral Cortex Page 3 of 15

at Colum

bia University Libraries on F

ebruary 16, 2011cercor.oxfordjournals.org

Dow

nloaded from

Figure 2. Cerebral GM, WM, and ventricle (red) ROI segmentation (A, B). Coronal images of prefrontal ROIs (C--F). 3D surface renderings of the cortex (G--I) and white mater(J--L) ROIs. The PFC was segmented into the FP (purple), the SFG (red), the ACC (magenta), the dlPFC (green), the IFG (yellow), the vmPFC (blue), the periarcuate gyrus (8A;cyan), and the prefrontal WM (dark gray).

Page 4 of 15 Regional Brain Volume and Memory Loss in Aged Monkeys d Shamy et al.

at Colum

bia University Libraries on F

ebruary 16, 2011cercor.oxfordjournals.org

Dow

nloaded from

traced on every third coronal section and on the most anterior and

posterior sections in which it appeared. The propagate regions tool in

Analyze 5.0 was used to connect the traced sections into a 3D object.

Additional manual editing was completed as needed.

StriatumThe volume of the caudate and putamen were measured using

a technique similar to that described in Matochik et al. (2000, 2004)

(see Fig. 3E,F). Age-related striatal atrophy has been reported (Matochik

et al. 2000, 2004; Wisco et al. 2008), and therefore, we considered this

region as a positive control for age-related volumetric atrophy. Briefly, the

caudate and the putamen were traced on every fifth coronal section and

on the most anterior and posterior sections in which these structures

appeared. GM ventral to the anterior commissure was excluded, as was

the tail of the caudate nucleus. The propagate regions tool in the Analyze

5.0 program was then used to connect the regions traced in the coronal

plane. Additional manual editing was completed as needed.

HippocampusDetailed descriptions of the hippocampal tracing rules are reported in

Shamy et al. (2006) (Fig. 3G,H). The boundary line of the hippocampus

included the dentate gyrus, CA1--3 fields of the hippocampus,

subiculum, presubiculum, and parasubiculum. The rostral and caudal

limits of the hippocampus were traced in sagittal images, and the

coronal plane was used to indentify borders through the body of the

structure. This method proved optimal for distinguishing between the

rostral hippocampus and amygdala and between the caudal hippocampus

and retrosplenial cortex.

Data AnalysisUnivariate statistical analyses of regional volumes were performed

using SigmaStat for Windows, version 3.1 (SPSS). To evaluate potential

age effects on acquisition on each of the behavioral tests, Student’s

t-tests were used. Mann--Whitney rank sum tests were employed when

the distribution of the data failed tests of normality. To evaluate

potential age effects on performance across increasing delays in the DR

and DNMS tasks, repeated-measures analyses of variance (ANOVAs)

were used. When statistically significant interactions were found,

Holm--Sidak pairwise comparisons were computed. Student’s t-tests

were used to assess the influence of age on cerebral and ventricular

volumes. For regional analyses, 2-way ANOVAs were used to evaluate

the potential influence of age on regional volume as a function of

hemisphere and gender. Unless noted otherwise, the results did not

differ as a function of these factors.

Pearson r correlation coefficients were used to explore potential

relationships between the volumetric effects of aging and memory

performance. Multiple sets of analyses were conducted to describe fully

the nature of observed associations. First, 2 sets of correlations were

computed for the raw values, one for the entire subject group and

Figure 3. Digitized boundaries of the calcarine cortex (A--D), striatum (E, F), and hippocampal (G, H) ROIs. The calcarine cortex and striatum were traced in the coronal plane andautomatically completed using the software program. The body of the hippocampus was traced in the coronal plane (G), as indicated by the white border, and the head and tailwere traced in the sagittal plane (H), as indicated by the black border. C, calcarine cortex; Cd, caudate; Put, putamen; Limit, line restricting striatal tracings to regions dorsal to theanterior commissure; H, hippocampus; A, amygdala.

Cerebral Cortex Page 5 of 15

at Colum

bia University Libraries on F

ebruary 16, 2011cercor.oxfordjournals.org

Dow

nloaded from

another for the aged subjects alone. This permitted assessment of

relationships between brain structure and function that might become

more tightly coupled later in the lifespan. Next, when the raw volumes

were statistically correlated with behavior or age, normalized volume

estimates were evaluated to account for individual differences in overall

brain size. Specifically, the ROIs were adjusted using the analysis of

covariance equation: volumeadjusted = volumeraw – b (the subject’s

ICV – mean ICV of all subjects), where b is the slope of the regression

line formed when the volume of the ROI is plotted against the ICV

(Matochik et al. 2004; Raz et al. 2004). Pearson r correlations were

computed between the resulting residual values and chronological age

or the behavioral measures. Reported correlations are explicitly

identified as ‘‘raw’’ and ‘‘normalized.’’ Standardization results in slight

adjustments to the raw values that take into account overall differences

in brain size while preserving individual variability in regional volume.

Lastly, when raw ROI volumes were significantly correlated with

a behavioral measure, the raw volumes were controlled for age by using

the analysis of covariance equation: volumeadjusted = volumeraw – b

(age), where b is the slope of the regression line formed when the

volume of the ROI is plotted against age. Pearson r correlations were

computed between the resulting residual values and the behavioral

data. Statistical significance was set at a = 0.05.

The current study, comprising detailed neuropsychological assess-

ment and brain imaging analysis in 20 young and aged monkeys, is to

our knowledge among the largest reported (see also Makris et al. 2010).

Nonetheless, studies of aged nonhuman primates face the joint

challenge of limited animal availability, high cost, and the need to

minimize subject numbers. As a consequence, sample size is typically

small in comparison with parallel research in people, and recognizing

this limitation, marginal but nonsignificant correlations (i.e., 0.05 < a <

0.10) are denoted in Table 2 for informational purposes only.

Results of the present study were not corrected for multiple

comparisons due to practical limitations on sample size in nonhuman

primate research. It should be noted in this context, however, that the

number of significant correlations observed between behavioral perfor-

mance and regional volumetric results among the aged animals greatly

exceeded that predicted by chance (see Results). Univariate analyses are

best applied to obvious focal changes. However, there is a risk of

‘‘correcting away’’ such effects, resulting in type II errors. In contrast to

univariate analyses, a multivariate analysis may better identify subtle and

distributed age-related changes in the volumetric data. Multivariate and

univariate analyses should be seen as complimentary since each is geared

toward different aspects of the volumetric data.

A multivariate analysis was performed to evaluate whether changes

across a network of brain regions better predicts age-related memory

impairment. First, an age-related covariance pattern was obtained from

the volumetric data that distinguished young from aged monkeys. To

do this, a version of the Subprofile Scaling Model (SSM) (Moeller et al.

1987; Moeller and Strother 1991) was performed on the volumetric

data. This technique has been used numerous times in clinical research

to analyze both ROI and voxel-based morphometric data (Moeller et al.

1999; Scarmeas et al. 2004; Alexander et al. 2008). Briefly, log-

transformed ROI values of all monkeys were pooled together and

a principal component analysis (PCA) was performed. A PCA is

commonly used to evaluate changes across a network of brain regions

because it reduces a large data set and captures the major sources of

variance by expressing it in terms of a few group-invariant PCs. In this

study, we confined ourselves to the first 5 PCs since they accounted for

80% of the variance in the data. Thus, rather than using the bilateral ROI

volumes for each monkey (24 values) only one number for each PC was

needed per subject (5 values).

Next we performed 5 linear discriminant regressions in which group

membership (young = 1 and aged = 0) was entered as the dependent

variable, that is, a 20-component column vector with age status as

a row entry for each monkey. The independent variables for these 5

regressions were the subject expression values of all possible

contiguous subsets of the first 5 PCs for all monkeys and had a similar

20-component format per each independent variable. For the first

regression, only PC1 was used, for the second regression PC1 and PC2

were used, and so on (regression 3: PCs 1--3; regression 4: PC1--4;

regression 5: PC1--5). SSM removes whole-brain differences from the

data prior to the multivariate decomposition. Since the volumetric data

possesses absolute quantification, the whole-brain average for each

subject was added as a separate independent variable in the regressions.

From these 5 regressions the one that yielded the largest area under the

receiver operating characteristic (ROC) curve was chosen. The ROC

curve plots the fraction of false positives against the fraction of true

positives when the threshold for criterion is defined across the entire

data range. In this study, the false positives were defined as elderly

monkeys whose network expression values fell above threshold, and,

according to chronological age, were thus incorrectly classified as

young. Likewise, the true positives were the correctly classified portion

of young monkeys whose pattern expression fell above the threshold.

The larger the area under the ROC curve, the better the discrimination

between groups. When the ROC curve is unity, it indicates that the

model has perfect discriminability. From the selected discriminant

regression, a covariance pattern was then constructed using the

obtained regression coefficients and applying them to the appropriate

PCs. Neither was there an inferential judgment employed in the

selection process of the PC set nor an iterative stepping procedure. By

design, this one-shot ‘‘scanning’’ procedure selects a set of PCs without

‘‘data dredging’’ and minimizing P-level inflation.

Subsequently, subject expression of this covariance pattern was tested

to determine whether it could account for certain aspects of

performance on the DR and DNMS tasks. To provide summary measures

for this correlation, the percent of correctly identified items at each

retention interval was regressed against the 5 delay periods (5, 10, 15, 30,

60 s for DR; 15, 30, 60, 120, 600 s for DNMS). The regression weight, or

‘‘accuracy slope,’’ of this relationship quantifies how much additional

information is gained or lost by imposing a delay. In the current

experiment, this number is ‘‘negative,’’ indicating information loss, and has

the unit of 1 percent/s. No memory impairment across increasing delays

was indicated by a slope of 0, and greater memory impairment across

increasing delays was indicated by more negative, or smaller, values. A

brain--behavioral correlation was anticipated such that a more youthful

profile should imply better memory across increasing delays. In order for

this anticipated correlation to be positive, we defined both volumetric as

well as behavioral measures such that increasing values signaled

benign outcomes. Specifically, a ‘‘larger’’ pattern expression signaled

a younger chronological age, and a larger accuracy slope signaled better

performance across increasing delay lengths in the behavioral tasks.

Next, a bootstrap procedure was completed to estimate the

reliability of each ROI’s loading in the covariance pattern (Efron and

Tibshirani 1994). This resampling test was used primarily to visualize

and approximate the true population variability underlying the data.

The pattern derivation was performed 10 000 times from resampled

data, including PCA and discriminant regression using the set of PCs

selected earlier. This resampling scheme left the group assignment

intact but sampled from the data with replacement. If 95% of the

bootstrap samples yielded values either larger or smaller than zero, then

the ROI loadings were deemed reliably positive or negative, re-

spectively. Positive loadings indicated that ROI volumes were smaller in

the aged monkeys, whereas negative loadings indicated that ROI

volumes were larger in the aged monkeys. The critical variable in the

bootstrap procedure was the overall expression of the pattern and not

the individual loadings in the pattern. Therefore, even when individual

loadings are not deemed reliable, the age-related pattern can still be

successfully extracted from the covariance relationships—it just cannot

be localized to a low number of key ROIs. The expression of the age-

related pattern, computed as a dot product between the covariance

pattern and the subjects’ volumetric data, is more stable than the

individual ROI loadings (Habeck et al. 2005).

Results

Behavioral Characterization

Means and standard errors for measures of the DR and DNMS

tasks are reported in Table 1. Compared with DR performance

observed in a much larger group of young monkeys, the subset

available for imaging in this study performed somewhat less

Page 6 of 15 Regional Brain Volume and Memory Loss in Aged Monkeys d Shamy et al.

at Colum

bia University Libraries on F

ebruary 16, 2011cercor.oxfordjournals.org

Dow

nloaded from

accurately on average but within the normal range for both task

acquisition and performance across increasing retention inter-

vals (Rapp et al. 2003, 2004). Aged and young monkeys learned

the DR task with 0- and 1-s delays at statistically equivalent rates,

despite a substantial numerical difference at the 1-s interval (0 s,

Mann--Whitney U = 70.5, P = 0.380; 1 s, U = 47.0, P = 0.272; Fig.

4A). Accuracy on the DR task declined in both groups with

increasing delay length (main effect of delay: F4,68 = 43.95, P <

0.001). In this sample of subjects, performance was only

numerically different as a function of age (main effect of age:

F1,68 = 3.12, P = 0.095), nor was the interaction between age and

delay length statistically reliable (F4,68 = 0.57, P = 0.683; Fig. 4B).

Nonetheless, the overall pattern of results is qualitatively similar

to many earlier studies demonstrating that DR performance is

reliably impaired in aged monkeys (Rapp and Amaral 1991; Rapp

et al. 2003). The lack of statistically significant group effects in

the present case is primarily attributable to high variability

among the young adults rather than preserved performance in

the aged group (Fig. 4B).

Behavioral characterization in this sample of subjects yielded

DNMS results similar to previous reports (Rapp and Amaral

1991; Rapp et al. 2004). Aged monkeys required many more

trials than young monkeys to learn the DNMS procedure

initially with a short 10-s delay (Mann--Whitney; U = 24.0, P =0.002; Fig. 4C). When successively longer retention intervals

were introduced, aged monkeys scored significantly worse

than did young subjects (main group effect: F1,68 = 18.95, P <

0.001; Fig. 4D). The significant interaction between age and

accuracy across delays (F4,68 = 3.38, P = 0.014) confirmed the

result of studies in larger numbers of subjects demonstrating

that aged monkeys scored comparably to young subjects at

the 15-s retention interval (t = 1.48, P = 0.143) but displayed

impaired recognition at longer delays (30 s, t = 2.35, P = 0.022;

60 s, t = 2.02, P = 0.047; 120 s, t = 2.32, P = 0.024; and 600 s, t =5.53, P < 0.001). These behavioral data provided a basis for

testing whether recognition memory impairment that occurs

during normal aging is coupled with volumetric changes in the

primate brain.

Age-Related Volumetric Differences

Means and standard errors for all ROIs are reported in Table 1,

and correlations between the ROIs and behavioral measures are

reported in Table 2. Volumes for individual subjects are plotted

against age in Figure 5.

Volumetric assessment revealed that age-related atrophy in

the rhesus monkey is not diffusely distributed but instead

localized to selective brain regions. Neither cerebral nor

ventricular volume was significantly different in the group of

aged monkeys compared with young adults (t = 0.24, P = 0.81;

Mann--Whitney: U = 74.0, P = 0.386). The lack of significant

findings in the current study compared with our earlier report

(Shamy et al. 2006) may be partially due to the addition of

several subjects in their early 20s. At this age, degenerative

changes are likely to be less widespread compared with

monkeys in their late 20s and early 30s. Notably, in subjects

over 20 years old, reduction of cerebral volume (r = –0.54, P =0.045; standardized: r = –0.67, P = 0.008) and ventricular

enlargement (r = 0.61. P = 0.02; standardized: r = 0.60, P = 0.022)

Table 1Descriptive statistics for age, behavioral measures and regional cortical volumes

Young Aged

Mean SE N Mean SE N

Agea 11.2 0.5 6 24.2 0.7 14DR 0 sb 98.2 36.1 6 62.4 22.7 13DR 1 sb 58.3 45.8 6 120.7 40.4 13DR avec 80.5 4.3 6 73.3 2.0 13DNMS 10 sb 223.2 47.8 6 906.0 121.1 13DNMS avec 87.0 1.2 6 79.7 1.0 13ROI volumesd

ICV 95953.3 1862.7 6 97341.2 2926.1 14Cerebrum 73459.3 1030.9 6 74410.0 2526.6 14Ventricles 528.5 56.5 6 568.3 104.3 14Calcarine Ctx 952.2 52.1 6 973.4 36.1 14PFC 4961.0 80.0 6 4905.6 206.9 14PFC GM 3726.2 97.8 6 3555.2 134.0 14PFC WM 1234.8 55.8 6 1350.4 79.5 14PFC GM/WMe 3.1 0.2 6 2.7 0.1 14FP 235.6 28.2 6 249.2 19.7 14SFG 905.4 44.2 6 843.6 34.9 14dlPFC 576.1 25.1 6 518.1 14.5 14IFG 254.6 17.1 6 257.3 12.2 14vmPFC 972.1 15.3 6 960.2 35.0 14ACC 418.4 26.4 6 380.3 10.2 148A 363.8 13.0 6 346.5 20.9 14Striatum 426.1 7.0 6 389.2 13.7 14Caudate 635.9 13.8 6 561.2 22.8 14Putamen 1062.1 17.2 6 950.5 30.5 14Hippocampus 417.3 18.1 6 396.5 10.0 14

Note: 8A, periarcuate gyrus.aAge at MRI acquisition in years.bNumber of trials to 90% criterion.cMean percent correct across increasing delays.dVolumetric data in cubic millimeter.eRatio in arbitrary units.

Figure 4. Mean trials to criterion (A, C) and recognition accuracy across longerdelays (B, D) on the DR (top panels) and DNMS tasks (lower panels). Behavioralresults for the subset of monkeys that participated in the present analysis weresimilar to findings for the much larger groups of young and monkeys tested over thecourse of this project. Aged monkeys learned the DR task as rapidly as youngsubjects without a delay (A). Despite a substantial numerical difference, the age-related impairment in DR accuracy with delays of 1 s and longer was not statisticallyreliable (B), primarily due to variability across the small number of young adults. Theaged group exhibited robust deficits learning DNMS initially with a 10-s delay (C) andsubsequently scored as well as adult controls at a 15-s retention interval (D). Reliableage-related deficits were when recognition memory was challenged with longerretention intervals (D).

Cerebral Cortex Page 7 of 15

at Colum

bia University Libraries on F

ebruary 16, 2011cercor.oxfordjournals.org

Dow

nloaded from

reliably correlated with age. As discussed in the study by Shamy

et al. (2006) that included a subset of the monkeys examined

here, the ventricular hypertrophy effect was attributable to

outlier data from 2 aged monkeys with grossly enlarged

ventricles.

Total PFC volume (F1,18 = 0.03, P = 0.866), PFC GM volume

(F1,18 = 0.62, P = 0.441), and prefrontal WM volume (F1,18 = 0.81,

P = 0.38) were not significantly different across the aged and

young groups. Among the aged individuals, the inverse

correlation between chronological age and prefontal WM

volume approached significance (r = –0.53, P = 0.053;

standardized: r = –0.52, P = 0.056). Whereas the volume of

prefrontal GM was not related to chronological age, the ratio of

PFC GM to WM significantly correlated with age (r = 0.55, P =0.042). Regional analysis of the PFC revealed selective reduction

of the dlPFC (F1,18 = 4.48, P = 0.048) and preservation of the

other PFC subdivisions in the aged group (FP: F1,18 = 0.15, P =0.706; SFG: F1,18 = 1.03, P = 0.324; IFG: F1,18 = 0.02, P = 0.902;

vmPFC: F1,18 = 0.05, P = 0.832; ACC: F1,18 = 2.76, P = 0.114; 8A:

F1,18 = 0.29, P = 0.611). Volumes of the ACC and dlPFC

significantly correlated with age across all subjects (raw: r = –

0.45, P = 0.045; standardized: r = –0.47, P = 0.037, and raw:

r = –0.57, P = 0.008; standardized: r = –0.64, P = 0.003,

respectively), and for dlPFC volume, among the aged animals

alone (raw: r = –0.55, P = 0.043; standardized: r = –0.56, P =0.036). Correlations between the other PFC subregion volumes

and age were not statistically significant. Although there was no

laterality effect for total PFC volume, the dlPFC and vmPFC were

larger in the right than in the left hemisphere across all subjects

(F1,18 = 16.65, P < 0.001; F1,18 = 10.51, P = 0.005, respectively).

The aged monkeys exhibited significantly reduced striatal

volume compared with their younger counterparts (F1,18 =5.27, P = 0.034), consistent with previous findings from

Matochik et al. (2000). When the caudate and putamen were

considered separately, group differences only approached

Table 2Correlation matrix for age, behavioral and regional volumes for all subjects and aged subjects

alone

Age DR Acq 1-sa DR Aveb DNMS Acq 10-sc DNMS Aved

All Aged All Aged All Aged All Aged All Aged

N 20 14 19 13 19 13 19 13 19 13Brain �0.14 �0.54* 0.07 0.00 0.11 0.32 �0.19 �0.491 0.31 0.66*Ventricles 0.25 0.61* 0.13 0.07 0.05 0.06 0.37 0.501 �0.421 �0.68*Calcarine 0.00 �0.28 0.26 0.11 0.14 0.481 0.12 0.12 �0.12 0.04PFC �0.22 �0.51þ 0.21 0.17 0.06 0.25 �0.25 �0.41 0.34 0.61*PFC GM �0.33 �0.48þ 0.23 0.21 0.16 0.32 �0.33 �0.37 0.40þ 0.58*PFC WM 0.02 �0.53* 0.12 0.09 �0.12 0.11 �0.05 �0.45 0.15 0.60*PFC GM/WM �0.25 0.55* 0.04 0.07 0.27 0.19 �0.17 0.43 0.11 �0.41FP �0.07 �0.48þ 0.02 0.13 0.06 0.31 �0.14 �0.46 0.25 0.53þ

SFG �0.34 �0.42 0.22 0.15 0.12 0.19 �0.23 �0.15 0.40þ 0.61*dlPFC �0.57* �0.55* 0.26 0.28 0.15 0.31 �0.57* �0.54þ 0.55* 0.60*IFG �0.10 �0.43 0.18 0.01 �0.02 0.44 �0.30 �0.57* 0.06 0.38vmPFC �0.19 �0.42 0.06 0.02 0.21 0.31 �0.15 �0.23 0.35 0.59*ACC �0.45* �0.52þ 0.37 0.46 0.09 0.27 �0.48* �0.55* 0.28 0.398A �0.22 �0.32 0.32 0.49þ 0.20 0.26 �0.20 �0.28 0.27 0.36Striatum �0.55* �0.37 �0.31 �0.16 0.46* 0.48þ �0.52* �0.34 0.57* 0.47Caudate �0.49* �0.49þ �0.07 0.06 0.29 0.33 �0.54* �0.47 0.54* 0.58*Putamen �0.47* �0.21 �0.42þ �0.28 0.48* 0.46 �0.39 �0.15 0.47* 0.26Hippocampus �0.14 0.19 �0.52* �0.57* 0.26 0.59* 0.03 0.33 �0.03 �0.03

Note: 8A, periarcuate gyrus.aNumber of trials to 90% criterion with 1-s delay.bMean percent correct across 5- to 90-s delay.cNumber of trials to 90% criterion with 10-s delay.dMean percent correct across 15- to 600-s delay.

*P # 0.05; 10.05 # P # 0.10.

Figure 5. When chronological age was plotted against ROI volumes (A--N), age wassignificantly predictive of dlPFC (G), striatal, putamen, and caudate (M) volumesacross the entire subject group. In the aged group alone, age was correlated withcerebral (A), prefrontal WM (C), the ratio of prefrontal GM to WM (D), the dlPFC (G),and ACC (H) volumes. Regression lines indicate significant correlations betweenregional volume and age. St, striatum; Put, putamen; Cd, caudate; Hipp,hippocampus.

Page 8 of 15 Regional Brain Volume and Memory Loss in Aged Monkeys d Shamy et al.

at Colum

bia University Libraries on F

ebruary 16, 2011cercor.oxfordjournals.org

Dow

nloaded from

statistical significance (F1,18 = 2.88, P = 0.107; F1,18 = 4.21, P =0.055, respectively). Chronological age was inversely correlated

with the volume of the striatum (raw: r = –0.55, P = 0.012;

standardized: r = –0.71, P < 0.001), caudate (raw: r = –0.49, P =0.028; standardized: r = –0.52, P = 0.019), and putamen (raw: r =–0.47, P = 0.038; standardized: r = –0.62, P = 0.004). The right

striatum and putamen were significantly larger than the left

when all subjects were considered in the analysis (F1,18 = 14.90,

P = 0.001; F1,18 = 21.48, P < 0.001, respectively), but these

effects were not reliable for the aged subjects alone.

As reported in Shamy et al. (2006) and confirmed in this

larger sample, hippocampal volume failed to correlate with age

(F1,18 = 1.17, P = 0.294). There was also no significant cor-

relation between hippocampal volume and age among mon-

keys older than 20 years. Correlations with calcarine cortex

volume were not statistically reliable for either the aged group

alone or all subjects considered together.

Volumetric Correlates of DR Performance

In contrast to a priori expectations, PFC volume was largely

unrelated to the status of spatiotemporal working memory,

whereas volumes of the hippocampus and striatum correlated

with individual differences in various aspects of DR perfor-

mance in aged monkeys (Table 2). Specifically, cerebral,

ventricular, and PFC volumes failed to correlate with any

measure of DR performance. In contrast, hippocampal volume

correlated with the number of trials to criterion on the DR task

with a 1-s delay across all subjects (raw: r = –0.52, P = 0.022;

standardized: r = –0.53, P = 0.019) and in the aged group alone

(raw: r = –0.57, P = 0.04; standardized: r = –0.71, P = 0.007;

Fig. 6A,C). When hippocampal volume was controlled for age,

the association remained significant for the entire subject

sample (r = –0.52, P = 0.022) but not for the aged animals alone.

Hippocampal volume among the aged monkeys also positively

correlated with DR accuracy averaged across increasing reten-

tion intervals (5--60 s, raw: r = 0.59, P = 0.034; standardized: r =0.36, P = 0.220; Fig. 6D), and this effect remained significant

when chronological age was included as a covariate (r = 0.56,

P = 0.045). Among all subjects, performance across delays on

the DR task was additionally coupled with putamen (raw: r =0.48, P = 0.038; standardized: r = 0.28, P = 0.250) and overall

striatum volumes (raw: r = 0.46, P = 0.048; standardized: r =0.29, P = 0.221; Fig. 6B), although neither association remained

reliable when the influence of age was controlled.

Volumetric Correlates of DNMS Performance

A novel observation in this study is that DNMS performance

was coupled with the volumes of multiple brain regions outside

the hippocampus, including subdivisions of the PFC and

striatum. Volumetric correlations with DNMS acquisition and

recognition accuracy across delays are shown in Figures 7 and

8, respectively, and listed in Table 2. Acquisition of the DNMS

task with a short delay was negatively correlated with ACC

Figure 6. Volumetric correlates of the DR task. Hippocampal volume predicts DRtask acquisition (A), whereas striatum and putamen volume are predictive of averageDR performance (B) across the entire subject group. When only the aged subjectswere included in the analysis, hippocampal volume was predictive of both DRacquisition (C) and performance (D).

Figure 7. Acquisition of the DNMS task was related to overall striatal and caudate(A), dlPFC (B), and ACC (C) volumes. When only the aged subjects were included inthe analysis, DNMS acquisition was predicted by IFG volume (D).

Cerebral Cortex Page 9 of 15

at Colum

bia University Libraries on F

ebruary 16, 2011cercor.oxfordjournals.org

Dow

nloaded from

(raw: r = –0.48, P = 0.038; standardized: r = –0.51, P = 0.026) and

dlPFC volumes (raw: r = –0.57, P = 0.012; standardized: r = –0.64,

P = 0.003) in the PFC. The dlPFC values also correlated with the

average recognition accuracy across longer retention intervals

(raw: r = 0.55, P = 0.015; standardized: r = 0.5, P = 0.030). For

the aged subjects considered alone, IFG volume was the only

PFC measure that was related to task acquisition (raw: r = –0.57,

P = 0.044; standardized: r = –0.69, P = 0.009), but total PFC

volume (r = 0.61, P = 0.027; standardized: r = 0.52, P = 0.07),

PFC GM (r = 0.58, P = 0.036; standardized: r = 0.519, P = 0.069),

and prefrontal WM (r = 0.60, P = 0.032; standardized: r = 0.43,

P = 0.142) all correlated with average DNMS performance.

Subregion analysis revealed that the relationship between

prefrontal GM and recognition accuracy was largely accounted

for by correlations for the SFG (raw: r = 0.61, P = 0.027;

standardized: r = 0.54, P = 0.059), the dlPFC (raw: r = 0.6, P =0.031; standardized: r = 0.47, P = 0.107), and the vmPFC (raw:

r = 0.59, P = 0.032; standardized: r = 0.56, P = 0.046). The

contribution of aging was critical to these relationships as none

of the identified associations remained significant when

chronological age was included as a covariate in the analysis.

Striatal correlates of DNMS performance included a signifi-

cant inverse relationship between learning the task initially and

total volume of the striatum (raw: r = –0.52, P = 0.024;

standardized: r = –0.68, P = 0.001), which was largely attribut-

able to a coupling between acquisition scores and the volume

of the caudate (raw: r = –0.54, P = 0.017; standardized: r = –0.61,

P = 0.005). Smaller striatum (raw: r = 0.057, P = 0.01;

standardized: r = 0.54, P = 0.016), caudate (raw: r = 0.54, P =0.017; standardized: r = 0.52, P = 0.021), and putamen volumes

(raw: r = 0.47, P = 0.043; standardized: r = 0.39, P = 0.096) were

also associated with lower average recognition accuracy across

increasing memory delays. Similar to the findings for the PFC,

these effects appeared related to the joint influence of age on

both striatal volume and DNMS performance.

Finally, for the aged subjects alone, smaller cerebrum volume

(r = 0.66, P = 0.014; standardized: r = 0.57, P = 0.043) and

enlarged ventricles (r = –0.68, P = 0.011; standardized: r = –0.68,

P = 0.011) were coupled with impaired recognition accuracy.

The correlation between ventricular volume and DNMS

performance remained significant when 2 outliers with gross

ventricular enlargement were omitted from the analysis (r =–0.79, P = 0.004) and when the influence of age was evaluated

(r = –0.55, P = 0.05).

Multivariate Analysis

Results of the multivariate analysis are displayed in Figure 9. A

PCA was performed on the combined ROI values of all monkeys

and selected the set of first 3 PCs to construct a covariance

pattern with optimal discrimination performance as measured

by the area under the ROC curve (area under the curve = 0.90).

The unequal-samples T-statistic for the group difference of the

pattern expression was T = 2.54. Also, a permutation test was

performed by repeating the derivation on resampled data with

a broken group-subject assignment for 10 000 times and

then computing the null distribution for the unequal-samples

T-contrast of the pattern expression. This test yielded a non-

significant result, P(T[null] > 2.54) = 0.27. Still, when age was

used as a continuous variable, an association was found with

the correct sign: pattern expression correlated inversely with

chronological age (r = –0.53, P = 0.02).

To determine whether the pattern expression could account

for performance in the DR and DNMS tasks, correlations were

calculated between pattern expression and the measures of

accuracy decline on the respective tasks. Whereas there was no

significant correlation of pattern expression with accuracy on

the DR task (r = –0.20, P = 0.42), pattern expression correlated

with DNMS accuracy (r = 0.66, P = 0.0025) such that a younger

profile was predictive of better performance across delays.

The bootstrap procedure was then used to identify ROIs

with a robust loading in the covariance pattern. Four such ROIs

were found. The first, the right prefrontal white matter (rWM)

had a smaller rWM volume in the young monkeys compared

with the aged monkeys (i.e., negative loading in covariance

pattern). The remaining 3 areas, bilateral putamen (rPut, lPut)

and left ACC, had significantly increased volumes in the young

compared with aged monkeys (i.e., positive loadings in covari-

ance pattern). Overall, most of the loadings in the covariance

Figure 8. Average performance across increasing delays on the DNMS task wasrelated to dlPFC (A), striatal, caudate, and putamen (B) volumes. When only the agedsubjects were included in the analysis, the cerebrum (C), ventricles (D), subjects withenlarged ventricles excluded), total PFC volume, PFC GM, prefrontal WM (E), SFG (F),dlPFC (G), and vmPFC (H) each predicted average performance across delays.

Page 10 of 15 Regional Brain Volume and Memory Loss in Aged Monkeys d Shamy et al.

at Colum

bia University Libraries on F

ebruary 16, 2011cercor.oxfordjournals.org

Dow

nloaded from

pattern were positive, indicating larger mean structural

volumes in the young monkeys compared with elderly

monkeys. This resulted in an average value of the loadings in

the covariance pattern of +0.07. Normalization of the loadings

in the covariance pattern was such that the Euclidean norm

was unity.

To test whether there was a residual correlation between

task performance and ROI volumetric measures that was

unaccounted for by the covariance pattern and task perfor-

mance, the data were residualized with respect to the covari-

ance pattern. Further, the covariance pattern was checked to

determine whether there were ROI-wise associations between

the residuals and the accuracy slopes from both DR and DNMS

tasks. No significant association was found at an uncorrected

P level of 0.05, which indicated that the covariance pattern

did indeed capture the major source of age- and task-related

variance in the data.

Discussion

The present study used univariate and multivariate analyses to

investigate potential volumetric alterations across multiple

brain regions and their relationship to age-related memory

impairment in the rhesus monkey. Results of the univariate

analyses revealed that aging is not associated with gross

structural change uniformly distributed throughout the ma-

caque brain. Dorsolateral PFC and striatal volumes were smaller

in aged monkeys compared with young adults. Additionally,

there was a negative correlation of prefrontal WM volume with

age selectively among the older subjects. In contrast, relative

volumetric preservation was noted in other PFC subregions,

the calcarine cortex, and the hippocampus. The overall pattern

of results was similar to findings from studies of human brain

aging, in which much larger numbers of subjects are typically

available for analysis. For example, age-related volumetric

decline in prefrontal GM (Raz et al. 1997, 2004, 2005; Salat

et al. 1999, 2001; Tisserand et al. 2002), prefrontal WM (Raz

et al. 1997, 2004; Salat et al. 2001), and striatum (Raz et al. 1995,

2005; Matochik et al. 2000, 2004) also has been documented in

elderly people, together with stability of the calcarine cortex

(Raz et al. 1997). In contrast to findings in nonhuman primates

(Shamy et al. 2006), however, hippocampal atrophy (Kaye et al.

1997; Raz et al. 1998; Tisserand et al. 2000) and ventriculome-

galy (Foundas et al. 1998) are frequently reported in human

aging, suggesting a possible contribution of incipient AD

(Soininen et al. 1994; Laakso et al. 1998; Sullivan et al. 2005),

vascular disease (Raz et al. 2007), or metabolic disease (Anan

et al. 2010).

In humans, prefrontal GM and WM volume measurements

reportedly predict impairments of recall, executive function,

and spatial working memory (Raz et al. 1998; Head et al. 2002;

Gunning-Dixon and Raz 2003), whereas compromised integrity

of the medial temporal lobe, particularly the hippocampus, is

coupled with declarative memory deficits (Raz et al. 1998).

Corresponding comparisons for the current sample of monkeys

indicated that PFC volume correlated with recognition

accuracy (i.e., DNMS), hippocampal volume correlated with

spatiotemporal memory in aged subjects (i.e., DR), and striatal

Figure 9. Results of the multivariate SSM analysis. When subject expression was plotted against age group (A), the pattern expression was not significantly different betweengroups. The use of the bootstrap procedure (B) revealed that most of the loadings in the covariance pattern were positive, indicating decreased mean structural volume in theaged monkeys. Additionally, 4 ROIs had robust loadings, as indicated by the larger size of the dots in the scatter plot. The rWM had a significantly negative loading, indicatingreduced volume of this ROI in the young monkeys compared with the old monkeys. Areas of significantly increased volume in the young included the putamen bilaterally (rPut,lPut) and the left ACC. The subject expression of the covariance pattern and the decline in accuracy across longer delays in the DNMS task were correlated (C), indicating thatincreased pattern expression may be beneficial in performance.

Cerebral Cortex Page 11 of 15

at Colum

bia University Libraries on F

ebruary 16, 2011cercor.oxfordjournals.org

Dow

nloaded from

volume correlated with measures of both spatiotemporal and

recognition memory. Chronological age largely accounted for

the relationships between recognition memory and PFC volume,

whereas the relationship between hippocampal volume and

DR performance was independent of age. Studies evaluating

structural MR images in humans and macaque monkeys have

revealed that cerebral volume is moderately predictive of age-

related recognition memory impairment (Coffey et al. 2001;

Shamy et al. 2006). Additionally, an inverse correlation was found

in the current study between ventricular volume and recogni-

tion memory performance. These findings raise the possibility

that age-related recognition memory impairment might result

from atrophy distributed across multiple memory-related

structures.

To consolidate the regionally selective univariate findings

under a single neural substrate that could account for both age

as well as task performance, a multivariate analysis was

conducted. An age-related covariance pattern was found whose

expression distinguished young from aged monkeys and also

correlated with impairments across delays on the DNMS task.

Overall, the loadings of individual brain regions were positively

weighted, indicating that smaller volumes were predictive of an

older profile and impaired recognition memory performance.

The most reliable regional volumes within the network

included the putamen bilaterally and the left ACC, which had

a positive loading, and the rWM, which had a negative loading.

As monkeys age, expression of this pattern decreases and this

change across the network of brain regions predicts greater

DNMS impairment.

A related study from Alexander et al. (2008) combined voxel-

based morphometry (VBM) with SSM to evaluate age-related

structural changes in the context of memory performance and

included results from a subset of subjects analyzed here. In

contrast to the present analysis, in which volumes were quan-

tified using manually acquired ROIs, VBM is a method in which

brains are morphed into a single template space, and compari-

sons of image density are made between subjects on a voxel-by-

voxel basis (Alexander et al. 2008). Reduced GM concentration

reported in VBM analyses is often interpreted as atrophy

(Keller et al. 2002; Wessels et al. 2006, but see Eriksson et al.

2009), and in this context, manually traced ROI volumetric

measurements are sometimes treated as a standard for

validating VBM results (Kubicki et al. 2002). However, obser-

vations from VBM compared with manual ROI analyses can

differ significantly in magnitude or location within the same

subjects (Good et al. 2002; Testa et al. 2004; Giuliani et al. 2005;

Kennedy et al. 2009), perhaps partly attributable to decreased

sensitivity of ROI analyses to focal changes (Voormolen et al.

2010) or alignment and smoothing artifacts in VBM analyses

(Eriksson et al. 2009).

To our knowledge, this is the first pair of complementary

studies to use VBM and ROI analyses to assess relationships

between age-related structural and cognitive changes in rhesus

monkeys. Across both investigations, dlPFC was reduced, toge-

ther with preservation in the visual cortex. Concomitant reduc-

tions in GM were also reported in the vmPFC, IFG, and superior

temporal cortex, alongside preservation in several regions

outside the scope of our ROI analysis. Higher expressions of

this pattern were correlated with DR performance and DNMS

acquisition. Notably, striatal GM was reportedly preserved in

the VBM assessment (Alexander et al. 2008), contrary to data

obtained in the current study and by Matochik et al. (2000). As

discussed previously (Alexander et al. 2008), this may be due in

part to the lower accuracy of anatomical parcellation in VBM

assessment compared with manually traced ROIs. A corre-

sponding advantage of VBM, however, is that by surveying the

entire brain unconstrained by ROI boundaries, this approach

can enhance sensitivity for detecting localized differences in

GM. This decreased sensitivity to local changes in ROI tracing

methods may partly explain the failure to find significant

correlations between age and vmPFC and IFG volumes in the

current study. On the other hand, the computation of ROIs

using anatomical landmarks is less prone to potential errors

induced by image preprocessing which can influence group-

level results in structural as well as functional neuroimaging

(Strother et al. 2004; Strother 2006). Furthermore, ROI com-

putation increases the signal-to-noise ratio, which is beneficial

regardless of whether a univariate or multivariate analysis is

employed.

For completeness, we used SSM on our ROI data to see

whether a single age-related dimension in the data could be

established that also had predictive power with respect to

behavioral performance on the DNMS task. After residualizing

the volumetric data with respect to the pattern, no significant

brain--behavioral or age correlations persisted, suggesting that

the pattern indeed captured the main source of age- and task-

related variance in the results. Inferring biological meaning

from the correlative relationships in the covariance pattern is

difficult. Instead, our analysis identifies topographic common-

alities in age-related volumetric change across all monkeys,

without postulating any ‘‘interaction’’ or ‘‘communication’’

among the implicated brain areas.

The pattern of correlations we report between regional

brain volume and behavior has precedence in previous studies

of aged nonhuman primates. The findings are also notably

different, however, than might be predicted from a standard

neuropsychological framework, on the basis of the behavioral

effects of experimental lesions in young subjects. Similar to the

current results, for example, hippocampal glucose metabolism,

measured by 18F-fluorodeoxyglucose positron emission tomog-

raphy (FDG PET) imaging, correlates with DR performance

among young and aged monkeys (Eberling et al. 1997). Given

that DR emphasizes spatiotemporal memory, such correlations

may arise from task demands on hippocampus-dependent

processing capacities, including the spatial (Baylis and Moore

1996; Beason-Held et al. 1999; Lavenex et al. 2006) and

temporal organization of memory (Buckmaster et al. 2004;

Alvarado and Bachevalier 2005; Lavenex et al. 2006). With

respect to our findings for the PFC whereas an earlier

stereological quantification of histological material failed to

detect reliable associations between dlPFC volume and DR

performance measures in aged monkeys (O’Donnell et al.

1999), several studies have reported that DNMS performance is

coupled with various age-related alternations in the dlPFC

(reviewed in Luebke et al. 2010), including changes in

myelinated axons in the deep layers of area 46 (Moore et al.

2005), monoamine receptor binding (Moore et al. 2005), the

electrotonic structure of both long and local projection

neurons (Kabaso et al. 2009), and firing rates of layers II and

III neurons (Chang et al. 2005). Recognition is understood to

comprise a multicomponent process, and these correlations

may reflect the reported differential involvement of PFC

subregions to the relevant capacities, including recollection,

familiarity, and novelty detection (Daselaar et al. 2006).

Page 12 of 15 Regional Brain Volume and Memory Loss in Aged Monkeys d Shamy et al.

at Colum

bia University Libraries on F

ebruary 16, 2011cercor.oxfordjournals.org

Dow

nloaded from

Overall, our findings demonstrate that normal cognitive

aging in nonhuman primates is associated with regionally

selective morphometric alteration distributed across a network

of memory-related regions. The additional implication is that

normal brain aging comprises a distinct neurobiological setting

in which cognitive outcomes are not easily predicted from the

known functional organization of the affected areas. Thus far, in

vivo imaging studies in behaviorally characterized monkeys

have been primarily cross-sectional and are therefore suscep-

tible to cohort effects. An advantage of this approach is the

ability to obtain MRI scans at multiple time points, and an

important future direction is to determine whether longitudi-

nal change occurs across this network of brain regions and if

the rate of change predicts trajectories of cognitive aging in

rhesus macaques.

Funding

National Institutes of Health (AG 003376 to C.A.B., AG10606

AG09973 to P.R.R., EB006204 to C.H., NS32892 and NS16980 to

D.G.A., MH58911 to P.R.H., MH62448, RR-000169 to the

California National Primate Research Center), the McKnight

Brain Research Foundation (to C.A.B); the Intramural Research

Program of the National Institute on Aging (to P.R.R.).

Notes

We would like to thank Mary Roberts, Carmel Stanko, Tracy Gilstrap, Dr

Cynthia Erickson, Ilana Seror, and Kevin Kelliher for expert technical

assistance and helpful discussion. Conflict of Interest : None declared.

References

Alexander GE, Chen K, Aschenbrenner M, Merkley TL, Santerre-

Lemmon LE, Shamy JL, Skaggs WE, Buonocore MH, Rapp PR,

Barnes CA. 2008. Age-related regional network of magnetic

resonance imaging gray matter in the rhesus macaque. J Neurosci.

28:2710--2718.

Alvarado MC, Bachevalier J. 2005. Selective neurotoxic damage to the

hippocampal formation impairs performance of the transverse

patterning and location memory tasks in rhesus macaques.

Hippocampus. 15:118--131.

Anan F, Masaki T, Shimomura T, Fujiki M, Umeno Y, Eshima N,

Saikawa T, Yoshimatsu H. 2010. Abdominal visceral fat accumulation

is associated with hippocampus volume in non-dementia patients

with type 2 diabetes mellitus. Neuroimage. 49:57--62.

Bachevalier J, Landis LS, Walker LC, Brickson M, Mishkin M, Price DL,

Cork LC. 1991. Aged monkeys exhibit behavioral deficits indicative

of widespread cerebral dysfunction. Neurobiol Aging. 12:99--111.

Barbas H. 2000. Connections underlying the synthesis of cognition,

memory, and emotion in primate prefrontal cortices. Brain Res Bull.

52:319--330.

Baylis GC, Moore BO. 1996. Hippocampal lesions cause forgetting in

a spatial response task. Eur J Neurosci. 8:853--860.

Beason-Held LL, Rosene DL, Killiany RJ, Moss MB. 1999. Hippocampal

formation lesions produce memory impairment in the rhesus

monkey. Hippocampus. 9:562--574.

Bowley MP, Cabral H, Rosene DL, Peters A. 2010. Age changes in

myelinated nerve fibers of the cingulate bundle and corpus callosum

in the rhesus monkey. J Comp Neurol. 518:3046--3064.

Brody H. 1955. Organization of the cerebral cortex. III. A study of aging

in the human cerebral cortex. J Comp Neurol. 102:511--516.

Buckmaster CA, Eichenbaum H, Amaral DG, Suzuki WA, Rapp PR. 2004.

Entorhinal cortex lesions disrupt the relational organization of

memory in monkeys. J Neurosci. 24:9811--9825.

Carmichael ST, Price JL. 1994. Architectonic subdivision of the orbital

and medial prefrontal cortex in the macaque monkey. J Comp

Neurol. 346:366--402.

Carmichael ST, Price JL. 1995. Limbic connections of the orbital and

medial prefrontal cortex in macaque monkeys. J Comp Neurol.

363:615--641.

Cavada C, Company T, Tejedor J, Cruz-Rizzolo RJ, Reinoso-Suarez F.

2000. The anatomical connections of the macaque monkey

orbitofrontal cortex. A review. Cereb Cortex. 10:220--242.

Chang YM, Rosene DL, Killiany RJ, Mangiamele LA, Luebke JI. 2005.

Increased action potential firing rates of layer 2/3 pyramidal cells in

the prefrontal cortex are significantly related to cognitive perfor-

mance in aged monkeys. Cereb Cortex. 15:409--418.

Coffey CE, Ratcliff G, Saxton JA, Bryan RN, Fried LP, Lucke JF. 2001.

Cognitive correlates of human brain aging: a quantitative magnetic

resonance imaging investigation. J Neuropsychiatry Clin Neurosci.

13:471--485.

Daselaar SM, Fleck MS, Cabeza R. 2006. Triple dissociation in the medial

temporal lobes: recollection, familiarity, and novelty. J Neurophysiol.

96:1902--1911.

Duan H, Wearne SL, Rocher AB, Macedo A, Morrison JH, Hof PR. 2003.

Age-related dendritic and spine changes in corticocortically projec-

ting neurons in macaque monkeys. Cereb Cortex. 13:950--961.

Dumitriu D, Hao J, Hara Y, Kaufmann J, Janssen WG, Lou W, Rapp PR,

Morrison JH. 2010. Selective changes in thin spine density and

morphology in monkey prefrontal cortex correlate with aging-

related cognitive impairment. J Neurosci. 30:7507--7515.

Eberling JL, Roberts JA, Rapp PR, Tuszynski MH, Jagust WJ. 1997.

Cerebral glucose metabolism and memory in aged rhesus macaques.

Neurobiol Aging. 18:437--443.

Efron B, Tibshirani RJ. 1994. An introduction to the bootstrap. New

York: CRC Press, LLC.

Eriksson SH, Free SL, Thom M, Symms MR, Martinian L, Duncan JS,

Sisodiya SM. 2009. Quantitative grey matter histological measures do

not correlate with grey matter probability values from in vivo MRI in

the temporal lobe. J Neurosci Methods. 181:111--118.

Feldman ML, Peters A. 1998. Ballooning of myelin sheaths in normally

aged macaques. J Neurocytol. 27:605--614.

Foundas AL, Zipin D, Browning CA. 1998. Age-related changes of the

insular cortex and lateral ventricles: conventional MRI volumetric

measures. J Neuroimaging. 8:216--221.

Fuster JM. 1985. The prefrontal cortex, mediator of cross-temporal

contingencies. Hum Neurobiol. 4:169--179.

Fuster JM. 2000. Memory networks in the prefrontal cortex. Prog Brain

Res. 122:309--316.

Fuster JM. 2001. The prefrontal cortex--an update: time is of the

essence. Neuron. 30:319--333.

Gazzaley AH, Thakker MM, Hof PR, Morrison JH. 1997. Preserved

number of entorhinal cortex layer II neurons in aged macaque

monkeys. Neurobiol Aging. 18:549--553.

Geuze E, Vermetten E, Bremner JD. 2005. MR-based in vivo

hippocampal volumetrics: 1. Review of methodologies currently

employed. Mol Psychiatry. 10:147--159.

Giuliani NR, Calhoun VD, Pearlson GD, Francis A, Buchanan RW. 2005.

Voxel-based morphometry versus region of interest: a comparison

of two methods for analyzing gray matter differences in schizo-

phrenia. Schizophr Res. 74:135--147.

Goldman-Rakic PS, Selemon LD, Schwartz ML. 1984. Dual pathways

connecting the dorsolateral prefrontal cortex with the hippocampal

formation and parahippocampal cortex in the rhesus monkey.

Neuroscience. 12:719--743.

Good CD, Scahill RI, Fox NC, Ashburner J, Friston KJ, Chan D, CrumWR,

Rossor MN, Frackowiak RS. 2002. Automatic differentiation of

anatomical patterns in the human brain: validation with studies of

degenerative dementias. Neuroimage. 17:29--46.

Gunning-Dixon FM, Raz N. 2003. Neuroanatomical correlates of

selected executive functions in middle-aged and older adults:

a prospective MRI study. Neuropsychologia. 41:1929--1941.

Habeck C, Krakauer JW, Ghez C, Sackeim HA, Eidelberg D, Stern Y,

Moeller JR. 2005. A new approach to spatial covariance modeling of

functional brain imaging data: ordinal trend analysis. Neural

Comput. 17:1602--1645.

Harlow HF, Bromer JA. 1939. A test apparatus for monkeys. Psychol Rec.

2:434--436.

Cerebral Cortex Page 13 of 15

at Colum

bia University Libraries on F

ebruary 16, 2011cercor.oxfordjournals.org

Dow

nloaded from

Haug H, Knebel G, Mecke E, Orun C, Sass NL. 1981. The aging of

cortical cytoarchitectonics in the light of stereological investiga-

tions. Prog Clin Biol Res. 59B:193--197.

Head D, Raz N, Gunning-Dixon F, Williamson A, Acker JD. 2002. Age-

related differences in the course of cognitive skill acquisition: the

role of regional cortical shrinkage and cognitive resources. Psychol

Aging. 17:72--84.

Herndon JG, Moss MB, Rosene DL, Killiany RJ. 1997. Patterns of

cognitive decline in aged rhesus monkeys. Behav Brain Res.

87:25--34.

Hof PR, Nimchinsky EA, Young WG, Morrison JH. 2000. Numbers of

meynert and layer IVB cells in area V1: a stereologic analysis in

young and aged macaque monkeys. J Comp Neurol. 420:113--126.

Jacobs B, Driscoll L, Schall M. 1997. Life-span dendritic and spine

changes in areas 10 and 18 of human cortex: a quantitative Golgi

study. J Comp Neurol. 386:661--680.

Kabaso D, Coskren PJ, Henry BJ, Hof PR, Wearne SL. 2009. The

electrotonic structure of pyramidal neurons contributing to pre-

frontal cortical circuits in macaque monkeys is significantly altered

with age. Cereb Cortex. 19:2238--2268.

Kaye JA, Swihart T, Howieson D, Dame A, Moore MM, Karnos T,

Camicioli R, Ball M, Oken B, Sexton G. 1997. Volume loss of the

hippocampus and temporal lobe in healthy elderly persons destined

to develop dementia. Neurology. 48:1297--1304.

Keller SS, Mackay CE, Barrick TR, Wieshmann UC, Howard MA,

Roberts N. 2002. Voxel-based morphometric comparison of

hippocampal and extrahippocampal abnormalities in patients with

left and right hippocampal atrophy. Neuroimage. 16:23--31.

Kennedy KM, Erickson KI, Rodrigue KM, Voss MW, Colcombe SJ,

Kramer AF, Acker JD, Raz N. 2009. Age-related differences in regional

brain volumes: a comparison of optimized voxel-based morphometry

to manual volumetry. Neurobiol Aging. 30:1657--1676.

Keuker JI, de Biurrun G, Luiten PG, Fuchs E. 2004. Preservation of

hippocampal neuron numbers and hippocampal subfield volumes in

behaviorally characterized aged tree shrews. J Comp Neurol.

468:509--517.

Keuker JI, Luiten PG, Fuchs E. 2003. Preservation of hippocampal neuron

numbers in aged rhesus monkeys. Neurobiol Aging. 24:157--165.

Kubicki M, Shenton ME, Salisbury DF, Hirayasu Y, Kasai K, Kikinis R,

Jolesz FA, McCarley RW. 2002. Voxel-based morphometric analysis

of gray matter in first episode schizophrenia. Neuroimage.

17:1711--1719.

Laakso MP, Soininen H, Partanen K, Lehtovirta M, Hallikainen M,

Hanninen T, Helkala EL, Vainio P, Riekkinen PJ, Sr. 1998. MRI of the

hippocampus in Alzheimer’s disease: sensitivity, specificity, and

analysis of the incorrectly classified subjects. Neurobiol Aging.

19:23--31.

Lavenex PB, Amaral DG, Lavenex P. 2006. Hippocampal lesion prevents

spatial relational learning in adult macaque monkeys. J Neurosci.

26:4546--4558.

Luebke J, Barbas H, Peters A. 2010. Effects of normal aging on prefrontal

area 46 in the rhesus monkey. Brain Res Rev. 62:212--232.

Makris N, Kennedy DN, Boriel DL, Rosene DL. 2010. Methods of MRI-

based structural imaging in the aging monkey. Methods.

50:166--177.

Marner L, Nyengaard JR, Tang Y, Pakkenberg B. 2003. Marked loss of

myelinated nerve fibers in the human brain with age. J Comp

Neurol. 462:144--152.

Matochik JA, Chefer SI, Lane MA, Roth GS, Mattison JA, London ED,

Ingram DK. 2004. Age-related decline in striatal volume in rhesus

monkeys: assessment of long-term calorie restriction. Neurobiol

Aging. 25:193--200.

Matochik JA, Chefer SI, Lane MA, Woolf RI, Morris ED, Ingram DK,

Roth GS, London ED. 2000. Age-related decline in striatal volume in

monkeys as measured by magnetic resonance imaging. Neurobiol

Aging. 21:591--598.

Merrill DA, Roberts JA, Tuszynski MH. 2000. Conservation of neuron

number and size in entorhinal cortex layers II, III, and V/VI of aged

primates. J Comp Neurol. 422:396--401.

Miller EK, Cohen JD. 2001. An integrative theory of prefrontal cortex

function. Annu Rev Neurosci. 24:167--202.

Mishkin M. 1978. Memory in monkeys severely impaired by combined

but not by separate removal of amygdala and hippocampus. Nature.

273:297--298.

Mishkin M, Pribram KH. 1956. Analysis of the effects of frontal lesions in

monkey: II. Variations of delayed response. J Comp Physiol Psychol.

49:36--40.

Moeller JR, Nakamura T, Mentis MJ, Dhawan V, Spetsieres P, Antonini A,

Missimer J, Leenders KL, Eidelberg D. 1999. Reproducibility of

regional metabolic covariance patterns: comparison of four pop-

ulations. J Nucl Med. 40:1264--1269.

Moeller JR, Strother SC. 1991. A regional covariance approach to the

analysis of functional patterns in positron emission tomographic

data. J Cereb Blood Flow Metab. 11:A121--A135.

Moeller JR, Strother SC, Sidtis JJ, Rottenberg DA. 1987. Scaled subprofile

model: a statistical approach to the analysis of functional patterns in

positron emission tomographic data. J Cereb Blood Flow Metab.

7:649--658.

Moore TL, Schettler SP, Killiany RJ, Herndon JG, Luebke JI, Moss MB,

Rosene DL. 2005. Cognitive impairment in aged rhesus monkeys

associated with monoamine receptors in the prefrontal cortex.

Behav Brain Res. 160:208--221.

Moss MB, Killiany RJ, Lai ZC, Rosene DL, Herndon JG. 1997.

Recognition memory span in rhesus monkeys of advanced age.

Neurobiol Aging. 18:13--19.

Moss MB, Rosene DL, Peters A. 1988. Effects of aging on visual

recognition memory in the rhesus monkey. Neurobiol Aging.

9:495--502.

O’Donnell KA, Rapp PR, Hof PR. 1999. Preservation of prefrontal

cortical volume in behaviorally characterized aged macaque

monkeys. Exp Neurol. 160:300--310.

O’Sullivan M, Jones DK, Summers PE, Morris RG, Williams SC, Markus HS.

2001. Evidence for cortical "disconnection" as a mechanism of age-

related cognitive decline. Neurology. 57:632--638.

Peters A. 1996. Age-related changes in oligodendrocytes in monkey

cerebral cortex. J Comp Neurol. 371:153--163.

Petrides M, Pandya DN. 1999. Dorsolateral prefrontal cortex: compar-

ative cytoarchitectonic analysis in the human and the macaque

brain and corticocortical connection patterns. Eur J Neurosci.

11:1011--1036.

Presty SK, Bachevalier J, Walker LC, Struble RG, Price DL, Mishkin M,

Cork LC. 1987. Age differences in recognition memory of the rhesus

monkey (Macaca mulatta). Neurobiol Aging. 8:435--440.

Rapp PR. 1988. Toward a nonhuman primate model of age-dependent

cognitive dysfunction. Neurobiol Aging. 9:503--505.

Rapp PR, Amaral DG. 1989. Evidence for task-dependent memory

dysfunction in the aged monkey. J Neurosci. 9:3568--3576.

Rapp PR, Amaral DG. 1991. Recognition memory deficits in a sub-

population of aged monkeys resemble the effects of medial

temporal lobe damage. Neurobiol Aging. 12:481--486.

Rapp PR, Kansky MT, Stanko CT, Fong SG, McKay HL, Roberts JA. 2004.

Cognitive aging in the rhesus monkey: a parametric analysis.

Program No. 82.7. Washington, DC: Society for Neuroscience.

Rapp PR, Morrison JH, Roberts JA. 2003. Cyclic estrogen replacement

improves cognitive function in aged ovariectomized rhesus

monkeys. J Neurosci. 23:5708--5714.

Raz N, Gunning FM, Head D, Dupuis JH, McQuain J, Briggs SD,

Loken WJ, Thornton AE, Acker JD. 1997. Selective aging of the

human cerebral cortex observed in vivo: differential vulnerability of

the prefrontal gray matter. Cereb Cortex. 7:268--282.

Raz N, Gunning-Dixon FM, Head D, Dupuis JH, Acker JD. 1998.

Neuroanatomical correlates of cognitive aging: evidence from

structural magnetic resonance imaging. Neuropsychology. 12:

95--114.

Raz N, Gunning-Dixon F, Head D, Rodrigue KM, Williamson A, Acker JD.

2004. Aging, sexual dimorphism, and hemispheric asymmetry of the

cerebral cortex: replicability of regional differences in volume.

Neurobiol Aging. 25:377--396.

Raz N, Lindenberger U, Rodrigue KM, Kennedy KM, Head D,

Williamson A, Dahle C, Gerstorf D, Acker JD. 2005. Regional brain

changes in aging healthy adults: general trends, individual differ-

ences and modifiers. Cereb Cortex. 15:1676--1689.

Page 14 of 15 Regional Brain Volume and Memory Loss in Aged Monkeys d Shamy et al.

at Colum

bia University Libraries on F

ebruary 16, 2011cercor.oxfordjournals.org

Dow

nloaded from

Raz N, Rodrigue KM, Kennedy KM, Acker JD. 2007. Vascular health and

longitudinal changes in brain and cognition in middle-aged and

older adults. Neuropsychology. 21:149--157.

Raz N, Torres IJ, Acker JD. 1995. Age, gender, and hemispheric

differences in human striatum: a quantitative review and new data

from in vivo MRI morphometry. Neurobiol Learn Mem. 63:133--142.

Salat DH, Kaye JA, Janowsky JS. 2001. Selective preservation and

degeneration within the prefrontal cortex in aging and Alzheimer

disease. Arch Neurol. 58:1403--1408.

Salat DH, Stangl PA, Kaye JA, Janowsky JS. 1999. Sex differences in

prefrontal volume with aging and Alzheimer’s disease. Neurobiol

Aging. 20:591--596.

Scarmeas N, Habeck CG, Zarahn E, Anderson KE, Park A, Hilton J,

Pelton GH, Tabert MH, Honig LS, Moeller JR, et al. 2004. Covariance

PET patterns in early Alzheimer’s disease and subjects with

cognitive impairment but no dementia: utility in group discrimina-

tion and correlations with functional performance. Neuroimage.

23:35--45.

Shamy JL, Buonocore MH, Makaron LM, Amaral DG, Barnes CA, Rapp PR.

2006. Hippocampal volume is preserved and fails to predict

recognition memory impairment in aged rhesus monkeys (Macaca

mulatta). Neurobiol Aging. 27:1405--1415.

Smith DE, Rapp PR, McKay HM, Roberts JA, Tuszynski MH. 2004.

Memory impairment in aged primates is associated with focal death

of cortical neurons and atrophy of subcortical neurons. J Neurosci.

24:4373--4381.

Soininen HS, Partanen K, Pitkanen A, Vainio P, Hanninen T,

Hallikainen M, Koivisto K, Riekkinen PJ, Sr. 1994. Volumetric MRI

analysis of the amygdala and the hippocampus in subjects with age-

associated memory impairment: correlation to visual and verbal

memory. Neurology. 44:1660--1668.

Squire LR, Zola-Morgan S. 1991. The medial temporal lobe memory

system. Science. 253:1380--1386.

Strother SC. 2006. Evaluating fMRI preprocessing pipelines. IEEE Eng

Med Biol Mag. 25:27--41.

Strother S, La Conte S, Kai Hansen L, Anderson J, Zhang J, Pulapura S,

Rottenberg D. 2004. Optimizing the fMRI data-processing pipeline

using prediction and reproducibility performance metrics: I. A

preliminary group analysis. Neuroimage. 23(Suppl 1):S196--S207.

Sullivan EV, Marsh L, Pfefferbaum A. 2005. Preservation of hippocampal

volume throughout adulthood in healthy men and women. Neuro-

biol Aging. 26:1093--1098.

Tang Y, Nyengaard JR, Pakkenberg B, Gundersen HJ. 1997. Age-induced

white matter changes in the human brain: a stereological in-

vestigation. Neurobiol Aging. 18:609--615.

Testa C, Laakso MP, Sabattoli F, Rossi R, Beltramello A, Soininen H,

Frisoni GB. 2004. A comparison between the accuracy of voxel-

based morphometry and hippocampal volumetry in Alzheimer’s

disease. J Magn Reson Imaging. 19:274--282.

Tisserand DJ, Pruessner JC, Sanz Arigita EJ, van Boxtel MP, Evans AC,

Jolles J, Uylings HB. 2002. Regional frontal cortical volumes decrease

differentially in aging: an MRI study to compare volumetric approa-

ches and voxel-based morphometry. Neuroimage. 17:657--669.

Tisserand DJ, Visser PJ, van Boxtel MP, Jolles J. 2000. The relation

between global and limbic brain volumes on MRI and cognitive

performance in healthy individuals across the age range. Neurobiol

Aging. 21:569--576.

Voormolen EH, Wei C, Chow EW, Bassett AS, Mikulis DJ, Crawley AP.

2010. Voxel-based morphometry and automated lobar volumetry:

the trade-off between spatial scale and statistical correction.

Neuroimage. 49:587--596.

Warrington EK, Weiskrantz L. 1982. Amnesia: a disconnection syn-

drome? Neuropsychologia. 20:233--248.

Wessels AM, Simsek S, Remijnse PL, Veltman DJ, Biessels GJ, Barkhof F,

Scheltens P, Snoek FJ, Heine RJ, Rombouts SA. 2006. Voxel-based

morphometry demonstrates reduced grey matter density on brain

MRI in patients with diabetic retinopathy. Diabetologia. 49:2474--2480.

West MJ. 1993. New stereological methods for counting neurons.

Neurobiol Aging. 14:275--285.

Wisco JJ, Killiany RJ, Guttmann CR, Warfield SK, Moss MB, Rosene DL.

2008. An MRI study of age-related white and gray matter volume

changes in the rhesus monkey. Neurobiol Aging. 29:1563--1575.

Zola SM, Squire LR, Teng E, Stefanacci L, Buffalo EA, Clark RE. 2000.

Impaired recognition memory in monkeys after damage limited to

the hippocampal region. J Neurosci. 20:451--463.

Cerebral Cortex Page 15 of 15

at Colum

bia University Libraries on F

ebruary 16, 2011cercor.oxfordjournals.org

Dow

nloaded from