Age moderates the relationship between cortical thickness and cognitive performance

Neuropsychologia 132 (2019) 107136

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Age moderates the relationship between cortical thickness and cognitive performance

T

Marianne de Chastelaine∗, Brian E. Donley, Kristen M. Kennedy, Michael D. Rugg Center for Vital Longevity and School of Behavioral and Brain Sciences, The University of Texas at Dallas, Dallas, TX, 75235, USA

A R T I C LE I N FO

A B S T R A C T

Keywords: Ageing Magnetic resonance imaging (MRI) Memory Lifespan development

Findings from cross-sectional and longitudinal magnetic resonance imaging (MRI) studies indicate that cortical thickness declines across the adult lifespan, with regional differences in rate of decline. Global and regional thickness have also been found to co-vary with cognitive performance. Here we examined the relationships between age, mean cortical thickness, and associative recognition performance across three age groups (younger, middle-aged and older adults; total n = 133). Measures of cortical thickness were obtained using a semi-automated method. Older age was associated with decreased memory performance and a reduction in mean cortical thickness. After controlling for the potentially confounding effects of head motion, mean cortical thickness was negatively associated with associative memory performance in the younger participants, but was positively correlated with performance in older participants. A similar but weaker pattern was evident in the relationships between cortical thickness and scores on four cognitive constructs derived from a neuropsychological test battery. This pattern is consistent with prior findings indicating that the direction of the association between cortical thickness and cognitive performance reverses between early and later adulthood. In addition, head motion was independently and negatively correlated with associative recognition performance in younger and middle-aged, but not older, participants, suggesting that variance in head motion is determined by multiple factors that vary in their relative influences with age.

1. Introduction The development of automated analysis pipelines for structural MRI data (e.g., Avants et al., 2011; Fischl et al., 2002; Patenaude et al., 2011) has resulted in a large literature describing studies that relate global and regional measures of neocortical (henceforth, cortical) thickness to variables such as chronological age and cognitive performance (see, for example, Fjell et al., 2009; Fjell et al., 2015; Karama et al., 2014; Schnack et al., 2015). The findings from cross-sectional and longitudinal studies converge to indicate that cortical thickness declines across the adult lifespan, with regional differences in rate of decline (e.g., Lee et al., 2018; Madan and Kensinger, 2018; Rast et al., 2017). It has also been reported that both global and regional thickness positively co-vary in older adults with multiple cognitive abilities, as well as with measures of general intelligence (e.g., Knopman et al., 2018; Schnack et al., 2015; Yuan et al., 2018). Importantly, and highly relevant to the findings presented below, a negative correlation between cortical thickness and cognitive performance has been reported in studies examining this relationship in adolescence and early adulthood (e.g., Schnack et al., 2015; Tamnes et al., 2010). Together, these

∗

findings imply that the slopes of the regression lines relating cortical thickness to cognitive performance are a non-monotonic function of age, an issue to which we return in the Discussion. Here, we present analyses of an MRI data set obtained from cohorts of healthy younger, middle-aged and older adults (de Chastelaine et al., 2015, 2016a; 2016b, 2017). The data included structural MRI measures, along with measures of performance on an experimental memory test (associative recognition) and a range of standard neuropsychological tests. Using multiple regression analyses, we examined whether age acted as a moderator of the relationship between mean cortical thickness and cognitive performance, as might be expected on the basis of the findings reviewed above. In follow-up analyses, we asked whether relationships between age, cognitive performance and thickness were differentially expressed across the cortical mantle. The application of automated measurement methods to MR images is not without its issues (e.g., Wenger et al., 2014), including the potentially confounding influence of within-scan head motion on estimates of cortical thickness. This issue was first brought to light by Reuter et al. (2015), who used the popular FreeSurfer analysis pipeline (Dale et al., 1999; Fischl and Dale, 2000; Fischl et al., 2002) to compare

Corresponding author. 1600 Viceroy Drive, Suite 800, Dallas, TX, 75235, USA. E-mail address: [email protected] (M. de Chastelaine).

https://doi.org/10.1016/j.neuropsychologia.2019.107136 Received 16 April 2019; Received in revised form 15 June 2019; Accepted 5 July 2019 Available online 06 July 2019 0028-3932/ © 2019 Elsevier Ltd. All rights reserved.

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The remainder of the battery comprised the California Verbal Learning Test-II (CVLT; Delis et al., 2000), the Wechsler Memory Scale (WMSIV), the Digit Span Forward and Backward test of the Wechsler Adult Intelligence Scale Revised (WAIS-R) (Wechsler, 2001), the Digit/ Symbol Coding test of the WAIS-R, Trail Making Tests A and B, letter and category fluency tests, the Wechsler Test of Adult Reading (WTAR; Wechsler, 2001) and Raven's Progressive Matrices (short version). Exclusion criteria for these tests were scores > 1.5 SDs below the ageappropriate norm on any sub-test of the long-term memory tests (CVLT or WMS) or on any two of the other tests, or an estimated full-scale IQ < 100 as indexed by performance on the WTAR. Our cut-off criterion for the WTAR led to the exclusion of < 10% of otherwise-eligible participants. These criteria were applied to minimize the likelihood of including participants with mild cognitive impairment or other cognitive difficulties. Along with other selection biases that are inevitable in studies such as this (Rugg, 2016), they mean that the present samples should not be taken to be representative of the general population.

thickness estimates derived from T1-weighted MR images acquired while participants remained still during image acquisition with estimates derived from scans acquired while participants deliberately moved their heads. The authors reported that motion resulted in an underestimation of ‘true’ thickness (for similar findings, see Geerligs et al., 2017). Savalia et al. (2017) reported analogous findings using a measure of head motion acquired not during the T1-weighted scan itself, but from a series of temporally adjacent functional scans. These findings suggest that estimates of between-volume head displacement during functional scans can serve as a reliable predictor of the amount of motion to be expected from a participant during the structural scan. Building on the findings of Savalia et al. (2017), here we used estimates of head motion derived from functional scans to examine whether relationships between cortical thickness and cognitive performance, and the dependency of these relationships on age, are evident when head motion is controlled for. This question is important in light of evidence that head motion increases with age (e.g., D'Esposito et al., 1999; Geerligs et al., 2017), raising the possibility that motion could act as a confounder when examining thickness x performance × age interactions. In addition, we asked whether motion is associated with cognitive performance independently of cortical thickness. This last question was motivated by prior findings suggesting that head motion may, in part, reflect a trait that is associated with individual differences in functional brain activity and cognitive ability. For instance, Zeng et al. (2014) reported that between- but not within-participant differences in head motion moderated estimates of resting state connectivity, while Geerligs et al. (2017) reported that estimates of head motion were negatively associated with fluid IQ and trial-wise variability in reaction time.

2.3. Procedure Functional scanning for the experimental task (associative recognition) took place across two separate scan sessions separated by a 15 min break. Fig. 1 provides a schematic overview of study and test procedures. During the first scan session, word pairs for the study phase of the experiment were presented during two scanning runs separated by a rest interval. The study task required a judgment as to which of the objects denoted by the words would ‘fit’ into the other. After completing the study phase, participants exited the scanner for 15 min before re-entering for the test phase. During this second session, participants undertook an associative memory test administered across three consecutive scanning runs that were separated by short (ca. 30 sec) rest intervals. For the memory test, participants were required to press one of three keys to indicate whether a test pair was intact (studied word pairs), rearranged (re-paired studied words) or new (not studied). The second scanning session concluded with diffusion tensor (DTI; ca. 3.5 min) and high resolution T1-weighted scans (ca. 4 min).

2. Methods More detailed descriptions of the methods can be found in five prior publications where analyses of the fMRI data acquired in this study were reported (de Chastelaine et al., 2015, 2016a; 2016b, 2017; King et al., 2018). Here, we report analyses of the structural MRI data acquired during that study. These analyses have not been reported in detail previously.

2.4. MRI data acquisition

2.1. Participants

A Philips Achieva 3T MR scanner (Philips Medical System, Andover, MA USA) equipped with a 32 channel head coil was used to acquire functional and anatomical images. Functional scans were acquired with

The present participants were the same as those described in the prior publications based on this study (see above) with the exception of three individuals who were excluded because the low quality of their MRI images precluded reliable estimation of cortical thickness (1 middle-aged, 2 older). Therefore, the present samples comprised 36 younger (18–29 yrs; M = 22 yrs; SD = 3.0 yrs; 17 female), 35 middleaged (43–55 yrs; M = 49 yrs; SD = 3.5 yrs; 19 female) and 62 older (63–76 yrs; M = 68 yrs; SD = 3.6 yrs; 34 female) adults. The participants were recruited from the University of Texas at Dallas and the surrounding communities. All were right-handed, fluent in English by age 5, in good general health, not taking central nervous system-active medication, free from psychiatric and neurological disease and had normal or corrected to normal vision. Exclusion criteria based on neuropsychological test scores are described below. Participants gave informed consent in accordance with the UT Dallas and UT Southwestern Institutional Review Boards and were compensated at the rate of $30 per hour. 2.2. Neuropsychological test battery On a day prior to the experimental MRI session all participants completed a neuropsychological test battery, which assessed a range of cognitive functions known either to decline or to be maintained with age. The Mini-Mental State Examination (MMSE) was employed to screen participants for dementia using a nominal cutoff score of 27/30.

Fig. 1. Schematic overview of the study and test procedures. 2

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a T2*–weighted echo-planar image (EPI) (TR 2 s, TE 30 ms, flip angle 70°, FOV 240 × 240, matrix size 80 × 78). Each functional volume comprised 33 slices (3 mm thickness, 1 mm inter-slice gap) with an inplane resolution of 3 × 3 mm. Slices were oriented parallel to the ACPC line, acquired in ascending order, and positioned for full coverage of the cerebrum and most of the cerebellum. fMRI data (and hence estimates of head motion – see below) were acquired during both the study and test phases (311 and 351 vol for each study and test block, respectively). A 3D MP-RAGE pulse sequence (TR = 8.1 ms, TE = 3.7 ms, FOV = 256 × 224, voxel size 1 × 1x1 mm, 160 slices, sagittal acquisition) was employed for T1-weighted anatomical image acquisition.

each of the 5 functional runs. 2.7. Multiple regression analyses We were interested in examining relationships between cortical thickness and cognitive performance, and whether and how these relationships were moderated by age group. In addition, given findings that head motion increases with age, we wished to examine whether either age-invariant or age-dependent relationships between cortical thickness and cognitive performance were independent of head motion, as well as whether head motion itself was associated with cognitive performance. We addressed these questions by constructing multiple regression models in which cognitive performance (most importantly, our experimental measure of associative recognition performance, pR) was the dependent variable, using as predictor variables age group, mean cortical thickness and FD (i.e., head motion), and the interaction terms of age group x thickness, and age group x FD. In this and all other regression analyses predictor variables were mean-centered. In additional regression analyses that are not reported here, we enlarged the models to include the quadratic and cubic expansions of FD (and their interactions with age group). These enlarged models yielded findings that differed in only minor respects from those reported below. Although highly redundant with the regression models described above which, as is typical, employ behavioral measures as the dependent variables, we also constructed complementary models in which mean cortical thickness was the dependent variable, and the predictor variables were age group, cognitive performance, FD, and the age group x cognitive performance and age group × FD interaction terms. The rationale for these models was that they permit a direct comparison between regression analyses employing mean thickness and the vertexwise regression analyses described below in which, of necessity, thickness estimates were dependent rather than predictor variables.

2.5. Cortical thickness estimates Estimates of cortical thickness for the T1-weighted images from each participant were calculated using the semi-automatic processing pipeline of FreeSurfer v5.3 (http://surfer.nmr.mgh.harvard.edu/fswiki; Dale et al., 1999; Fischl and Dale, 2000; Fischl et al., 2002). This analysis provides volumetric segmentation (i.e., measures of volumetric anatomy such as gray/white matter volume and subcortical gray matter volume) and surface-based cortical reconstruction, the latter allowing estimates of cortical gray matter thickness and surface area. After the images had been subjected to automated analysis, gray/white matter surfaces were visually inspected by two trained raters who consulted standard neuroanatomical atlases (and conferred with each other), each rater reviewing independent sets of scans. While the automated analysis generally succeeded in segmenting white and gray matter, certain brain regions (e.g., orbito-frontal, insula and temporal regions, and boundaries between gray matter and the pial surface and the cerebellum) required further manual edits. Segmentation errors were corrected manually by editing the white matter and brain masks returned by FreeSurfer. Also, ‘control points’ were sometimes manually added to voxels close to what were determined to be white matter paths that had been excluded by the automated analysis. Control points serve to increase a voxel's intensity such that voxels corresponding to these points (and the surrounding voxels) are likely to be correctly identified as white matter by the automated analysis when reiterated. Manual edits were repeated as necessary to ensure that tissue classification was as accurate as possible. Cortical thickness was measured as the distance from the gray/white matter boundary to the pial surface on a vertex-byvertex basis across the entire cortical mantle. Mean whole-brain cortical thickness was calculated by averaging cortical thickness values generated for the left and right hemispheres, and the principal analyses reported below were conducted using these values.

2.8. Vertex-wise analyses In addition to the analyses of mean whole-brain cortical thickness – the primary focus of this study – we also undertook exploratory vertexwise (surface-based) analyses. The aim of these analyses was to determine if the findings obtained for the mean thickness measures exhibited statistically significant regional maxima. For these analyses, segmented images from each participant were resampled into a common cortical space (i.e., FreeSurfer's ‘fsaverage’) and spatially smoothed at 10 mm FWHM. The images were subjected to two vertexwise regression analyses with the use of FreeSurfer's ‘mri_glmfit’ utility. The first of these analyses examined the effect of age group on cortical thickness after controlling for FD. The second set of analyses examined the extent to which interactions between age group and cognitive performance measures accounted for between participant variance in cortical thickness after controlling for the effects of age group, cognitive performance and FD. Statistical significance in these models was evaluated by combining a vertex-wise height threshold of p < 0.001 with a FWE cluster-wise correction set at p < 0.05. Cluster extent thresholds meeting these criteria were estimated by Monte Carlo simulation (10,000 iterations), as described in Hagler et al. (2006).

2.6. Head motion estimates Following Power et al. (2012), we used framewise displacement (FD) as our measure of in-scanner head motion. FD was calculated as the sum of the absolute frame-to-frame motion displacement across the six head motion parameters derived from the 5 functional runs (2 study runs and 3 test runs) using the motion correction routines incorporated into SPM8. For a given functional run, the six realignment motion parameters estimated from SPM8 (three translational (mm) and three rotational vectors (radians)) indexed the absolute displacement of the participant's head at each TR relative to the first TR of the run. To calculate FD, first, rotational estimates were converted from radians to mm displacement relative to a sphere with a radius of 50 mm (the approximate distance from the cerebral cortex to the center of the head). The resulting six-dimensional time-series was then differentiated in order to index the relative displacement of the participant's head in each dimension for each TR relative to the immediately preceding TR across a run. Volume-wise FD was calculated as the sum of the absolute values of the six differentiated realignment parameters at each TR of a run (i.e., total movement for each TR was summarized as a positive displacement value). Mean volume-wise FD was then calculated for

3. Results 3.1. Neuropsychological data Full details of the raw neuropsychological test scores for the present samples have been reported previously (e.g., de Chastelaine et al., 2015). In brief (see Table 1), the three groups showed equivalent performance on tests that are typically preserved with age (i.e., Digit Span, Letter and Category Fluency and a measure of crystallized intelligence, the WTAR). In contrast, composite recall of word lists (CVLT) was significantly lower in the older, but not in the middle-aged group, 3

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Table 1 Demographic and neuropsychological data (mean, SD, and range) for young, middle-aged, and older adults. Young adults

Age Years of Educationa† Mini Mental State Exam CVLTa Composite Recalla† CVLTa Recognitiona‡, b∗ CVLTa False Positivesa†, b∗ WMSb Composite Score Forward/Backward Digit Span Digit Symbol Substitutiona‡, b†, Trail Aa‡, c‡ Trail Ba‡, c‡ Letter Fluency Category Fluency WTARc Raw Score Raven's Prog. Matricesda‡, b∗∗

c†

Middle-aged adults

Older adults

Mean

SD

Range

Mean

SD

Range

Mean

SD

Range

22.2 15.5 29.6 13.3 15.6 0.9 29.0 18.0 61.4 20.9 46.7 43.6 24.8 112.3 11.2

3.0 2.4 0.6 1.9 0.6 1.0 6.6 3.9 10.1 7.3 17.3 11.8 5.9 5.0 1.0

18–29 11–22 28–30 9.3–16 14–16 0–4 15.5–41 11–26 39–83 11–47 23–108 23–65 16–42 102–119 8–12

49.4 16.3 29.3 12.4 15.2 1.8 27.0 18.2 55.0 24.4 52.2 47.1 23.8 111.2 10.3

3.5 2.6 0.8 2.0 0.9 2.2 6.3 3.5 7.8 6.6 16.9 12.0 6.0 5.1 1.5

43–55 10–22 28–30 9–16 13–16 0–8 14–44.5 13–26 41–70 14–39 27–95 28–69 13–36 101–118 7–12

68.3 17.2 29.3 12.0 14.9 1.9 27.6 18.3 50.0 32.5 74.2 45.7 22.4 112.8 9.8

3.6 2.3 0.8 2.5 1.3 2.2 5.3 4.5 8.5 11.6 47.4 12.6 5.6 5.4 1.9

63–76 12–22 27–30 5.5–16 12–16 0–10 18.3–39.5 12–27 31–74 15–88 31–360 21–81 12–40 101–119 3–12

Note: Statistically significant difference between a) young and older adults, b) young and middle-aged adults, c) middle-aged and older adults. *P < 0.05, **P < 0.01, †P < 0.005, ‡P < 0.001, 2-tailed t-tests. a California Verbal Learning Test. b Wechsler Memory Scale (WMS-IV). c Wechsler Test of Adult Reading Full Scale Intellectual Quotient. d Short version of Raven's Progressive Matrices.

compared to the younger participants. Older participants also demonstrated poorer performance on tests of speeded cognition relative to both the younger and middle-aged groups. Younger participants outperformed both older and middle-aged participants on a test of fluid intelligence (Raven's Matrices, short form). We also note that older adults had significantly more years of education than younger adults, but years of education did not differ significantly between the middleaged and either of the two other age groups. It is likely that this difference between the younger and older groups was because a substantial fraction of the younger participants were still attending college. Here we further report neuropsychological test performance after dimension reduction by principal components analysis (PCA). The following variables were included in this analysis: CVLT composite recall (average number of words recalled on the short- and long-delay freeand cued-recall tests), number of CVLT recognition hits, number of CVLT recognition false alarms, logical memory composite recall (average of immediate and delayed recall), completion time for Trails A and B, number of valid responses on the SDMT, FAS, and Raven's, and estimated full-scale intelligence quotient derived from the WTAR. The scores were standardized and then subjected to PCA. Principal components with eigenvalues > 1 were retained and rotated using Varimax rotation (Kaiser, 1958). Four retained components explained 64.1% of the variance in the data prior to rotation. The rotated components broadly correspond to factors representing processing speed (C1), memory (C2), crystallized intelligence (C3), and fluency (C4). The loadings for the rotated factors are shown in Table 2. The loadings were applied to each participant's standardized test scores to obtain the factor scores for the analyses reported here. Note that we reversed the signs of the factor loadings for the speed factor (C1) so that larger factor scores represented better (i.e., faster) performance, consistent with the scores for the other three factors. Mean (SD) factor scores for the younger, middle-aged and older groups were, respectively, 1.74 (1.78), 0.5 (1.73) and −1.26 (2.78) for processing speed, 1.64 (1.87), −0.06 (2.09) and −0.76 (2.66) for memory, 0.25 (1.79), 0.04 (1.66) and −0.15 (2.17) for crystallized intelligence, and 0.08 (1.32), 0.06 (1.39) and −0.10 (1.57) for fluency. One-way ANOVAs revealed that the factor scores for the three groups differed significantly for processing speed (F2, 132 = 20.53, p < 0.001) and memory (F2, 132 = 12.24, p < 0.001). Pairwise contrasts (t-tests, equal variances not assumed) revealed that, for

Table 2 Rotated factor loadings from the PCA (after Varimax rotation) of the neuropsychological test data.

CVLT Composite CVLT Hits CVLT False Alarms Logical Memory Composite Trails A Trails B SDMT Digit Span Category Fluency (Animals) FAS WTAR (Full-Scale IQ) Raven's (List 1)

Speed (C1)

Memory (C2)

Crystallized IQ (C3)

Fluency (C4)

-.19 -.20 .21 .10

.84 .42 -.69 .67

.08 .23 .26 .18

-.15 -.64 -.17 .02

.91 .85 -.59 -.16 -.34

-.09 -.09 .40 .01 .23

-.05 -.28 .08 .80 .14

-.14 .08 .30 -.08 .63

-.12 -.12

.06 .12

.46 .79

.57 .21

-.33

.48

.10

.05

processing speed, the younger group out-performed both the middleaged (t69 = 2.98, p < 0.005) and the older groups (t95 = 6.51, p < 0.001), and that the middle-aged group out-performed the older group (t94 = 3.84, p < 0.001). For the memory factor, the younger group achieved higher scores than the middle-aged (t68 = 3.61, p < 0.001) and the older group (t92 = 5.23, p < 0.001), but there was no significant difference between the two older groups (t < 1.45). The ANOVAs did not reveal significant group differences for the crystallized intelligence (F < 1) or fluency (F < 1) factors.

3.2. Performance on the experimental memory test Associative recognition performance is described here only briefly as comprehensive treatments of these data are available elsewhere (de Chastelaine et al., 2015, 2016a; 2016b, 2017). As in those prior reports, we operationalized the ability to recollect studied associates as the difference between the proportion of intact test pairs correctly endorsed as intact (associative hits) and the proportion of rearranged test pairs incorrectly judged intact (associative false alarms). Mean (SD) recollection scores were 0.48 (0.19), 0.39 (0.14) and 0.32 (0.15) for the 4

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younger, middle-aged and older groups, respectively. A one-way ANOVA revealed that these means differed significantly (F2, 132 = 11.93, p < 0.001). Pairwise contrasts (t-tests, equal variances not assumed) revealed a graded decline with age: younger participants were more accurate than both middle-aged (t64 = 2.30, p < 0.05) and older participants (t60 = 4.40, p < 0.001), and middle aged participants were more accurate than the older participants (t74 = 2.35, p < 0.05). 3.3. Cortical thickness Mean (SD) cortical thickness estimates (see Methods), were 2.53 (0.08) mm, 2.39 (0.09) mm and 2.30 (0.11) mm for the younger, middle-aged and older groups, respectively. ANOVA revealed that the estimates differed significantly according to group (F2, 132 = 65.52, p < 0.001). Thickness was greater in the younger group compared to both the middle-aged (t69 = 6.87, p < 0.001) and older groups (t88 = 11.76, p < 0.001), and was greater in the middle-aged than in the older group (t84 = 4.64, p < 0.001).

Fig. 2. Adjusted mean thickness values (after controlling for FD) for each age group.

head motion estimates and age. To examine the possible confounding effects of motion on the between-group differences in thickness, we employed an analysis of covariance (ANCOVA) model with mean thickness as the dependent variable, age group as a fixed factor and FD as a covariate. Echoing the one-way ANOVA reported above, the main effect of age group was highly significant (F2, 132 = 41.46, p < 0.001) (see Fig. 2). Follow-up pairwise t-tests (equal variances not assumed) indicated that, as before, thickness estimates were greater in younger adults compared to both middle-aged and older adults, and thickness was greater in the middle-aged than in the older adults (all ps < 0.001).

3.4. Framewise displacement As detailed in the Methods, mean FD was calculated for each participant for each of the two study and three test runs. An ANOVA examining FD as a function of run and age group revealed significant main effects of run (F2.3, 297.4 = 19.42, p < 0.001) and age group (F2, 130 = 25.01, p < 0.001), with no interaction between the two factors (F < 1). The effects of run reflected a monotonic increase in FD across the five runs (across-participants means (SDs) ranging from 0.23 (0.08) mm for run 1 to 0.28 (0.12) mm for run 5). Group mean FD values, collapsed across all five runs, were 0.19 (0.05) mm, 0.26 (0.08) mm and 0.32 (0.11) mm for the young, middle-aged and older participants, respectively. Younger participants moved less than both the middle-aged (t57 = 4.52, p < 0.001) and the older participants (t92 = 8.11, p < 0.001), while motion was greater in the older than in the middle-aged group (t88 = 3.07, p < 0.005). These group differences in mean FD were accompanied by differences in the variance of the FD estimates: Levene's tests revealed that the variances of the middle-aged and older groups' estimates did not significantly differ (p > 0.2), but that both were greater than the variance of the estimates in the younger group (max p < 0.025). As summarized in Table 3, simple correlations of the FD values across participants indicated strong positive relationships between all runs, with the strongest correlations between temporally adjacent runs. Given that study runs occurred in a separate scan session prior to the test runs (and prior to a 15 min break outside of the scanner), and anatomical scanning occurred in the same scan session as the test runs, we used FD values from the three test runs as a proxy for head movement during the anatomical scan, averaging these values across the runs to generate a single FD value for each participant. These mean FD values were employed in all further analyses.

3.6. Predictors of cognitive performance The regression model predicting associative recognition performance (pR; see Methods) was highly significant (F5,132 = 10.78, p < 0.001, adjusted R2 = 0.270). As can be seen in Table 4, mean thickness did not significantly predict pR across age groups, but this null finding was qualified by an interaction between thickness and age. Additionally, while FD significantly predicted pR across age groups, this finding was also qualified by an interaction with age group. To elucidate these interactions, for each age group, we calculated the partial correlations between cortical thickness and pR (controlling for FD), and between FD and pR (controlling for thickness). As is illustrated in Fig. 3, there was a significant negative correlation between thickness and pR in the younger group (r(33) = −0.448, p < 0.01), no significant correlation between the two variables in the middle-aged group (p > 0.4) and a significant positive correlation between these variables in the older group (r(59) = 0.317, p < 0.025). In the case of FD, there were negative relationships with pR in the younger (r(33) = −0.408, p < 0.025) and middle-aged groups (r (32) = −0.362, p < 0.05), but no significant correlation was evident for the older group (r(59) = −0.004, p > 0.9) (Fig. 4). The analogous regression models that included each of the four neuropsychological factor scores as predictor variables were significant for the processing speed (F5,132 = 10.48, p < 0.001, adjusted R2 = 0.264) and memory (F5,132 = 6.64, p < 0.001, adjusted

3.5. 5 head motion and the relationship between thickness and age As noted above, we identified a negative relationship between mean cortical thickness and age group, but a positive relationship between

Table 4 Results of the regression model investigating variables (age group, FD, mean thickness, age x FD and age × thickness interactions) that predict pR.

Table 3 Across participants correlation matrix table for study and test run FD estimates.

Study 1 Study 2 Test 1 Test 2 Test 3

Study 1

Study 2

1 .91 .84 .75 .68

1 .87 .81 .74

Test 1

1 .91 .85

Test 2

1 .90

Test 3

Model

b

SE b

β

p value

1

Age group Mean thickness FD Mean thickness x Age group FD x Age group

-.067 -.185 -.613 .565 .650

.024 .143 .163 .143 .186

-.329 -.148 -.411 .334 .346

.005 .197 .000 .000 .000

5

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Fig. 3. Partial plots showing the relationships across participants between mean cortical thickness and pR (after controlling for FD) in each age group.

R2 = 0.176) factors, but not for crystallized intelligence or for fluency (Fs < 1). Although these latter two models were not significant, we nonetheless report the thickness x age group and FD x age group interaction terms for these scores in order to assess the comparability of these effects with those observed for pR. As is evident from Table 5A–D, the interaction terms for age group x mean thickness (but, with the exception of fluency, not for age group x FD) predicted three of the factor scores, namely, processing speed, intelligence and fluency, and demonstrated a trend towards predicting memory scores. Follow-up group-wise analyses revealed patterns similar to those obtained for pR (see above), with negative (albeit non-significant) correlations between thickness and the 4 factor scores in the younger group, and positive correlations in the older group (significant, however, only for the factor scores of processing speed and memory). The regression model predicting mean cortical thickness (with age group, pR, FD, age group x pR and age group x FD as predictor variables – see Methods) was highly significant (F5,132 = 33.31, p < 0.001, adjusted R2 = 0.550). As can be seen in Table 6, age group and FD were both significant predictors of mean thickness, as was the age group × pR interaction term. The analogous regression models that each included one of the four neuropsychological factor scores as predictor variables, rather than pR, were also significant: processing speed (F5,132 = 30.39, p < 0.001, adjusted R2 = 0.527); memory (F5,132 = 30.37, p < 0.001, adjusted R2 = 0.527); crystallized intelligence (F5,132 = 30.20, p < 0.001, adjusted R2 = 0.525); and fluency (F5,132 = 30.14, p < 0.001, adjusted R2 = 0.525). Notably, in each case, the age group x factor score interaction term was a significant predictor of mean cortical thickness.

Table 5 Results of the regression model investigating variables (age group, FD, mean thickness, age x FD and age × thickness interactions) that predict each of the four neuropsychological constructs. A Predicting processing speed Model

b

SE b

β

p value

Age group Mean thickness FD Mean thickness x Age group FD x Age group

1.341 −1.229 1.976 −5.740 −2.550

.363 2.193 2.495 2.193 2.854

.431 -.064 .087 -.222 .089

.000 .576 .430 .010 .373

Model

b

SE b

β

p value

Age group Mean thickness FD Mean thickness x Age group FD x Age group

-.693 1.748 −4.708 4.045 5.244

.370 2.235 2.543 2.235 2.909

-.232 .095 -.214 .162 .189

.063 .436 .066 .073 .074

Model

b

SE b

β

p value

Age group Mean thickness FD Mean thickness x Age group FD x Age group

-.123 .069 −1.927 −3.978 2.982

.313 1.889 2.149 1.888 2.458

-.053 .005 -.114 .207 .140

.696 .971 .371 .037 .227

Model

b

SE b

β

p value

Age group Mean thickness FD Mean thickness x Age group FD x Age group

-.085 -.281 −1.088 3.324 4.202

.231 1.397 1.590 1.397 1.818

-.049 -.026 -.086 .231 .263

.712 .841 .495 .019 .022

B Predicting memory

C Predicting crystallized IQ

D Predicting fluency

3.7. Vertex-wise analyses The first of the vertex-wise analyses, investigating the main effect of age group on cortical thickness after controlling for FD, identified numerous clusters in both hemispheres that survived correction. As can be seen in Fig. 5, the clusters were concentrated bilaterally in medial/superior anterior and posterior cortex, and posterior lateral and inferior prefrontal cortex. The second vertex-wise analysis, investigating the effect of the age group × pR interaction on cortical thickness after controlling for age

Fig. 4. Partial plots showing the relationships across participants between FD and pR (after controlling for mean thickness) in each age group. 6

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4. Discussion

Table 6 Results of the regression model investigating variables (age group, pR, FD, age x pR and age × FD interactions) that predict mean cortical thickness. Model

b

SE b

β

p value

Age group pR FD pR x Age group FD x Age group

-.175 -.015 -.323 .209 .178

.024 .053 .104 .057 .116

−1.077 -.018 -.272 .550 .119

.000 .780 .002 .000 .128

Replicating numerous prior findings (e.g., Lee et al., 2018; Madan and Kensinger, 2018; Rast et al., 2017; and reported in brief for this data-set in King et al., 2018), we observed a robust negative relationship between age group and mean cortical thickness, and the inclusion of an estimate of in-scanner head motion as a covariate had only a modest impact on this relationship. Additionally, head motion itself was negatively correlated with thickness estimates independently of age. These findings are consistent with previous reports that estimates of cortical thickness are negatively biased by head motion (Reuter et al., 2015; Savalia et al., 2017), while also suggesting that the bias does not seriously confound the effects of age on thickness estimates. Crucially, mean cortical thickness predicted performance on both the experimental memory test and cognitive constructs derived from scores on a battery of standardized neuropsychological tests. These associations between thickness and cognitive performance were negative in the younger participants, but positive in the older sample. Below, we discuss the implications of these and related findings. As just noted, estimates of mean cortical thickness in both the younger and older groups correlated robustly with associative recognition performance (pR). Strikingly, these correlations were in opposite directions in the two age-groups, yielding the robust age group × thickness interaction in the regression model summarized in Table 4. The resulting pattern conforms to the prediction outlined in the Introduction of a U-shaped function across age groups for the regression slopes relating estimates of thickness and cognitive performance. Analogous but weaker results were obtained for the cognitive constructs derived from the neuropsychological test scores: the age group × thickness interaction term was reliable for the regression analyses conducted on three of the constructs – processing speed, crystallized intelligence and fluency - and the term approached significance for the memory construct. In each case the component scores correlated positively with cortical thickness in the older sample, albeit non-significantly in the case of crystallized intelligence and fluency, and correlated negatively, although non-significantly, in the younger group. The absence of significant negative correlations for these constructs in the younger group should be treated with caution: the correlations were of moderate size and in the appropriate direction (−0.232, −0.165, −0.222 and −0.257 for speed, memory, intelligence and fluency respectively), and may have been attenuated by virtue of the lower variance of the scores in the younger relative to the older group. The question arises as to why the relationship between mean cortical thickness and pR tended to be stronger than its relationship with the component scores derived from the neuropsychological test battery. One potential answer, noted above, is that the differing strengths of the relationships reflect differences in across-participant variance in the

Crystallized intelligence (F5,132 = 30.20, p < 0.001, adjusted R2 = 0.525); and fluency (F5,132 = 30.14, p < 0.001, adjusted R2 = 0.525). Notably, in each case, the age group x factor score interaction term was a significant predictor of mean cortical thickness.

Fig. 5. Clusters demonstrating where age group predicts cortical thickness after controlling for FD. This effect is superimposed on the bilateral inflated surfaces of a standardized brain.

group, pR and FD, identified three small clusters – in medial prefrontal, occipital and posterior cingulate cortex – that survived correction (see Fig. 6). To elucidate the relationships between cortical thickness and pR in these regions, we extracted mean thickness measures for each participant from the vertices forming each of the three clusters. Partial correlation analyses between the measures and pR (controlling for FD) were then conducted separately for each age group. Consistent with the analyses of whole brain thickness, robust negative correlations between thickness and pR were evident for each region in the younger group (min r = −0.495, max p < 0.005), whereas positive correlations were identified in the older group (min r = 0.361, max p < 0.005). We repeated the foregoing analysis after replacing pR with each of the four cognitive factor scores in turn. In no case did any clusters survive whole brain correction for multiple comparisons.

Fig. 6. Clusters demonstrating where the age group × pR interaction term predicts cortical thickness after controlling for age group, pR and FD. This effect is superimposed on the bilateral inflated surfaces of a standardized brain. 7

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participants in terms of the functional benefit that arises from ‘pruning’ processes that optimize the efficiency of local and distal neural connectivity during cortical development. The present findings for our younger sample are highly reminiscent of those reported by Schnack et al. (2015), and there seems little reason to suppose that a similar account would not apply here. To the extent that the account is valid, it implies that the functional consequences of individual differences in cortical development are detectable well into early adulthood (some 60% of our sample were aged between 21 and 29 yrs). The reversal of this association in older individuals implies that, after optimal cortical thickness has been attained, any additional thinning that occurs later in life is detrimental for cognitive function. Of course, this does not imply that the underlying mechanisms and microstructural correlates of individual differences in cortical thickness are invariant across the lifespan. Arguably, it is more plausible to suppose that the factors mediating the negative relationship between cortical thickness and cognition in younger individuals (e.g., ‘synaptic pruning’) are different from the factors (e.g., myelin loss) mediating the reverse relationship that emerges in later life. Future research employing MRI methods sensitive to microstructural cortical architecture (e.g., Zhang et al., 2012) might allow this and related issues to be resolved. It should be noted that whereas we used cortical thickness to predict performance on an experimental memory test and on component scores derived from a neuropsychological test battery, Schnack et al. (2015) operationalized cognitive ability in terms of WAIS/WISC IQ. We used our data to approximate this metric by summing the standardized scores for the tests in our battery that corresponded most closely to WAIS sub-tests, namely, Digit-Symbol substitution, WTAR, Raven's and Digit-Span. The means (standard deviations) of this metric were 1.20 (2.31) and −0.630 (2.74) for the younger and older groups respectively (t(95) = −6.51, p < 0.001, equal variances not assumed). Consistent with the findings of Schnack et al. (2015), this very rough proxy for IQ correlated positively with cortical thickness in our older sample (partial r = 0.299, controlling for head motion, p < 0.02) and negatively, albeit non-significantly, in the younger group (partial r = −0.186). As is evident from Fig. 5, the relationship between cortical thickness and age group was sufficiently robust to survive vertex-wise correction for multiple comparisons in several regions, prominent among which were dorsomedial prefrontal and posteromedial cortices. These whole brain findings are consistent with those from prior cross-sectional studies that adopted similar approaches to examining the relationship between age and cortical thickness over the lifespan at a regional level (e.g., Fjell et al., 2009). Additional findings for the mean thickness estimates indicated that they were differentially associated with associative recognition performance across age groups, as evidenced by the highly significant age group x thickness and age group × pR interaction terms in the regression models employed to predict pR and cortical thickness respectively (Tables 4 and 6). These findings also had regionally localized counterparts: when we expanded the second of these regression models to a vertex-wise analysis, the age group × pR interaction term was a significant predictor of thickness in the three small clusters illustrated in Fig. 6. In all three cases, follow-up analyses indicated that the interaction reflected a cross-over in the slopes of the regression lines analogous to those illustrated in Fig. 3. One of these regions – in the vicinity of the posterior cingulate – overlapped the medial posterior region where thickness declined with increasing age (cf. Figs. 5 and 6). This overlap suggests that the structural characteristics of the posterior cingulate are not only age-sensitive, but play a role in mediating associative memory performance (indeed, we think it is no accident that this region demonstrates robust ‘negative’ subsequent memory effects in fMRI studies of associative encoding, and that these effects are attenuated with increasing age; Park et al., 2013; de Chastelaine et al., 2015; de Chastelaine et al., 2011). The present findings seem most easily accommodated by the proposal (see above) that the computational efficacy of the posterior cingulate is negatively related to thickness in younger adults but is positively related to

respective measures. Another possibility, noted by an anonymous reviewer, is that pR is a more reliable (less ‘noisy’) measure than the construct scores. A final, more interesting, possibility is that pR more effectively captures variance in the latent cognitive construct (or constructs) that mediates the relationship between cognition and cortical thickness. By this argument, our experimental memory procedure either makes heavier demands on this construct than do the tests in our neuropsychological battery, or it is a more reliable proxy for the construct. In support of these possibilities, we note that pR correlated moderately with each of the component scores (partial r, controlling for age group, = 0.304, 0.387, 0.339 and 0.287 for the speed, memory, crystallized IQ and fluency components respectively, all ps < 0 0.001). Despite these correlations, however, when the regression model summarized in Table 4 was expanded to include the component scores for each construct as four additional predictor variables, the age group x cortical thickness interaction term remained a highly significant predictor of pR (standardized beta = 0.251, p = 0.002), indicating that the interaction term was sensitive to variance unique to pR. By contrast, adding pR to the regression models predicting the component scores for each of the constructs rendered each interaction term far from significant (min p = 0.116). These findings are consistent with the proposal that the interactions (or, in the case of the memory component, the near-significant interaction) observed between age group and cortical thickness for the different cognitive constructs were each explaining variance shared with pR. Which of the doubtless numerous cognitive abilities reflected in this measure of associative recognition performance is responsible for its strong association with cortical thickness is uncertain and requires further research. On the basis of the findings of Schnack et al. (2015) however (see below), we suspect the answer might lie in the extent to which pR uniquely shares variance with general intelligence, another cognitive measure that demonstrates a robust relationship with cortical thickness. The findings for the older group are consistent with prior reports of a positive association between cortical thickness and cognitive performance in healthy older adults (e.g., Knopman et al., 2018; Schnack et al., 2015; Yuan et al., 2018). It is tempting to interpret these findings in terms of differential aging effects (that is, as reflecting individual differences in cortical thinning over time). It should be acknowledged, however, that with cross-sectional, within-cohort data such as those comprising the present data-set we cannot rule out the possibility that the relationship between cortical thickness and cognitive performance in our older participants was also present in early life, rather than emerging over the course of their lifespans (cf. Rugg, 2016). This possibility is lessened by the negative correlation that we observed between cortical thickness and cognitive performance in the younger group, and the null relationship evident in the middle-aged group. These differing patterns of correlations between cortical thickness and cognitive performance in the three age groups seem unlikely to be attributable to birth cohort or related confounds (although, as noted by an anonymous reviewer, the possibility that the different patterns reflect an interaction between aging and cohort effects cannot be ruled out). Thus, they point to a dynamic relationship between thickness and cognition across the lifespan of the kind proposed by Schnack et al. (2015) and discussed below. As was noted in the Introduction, negative associations between cognitive performance and cortical thickness in adolescents and younger adults have been reported previously (Tamnes et al., 2010; Schnack et al., 2015; see Daugherty et al., 2017, and Schlichting et al., 2017, for analogous findings for hippocampal volume). Notably, in a study that combined cross-sectional and longitudinal approaches, Schnack et al. (2015) reported that full-scale IQ was negatively correlated with rate of cortical thinning (predominantly in the left hemisphere) between the ages of approximately 10 and 21 years, and with mean cortical thickness (again mainly in the left hemisphere) until approximately age 30. These correlations began to reverse direction in middle age. The authors interpreted the findings from their younger 8

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Acknowledgements

thickness in older individuals. We think it very likely that there are other specific cortical regions where thickness also drives the relationships we observed with pR, but which were not identified in the present study due to the combination of relatively small sample sizes and the conservative statistical thresholds required for whole-brain vertex-wise analyses. Identification of these additional regions (should they exist) will have to await a larger and better powered study. Finally, we comment on our findings for head motion (FD). FD estimates derived from the five functional scanning sessions were strongly correlated across participants. Notably, even for the scans occurring on either side of an approximately 15 min rest outside of the scanner, the correlation between motion estimates approached 0.9. These findings are consistent with prior suggestions that across-participant variance in within-scanner head motion originates in part from stable individual differences in predilection for movement (Geerligs et al., 2017). Further support for this idea comes from the finding that in the younger and middle-aged participants, though not in the older group, motion estimates were predictive of pR. One interpretation of these findings arises from the assumption that in-scanner motion has multiple determinants. We assume that one of these determinants is the ability to comply with instructions to remain immobile and focus on the experimental task. We further assume that individuals in whom this ability is lacking are more distractible (see Couvy-Duchesne et al. (2016) for evidence that propensity for motion and distractibility have a common genetic basis), less attentive to task demands and, in the present case, less likely to engage in effective encoding and retrieval processing. A second determinant of motion, however, is more situation-specific and reflects amount of physical discomfort – and hence the need for regular postural adjustment – experienced in the scanner. We conjecture that this second determinant made an especially large contribution to across participant variance within the older age group. This over-shadowed the influence of the trait-like factor associated with task performance, diluting the relationship between motion and performance that was evident in younger and middle-aged participants. In conclusion, the present findings add to the evidence that the nature of the relationship between cortical thickness and cognitive performance is age-dependent, reversing its slope between younger and older adults and highlighting that when it comes to the (younger) brain, bigger is not always better. In addition, the findings replicate prior reports that in-scanner head motion negatively biases cortical thickness estimates, and suggest that, in younger and middle-aged individuals at least, head motion is predictive of in-scanner cognitive performance independently of its impact on measures of cortical thickness.

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Funding This work was supported by the National Institute on Aging (grant number 1RF1AG039103).

Declarations of interest None.

CRediT authorship contribution statement Marianne de Chastelaine: Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Visualization, Writing - original draft, Writing - review & editing. Brian E. Donley: Investigation, Methodology, Project administration. Kristen M. Kennedy: Methodology, Resources, Software, Validation, Writing review & editing. Michael D. Rugg: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Software, Supervision, Validation, Writing - original draft, Writing review & editing. 9

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