Is retaining the youthful functional anatomy underlying ... · The functional anatomy of efficient information processing with the inspection time task has been studied, to date,

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NeuroImage 41 (2008) 581–595
Is retaining the youthful functional anatomy underlying speed ofinformation processing a signature of successful cognitive ageing?An event-related fMRI study of inspection time performance

Gordon D. Waiter,a Helen C. Fox,b Alison D. Murray,a John M. Starr,c Roger T. Staff,d

Victoria J. Bourne,e Lawrence J. Whalley,b and Ian J. Dearyf,⁎

aDepartment of Radiology, College of Life Sciences and Medicine, University of Aberdeen, UKbDepartment of Mental Health, College of Life Sciences and Medicine, University of Aberdeen, UKcGeriatric Medicine, Department of Clinical and Surgical Sciences, University of Edinburgh, UKdDepartment of Nuclear Medicine, Aberdeen Royal Infirmary, UKeSchool of Psychology, University of Dundee, UKfDepartment of Psychology, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, Scotland, UK

Received 8 October 2007; revised 18 February 2008; accepted 21 February 2008Available online 6 March 2008

It has been hypothesized that individual differences in cognitive ageingmight in part be based on the relative preservation of speed of informationprocessing. However, the biological foundations of processing speed arenot understood. Here we compared two groups of non-demented olderpeople who had relatively similar IQs at age 11 but differed markedly innon-verbal reasoning ability at age 70: ‘cognitive sustainers’ (n=25), and‘cognitive decliners’ (n=15). Using an event-related fMRI design, westudied the BOLD response while they performed an inspection time task.Inspection time is a two-alternative forced choice, backward masking testof the speed of the early stages of visual information processing. Inspectiontime has a well-established, significant association with higher cognitiveabilities. The group of cognitive sustainers showed a pattern of BOLDactivation–deactivation in response to inspection time stimulus durationdifferences that was similar to a healthy young sample [Deary, I.J.,Simonotto, E., Meyer, M., Marshall, A., Marshall, I., Goddard, N.,Watdlaw, J.M., 2004a. The functional anatomy of inspection time: anevent-related fMRI study. NeuroImage 22, 1466–1479]. The group ofcognitive decliners lacked these clear neural networks. The relativepreservation of complex reasoning skills in old agemay be associated withthe preservation of the neural networks that underpin fundamentalinformation processing in youth.© 2008 Elsevier Inc. All rights reserved.

⁎ Corresponding author. Fax: +44 131 651 1771.E-mail address: [email protected] (I.J. Deary).Available online on ScienceDirect (www.sciencedirect.com).

1053-8119/$ - see front matter © 2008 Elsevier Inc. All rights reserved.doi:10.1016/j.neuroimage.2008.02.045

Introduction

There is a growing proportion of older people in society.Therefore, the problems of the older person have become a higherpriority for researchers (House of Lords, 2005). Age-related cognitivedecline is among the most feared and most burdensome aspects ofgrowing old (Martin, 2004; Stern and Carstensen, 2000). Cognitiveageing is the harbinger of pathological states of cognitive decline,such as the dementias (Tierney et al., 2005). In order to providerational bases for treatments to counter or ameliorate cognitiveageing, it is necessary to understand its psychological and biologicalfoundations.

There is much descriptive data on the cognitive ability changesthat occur with ageing (Hedden and Gabrieli, 2004; Salthouse andFerrer-Caja, 2003; Schaie, 2005). Some cognitive capabilities arewell retained in older people. Examples are vocabulary, somenumber skills, and general knowledge. Other cognitive functionsshow decline, similar in trajectory to physical functions, such asgrip strength (Frederiksen et al., 2006). Examples are reasoning,some aspects of memory, spatial ability, executive functions, andspeed of information processing. There are marked individualdifferences in human cognitive ageing (Schaie, 2005; Wilson et al.,2002). A large proportion of this variation appears to be caused bygeneral cognitive decline, such that, when one domain of cognitiveability starts to decline, others also tend to deteriorate, though thereis also additional age-related decline in specific cognitive abilities(Salthouse, 1996; Salthouse and Czaja, 2000; Wilson et al., 2002).

There are attempts within cognitive ageing to explain the behav-ioral phenomena using a smaller number of more “fundamentalcognitivemechanisms or ‘primitives’” (Braver andWest, 2008, p.311).In this mode, there are accounts which place working memory and,

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http://dx.doi.org/10.1016/j.neuroimage.2008.02.045

Fig. 1. The cue, stimuli, and backward mask for the inspection time task. Seetext for procedure.

582 G.D. Waiter et al. / NeuroImage 41 (2008) 581–595

especially, executive control in explanatory positions regarding theageing of disparate cognitive functions. Another influential hypothesisin cognitive ageing is that speed of information processing has a specialplace in understanding what happens in the older person's brain (Bugget al., 2006; Charlton et al., in press; Finkel et al., 2005; Hertzog et al.,2003; Salthouse, 1996;Verhaeghen andSalthouse, 1997; Zimprich andMartin, 2002). In this view, processing speed might not merely be onedomain of cognitive function that declines with age. It might be apartial foundation of other cognitive functions that decline; age-relatedchanges in processing speed might account for some of the age-relatedvariance in other mental functions. That is, if speed of informationprocessing slows down, then perhaps other cognitive functionsdeteriorate too, by their being partly dependent on intact speed ofprocessing for their efficiency. Speed of information processing can beassessed in humans by psychometric tests, such as the Wechslerbattery's digit symbol and similar tests (Hoyer et al., 2004; Salthouse,1996). However, these are complex, involving, probably, other high-level functions when people perform them (Deary, 2000, Chapter 8).Better evidence for the ageing of processing speed comes fromconceptually simpler, lower-level tasks. These include reaction timesthat come from cognitive-experimental psychology, and the inspectiontime task that comes from psychophysics. Reaction times speed upduring childhood, and are fastest in late adolescence/early adulthood.Thereafter, there is, on average, a marked non-linear slowing inreaction time with age, starting from early adulthood, which parallelsage-related changes in physical functions (Der andDeary, 2006). Thereis statistical evidence that reaction times can account for a substantialproportion of the age-related variation in other cognitive domains,including memory (Salthouse, 1996). Slowing of reaction time isassociated with an increased risk of mortality, as well as cognitivedecline (Deary and Der, 2005; Shipley et al., 2006, 2007). It is possiblethat the speeding up of reaction times during childhood and the slowingduring middle and later adulthood are caused in part by the maturationand deterioration, respectively, of white matter tracts in the brain(Deary et al., 2006a). Despite this evidence for their importance in oldage, a limitation of reaction times is that they involve speeded physicalreactions, which could be slowed for other, non-cognitive reasons inolder people, such as arthritis.

Here, to investigate further the biological foundations of humancognitive ageing, we employ a psychophysical task of speed ofinformation processing that assesses the efficiency of iconic memoryin the early stages of visual information processing. It is a visualbackwardmasking task called inspection time (Deary, 2001). The taskrequires only a very simple visual discrimination, one that is almosterror-free at longer stimulus exposure durations, such as the 150 msand 200 ms durations included in the present study. The inspectiontime task, in its most usual format, is a two-alternative, forced choiceprocedure. Subjects are first cued that a stimulus is about to appear. Ina typical version of the task, subjects are shown two parallel, verticallines, joined at the topwith a horizontal line (Fig. 1). There is amarkeddifference in length between the two lines. Subjects indicate, at leisureand with no requirement for a speeded response, whether the longerline was on the right or the left. The stimulus is presented at a numberof durations that vary from a few hundredmilliseconds (easy trials), tojust a few milliseconds (difficult trials). The stimulus is backward-masked with a visual pattern mask (Fig. 1) after stimulus offset, toprevent further processing. The function of the stimulus durationversus the proportion of correct responses is described by a cumu-lative normal ogive (Deary et al., 1993).

There are individual differences in the efficiency of informationprocessing assessed by the inspection time procedure. These are

significantly correlated with individual differences in higher levelmental abilities, including psychometric tests of intelligence(Grudnik and Kranzler, 2001). Subjects who make a higherproportion of correct discriminations in the inspection time tasktend to score better on cognitive ability tests. This is the case forchildren (Edmonds et al., in press), healthy adults (Crawford et al.,1998), healthy older adults (Nettelbeck and Rabbitt, 1992), andcognitively impaired older adults (Bonney et al., 2006). There is astronger association between inspection time and those tests ofmental ability that decline with age than with those that don't,especially tests of processing speed (Burns and Nettelbeck, 2003;Crawford et al., 1998; Luciano et al., 2004). Inspection timemediates the association between age and the decline in other mentalfunctions (Deary, 2000, p. 245; Nettelbeck and Rabbitt, 1992).Inspection time performance, and performance on other visualbackward masking tasks, is markedly poorer in older adults withpathological cognitive decline, including mild cognitive impairment(Bonney et al., 2006; Lu et al., 2005) and dementia (Deary et al.,1991), and it is suggested that tests of this type of processingefficiency might be useful early detectors—‘biomarkers’—of age-related cognitive decline (Nettelbeck and Wilson, 2004, 2005).

A principal reason that inspection time—and its correlations withhigher cognitive ability tests—has attracted much attention is thesimplicity of the task (Deary, 2000). As Nettelbeck (2001, p. 460)stated, “the measurement of IT is straightforward. The procedurerequires a very simple judgment about which of two vertical lines,joined horizontally across the top, is longer (or shorter). If viewingtime is not restricted, then no one makes any errors.” Thus, inspec-tion time is simple in the senses that: the very easy discrimination ismade almost always without error when the stimulus duration issufficiently long; the stimulus–response characteristics are fixed;and the task can be done reliably even by children and people withdementia. The psychophysical task of inspection time is far simplerin conception and execution than the complex mental tests withwhich it correlates. Indeed, the task was designed by its originatorVickers (1979; Vickers et al., 1972) to find the exposure durationthat individual subjects required in order to resolve a simple visualjudgment to a given level of accuracy. Of course, being cognitivelysimple would not rule out higher level brain areas being activatedduring the performance of the task.

A major missing link in the understanding of cognitive ageing isthe lack of a biological foundation for the efficiency of informationprocessing (Deary, 2000, Chapter 8; Finkel et al., 2005; Salthouse,2000). Tasks such as inspection time offer candidate tools to close thisexplanatory gap, by acting as endophenotypes between highercognitive functions and brain biology. Thus, impaired iconic memoryhas been suggested as a biomarker of accelerated ageing (Lu et al.,

583G.D. Waiter et al. / NeuroImage 41 (2008) 581–595

2005). Supporting this suggestion, inspection time is partly heritable,and has a sizeable genetic correlation with higher cognitive abilities inchildren and in adults (Edmonds et al., in press; Luciano et al., 2001,2004). The functional anatomy of inspection time in young adults hasbeen described using block- and event-related fMRI study designs(Deary et al., 2001, 2004a). In the event-related fMRI study, subjectsperformed an inspection time task while fMRI took place. The BOLDactivation–deactivation pattern was mapped in relation to a linearfunction of the inspection time stimulus duration. Task difficulty wasassociated with bilateral activation in the inferior fronto-opercularcortex, superior/medial frontal gyrus, and anterior cingulate gyrus,and bilateral deactivation in the posterior cingulate gyrus andprecuneus. Therefore, in young adults, functional imaging has alreadyprovided information about brain areas which might be sufficient fortask performance (Deary et al., 2004a).

In summary, there is good evidence that retaining efficientinformation processing is a key aspect of successful cognitive ageing.The inspection time technique has many findings that make it anattractive procedure—biomarker or endophenotype—for furtherexploring the neural foundations of successful cognitive ageing.The functional anatomy of efficient information processing with theinspection time task has been studied, to date, solely in youngerpeople. Here, for the first time, we examine whether successfullifetime cognitive ageing, based on mental test scores taken over50 years apart, is associated with the retention or loss of thosefunctional brain networks that support efficient information proces-sing in younger people (cf. Deary et al., 2004a). There are fourtheoretical models that attempt to account for how the average olderbrain operates when it tackles the samemental material as the averageyounger brain, viz. compensatory reorganization, dedifferentiation,computational capacity limitation, and neural inefficiency (Zarahnet al., 2007). The first twomodelsmake opposite predictions about thebrain activation patterns of higher and lower mentally functioningolder people when compared with younger people. Compensatoryreorganization hypothesizes that the lower functioning older peoplewill more closely resemble younger people, and dedifferentiationhypothesizes that it is the higher functioning older people who wouldhave brain activation patterns more like the young (Cabeza et al.,2002, 2004; Zarahn et al., 2007). The present study provides evidencerelevant to deciding between these hypotheses in the context ofinspection time performance.

We examined individuals within a uniquely valuable cohortwhose cognitive ability was assessed at age 11 and then again in theirmiddle-to-late 60s: the Aberdeen Birth Cohort 1936 (e.g. Dearyet al., 2004b; Whalley et al., 2005). Briefly, the design of theexperiment was as follows. We compared two groups of non-demented 70-year-olds who, at age 11, had relatively similar generalcognitive ability but who, in old age, had diverged, with one groupdemonstrating relatively successful, and the other one unsuccessful,cognitive ageing. We examined whether, in older people withrelatively successful cognitive ageing, their BOLD activation–deactivation pattern while they performed an inspection time taskwas more or less similar to those of healthy younger individuals thanolder people with relatively unsuccessful cognitive ageing.

Methods

Participants and mental tests

Written informed consent for the studywas obtained and the studywas approved by the Grampian Research Ethics Committee. Subjects

in the study were surviving participants of the ScottishMental Survey1947 (SMS1947; ScottishCouncil for Research inEducation [SCRE],1949). This was a nationwide exercise including almost all school-children born in 1936 and attending Scottish schools on June 4th 1947(N=70,805). They sat a version of the Moray House Test No. 12(MHT). This is a group-administered mental test with a time limit of45 min, a maximum score of 76, and a range of questions includingverbal, numerical and spatial reasoning, as well as other types of item.It has a very high correlation (~0.8) with the individually-administered Stanford–Binet mental test (SCRE, 1949). Data fromthe SMS1947 were retained by the SCRE.With ethical approval, 508Aberdeen residents who had taken part in the SMS1947 at age 11wererecruited into a prospective, longitudinal study of brain ageing andhealth from 1999. They are known as theAberdeenBirth Cohort 1936(ABC1936). They have undertaken a series of substantial medical andcognitive assessments (Deary et al., 2004b). By the time the currentfMRI study was conducted, members of the ABC1936 had alreadybeen assessed for the third time within older age. The three waves oftesting took place around 2000, 2002, and 2004, when cohortmembers were about 64, 66, and 68 years old, respectively. Thecognitive assessment on each of these occasions within older ageincluded a test of non-verbal reasoning (Raven's Standard ProgressiveMatrices test [RPM]; Raven et al., 1977). This is a 60-item test inwhich subjects examine abstract patterns arrayed as a 3×3 matrix. Ineach item the bottom, right hand component of the matrix is missing.The subject's task is to examine the rest of the matrix and, by inducingand then applying the logical rules underlying the rest of the pattern,indicate which of the answer options correctly completes the pattern.The RPM loads highly on the general cognitive ability factor, makingit a good indicator of general mental ability (Carroll, 1993). RPMscores obtained in old age correlate moderately and significantly withthe MHT scores from age 11 (Deary et al., 2004b). In the present,ABC1936, sample, the two-tailed Pearson correlation between MHTand RPM at age about 68 (wave 3, N=294) was 0.56 (pb0.001).

Participants with relatively successful and unsuccessful cognitiveageing

The availability of MHT (at age 11) and RPM (from the mid-sixties, onwards) scores for members of the ABC1936 meant thatsubgroups of subjects could be identified with more or less successfulcognitive ageing in the long period between age 11 and the middle-to-late 60s. The aim was to select two subgroups of subjects withrelatively similar MHT scores at age 11, but divergent RPM scores inlater life. TheMoray House Test score at age 11was used as a measureof prior ability. It provided the baseline from which relatively success-ful and unsuccessful cognitive change in older age was determined.From theABC1936 sample, 58 individuals (aged 69–70 years by then;28 female) with an age 11 IQ (calculated from MHT raw scores)between 85 and 115were invited for fMRI scanning; i.e., at age 11 theywere within 1 SD of the sample mean. The age 11 IQ score wascalculated by first transforming the ABC1936 sample's age 11 MHTraw scores to a standard IQ-type scale with a mean of 100 and SD of15. Thereafter, only subjects with integer IQs greater than 84 and lessthan 116 were considered for the present study. Participants within this‘normal’ childhood IQ band were grouped, according to RPM scoresin later life, into relative cognitive sustainers and decliners.

To form a metric for cognitive ‘decliners’ and ‘sustainers’, theABCwave 3 testing data were used. First, to ensure that subjects hadsimilar experience with the RPM test, the ABC1936 participantsused in this exercise had to have taken RPM at wave 1 and wave 3 of


the ABC1936 study. Then, they were divided according to theirRPM score at wave 3, around age 68. The entire cohort's mean andstandard deviation (SD) for RPM at wave 3 were computed. Themean was 37.8 and the SD was 7.6. Potential decliners were definedas those individuals with a childhood IQ score between 85 and 115andwho scored at least 0.5 SD below the groupmean on RPM at ageabout 68. In practice, the decliners were subjects with a RPM scoreof ≤34 (n=27, 16 female). The sustainers were defined as thoseindividuals with a childhood IQ score between 85 and 115 and whoscored at least 0.5 SD above the ABC1936 wave 3 group mean onRPM at age about 68. In practice, the sustainers were subjects with aRPM score of ≥41 (n=31, 12 female).

Thus, the subgroup of the ABC1936 who took part in thepresent study were included on the following bases: they met thechildhood and later life mental test score criteria described above;they had been able to perform the inspection time test at a previousexamination; their previous MRI brain scan had produced asatisfactory structural scan; they had no medical contraindications;and they agreed to take part in this study when contacted to do so.There was funding to scan 60 subjects and participants wereinvited from the ABC1936 until all slots were filled.

In summary, starting with a group of people in the ‘normal’ IQrange in childhood, we defined groups in later adulthood withrelatively good (cognitive ‘sustainers’) and poor (cognitive ‘decli-ners’) lifetime cognitive ageing. We then proceeded to examine andcompare the brain functional anatomy of inspection time in these twogroups.

Inspection time testing

Baseline inspection time testingThe inspection time tasks employed in the baseline and imaging

sessions were replicated as closely as possible from those describedin Deary et al. (2004a). In all inspection time tests, the participantswere required to make a simple visual discrimination. They wereasked to indicate, with no pressure on response time, which of twoparallel, vertical lines of markedly different lengths was longer(Fig. 1). Subjects were informed clearly that accuracy and notspeed of responses was being assessed.

As a part of the cognitive testing session, almost all ABC1936subjects undertook inspection time testing at wave 3 at a mean age of68.63 years (SD=0.69). For the subgroup of 58 ABC1936 subjectswho took part in the present fMRI study, this ‘baseline’ inspectiontime session had familiarized participants with the task before brainimaging, which took place an average of 14 months afterwards. Theinspection time test was constructed, run, and analyzed using E-Prime1 [Psychology Software Tools (PST), Pittsburgh, PA]. Thestimulus lines were 5 cm for the longer line and 2.5 cm for the shorterline. They were joined at the top with a 2.5 cm crossbar. The lineswere about 1.6 mm wide. The backward mask was constructed of ajumble of vertical lines 1.6 mmwide that overwrote the vertical linesin the stimulus (Fig. 1). Participants were seated comfortably, withtheir eyes about 75 cm from a computer screen. The cue lasted500 ms, and there was a blank interval of 800 ms between cue offsetand stimulus onset. Ten trials were presented at each of 15 durations(rounded to the nearest millisecond): 6, 12, 19, 25, 31, 37, 44, 50, 62,75, 87, 100, 125, 150, and 200. The backward mask lasted 500 ms.All stimuli were presented on a computer screen running at a vertical

1 The program was written by Michael Allerhand to a design by IanDeary.

refresh rate of 170 Hz. On each trial, after mask offset, participantsindicated the position of the longer line by pressing 1 (with the indexfinger of the right hand for ‘left’) or 2 (with the middle finger of theright hand for ‘right’) on the number pad of a computer keyboard.

Brain imaging inspection time testingParticipants who volunteered for the present functional imaging

study had, therefore, previously undertaken the inspection time task.Immediately before brain imaging the participants practiced theinspection time task again to make sure they were completelyfamiliar with the task demands. Two further inspection time sessionstook place in theMRI scanner (imaging inspection time test sessions1 and 2). Inspection time was again assessed using a two-alternative,forced choice procedure. The procedure was designed to be assimilar to that described inDeary et al. (2004a) as possible. Themaindifference from the study of Deary et al. in younger subjects was thatthe older subjects in the present study had the additional task-reminder session prior to performing the task in the fMRI setting,with about 15 min of practice to refresh them concerning the test'srequirements. In the imaging inspection time sessions, twenty trialswere presented at each of eight durations: 6, 12, 25, 37, 50, 75, 100,and 150. Paradigms were programmed in Presentation (Neurobe-havioral Systems Inc., CA) with instructions and stimuli beingpresented visually on the computer monitor and viewed via themirror on the head coil. The eye-to-screen distance was about 5 m,and the dimensions of the stimuli were increased by a factor of fourcompared with the baseline task. Visual acuity was assessedimmediately before scanning and corrected with MR compatiblelenses as necessary. Participants were provided with pushbuttonunits to allow their responses to be logged by the software.Participants indicated the position of the longer line by pressing akey with the left index finger (for ‘left’) or a key with the right indexfinger (for ‘right’). The same optimal ISI sequence was used for allsessions. The same random sequence of stimulus durations waspresented to all subjects.

Brain imaging

Scanning was performed on a 1.5-T GE Signa NVi scanner(General Electric Healthcare, Milwaukee, WI), using the standardhead coil. Participants began and ended the fMRI imagingsession with inspection time tests (160 trials in each). Between thetwo inspection time tests, a working memory task was carried out(N-back; results reported separately). Following the secondinspection time task a T1-weighted structural scan was acquired.Contiguous T2⁎-weighted gradient-echo echo-planar images (EPI)were acquired in the axial orientation with TR/TE of 2500/40 ms,matrix 64×64, field of view of 24 cm2, thickness of 5 mm, 30 slicesper volume. In total, 292 volumes per fMRI test were collected, ofwhich the first 4 volumes were discarded. The total scanning timeper fMRI test was 12 min and 10 s.

Analysis

Post-processing was performed off-line on a workstation usingSPM2 (http://www.fil.ion.ucl.ac.uk/spm/), a suite of programsrun within the MATLAB environment. Firstly correction for theacquisition delay between slices was applied. Intrasubject registra-tion was then performed by aligning all volumes of each session tothe first volume of that session using a 6 parameter rigid body linearregistration algorithm. Normalisation to the standard SPM2 EPI

http://www.fil.ion.ucl.ac.uk/spm/

Table 1Mean (SD) of participant characteristics

Whole ABC1936sample at wave 3

Sustainers Decliners Difference between sustainersand decliners (Cohen's d units)

Moray House Test score at age 11 103.7 (13.1) 109.3 (6.6) 102.1 (6.0) 0.55 a

Raven's Progressive Matrices 37.8 (7.6) 46.2 (3.9) 29.1 (3.8) 2.25 a

Total number of correct responses (out of 320) in the twoimaging inspection time sessions

– 230.5 (14.2) 218.6 (17.2) 0.75

Age at scanning (years) – 69.8 (0.6) 69.8 (0.4) 0

a This is based on the standard deviation of the whole ABC1936 sample, not on the standard deviations of the subsamples.

Fig. 2. a. Relation between accuracy and stimulus duration in the baselineinspection time task. b. Relation between accuracy and stimulus duration forsustainers and decliners averaged over both imaging inspection time tests.


template was achieved by intersubject registration using a 12parameter affine transformation, warping each participant's imagesinto a standard template with a voxel size of 2×2×2 mm. Finally,Gaussian smoothing was applied, at 6 mm FWHM.

Functional activity was modeled using one regressor for trialswith correct responses and one for trials with incorrect responses. Allthe regressors were obtained by convolving the vector of stimulusonsets with a canonical hemodynamic response as defined in SPM2.This resulted in 16 predictors of brain activity for each experimentalrun, 8 for the inspection time trials with correct responses and 8 forthe inspection time trials with incorrect responses. Head movementestimates were included in each model and data were filtered in thetime domain using a high-pass filter (cut off period=150 s).Proportional scaling was used to correct for inter volume fluc-tuations in signal. Following the estimation of the model thefollowing analysis was performed. The analyses replicated that ofDeary et al. (2004a), where regions of positive and negativecorrelation of brain response with inspection time difficulty weredetermined by computing a linear weighting of all eight stimulusdurations used in the imaging inspection time test sessions. Thecomputation of contrasts was limited to the eight predictors of brainactivity associated with inspection time trials with correct responses.For each participant, we averaged the two contrast maps for theimaging inspection time sessions; the average maps were entered ina second-level random-effects analysis. The cohort, as a whole, wasassessed with a one-sample t-test, and group differences wereassessed with an independent samples t-test.

Taking the coordinates of two regions reported by Deary et al.(2004a; 1: x=0, y=−60, z=36; 2: x=−42, y=12, z=−2) we alsocomputed functional connectivity maps (Friston, 1996); that is,maps of cross-correlation coefficients between a specific brainregion, the seed of the map, and any other brain voxel.We calculatedcross-correlation coefficients following the method described byDeary et al. (2004a) summarized below. The cross-correlationcoefficients were computed on the residuals (in time) after fitting thesame model that was used for functional localization; the raw timecourses were first extracted, corrected for changes in global signal,filtered in time, and fitted to the model so that the residuals afterfitting could be computed. Cross-correlation maps were computedseparately for each subject, for each fMRI test and for each seed;maps were transformed using the Fisher r-to-z transform thattransforms cross-correlation coefficients into variables with ap-proximately Gaussian distributions. Cumulative distributions foreach of the maps were fitted to a normal curve (Hampson et al.,2002; Lowe et al., 1998) to estimate the mean, SD, and area of thecurve and to remove the fitted mean from the data. For each subjectand for each seed, the two z-maps were averaged and smoothed witha Gaussian kernel of 6×6×6 mm3. Regionally specific effects werecomputed by entering all the corrected maps in a simple t-test

analysis in SPM2. Each seed was analyzed separately. The group asa whole and the sustainer and decliner groups were analyzed.

In inspection time testing, very brief durations have near-tochance responding, and the longer durations are associated withalmost perfect responding. Durations between the extremes show alinear increase in performance with increasing duration. Therefore,we re-examined the locations of brain activation firstly showingeither a signal increase or decrease as a function of task difficultyand secondly as a correlation of magnitude of event-related activitywith age 11 IQ, including only the inspection time durations of 25,37, 50, and 75 ms. We also looked at the main effect of the BOLDresponse for inspection time durations of 6, 12, 100 and 150 msand 25, 37, 50, and 75 ms separately. The first-level analysis wasentered as explained above. A GLM included separate predictors


for each inspection time duration and, within each of the durations,there were separate predictors for trials with correct and with non-correct responses. After estimating the statistical model, a contrastof the parameter estimates was entered, which was either weightedin a linear fashion over the predictors for inspection times 25, 37,50, and 75 ms or a constant for the trials with correct response.

To test the hypothesis that older people with comparativelysuccessful ageing are more similar to young people than olderpeople with comparatively unsuccessful ageingwe retrieved the datafrom the young cohort from the Deary et al. (2004a) paper andreanalyzed it using SPM2 following the same procedures as detailedabove. We then performed group analysis as detailed above.

Clusters were defined as a contiguous group of voxels passing asignificance criterion of pb0.05 (corrected for multiple compar-isons, extent threshold k=400 voxels, with an uncorrected voxelsignificance level of pb0.01). Coordinates are quoted in standardTalairach and Tournoux space following application of a conversionfactor (http://www.mrc-cbu.cam.ac.uk/Imaging/mnispace.html),from the MNI space employed by SPM. Regional designation ofgrey matter differences was determined by the Talairach Daemon(Lancaster et al., 1997, 2000) and confirmed by comparison of localanatomy with a standard atlas (Ono et al., 1990).

Results

Behavioral data

Of the 58 members of the ABC1936 group invited for fMRIscanning 13 fell below a pre-determined threshold of 18 correct

Fig. 3. Relationship between BOLD activation and a linear derivative of stimulus dthe cohort as a whole: Middle row, sustainers: Bottom row, group difference betweenRed denotes increase; green, decrease.

responses out of a total of 20 for the 150 ms duration in bothscanning sessions. This included 5 sustainers and 8 decliners.Those that did pass the threshold included a pre-defined sustainergroup containing 25 participants (11 female), and a pre-defineddecliner group containing 15 participants (9 female) (Table 1).There was no significant difference in age at scanning between thesustainers and decliners (p=0.71) (Table 1). There was acomparatively modest difference in Moray House Test IQ scoresat age 11 between the two groups (p=0.012, Cohen's d=0.55), buta much larger difference in Raven's Progressive Matrices at aboutage 70 (p=0.001; Cohen's d=2.25) (Table 1).

The mean psychometric curve for the baseline inspection timetest is shown in Fig. 2a, for sustainers and decliners. This shows, asexpected, responses at almost chance at the shortest durations,increasing to an almost perfect responses above 75 ms. The meanpsychometric curves for the two groups are similar; these baselineinspection time data contain fewer items per stimulus duration thanthe task performed within the brain scanner, making the means ateach stimulus more prone to error. The curves for the inspectiontime tasks undertaken within the MR scanner are shown in Fig. 2b.Sustainers had more correct responses overall in the fMRIinspection time sessions (Table 1). Performing a two-way mixedANOVAwith group (sustainers or decliners) as the fixed effect andinspection time duration as the repeated measure revealed asignificant group difference (Wilk's Lambda=0.844, F=5.37,p=0.028). The interaction of group (sustainers and decliners)and stimulus duration had a borderline significant p value (Wilk'sLambda=0.578, F=2.40, p=0.053; see Fig. 2b). However, thisinteraction includes very brief and very long stimulus durations

uration in the inspection time task (correctly answered trials only). Top row,sustainers and decliners (no significant relationship was found for decliners)

http://www.mrc-cbu.cam.ac.uk/Imaging/mnispace.html

Table 2Location of brain activation foci for the cohort as a whole showing either a signal increase or decrease as a function of task difficulty (correctly answered trials only)

Talairach coordinates (XYZ) Hemisphere Region BA Cluster p value (corrected) Voxel t value Extent

Positive correlation10, 33, 33 Right Medial frontal gyrus 9 0.000 5.71 18856, 24, 47 Right Medial frontal gyrus 8 5.608, 16, 47 Right Medial frontal gyrus 6 5.60−36, −2, 30 Left Precentral gyrus 6 0.003 4.66 880−46, 23, −5 Left Inferior frontal gyrus 47 4.13−53, 5, 18 Left Inferior frontal gyrus 44 3.78

Negative correlation48, −72, 0 Right Inferior temporal gyrus 19 0.001 6.78 96934, −61, −14 Right Fusiform gyrus 37 3.6644, −55, −11 Right Fusiform gyrus 37 3.45−4, −50, 17 Left Posterior cingulate 30 0.000 5.13 145912, −42, 8 Right Posterior cingulate 29 4.664, −50, 14 Right Posterior cingulate 30 4.60−30, −84, 21 Left Middle occipital gyrus 19 0.050 4.97 524−32, −80, 39 Left Precuneus 19 4.31−51, −67, 25 Left Middle temporal gyrus 39 4.02


that would not be expected to show significant differences.Separate analyses of individual durations on the more informativepart of the psychometric curve (Fig. 2b) showed significantly(based on Student's t-test) lower accuracy for decliners than forsustainers: 50 ms, p=0.002; 75 ms, pb0.001; 100 ms, pb0.001.Durations of 6 ms, 12 ms, 37 ms and 150 ms showed no significantdifferences, but these are similarly close to chance or perfectresponding in both groups.

Individual differences in inspection performance across thegroup as a whole were stable between baseline and both imaginginspection time tasks (baseline versus inspection time imagingsession 1, r=0.98, pb0.001; baseline versus inspection timeimaging session 2, r=0.99, pb0.001). There was a trend toward asignificant partial correlation (r=0.43, p=0.06) between Raven'sscore nearest to the time of the fMRI brain imaging session, and thetotal number of correct responses during the fMRI inspection timetest, keeping Moray House Test score at age 11 fixed.

Table 3Location of brain activation foci for sustainers showing either a signal increase or

Talairach coordinates (XYZ) Hemisphere Region

Positive correlation6, 16, 49 Right Superior frontal gyrus−2, 18, 53 Left Superior frontal gyrus−8, 12, 55 Left Superior frontal gyrus36, 30, 24 Right Middle frontal gyrus40, 33, 32 Right Middle frontal gyrus34, 42, 29 Right Middle frontal gyrus

Negative correlation46, −66, 33 Right Angular gyrus51, −72, 5 Right Middle occipital gyrus40, −87, 6 Right Middle occipital gyrus−6, −50, 14 Left Posterior cingulate10, −44, 8 Right Posterior cingulate2, −37, 30 Right Cingulate gyrus−42, −79, 8 Left Middle occipital gyrus−28, −81, 41 Left Precuneus−40, −70, 33 Left Precuneus

Correlation of BOLD brain activity in response to inspection timetask difficulty

The results of the random-effects analysis, which included bothof the fMRI inspection time test sessions for all of the 40 partici-pants, are shown in Fig. 3. For the group as a whole, we calculatedBOLD activity for correctly answered trials only, yieldingsignificant positive and negative correlation with inspection timestimulus duration in various brain regions (Table 2, Fig. 3). Frontalareas were associated with a positive correlation between activationand inspection time difficulty (shorter stimulus durations) includingthe medial frontal gyrus and left inferior frontal gyrus. Posteriorregions were associated with a negative correlation betweenactivation and inspection time difficulty, including the right inferiortemporal gyrus, posterior cingulate, and left middle occipital gyrus.

Investigating the sustainer and decliner groups individually, it wasfound that the decliners did not show any areas of significant positive

decrease as a function of task difficulty (correctly answered trials only)

BA Cluster p value (corrected) Voxel t value Extent

8 0.000 5.24 11636 4.906 4.779 0.049 4.90 4469 4.299 3.21

39 0.000 6.66 137137 5.5419 5.5330 0.000 5.45 146829 5.3131 4.4119 0.000 4.55 98219 4.4839 4.47

Table 4Location of brain activation foci for regions showing activation greater in sustainers than in decliners as function of task difficulty (correctly answered trials only)


4, 41, −5 Right Anterior cingulate 32 0.041 5.81 5452, 47, 5 Right Anterior cingulate 32 4.4010, 35, 0 Right Anterior cingulate 24 3.41


or negative correlation whereas the sustainers (Table 3, Fig. 3)maintained the pattern shown by the whole group. Performing astatistical analysis of the group differences (Table 4), a region in theanterior cingulate was found to show greater BOLD activation in thesustainers than in the decliners. No regions of significant differencewere found for the comparison of decliners greater than sustainers.

Comparing the sustainer and decliner groups to the young groupobtained from the analyses described in Deary et al. (2004a,b), it wasfound that the sustainers did not show any areas of significantdifference compared with the young group, whereas the declinersshowed a number of regions of reduced BOLD activation whencompared with the young group (Table 5, Fig. 4).

Results including only the 25, 37, 50, and 75 ms stimulus durations

Correlation of the magnitude of the event-related activity withstimulus duration, considering only the durations of 25, 37, 50, and75 ms, with age 11 IQ as a covariate, resulted in no significantactivity at the pb0.05 level. This was a random-effects analysis,corrected for multiple comparisons, extent threshold k=400 voxels,with an uncorrected voxel significance level of pb0.01. Comparisonof the main effect of the BOLD response during the inspection timedurations of 25, 37, 50, and 75 ms showed no appreciable differencein activity pattern with the BOLD response during the inspectiontime durations of 6, 12, 100 and 150 ms (see Supplementary Figs. 1and 2).

Functional connectivity

A functional connectivity analysis was performed to discoverthose brain regions in which the activation was correlated with twoseed loci. The seed loci were those reported by Deary et al. (2004a).Activation, for the cohort as a whole, in the volume of interestsurrounding the precuneus (x=0, y=−60, z=36) was correlatedpositively with a region on the right superior frontal gyrus, BA10,and bilaterally in the thalamus. Negative correlations were found inthe right middle temporal gyrus, right pre-central gyrus and left

Table 5Location of brain activation foci for regions showing activation greater in the youtrials only)


4, 12, 45 Right Medial frontal gyrus0, 16, 51 Left Superior frontal gyrus0, 21, 38 Left Cingulate gyrus44, 12, 1 Right Insula32, 22, 6 Right Insula40, 15, −13 Right Inferior frontal gyrus4, −81, 2 Right Lingual gyrus20, −64, 3 Right Lingual gyrus−2, −89, −1 Left Lingual gyrus

middle frontal gyrus (Table 6, Fig. 5). Investigating the sustainergroup separately, positive correlations were found in the rightangular gyrus, left middle temporal gyrus, and right medial frontalgyrus. Negative correlations were found in the right fusiform gyrus,left pre-central gyrus, left superior frontal gyrus and the cerebellumbilaterally, (Table 7, Fig. 5). The decliners showed positive cor-relations in the right angular gyrus and left middle temporal gyrus,with negative correlation in the left middle temporal gyrus, (Table 8,Fig. 5).

Activation, for the cohort as a whole, in the volume of interestsurrounding the left anterior insula (x=−42, y=12, z=−2) wascorrelated positively with the right inferior frontal gyrus and rightcingulate gyrus. Negative correlations were found in the left posteriorcingulate, right lingual gyrus, left middle occipital gyrus, right middlefrontal gyrus, left parahippocampal and the left fusiform gyrus(Table 9, Fig. 6). Positive correlations for the sustainer group werefound in the right anterior insula, and the left anterior cingulate(Table 10, Fig. 6). Negative correlations were found in the leftposterior cingulate left cerebellum, right parahippocampal gyrus, leftcuneus, left fusiform gyrus and bilaterally in the middle occipitalgyrus (Table 11, Fig. 6). Positive correlations for the decliner groupwere found in the left insula, right inferior frontal gyrus and the righttransverse temporal gyrus. No significant negative correlations werefound.

Discussion

In this group of older individuals, inspection time performanceshowed the expected stimulus duration versus accuracy association(Deary et al., 1993), and showed the expected association withmentalability test scores (Grudnik andKranzler, 2001). The group as a wholeshowed a clear pattern of BOLD activation and deactivation inassociation with stimulus difficulty. For correct responses, processingmore relatively difficult stimuli (shorter durations) involved relativelygreater activation in the medial, pre-central and inferior frontal gyri,and relatively more deactivation in a number of more posteriorregions.

ng group than in decliners as function of task difficulty (correctly answered


6 0.001 5.66 10698 4.7132 4.1213 0.007 4.83 74213 4.7447 4.0818 0.030 3.76 56619 3.4418 3.40

Fig. 4. Regions of significant difference in BOLD activity comparing the young cohort with cognitive decliners.


The pattern was similar in the subgroup known as the cognitivesustainers, but there were far fewer significant regions in thecognitive decliners. The subgroup of cognitive sustainers showedsignificantly more BOLD activation in the anterior cingulate regionthan the relative decliners. The functional connectivity maps showeda similar pattern, with more extensively associated regions in thegroup of cognitive sustainers versus decliners.

The patterns of association between stimulus duration and BOLDactivation and deactivation were similar to those reported for the sameinspection time task in healthy young individuals, who werecognitively able (Deary et al., 2004a). Co-incident in both studies,the region showing the largest activation in response to a decrease ininspection time stimulus duration in the cohort as a whole was themedial frontal gyrus region (cluster maximum x=10, y=33, z=33),described as the “LH Rostral SMA” (cluster maximum x=−2, y=18,z=54) in Deary et al. (2004a). This region extends into the anteriorcingulate gyrus also. Furthermore, we observed an increase in activityin the left pre-central gyrus extending into the ventral part of theanterior insula, co-incident with the region described as “LH ventralanterior insula” (cluster maximum x=−42, y=12, z=−2) in Dearyet al. (2004a). The region that shows the largest activation in responseto an increase in inspection time stimulus duration was the rightinferior temporal gyrus (cluster maximum x=48, y=−72, z=0). Thisregion is co-incident with the “RH inferior temporal gyrus” (clustermaximum x=48, y=−62, z=−2) fromDeary et al. (2004a). A secondregion, the middle occipital gyrus (cluster maximum x=−30, y=−80,z=39) extending into the middle temporal gyrus overlaps the “LHmiddle temporal gyrus” region listed in Deary el al. (2004a). Thesustainers followed a similar pattern of overlap with the young group,contrasting with the decliners who did not demonstrate any regionswith a significant correlation between activity and stimulus duration.Reducing the threshold of the displayed results to pb0.05,uncorrected for multiple corrections at the voxel level, (Supplemen-tary Figs. 3 and 4) shows that the decliners have an activation pattern

Table 6Functional connectivity for cohort as a whole: regions with positive and negative


Positive4, −64, 36 Right Precuneus8, 56, −1 Right Superior frontal gyrus−8, −19, 6 Left Thalamus10, −19, 8 Right Thalamus−14, −1, 11 Left Thalamus

Negative40, −4, −32 Right Middle temporal gyrus53, −1, 48 Right Precentral gyrus−40, 42, 27 Left Middle frontal gyrus

that is markedly different from the sustainers with reduced regions ofpositive correlation between BOLD activation and IT duration in thefrontal and parietal regions and reduced regions of negativecorrelation between BOLD activation and IT duration in the occipitalregions. There are regions of overlap between groups and regions,particularly in the orbito-frontal cortex, where the decliners have apositive correlation with IT duration that the sustainers do not. Itshould be noted however, that this description contains non-significant clusters and these differences are only reported to moreclearly demonstrate the differences between the groups.

Functional connectivity with a seed in the precuneus resulted inpositive correlations in an extensive posterior region surrounding theprecuneus (cluster maximum x=4, y=−64, z=36) extendingbilaterally into the temporal lobes. This region is co-incident withthe results reported in the young persons' study of Deary et al.(2004a), who reported regions in the “Precuneus” (cluster maximumx=0, y=−64, z=36), “RH mid temporal gyrus” (cluster maximumx=52, y=−62, z=22) and “LH mid temporal gyrus” (cluster maxi-mum x=−54, y=−68, z=26). Negative correlations were found inan extensive frontal region (cluster maximum x=53, y=−1, z=48)that was co-incident with the “RH medial frontal gyrus” (clustermaximum x=26, z=−4, z=40), “LH insula” (cluster maximum x=−34, y=−2, z=26) and “RH mid frontal gyrus” (cluster maximumx=16, y=−8, z=66) regions reported by Deary et al. (2004a). Thepositive correlation in the precuneus and bilateral temporal lobes ispresent in both sub groups. However, it is greatly reduced in thedecliners group compared with the sustainers group. The secondseed point was placed in the left anterior insula and showed positivecorrelation in the right insula/inferior frontal gyrus (cluster maxi-mum x=40, y=23, z=−6) and the right cingulate gyrus (clustermaximum x=8, y=−14, z=27). The bilateral positive correlations inthe insula were also found in the young cohort reported by Dearyet al. (2004a) (cluster maxima x=−42, y=12, z=2, and x=38, y=0,z=−2), as well as a “RH inferior frontal gyrus” co-incident with the

correlations with seed ‘0 −60 36’ (pb0.05, kN10) (precuneus)

BA Cluster p value (Corrected) Voxel t value Extent

7 0.00 27.00 14,93310 0.00 9.09 1040MDN 0.00 7.56 59MDN 0.00 6.91 45VAN 0.00 6.30 16

21 0.00 8.84 72136 0.00 8.34 13,34346 0.02 6.26 396

Fig. 5. Functional connectivity maps. Top row, all participants: Middle row, sustainers: Bottom row, decliners. Seed in precuneus (x=0, y=−60, z=36). Redshows positive correlation, green shows negative correlation.


cluster right insula/inferior frontal gyrus region (cluster maximumx=40, y=23, z=−6).

Therefore, while performing a psychophysical task that deterio-rateswith ageing—andwhich can account for the association betweenage and higher cognitive functions (Nettelbeck and Rabbitt, 1992)—older individuals whose non-verbal reasoning ability has remainedrelatively intact with age have a BOLD activation–deactivationpattern that closely resembles those of healthy, cognitively able,younger individuals. On the other hand, the subgroup whose non-

Table 7Functional connectivity for sustainers: regions with positive and negative correlat

Talairach coordinates (XYZ) Hemisphere Region B

Positive6, −53, 38 Right Precuneus 751, −65, 29 Right Angular gyrus 3−48, −59, 25 Left Middle temporal gyrus 36, 52, −9 Right Medial frontal gyrus 12, 53, 19 Right Medial frontal gyrus 9

Negative44, −37, −8 Right Fusiform gyrus 3−63, −12, 30 Left Precentral gyrus 4−18, −12, 71 Left Superior frontal gyrus 626, −52, −31 Right cerebellum Cerebellar tonsil−2, −8, −11 Left brainstem M6, −40, −23 Right cerebellum16, 7, 62 Right Superior frontal gyrus 6−34, −41, 4 Left Caudate C

verbal reasoning ability has declined with age showed a far lessextensive pattern of associations. This might be taken to indicate thattheir neural networks underlying efficient information processing areless intact than those who showed successful cognitive ageing. Theregion that was significantly different in BOLD activation betweenthe sustainer and decliner subgroups—the anterior cingulate gyrus—was an area in which BOLD activation was significantly associatedwith stimulus difficulty level in younger people (Deary et al., 2004a).Of course, these differences in old age might not have their origins

ions with seed ‘0 −60 36’ (pb0.05, kN10) (precuneus)

A Cluster p value (corrected) Voxel t value Extent

0.00 24.80 63599 0.00 15.70 10199 0.00 12.80 12610 0.00 8.59 84

0.00 8.35 155

7 0.00 9.16 6800.00 7.35 3200.00 6.80 29890.00 5.84 460

ammillary body 0.00 5.81 4200.00 5.49 3580.00 5.23 498

audate tail 0.00 5.06 349

Table 8Functional connectivity for decliners: regions with positive and negative correlations with seed ‘0 −60 36’ (pb0.05, kN10) (precuneus)


Positive−2, −55, 36 Left Precuneus 7 0.00 16.10 234246, −61, 33 Right Angular gyrus 39 0.00 9.72 88−40, −59, 23 Left Middle temporal gyrus 39 0.00 9.69 64

Negative−44, −3, −18 Left Middle temporal gyrus 21 0.03 9.18 97


solely in grey matter. It is possible that the differences in activationpattern are in part caused by differences in connectivity which haveorigins in the integrity of the white matter tracts connecting therelevant regions (Deary et al., 2006a; Charlton et al., in press). Thedifferences in functional connectivity patterns observed betweensustainers and decliners may provide supporting evidence thatreduced integrity of white matter may result in reduced cognitivecapacity. Deary et al. (2004a) states that “a functional network—consisting fronto-opercular and intrasylvian areas in coordinationwith the medial frontal gyrus and the anterior cingulate cortex—reflects effort-related process necessary to deal with attempting toprocess a visually degraded percept. A posterior network of moresensory-related regions in conjunction with associative regions mayreflect the optimal processing of a visual discrimination task thatrequires high processing demands and combines several fundamentalcognitive domains (attention, working memory, imagery, andvision)”.

The results using only the 25, 37, 50, and 75 ms stimulusdurations and correcting for the age 11 IQ show no significantclusters, indicating that differences in the way in which individualsperform this task might be explained by their childhood abilities.This suggests an alternative explanation for our findings. Sincethere is a small but significant difference between the decliners andsustainers, with the sustainer scoring higher on the age 11 IQ test, itis possible that the differences observed are pre-existing or resultfrom life long experiences brought about by the advantage of ahigher IQ in childhood. The findings in younger people (Dearyet al., 2004a) were produced with a sample described as ‘cog-nitively able’ and drawn from the student body of faculty at theUniversity of Edinburgh. The older sample is drawn a broaderpopulation and it may well be that our findings are brought about

Table 9Functional connectivity for cohort as a whole: regions with positive and negative


Positive−42, 9, −6 Left Insula40, 23, −6 Right Inferior frontal gyrus8, −14, 27 Right Cingulate gyrus

Negative0, −52, 17 Left Posterior cingulate6, −80, −8 Right Lingual gyrus−46, −76, −6 Left Middle occipital gyrus34, 33, 46 Right Middle frontal gyrus−16, −18, −9 Left Parahippocampal gyrus−34, −55, −4 Left Parahippocampal gyrus−36, −80, −14 Left Fusiform gyrus

by different levels of original ability level, or perhaps thedifferences in socio-economic advantage with which these areassociated, rather that different degrees of cognitive ageing per se.Investigation of this possibility is not within the scope of ourcurrent work but is a possible future direction for investigation.

A strength of the present studywas the availability of informationon childhoodmental ability. Thus, wewere in the unusual position ofbeing able to form subgroups with relatively successful andunsuccessful cognitive ageing based on truly premorbid mentalability. The subgroups comprised people with quite similar cognitivefunction in youth who diverged markedly later in life. It should benoted that the cognitive test scores at age 11 were not identical. Atage 11, the sustainers had higher test scores than the decliners.However, with respect to the whole ABC1936 sample distribution,the difference in childhood mental ability was not large (0.55 SDunits), whereas the difference in old age was much larger (2.25 SDunits). In the inspection time sessions conducted in the fMRI setting,the subgroup of cognitive decliners had significantly poorerinspection time performance on the durations associated with theinformative, ascending part of the psychometric curve. However, forboth subgroups we used the BOLD data only to correct responses. Itis also noted that the decliner subgroup was smaller in number thanthe sustainers, so it is possible that some of the lack of clearassociations between BOLD activation and stimulus duration mighthave been due to less statistical power in this subgroup. Thispossibility was investigated. A bootstrap analysis was performed,limited to a number of random subsamples of 15 participants fromthe sustainer group. The pattern of activation present in the wholesustainer group was maintained (data not shown). Therefore, thesmaller size of the decliner group is not responsible for thedifferences in activation–deactivation patterns demonstrated.

correlations with seed ‘−42 12 −2’ (pb0.05, kN10) (left anterior insula)


13 0.00 32.10 15,83547 0.00 18.80 10,73723 0.00 6.48 39

23 0.00 11.30 278618 0.00 7.58 153919 0.00 7.17 348 0.02 6.22 1035 0.01 5.87 1919 0.01 5.56 1619 0.01 5.51 12

Fig. 6. Functional connectivity maps. Top row, all participants: Middle row, sustainers: Bottom row, decliners. Seed in left anterior insula (x=−42, y=12, z=−2). Red shows positive correlation, green shows negative correlation.


These data on the differential functional anatomy of processingspeed in older people with successful and less successful cognitiveageing add new information to the processing speed theory ofcognitive ageing, making it less dependent on the statistical analysesof associations among psychometric tests (cf. Salthouse, 1996;Zimprich and Martin, 2002). These data on BOLD activationpatterns also require to be integrated with complementary data onageing cohorts which suggest that white matter integrity is asso-ciated with successful cognitive ageing (Leaper et al., 2001; Dearyet al., 2003; Charlton et al., in press), and that the effect of white

Table 10Functional connectivity for sustainers: regions with positive and negative correlat


Positive−42, 9, −6 Left Insula38, 18, 3 Right Insula−2, 35, −5 Left Anterior cingulate

Negative−2, −40, 15 Left Posterior cingulate−8, −79, −18 Left cerebellum Declive−8, −99, 10 Left Cuneus−26, −53, −19 Left Cerebellum Culmen30, −37, −5 Right Parahippocampal gyrus22, −101, 7 Right Middle occipital gyrus0, −82, 39 Left Cuneus30, −56, 1 Right Parahippocampal gyrus−46, −80, −1 Left Middle occipital gyrus−26, −78, −1 Left Lingual gyrus−36, −53, −6 Left Fusiform gyrus

matter integrity on general intelligence in old age is mediated viaprocessing speed as measured using reaction time (Deary et al.,2006a). However, some have found that white matter lesions areassociated with impaired processing speed and executive function inold age, but not intelligence more generally (Rabbitt et al., 2007).Also relevant is the finding that inspection time and higher cognitivefunctions share genetic variance (Luciano et al., 2001, 2004). Takentogether with the finding that there are genetic and environmentalcontributions to cognition in old age (Deary et al., 2006b), thereshould be further investigation into the genetic and environmental

ions with seed ‘−42 12 −2’ (pb0.05, kN10) (left anterior insula)


13 0.00 27.60 824413 0.00 14.60 503032 0.00 8.07 334

29 0.00 10.80 9860.00 10.10 1916

18 0.00 10.10 3840.00 9.51 158

36 0.00 9.12 4118 0.00 8.69 7519 0.00 8.19 6230 0.00 7.64 5119 0.00 7.64 2318 0.01 7.23 1137 0.01 6.91 11

Table 11Functional connectivity for decliners: regions with positive correlations with seed ‘−42 12 −2’ (pb0.05, kN10) (left anterior insula)


Positive−40, 4, 2 Left Insula 13 0.00 21.70 396330, 23, −8 Right Inferior frontal gyrus 47 0.00 16.20 142046, −23, 14 Right Transverse temporal gyrus 41 0.00 9.12 27


foundations of processing speed, its change with age, and the degreeto which the same genetic and environmental influences affect brainfunction and structure.

The present data address the wider issue of whether, across anumber of mental tasks and domains, older people activate similar ordifferent, andmore or less distributed, neural networks. Theymake acontribution to discovering whether, in the ageing brain, there is aprocess of compensatory reorganization or dedifferentiation (Zarahnet al., 2007). They suggest that less successful cognitive ageing isassociated with a process of dedifferentiation, because the load-dependent BOLD activation in people with successful cognitiveageing was more like that of younger people than in the relativecognitive decliners. Supplementary Figs. 1 and 2 (available online)show positive and negative correlations respectively betweeninspection time duration and BOLD activity in the sustainers anddecliners at a statistical threshold of pb0.05 uncorrected for multiplecomparisons. These figures demonstrate an activity pattern that,although including non-significant clusters, demonstrates functionalbrain regions that are non-identical in the decliners compared withthe sustainers. However some cautions are necessary. First, noyoung subjects with cognitive testing were included in the presentstudy; therefore, conclusions concerning the compensation ordedifferentiation issue are made with caution. Second, such a resultapplies to load-dependent aspects of inspection time, and does notnecessarily apply either to other aspects of the task or to other mentaltests. Zarahn et al., (2007) found that, even within a single task,different task parameters elicited brain activation data that supporteddifferent ageing hypotheses. Another example exemplifies thisfurther. In a PET activation study investigating source memory,Cabeza et al. (2002) concluded that “young adults and low-performing older adults recruited similar right PFC (pre-frontalcortex) regions, whereas high-performing older adults engaged PFCregions bilaterally. These results suggest that low-performing olderadults recruit a similar network of brain regions as young adults butuse them inefficiently, whereas high-performing older adultscounteract age-related neural decline by reorganizing brain func-tions”. This, although superficially opposite to the findings of thepresent study, suggests that there may be a reduction in low levelprocessing occurring in age-related cognitive decline. This, althoughinvolving a different mental task and different imaging technology,adds support to the results shown here where so-called cognitivedecliners show a reduced activation pattern in response to aninspection time task whereas the cognitive sustainers show a patternsimilar to a young cohort.

A third caution concerns the design of the present study. In thetheoretically-driven study by Zarahn et al. (2007), which used adelayed item-recognition task, fMRI-derived brain activation wascompared quantitatively (using canonical variates analysis) in theyounger and older individuals. In the present study, the quantitativecomparison was between groups of older people with uniquelyvaluable data on cognitive change between childhood and old age,whereas the comparisons between these older groups and the

younger sample were made by visual inspection and comparison ofbrain coordinates. Thus, different study designs, none of which iscomprehensive, can be used to test hypotheses about age-relatedchanges in brain function.

The present results on normal cognitive ageing have implicationsfor pathological cognitive ageing. The identification of the earliestchanges in Alzheimer's disease is now at a critical stage in clinicalneuroscience. Longitudinal studies of volunteers initially withoutcognitive change, but at increased risk of dementia on grounds of ageor family history suggest that global dementia is preceded by anenduring stage of progressive cognitive decline. Cliniconeuropatho-logical correlative studies show that diffuse and sometimesadvanced pathology, extending beyond the medial-temporal lobeis present during this ‘pre-clinical phase’ of dementia. The earlyidentification of prodromal features of dementia at a stage that mightbe arrested or even reversed by novel therapies, though ambitious,might be attainable. Such clinical achievements will need to bebased on the fundamental psychological and biological foundationsof the transformation from cognitive ageing into dementia. Thus, assuggested by Bonney et al. (2006), processes such as inspection timerequire more investigation as the heralds of imminently worseningcognitive decline in old age.

In conclusion, age-related cognitive change in humans, despitebeing well described, is poorly understood. A prominent idea, withsome strong empirical support, is that the slowing of informationprocessing speed has an important, quite general, part to play in howthe brain performs as people grow older. The biological foundationsof the age changes in processing speed are poorly understood. Thepresent study adds new findings. The older people we have defined,based on complex tests of reasoning, as cognitive sustainers are thegroup whose brains resemble younger people's during a visualinformation processing task. That is, older people whose non-verbalreasoning is relatively well retained, based on their childhoodmentalability, show similar neural networks underlying speed of informa-tion processing to those of younger individuals.

Acknowledgments

We thank Enrico Simonotto for retrieving and supplying uswith the raw data from the study by Deary et al. (2004a,b). Thestudy was supported by a grant from the Alzheimer Research Trust.Earlier waves (1 and 2) of data collection on the Aberdeen BirthCohort 1936 mentioned here were supported, respectively, by theUK's Biotechnology and Biological Sciences Research Counciland the Wellcome Trust. Ian Deary is the recipient of a RoyalSociety-Wolfson Research Merit Award.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.neuroimage.2008.02.045.

http://dx.doi.org/doi:10.1016/j.neuroimage.2008.02.045


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Is retaining the youthful functional anatomy underlying ... · The functional anatomy of efficient information processing with the inspection time task has been studied, to date,