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functions guarantee that the local warps are smoothly joined at the boundaries of the cubes. By changing one or more of these grid points, the location of the voxel can be adjusted. Because this adjustment is dependent on local parameters only (the locations of the neigh- boring 64 grid points), we can obtain a finer anatomi- cal match than is achievable using linear or nonlinear globally parameterized transformations. The multigrid approach refers to using control point grids of succes- sively finer mesh. We used 32-, 16-, 8-, 4-, and 2-mm control point separations in succession.

Normalization of the EPI images posed a challenge because of their lack of anatomical detail and also an inherent nonlinear field distortion when compared with the anatomical images. To overcome these difficulties we first linearly aligned (12-parameter) each subject’s mean EPI with their coplanar T2-weighted image, which afforded better gross boundary contrasts than the T1. The T2-weighted image was, in turn, coregistered with the T1. We then used a coarse-grid (32 mm) spline warp to adjust the EPI field distortion.

fMRI Data Analyses

For each task, each individual’s spatially normalized data were modeled using a modified general linear model (GLM) as implemented in VoxBo (www.voxbo.org). Covariates representing the contrast of activity during each task relative to its respective baseline condition were constructed by convolving a boxcar function with a hemodynamic response function. Additional nuisance covariates modeled motion-correlated signals, global signal changes (orthogonalized with respect to the design matrix) (Desjardins, Kiehl, & Liddle, 2001), inter- scan baseline shifts, and an intercept. Each GLM also included filters to remove frequencies below 0.02 Hz and above 0.25 Hz.

Next, a random-effects analysis was used to identify areas of activation observed across the entire group of subjects. In this analysis, images of parameter estimates were derived for each contrast for each subject and entered into a second-level, one-sample t test in which the mean estimate across participants at each voxel was tested against zero. Significant regions of activation were identified using an uncorrected one-tailed threshold of p < .001 and a minimum cluster size of 10 contiguous voxels.

To examine correlations between WMH volume and PFC activation, we first defined prefrontal ROIs based on the group-averaged statistical parametric map (SPM) by selecting all contiguous suprathreshold voxels in ana- tomically constrained areas, the middle frontal gyrus (BA 9/46) for dorsal PFC and the inferior frontal gyrus (BA 44/45/47) for ventral PFC. Each ROI was then used as a mask and applied to single-subject data. Parameter estimates, indexing activation during each task relative to its baseline condition, were averaged over the entire


Journal of Cognitive Neuroscience

mask and then entered into second-level analyses with subjects as a random variable. Pearson correlation co- efficients were derived to identify the relationship be- tween WMH volume and averaged parameter estimates for each ROI. A Fisher’s r to z transformation was carried out to determine whether the correlation coefficient was significantly different from zero.

We also defined task-related ROIs of activity outside of the PFC to explore the possibility that dorsal PFC WMH volume may also be associated with activity in other regions that are functionally connected. The additional ROIs examined were based on previous functional im- aging studies as well as studies of anatomical connectiv- ity and are discussed separately for each task. The ROIs were delineated based on the group-averaged activa- tions for each task, and mean parameter estimates were correlated with dorsal PFC WMH volumes.


Consistent with previous studies (e.g., de Leeuw et al., 2001; Breteler, van Amerongen, et al., 1994), we found a positive correlation between age and global WMH vol- ume (R = .590, p = .02). However, age was not sig- nificantly correlated with brain activity in any of the PFC ROIs examined. Thus, age confounds could not account for any of the observed relationships between WMH and PFC activity.

In order to compare the extent of WMH in this sample relative to the general population, we exam- ined how subjects in this sample compared to percent- iles from a larger sample of nondemented individuals from a population-based study (Wu et al., 2002). We found that 87% of subjects in the current study had WMH volumes less than the 75th percentile of the larger study. Thus, the majority of subjects in this study had minimal to moderate WMH volumes. Indi- vidual examples of the extent of WMH are depicted in Figure 2.

Behavioral Results Episodic Memory Task

An immediate retrieval task was administered after the study phase (mean accuracy: 0.82, SD = .08), and after a delay of 1 hr, a delayed retrieval task was administered during scanning (mean accuracy: 0.75, SD = .12). Per- formance was not significantly correlated with age (im- mediate: R = .322, p = .25; delayed: R = .241, p = .39). The correlations between performance and global WMH volume were as follows: immediate; R = .394, p = .15; delayed; R = .494, p = .06, and correlations between performance and dorsal PFC WMH volume were as follows: immediate; R = .555, p = .03; delayed; R = .477, p = .07.

Volume 18, Number 3

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