Age-related slowing of task switching is associated with decreased integrity of frontoparietal white matter

Neurobiology of Aging 31 (2010) 512–522

Age-related slowing of task switching is associated with decreased integrity of frontoparietal white matter Brian T. Gold a,∗ , David K. Powell d , Liang Xuan d , Greg A. Jicha b,c , Charles D. Smith b,c,d a

Department of Anatomy and Neurobiology, Chandler Medical Center, University of Kentucky School of Medicine, Lexington, KY 40536-0298, USA b Department of Neurology, Chandler Medical Center, University of Kentucky, Lexington, KY, USA c Alzheimer’s Disease Center and Sanders-Brown Center on Aging, Chandler Medical Center, University of Kentucky, Lexington, KY, USA d Magnetic Resonance Imaging and Spectroscopy Center, Chandler Medical Center, University of Kentucky, Lexington, KY, USA Received 20 November 2007; received in revised form 28 March 2008; accepted 10 April 2008 Available online 20 May 2008

Abstract A body of research has demonstrated age-related slowing on tasks that emphasize cognitive control, such as task switching. However, little is known about the neural mechanisms that contribute to this age-related slowing. To address this issue, the present study used both fMRI and DTI in combination with a standard task switching paradigm. Results from the fMRI experiment demonstrated task switching cost (switching vs. nonswitching) activations in a network of frontoparietal and striatal regions in the young group. The older group recruited a similar network of regions, but showed decreased spatial extent of activation and recruited several regions not activated in the young group. White matter (WM) ROIs bordering the cortical network showing task switching activation were then selected to explore potential relationships between task switching reaction time (RT) cost and fractional anisotropy (FA) in the same groups of participants. Results demonstrated a negative correlation between switch cost RT and FA in left frontoparietal WM in both young and older groups. In addition, age-related FA decline in the same frontoparietal WM region was found to mediate age-related increases in RT switch costs. These findings identify decreased integrity of frontoparietal WM as one mechanism contributing to age-related increases in RT switch costs. © 2008 Elsevier Inc. All rights reserved. Keywords: Aging; Neuroimaging; White matter; Fractional anisotropy; Task switching; Cognitive control

1. Introduction Normal human aging is associated with slowing on a number of cognitive tasks. Age-related slowing has often been observed on tasks that emphasize cognitive control processes (Salthouse et al., 1998; West, 1996; Kramer et al., 1999). Cognitive control refers to a set of processes that enable humans to flexibly shape thoughts and behavior in order to accomplish internal goals. An important component of cognitive control involves the ability to redirect attention in a flexible manner in order to meet shifting task demands. One of the most common ways to explore this cognitive skill is through the use of the task switching paradigm, in which participant reaction time (RT) is compared when they switch between two simple ∗

Corresponding author. Tel.: +1 859 323 4813; fax: +1 859 257 6700. E-mail address: [email protected] (B.T. Gold).

0197-4580/$ – see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.neurobiolaging.2008.04.005

tasks vs. performing either task alone (Jersild, 1927). When participants are required to switch between two tasks there is an increase in RT, termed a switch cost, compared to performing either task in isolation (Rogers and Monsell, 1995; Kramer et al., 1999). A body of data has demonstrated age-related slowing of task switching (Kramer et al., 1999; Kray and Lindenberger, 2000; Cepeda et al., 2001). For example, a study of participants between 7 and 82 years old performing a task switching paradigm resulted in a U-shaped function for RT switch costs. The largest switch costs were observed for young children and older adults (Cepeda et al., 2001). Functional neuroimaging studies have provided important information related to the neural bases of task switching in young and older adults. A number of studies have demonstrated that young adults recruit a distributed network of frontoparietal and striatal regions when switching between tasks (DiGirolamo et al.,

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2001; Dove et al., 2000; Brass and von Cramon, 2002; Braver et al., 2003; Reynolds et al., 2004; Badre and Wagner, 2006). Compared to young adults, older adults have been found to under-recruit some of these frontoparietal regions and recruit additional regions during task switching and other executive control tasks (Reuter-Lorenz et al., 2000; DiGirolamo et al., 2001; Milham et al., 2002). Despite such important knowledge related to functional neuroanatomy, the neural basis of age-related slowing of task switching remains poorly understood. This may be due in part to the fact that, until recently, no technique existed for the measurement of microstructural properties of cerebral white matter (WM) in vivo. A long-standing hypothesis is that cognitive RT depends on microstructural properties of cerebral WM, such as degree of myelination (Flechsig, 1920). Recently, a magnetic resonance technique called diffusion tensor imaging (DTI) has been shown to provide information about WM microstructure in vivo (Basser et al., 2000; Le Bihan, 2003). DTI is a structural technique that does not reflect participant responses during scanning. Rather, the behavioral and imaging data are acquired separately and subsequently correlated. The DTI technique is based upon sensitizing the MR signal to the self-diffusion of water and provides a voxel-by-voxel estimate of both the degree and orientation of directionality along which water molecules diffuse preferentially. The degree to which molecular displacements are directionally dependent is referred to as fractional anisotropy (FA). The FA ranges from 0, representing diffusion that is equal in all directions, to 1, representing diffusion that occurs exclusively along one direction. The biological variables contributing to FA have not been fully identified but include degree of myelination, and the density and orientational coherence of axons (reviewed in Beaulieu, 2002). For example, histological studies have demonstrated that myelination of axons increases anisotropy (Wimberger et al., 1995) and demyelination of axons decreases anisotropy (Werring et al., 1999). Consequently, The FA measure varies systematically across different compartments of the brain. For example, FA is low in the ventricles, where water movement is relatively unconstrained and thus isotropic. In contrast, FA is relatively high in cerebral WM, because the highly organized structure of WM fiber tracts causes water diffusion to be anisotropic, or unequal across different directions (Basser et al., 1994a, 2000; Le Bihan, 2003; Catani, 2006). The FA measure has been used primarily as an index of WM integrity (Pfefferbaum et al., 2000; Sullivan et al., 2001; O’Sullivan et al., 2001). More recently, the FA measure has also been shown to be useful in the identification of WM correlates of RT. The established relationship between degree of myelination and speed of nerve conduction velocity (Jack et al., 1983) suggests that increased FA in specific WM regions may be associated with behavioral RT. Several studies have reported a relationship between regional FA and RT, across a number of different cognitive tasks in healthy young adults (Madden et al., 2004; Nagy et al., 2004; Tuch et al., 2005;

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Gold et al., 2007) and older adults (Madden et al., 2004, 2007; Bucur et al., 2007). A potentially powerful design that could be used to study the neural bases of age-related slowing of task switching would be one that combines a standard task switching behavioral paradigm with both fMRI and DTI methods. With such a design, fMRI could be used to identify the network of cortical regions involved in task switching in young and older adults. Knowledge about the cortical network involved in task switching in could then be used to guide selection of WM ROIs that border the task switching cortical network in the same groups of participants. One could then ask whether (1) speed of task switching correlates with FA in WM regions proximal to task-relevant cortical regions and, if so, (2) whether age-related FA decreases in any of these WM regions contribute to age-related slowing of task switching. In the present study, we employed both fMRI and DTI methods in combination with a standard task switching behavioral paradigm. The goal of the study was to identify neural correlates of age-related slowing in task switching. We therefore used a blocked fMRI design that focused on mixing costs (the difference between average performance in the mixed task condition vs. average performance in the single task condition) rather than trial-by-trial switching costs (the difference between average performance for switch trials in the mixed task condition vs. nonswitch trials in the mixed task condition) because age-related switching differences tend to be larger for mixing costs than trial-by-trial switching costs (Kray and Lindenberger, 2000; Meiran et al., 2001).

2. Methods 2.1. Participants The research procedures were approved by the Institutional Review Board of the University of Kentucky Medical Center, and all participants provided informed written consent. Participants were 20 young adults (10 men) between the ages of 19 and 27 (M = 24.3, S.D. = 3.9) and 20 older adults (10 men) between the ages of 63 and 76 (M = 68.3, S.D. = 4.6). Young and older groups had comparable number of years of education (younger adults’ M = 15.7; older adults’ M = 16.2). Participants were community-dwelling individuals who were right-handed, with normal or correctedto-normal visual acuity. Potential participants completed a basic medical screening questionnaire. All participants reported to being free of hypertension, and neurological/psychiatric disorders. None of the participants reported to taking psychotropic medications. Participants scored a minimum of 28 points on the Mini Mental State Exam. A senior neurologist (C.D.S.) reviewed the T1-weighted images for cortical atrophy and ventricular enlargement and the T2weighted images for white matter signal hyperintensities, using previously described methods (Smith et al., 2000a,b).

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All participants were found to be within normal age-related limits. 2.2. Stimuli Stimuli were number-letter pairs (e.g., “4e”) from a set of 10 letters and 10 digits. The set of 10 letters were five consonants (“b”, “k”, “n”, “s”, and “t”) and five vowels (“a”, “e”, “i” “o”, and “u”), and the set of 10 digits were five odd numbers (“1” “3”, “5”, “7”, and “9”), and five even numbers (“0”, “2”, “4”, “6”, and “8”). The number and letter positions were counterbalanced across pairs (e.g., “4n” or “n4”). 2.3. Task and procedure The number-letter task (Rogers and Monsell, 1995) was used. On each trial, a cue (either LETTER or NUMBER) appeared for 200 ms, indicating that the upcoming task would be a letter decision, or a number decision. The cue was followed immediately by a number-letter pair (e.g., “4e”), which appeared for 2300 ms. In the letter task, participants categorized the letter as a consonant or vowel. In the number task, participants categorized the number as odd or even. Decisions were indicated via a button press using the left or right hand, which was counterbalanced across subjects. A blocked design was used involving task blocks, and fixation blocks [in which participants focused their vision on a central cross hair (+)]. Task blocks were 60 s in duration, and fixation blocks were 30 s in duration. There were three runs. Run structure was similar to that used in DiGirolamo et al. (2001). Each run contained 4 task blocks and 5 fixation blocks and lasted 6.5 min. One run consisted of two blocks of each of the letter task and number task. The other two runs contained one block each of the letter task and number task and two switching blocks (in which the letter and number tasks switched pseudorandomly; every second or third trial on average). The order of runs and task blocks within runs was counterbalanced across participants. Prior to the scanning session, participants completed a practice session outside the magnet. The practice session consisted of 30 trials for each task singularly, and then 50 trials of the switching condition, divided evenly between switch and nonswitch trials. 2.4. MRI acquisition Data were collected on a 3T Siemens Magnetom Trio MRI scanner, using an 8-channel head array coil, at the University of Kentucky Magnetic Resonance Imaging and Spectroscopy Center. Foam padding was used to limit head motion within the coil. Five types of image sequences were collected for each participant: (1) a standard low-resolution anatomic localizer; (2) a high-resolution, T1-weighted sequence for the subsequent localization of fMRI activity in standard stereotactic space; (3) T2*-weighted images sensitive to the BOLD signal for estimation of fMRI activity; (4) a B0 field

map sequence for subsequent geometric unwarping of T2*weighted images; (5) diffusion tensor images for estimation of fractional anisotropy. T2*-weighted images were acquired using a gradientecho EPI sequence (TR = 2500, TE = 30 ms, flip angle = 81◦ , 38 axial slices, FOV = 224 × 224, image matrix = 64 × 64, isotropic 3.5 mm voxels), covering the entire cerebrum and the upper cerebellum. High-resolution, 3D anatomic images were acquired using an MP-RAGE sequence (TR = 2100 ms, TE = 2.93 ms, TI = 1100 ms, flip angle = 12◦ , FOV = 224 mm × 256 mm × 192 mm, 1 mm isotropic voxels, sagittal partitions). Diffusion tensor imaging used a fluid attenuated inversion recovery EPI sequence (TR = 13,600 ms, TE = 84 ms, TI = 2500 ms, flip angle = 90◦ , FOV = 224 × 224, image matrix = 128 × 128, 6 signal averages, 40 axial slices of 3 mm thick, with no interslice gap, in-plane voxel resolution = 1.75 mm2 ), covering the whole cerebrum. Diffusion was measured in six directions, and one image with no diffusion weighting. The directions were (x, y, z) = (0, 0, 0), (1, 1, 0), (1, −1, 0), (1, 0, 1), (1, 0, −1), (0, 1, 1), (0, 1, −1), where 1 indicates a gradient applied in that direction (Basser and Pierpaoli, 1996). 2.5. Analysis of fMRI data fMRI data was analyzed with AFNI software (Cox, 1996) using a series of steps similar to those in Gold et al. (2006). Preprocessing of the fMRI data included the following steps. After discarding the first few functional volumes (8 s) of each run, differences in timing between slices were adjusted with sinc interpolation. Images were then registered to the image collected closest in time to the anatomical image using a 6-parameter rigid body transformation (Cox and Jesmanowicz, 1999). Images were then unwarped via B0 field maps (using FSL software; http://www.fmrib.ox.ac.uk/fsl) to reduce non-linear magnetic field distortions. Finally, images were smoothed with a 4 mm FWHM Gaussian filter and intensity normalized to enable activation measures expressed as percent signal change. Multiple regression was performed on each participant’s preprocessed time-series to provide simultaneous parameter estimates of each condition (Glover, 1999). Each regressor of interest consisted of a square wave for each condition compared to baseline fixation, convolved with a standard γ-variate function to account for the slow hemodynamic response (Cohen, 1997). Nuisance regressors included in the model were each run’s mean, linear trend and movement parameters estimated during image registration. Each participant’s regressor files were then transformed into standardized space using a combined young-older target. Participant’s T1weighted images were first transformed into standard space (Talairach and Tournoux, 1988) and then averaged using AFNI’s 3dmerge program. Each participant’s original T1weighted images in native space were then transformed to this merged young–old target. Participant’s regressor files were then transformed to standard space, using landmarks from

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their anatomical datasets, and resampled at 2 mm3 resolution using cubic spline interpolation. Group-based, mixed-effects t-tests were then performed on regressor datasets in standard space. For each age group, ttests compared switching vs. nonswitching conditions (fixed effect), with each participant serving as the repeated measure (random effect). Whole-brain maps were thresholded at p < 0.001, and a cluster threshold of 5 contiguous voxels. In addition, young and older groups’ task switching activation patterns were compared directly to each other via a group (young vs. older) by condition (switching vs. nonswitching) interaction (conjunction) analysis. Maps for the conjunction analysis were thresholded at p < 0.01 and a cluster threshold of 5 contiguous voxels. Activation maps were projected onto a semi-inflated surface using Caret software (Van Essen et al., 2001). In order to obtain unbiased BOLD magnitude estimates within ROIs, group-specific ROI masks were generated to surround that group’s peak coordinates from the comparison of both active conditions with baseline fixation. ROI masks were generated to include all voxels activated at p < 0.001, within 12 mm surrounding the peak and were then applied to each subject’s regressor image to extract mean percent signal change from each condition compared to baseline fixation. 2.6. Analysis of DTI data Preprocessing of diffusion weighted images was carried out using FSL software (http://www.fmrib.ox.ac.uk/fsl). Registration was performed using FLIRT and used a 12parameter affine transformation of the images from each direction to the non-diffusion weighted image to correct for motion and residual eddy current distortion. Each participant’s diffusion tensor was then calculated and diagonalized. Fractional anisotropy was then calculated using procedures described previously (Basser et al., 1994b). Briefly, three eigenvectors that define the diffusion ellipsoid were calculated in each voxel from the diffusion tensor. These eigenvectors correspond to three eigenvalues, which represent the magnitude of diffusivity in the three principal directions. Based upon the three principal diffusivities, FA was calculated in each voxel (Basser and Pierpaoli, 1996). Analyses were conducted on FA images in native space (Madden et al., 2004, 2007; Bucur et al., 2007) in order to avoid confounds associated with spatial normalization and smoothing of DTI data (Jones et al., 2005). ROIs were traced on each participant’s FA volume in native space using Analyze 6.0 software. Images were first corrected for minor head rotation. All ROIs were traced on axial slices. Following Madden et al. (2004, 2007), prior to tracing, the maximum FA image intensity was set to a low value (0.25) so that all voxels with FA values greater than this maximum were saturated and appeared homogeneously as pure white. This allowed visualization and exclusion of pixels with low FA values (e.g., below 0.25) from our ROIs that are likely to represent grey

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matter or WM consisting of multiple crossing tracts. ROIs were then drawn to include all saturated (white) pixels within the specified boundaries. Intra-rater reliability was assessed by having the rater re-trace each ROI on 10 randomly selected participants. The total volume in each ROI was then computed automatically by the Analyze program. Intraclass correlations were run between ROI volumes in order to estimate the degree of spatial overlap between tracings. Reliabilities were calculated separately for each hemisphere. A minimum intraclass reliability coefficient of 0.90 was required for an ROI to be considered in subsequent RT–FA analyses. The RT–FA relationship was explored in 12 WM ROIs. We selected WM ROIs bordering the distributed network of task switching cortical regions (from the switching–nonswitching contrast) activated by the same participants in the fMRI experiment. All WM ROIs except one (the uncinate fasciculus; discussed below) bordered task switching fMRI activations that were common to both the young and older adult groups. The commonly activated cortical regions included bilateral frontoparietal cortices, cingulate gyrus, and striatal nuclei (caudate, putamen). Thus, WM ROIs were: dorsal portions of the superior longitudinal fasciculus (SLF) connecting lateral frontal cortex with parietal cortex; WM in a pericallosal frontal (PCF) region and WM in the anterior limb of the internal capsule (ALIC), both of which contain connections between lateral prefrontal cortex and striatal nuclei (Behrens et al., 2003); the cingulum (CIN) bundle connecting portions of the cingulate gyrus (and the cingulate gyrus with medial temporal lobe structures); the genu (GN) of the corpus callosum interconnecting left and right frontal cortices; the splenium (SPN) of the corpus callosum, interconnecting left and right parieto-occipito-temporal cortices. The older adult group also showed activation in lateral temporal cortex, raising the possibility that their task switching RT was moderated by FA in a frontotemporal network. To explore this possibility, an additional WM ROI was placed in the uncinate (UNC) fasciculus connecting lateral temporal regions with lateral frontal regions. A representation of the ROIs is presented in Fig. 1. The GN, SPN and ALIC ROIs were clearly visible on FA images and standard boundaries were used in tracing these regions (Haines, 2004). Boundaries for tracing other WM regions were those suggested in previous research (Madden et al., 2004, 2007; Bucur et al., 2007). The PCF was traced on slices containing the genu and used boundaries suggested to target the anterior centrum semiovale, where fibers associated with the dorsolateral prefrontal cortical areas and related subcortical striatal structures would be expected to course (Madden et al., 2004). The PCF ROI was bounded anterior-laterally by grey matter at the fundi of the frontal sulci, medially by the anterior cingulate, and posteriorly by insular cortex. The frontoparietal ROIs targeted the superior portion of the SLF connecting lateral prefrontal cortex with parietal cortex. This ROI was bounded anteriorly by prefrontal grey matter, posteriorly by parietal grey matter, laterally by temporoparietal grey matter, and medially by regions of low FA, likely cor-

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Fig. 1. Representations of ROIs. The twelve ROIs are shown on the FA images of a representative participant from the older group (left and middle panels) and younger group (right panel). The ROIs were: the superior longitudinal fasciculus (SLF); the cingulum (CIN) bundle; pericallosal frontal (PCF) WM; the genu (GN) of the corpus callosum; the anterior limb of the internal capsule (ALIC); the splenium (SPN) of the corpus callosum; and the uncinate (UNC) fasciculus.

responding to WM containing a mixture of fibers from the SLF and descending corona radiata (Wakana et al., 2004). This ROI was defined on the first slice in which the lateral ventricles were no longer present and ended when the white matter tracts were no longer present. The CIN was bounded medially by grey matter from the cingulate gyrus and laterally by a vertical line drawn from the most lateral extension of the anterior cingulate gyrus to grey matter from the precuneus. The CIN ROI was defined beginning on the same first slice as the SLF and ending when the anterior cingulate gyrus was no longer distinct from the body. The uncinate fasciculus was bounded anteriorly by a diagonal line drawn from grey matter of the medial temporal lobe to grey matter from the diencephalon, and posteriorly by a horizontal line drawn from the most anterior portion of the midbrain. This ROI was defined on the last slice in which the midbrain was present. Mean FA was computed in each ROI for each participant. The study was concerned with higher attention components of task switching, as opposed to visual perceptual or motor output (button press) components. Thus, participant’s mean FA values in each ROI were correlated with their switch cost RTs (switching–nonswitching) from correct trials in order to account for age-difference in perceptual and motor speed (which should be consistent across conditions). Multiple regression analyses were then used to isolate RT variance uniquely associated with FA in each ROI. Four separate models were run. In one model all participants were included regardless of age group to maximize the sample size/power of detecting RT–FA relationships. The other models were intended to identify RT–FA relationships that are common or distinct between the two age groups and were thus run separately for younger and older adult groups. In each model, mean RTs were entered as outcome variables, with ROI and sex as predictor variables. The forth regression analysis was conducted to determine whether the strength of any RT–FA relationships observed in both young and older groups were moderated by age group. The predictor vari-

ables for this model were ROI, age and their interaction terms. Further analyses were conducted to explore the possibility of age-related FA declines in the WM ROIs employed in the above RT–FA analysis. Multivariate analysis of variance (MANOVA) was conducted using age group as the between-subjects variable and the ROIs as separate dependent variables. Univariate tests of the overall significant MANOVA age group difference were then conducted for each ROI.

3. Results 3.1. Behavioral Switching costs were computed as participants’ mean RT for the switching blocks minus their mean RT for nonswitching blocks (sometimes referred to as mixing costs). Table 1 summarizes RTs on correct trials and accuracies of each group. Overall, older adults had longer RTs than young adults [F(1, 37) = 49.1, p < 0.001]. Both groups had longer RTs during the switching condition than the nonswitching condition [F(1, 38) = 93.4, p < 0.001]. Finally, there was an age × condition interaction such that older adults had larger RT switch costs than young adults [F(1, 38) = 5.5 p < 0.01]. In terms of accuracy, the older adult group made significantly more errors than the younger group [F(1, 37) = 14.1 p < 0.05], and both groups made significantly more errors during the switching condition than the nonswitching condition [F(1, 38) = 23.5 p < 0.001]. However, there was no age × condition interaction in accuracy switch costs [F(1, 38) = 2.63 p = 0.16]. Fig. 2 presents results from the fMRI experiment. In the young adult group, increased activation in the switching condition compared to the nonswitching condition (switch cost activation) was observed in a network of predominantly left-hemisphere frontoparietal regions including: dorsolateral

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Table 1 Mean performance data (standard deviations) and switch costs according to age group Reaction time

Young adult Older adult

Accuracy

Noswitch

Switch

Cost

Noswitch

Switch

Cost

692 (83) 795 (98)

865 (58) 1077 (127)

173 282

98 (1.8) 95 (2.1)

93 (1.9) 85 (2.6)

5 10

prefrontal cortex (DLPFC: ∼BA 46), anterior left inferior prefrontal cortex (aLIPC: ∼BA 45), posterior left inferior prefrontal cortex (pLIPC: ∼BA 44/6), cingulate gyrus (∼BA 24/32), inferior parietal cortex (IPC: ∼BA 7/40), and striatal regions (caudate and putamen; not shown). The same comparison in the older group resulted in activation of a similar network of frontoparietal regions. The observation that the two groups showed overlapping fMRI activation patterns led us to select a common set of WM ROIs to explore potential RT–FA relationships in the young and older groups (see Section 2.6 for a description of WM ROIs). However, further analyses demonstrated that there were also significant differences in fMRI activation patterns between the young and older groups. A direct comparison

between the two groups’ whole-brain switch cost activation patterns was conducted via a group by condition interaction analysis. Results from the interaction analysis (panel B) demonstrated that the young group showed a larger spatial extent of activation than the older group in four left frontoparietal regions: DLPFC, aLIPC, pLIPC, and IPC. By contrast, the older group showed activation in two regions not activated in the young group: left middle temporal cortex (∼BA 22) and right anterior prefrontal cortex (∼BA 45). The above results address the issue of spatial extent of activation but not magnitude of activation. In a second set of analyses, BOLD magnitudes were explored in the four frontoparietal regions (DLPFC, aLIPC, pLIPC, and IPC) that showed age-related decreases in spatial extent of activation

Fig. 2. fMRI results. (A) Whole-brain activation maps from the switching–nonswitching contrast in the young (left) and older (right) groups. The level of significance is indicated by the color bar at the bottom. (B) Whole-brain activation maps from a direct comparison of the switching–nonswitching contrast in the young group compared to the older group. The level of significance is indicated by the color bar at the bottom. (C) Mean BOLD magnitudes for each group in the left DLPFC and aLIPC ROIs for the nonswitching (black) and switching (white) conditions compared to fixation. Note: * p < 0.05; ** p < 0.01; *** p < 0.001.

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in the above switching–nonswitching contrast. In order to explore the possibility of group by condition interactions, a series of two-factor ANOVAs were performed, with magnitudes in each of the four ROIs as the dependent variable, and group (young vs. older) and condition (switching vs. nonswitching) as independent variables. No age by condition interactions were observed in any of the four ROIs (all ps > 0.24). However, given that older adults did show numerically smaller increases than young adults in the switching compared to the nonswitching condition (see below), the lack of an interaction could be due to the limited sample sizes included in the comparison. A series of t-tests were then performed to compare each group’s activation patterns in each condition relative to baseline fixation. Results indicated that, in each of the four ROIs, both groups showed significantly higher magnitudes during the nonswitching condition than baseline fixation, and a significant increase in the magnitude of response under the switching condition that emphasized cognitive control. An example of this pattern is presented for the left DLPFC and aLIPC regions in Fig. 2 (panel C). Together, the whole-brain and ROI magnitude results demonstrate that the older group showed continued ability to increase frontoparietal activation in response to increasing demands for cognitive control, albeit in more circumscribed portions of cortex than the young group.

Fig. 3. Results from the RT–FA analysis. The regression plot shows the significant correlations between RT switch costs and FA in left superior longitudinal fasciculus (SLF) in young and older adult groups. Each circle represents one of the 20 participants in each group. Note: Black filled circles = young adults and grey unfilled circles = older adults.

Fig. 3 presents the regression plot of the significant RT–FA negative correlation in the left SLF for both the young group [r (20) = −0.42, p < 0.05] and the older group [r (20) = −0.47, p < 0.05]. Younger and older individuals with relatively small RT switch costs tended to have high FA in the left SLF. These results demonstrate that task switching RT is in part associated with the degree of directional coherence of SLF fibers connecting left frontal and parietal regions.

3.2. RT–FA correlations

3.3. FA group differences

The multiple regression analysis that considered all participants, regardless of age group, determined that RT switch costs correlated with FA in the left superior longitudinal fasciculus (F = 5.4, p < 0.01), the right SLF (F = 4.1, p < 0.05), the left pericallosal frontal region (F = 3.7, p < 0.05), and the right PCF (F = 3.9, p < 0.05). The remaining regressors were uncorrelated with RT [sex (p = 0.31), all other ROIs (p ≥ 0.48)]. In each ROI, the RT–FA correlation was negative (left SLF [r (40) = −0.44, p < 0.001], right SLF [r (40) = −0.31, p < 0.05], left PCF [r (40) = −0.32, p < 0.05] and right PCF [r (40) = −0.34, p < 0.05]. The model for the young group indicated a relationship between RT switch costs and FA in the left SLF (F = 4.8, p < 0.05). There was a trend for a relationship between young adult’s RT switch costs and their FA in the right PCF (p = 0.10). The remaining regressors were uncorrelated with RT [sex (p = 0.46), all other ROIs (p ≥ 0.33)]. The model for older adults also revealed a correlation between RT and FA in the left SLF (F = 5.3, p < 0.01). The remaining regressors were uncorrelated with RT [sex (p = 0.34), all other ROIs (p ≥ 0.19)]. A forth model was run to determine whether the strength of the RT–FA relationship in the left SLF observed in each age group was moderated by age. Results revealed relationships between age and RT (F = 9.7, p < 0.001) and between FA in the left SLF and RT (F = 3.9, p < 0.05). However, there was no age by left SLF interaction (p = 0.78). This indicates that the relation between FA in the left SLF and RT did not differ as a function of age group.

Further analyses were conducted to explore the possibility of between-group FA differences in the WM ROIs employed in the above analysis. Preliminary analyses determined that no lateralization differences approached significance in mean FA for the ROIs that were bilateral. The average left–right FA was thus used in these ROIs. Fig. 4 presents the mean FA values in each ROI for each age group. MANOVA was conducted using age group as the between-subjects variable and the seven ROIs as separate dependent variables. Results revealed an effect of age group for the seven ROIs, F(7, 32) = 4.94, p < 0.001. Univariate tests revealed that the older group had lower FA than the young group in the following

Fig. 4. Mean FA values in each ROI by age group. For abbreviations see legend for Fig. 1. Note: Filled bars = young adults; unfilled bars = older adults; * p < 0.01; ** p < 0.001.

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Table 2 Effects of age and FA for RT switch costs r2 Model 1 Age

0.232

Model 2 FA-SLF Age

0.262 0.350

Note:

Increment in r2

F

Attenuation

% Attenuation

0.144

62.07

9.32**

0.088

13.24** 4.78*

* p < 0.05, ** p < 0.01.

WM ROIs: pericallosal frontal (p = 0.001), genu (p < 0.01), superior longitudinal fasciculus (p < 0.01) and cingulum bundle (p = 0.001). No age-related FA differences were observed in the anterior limb of the internal capsule, the uncinate fasciculus, or the splenium (all ps ≥ 0.32). 3.4. Mediation analysis The above analyses determined that the left SLF was the only WM ROI to show an RT–FA correlation in both young and older groups, and that this region also showed an age-related FA decline. Together, these findings raise the possibility that age-related FA decreases in the left SLF may mediate age-related increases in switch cost RT. Hierarchical regression analyses predicting switch cost RT were conducted to explore this possibility (Table 2). In model 1, when age group was the only predictor, 23% of the variance in switch cost RT was age-related. In model 2, when FA in the left SLF was entered first, followed by age group, FA in the left SLF was a significant predictor, accounting for 26% of the variance in RT switch costs. After controlling for FA in the left SLF, age group still accounted for significant variance in switch cost RT. However, the relationship between age and RT was significantly reduced when FA in the left SLF was taken into account. In order to assess the degree to which FA in the left SLF attenuated the amount of variance in switch cost RT that can be explained by age, we followed a procedure suggested by Salthouse (1993), and used recently by Bucur et al. (2007), in which we subtracted the amount of variance in switch cost RT uniquely associated with age (when FA in the left SLF was included in the model) from the amount of variance in switch cost RT associated with age as a sole predictor. We then divided this difference by the amount of age-related variance in switch cost RT when age was the sole predictor. Results indicated that FA in the left SLF attenuated 62% of the agerelated variance in switch cost RT.

4. Discussion The ability to redirect attention in a flexible manner in order to meet shifting task demands is an important component of cognitive control. A body of data has demonstrated that this ability, as assessed by the task switching paradigm, undergoes age-related slowing (Kramer et al., 1999; Kray

and Lindenberger, 2000; Cepeda et al., 2001). The present study focused on switch costs associated with the difference between average performance in the mixed task condition vs. average performance in the single task condition (sometimes referred to as mixing costs). By combining a standard task switching manipulation with both fMRI and DTI, the present study was able to demonstrate that variations in the microstructure of frontoparietal white matter contribute to task switching RT cost in young and older adults. In addition, results indicate that age-related increases in task switching RT costs are associated, in part, with reduced integrity of frontoparietal WM bordering a task-relevant cortical network. Results from the fMRI study demonstrated that the young adult group showed switch cost activation (increased activation resulting from the mixed task condition vs. the single task condition) in a network of frontoparietal and striatal regions, consistent with previous studies (Dove et al., 2000; Brass and von Cramon, 2002; Braver et al., 2003; Reynolds et al., 2004; Badre and Wagner, 2006). The same comparison in the older group resulted in activation of a similar network of regions. However, several age-related differences in activation patterns were observed. In particular, the older group showed decreased spatial extent of activation in left frontoparietal regions and activated additional regions not recruited by the young group. These results are consistent with a body of data suggesting that poorer performance of older adults on difficult cognitive tasks tends to be accompanied by decreased activation in regions most prominently activated in young adults, or ‘task-related under-recruitment’ and activation of additional regions not recruited by young adults, or ‘nonspecific recruitment’ (Grady et al., 1995, 1999; Backman et al., 1997; Cabeza et al., 1997a; Madden et al., 1999; Anderson et al., 2000; Reuter-Lorenz et al., 2000; Logan et al., 2002; Milham et al., 2002). The fMRI results were used to guide the selection of WM ROIs bordering the task-relevant frontoparietal cortical network for an RT–FA analysis in the same groups of young and older participants. In the regression analysis that explored RT–FA relationships across all participants, regardless of age group, correlations were observed in four WM ROIs. Specifically, negative correlations between switch cost RT and FA were observed in the left and right superior longitudinal fasciculus ROIs, and in the left and right pericallosal frontal ROIs. These results indicate that switch cost RT is in part associated with the extent of directional coherence of a distributed

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network of WM fibers connecting frontal cortex with parietal and striatal regions. The main focus of the study was to determine if agerelated decreases in FA mediate age-related increases in task switching RT cost. In the regression analyses conducted separately on data from the young and older age groups, the only ROI that showed an RT–FA association in both groups was the left SLF. Results from DTI-based tractography studies suggest that this portion of the SLF originates at or near the supramarginal gyrus (BA 40), occupies the white matter of the pericentral and frontal opercular regions and connects with lateral prefrontal regions including pLIPC (BA 44/6), and DLPFC (BA 46) (Makris et al., 2005; Catani et al., 2005). The notion that switch cost RT is influenced by microstructural properties of WM linking left lateral prefrontal and parietal cortical regions is intriguing because it is in-keeping with previous functional neuroimaging results suggesting that executive control tasks require the coordinated activity of lateral prefrontal and parietal cortical regions (Cabeza and Nyberg, 2000). It has been suggested that lateral prefrontal cortex, in particular the left DLPFC, and parietal cortical regions work together to bias attention to the selection of task-relevant information (Banich et al., 2000). The older group showed decreased FA in the SLF and several of the other WM ROIs employed in the RT–FA analysis. Specifically, in addition to the SLF, the older group also showed reduced FA in pericallosal frontal WM, the genu of the corpus callosum, and the cingulum bundle. In contrast, there were several regions in which age-related FA declines were not observed: the anterior limb of the internal capsule, the uncinate fasciculus and the splenium of the corpus callosum. These findings are broadly consistent with recent data suggesting that age-related FA decline follows a general anterior–posterior gradient, with WM in frontal regions showing the largest decreases but with FA decreases also evident in some posterior regions (Pfefferbaum et al., 2000; O’Sullivan et al., 2001; Head et al., 2004; Madden et al., 2004, 2007; Bucur et al., 2007). Similarly, recent imaging studies have demonstrated the largest age-related decreases in white matter volume in the genu of the corpus callosum and forceps region (Smith et al., 2007). Although age-related FA declines were evident in several ROIs bordering the task-related cortical network, only FA in the SLF was found to be associated with switch cost RT in both groups, raising the possibility that agerelated FA declines in this region may contribute age-related increases in switch cost RT. Hierarchical regression analyses demonstrated that the relationship between age and switch cost RT was significantly reduced when FA in the left SLF was taken into account. We interpret this finding to suggest that age-related slowing of task switching is in part related to decreased integrity of WM association fibers supporting transmission of information between a distributed network of frontoparietal cortical regions. These results provide a potential anatomical basis for previous findings that age-related performance declines can be associated with

decreased coordination of functional connectivity between frontal and parietal cortical regions (Cabeza et al., 1997b; Esposito et al., 1999). The present results are in-line with the view that performance declines can result from a decrease in the strength of connectivity between some task-related cortical regions. Geschwind (1965) emphasized the idea that acquired reading disorders are manifestations of a ‘disconnection syndrome’ in which damage to white matter disrupts communication between key cortical reading areas. More recently, results from DTI studies have demonstrated that age-related FA declines can influence cognitive performance on different tasks, depending upon the WM region affected (O’Sullivan et al., 2001; Sullivan et al., 2001; Bucur et al., 2007). There are several important limitations associated with the current study. First, it is important to note that the FA metric is only an indirect marker of WM microstructural properties and is influenced by factors other than degree of myelination and orientational coherence of axons, including the density and diameter distribution of axons (Beaulieu, 2002). Second, ROIs are likely to reflect more than one WM tract. For example, our SLF ROI is likely to include smaller frontal and temporal association fibers that are not part of the SLF proper. Present methods do not allow for exclusive definition of a single fiber tract in vivo as even tractography estimates of trajectory are limited by inherent noise within the MR imaging system (Catani et al., 2005). Third, cognitive RT is likely to be influenced by a number of factors other than FA, such as grey/white matter volume and/or functional activation patterns, as evident from the observation that age group continued to account for significant variance in switch cost RT when FA in the SLF was taken into account. For example, age-related fMRI ‘under-recruitment’ or ‘non-specific recruitment’ observed in the present study may have contributed to increased task switching RT costs. Future studies will be required to determine if such age-related fMRI patterns provide an independent contribution to age-related RT increases or are a consequence of decreased WM integrity. Further DTI research will also be required to identify a more complete network of WM regions that are likely to contribute to age-related increases in task switching RT costs. For example, in the present study, in the analysis that maximized power by including both young and older participants, correlations were observed between RT switch cost and FA in bilateral SLF and pericallosal frontal ROIs. Subsequent mediation analyses focused on the left SLF because it was the only ROI in which an RT–FA relationship was observed in both young and older groups. However, decreased integrity of FA in these other ROIs showing an RT–FA relationship may contribute to age-related increases in task switching RT costs. Future studies with larger sample sizes will be needed to identify the more complete network of WM regions likely to mediate task switching RT. In addition, future DTI research will be required to determine whether decreased integrity in left frontoparietal WM also contributes to age-related slowing of other executive

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control processes that prominently recruit frontoparietal cortical regions, such as inhibitory control or dividing attention. However, results from previous studies demonstrate that FA declines in left frontoparietal WM do not broadly underlie age-related slowing across all cognitive domains (Madden et al., 2004; Bucur et al., 2007). For example, Bucur et al. (2007) observed that declines in the genu of the corpus callosum and pericallosal frontal WM, rather than frontoparietal WM, moderated age-related slowing on an episodic memory task. In conclusion, a body of data has demonstrated agerelated slowing of executive control processes, such as task switching. The present study identified decreased integrity of frontoparietal WM, as indexed by FA in the left SLF, as one neurobiological mechanism contributing to this age-related slowing. Decreased integrity of WM in the SLF could contribute to age-related slowing of task switching by slowing the rate of information transfer between a distributed network of task-relevant frontoparietal cortical regions.

Conflicts of interest The authors have no actual or potential conflicts of interest associated with this research.

Acknowledgments This research was supported by National Institutes of Health grants DC007315 and P50 AG05144-21. The authors thank and Sara Jones and Jeff Covell for help with data collection. We also thank Drs. Anders Andersen and three anonymous reviewers for thoughtful comments.

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