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. 2013 Jan 30;35(3):1085–1100. doi: 10.1002/hbm.22236

Associations between T 1 white matter lesion volume and regional white matter microstructure in aging

Elizabeth C Leritz 1,2,3,4,✉, Juli Shepel 1,2,4, Victoria J Williams 1,2,4, Lewis A Lipsitz 5, Regina E McGlinchey 1,2,3, William P Milberg 1,2,3, David H Salat 1,3,4
PMCID: PMC4356252  NIHMSID: NIHMS606832  PMID: 23362153

Abstract

White matter lesions, typically manifesting as regions of signal intensity abnormality (WMSA) on MRI, increase in frequency with age. However, the role of this damage in cognitive decline and disease is still not clear, as lesion volume has only loosely been associated with clinical status. Diffusion tensor imaging (DTI) has been used to examine the quantitative microstructural integrity of white matter, and has applications in the examination of subtle changes to tissue that appear visually normal on conventional imaging. The primary goal of this study was to determine whether major macrostructural white matter damage, (total WMSA volume), is associated with microstructural integrity of normal appearing white matter, and if these macrostructural changes fully account for microstructural changes. Imaging was performed in 126 nondemented individuals, ages 43–85 years, with no history of cerebrovascular disease. Controlling for age, greater WMSA volume was associated with decreased fractional anisotropy (FA) in widespread brain regions. Patterns were similar for FA and radial diffusivity but in contrast, WMSA was associated with axial diffusivity in fewer areas. Age was associated with FA in several regions, and many of these effects remained even when controlling for WMSA volume, suggesting the etiology of WMSAs does not fully account for all age‐associated white matter deterioration. These results provide evidence that WMSA volume is associated with the integrity of normal‐appearing white matter. In addition, our results suggest that overt lesions may not account for the association of increasing age with decreased white matter tissue integrity. Hum Brain Mapp 35:1085–1100, 2014. © 2013 Wiley Periodicals, Inc.

Keywords: white matter lesions, white matter microstructure, aging, diffusion tensor imaging

INTRODUCTION

White matter signal abnormalities (WMSAs), seen as dark hypointense areas on T 1‐weighted images and bright hyperintense areas on T 2‐weighted images, are a common feature on radiological examination of older adults and are found to be associated with age‐associated disease and cognitive decline (Carmichael et al., 2010; Debette et al., 2010; Gunning‐Dixon et al., 2009). Although increased in individuals with elevated risk for cerebrovascular disease (Enzinger et al., 2006; Raz et al., 2011; Shrestha et al., 2009), WMSAs are found in even those within the normal range of cerebrovascular health (Gunning‐Dixon et al., 2009), and prior work has noted that WMSAs can arise from subtle vascular and ischemic‐related events that do not manifest overtly (Assareh et al., in press; Fischer et al., 2007). These lesions are often accompanied by a range of pathologies including demyelination and axonal loss (Gouw et al., 2008a; Smith et al., 2000; Wang et al., 2011). The presence of WMSAs impacts cognition and clinical status as several studies have reported a direct relationship between lesions and behavior (Ishikawa et al., 2012; Jacobs et al., 2012; Libon et al., 2008; Maillard, et al., 2012; Meier, et al., 2012; Murray et al., 2010; O'Brien et al., 2002; Stavitsky et al., 2010). WMSA volume has also been directly tied to functional status, including aspects of activities of daily living such as mobility and gait (Murray et al., 2010; Nadkarni et al., 2009; Wakefield et al., 2010). However, despite this evidence, WMSA burden does not always associate statistically with cognitive performance or function, and findings are inconsistent with regard to the severity of WMSAs in seemingly healthy aging populations (Frisoni et al., 2007; Galluzzi et al., 2008).

Part of the reason for these varying findings may stem in part from the fact that while WMSAs tend to congregate in predictable brain regions, such as periventricular and deep subcortical white matter, there is heterogeneity with regard to the individual functional expression of total WMSA burden. Moreover, there have been few studies to date that have examined how WMSAs relate to the health of the remaining, normally appearing white matter (NAWM). Indeed, alterations to the microstructural properties of NAWM have also been tied to age‐associated cognitive and functional decline, as well as to brain structural abnormalities (Salat et al., 2005a), providing evidence that even healthy‐seeming tissue on MRI may have underlying, more subtle damage that contributes to age‐related changes. It is currently unclear, however, how total WMSA volume relates to these microstructural properties. Specifically, it is unknown whether the widely reported age‐associated changes in NAWM are simply due to similar processes to those underlying WMSA development, or whether the more subtle changes in NAWM arise from distinct mechanisms. Therefore, a better understanding of how WMSAs relate to the health of NAWM across the entire brain may provide more insight into age‐related white matter alterations. The examination of associations between macrostructural (WMSA) and microstructural white matter changes, and how these vary with age‐related alterations, may answer this question.

Diffusion tensor imaging (DTI) assesses the translational movement of water throughout tissue and allows for the examination of the microstructural integrity of NAWM. This procedure has been used in a wide range of studies of aging, dementia, and white matter disease (Salat et al., 2005b; Wozniak and Lim, 2006; Zhang et al., 2009). Several regional measures of tissue microstructure can be calculated, all of which are derived from the directional information. Axial diffusivity (AD) describes the diffusion of water along the principal direction of the fiber (parallel diffusion) in mm2/s, whereas radial diffusivity (RD) described diffusion across the fiber in nonprincipal directions (perpendicular diffusion). Higher diffusivity is indicative of less restriction of water movement. Fractional anisotropy (FA) is a metric that combines both axial and RD, providing a summary measure of the prominence of diffusion directionality along a white matter fiber. Because myelinated fibers create an environment that is more restrictive for water diffusion, FA values are high in tracts with densely packed fibers or heavy myelination and are low in those that are less myelinated or less neuronally dense. Normal aging has a profound effect on white matter microstructure (Gunning‐Dixon et al., 2009; Salat et al., 2005a; Voineskos et al., 2012), and regional patterns of this degeneration differ from abnormal conditions such as Alzheimer's disease (Damoiseaux et al., 2009; Douaud et al., 2011; Huang et al., 2011; Salat et al., 2010), cerebral amyloid angiopathy (Salat et al., 2006; Viswanathan et al., 2008), and cerebrovascular disease risk (Leritz et al., 2010; Salat et al., 2012; Williams et al., 2012). While DTI properties of microstructural integrity have been associated with a variety of different parameters, such as age, we sought to relate these properties to an identifiable “burden” volume (total WMSA volume), and to thus provide meaningful information regarding the relationship between macrostructural damage and microstructural integrity. We then aimed to use this information to explore whether age‐associated changes to white matter microstructure occur as a single mechanism (i.e., due to the same process as WMSAs) or to multiple mechanisms. In doing so, our goal was also to determine which regions in the NAWM are most vulnerable to degeneration in generally healthy older adults with WMSA. For the purposes of this study, we define NAWM as regions of white matter tissue that are outside of regions where WMSAs are found.

Total WMSA volume was related to diffusion metrics throughout brain white matter in sample of healthy middle to older adults ranging in age from 43 to 85 years. In addition, we examined how age influences these relationships, how age relates to the overall distribution of WMSAs, and how the relationship between age and white matter microstructure is influenced by WMSA volume. We expected that overall, higher WMSA volume would be associated with lower FA, but with higher RD and AD, given the fact that greater diffusivity indicates less coherence in fiber tracts and thus likely reduced integrity. We expected that all DTI measures would be associated with WMSA volume in regions where WMSAs are typically found, such as periventricular brain areas. However, given what is known about age‐related white matter changes, we also expected that the spatial pattern of the relationship between WMSAs and white matter would also extend to regions that are NAWM on conventional MRI imaging and not as typical for WM lesions. Finally, we hypothesized that WMSA would account for a portion of total age‐associated decrease in white matter integrity, but that regional residual age‐associated changes would still be apparent when statistically controlling for WMSA.

MATERIALS AND METHODS

Participants

One hundred twenty‐six participants (77 females/49 males) participated in this study. All participants provided informed consent for participation in this research. Participants were recruited from two separate but overlapping studies examining how common cerebrovascular risk factors impact brain structure and cognition. Forty participants were selected (based on their agreement to undergo structural MRI) from a larger sample recruited by the Harvard Cooperative Program on Aging Claude Pepper Older American Independence Center. Participants in this program were recruited from the community in response to an advertisement asking for healthy community‐dwelling older African Americans. Eighty‐six participants were recruited through the Understanding Cardiovascular and Alzheimer's Risk in the Elderly program. Participants in this study were recruited through the Boston University Alzheimer's Disease Center based on the initial criteria of being neurologically healthy and having a first‐degree family relative with dementia. Inclusion criteria for both studies included age of 40–85 years. Participants were excluded for the following reasons: a history of head trauma of “mild” severity or greater according to the criteria of Fortuny et al. (2007; e.g., loss of consciousness for >10 min), any history of more than one head injury (due to possible cumulative neuropathological effects), diagnosis of any form of dementia (i.e., Parkinson's disease, Alzheimer's disease, vascular dementia), any severe psychiatric illness, or history of brain surgery. All participants were literate with at least a sixth‐grade education. One hundred twenty‐two of the participants were right‐handed. Mini‐mental state examination (MMSE) scores ranged from 23 to 30. These scores are in a range outside of a dementia diagnosis, according to normative data for the two racial groups (Caucasian, African American) in this sample (Bohnstedt et al., 1994). Twenty‐eight percent (40) of the sample presented with mild‐moderately elevated blood pressure (BP) and 4 % (5) with severely elevated BP, 51% (64) presented with mild‐moderately elevated cholesterol and 9% (11) with severely elevated cholesterol. Three percent of individuals had evidence of possible diabetes.

MRI Image Acquisition

Each participant received two whole‐brain high‐resolution T 1‐weighted magnetization prepared rapid acquisition gradient echo (MPRAGE) scans as well as a whole‐head high resolution DTI scan. Ten participants were scanned using a Siemens 1.5 Tesla Sonata system, with the following parameters: MPRAGE; T 1 = 1,000 ms, TR = 2.73 s, TE = 3.39 ms, flip angle = 7°, slice thickness = 1.33 mm, 128 slices, field of view (FOV) = 256 × 256 mm; DTI: repetition time (TR) = 9,000 ms echo time (TE) = 68 ms, 60 slices total, acquisition matrix = 128 × 128 (FOV = 256 × 256 mm), slice thickness = 2 mm (for 2 mm3 isotropic voxels) with 0 mm gap, with a b value = 700 s/mm2, 10 T 2 and 60 diffusion weighted images, and one image, the T 2‐weighted “low b” image with a b value = 0 s/mm2 as an anatomical reference volume. The remaining 116 participants were scanned on the upgraded Siemens 1.5 Avanto System, with slightly different parameters; MPRAGE: T 1 = 1,000 ms, TR = 2.73 s, TE = 3.31 ms, flip angle = 7°, slice thickness = 1.3 mm, 128 slices, FOV = 256 × 256 mm; DTI: repetition time (TR) 7,200‐ms echo time (TE) = 77 ms, 60 slices total, acquisition matrix = 128 × 128 (FOV = 256 × 256 mm), slice thickness = 2 mm (for 2 mm3 isotropic voxels) with 0 mm gap, with a b value = 700 s/mm2, 10 T 2 and 60 diffusion weighted images, and one image, the T 2‐weighted “low b” image with a b value = 0 s/mm2 as an anatomical reference volume.

MPRAGE scans were averaged together for each participant to create a single image volume with high contrast‐to‐noise ratio. DTI acquisitions used a twice‐refocused balanced echo to reduce eddy current distortions (Reese et al., 2003). All scans have been empirically optimized for high contrast between gray and white matter, as well as gray matter and cerebrospinal fluid for optimal structural and surface segmentation. Total imaging time was ∼20 min.

Image Analysis and Preprocessing

Diffusion data were processed using the FSL (http://www.fmrib.ox.ac.uk.fsl/) processing stream (Smith et al., 2004; Woolrich et al., 2009). Diffusion volumes were eddy current and motion corrected using FSLs Eddy Correct tool. A T 2‐weighted structural volume, collected using identical sequence parameters as the directional volumes with no diffusion‐weighting and thus in register with the final diffusion maps, was used for all registration and motion correction using a 12‐parameter affine mutual information procedure in FMRIB's Linear Image Registration Tool (Jenkinson, 2003; Jenkinson et al., 2002; Jenkinson and Smith, 2001). The diffusion tensor (including the six components, three eigenvectors, and three eigenvalues) was calculated for each voxel using a least‐squares fit to the diffusion signal (Pierpaoli and Basser, 1996). The T 2‐weighted low b volume was skull stripped using FSLs Brain Extraction Tool, which was then used as a brain mask for all other diffusion maps (Smith, 2002). FA, RD, and AD metrics were derived from the three‐dimensional maps of the three eigenvalues in the diffusion tensor as described previously (Pierpaoli et al., 1996). Data were then prepared for statistical analysis using Tract‐Based Spatial Statistics (TBSS; Smith et al., 2006), distributed as part of the FSL package. TBSS was used for the analyses with FA, RD, and AD; the FA metric was processed first. First, all participants' FA data were aligned into a common space using the nonlinear registration tool FNIRT (Andersson et al., 2007), which uses a b‐spline representation of the registration warp field (Rueckert et al., 1999). Next, the mean FA image was created and thinned to create a mean FA skeleton that represents the centers of all fiber tracts common to the entire sample. The mean skeleton was masked to display only voxels with FA values greater than 0.2 to avoid inclusion of regions that could be composed of multiple tissue types or fiber orientations. Each subject's aligned, common‐space FA data were then projected onto this skeleton to create a four‐dimensional skeletonized volume; this was then fed into voxelwise group statistics. Data along the skeleton were smoothed using an anatomical constraint to limit the smoothing to neighboring data within adjacent voxels along the skeleton. The exact transformations derived for the FA maps were then applied to the other diffusivity volumes (RD and AD) for matched processing of image volumes per subject. Statistical maps were dilated from the TBSS skeleton for visualization purposes. For voxel‐based region of interest (ROI) analysis, FA values were derived from each participant's native space volume.

MPRAGE scans were also used to generate total brain volume (TBV) for each subject. This volume was corrected for intracranial volume (ICV) using a standardized atlas normalization procedure that takes into account an age‐appropriate template (Buckner et al., 2004).

Atlas‐Based Regions of Interest

ROIs were generated, based on significance maps of the relationship between WMSA volume and FA, for further illustrating effects in scatter plots. ROIs were based on the Johns Hopkins University (JHU) white matter atlas, available as part of the FSL package (Hua et al., 2008; Wakana et al., 2007), and the MNI152 average T 1 brain, available through the FreeSurfer package. The JHU atlas labels refer to deeper subcortical white matter tracts, whereas the FreeSurfer white matter segmentations provide labeling of white matter regions that are closer to the cortical surface. First, the mean FA skeleton volume was binarized and used as a mask in order to assign each voxel along the skeleton a particular segmentation value (based on either the JHU or FreeSurfer atlas designations). Then, the ROI‐segmented mean skeleton was deprojected from TBSS standard space to each participant's native space, using an inverse transform applied during the nonlinear registration of all subjects to the target (TBSS preprocessing). Statistics for selected ROI segmentations were then extracted from each participant's native space map; each segmentation contained voxels representing the centermost part of each tract (Fig. 1).

Figure 1.

Figure 1

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Segmentation of the skeleton to create ROIs, including the deprojection step to measure voxels from an individual's native DTI space.

WMSA Volume

Segmentation of white matter lesions was performed with the Freesurfer image analysis suite, which is documented and freely available for download online (http://surfer.nmr.mgh.harvard.edu/). The technical details of these procedures are described in prior publications (Brands et al., 2006; Dale et al., 1999; Fischl and Dale, 2000; Fischl et al., 2001; Fischl et al., 2002; Fischl et al., 2004a; Fischl et al., 1999; Fischl et al., 2004b). Briefly, this processing includes motion correction and averaging of multiple volumetric T 1‐weighted images (when more than one is available), removal of nonbrain tissue using a hybrid watershed/surface deformation procedure (Segonne et al., 2004), automated Talairach transformation, and segmentation of the subcortical white matter and deep gray matter volumetric structures (Fischl et al., 2002; Fischl et al., 2004a). FreeSurfer morphometric procedures have been demonstrated to show good test‐retest reliability across scanner manufacturers and across field strengths (Han et al., 2006). In addition, the automated segmentation procedures have demonstrated comparable reliability and validity when compared with manual procedures (Fischl et al., 2002). WMSAs were labeled using a probabilistic procedure subsequently extended to label white matter lesions (Fischl et al., 2002). Total WMSA (hypointensity) volume was then calculated for each hemisphere; these were averaged together to create a single WMSA volume for each subject. This procedure has demonstrated sensitivity in measuring white matter damage in individuals with Alzheimer's disease (Salat et al., 2008). These WMSAs are correlated with hyperintensity volumes measured on T 2/FLAIR, but tend to be labeled in a more restricted portion of tissue compared with these other imaging domains.

Analysis

Total WMSA, indexed as a single volume number for each subject, was compared when each diffusion measure (FA, RD, and AD) across the entire white matter TBSS skeleton on a voxel‐by‐voxel basis using TBSS procedures (Smith et al., 2006; Smith et al., 2004). Age was used as a covariate in these analyses. Individual FA images were warped onto the TBSS skeleton (see Preprocessing) for examining voxelwise correlations between WMSA volume and either FA, RD, or AD. Permutation testing was conducted using the TBSS “randomize” tool, which is specifically designed for permutation testing with nonparametric values. The number of permutations was set at 5000, and significance threshold was set at P < 0.05, corrected for multiple comparisons across voxels using the threshold‐free cluster‐enhancement option. Of note, this procedure did not distinguish between regions that are NAWM and regions that contained areas of WMSA. For this article, we define NAWM as regions “outside” of WMSA tissue measured on T 1 imaging. This TBSS procedure was repeated first for associations between WMSA volume and all diffusion metrics (FA, AD, and RD), as well as for the relationship of age to FA, and then for age to FA while controlling for overall WMSA volume. Next, scatter plots of significant regions were created using the atlas‐based ROIs described above. ROIs were based on regions in which there was a significant association between WMSA volume and diffusion parameters, and also in regions that were deemed to be peripheral to regions in which WMSAs are typically located.

Finally, we created an overlap map of the frequency distribution of WMSAs, overlaid on the significance maps derived from the randomize procedure described above. The purpose of this figure was to graphically demonstrate areas of overlap and nonoverlap so as to visually inspect if associations were present in regions in which WMSAs are not typically found. The end result of this procedure was to transform both the WMSA frequency distribution and the significance map (created using the TBSS skeleton) to the same space. First, the WMSA label from each participants FreeSurfer volume was binarized and transformed to native TBSS space, leaving only voxels representing areas of WMSA. The TBSS warp was then applied to this binarized volume to transform it to standard TBSS space. These volumes were added together across participants to get a frequency distribution of WMSA per voxel, and this summed volume was then used as an overlay on the existing map created using the TBSS randomize procedure described above.

RESULTS

Demographic Data

Demographic and mean WMSA data are presented in Table 1.

Table 1.

Demographic and mean WMSA data

Mean Standard deviation Range
Age (years) 67.97 9.43 43–87
Education (years) 14.84 2.65 9–20
MMSE score (n = 106) 27.82 1.83 23–30
White matter hypointensity data (mm3)
Left hemisphere 2516.12 1586.56 765–8735
Right hemisphere 2305.85 1314.73 690–6289
Average 2411.28 1416.21 864–7259
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Relationship Between Total WMSA Volume and DTI Indices

Maps of the relationship between WMSA volume and FA, RD, and AD are presented in Figure 2. Total WMSA volume was associated with lower FA in widespread brain regions, with only modest relationships in temporal lobe white matter. However, there was a small region in the left temporal lobe white matter in which lower FA correlated with greater lesion volume. As expected, higher WMSA volume was associated with higher RD across many brain regions, in a pattern that was consistent with that observed for FA. Higher WMSA volume was associated with greater AD values in fewer regions, in a regional pattern that was somewhat overlapping with that observed for RD and FA. As with FA, there were a few areas in which an opposite effect was observed (higher RD and lower WMSA volume). Notably, there were somewhat less anterior and posterior effects with AD, mainly in the genu and splenium of the corpus callosum. However, we did find an opposite effect, in which higher AD was associated with lower WMSA volume, in bilateral posterior white matter regions (Fig. 2). All analyses were also run with age as a covariate (Fig. 3); as can be seen, results were very similar to those observed in Figure 2. Subtle differences were noted with overall slightly smaller areas of significance while controlling for age, indicating that the relationship between WMSA volume and DTI indices of tissue microstructure are not largely influenced by age in this sample. Gender was also included as a covariate but did not alter results; therefore, all results are presented with only age as a covariate.

Figure 2.

Figure 2

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Significance maps of the relationship between WMSA volume and fractional anisotropy (FA), radial (RD), and axial diffusivity (AD), for significance values of P < 0.05. Blue indicates a positive relationship such that higher WMSA volume is associated with lower FA or diffusivity (RD or AD); Yellow/red indicates a positive relationship such that higher WMSA is associated with higher FA or diffusivity. All statistics were run on the standard skeleton obtained using TBSS procedures, but are displayed on thickened skeletons for ease of viewing.

Figure 3.

Figure 3

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Significance maps of the relationship between WMSA volume and FA, RD, and axial AD while controlling for the effects of age.

Relationship Between WMSA‐FA Significance Map and Overall WMSA Distribution

Figure 4 demonstrates the frequency distribution of WMSAs overlaid on the map of the relationship between FA and WMSA volume. Consistent with prior findings, the majority of WMSAs are located in periventricular regions. While there are significant areas of overlap, there are also associations between WMSA volume and lower FA in regions outside of the primary concentration of hypointense regions on T 1, primarily in anterior brain regions. Of note, the regions of significance outside of the regions containing WMSAs (blue regions) are areas that we consider to be NAWM. Scatter plots demonstrating the relationship between FA and WMSA volume in selected ROIs are presented in Figure 5a. Figure 5b presents the specific regions from which these scatter plots were created.

Figure 4.

Figure 4

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Frequency distribution of WMSAs overlaid on the map of the relationship between FA and WMSA volume. Green represents the distribution of WMSA (darker green is higher frequency) hypointensities labeled on T 1‐weighted scans), while blue represents the significance map of the relationship between total WMSA volume and FA. The scatter plot to the right of the figure demonstrates the relationship between FA and WMSA volume in a region (circled in yellow) that is peripheral to the typical regions of WMSA distribution, thus demonstrating the global nature of this effect.

Figure 5.

Figure 5

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(a) Scatter plots in selected ROIs from TBSS maps demonstrating the association between WMSA volume and FA. (b) Regions of ROIs selected for scatter plots. Purple, right anterior corona radiata; blue, left anterior corona radiata; pink, right anterior limb of the internal capsule; green, left anterior limb of the internal capsule; red, left precuneus; aqua, right precuneus.

Relationship Between Age and FA

A final set of analyses examined the specific relationship between age and FA in our sample; these maps are presented in Figure 6. As can be seen, lower FA was associated with greater age on a relatively global level, with a small region demonstrating the opposite association. The overall pattern of these relationships was consistent with that seen in the relationship between FA and WMSA volume (Fig. 2). Thus, a secondary analysis was conducted between FA and age while controlling for WMSA volume, in order to determine the degree to which the relationship between age and tissue structure is due to overall WMSA volume (Fig. 7). These maps revealed somewhat reduced significance when compared with the maps examining age and FA alone, but still reveal significant areas of association, primarily in anterior brain regions. These associations were largely negative such that greater age was associated with reduced FA values, but there was a small region demonstrating the opposite effect.

Figure 6.

Figure 6

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Significance map of the relationship between age and FA.

Figure 7.

Figure 7

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Significance map of the relationship between age and FA while controlling for the effect of total WMSA volume.

Relationship Between WMSA Volume and Total Brain Volume

A partial correlation, controlling for age, was conducted between total WMSA volume and ICV‐corrected TBV, to determine if TBV is related to hypointensity volume. This correlation was nonsignificant (r = 0.162, P > 0.05), demonstrating that total brain volume likely did not have a significant impact on our data.

Results of Permutation and Multiple Comparison Testing

Permutation testing using the TBSS randomize procedure revealed ∼130 significant clusters that remained after correcting for multiple comparisons on the linear model examining FA and WMSA volume. These clusters remained along the entire skeleton and the multiple comparison results resembled those observed in the uncorrected model, with clusters occurring throughout the skeleton. Similarly, clusters present in anterior and posterior regions represented regions of strongest associations. The combined results of both of these methods for adjusting for multiple comparisons suggest that our results are not likely due to chance and represent significant associations between WMSA volume and DTI measures of microstructural integrity, and between age and measures of microstructural integrity.

Regions of Interest

To fully demonstrate the effects and to display the distribution of our data, ROIs were selected based on areas in which there was a strong relationship between DTI and WMSA volume. Scatter plots for the association between WMSA volume and FA are displayed in Figure 5a. These scatter plots demonstrate that the distribution of our data is even across ROIs.

Distribution of WMSA Data

Because of the fact that the distribution is positively skewed, we ran primary analyses (associations between FA and WMSA volume) after conducting logarithmic transformations of the data (log10). Results did not differ when compared with nontransformed data. Raw data is therefore presented for ease of interpretation.

Impact of Scanner Differences

Ten participants were scanned on an earlier Siemens scanner system (Sonata) before the upgrade to the Avanto system. While magnet strength was the same across scanners, parameters for both the DTI and T 1 MPRAGE scans differed slightly following the upgrade. To ensure that these differences in acquisition protocols did not influence results, we conducted the primary analyses (Fig. 2; associations between WMSA volume and FA) while also including scanner as a covariate. This did not alter results. In addition, we conducted analyses with and without the 10 participants scanned before the scanner upgrade; results were also not altered. Thus, we are confident that scanner differences did not influence our findings.

DISCUSSION

We found robust associations between total WMSA volume and DTI properties of white matter, including FA, RD, and AD. Greater WMSA volume was associated with lower FA in periventricular and deeper subcortical white matter regions, as well as in more peripheral areas. Associations revealed relative sparing of temporal lobe white matter and the posterior limb of the internal capsule, suggesting that higher WMSA volume, an indicator of lesion burden, is associated with reduced integrity in these regions. Both RD and AD were positively related to overall WMSA volume, but the spatial pattern of AD was somewhat reduced relative to RD, particularly in the spelnium of the corpus callosum. By itself, age was related to FA, even when removing variance due to overall WMSA volume. These findings were generally consistent with our predictions and provide evidence that although associations between WMSA volume and integrity of normal appearing white matter were somewhat widespread, there appears to be a spatial pattern to the relationships. The current study demonstrates that, although WMSA are strongly associated with normal appearing tissue integrity, these overt lesions alone do not account for the total association of increasing age with decreasing tissue integrity. Specifically, regions within the frontal white matter showed prominent associations with age, even after statistically controlling for WMSA. These results may suggest differing mechanisms of white matter degeneration with aging and that age and total WMSA volume may have independent roles in age‐associated WM alterations; however, it is important to caveat interpretations of these statistical results.

Spatial Patterns of the Relationship Between WMSA Volume and DTI Indices

Our results provide evidence that WMSA volume, an indicator of overall lesion burden may be linked to regionally specific changes in different aspects of WM tissue. While many of the WMSA‐FA associations were, as expected, in regions with a higher frequency of hypointensities and where WMSAs are commonly found (such as periventricular and deeper subcortical white matter regions), we also found significant relationships in more peripheral areas of NAWM (Fig. 3), including prefrontal regions, as well as posterior brain areas such as parietal lobe white matter. It has been suggested that WMSAs and alterations to NAWM have similar underlying etiologies, and that the more subtle reductions to integrity revealed through DTI are precursors to the development of more obvious lesions (Gunning‐Dixon et al., 2009; Hugenschmidt et al., 2008). Pathologic processes believed to underlie both include vascular changes, myelin reduction, increase in perivascular spaces, and gliosis, all of which are common in the aging brain. Our results are in partial support of this notion, as we found robust associations between DTI and lesion volume across multiple brain regions. While the majority of these associations were positive, such that reduced white matter integrity (lower FA, or greater AD or RD) was related to greater WMSA volume, we did find small regions in which the opposite effect was observed (Fig. 2). Across the three diffusion parameters, these contrary effects were found in areas that were also peripheral to regions in which WMSAs are typically observed, and may suggest that there are some areas of brain white matter integrity that are not affected by the WMSA process; these areas may in fact compensate for the areas which show a significant decrease in integrity with increasing WMSA volume. This may particularly be the case in the relationship between AD and WMSA volume, as this opposite association was observed in bilateral white matter. Given these small effects, however, future work will be needed to confirm this speculation.

The idea that WMSAs represent the extreme ends of white matter injury has been proposed, and it has even been suggested that DTI indices such as FA may be more sensitive to these pending alterations particularly in the early stages of damage such as is commonly seen in aging (Lee et al., 2009). Along these lines, it may be that the existence of WMSAs are an indicator of general white matter health, and that perhaps WMSAs influence alterations to NAWM via processes such as diaschisis, which would also explain strong correlations between DTI and WMSA volume. It is important to note that in this study, associations were found in both regions where WMSAs were found as well as in regions that appeared normal (NAWM) on conventional imaging. While the statistically significant association between WMSA volume and reduced FA does not necessarily imply a causative impact on tissue integrity, it nonetheless suggests that macrostructural brain changes may be associated with microstructural integrity across multiple brain regions. This idea is supported by premortem and postmortem studies of individuals with leukoaraiosis, a disease marked by periventricular white matter lesions, which have demonstrated damage to white matter peripheral to the overt lesion areas (Englund et al., 2004; O'Sullivan et al., 2001). In the current study, we measured associations in areas with clearly marked lesions, as well as in areas that appear normal on conventional imaging, demonstrating the relatively global nature of the effect.

We also found significant relationships between FA and age, even when controlling for WMSA volume, whereby higher age was mainly associated with reduced FA. These findings suggest that some of the NAWM changes may not be directly related to WMSAs, or may occur independently. It may be that at least some of the observed age‐related microstructural changes arise via different mechanisms than those that underlie WMSAs, and may not always precede the development of an overt lesion (Liao et al., 2010). The fact that WMSA volume‐independent associations between age and FA also occurred in regions peripheral to areas with high frequencies of hypointensities also supports this idea, as it suggests that some changes to NAWM are process independent of those that eventually develop into overt lesions. Subtle white matter changes common to aging, such as demyelination, may not necessarily appear as WMSAs and thus may be a manifestation of microstructural tissue changes. Moreover, DTI is a more sensitive technique that is perhaps capturing more subtle changes that will not necessarily progress to a WMSA. The spatial pattern of our relationships, with associations predominantly in anterior brain regions, were also notably similar to what was observed, both in this study and in several prior studies, when looking at age‐related white matter changes using DTI (Head et al., 2004; Michielse et al., 2010; Salat et al., 2005a). Our age‐related findings are in contrast to a prior study reporting that age‐related diffusion changes were almost completely explained by white matter lesions (Vernooij et al., 2008). Our study used T 1 imaging to quantify lesions, which may in fact be more sensitive than T 2 (Sailer et al., 2001), but nonetheless, it is possible that this important methodological difference may be a factor in the discrepancy. Nonetheless, we are confident that our findings suggest multiple processes underlying age‐associated decrements to white matter integrity. It is worth noting that there was a small region in which greater age was associated with higher FA values (Fig. 6), suggesting that potentially there are areas of white matter that may become better organized in response to age‐related changes, or may compensate for areas that demonstrate a decrease in integrity. However, this finding warrants further investigation.

Our results suggest that perhaps there are multiple mechanisms underlying age‐related white matter changes, from both macroscopic and microscopic levels. It may be that similar pathological mechanisms underlie both WMSAs and microstructural changes, but that the aging brain also is subject to additional neural alterations, and that ultimately, white mater changes are the result of several different mechanisms. While our results cannot speak to this directly, our discrepant findings may in part help to explain the lack of consistency across studies regarding the relationship of WMSAs to clinical or cognitive behavior. Variation in neural parameters is not uncommon in aging, and one potential source is risk factors for cerebrovascular disease, which increase dramatically in prevalence with increasing age (Gouw et al., 2008b; Mostafaie et al., 2010). Vascular risk was present in our sample, as a percentage presented with elevations in factors such as BP and cholesterol, and it is certainly likely that these can affect WMSA development as well as the association with microstructural changes. It is also interesting to note that when age was related with FA while controlling for WMSA volume (Fig. 6), much of the posterior effects associated with age (seen in Fig. 5) were removed, potentially suggesting that the age‐related effects in posterior white matter may be related to overall WMSA burden and not simply to age alone. Prior work has found that WMSA in posterior regions is associated with Alzheimer's disease (Brickman et al., 2012), and that Alzheimer's disease in general is associated with microstructural changes in posterior white matter (Head et al., 2004). Thus, it may be that WMSA volume is associated with a different type of age and disease‐associated change than damage to the microstructure in isolation. Our future work will examine how parameters of vascular health such as elevated BP and cholesterol, and diagnoses such as Alzheimer's disease or cerebrovascular disease, influence these relationships.

Differential Relationships Among FA, Axial, and Radial Diffusivity Maps

The spatial similarities between significance maps for FA and RD are consistent with an abundance of prior evidence that these metrics are the most sensitive to age‐related WM degeneration (Bennett et al., 2010; Sala et al., 2012); these maps also demonstrated overall more areas of significance relative to AD. RD is believed by some to reflect myelin integrity, although this may be less likely in regions of crossing fibers (Wheeler‐Kingshott and Cercignani, 2009). Taken together with our findings, this supports the idea that the process of aging is primarily due to breakdowns in myelin as opposed to axonal integrity. Indeed, while there are reports of alterations to both, RD is believed to be more sensitive to age‐related changes (Brickman et al., 2012; Davis et al., 2009; Sala et al., in press). It may also be that RD is more vulnerable to sources of variation in age‐related brain changes, such as cerebrovascular risk factors. However, a substantial spatial relationship was found with AD, suggesting that microstructural changes along the primary pathway are also a part of aging and any age‐related neural variations. It is also worth noting that in many regions, the significance maps for AD were somewhat complementary to those observed for FA and RD, and the finding that radial and axial patterns often border each other has been reported in prior studies (Salat et al., 2008). Ultimately, the variance in patterns for diffusion metrics suggests that future studies examine FA, RD, and AD to fully assess the microstructural integrity of NAWM.

Clinical Implications

Although we cannot comment directly on the functional relationships of our findings, they do have potential clinical implications. WMSAs, as well as reduced white matter integrity, have both been independently tied to clinical status such as dementia or mild cognitive impairment (O'Dwyer et al., 2011; Salat et al., 2009). It may be that the relationship between WMSAs and white matter integrity may have specific impacts on cognition, such that the degree of association may be tied to certain cognitive profiles. Moreover, the spatial pattern of the associations among the different diffusion properties may even more specifically define these profiles. It may be that the spatial maps of the relationship of WMSA volume with each of the diffusion parameters (FA, RD, and AD) are associated with slightly different clinical profiles, and this may vary at different points along the aging spectrum. Future work will be needed to confirm this speculation, and our future directions will include relating these findings to cognitive and clinical status.

Methodological Considerations

Our use of T 1‐weighted images to calculate WMSA volume is a potential methodological limitation, as T 2‐weighted images are more commonly used for this purpose, and may in fact provide different information than that obtained from T 1 imaging. WMSA lesions themselves are pathologically heterogeneous, and it also may not be appropriate to lump them together in one single volume. However, use of T 1 to estimate WMSA volume has been reported in other studies using similar methods to what is described here (Bagnato et al., 2010; Burns et al., 2005; Salat et al., 2008). High correlations between WMSAs measured using T 1‐weighted imaging and those measured using T 2‐weighted or FLAIR imaging have been established (Benedict et al., 2004; Rovaris et al., 1999; Simon et al., 2000), and it has been suggested that lesions assessed through T 1 weighting may in fact show a closer association with clinical symptoms (Bagnato et al., 2010; Miller et al., 1998; Sailer et al., 2001). Our procedure for calculating WMSA volume is based on standard procedures as part of FreeSurfer, which are validated highly accurate in labeling lesions based on tissue class (Fischl et al., 2002). These methods have been reported in prior studies (Smith et al., 2010), and although different from measurement by T2, provide a valid measure of altered tissue properties. Nonetheless, it is certainly possible that different results might be apparent when measuring WMSAs with alternative procedures such as T 2‐weighted imaging.

Our use of overlapping procedures (lesion volume and creation of the white matter skeleton) to examine the associations between WMSA volume and diffusion properties is also a potential limitation. Alternate methodologies exist which first create a lesion mask before generating the normal‐appearing white matter skeleton; this mask is then applied during voxelwise statistics (Giorgio et al., 2010), thus ensuring that significant associations are solely in NAWM regions without over lesions. Our approach, which did not use a lesion mask, could certainly impact statistics, and it is possible that there are areas of overlapping lesions and NAWM. However, given our substantial regions of significance, we are confident that our findings accurately reflect the association between macrostructure and microstructure.

The use of different scanners is also a potential limitation of this study. The fact that a smaller subset of subjects received slightly different protocols on a different scanner is not ideal, and could potentially impact results. However, there are data that exist to suggest that several of the procedures used in this study are reliable across different scanners (Jovicich et al., 2009). In addition, analyses did not change when excluding subjects who were scanned on the earlier model, and using scanner as a covariate did not alter results. Thus, we are confident that the use of different scanners in this article does likely not affect interpretation of our findings. Finally, it is important to note that our results do not demonstrate a causative relationship between WMSA volume and NAWM. Longitudinal work in our laboratory is ongoing to clarify this issue.

CONCLUSIONS

In summary, we found robust relationships between WMSA volume, assessed using overall T 1‐weighted hypointensity volume, and DTI indices of white matter microstructure, including FA, RD, and AD. These relationships were independent of age, and importantly were linear, suggesting that the association may be subtler, as opposed to based on a single event, for example, that occurs when WMSA volume reaches a certain level. Our results also suggest that different mechanisms may underlie age‐associated macrostructural and microstructural alterations. These findings are some of the first to quantitatively examine the relationship between WMSA volume and DTI indices of tissue microstructure, and provide preliminary evidence that using WMSA volume or DTI as independent assessments of white matter integrity may not be sufficient. Our results highlight the importance of multiple imaging measures in assessing structural brain integrity, as both diffusion and measurement of WMSAs may provide different yet complementary estimations of age‐related white matter alterations, and may provide a comprehensive picture of brain health in aging.

ACKNOWLEDGMENTS

This research was conducted in compliance with the Code of Ethics of the World Medical Association and the standards established by the Institutional Review Boards at VA Boston Healthcare System and Brigham & Women's Hospital. All participants provided informed consent for participation in this research. The authors would like to thank Emily Lindemer for assistance with Figures.

National Institute of Neurologic Disorders and Stroke (K23NS062148); the National Institute of Nursing Research (R01NR010827), the National Institute on Aging (P60AG08812 and P01AG004390); and by Medical Research Service VA.

REFERENCES

  1. Andersson JLR, Jenkinson M, Smith SM (2007): Non‐Linear Registration, aka Spatial Normalisation. FMRIB Technical Report TR07JA2. Oxford. Available at: http://www.fmrib.ox.ac.uk/analysis/techrep, (Accessed on February 1, 2011).
  2. Assareh A, Mather KA, Schofield PR, Kwok JB, Sachdev PS (2011): The genetics of white matter lesions. CNS Neurosci Ther 17:525–540. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bagnato F, Salman Z, Kane R, Auh S, Cantor FK, Ehrmantraut M, Gallo A, Ikonomidou VN, Ohayon J, Pellicano C, Stern SK, McFarland HF (2010): T1 cortical hypointensities and their association with cognitive disability in multiple sclerosis. Mult Scler 16:1203–1212. [DOI] [PubMed] [Google Scholar]
  4. Benedict RH, Weinstock‐Guttman B, Fishman I, Sharma J, Tjoa CW, Bakshi R (2004): Prediction of neuropsychological impairment in multiple sclerosis: Comparison of conventional magnetic resonance imaging measures of atrophy and lesion burden. Arch Neurol 61:226–230. [DOI] [PubMed] [Google Scholar]
  5. Bennett IJ, Madden DJ, Vaidya CJ, Howard DV, Howard JH Jr (2010): Age‐related differences in multiple measures of white matter integrity: A diffusion tensor imaging study of healthy aging. Hum Brain Mapp 31:378–390. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Bohnstedt M, Fox PJ, Kohatsu ND (1994): Correlates of Mini‐Mental Status Examination scores among elderly demented patients: The influence of race‐ethnicity. J Clin Epidemiol 47:1381–1387. [DOI] [PubMed] [Google Scholar]
  7. Brands AM, Kessels RP, Hoogma RP, Henselmans JM, van der Beek Boter JW, Kappelle LJ, de Haan EH, Biessels GJ (2006): Cognitive performance, psychological well‐being, and brain magnetic resonance imaging in older patients with type 1 diabetes. Diabetes 55:1800–1806. [DOI] [PubMed] [Google Scholar]
  8. Brickman AM, Meier IB, Korgaonkar MS, Provenzano FA, Grieve SM, Siedlecki KL, Wasserman BT, Williams LM, Zimmerman ME (2012): Testing the white matter retrogenesis hypothesis of cognitive aging. Neurobiol Aging 33:1699–1715. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Buckner RL, Head D, Parker J, Fotenos AF, Marcus D, Morris JC, Snyder AZ (2004): A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas‐based head size normalization: Reliability and validation against manual measurement of total intracranial volume. Neuroimage 23:724–738. [DOI] [PubMed] [Google Scholar]
  10. Burns JM, Church JA, Johnson DK, Xiong C, Marcus D, Fotenos AF, Snyder AZ, Morris JC, Buckner RL (2005): White matter lesions are prevalent but differentially related with cognition in aging and early Alzheimer disease. Arch Neurol 62:1870–1876. [DOI] [PubMed] [Google Scholar]
  11. Carmichael O, Schwarz C, Drucker D, Fletcher E, Harvey D, Beckett L, Jack CR Jr, Weiner M, DeCarli C (2010): Longitudinal changes in white matter disease and cognition in the first year of the Alzheimer disease neuroimaging initiative. Arch Neurol 67:1370–1378. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Dale AM, Fischl B, Sereno MI (1999): Cortical surface‐based analysis. I. Segmentation and surface reconstruction. Neuroimage 9:179–194. [DOI] [PubMed] [Google Scholar]
  13. Damoiseaux JS, Smith SM, Witter MP, Sanz‐Arigita EJ, Barkhof F, Scheltens P, Stam CJ, Zarei M, Rombouts SA (2009): White matter tract integrity in aging and Alzheimer's disease. Hum Brain Mapp 30:1051–1059. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Davis SW, Dennis NA, Buchler NG, White LE, Madden DJ, Cabeza R (2009): Assessing the effects of age on long white matter tracts using diffusion tensor tractography. Neuroimage 46:530–541. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Debette S, Beiser A, DeCarli C, Au R, Himali JJ, Kelly‐Hayes M, Romero JR, Kase CS, Wolf PA, Seshadri S (2010): Association of MRI markers of vascular brain injury with incident stroke, mild cognitive impairment, dementia, and mortality: The Framingham Offspring Study. Stroke 41:600–606. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Douaud G, Jbabdi S, Behrens TE, Menke RA, Gass A, Monsch AU, Rao A, Whitcher B, Kindlmann G, Matthews PM, Smith S (2011): DTI measures in crossing‐fibre areas: Increased diffusion anisotropy reveals early white matter alteration in MCI and mild Alzheimer's disease. Neuroimage 55:880–890. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Englund E, Sjobeck M, Brockstedt S, Latt J, Larsson EM (2004): Diffusion tensor MRI post mortem demonstrated cerebral white matter pathology. J Neurol 251:350–352. [DOI] [PubMed] [Google Scholar]
  18. Enzinger C, Smith S, Fazekas F, Drevin G, Ropele S, Nichols T, Behrens T, Schmidt R, Matthews PM (2006): Lesion probability maps of white matter hyperintensities in elderly individuals: Results of the Austrian stroke prevention study. J Neurol 253:1064–1070. [DOI] [PubMed] [Google Scholar]
  19. Fischer P, Krampla W, Mostafaie N, Zehetmayer S, Rainer M, Jungwirth S, Huber K, Bauer K, Hruby W, Riederer P, Tragl KH (2007): VITA study: White matter hyperintensities of vascular and degenerative origin in the elderly. J Neural Transm Suppl 72:181–188. [DOI] [PubMed] [Google Scholar]
  20. Fischl B, Dale AM (2000): Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A 97:11050–11055. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Fischl B, Liu A, Dale AM (2001): Automated manifold surgery: Constructing geometrically accurate and topologically correct models of the human cerebral cortex. IEEE Trans Med Imaging 20:70–80. [DOI] [PubMed] [Google Scholar]
  22. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM (2002): Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron 33:341–355. [DOI] [PubMed] [Google Scholar]
  23. Fischl B, Salat DH, van der Kouwe AJ, Makris N, Segonne F, Quinn BT, Dale AM (2004a): Sequence‐independent segmentation of magnetic resonance images. Neuroimage 23( Suppl 1):S69–S84. [DOI] [PubMed] [Google Scholar]
  24. Fischl B, Sereno MI, Dale AM (1999): Cortical surface‐based analysis. II: Inflation, flattening, and a surface‐based coordinate system. Neuroimage 9:195–207. [DOI] [PubMed] [Google Scholar]
  25. Fischl B, van der Kouwe A, Destrieux C, Halgren E, Segonne F, Salat DH, Busa E, Seidman LJ, Goldstein J, Kennedy D, Caviness V, Makris N, Rosen B, Dale AM (2004b)Automatically parcellating the human cerebral cortex. Cereb Cortex 14:11–22. [DOI] [PubMed] [Google Scholar]
  26. Fortuny LA, Briggs M, Newcombe F, Ratcliff G, Thomas C (1980): Measuring the duration of post traumatic amnesia. J Neurol Neurosurg Psychiatry 43:377–379. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Frisoni GB, Galluzzi S, Pantoni L, Filippi M (2007): The effect of white matter lesions on cognition in the elderly—Small but detectable. Nat Clin Pract Neurol 3:620–627. [DOI] [PubMed] [Google Scholar]
  28. Galluzzi S, Lanni C, Pantoni L, Filippi M, Frisoni GB (2008): White matter lesions in the elderly: Pathophysiological hypothesis on the effect on brain plasticity and reserve. J Neurol Sci 273:3–9. [DOI] [PubMed] [Google Scholar]
  29. Giorgio A, Palace J, Johansen‐Berg H, Smith SM, Ropele S, Fuchs S, Wallner‐Blazek M, Enzinger C, Fazekas F (2010): Relationships of brain white matter microstructure with clinical and MR measures in relapsing‐remitting multiple sclerosis. J Magn Reson Imaging 31:309–316. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Gouw AA, Seewann A, Vrenken H, van der Flier WM, Rozemuller JM, Barkhof F, Scheltens P, Geurts JJ (2008a): Heterogeneity of white matter hyperintensities in Alzheimer's disease: Post‐mortem quantitative MRI and neuropathology. Brain 131( Pt 12):3286–3298. [DOI] [PubMed] [Google Scholar]
  31. Gouw AA, van der Flier WM, Pantoni L, Inzitari D, Erkinjuntti T, Wahlund LO, Waldemar G, Schmidt R, Fazekas F, Scheltens P, Barkhof F (2008b)On the etiology of incident brain lacunes: Longitudinal observations from the LADIS study. Stroke 39:3083–3085. [DOI] [PubMed] [Google Scholar]
  32. Gunning‐Dixon FM, Brickman AM, Cheng JC, Alexopoulos GS (2009): Aging of cerebral white matter: A review of MRI findings. Int J Geriatr Psychiatry 24:109–117. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Han X, Jovicich J, Salat D, van der Kouwe A, Quinn B, Czanner S, Busa E, Pacheco J, Albert M, Killiany R, Maguire P, Rosas D, Makris N, Dale A, Dickerson B, Fischl B (2006): Reliability of MRI‐derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer. Neuroimage 32:180–194. [DOI] [PubMed] [Google Scholar]
  34. Head D, Buckner RL, Shimony JS, Williams LE, Akbudak E, Conturo TE, McAvoy M, Morris JC, Snyder AZ (2004): Differential vulnerability of anterior white matter in nondemented aging with minimal acceleration in dementia of the Alzheimer type: Evidence from diffusion tensor imaging. Cereb Cortex 14:410–423. [DOI] [PubMed] [Google Scholar]
  35. Hua K, Zhang J, Wakana S, Jiang H, Li X, Reich DS, Calabresi PA, Pekar JJ, van Zijl PC, Mori S (2008): Tract probability maps in stereotaxic spaces: Analyses of white matter anatomy and tract‐specific quantification. Neuroimage 39:336–347. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Huang H, Fan X, Weiner M, Martin‐Cook K, Xiao G, Davis J, Devous M, Rosenberg R, Diaz‐Arrastia R (2011): Distinctive disruption patterns of white matter tracts in Alzheimer's disease with full diffusion tensor characterization. Neurobiol Aging 33:2029–2045. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Hugenschmidt CE, Peiffer AM, Kraft RA, Casanova R, Deibler AR, Burdette JH, Maldjian JA, Laurienti PJ (2008): Relating imaging indices of white matter integrity and volume in healthy older adults. Cereb Cortex 18:433–442. [DOI] [PubMed] [Google Scholar]
  38. Ishikawa H, Meguro K, Ishii H, Tanaka N, Yamaguchi S (2012): Silent infarction or white matter hyperintensity and impaired attention task scores in a nondemented population: the Osaki‐Tajiri Project. J Stroke Cerebrovasc Dis 21:275–282. [DOI] [PubMed] [Google Scholar]
  39. Jacobs HI, Visser PJ, Van Boxtel MP, Frisoni GB, Tsolaki M, Papapostolou P, Nobili F, Wahlund LO, Minthon L, Frolich L, Hampel H, Soininen H, van de Pol L, Scheltens P, Tan FE, Jolles J, Verhey FR (2012): The association between white matter hyperintensities and executive decline in mild cognitive impairment is network dependent. Neurobiol Aging 33:201, e1–8. [DOI] [PubMed] [Google Scholar]
  40. Jenkinson M (2003): Fast, automated, N‐dimensional phase‐unwrapping algorithm. Magn Reson Med 49:193–197. [DOI] [PubMed] [Google Scholar]
  41. Jenkinson M, Bannister P, Brady M, Smith S (2002): Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17:825–841. [DOI] [PubMed] [Google Scholar]
  42. Jenkinson M, Smith S (2001): A global optimization method for robust affine registration of brain images. Med Image Anal 5:143–156. [DOI] [PubMed] [Google Scholar]
  43. Jovicich J, Czanner S, Han X, Salat D, van der Kouwe A, Quinn B, Pacheco J, Albert M, Killiany R, Blacker D, Maguire P, Rosas D, Makris N, Gollub R, Dale A, Dickerson BC, Fischl B (2009): MRI‐derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths. Neuroimage 46:177–192. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Lee DY, Fletcher E, Martinez O, Ortega M, Zozulya N, Kim J, Tran J, Buonocore M, Carmichael O, DeCarli C (2009): Regional pattern of white matter microstructural changes in normal aging, MCI, and AD. Neurology 73:1722–1728. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Leritz EC, Salat DH, Milberg WP, Williams VJ, Chapman CE, Grande LJ, Rudolph JL, Schnyer DM, Barber CE, Lipsitz LA, McGlinchey RE (2010): Variation in blood pressure is associated with white matter microstructure but not cognition in African Americans. Neuropsychology 24:199–208. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Liao J, Zhu Y, Zhang M, Yuan H, Su MY, Yu X, Wang H (2010): Microstructural white matter abnormalities independent of white matter lesion burden in amnestic mild cognitive impairment and early Alzheimer disease among Han Chinese elderly. Alzheimer Dis Assoc Disord (Epub ahead of print). [DOI] [PubMed] [Google Scholar]
  47. Libon DJ, Price CC, Giovannetti T, Swenson R, Bettcher BM, Heilman KM, Pennisi A (2008): Linking MRI hyperintensities with patterns of neuropsychological impairment: Evidence for a threshold effect. Stroke 39:806–813. [DOI] [PubMed] [Google Scholar]
  48. Maillard P, Carmichael O, Fletcher E, Reed B, Mungas D, Decarli C (2012): Coevolution of white matter hyperintensities and cognition in the elderly. Neurology 79:442–448. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Meier IB, Manly JJ, Provenzano FA, Louie KS, Wasserman BT, Griffith EY, Hector JT, Allocco E, Brickman AM (2012): White matter predictors of cognitive functioning in older adults. J Int Neuropsychol Soc 18:414–427. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Michielse S, Coupland N, Camicioli R, Carter R, Seres P, Sabino J, Malykhin N (2010): Selective effects of aging on brain white matter microstructure: A diffusion tensor imaging tractography study. Neuroimage 52:1190–1201. [DOI] [PubMed] [Google Scholar]
  51. Miller DH, Grossman RI, Reingold SC, McFarland HF (1998): The role of magnetic resonance techniques in understanding and managing multiple sclerosis. Brain 121( Pt 1):3–24. [DOI] [PubMed] [Google Scholar]
  52. Mostafaie N, Huber KR, Sebesta C, Krampla W, Jungwirth S, Zehetmayer S, Hinterberger M, Krugluger W, Tragl KH, Fischer P (2010): Risk factors for cerebrovascular and cardiovascular diseases beyond age 75 years. J Neural Transm 117:1247–1252. [DOI] [PubMed] [Google Scholar]
  53. Murray ME, Senjem ML, Petersen RC, Hollman JH, Preboske GM, Weigand SD, Knopman DS, Ferman TJ, Dickson DW, Jack CR Jr (2010): Functional impact of white matter hyperintensities in cognitively normal elderly subjects. Arch Neurol 67:1379–1385. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Nadkarni NK, McIlroy WE, Mawji E, Black SE (2009): Gait and subcortical hyperintensities in mild Alzheimer's disease and aging. Dement Geriatr Cogn Disord 28:295–301. [DOI] [PubMed] [Google Scholar]
  55. O'Brien JT, Wiseman R, Burton EJ, Barber B, Wesnes K, Saxby B, Ford GA (2002): Cognitive associations of subcortical white matter lesions in older people. Ann N Y Acad Sci 977:436–444. [DOI] [PubMed] [Google Scholar]
  56. O'Dwyer L, Lamberton F, Bokde AL, Ewers M, Faluyi YO, Tanner C, Mazoyer B, O'Neill D, Bartley M, Collins DR, Coughlan T, Prvulovic D, Hampel H (2011): Using diffusion tensor imaging and mixed‐effects models to investigate primary and secondary white matter degeneration in Alzheimer's disease and mild cognitive impairment. J Alzheimers Dis 26:667–682. [DOI] [PubMed] [Google Scholar]
  57. O'Sullivan M, Summers PE, Jones DK, Jarosz JM, Williams SC, Markus HS (2001): Normal‐appearing white matter in ischemic leukoaraiosis: A diffusion tensor MRI study. Neurology 57:2307–2310. [DOI] [PubMed] [Google Scholar]
  58. Pierpaoli C, Basser PJ (1996): Toward a quantitative assessment of diffusion anisotropy. Magn Reson Med 36:893–906. [DOI] [PubMed] [Google Scholar]
  59. Pierpaoli C, Jezzard P, Basser PJ, Barnett A, Di Chiro G (1996): Diffusion tensor MR imaging of the human brain. Radiology 201:637–648. [DOI] [PubMed] [Google Scholar]
  60. Raz N, Yang Y, Dahle CL, Land S (2011): Volume of white matter hyperintensities in healthy adults: Contribution of age, vascular risk factors, and inflammation‐related genetic variants. Biochim Biophys Acta 22:361–369. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Reese TG, Heid O, Weisskoff RM, Wedeen VJ (2003): Reduction of eddy‐current‐induced distortion in diffusion MRI using a twice‐refocused spin echo. Magn Reson Med 49:177–182. [DOI] [PubMed] [Google Scholar]
  62. Rovaris M, Comi G, Rocca MA, Cercignani M, Colombo B, Santuccio G, Filippi M (1999): Relevance of hypointense lesions on fast fluid‐attenuated inversion recovery MR images as a marker of disease severity in cases of multiple sclerosis. AJNR Am J Neuroradiol 20:813–820. [PMC free article] [PubMed] [Google Scholar]
  63. Rueckert D, Sonoda LI, Hayes C, Hill DL, Leach MO, Hawkes DJ (1999): Nonrigid registration using free‐form deformations: Application to breast MR images. IEEE Trans Med Imaging 18:712–721. [DOI] [PubMed] [Google Scholar]
  64. Sailer M, Losseff NA, Wang L, Gawne‐Cain ML, Thompson AJ, Miller DH (2001): T1 lesion load and cerebral atrophy as a marker for clinical progression in patients with multiple sclerosis. A prospective 18 months follow‐up study. Eur J Neurol 8:37–42. [DOI] [PubMed] [Google Scholar]
  65. Sala S, Agosta F, Pagani E, Copetti M, Comi G, Filippi M (2012): Microstructural changes and atrophy in brain white matter tracts with aging. Neurobiol Aging 33:488–498. [DOI] [PubMed] [Google Scholar]
  66. Salat DH, Greve DN, Pacheco JL, Quinn BT, Helmer KG, Buckner RL, Fischl B (2009): Regional white matter volume differences in nondemented aging and Alzheimer's disease. Neuroimage 44:1247–1258. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Salat DH, Smith EE, Tuch DS, Benner T, Pappu V, Schwab KM, Gurol ME, Rosas HD, Rosand J, Greenberg SM (2006): White matter alterations in cerebral amyloid angiopathy measured by diffusion tensor imaging. Stroke 37:1759–1764. [DOI] [PubMed] [Google Scholar]
  68. Salat DH, Tuch DS, Greve DN, van der Kouwe AJ, Hevelone ND, Zaleta AK, Rosen BR, Fischl B, Corkin S, Rosas HD, Dale AM (2005a)Age‐related alterations in white matter microstructure measured by diffusion tensor imaging. Neurobiol Aging 26:1215–1227. [DOI] [PubMed] [Google Scholar]
  69. Salat DH, Tuch DS, Hevelone ND, Fischl B, Corkin S, Rosas HD, Dale AM (2005b)Age‐related changes in prefrontal white matter measured by diffusion tensor imaging. Ann N Y Acad Sci 1064:37–49. [DOI] [PubMed] [Google Scholar]
  70. Salat DH, Tuch DS, van der Kouwe AJ, Greve DN, Pappu V, Lee SY, Hevelone ND, Zaleta AK, Growdon JH, Corkin S, Fischl B, Rosas HD (2008): White matter pathology isolates the hippocampal formation in Alzheimer's disease. Neurobiol Aging 31:244–256. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Salat DH, Tuch DS, van der Kouwe AJ, Greve DN, Pappu V, Lee SY, Hevelone ND, Zaleta AK, Growdon JH, Corkin S, Fischl B, Rosas HD (2010): White matter pathology isolates the hippocampal formation in Alzheimer's disease. Neurobiol Aging 31:244–256. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Salat DH, Williams VJ, Leritz EC, Schnyer DM, Rudolph JL, Lipsitz LA, McGlinchey RE, Milberg WP (2012): Inter‐individual variation in blood pressure is associated with regional white matter integrity in generally healthy older adults. Neuroimage 59:181–192. [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Segonne F, Dale AM, Busa E, Glessner M, Salat D, Hahn HK, Fischl B (2004): A hybrid approach to the skull stripping problem in MRI. Neuroimage 22:1060–1075. [DOI] [PubMed] [Google Scholar]
  74. Shrestha I, Takahashi T, Nomura E, Ohtsuki T, Ohshita T, Ueno H, Kohriyama T, Matsumoto M (2009): Association between central systolic blood pressure, white matter lesions in cerebral MRI and carotid atherosclerosis. Hypertens Res 32:869–874. [DOI] [PubMed] [Google Scholar]
  75. Simon JH, Lull J, Jacobs LD, Rudick RA, Cookfair DL, Herndon RM, Richert JR, Salazar AM, Sheeder J, Miller D, McCabe K, Serra A, Campion MK, Fischer JS, Goodkin DE, Simonian N, Lajaunie M, Wende K, Martens‐Davidson A, Kinkel RP, Munschauer FE IIII (2000): A longitudinal study of T1 hypointense lesions in relapsing MS: MSCRG trial of interferon beta‐1a. Multiple Sclerosis Collaborative Research Group. Neurology 55:185–192. [DOI] [PubMed] [Google Scholar]
  76. Smith CD, Snowdon D, Markesbery WR (2000): Periventricular white matter hyperintensities on MRI: Correlation with neuropathologic findings. J Neuroimaging 10:13–16. [DOI] [PubMed] [Google Scholar]
  77. Smith EE, Nandigam KR, Chen YW, Jeng J, Salat D, Halpin A, Frosch M, Wendell L, Fazen L, Rosand J, Viswanathan A, Greenberg SM (2010): MRI markers of small vessel disease in lobar and deep hemispheric intracerebral hemorrhage. Stroke 41:1933–1938. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Smith SM (2002): Fast robust automated brain extraction. Hum Brain Mapp 17:143–155. [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Smith SM, Jenkinson M, Johansen‐Berg H, Rueckert D, Nichols TE, Mackay CE, Watkins KE, Ciccarelli O, Cader MZ, Matthews PM, Behrens TE (2006): Tract‐based spatial statistics: Voxelwise analysis of multi‐subject diffusion data. Neuroimage 31:1487–1505. [DOI] [PubMed] [Google Scholar]
  80. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen‐Berg H, Bannister PR, De Luca M, Drobnjak I, Flitney DE, Niazy RK, Saunders J, Vickers J, Zhang Y, De Stefano N, Brady JM, Matthews PM (2004): Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23( Suppl 1):S208–S219. [DOI] [PubMed] [Google Scholar]
  81. Stavitsky K, Du Y, Seichepine D, Laudate TM, Beiser A, Seshadri S, Decarli C, Wolf PA, Au R (2010): White matter hyperintensity and cognitive functioning in the racial and ethnic minority cohort of the Framingham Heart Study. Neuroepidemiology 35:117–122. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Vernooij MW, de Groot M, van der Lugt A, Ikram MA, Krestin GP, Hofman A, Niessen WJ, Breteler MM (2008): White matter atrophy and lesion formation explain the loss of structural integrity of white matter in aging. Neuroimage 43:470–477. [DOI] [PubMed] [Google Scholar]
  83. Viswanathan A, Patel P, Rahman R, Nandigam RN, Kinnecom C, Bracoud L, Rosand J, Chabriat H, Greenberg SM, Smith EE (2008): Tissue microstructural changes are independently associated with cognitive impairment in cerebral amyloid angiopathy. Stroke 39:1988–1992. [DOI] [PMC free article] [PubMed] [Google Scholar]
  84. Voineskos AN, Rajji TK, Lobaugh NJ, Miranda D, Shenton ME, Kennedy JL, Pollock BG, Mulsant BH (2012): Age‐related decline in white matter tract integrity and cognitive performance: A DTI tractography and structural equation modeling study. Neurobiol Aging 33:21–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Wakana S, Caprihan A, Panzenboeck MM, Fallon JH, Perry M, Gollub RL, Hua K, Zhang J, Jiang H, Dubey P, Blitz A, van Zijl P, Mori S (2007): Reproducibility of quantitative tractography methods applied to cerebral white matter. Neuroimage 36:630–644. [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Wakefield DB, Moscufo N, Guttmann CR, Kuchel GA, Kaplan RF, Pearlson G, Wolfson L (2010): White matter hyperintensities predict functional decline in voiding, mobility, and cognition in older adults. J Am Geriatr Soc 58:275–281. [DOI] [PMC free article] [PubMed] [Google Scholar]
  87. Wang L, Goldstein FC, Levey AI, Lah JJ, Meltzer CC, Holder CA, Mao H (2011): White matter hyperintensities and changes in white matter integrity in patients with Alzheimer's disease. Neuroradiology 53:373–381. [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Wheeler‐Kingshott CA, Cercignani M (2009): About “axial” and “radial” diffusivities. Magn Reson Med 61:1255–1260. [DOI] [PubMed] [Google Scholar]
  89. Williams VJ, Leritz EC, McGlinchey RE, Milberg WP, Rudolph JL, Lipsitz LA, Salat DH (2012): Interindividual variation in serum cholesterol is associated with regional white matter tissue integrity in older adults. Hum Brain Mapp (Epub ahead of print). [DOI] [PMC free article] [PubMed] [Google Scholar]
  90. Woolrich MW, Jbabdi S, Patenaude B, Chappell M, Makni S, Behrens T, Beckmann C, Jenkinson M, Smith SM (2009): Bayesian analysis of neuroimaging data in FSL. Neuroimage 45( 1 Suppl):S173–S186. [DOI] [PubMed] [Google Scholar]
  91. Wozniak JR, Lim KO (2006): Advances in white matter imaging: A review of in vivo magnetic resonance methodologies and their applicability to the study of development and aging. Neurosci Biobehav Rev 30:762–774. [DOI] [PMC free article] [PubMed] [Google Scholar]
  92. Zhang Y, Schuff N, Du AT, Rosen HJ, Kramer JH, Gorno‐Tempini ML, Miller BL, Weiner MW (2009): White matter damage in frontotemporal dementia and Alzheimer's disease measured by diffusion MRI. Brain 132( Pt 9):2579–2592. [DOI] [PMC free article] [PubMed] [Google Scholar]

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