Neuroanatomical correlates of selected executive functions in middle-aged and older adults: a prospective MRI study

Neuropsychologia 41 (2003) 1929–1941

Neuroanatomical correlates of selected executive functions in middle-aged and older adults: a prospective MRI study Faith M. Gunning-Dixon a,∗ , Naftali Raz b a

Department of Psychology, Kaufman Building, Hillside Division of Long Island Jewish Medical Center, 75-59 263rd St, Glen Oaks, NY 11040, USA b Department of Psychology, Institute of Gerontology, Wayne State University, Detroit, MI, USA Received 29 March 2001; received in revised form 5 July 2001; accepted 20 May 2003

Abstract Neuroanatomical substrates of age-related differences in working memory and perseverative behavior were examined in a sample of healthy adults (50–81 years old). The participants, who were screened for history of neurological, psychiatric, and medical conditions known to be linked to poor cognitive performance, underwent magnetic resonance imaging (MRI) and were administered tests of working memory and perseveration. Regional brain volumes and the volume of white matter hyperintensities (WMH) were measured on magnetic resonance images. The analyses indicate that the volume of the prefrontal cortex (PFC) and the volume of white matter hyperintensities in the prefrontal region are independently associated with age-related increases in perseverative errors on the Wisconsin Card Sorting Test (WCST). When participants taking antihypertensive medication were excluded from the analysis, both the volume of the prefrontal cortex and the frontal white matter hyperintensities (FWMH) still predicted increases in perseveration. Neither reduced volume of the prefrontal cortex nor the FWMH volume was linked to age-associated declines in working memory. The volumes of the fusiform gyrus (FG) and the temporal white matter hyperintensities (TWMH) were unrelated to cognitive performance. © 2003 Elsevier Ltd. All rights reserved. Keywords: Brain; Prefrontal; White matter; Executive functions; Hypertension; MRI

1. Introduction Cognitive aging is a selective process. Advancing age is associated with significant declines in performance on tasks that demand substantial mental effort, rely heavily on processing speed, and are characterized by novelty and complexity of the stimuli, whereas the tasks for which success depends on overlearned skills, knowledge, and expertise are relatively immune to the ill effects of senescence (Horn, 1986). For example, executive functions and working memory show significant decline with advancing age (see Salthouse, 1994; West, 1996 for reviews); vocabulary appears relatively stable in middle to later adulthood (e.g. Alwin & McCammon, 2001). This selective aging of mental abilities is contemporaneous with differential age-related changes in the brain (for reviews see Kemper, 1994; Raz, 2000). In vivo studies of normal human aging suggest the greatest age-related volume reduction in the prefrontal cortex (PFC), and the smallest in sensory cortices (Raz, 2000). Furthermore, while the prepon∗

Corresponding author. Tel.: +1-718-470-8382; fax: +1-718-470-9402. E-mail address: [email protected] (F.M. Gunning-Dixon).

0028-3932/$ – see front matter © 2003 Elsevier Ltd. All rights reserved. doi:10.1016/S0028-3932(03)00129-5

derance of in vivo studies suggests that age-related volume reductions are greater in the cortex relative to the subcortical white matter, abnormal foci of white matter hyperintensity (WMH) in periventricular regions and the centrum semiovale are frequently observed on brain MR images of asymptomatic persons (De Leeuw, De Groot & Breteler, 2000). Pathophysiological origins of WMH are diverse, and they represent a manifold of cerebrovascular and neuropathological events. These events include cerebral hypoperfusion coupled with greater vulnerability of the watershed zones (Brant-Zawadzki, 1992), subclinical ischemia (Pantoni, Inzitari & Wallin, 2000), and état criblé that begins with age-related neuronal loss, results in axonal degeneration, and finally produces an extensive network of fluid-filled peri vascular spaces (Ball, 1989). The most frequently observed pathological correlates of WMH include gliosis (Chimowitz, Estes, Furlan & Awad, 1992; Fazekas et al., 1993; Fazekas, Schmidt & Scheltens, 1998), myelin pallor (Awad, Johnson, Spetzler & Hodak, 1986; Fazekas et al., 1993, 1998; Takao et al., 1999), atrophy of the neuropil (Fazekas et al., 1998), and breakdown of the ependymal ventricular lining (Leifer, Buonanno & Richardson, 1990; Scarpelli et al., 1994). The significance of WMH in understanding the relationship

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between the aging brain and behavior is underscored by the fact that white matter abnormalities observed on neuroimaging are associated with attenuations in performance on tasks of processing speed, immediate and delayed memory, and executive functions (Gunning-Dixon & Raz, 2000). The term “executive functions” is somewhat vague and ill-defined. It encompasses a broad range of skills and abilities, such as planning, self-monitoring, inhibiting prepotent responses, and altering behavior in response to changing task demands. A frequently used example of a failure of an executive function is perseveration on the Wisconsin Card Sorting Test (WCST). Although perseveration on the WCST may occur for a host of reasons, structural equation modeling of executive functions suggests that perseverative errors are primarily related to the ability to shift cognitive sets (Miyake, Friedman, Emerson, Witzki & Howerter, 2000). Neuroanatomical evidence suggests that age-related increases in perseverative errors on the WCST are associated with shrinkage of the prefrontal cortex (Raz, Gunning-Dixon, Head, Dupuis & Acker, 1998) and loss of axonal integrity in the prefrontal white matter (Valenzuela et al., 2000) however, these skills also depend on distributed cortical networks including multiple prefrontal regions and posterior association areas (Anderson, Damasio, Jones & Tranel, 1991; Berman et al., 1995; Eslinger & Grattan, 1993). Working memory (WM), an age-sensitive ability that allows simultaneous short-term storage and processing of information (Salthouse, 1994), is also linked to perseverative errors on the WCST (Dunbar & Sussman, 1995; Hartman, Bolton & Fehnel, 2001; Kimberg & Farah, 1993; Raz et al., 1998). Working memory tasks vary in complexity and processing demands. In some tasks, success depends on simple storage and maintenance of information, in others, active manipulation of information is necessary. Functional imaging studies in young adults most frequently implicate parietal association cortices in the temporary storage of information (Cabeza & Nyberg, 2000; Paulseau, Frith & Frackowiak, 1993) and, prefrontal regions, to varying degrees, in the storage, maintenance, manipulation and updating components of working memory (D’Esposito, Postle & Rympa, 2000; Smith & Jonides, 1999). Regarding aging of working memory, limited neuroanatomical evidence suggests that age-related declines on complex span tasks, which presumably rely on all of the aforementioned working memory components, may be linked to shrinkage of the prefrontal and visual association cortices (Raz et al., 1998). The complex nature of perseveration and working memory calls for examination of multiple aspects of brain structure and function. It is plausible, for instance, that regional age-related shrinkage of the prefrontal cortex and structural alterations in the cerebral white matter exert additive influence on these cognitive functions. Thus, a simultaneous examination of those neuroanatomical parameters may yield significant non-redundant information. Such an assessment

of the relative contribution of select cerebral cortical regions and subcortical white matter to an age-sensitive function is the focus of this report. Our objective was to examine the role of specific brain regions in age-associated differences in working memory and perseveration. On the basis of the reviewed literature, we predicted that both working memory and perseveration would be associated with shrinkage of the prefrontal cortex, and increased WMH volume in subcortical frontal regions. Whether the influence of the WMH and the PFC volume on perseveration would be additive or redundant was a question to which we sought an answer in the data without preconception. We expected to observe a number of dissociations between the PFC and the control region indexed by the volumes of the fusiform gyrus (FG) and of the temporal lobe WMH. We predicted that neither the fusiform gyrus volume nor temporal lobe WMH burden, both of which evidence significant age-related differences (Raz et al., 1998, also see Gunning-Dixon & Raz, submitted, for a review), would mediate age-related differences in perseveration. While some of our previous MRI findings indicate that inferior parietal cortical volumes do not evidence age-related shrinkage and correlate only modestly with working memory, age-related volume reductions in the FG mediate age-related differences in working memory performance (Raz et al., 1998). Thus, we chose the FG as a control region and predicted that the FG volume differences would contribute to the age-related declines in working memory. 2. Method 2.1. Participants The data for this study were collected in an ongoing investigation of neuroanatomical correlates of age-related differences in cognition. Participants completed a mail-in health questionnaire and underwent a telephone interview as the means of screening for history of neurological and psychiatric conditions, head trauma with loss of consciousness exceeding five minutes, alcohol and/or drug abuse, hypo- or hyperthyroidism, and diabetes. None of the participants included in this sample reported major cardiac problems nor had anyone undergone a cardiac or vascular surgery. In addition, all participants were screened for dementia and depression using a modified Blessed Information–Memory–Concentration Test (Blessed, Tomlinson & Roth, 1968) and Geriatric Depression Questionnaire (Radloff, 1977), with cut-off scores of 30 and 15, respectively. All participants were strongly right-handed (75% and above on the Edinburgh Handedness Questionnaire, (Oldfield, 1971). The sample consisted of 139 participants (56 men and 83 women). Their age ranged from 50 to 81 years, M = 63.71, standard deviation (S.D.) = 7.97. Twenty-five participants reported receiving a diagnosis of hypertension from their physicians. These participants were

F.M. Gunning-Dixon, N. Raz / Neuropsychologia 41 (2003) 1929–1941

taking antihypertensive medication at the time of the study (mean length of treatment = 6.40 years, S.D. = 5.50 years). Thirty-six persons in this sample (26%) participated in a previously reported study (Raz et al., 1998). 2.2. MR imaging protocol Imaging was performed at the Diagnostic Imaging Center, Baptist Memorial Hospital, Memphis, Tennessee on a 1.5 T Signa scanner General Electric Co., Milwaukee, Wisconsin. Volumetric measures of the prefrontal cortex and fusiform gyrus were performed on reformatted images acquired using a T1 -weighted three-dimensional spoiled gradient recalled acquisition sequence (SPGR, 124 contiguous axial slices, TE = 5 ms, and TR = 24 ms, FOV = 22 cm, acquisition matrix 256×192, slice thickness = 1.3 mm, and flip angle = 30◦ ). The WMH volumes were estimated from T2 -weighted axial images from an axial double-echo T2 - and protondensity-weighted fast spin echo (FSE) sequence. The FSE sequence parameters were TR = 3300 ms, effective TE = 90 ms, for T2 slices and effective TE = 18 ms, for proton density slices, FOV = 20 cm×20 cm, matrix 256×256, slice thickness of 5 mm, and inter-slice gap of 2.5 mm. Between 18 and 20 T2 -weighted axial slices per head were available. All MR scans were examined for signs of space-occupying lesions and all suspicious cases were evaluated by an experienced neuroradiologist. 2.2.1. MR Image processing After acquisition, all MR images were processed and analyzed on a Power Macintosh 8100 (Apple Computer Corp. Cupertino, CA). The T1 -weighted images were reformatted off-line and corrected for undesirable effects of head tilt (to the left or right shoulder), pitch (forward or backward), and rotation (to the right or to the left) using BrainImage 2.3.3 software, obtained from the World Wide Web at http://sol.med.jhu.edu/Brainimage.html). To correct head tilt, pitch and rotation and to bring each brain into a unified system of coordinates the operator uses standard neuroanatomical landmarks. The re-alignment process consists of the following steps. First, to correct for head pitch, the axial plane is tilted so it passes through the anterior and posterior commissures (incorporating the AC-PC line). Because an infinite number of tilted planes can pass though the AC-PC line, an additional landmark is necessary to define the axial plane. Therefore, in the next step, the axial plane is fixed interactively by forcing it through the orbits in such a way that the axial cross section of the orbits on the right and the left side of the head is of equal diameter. After correcting for head tilt, the sagittal plane is moved to pass along the straight line drawn through the extreme anterior and posterior points of the interhemispheric fissure (IHF). Finally, the coronal plane is drawn perpendicular to the axial and sagittal planes as defined. To compensate for head rotation, the equivalence of right and left orbital diameters is

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ascertained throughout the brain volume by inspection of all relevant coronal slices. In some cases, a complete correction of the rotation by insuring orbit equivalence is difficult and requires the operator to check back and forth to maintain the IHF as the middle anchor of the volume. When inconsistency arises, alignment along the IHF takes precedence over the coronal orbital alignment. 2.3. Volumetric image analysis The reformatted T1 images were cut into 1.5 mm coronal sections. The ROI (region of interest) areas were measured with NIH Image public domain software (Version 1.60, available at the World Wide Web URL http://rsb.info. nih.gov/nih-image). Images were displayed on a 21 in. monitor and each ROI was traced manually using a digitizing tablet. Regional volumes were computed using the Cavalieri estimate (Rosen & Harry, 1990). 2.4. Quantification of white matter hyperintensities Volumetric measures of WMH were obtained using nine to 12 axial, T2 -weighted images per subject. Because of limited number of slices, lack of inter-slice contiguity, and relatively large slice thickness, the FSE T2 -weighted images were not realigned to avoid distortion. The axial slices were divided into frontal, temporal, parietal, and occipital regions of interests (ROIs), as described later. Hyperintense regions, defined as circumscribed areas of increased signal intensity within the white matter, were identified and measured on axial slices of the T2 -weighted images beginning at the most inferior slice on which the inferior horn of the lateral ventricles were present. Due to the difficulty in distinguishing WMH from emerging sulci and blood vessels in the superior convexity, the last slice on which WMH were quantified was located three slices below the vertex. All identifiable WMH-periventricular and deep white matter, were included. A total WMH volume was obtained by summing the volumes of hyperintensities from all of the ROIs and multiplying by the sum of the interslice gap and slice thickness. Because the distinction between gray and white matter is clearer on the PD-weighted images, the latter were used as reference in making decisions about lesion location and neuroanatomical boundaries. All questionable cases were resolved by consulting the correlative images in an MRT neuroanatomy atlas (Damasio, 1995; DeArmond, Fusco & Dewey, 1976; Montemurro & Bruni, 1988; Ono, Kubik & Abernathy, 1990; Talairach & Tournoux, 1988). Both inter- and intrarater reliability coefficients were computed. Interrater reliability was computed from measures by two trained operators who traced a random sample of 10 brains. The intraclass correlation formula for two random raters (ICC(2) (Shrout & Fleiss, 1979) was used, and the resulting reliability estimates for all ROIs and all types of WMH exceeded 0.80. Intrarater reliability of the WMH volumes was assessed from the data collected by one rater who

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traced 10 randomly chosen brains on two separate occasions 7 days apart. For practical and logistic reasons only one operator unaware of the subjects’ exact age and cognitive status (FGD) performed all WMH measurements reported here. This operator was trained to identify WMH by a neuroradiologist. The ROIs for WMH volume estimation were defined as the following. 2.4.1. Frontal white matter hyperintensities (FWMH) On ventral slices the frontal region included the region anterior to the lateral sulcus. When the central sulcus emerged on dorsal slices, it served as the caudal boundary. The intrarater reliability of this measure was ICC(2) = 0.92. 2.4.2. Parietal white matter hyperintensities (PWMH) The parietal ROI began on the most inferior slice when the central sulcus could first be identified. On ventral slices the central sulcus served as the anterior boundary whereas the lateral fissure was the posterior boundary (separating the parietal from temporal regions). On dorsal slices (beginning with the last slice on which the corpus callosum is present) all white matter posterior to the central sulcus was included in the parietal region. The intrarater reliability was ICC(2) = 0.89. 2.4.3. Temporal white matter hyperintensities (TWMH) On ventral slices the anterior boundary of the temporal region was the lateral fissure and the temporal-occipital incisure served as the posterior boundary. On superior slices, the parieto-occipital sulcus appeared medially and a horizontal line was drawn from the parieto-occipital sulcus to the lateral surface of the cortex to form the boundary between the occipital and temporal regions. The TWMH were traced until the slice on which the superior temporal gyrus could no longer be seen (usually the slice inferior to the last slice of the corpus callosum). This measure had an intrarater reliability of ICC(2) = 0.93. 2.4.4. Occipital white matter hyperintensities (OWMH) The occipital region included white matter posterior to the temporal-occipital fissure (laterally) and the parietooccipital sulcus (medially). The reliability of that measure was ICC(2) = 0.91. 2.4.5. Total white matter hyperintensities Measurements of WMH from frontal, temporal, parietal, and occipital were summed to obtain total WMH. The reliability of the combination was ICC(2) = 0.97. 2.5. Volumetric regional measures For the volumetric regional measurements, performed on T1 -weighted images, a set of slices containing each ROI was split, at random, into two equal groups, and each was traced by one of two trained operators. The inter-rater reliability for all T1 ROIs was high (ICC(2) exceeded 0.90)

and was assessed on randomly chosen sets of 10 brains. In occasional regions of partial voluming, the operator interpolated the line between two clearly definable segments of the cortical border. We resolved all questionable cases by consulting the correlative MRI brain atlases (Damasio, 1995; DeArmondet al., 1976; Montemurro & Bruni, 1988; Ono, Kubik & Abernathy, 1990; Talairach & Tournoux, 1988). The rules for MRI-based volumetry used in our laboratory have been published elsewhere (Raz et al., 1995, 1997; Raz, Briggs, Marks & Acker, 1999), and here we provide an abridged version. All structures were measured separately for each hemisphere. The examples of traced ROIs are depicted in Fig. 1. 2.5.1. Prefrontal cortex (PFC) The volume of the dorsolateral prefrontal cortex (DLPFC) was computed from 8 to 12 coronal slices located rostrally to the genu of corpus callosum. This ROI included superior, middle, and inferior frontal gyri and covers Brodmann areas 8, 9, 10, 46, and part of area 45. The DLPFC was defined as the gray matter located between the most dorsomedial point of the cortex and the orbital sulcus. Volume of the orbitofrontal cortex (OFC) was estimated from the same slices, using the most lateral branch of the orbital sulcus as the lateral boundary and the olfactory sulcus as the medial boundary. The OFC included parts of Brodmann areas 11 and 47. Based on our previous data suggesting that DLPFC and OFC volumes tend to be highly correlated (Raz et al., 1998), we decided a priori to treat these regions as complementary parts of an ROI labeled the PFC. To determine the range of inclusion, the number of slices located between the frontal poles and the slice rostral to the genu of the corpus callosum were calculated. The operators measured the DLPFC and the OFC on the caudal 40% of these slices. Only the continuous cortical ribbon was measured; gray matter was excluded if it was completely enclosed by white matter. 2.5.2. Fusiform gyrus (FG) The FG spans temporal and occipital lobes, and was traced according to the following rules. The most anterior slice on which the fusiform gyrus was measured was the one on which the anterior commissure first appeared. The most posterior slice on which the temporal portion of the FG was measured was the last slice on which the splenium of the corpus callosum could be identified. The occipital portion of the FG spanned the anterior 33% of the ranged between the occipital poles and the slice caudal to the one on which splenium is no longer present. Between the first slice and the end of the splenium, the occipito-temporal sulcus served as the lateral boundary of the FG. Starting at the first slice caudal to the splenium, the most ventro-lateral sulcus became the lateral boundary. In most cases, this still was the occipito-temporal sulcus. The collateral sulcus served as the medial boundary of the FG. In the posterior portion of the FO, the collateral sulcus splits into two sulci. The most

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Fig. 1. Examples of the brain ROIs used in volumetric analysis. Traced cortical areas are colored white for illustration purpose: (A) the prefrontal cortex; (B) the fusiform gyrus; (C) white matter hyperintensities (WMH) in the deep white matter and periventricular regions.

lateral sulcus of them was used as the boundary. The medial (lingual) sulcus was not included in the FG ROI. 2.6. Cognitive measures 2.6.1. Executive functions: Wisconsin Card Sorting Test (WCST) The computerized version of the WCST (Heaton, 1981) (Neuroscan Corp., Herndon, VA) was administered. On the WCST, the participants sort cards with geometric designs into categories by shape, color, or number of the designs. The participants were asked to match a card that appeared in the lower right corner of the computer screen with one of the four cards displayed at the top of the screen. The audible feedback (tone for correct decision, buzz for an incorrect one) was provided by the computer to the participant afler each trial. The examiner did not intervene in administration of the task. The WCST provides a number of indices intended to measure executive functions, the majority of which are highly intercorrelated. Thus, for the purpose of this study

only the number of perseverative errors was used. The reported long-term test–retest reliability estimates for perseverative errors range between 0.24 and 0.90, with the median of 0.75 (Paolo, Axlerod & Tröster, 1996; Pennington, Bennetto, McAleer & Roberts, 1996). However, the more recent estimates of test-retest reliability of perseverative responses fall within the higher end of this range (Paolo, Axlerod & Tröster, 1996). 2.6.2. Working memory The participants performed two verbal working memory tasks: Computation Span (CSPAN) and Listening Span (LSPAN) (Salthouse, Mitchell, Skovronek & Babcock, 1990). Both measure the ability for simultaneous storage and processing of verbal information, and are very similar in structure, administration procedure, and scoring. In CSPAN, the subject is asked to solve simple arithmetic problems while simultaneously remembering the last digit in each problem. In LSPAN, the subjects listen to simple sentences. After each sentence, they are asked to answer a question about its content and to report its final word. The

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first three trials of each of these tasks contain one item each: an arithmetic problem (CSPAN), or a sentence (LSPAN). On the additional groups of three trials, the number of items is incremented by one, up to seven, after which the task is stopped. There are three ways of quantifying participants’ performance on these working memory tasks, and three indices produced by these approaches: simple span (SS), absolute span (AS), and total span (TS). To get an item correct, the subject must solve the arithmetic problem (CSPAN) or answer the question correctly (LSPAN), and correctly identify the final number (CSPAN) or the final word (LSPAN). To get a trial correct, the subject must get all items correct within that trial. The SS is computed on each block of three trials: a correct response on two or three of the trials earns one point, and a correct answer on only one out of three trials is scored as half a point. Failure on two out of the three trials in the same block results in discontinuation of the SS scoring. The AS is calculated by summing the number of correct items across all correct trials. For example, getting a four-item trial correct, adds four points to the AS score. The TS is scored as the total number of correct items, regardless of missing or passing other items within that trial. In this study, we opted for the AS as an index of working memory performance. The SS produces too narrow a range to make it useful for correlational analyses, and the TS scores may tend to capitalize on chance because it counts single successful items scored on the trials even when the participant fails the rest.

3. Results To reduce the number of variables in the models (in keeping with the customary 1:10 variable per observation ratio) and for the lack of specific hypotheses about hemisphericity, we combined hemispheric volumes for each region. To compensate for body-size differences between the sexes, all cortical ROIs were adjusted for height using a formula: ROI volumeadj = ROI volumeraw −b×(height−heightmean ). In this formula, ROI volumeraw is subject’s raw volume of a given ROI, ROI volumeadj is subject’s height-adjusted volume, height is individual subject’s height, heightmean is the sample mean of height, and b is the unstandardized beta weight from the regression of ROT on height. An alternative correction factor would be intracranial volume (ICV), which reflects just as height does sexual dimorphism in body size. The disadvantage of ICV as a correction factor lies in its developmental association with brain volume (see Raz et al., 1997, for discussion). Thus, removing the variance of ICV from brain volumes could eliminate a significant proportion of true variance of the latter rather than ostensibly irrelevant variance of the body size. In any case, neither height nor intracranial volume correlated with age in men or women, and therefore, the choice of correction index would not affect the results. To maintain continuity with previous reports on the

population from which our sample was drawn we elected to use height as an adjustment factor for regional volume measures. White matter hyperintensities volumes were not corrected for the expected value of the volume in an ideal brain is zero. In addition, qualitative rating scales do not take cranial size into account and body size adjustments would create difficulty in comparing the previous findings with ours. 3.1. The effects of age, sex, history of hypertension on regional WMH and regional brain volumes Although all participants evidenced some WMH, the distribution of WMH volume differed across the regions. In the frontal region, all participants showed either deep or periventricular WMH, and most of them (91%) showed both. In the temporal regions, virtually all participants (99%) showed some type of WMH. Of those who had WMH, all evidenced periventricular WMH, whereas 24% showed deep subcortical WMH as well. In the parietal regions, only 22% of the participants had deep subcortical WMH, 82% showed periventricular WMH, and 20% revealed both types of WMH. However, less then 4% of the participants showed deep subcortical WMH in the occipital white matter and about 14% exhibited occipital periventricular hyperintensities, with 13% showing both types of WMH. It is plausible that the relatively small occipital region contributed to the weak correlations between age and occipital WMH. The hypertensive participants had a marginally greater predilection for occipital deep white matter WMH (Yates-corrected χ2 (1) = 3.65, P < 0.06) than those who carried no diagnosis of hypertension. No other significant differences in the distribution of WMH were observed between these groups. The analysis of the relationship of age, sex and hypertension to WMH was conducted using a general linear model (GLM) approach. To alleviate the extreme skew in WMH frequency distribution caused by excess of zero and near-zero scores (especially in the occipital region), all WMH volumes were transformed using a natural log-linear function: transformed volume = log(l + raw volume). The transformation brought the distribution considerably closer to normal. The GLM included three between subject factors, age (continuous), sex and history of hypertension (both categorical), with the log-transformed regional WMH volumes (ROI) serving as the dependent variable. The interactions among age, sex, and history of hypertension were not significant (all Fs < 1, ns), and were therefore removed from the model. In the reduced model, age emerged as a significant predictor of the total WMH burden: F(1, 135) = 32.48, P < 0.001. However, the magnitude of the age effect differed across the regions, as indicated by a significant ROI × age interaction: F(3, 405) = 8.23, P < 0.001 with Greenhouse–Geisser correction. A post-hoc analysis of simple effects associated with the significant ROI × age interaction revealed that the slope of regression of WMH volume on age was significantly steeper for the frontal (0.031 ± 0.006 cm3

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Table 1 Descriptive statistics for cortical ROI volumes and age, and correlations among them Age 0.47∗∗∗

TOTWMH

FWMH

TWMH

PWMH

OWMH

0.64∗∗∗ 0.73∗∗∗ 0.41∗∗∗ −0.13 −0.04

0.58∗∗∗ 0.42∗∗∗ −0.23∗∗ −0.04

0.50∗∗∗ −0.04 0.00

−0.13 −0.01

PFC

FG

Total WMH Frontal WMH Temporal WMH Parietal WMH Occipital WMH Prefrontal cortex Fusiform gyrus

0.40∗∗∗ 0.48∗∗∗ 0.32∗∗∗ 0.21∗ −0.30∗∗∗ −0.20∗

0.92∗∗∗ 0.85∗∗∗ 0.83∗∗∗ 0.48∗∗∗ −0.17∗ −0.02

Mean

63.71

10.05

4.40

3.72

1.70

0.23

22.79

17.41

S.D.

7.97

14.80

5.95

5.17

3.93

1.40

3.11

1.97

0.42∗∗∗

Notes: N = 139. All volume measures are in cm3 . Abbreviations, ROI: region of interest, WMH: white matter hyperintensity, the prefix TOT, F, T, P, and O in front of WMH stands for total, frontal, temporal, parietal, and occipital WMH volumes; all correlations involving WMH variables were computed using log-tranformed indices. All cortical ROI volumes are adjusted for height. ∗ P < 0.05. ∗∗ P < 0.01. ∗∗∗ P < 0.001.

per year), temporal (0.035 ± 0.006 cm3 per year), and parietal (0.026 ± 0.007 cm3 per year) regions than for the occipital one (0.008 ± 0.003 cm3 per year)1 . The WMH volume differed across the regions (F(3, 405) = 96.37, P < 0.001, but was associated with neither sex nor the diagnosis of hypertension: for both, main effect and interaction with ROI effects yielded F < 1, ns. Re-analyzing the data without four outliers (a 54-year-old woman, a 54-year-old man, a 65-year-old man and a 75-year-old man, all normotensive) did not alter the results. A similar set of linear models was fitted to the regional volumes (PFC and FG) data. The results of those analyses revealed significant main effects of age and sex (F(1, 135) = 16.87 and 7.71, P < 0.001 and 0.01, respectively), indicating that volumes of both PFC and FG were reduced with age, and that men had larger regional volumes then women did. No effect of hypertension was observed (F < 1). Shrinkage of the PFC exceeded that of the FG (F(1, 135) = 4.95, P < 0.05). The rest of the interactions were nonsignificant (all Fs < 1).

(Table 1), whereas the descriptive statistics for cognitive indices and their respective correlations with age are presented in Table 2. The correlations in Table 1 reveal moderate but significant age-related shrinkage of the prefrontal and fusiforrn cortices, along with an age-related increase in the total WMH volume. The correlations in Table 2 reflect frequently observed age-related declines in executive functions and working memory. The magnitude of age differences is somewhat attenuated in comparison to the figures reported in the previous study on a similar sample drawn from the same population (Raz et al., 1998) because of a relatively restricted age range in the current sample. Because of relative rarity of parietal and, especially, occipital WMH, we limited our consideration to frontal and temporal white matter hyperintensities (TWMH) that were found in virtually all participants. The matrix of zero-order correlations among age, regional brain volumes (corrected for height), frontal WMH and temporal WMH volumes, and cognitive variables is presented in Table 3.

3.2. Cognitive abilities, regional brain parameters, and age

3.2.1. Path analysis Zero-order correlations do not provide sufficient information for interpretation of associations among brain and cognitive variables, and assessment of their mutual influence is necessary. To accomplish that goal these data were examined

Due to strong correlations among cognitive variables and in and effort to reduce the number of variables as well as to increase the reliability of cognitive indices, we computed a working memory composite score. This working memory composite was computed by summing the standardized (z) scores of LSPAN and CSPAN. The relations among the regional brain volumes (corrected for height), the WMH burden, and age are presented in a zero-order correlation matrix The numbers in parentheses indicate ±standard errors (S.E.) of the unstandardized regression weight estimate. The 95% confidence limits (95% CI) of the slopes can be computed by multiplying S.E. by 1.96. Lack of overlap between 95% CI for a pair of slopes indicate that they are significantly different at P < 0.05. 1

Table 2 Descriptive statistics for cognitive measures and their correlations with age Variable

Mean

S.D.

Correlation (r) with age

WCST perseverative errors Computation span, absolute Listening span, absolute

29.40 13.05 18.52

19.32 9.64 10.96

0.27∗∗ −0.22∗ −0.26∗∗

Note: N = 139. ∗ P < 0.05. ∗∗ P < 0.01.

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Table 3 Zero-order correlations among cognitive indices, brain variables, and age Age Perseveration Working memory Prefrontal cortex Fusiform gyrus Frontal WMH Temporal WMH

0.27∗∗

−0.27∗∗ −0.29∗∗∗ −0.20 0.40∗∗∗ 0.48∗∗∗

Perseverative errors

WM

PFC

FG

FWMH

−0.25∗∗ −0.30∗∗∗ −0.22 0.34∗∗∗ 0.17∗

0.33∗∗∗ 0.19∗ −0.22∗ −0.19∗

0.42∗∗∗ −0.13 −0.23∗∗

−0.04 −0.04

0.64∗∗∗

Note: N = 139. WM: working memory composite. All cortical ROI volumes are adjusted for height. ∗ P < 0.05. ∗∗ P < 0.01. ∗∗∗ P < 0.001.

within a classic path analysis paradigm in which a system of linear regression equations is simplified (reduced) hierarchically until the difference in the model’s goodness of fit to the data reaches statistical significance (Pedhazur, 1982). In linear path models, the flow of variance is presumed unidirectional with upstream (independent) variables having the ability to affect downstream (dependent) variables but downstream variables cannot affect upstream variables. For example, in the model described later brain variables are placed upstream to cognitive variables because, presumably, brain volumes can affect the cognitive performance but the cognitive variables, at least under normal range of circumstances, are unlikely to affect gross brain volumes. Path coefficients are standardized regression coefficients of the models, in which each downstream variable is predicted by the connected upstream variables. If the connection is deemed negligible or untenable, on statistical or theoretical grounds, the path coefficient is set to zero and the path is eliminated from the model. It is worth noting that because of the essentially correlational nature of the analysis, paths do not represent causal relationships among the variables, only the distribution of variance in a given sample. Some of those relationships are direct (via straight paths), whereas others are indirect, mediated by the paths connecting other variables. The objective is to find the smallest number of variables and effects to account for the observed data. The relative goodness of fit of the hierarchical models is compared using a statistic distributed as χ2 with degrees of freedom equal to the number of restrictions imposed on the higher model (Pedhazur, 1982). The analysis starts with fitting a full model to the data. In the full model, all paths from upstream to downstream variables are presumed to differ from zero. As the paths are set to zero, the goodness of fit of increasingly restrictive models is compared until the most parsimonious model that is a good fit to the data is obtained. Removal of the paths in the hierarchy of reduced models is driven both by considerations of theoretical relevance and statistical significance. Theoretical and empirical considerations directed the creation of the hierarchical structure within the models. Age comprised the first tier because it was assumed to be measured without error and not influenced by any other variables. The second tier consisted of brain variables

representative regional cortical and WMH volumes from prefrontal and temporal regions (FWMH, TWMH, FG, and PFC). The brain variables were positioned in the second tier of the hierarchical model because they presumably could be affected by age but not by the cognitive variables. The cognitive indices formed the third tier (WM, PERSEV), under an assumption that such cognitive functions could be influenced by both age and brain variables. Preliminary analyses of the models showed that four observations were multivariate outliers (>3 S.D. from mean ROI and/or cognitive values). Therefore, four participants (one 54-year-old woman, and three men, age 54, 65, and 75) were excluded from the path analyses. The resulting sample (N = 135) retained the demographic characteristics of the full sample described in Section 2. The path diagram of the full model is presented in Fig. 2A. The reduced model (Fig. 2B) was constructed by removing all paths that were statistically nonsignificant. This model reflected the hypothesis that older adults would perform worse on tasks of working memory and executive functions and age-related differences in working memory and perseveration were expected to be mediated by smaller volumes of the prefrontal cortex and larger volumes of WMH in the subcortical frontal, but not temporal regions. Reduction of the model produced no significant drop in goodness of fit: χ2 (9) = 10.69, ns. However, further simplification of the model by setting additional paths to zero lead to significantly worse fit, and the reduced model was accepted as the best fit for the data.To examine further the nature of the relationship among age, cerebral volumes, and perseveration, we decomposed the correlations among the variables into direct and indirect effects (Pedhazur, 1982). In path analysis the direct effect is equal to the standardized coefficient of regression of a downstream variable (e.g., perseveration) on an upstream variable (e.g., age). As depicted in Fig. 2B, advanced age was not directly associated with increase in perseveration. However, an indirect association is present when an upstream variable is related to a downstream variable via a mediating variable. As shown in Fig. 2B, age is indirectly related to perseveration via its effects on the prefrontal structures (PFC and FWMH). Thus, the indirect relationship of age with perseveration via the FWMH is calculated by multiplying the path coefficient between age and FWMH by

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Fig. 2. (A) A full path model linking age, regional brain volumes, white matter hyperintensities and cognitive measures in the full sample. (B) A reduced path models linking age, regional brain volumes, white matter hyperintensities and cognitive measures. Only statistically significant paths are shown. The path from age to FG, although significant, is not shown because of lack of FG effects on the cognitive variables. The two-digit bold numbers on the paths are standardized path coefficients; the fourdigit numbers at the boxes are residuals. WM: working memory composite score; Persev: perseveration errors.

the path coefficient between FWMH and perseveration. This calculation indicates that the integrity of the frontal white matter is related indirectly to the increases in perseveration, accounting for 49% of the age-related variance in perseverative performance. Similarly, the PFC indirectly influenced age-related increases in perseveration. This indirect relationship accounted for 25% of the age-associated variance in perseveration. The remaining 26% of the variance could not be accounted for by any variable in the model. 3.3. The influence of history of hypertension on relationships among age, regional brain volumes and cognition The sample analyzed above included 24 individuals (18%) with a history of hypertension. Because hypertension is associated with increased WMH burden (Carmelli et al., 1999; De Leeuw et al., 2000) and cognitive deficits, especially in the executive domain (Waldstein, 1995), we repeated the

analyses in a subset of 111 participants who did not report a history of hypertension. There were no significant demographic differences between the hypertensive and normotensive individuals except that the group with a history of hypertension was significantly older than the normotensive group: normotensive group mean age = 63.10 ± 7.96 years, hypertensive group mean age = 66.58 ± 7.42, t(133) = 2.06, P < 0.05. The path analyses repeated on the subsample of self-reported normotensives resulted in a reduced model that was not significantly different from the full one: χ2 (9) = 6.79, ns. The reduced model revealed a pattern of results that was identical to the one observed in the full sample.

4. Discussion The main result of this study is that both the prefrontal cortical volume and the integrity of the subcortical frontal

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Fig. 3. A reduced path models of associations among age, brain variables and cognitive measures in the self-reported normotensives, N = 112. The numbers on the paths are standardized path coefficients; the numbers at the boxes are residuals.

regions contribute almost equally to increase in perseveration observed in middle-aged and older adults. This finding refines the classic view that perseverative behavior on the WCST is linked to the frontal lobe integrity (Milner, 1963). Although a quarter of the covariation between perseveration and age was due to unidentified causes (in accord with Salthouse, Mitchell, Skovronek & Babcock, 1990), a significant portion of variance was explained by the volume of PFC and frontal WMH (Figs. 2 and 3). The design and statistical methods employed in this study allowed for limited comparisons among regions and tasks, and afforded no opportunity to conduct multiple dissociations among tasks and regions. Nonetheless, the specificity of the findings is underscored by lack of significant links between perseveration and a posterior association region (FG). Despite this evidence of specificity, the results are also compatible with the notion that performance on WCST relies on a widely distributed neural network (Eslinger & Grattan, 1993) and therefore may be sensitive to changes in the integrity of white matter tracts. Given the extensive white matter connections both within the prefrontal regions and between the prefrontal regions and the rest of the brain (Cummings, 1993; Goldman-Rakic, 1987), it comes as no surprise that the volume of frontal white matter hyperintensities (FWMH) is directly related to perseveration on WCST. In regards to neural correlates of WM and of age related differences in WM performance, the outcome of this study is less informative. Despite the proposition that the PFC plays a major role in the aging of working memory (Moscovitch & Winocur, 1992), the negative association between age and working memory scores was not linked to age-related shrinkage of the PFC. To our knowledge these specific “loaded span” or “span-plus” tasks, in which holding information in short term memory is combined with manipulation of its

contents, have not been examined in functional neuroimaging paradigms. However, the literature suggests that young adults most consistently rely on BAs 9/46 during working memory tasks involving cognitive processes (i.e. on-line manipulation of stimuli versus maintenance) similar to those presumed necessary to perform the complex span tasks we studied (for review see Collette & Van der Linden, 2002). Furthermore, in contrast to earlier models that emphasized the link between the PFC and working memory, more recent findings suggest that the latter depends on the concerted effort of both anterior and posterior neural circuits connected by the extensive white matter tracts (for review see Cabeza & Nyberg, 2000). Thus, the lack of a significant relationship between WMH and working memory performance in this sample is perplexing. One possibility is that the complex nature of the working memory tasks used in this study may have obscured connections between neuroanatomical measures and working memory. It is also plausible that depending on the nature of the working memory task, performance may be attenuated by white matter abnormalities in specific white matter tracts. Unfortunately, the inability of the neuroimaging techniques used at the time of this study to identify and measure particular subcortical pathways precludes the detection of more exact relationships between specific white matter tracts and working memory performance. Finally, our choice of cortical regions may have limited our ability to detect neuroanatomical correlates of working memory. For example, based on our previous findings regarding age-related declines in working memory we chose to include the fusiform gyrus in our analyses rather than the posterior parietal regions that have been implicated more frequently in working memory performance (Cabeza & Nyberg, 2000; Paulseau, Frith & Frackowiak, 1993). Although we observed a significant relationship between calendar age and working memory performance, calendar age is

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a proxy for multiple physiological and pathological changes that affect the organism with the passage of time. Thus, the statistically significant age effect is an invitation to search for biological indices that are missing from the extant statistical models. Although hypertension may be a risk factor in age-related cognitive declines (Waldstein, 1995), in the sample examined in this study, taking into account the fact that some participants carried a diagnosis of hypertension did not alter the observed pattern of results. This finding should not be interpreted as a denial of the negative effects of hypertension on cognition and cerebrovascular system. It is possible that some of the self-reported normotensives actually suffered from preclinical hypertension that was yet to come to medical attention. Nonetheless, it is unlikely that in a highly educated sample of active middle-aged and older persons who are health conscious and have access to excellent health care such undetected illness could play a significant role. In a follow-up study currently underway, we plan to address this issue by obtaining direct measures of blood pressure rather than relying on self-reports about hypertension status. Furthermore, although the modest number of individuals with reported hypertension limits our power to detect an association between hypertension, cerebral regions, and cognition, we are able to infer with reasonable confidence that the observed relationship between the PFC, frontal WMH and age-related increases in perseveration is not due to the inclusion of hypertensive individuals in our sample. Regarding the influence of age in a sample of individuals who are relatively free of cerebrovascular risk factors (with the exception of those with medically controlled hypertension), age was modestly associated with WMH burden. Furthermore, the association between age and either frontal or temporal WMH volume was stronger than its association with the WMH volume in the occipital region. It is plausible that the relatively small size of the occipital region contributed to the weak correlations between age and occipital WMH. However, the greater vulnerability of frontal and temporal white matter to age-associated changes is consistent with the greater age-sensitivity of frontal and temporal cortices in comparison to the relative age-invariant primary and secondary visual cortices that comprise much of the occipital region (see Kemper, 1994; Raz, 2000 for reviews). These findings are also in accord with clinical observations (Pantoni et al., 2000). Whereas advancing age was associated with a greater burden of WMH, history of hypertension was not. The failure to detect a connection is not surprising considering a very modest relationship between hypertension and WMH in cognitively normal adults (r = 0.20 across multiple independent samples, (Gunning-Dixon & Raz, submitted), a relatively small number of people carrying the diagnosis of hypertension within the examined sample, and the probable overlap between normotensive and hypertensive groups discussed earlier.

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4.1. Methodological limitations and future directions The findings reported here should be viewed within the context of several methodological limitations. One drawback of the in vivo MRI methods used in the current study is that they cannot provide clues to the underlying cellular mechanisms of an age-related loss in volume. Losses of neuronal bodies, decrease in their volume, or decrease in dendritic arborization are all potential sources of volume reduction. Although one would expect all of such events to have a negative impact on behavioral correlates, structural MRI cannot clarifiy the specific neuroanatomical processes that account for such volume losses. In addition to the inability to elucidate specific biological mechanisms underlying volume loss, the methods of quantifying WMH are beset with similar limitations. Studies assessing the histology of WMH suggest that such white matter abnormalities reflect a number of pathological processes; however, the present methodology prevents a reliable discrimination among such mechanisms. Furthermore, the current technology does not allow identification of specific affected white matter tracts. In the future, one hopes, introduction of new MRI approaches (diffusion weighted, magnetization transfer, and fluid attenuated inversion recovery imaging) will help to achieve not only better differentiation between true white matter lesions and spurious unidentified bright objects but will also help to clarify the underlying pathological causes of the WMH (Kapeller, Ropele & Fazekas, 2000). For example, diffusion tensor imaging allows the assessment of the orientation and directional uniformity of the water diffusion in the brain, thus opening a new window on previously obscure properties of the white matter, such as orientation of individual tracts and bundles (Peled, Gudbjartsson, Westin, Kikinis & Jolesz, 1998). In a recent study, a step in that direction was made by demonstrating the association between age related increase in disruption of anterior white matter fibers and declines in cognitive performance in a small sample of healthy adults (O’Sullivan et al., 2001). Finally, the generalizability of the findings reported here is limited to those individuals who are highly educated, independent, and relatively free of age-associated cerebrovascular risk factors (e.g., uncontrolled hypertension, TIA/CVA, cardiovascular disease). Therefore, this sample is neither typical nor representative of the general population of older adults. It is probable that the selection criteria restricted the range of white matter abnormalities observed in this study rendering these results a conservative estimate of the role of WMH in age-related cognitive differences. It must be noted that in this sample in general, the magnitude of all associations involving age is underestimated because of the restriction of range of age. In summary, despite methodological caveats, the results reported here indicate that both the reduced prefrontal cortex volume and increased subcortical frontal WMH are associated with age-related augmentation in perseverative

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