Home Current Issue Previous Issues Published Ahead-of-Print Collections For Authors Journal Info
Skip Navigation LinksHome > December 15, 2007 - Volume 46 - Issue 5 > HIV-Associated Alterations in Normal-Appearing White Matter:...
JAIDS Journal of Acquired Immune Deficiency Syndromes:
doi: 10.1097/QAI.0b013e318159d807
Clinical Science

HIV-Associated Alterations in Normal-Appearing White Matter: A Voxel-Wise Diffusion Tensor Imaging Study

Stebbins, Glenn T PhD*; Smith, Clifford A PhD*; Bartt, Russell E MD*†‡; Kessler, Harold A MD*; Adeyemi, Oluwatoyin M MD*†‡; Martin, Eileen PhD§; Cox, Jennifer L PhD*; Bammer, Roland PhD∥; Moseley, Michael E PhD∥

Free Access
Supplemental Author Material
Article Outline
Collapse Box

Author Information

From *Rush University Medical Center, Chicago, IL; †John H. Stroger, Jr. Hospital of Cook County, Chicago, IL; ‡Ruth M. Rothstein Core Center, Chicago, IL; §University of Illinois-Chicago, IL; and ∥Stanford University, Stanford, CA.

Received for publication February 29, 2007; accepted August 29, 2007.

Supported by National Institute on Aging grant R21 AG23491 and a Rush University Medical Center-Cook County Collaborative Grant.

Correspondence to: Glenn T. Stebbins, PhD, Rush University Medical Center, 1725 W Harrison, Suite 309, Chicago, IL 60612 (e-mail: gstebbin@rush.edu).

Collapse Box


Objective: There are conflicting reports of adverse HIV-associated alterations in white matter integrity as measured by diffusion tensor imaging (DTI). We sought to address these conflicting reports by assessing, on a voxel-by-voxel basis, HIV-associated regional changes in radiologically defined normal-appearing white matter (NAWM) integrity using high-resolution DTI.

Methods: 30 HIV-seropositive (SP) and 30 HIV-seronegative (SN) nondemented, community-dwelling participants underwent DTI to derive whole-brain measures of white matter integrity (fractional anisotropy [FA] and mean diffusivity [MD]). For each participant, the white matter T2 volume was thresholded to remove regions of abnormal signal, resulting in a NAWM mask, which was then applied to the FA and MD volumes to extract voxel-wise NAWM measures of white matter integrity. Voxel-wise group comparisons of FA and MD were conducted (P < 0.005, extent threshold 5 voxels) while controlling for age and substance-abuse history.

Results: There were no significant differences between the groups for demographic or cognitive performance variables. Summary whole-brain measures of FA and MD were equivalent between the SP and SN samples. Among the SP sample, history of substance abuse was associated with significantly increased whole-brain NAWM MD, and coinfection with hepatitis C virus (HCV) was associated with a trend for increased MD. Correlations between whole-brain NAWM FA and MD with cognitive performance measures were not significant. Regional analyses of DTI measures revealed variable differences in NAWM FA in the SP sample, with findings of both decreased and increased FA. Differences in NAWM MD were more consistent, with widespread increases noted in the SP sample compared to the SN sample. Eight of the 10 regions displaying significantly increased FA in the SP sample were also found to have significantly increased MD compared to the SN sample.

Conclusions: Decreased white matter integrity is present even in radiologically defined NAWM in nondemented, community-dwelling patients with HIV. The decrease in NAWM integrity is best seen in increases in MD, a measure of generalized tissue breakdown. Indications of NAWM axonal integrity (FA) present a more complicated picture, with both decreased FA and increased FA in the SP sample. Our findings of variable HIV-associated FA changes in NAWM may account for previous conflicting reports of changes in DTI parameters in this population. The results of our study suggest that HIV infection contributes to variable changes in DTI values, reflecting both direct loss of axonal integrity and a loss of complexity to the underlying axonal matrix.

Back to Top | Article Outline


Click on the links below to access all the ArticlePlus for this article.

Please note that ArticlePlus files may launch a viewer application outside of your web browser.

* http://links.lww.com/QAI/A4

The use of highly active antiretroviral therapy (HAART) has contributed to a reduction in the direct and indirect effects of HIV infection within the central nervous system (CNS).1,2 Yet neurologic sequelae of HIV infection remains a substantial concern, because even mild CNS dysfunction can contribute to decreased life expectancy, diminished work capacity, and poor medication adherence.3-6 Structural magnetic resonance (MR) imaging has revealed that, in addition to global and regional gray matter atrophy,7-12 callosal and cortical thinning,13,14 and ventricular enlargement,14,15 macrostructural white matter integrity, as evidenced by white matter hyperintensities16,17 and reduced white matter volume,8,12 is often present with advanced HIV disease. In the early stages of HIV infection, however, white matter abnormalities are unreliably detected on structural MR.7 Functional MR techniques, including MR spectroscopy18-20 and functional MR imaging,21-26 have revealed CNS abnormalities in asymptomatic patients without cognitive deficits or abnormal findings on standard MRI examination, suggesting that microstructural brain alterations may portend more prominent structural abnormalities.

Diffusion tensor imaging (DTI) is an emerging technique that combines MR diffusion-weighted pulse sequences with tensor mathematics to measure molecular diffusion in 3 dimensions, thereby providing a noninvasive proxy measure of cerebral integrity.27 Within brain white matter, the diffusion of water molecules is not equal in all directions, because molecular restriction is greater across axonal fibers than along the major axis. From this, intact brain white matter promotes anisotropic diffusion, whereas damaged white matter promotes isotropic diffusion. The diffusion of water molecules can thus be characterized by the derived tensor from which a number of quantitative indices can be calculated, reflecting the magnitude and orientation of water diffusion. Whereas a variety of scalar indices can be calculated, to date, whole-brain and regional alterations in fractional anisotropy (FA; the ratio of anisotropic to isotropic diffusion at the voxel level) and mean diffusivity (MD; the overall free diffusion of water) have been associated with both healthy aging28-31 and a number of neurologic and psychiatric disorders, including Parkinson disease,32 mild cognitive impairment/Alzheimer disease,33-36 stroke,37 multiple sclerosis,38,39 alcoholism,40,41 and schizophrenia.42,43

An increasing number of studies report DTI abnormalities associated with HIV infection, yet such findings have not been universal.44-50 Reports of HIV-associated decreases in anisotropic diffusion in the genu,44,45 splenium,44,45,48 and frontal regions47 are in conflict with findings of no significant differences between HIV-seropositive and HIV-seronegative individuals in the genu,46-48 splenium,46,47 or frontal regions.44,48 Some studies have even reported significantly elevated anisotropic diffusion in HIV patients in the internal capsule, centrum semiovale, splenium, and parietal lobes.44,47 Reports of HIV-associated elevations in mean diffusivity44,45,48 have been contradicted by other studies that show no significant difference between seropositive and seronegative subjects.46,47 Further, alterations in fractional anisotropy and diffusivity have been associated with virologic and immunologic status in some studies,47,49 but not in other studies.44,46,50

DTI is an emerging and developing technique, and it is likely that there are multiple contributions to these discrepant results. Some of these include limited sample size,45,47,49-51 differing approaches to the acquisition and analysis of DTI parameters (eg, diffusion weights, eddy current correction, number of gradient directions), and varying definitions of regions of interest versus whole-brain analyses.44,45,47,49-52 Differences in the definition of regions for examination could be particularly problematic in that each laboratory may well be examining slightly different regions with resultant variations in the DTI results.

Given these equivocal and contradictory findings, the sensitivity and utility of DTI to identify early pathologic changes associated with HIV infection has been questioned.44,53 However, because others have reported DTI abnormalities associated with asymptomatic infection,52 DTI techniques may in fact be sensitive to early HIV-associated pathologic change. In the present study, we sought to evaluate HIV-associated changes in white matter integrity in a group of nondemented, community-dwelling participants. Specifically, in contrast to the whole-brain and region-of-interest (ROI) approaches that have been employed to date, we investigated regional changes in radiologically defined, normal-appearing white matter (NAWM) integrity using whole-brain, voxel-based, high-resolution diffusion tensor imaging (DTI) in subjects with HIV infection. Voxel-based methods applied to DTI analyses provide a global and comprehensive assessment of group differences uncomplicated by the potential biases and variations of ROI approaches. Such whole-brain, voxel-based techniques are automated and therefore have been noted to be less subject to issues of human-based tracing reliability and/or reproducibility.34,54 In addition, whole-brain voxel-based analyses assessing regional changes in DTI parameters, independent of a priori constraints on location, may reveal differences that are not encompassed by specific ROIs evaluated to date.

Back to Top | Article Outline


HIV Seropositive

Thirty right-handed, HIV-seropositive (SP) individuals were recruited from the Mark Weiss Infectious Disease Clinic at Rush University Medical Center, the Ruth M. Rothstein CORE Center, and John H. Stroger, Jr. Hospital of Cook County, and by word of mouth among enrolled participants. A board-certified neurologist (R.E.B) completed a neurological examination at the time of enrollment. No SP participant presented with a central nervous system symptom or disorder. Mean duration of known infection (defined as the time since first positive HIV antibody test) was 9.9 ± 5.8 years. Plasma CD4 cell count and HIV RNA viral load information were collected within 4 weeks of completing the imaging protocol. Viral load was measured by an ultrasensitive assay with a lower limit of detectability of 75 copies/mL; 13 participants (43%) presented with a detectable viral load. Twenty-three (77%) of the patients were taking a combination of antiretroviral medications. Eleven (37%) of the individuals with HIV infection were also antibody positive for hepatitis C virus (HCV). See Table 1 for additional demographic and clinical characteristics.

Table 1
Table 1
Image Tools
Back to Top | Article Outline
HIV Seronegative

Thirty HIV-seronegative (SN) individuals, representing friends, family members, or partners of the SP recruits meeting all inclusion and exclusion criteria listed below, were enrolled for comparison. All participants signed an informed consent, and the protocol was approved by the Institutional Review Boards at Rush University Medical Center and Cook County Bureau of Health Services.

Each participant fulfilled the following inclusion criteria: right-handedness, primary education in English, and estimated Full Scale IQ score ≥70 based on the Wechsler Test of Adult Reading (WTAR).55 Exclusionary criteria included: a Mini Mental State Examination (MMSE)56 score <17; a history of closed head injury with loss of consciousness >30 minutes; a previous diagnosis of dementia, schizophrenia, or nonaffective psychotic disorder; substance abuse within the past 6 months based on self-report; a history of stroke or seizure disorder; or having contraindications to MRI scanning, including resting tremor or the presence of certain types of surgically implanted devices (eg, pacemakers), metal clips, prostheses, or other ferromagnetic materials or fragments in high-risk regions of the body.

Back to Top | Article Outline
Neuropsychological Measures

In addition to the MMSE56 and WTAR55 completed at the time of enrollment, all participants completed a battery of neuropsychological measures that was designed to assess general cognition, memory, speeded processing, motor skills, and executive function. The battery of tests included the HIV Dementia Scale (HDS),57 California Verbal Learning Test II (CVLT II),58 grooved pegboard,59 Controlled Oral Word Association Test (COWAT),60 oral Symbol Digit Modalities Test (SDMT),61 Trail Making A and B,62 Stroop color-word interference tasks,63 and the letter-number sequencing (LN Seq) subtest from the Wechsler Adult Intelligence Test III.64 A board-certified neuropsychologist (C.A.S) administered all tests within 4 weeks of completing the neuroimaging protocol.

Back to Top | Article Outline
Imaging Acquisition

Scans were performed on a 1.5T GE scanner (General Electric, Milwaukee, WI) equipped with fast-gradient Horizon EchoSpeed upgrades (Rev. 11.4). Single-shot echo-planar diffusion-weighted imaging was used with the following parameters: repetition time (TR) = 12,100 ms, echo time (TE) = 97 ms, gradient duration δ = 20 ms, acquisition matrix 128 × 128, field of view (FOV) = 250 degrees, slice thickness = 3 mm 0 gap, 38 axial slices. Two degrees of diffusion weighting (b values) were used: b = 0 and b = 800 seconds/mm2. These diffusion weights were applied in 6 noncollinear directions (xy, yz, xz, -xy, -yz, -xz), with 3 repetitions of b = 0 and 6 repetitions of each diffusion weighted image. Images were transferred to an offline workstation (Sun Microsystems, Palo Alto, CA) for processing.

Back to Top | Article Outline
Image Processing

The first step in postacquisition processing of diffusion tensor images was the realignment of all images and the unwarping of eddy currents. Realignment of images involved registering each image to the first image using a rigid-body 12-parameter affine algorithm. Eddy current correction is necessary to adjust for geometric distortions introduced by the echo-planar diffusion-weighting gradients that can cause distortions in shear, magnification, and/or pixel shifts. A set of cerebrospinal fluid (CSF)-nulled inversion recovery images (time to inversion [TI] ∼2100 ms) are acquired, with b = 0 as a reference for unwarping eddy current effects in the diffusion-weighted images.65,66 Processing of unwarped DTI images involved the calculation of the 6 diffusion coefficients defining the 6 elements of the diffusion tensor.67 Eigenvectors defining the 3 principal directions of diffusion for each voxel were derived from the diffusion tensor. The magnitude of diffusivity in each direction was represented by the eigenvalues for the 3 eigenvectors. The MD and the FA were derived from the eigenvalues.68-70 From this postprocessing, 3 values were constructed for each slice: MD, FA, and b = 0 (0 diffusion-weighted image = T2 image).

Back to Top | Article Outline
DTI Data Analyses

Individual participant slice images for MD, FA, and T2 acquisitions were concatenated into whole-brain volumes in acceptable format. Whole-brain volumes were imported into Statistical Parametric Mapping software (SPM2), implemented in Matlab R14, sp1 (The MathWorks, Natick, MA) for analysis. To facilitate voxel-by-voxel comparison between groups, all images were spatially normalized to a standard template. To avoid the geometric distortions associated with diffusion-weighted echo-planar imaging, we used the 0 diffusion-weighted (eg, T2) image obtained during the scanning sequence for normalization. Although of relatively low resolution (1.95 mm × 1.95 mm × 3.0 mm voxel size), this image is acquired at the same time and same locations as the diffusion-weighted images, and thus does not require coregistration with the MD or FA images. The T2 weighted image was normalized to the standard T2 template in SPM2 using a 12-iteration affine transformation and a nonlinear transformation with 7 × 8 × 7 basis functions. Parameters from this transformation were then applied to the remaining DT images, and statistical maps were created for MD and FA values.

To limit our analysis to MD and FA values in white matter, we created individual subject mask volumes that were used to exclude voxels representing white matter abnormalities based on T2 signal, voxels from gray matter, CSF, and extracranial space. The first step in creating the masks was to segment the normalized T2 images into CSF, gray matter, and white matter compartments. The segmentation algorithms are based on signal intensity and prior probabilities for location. We defined radiologically NAWM based on a probability of >0.80 for white matter classification from the white matter segmented image.34,37 This process allowed us not only to exclude voxels classified as gray matter, CSF, and extracranial space, but also to exclude areas of white matter in which the T2 signal was altered due to white matter lesions, atrophic changes, or other abnormalities on an individual participant basis. Thus, only voxels surviving this threshold were included in the analyses of group differences. The individual white matter masks were then applied to individual subjects' MD and FA maps. A Gaussian filter was applied to these individual masked images (6 mm full width at half maximum [FWHM]) to increase signal-to-noise ratio and meet requirements of a Gaussian distribution for general linear analyses. A filter size of 6 mm was chosen to allow for interrogation of relatively small regions (eg, internal capsule) while assuring normal distribution of data across voxels. Group differences in voxel-level DTI values were assessed using these individual, masked MD and FA maps.

Back to Top | Article Outline
Statistical Analyses

Differences between the 2 groups of participants in demographic measures were assessed by 2-sample t test or χ2 analysis as appropriate in SPSS (SPSS, Chicago, IL). Group differences in MD and FA were examined for both averaged NAWM values and in separate voxel-wise comparisons. Averaged NAWM comparisons were based on the mean MD and FA values of all voxels and used participant age and substance abuse history as covariates of no interest in an analysis of covariance model (ANCOVA). We included these variables as covariates of no interest due to their known relationship to DTI-derived measures of FA and MD27,29,31 (age) and sample group differences in substance abuse history. These analyses allowed us to examine total-brain NAWM MD and FA differences between groups without regard to location in the cerebrum. Relationships between demographic, cognitive performance measures, and virologic variables and DTI measures were assessed by ANCOVA model or partial correlations, controlling for age and substance abuse history. Significance was determined with a P value of <0.05.

Voxel-wise NAWM group differences in FA and MD were assessed using the ANCOVA module in SPM2, and used participant age and substance abuse history as covariates of no interest. These analyses allowed us to examine regional differences in NAWM MD and FA between groups. Given the limited number of voxels examined in these comparisons due to masking for NAWM, we felt that standard methods used to control for multiple comparisons (eg, Bonferroni, family-wise error, false discovery rate) would be too restrictive. Therefore, we employed a conservative significance threshold for these analyses with a P value of <0.005 and an extent threshold of 5 voxels.71

Determination of the location of voxels demonstrating significantly different DTI values was accomplished by converting the xyz coordinates for the peak voxel within a cluster from the Montreal Neurological Institute (MNI) coordinates used in SPM2 analyses to Talairach coordinates72 using the MNI2TAL software (http://www.mrc-cbu.cam.ac.uk/Imaging/Common/mnispace.shtml). The resultant Talairach coordinates were entered into a software program that identifies lobar and Brodmann area locations.73

Back to Top | Article Outline


Group Differences in Demographic Factors and Whole-Brain DTI Indices

Examination of demographic data revealed no significant differences in sex, age, or years of education between the 2 groups. As highlighted in Table 1, drug-use history between the SP and SN groups was significantly different.

ANCOVA revealed no significant group difference in whole-brain NAWM volume, averaged whole-brain NAWM FA (SP: mean [M] = 0.434 [standard deviation {SD} 0.026]; SN: M = 0.445 [SD 0.033]), or averaged NAWM MD (SP: M = 776.8 [SD 20.0]; SN: M = 770.7 [SD 29.3]) between SP and SN subjects after covarying for age and substance-abuse history.

Back to Top | Article Outline
Demographic and Disease Characteristics Associated With Whole-Brain DTI Indices

Mean and standard deviations for immune and virologic markers are summarized in Tables 1 and 2. Duration of known HIV infection, absolute CD4 cell counts, percent of CD4 lymphocytes present, or viral load (detectable or undetectable) were not associated with either NAWM FA or NAWM MD when age and substance-abuse history were used as covariates of no interest. Among the SP subjects, time since last alcohol or drug abuse ranged from 12 to 276 months, with a similar range reported for the SN group (6 to 300 months). After controlling for age, those with a reported history of substance abuse demonstrated significantly higher NAWM MD (F2,27 = 18.2, P < 0.0005). There were no group differences in NAWM FA associated with substance-abuse history. Thirty-seven percent of the HIV-infected individuals were HCV-antibody positive. All HCV-positive SP subjects had a history of substance abuse. Therefore, analyses of DTI differences between HCV-positive and HCV-negative subjects controlled for age only. There was a trend for decreased FA (F2,27 2.9, P = 0.07) in the HCV-positive group compared to the HCV-negative group and a significant increase in MD (F2,27 11.2, P < 0.0005) in the HCV-positive group. Given the limited cell size and skewed distributions, examinations of group differences in CD4 categories based upon CDC classification or current antiretroviral therapy use were not conducted.

Table 2
Table 2
Image Tools
Back to Top | Article Outline
Neuropsychological Performance

Table 3 shows the raw neuropsychological performances (mean ± standard deviation) results for each of the tasks. There were no significant differences between the SN and SP groups on any neuropsychological measure. Within-group (SP only) correlational analyses between the DTI indices and the neuropsychological measures revealed no significant associations after controlling for age and substance-abuse history (Table 4).

Table 3
Table 3
Image Tools
Table 4
Table 4
Image Tools
Back to Top | Article Outline
Voxel-Wise Group Comparisons of NAWM FA and NAWM MD

Although there were no group differences in whole-brain average NAWM FA or MD, we examined the possibility of regional differences through voxel-wise comparisons. After controlling for age and substance-abuse history, we found significant regional group differences in both NAWM FA and NAWM MD. Regional decreases in NAWM FA in the SP group compared to the SN group were found in 8 regions (Fig. 1; Table 5 is available via the Article Plus feature at http://www.jaids.com. Locate this article, then click on the Article Plus link on the right), including the white matter of right middle frontal gyrus (near Brodmann areas 9 and 10), left cuneus (near Brodmann area 17), left precuneus (near Brodmann area 7), right precentral gyrus (near Brodmann areas 4 and 6), right cingulum (near Brodmann area 32), right insula (near Brodmann area 13), and right internal capsule near the pulvinar. Regional increases in NAWM FA in the SP subjects were found in 10 regions, including the white matter of the bilateral medial frontal lobes (near Brodmann areas 10 and 6), bilateral middle frontal gyrus (near Brodmann area 9), right inferior frontal lobe (near Brodmann area 45), left precentral gyrus (near Brodmann area 6), right cingulum (anterior, middle, and posterior; near Brodmann areas 30 and 32), and right parietal lobes (near Brodmann area 40). Although both decreased and increased FA were noted in selected cerebral locations in the seropositive patients, there was no direct overlap of specific lobar locations. Overall, there was an approximate 3-fold greater number of voxels, evidencing increased (n = 267) rather than decreased (n = 81) anisotropic diffusion in the SP sample compared to the SN sample.

Figure 1
Figure 1
Image Tools

Voxel-wise comparisons of NAWM MD between the groups revealed regions of significantly decreased and increased diffusivity in the SP subjects compared to the SN subjects (Fig. 2; Table 6 is available via the Article Plus feature at http://www.jaids.com. Locate this article, then click on the Article Plus link on the right). Decreased mean diffusivity was found in the NAWM in SP subjects in 2 regions, including the white matter of the right middle frontal gyrus (near Brodmann area 10) and the right ventriculus lateralis. Increased mean diffusivity was found in 21 regions of NAWM in the SP subjects, including white matter of the left superior frontal gyrus (near Brodmann area 10), bilateral middle frontal gyrus (near Brodmann area 9), bilateral inferior frontal gyrus (near Brodmann areas 45 and 47), bilateral medial frontal gyrus (near Brodmann area 6), extensive regions of the right cingulum (near Brodmann areas 24, 30, 31, and 32), bilateral precentral gyrus (near Brodmann area 6), right superior temporal gyrus (near Brodmann area 39), left middle temporal gyrus (near Brodmann area 21), left cuneus (near Brodmann area 18), and right anterior and posterior limbs of the internal capsule. Although both decreased and increased MD was noted in selected cerebral locations in the SP subjects, there was no direct overlap of specific lobar locations. Overall, there was an approximate 10-fold greater number of voxels evidencing increased mean diffusivity (n = 412), as opposed to decreased mean diffusivity (n = 43), in the SP sample compared to the SN sample.

Figure 2
Figure 2
Image Tools

Selected regions of increased NAWM FA in the SP sample overlapped with regions of increased NAWM MD in the same sample. Specifically, 8 of the 10 regions demonstrating increased anisotropic diffusion also evidenced increased diffusivity. These regions included white matter in the right cingulum, right medial frontal lobe, right inferior frontal lobe, bilateral middle frontal lobe, left precentral gyrus, and right parietal lobe.

Back to Top | Article Outline


To our knowledge, the present study is the first characterization of whole-brain alterations in radiologically defined NAWM of individuals infected with HIV using high-resolution diffusion tensor imaging. Although the exact cause of cerebral pathology in HIV is not completely understood, there is evidence of both immunologic and direct viral effects that may compromise cerebral integrity. HIV enters the brain early in the course of the disease74 and directly affects neuronal and synaptic function, as evidenced by decreased synaptophysin and microtubule-associated protein-2 labeling.75 Immunological effects result from the release of proinflammatory cytokines and chemokines76 with elevation of β-amyloid precursor protein,77 resulting in damage to axonal and myelin components to white matter.

Anisotropic diffusion has been associated with intact white matter, whereas isotropic diffusion has been associated with the loss of white matter integrity.27,68,70 Because of the semiorganized structure of intact white matter tracts, diffusion flows along the long axis of myelinated axons, resulting in increased diffusion directionality and higher DTI-measured fractional anisotropy. As damage occurs in white matter, the diffusion pattern is perturbed, which results in decreased diffusion directionality and lower DTI-measured fractional anisotropy. Through a voxel-by-voxel analytic approach, we found significant regional differences in DTI measures of white matter integrity, including changes in NAWM anisotropic diffusion and mean diffusivity in the SP sample compared to an SN comparison sample. These differences were present despite comparable performance on neuropsychological testing and after controlling for group differences associated with age and history of substance abuse, which are known to independently alter white matter integrity. These findings are consistent with previous studies that demonstrate decreased anisotropy in investigator-defined regions of interest,45,47,51 suggesting that decreased white matter integrity is not attributed to the effects of aging, substance abuse, or neuropsychological status alone.

In comparison to regional decreases in anisotropic diffusion in the SP sample, we also found regions with increased fractional anisotropy in these patients. Previous ROI studies have also found selected regions of increased anisotropic diffusion in SP samples in the centrum semiovale, splenium, parietal lobes, and internal capsule.44,47 The standard interpretation of these findings is that increased fractional anisotropy represents increased white matter integrity in the SP patients. However, an alternative interpretation is that increased anisotropic diffusion is due to a loss of complexity of the white matter matrix in these regions. If a given voxel contains only parallel white matter fibers, anisotropic diffusion would be high. On the other hand, if a given voxel contained a complex matrix of crossing or other nonparallel fiber orientations, anisotropic diffusion would be lower.78 Damage to the complex matrix that results in the loss of crossing and nonparallel fibers would preserve parallel fibers, and thus FA would paradoxically increase. One indicator of such a process could be increased anisotropic diffusion in regions with increased diffusivity. The expected relationship between FA and MD is inverse; as FA decreases, MD typically increases, representing both loss of parallel fibers (decreased FA) and general tissue damage (increased MD). We found multiple regions with the opposite relationship in the SP sample: FA increase was concomitant with MD increase. This may well represent a loss of complexity to the white matter matrix in these regions, because increased FA could represent loss of crossing and other nonparallel fibers and increased MD would reflect the generalized tissue loss associated with such damage. If HIV infection resulted in a loss of the complexity to the white matter matrix, anisotropic diffusion would be paradoxically increased in these regions. We believe this is the first report of such an atypical relationship between FA and MD in SP subjects. The finding needs to be replicated in other samples and extended by further decomposing the diffusion tensor for clues as to the underlying cause of this unique relationship between FA and MD.

Mean diffusivity, a nondirectional measure of free translational diffusion, was markedly increased in our sample of SP subjects compared to the SN subjects. The increased mean diffusivity was widespread in the NAWM regions. This finding is consistent with many previous ROI studies, which report associations between diffusivity and cognitive impairment,51,52,79 viral load (in some studies47 but not others44), glial metabolites, and inflammation and breakdown of the extracellular matrix.79 Two previous reports of whole-brain DTI studies, including both gray matter and white matter, found no significant differences in total mean diffusivity between SP and SN subjects.49,50 However, the combination of gray and white matter measures of diffusivity complicates the interpretation of these findings because of basic differences in the signal-to-noise ratio of gray and white matter using echo-planar imaging.80 Additionally, these previous reports did not control for the confounding effects of age and/or substance-abuse history. Indeed, in our study, significant group differences in NAWM MD were found when age and substance-abuse history were not used as covariates of no interest, but lost significance after correction for age and substance-abuse history. This points out the importance of controlling for the known influence of age27,29,31 and other potential confounders, such as substance-abuse history, on DTI values.

Macrostructural white matter integrity, as evidenced by white matter hyperintensities16,17 and reduced white matter volume,8,81 has been associated with HIV infection. However, in our study, the widespread alterations in anisotropy seem independent of white matter volume reduction, inasmuch as there was no significant difference in the white matter volume between the 2 subject groups. Further, by separating the b0 diffusion-weighted image into CSF, gray matter, and white matter segments and then applying the segmented white matter image to each FA and MD map, we avoid misinterpreting alterations associated with elements other than NAWM, such as hyperintensities, CSF, gray matter, and extracerebral matter. With the potential for echo-planar distortions at the brain/air/bone junctions, particularly in the frontal and inferior temporal regions, the exclusion of cortical gray matter further decreases the likelihood of imaging artifacts contributing to the demonstrated alterations.

Cognitively, there were no significant group differences on any neuropsychological measure, suggesting that the identified regional differences in anisotropy and diffusivity cannot be attributed to global differences in cognitive ability (ie, cognitive impairment), which has been associated with altered DTI values in HIV.46,48-51 Because regional DTI differences between the SP and SN subjects may be associated with unique patterns of cognitive function, correlational analyses between the whole-brain NAWM DTI indices and neuropsychological performance were conducted for the SP sample only. There were no significant associations among the DTI indices and cognitive performance measures after controlling for age and substance-abuse history. This discrepancy from previous reports may be due to multiple factors. First, we used age and substance abuse as covariates of no interest in our analyses. The previous studies finding significant associations between cognitive function and DTI values in SP samples did not employ such corrections. Indeed, when we did not correct for age and substance abuse, we found significant correlations between DTI measures and cognitive performance in the SP sample (data not shown). Second, the previous reports used either ROI analyses or whole-brain analyses that incorporated normal and abnormal white matter and gray matter. Voxel-wise correlational analyses between NAWM DTI measures and cognitive performance measures are yet to be conducted.

Within the SP sample, history of substance abuse was associated with significant increases in MD. This finding replicates a report of altered DTI measures of white matter integrity in SP individuals with a history of alcohol abuse that was not present in SP patients without such a history.46 Additionally, we found that SP patients with HCV coinfection also had significantly higher MD, and a trend for lower FA, compared to SP patients without coinfection. This finding is somewhat complicated by 2 issues. First, all SP patients with HCV coinfection reported a positive history for substance abuse, so it is impossible to differentiate the potential influences of coinfection and substance abuse on DTI markers. Second, although the SP patients with HCV coinfection were antibody-positive for the virus, they were not necessarily viremic at the time of assessment, so the effects of currently active infection versus past infection on DTI markers cannot be separated.

Although our sample was larger than that of most previous studies, it was still relatively small, and the results need to be replicated in independent samples. However, our sample size was sufficiently large to allow for examination of important covariates that had not been addressed in previous studies. By accounting for the differences in age and substance-abuse history between the SP and SN participants in our study, we were able to control for the possible interactions of age, substance-abuse history, and DTI measures of NAWM integrity. Examination of the different results of the noncorrected and corrected analyses highlight the importance of these covariates. Our sample size was not sufficiently large, however, to examine the influence of all potential covariates (eg, route of initial infection). Additionally, we had a limited number of subjects who were not on HIV therapy, so we could not investigate potential differences in DTI indices of white matter integrity in treated versus untreated patients. Both our SP and SN samples were not randomly selected from the population of all possible SP and SN individuals, but rather were selected from our tertiary-care center. The average CD4 count for our SP sample was somewhat elevated, making generalizations to patients with lower CD4 counts imprecise. Finally, our study was designed as a cross-sectional examination of DTI indices of NAWM pathology in SP individuals, and thus cannot address issues of treatment effects of longitudinal changes associated with infection.

Despite these potential limitations of the study, our results may help reconcile previous conflicting reports of both increases and decreases in anisotropic diffusion and mean diffusion. Previous studies using ROI approaches may have only selected regions that demonstrated increased indices of DTI parameters of anisotropic diffusion and mean diffusivity at the expense of missing regions that demonstrated decreased indices. By comparing differences on a voxel-by-voxel basis, whole-brain voxel-wise analysis, as conducted in our study, interrogates all regions of NAWM and captures both increased and decreased values averaged across all participants.

The use of HAART has contributed to a significant reduction in HIV-associated CNS disorders. HAART has not only been associated with improved immunological status, but has also been shown to improve metabolic abnormalities associated with HIV-cognitive motor complex82 and neuropsychological performance.83-85 However, findings from our investigation indicate that microstructural changes are evident throughout the cerebrum in nondemented, community-dwelling SP individuals, despite 85% of the sample receiving HAART intervention. Additionally, these alterations are not attributable to white matter abnormalities, as noted in previous studies;8,16,17 rather, these changes occur in radiologically defined NAWM that specifically excludes areas of white matter in which the T2 signal was altered due to white matter lesions, atrophic changes, or other adverse influences.

Back to Top | Article Outline


Data in this manuscript were collected at the Ruth M. Rothstein CORE Center for the Prevention, Care and Research of Infectious Diseases, a joint venture of John H. Stroger, Jr. Hospital of Cook County, the Cook County Bureau of Health Services, and Rush University Medical Center.

Back to Top | Article Outline


1. Maschke M, Kastrup O, Esser S, et al. Incidence and prevalence of neurological disorders associated with HIV since the introduction of highly active antiretroviral therapy (HAART). J Neurol Neurosurg Psychiatry. 2000;69:376-380.

2. Sacktor N. The epidemiology of human immunodeficiency virus-associated neurological disease in the era of highly active antiretroviral therapy. J Neurovirol. 2002;8(Suppl 2):115-121.

3. Price RW, Yiannoutsos CT, Clifford DB, et al. Neurological outcomes in late HIV infection: adverse impact of neurological impairment on survival and protective effect of antiretroviral therapy. AIDS Clinical Trial Group and Neurological AIDS Research Consortium study team. AIDS. 1999;13:1677-1685.

4. Hinkin CH, Hardy DJ, Mason KI, et al. Medication adherence in HIV-infected adults: effect of patient age, cognitive status, and substance abuse. AIDS. 2004;18(Suppl 1):S19-S25.

5. Heaton RK, Marcotte TD, Mindt MR, et al. The impact of HIV-associated neuropsychological impairment on everyday functioning. J Int Neuropsychol Soc. 2004;10:317-331.

6. Marcotte TD, Wolfson T, Rosenthal TJ, et al. A multimodal assessment of driving performance in HIV infection. Neurology. 2004;63:1417-1422.

7. Jernigan TL, Archibald S, Hesselink JR, et al. Magnetic resonance imaging morphometric analysis of cerebral volume loss in human immunodeficiency virus infection. The HNRC Group. Arch Neurol. 1993;50:250-255.

8. Stout JC, Ellis RJ, Jernigan TL, et al. Progressive cerebral volume loss in human immunodeficiency virus infection: a longitudinal volumetric magnetic resonance imaging study. HIV Neurobehavioral Research Center Group. Arch Neurol. 1998;55:161-168.

9. Archibald SL, Masliah E, Fennema-Notestine C, et al. Correlation of in vivo neuroimaging abnormalities with postmortem human immunodeficiency virus encephalitis and dendritic loss. Arch Neurol. 2004;61:369-376.

10. Ances BM, Roc AC, Wang J, et al. Caudate blood flow and volume are reduced in HIV+ neurocognitively impaired patients. Neurology. 2006;66:862-866.

11. Jernigan TL, Gamst AC, Archibald SL, et al. Effects of methamphetamine dependence and HIV infection on cerebral morphology. Am J Psychiatry. 2005;162:1461-1472.

12. Chiang MC, Dutton RA, Hayashi KM, et al. 3D pattern of brain atrophy in HIV/AIDS visualized using tensor-based morphometry. Neuroimage. 2007;34:44-60.

13. Thompson PM, Dutton RA, Hayashi KM, et al. Thinning of the cerebral cortex visualized in HIV/AIDS reflects CD4+ T lymphocyte decline. Proc Natl Acad Sci USA. 2005;102:15647-15652.

14. Thompson PM, Dutton RA, Hayashi KM, et al. 3D mapping of ventricular and corpus callosum abnormalities in HIV/AIDS. Neuroimage. 2006;31:12-23.

15. Pfefferbaum A, Rosenbloom MJ, Rohlfing T, et al. Contribution of alcoholism to brain dysmorphology in HIV infection: effects on the ventricles and corpus callosum. Neuroimage. 2006;33:239-251.

16. Hestad K, McArthur JH, Dal Pan GJ, et al. Regional brain atrophy in HIV-1 infection: Association with specific neuropsychological test performance. Acta Neurol Scand. 1993;88:112-118.

17. McArthur JC, Kumar AJ, Johnson DW, et al. Incidental white matter hyperintensities on magnetic resonance imaging in HIV-1 infection. Multicenter AIDS Cohort Study. J Acquir Immune Defic Syndr. 1990;3:252-259.

18. Meyerhoff D, Bloomer C, Cardenas V, et al. Elevated subcortical choline metabolites in cognitively and clinically asymptomatic HIV+ patients. Neurology. 1999;52:995-1003.

19. Suwanwelaa N, Phanuphak P, Phanthumchinda K, et al. Magnetic resonance spectroscopy of the brain in neurologically asymptomatic HIV-infected patients. Magn Reson Imaging. 2000;18:859-865.

20. Chang L, Lee PL, Yiannoutsos CT, et al. A multicenter in vivo proton-MRS study of HIV-associated dementia and its relationship to age. Neuroimage. 2004;23:1336-1347.

21. Tracey I, Hamberg LM, Guimaraes AR, et al. Increased cerebral blood volume in HIV-positive patients detected by functional MRI. Neurology. 1998;50:1821-1826.

22. Chang L, Speck O, Miller EN, et al. Neural correlates of attention and working memory deficits in HIV patients. Neurology. 2001;57:1001-1007.

23. Chang L, Tomasi D, Yakupov R, et al. Adaptation of the attention network in human immunodeficiency virus injury. Ann Neurol. 2004;56:259-272.

24. Ernst T, Chang L, Jovicich J, et al. Abnormal brain activation on functional MRI in cognitively asymptomatic HIV patients. Neurology. 2002;59:1343-1349.

25. Ernst T, Chang L, Arnold S. Increased glial metabolites predict increased working memory network activation in HIV brain injury. Neuroimage. 2003;19:1686-1693.

26. Castelo JM, Sherman SJ, Courtney MG, et al. Altered hippocampal-prefrontal activation in HIV patients during episodic memory encoding. Neurology. 2006;66:1688-1695.

27. Moseley M. Diffusion tensor imaging and aging - a review. NMR Biomed. 2002;15:553-560.

28. Nusbaum AO, Tang CY, Buchsbaum MS, et al. Regional and global changes in cerebral diffusion with normal aging. AJNR Am J Neuroradiol. 2001;22:136-142.

29. Pfefferbaum A, Sullivan EV, Hedehus M, et al. Age-related decline in brain white matter anisotropy measured with spatially corrected echo-planar diffusion tensor imaging. Magn Reson Med. 2000;44:259-268.

30. Abe O, Aoki S, Hayashi N, et al. Normal aging in the central nervous system: quantitative MR diffusion-tensor analysis. Neurobiol Aging. 2002;23:433-441.

31. Stebbins GT, Poldrack RA, Klingberg T, et al. Aging effects on white matter integrity and processing speed: a diffusion tensor imaging study. Neurology. 2001;56:A374. Abstract.

32. Stebbins GT, Carrillo MC, Goetz CG, et al. Dissociation of frontal and prefrontal white matter integrity to cognitive and motor behaviors in Parkinson‘s disease: a diffusion tensor imaging study. Mov Disord. 2002;17(Suppl 5):S172. Abstract.

33. Bozzali M, Falini A, Franceschi M, et al. White matter damage in Alzheimer's disease assessed in vivo using diffusion tensor magnetic resonance imaging. J Neurol Neurosurg Psychiatry. 2002;72:742-746.

34. Medina D, DeToledo-Morrell L, Urresta F, et al. White matter changes in mild cognitive impairment and AD: a diffusion tensor imaging study. Neurobiol Aging. 2006;27:663-672.

35. Kantarci K, Jack CR, Xu YC, et al. Mild cognitive impairment and Alzheimer disease: regional diffusivity of water. Radiology. 2001;219:101-107.

36. Sandson TA, Felician O, Edelman RR, et al. Diffusion-weighted magnetic resonance imaging in Alzheimer's disease. Dement Geriatr Cogn Disord. 1999;10:166-171.

37. Wang C, Stebbins GT, Nyenhuis DL, et al. Longitudinal changes in white matter following ischemic stroke: a three-year follow-up study. Neurobiol Aging. 2006;27:1827-1833.

38. Rovaris M, Iannucci G, Falautano M, et al. Cognitive dysfunction with mildly disabling relapsing-remitting multiple sclerosis: an exploratory study with diffusion tensor MR imaging. J Neurol Sci. 2002;195:103-109.

39. Stebbins GT, Katsamakis G, Carrillo MC, et al. White matter integrity in multiple sclerosis and its relationship to cognitive processing speed: a diffusion tensor imaging study. Neurology. 2003;60(Suppl 1):A303. Abstract.

40. Pfefferbaum A, Sullivan EV, Hedehus M, et al. In vivo detection and functional correlates of white matter microstructural disruption in chronic alcoholism. Alcohol Clin Exp Res. 2000;24:1214-1221.

41. Pfefferbaum A, Sullivan EV. Microstructural but not macrostructural disruption of white matter in women with chronic alcoholism. Neuroimage. 2002;15:708-718.

42. Kanaan RA, Kim JS, Kaufmann WE, et al. Diffusion tensor imaging in schizophrenia. Biol Psychiatry. 2005;58:921-929.

43. Kubicki M, McCarley R, Westin CF, et al. A review of diffusion tensor imaging studies in schizophrenia. J Psychiatr Res. 2007;41:15-30.

44. Thurnher MM, Castillo M, Stadler A, et al. Diffusion-tensor MR imaging of the brain in human immunodeficiency virus-positive patients. AJNR Am J Neuroradiol. 2005;26:2275-2281.

45. Filippi CG, Ulug AM, Ryan E, et al. Diffusion tensor imaging of patients with HIV and normal-appearing white matter on MR images of the brain. AJNR Am J Neuroradiol. 2001;22:277-283.

46. Pfefferbaum A, Rosenbloom MJ, Adalsteinsson E, et al. Diffusion tensor imaging with quantitative fibre tracking in HIV infection and alcoholism comorbidity: synergistic white matter damage. Brain. 2007;130:48-64.

47. Pomara N, Crandall DT, Choi SJ, et al. White matter abnormalities in HIV-1 infection: a diffusion tensor imaging study. Psychiatry Res. 2001;106:15-24.

48. Wu Y, Storey P, Cohen BA, et al. Diffusion alterations in corpus callosum of patients with HIV. AJNR Am J Neuroradiol. 2006;27:656-660.

49. Ragin AB, Storey P, Cohen BA, et al. Whole brain diffusion tensor imaging in HIV-associated cognitive impairment. AJNR Am J Neuroradiol. 2004;25:195-200.

50. Ragin AB, Storey P, Cohen BA, et al. Disease burden in HIV-associated cognitive impairment: a study of whole-brain imaging measures. Neurology. 2004;63:2293-2297.

51. Ragin AB, Wu Y, Storey P, et al. Diffusion tensor imaging of subcortical brain injury in patients infected with human immunodeficiency virus. J Neurovirol. 2005;11:292-298.

52. An H, Chen Y, Smith J, et al. Whole brain diffusion tensor analysis in HIV patients at various clinical stages [abstract]. Presented at: Scientific Assembly and Annual Meeting of the Radiological Society of North America; 2005; Chicago.

53. Berger JR, Avison MJ. Diffusion tensor imaging in HIV infection: what is it telling us? AJNR Am J Neuroradiol. 2001;22:237-238.

54. Good CD, Johnsrude IS, Ashburner J. A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage. 2001;14:21-36.

55. Wechsler Test of Adult Reading. Odessa, FL: Psychological Assessment Resources; 2001.

56. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189-198.

57. Power C, Selnes OA, Grim JA, et al. HIV Dementia Scale: a rapid screening test. J Acquir Immune Defic Syndr Hum Retrovirol. 1995;8:273-278.

58. Delis DC, Kramer JH, Kaplan E, et al. California Verbal Learning Test-II. San Antonio: The Psychological Corporation; 2000.

59. Matthews CG, Kløve H. Instruction Manual for the Adult Neuropsychology Test Battery. Madison, WI: University of Wisconsin Medical School; 1964.

60. Benton AL, Hamsher K, Sivan AB. Multilingual Aphasia Examination. 3rd ed. Iowa City, IA: AJA Associates; 1983.

61. Smith A. The symbol digit modalities test: a neuropsychologic test for economic screening of learning and other cerebral disorders. Learning Disord. 1968;3:83-91.

62. Army Individual Test Battery. Manual of Directions and Scoring. Washington, DC: War Department, Adjutant General's Office; 1994.

63. Stroop J. Studies of interference in serial verbal reactions. J Exp Psychol. 1935;18:643-662.

64. Wechsler D. Wechsler Adult Intelligence Scale-III. San Antonio: The Psychological Corporation; 2000.

65. de Crespigny AJ, Moseley ME. Eddy current induced image warping in diffusion weighted EPI. In: ISMRM Sixth Meeting Proc., 18-24 April 1998, Sydney, Australia. Berkeley, CA: Intl Soc Magn Res Medicine; 1998:661.

66. Haselgrove JC, Moore JR. Correction for distortion of echo-planar images used to calculate the apparent diffusion coefficient. Magn Reson Med. 1996;36:960-964.

67. Basser PJ, Mattiello J, LeBihan D. Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B. 1994;103:247-254.

68. Basser PJ. Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. NMR Biomed. 1995;8:333-344.

69. Basser PJ, Mattiello J, LeBihan D. MR diffusion tensor spectroscopy and imaging. Biophys J. 1994;66:259-267.

70. Basser PJ, Pierpaoli C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B. 1996;111:209-219.

71. Friston KJ, Worsley KJ, Frackowiak RSJ, et al. Assessing the significance of focal activations using their spatial extent. Hum Brain Mapp. 1994;1:214-220.

72. Talairach J, Tournoux P. Co-Planar Stereotaxic Atlas of the Human Brain. New York: Thieme Medical Publishers; 1988.

73. Lancaster JL, Woldorff MG, Parsons LM, et al. Automated Talairach atlas labels for functional brain mapping. Hum Brain Mapp. 2000;10:120-131.

74. Davis LE, Hjelle BL, Miller VE, et al. Early viral brain invasion in iatrogenic human immunodeficiency virus infection. Neurology. 1992;42:1736-1739.

75. Moore DJ, Masliah E, Rippeth JD, et al. Cortical and subcortical neurodegeneration is associated with HIV neurocognitive impairment. AIDS. 2006;20:879-887.

76. Conant K, Garzino-Demo A, Nath A, et al. Induction of monocyte chemoattractant protein-1 in HIV-1 Tat-stimulated astrocytes and elevation in AIDS dementia. Proc Natl Acad Sci U S A. 1998;95:3117-3121.

77. Raja F, Sherriff FE, Morris CS, et al. Cerebral white matter damage in HIV infection demonstrated using beta-amyloid precursor protein immunoreactivity. Acta Neuropathol (Berl). 1997;93:184-189.

78. Virta A, Barnett A, Pierpaoli C. Visualizing and characterizing white matter fiber structure and architecture in the human pyramidal tract using diffusion tensor MRI. Magn Reson Imaging. 1999;17:1121-1133.

79. Cloak CC, Chang L, Ernst T. Increased frontal white matter diffusion is associated with glial metabolites and psychomotor slowing in HIV. J Neuroimmunol. 2004;157:147-152.

80. Sun S, Song S, Hong C, et al. Improving relative anisotropy measurement using directional correlation of diffusion tensors. Magn Reson Med. 2001;46:1088-1092.

81. Olsen WL, Longo FM, Mills CM, et al. White matter disease in AIDS: findings at MR imaging. Radiology. 1988;169:445-448.

82. Chang L, Ernst T, Leonido-Yee M, et al. Highly active antiretroviral therapy reverses brain metabolite abnormalities in mild HIV dementia. Neurology. 1999;53:782-789.

83. Sacktor NC, Skolasky RL, Lyles RH, et al. Improvement in HIV-associated motor slowing after antiretroviral therapy including protease inhibitors. J Neurovirol. 2000;6:84-88.

84. Robertson KR, Robertson WT, Ford S, et al. Highly active antiretroviral therapy improves neurocognitive functioning. J Acquir Immune Defic Syndr. 2004;36:562-566.

85. Marra CM, Lockhart D, Zunt JR, et al. Changes in CSF and plasma HIV-1 RNA and cognition after starting potent antiretroviral therapy. Neurology. 2003;60:1388-1390.

Cited By:

This article has been cited 9 time(s).

Journal of Neurovirology
White matter tract injury and cognitive impairment in human immunodeficiency virus-infected individuals
Gongvatana, A; Schweinsburg, BC; Taylor, MJ; Theilmann, RJ; Letendre, SL; Alhassoon, OM; Jacobus, J; Woods, SP; Jernigan, TL; Ellis, RJ; Frank, L; Grant, I
Journal of Neurovirology, 15(2): 187-195.
Neuropsychology Review
MR Diffusion Tensor Imaging: A Window into White Matter Integrity of the Working Brain
Chanraud, S; Zahr, N; Sullivan, EV; Pfefferbaum, A
Neuropsychology Review, 20(2): 209-225.
Brain Imaging and Behavior
Gender Effects on HIV-Associated White Matter Alterations: A Voxel-Wise DTI Study
Smith, CA; Stebbins, GT; Bartt, RE; Kessler, HA; Adeyemi, OM; Martin, E; Bammer, R; Moseley, ME
Brain Imaging and Behavior, 2(3): 177-191.
Clinical Neurology and Neurosurgery
White matter changes in HIV-1 infected brains: A combined gross anatomical and ultrastructural morphometric investigation of the corpus callosum
Wohlschlaeger, J; Wenger, E; Mehraein, P; Weis, S
Clinical Neurology and Neurosurgery, 111(5): 422-429.
Behavioural Neurology
Changes in parahippocampal white matter integrity in amnestic mild cognitive impairment: A diffusion tensor imaging study
Rogalski, EJ; Murphy, CM; DeToledo-Morrell, L; Shah, RC; Moseley, ME; Bammer, R; Stebbins, GT
Behavioural Neurology, 21(): 51-61.
Neuropsychology Review
Drug Abuse and Hepatitis C Infection as Comorbid Features of HIV Associated Neurocognitive Disorder: Neurocognitive and Neuroimaging Features
Martin-Thormeyer, EM; Paul, RH
Neuropsychology Review, 19(2): 215-231.
White matter abnormalities revealed by diffusion tensor imaging in non-demented and demented HIV plus patients
Chen, YS; An, HY; Zhu, HT; Stone, T; Smith, JK; Hall, C; Bullitt, E; Shen, DG; Lin, WL
Neuroimage, 47(4): 1154-1162.
Movement Disorders
A Novel Splice Site Mutation in the SPG7 Gene Causing Widespread Fiber Damage in Homozygous and Heterozygous Subjects
Warnecke, T; Duning, T; Schirmacher, A; Mohammadi, S; Schwindt, W; Lohmann, H; Dziewas, R; Deppe, M; Ringelstein, EB; Young, P
Movement Disorders, 25(4): 413-420.
Behavioural Neurology
Diffusion tensor imaging in Alzheimer's disease and mild cognitive impairment
Stebbins, GT; Murphy, CM
Behavioural Neurology, 21(): 39-49.
Back to Top | Article Outline

magnetic resonance imaging; diffusion tensor imaging; white matter

Supplemental Digital Content

Back to Top | Article Outline

© 2007 Lippincott Williams & Wilkins, Inc.


Search for Similar Articles
You may search for similar articles that contain these same keywords or you may modify the keyword list to augment your search.