Tract-Based Spatial Statistics of Cerebral Small Vessel Disease in an HIV Population – OHBM 2020 Poster Session


Diffusion MRI (dMRI) signal can be reconstructed by fitting various signal models that reflect different aspects of white matter structure. Three of these models are diffusion tensor imaging (DTI),1 diffusion kurtosis imaging (DKI),2 and neurite orientation dispersion and distribution index (NODDI).3 DTI can be used to calculate fractional anisotropy (FA) and mean, axial, and radial diffusivities (MD, AD, and RD). DKI produces mean, axial, and radial kurtosis (MK, AK, and RK). NODDI produces neurite density (ICVF), orientation dispersion (ODI), and cerebrospinal fluid volume fraction (FISO). Cerebral small vessel disease (CSVD) is a vascular disease that encompass lesions such as white matter, lacunes, microbleeds and enlarged perivascular spaces. CSVD is twice as prevalent in an HIV population, likely due to chronic neuroinflammation.4  By studying an aging HIV population, we show that changes in diffusion metrics may reflect diffuse neuroinflammation due to HIV and global changes due to aging.


In an ongoing study, 180 subjects [(with CSVD: N=116, mean ± SD age = 55.6 ± 12.5 years); (without CSVD: N=65, mean ± SD age = 44.2 ± 14.4 years)] were evaluated for the presence of CSVD. All imaging was conducted on a 3T whole-body Siemens MAGNETOM Prisma scanner equipped with a 64-channel head coil. T1-weighted anatomical images were acquired for coregistration using the MPRAGE sequence with the following parameters: TI= 926 ms; TR= 1840 ms; TE= 2.45 ms; echo spacing (ESP) = 7.5 ms. dMRI  was performed using a 2D single-shot spin echo echo-planar imaging (SE-EPI) sequence with TR= 4300 ms; TE= 69.0 ms; ESP= 0.66 ms; 1.5 mm isotropic resolution. Diffusion gradients were applied along 64 directions with two shells (b=1,000 and 2,000 s/mm2) and 7 reference images. Preprocessing included BET, TOPUP, and EDDY in FMRIB’s Software Library (FSL, version 5.0.11).5 A diffusion tensor was fit to each voxel using a linear regression with sum of least-squares error and metric maps were produced using DTIFIT. Kurtosis metrics were calculated using the DIPY module in Python 3.6.6 NODDI metrics were processed using the NODDI Toolbox run on the MATLAB platform (R2018a). Tract-based spatial statistics (TBSS) was then performed on all metric maps comparing group differences due to CSVD using randomise in FSL (version 6.0.2) with 5000 permutations, controlling for HIV status and age, after registering each subject to MNI-152 1mm standard space using FLIRT and FNIRT. Interaction effects between HIV and CSVD were preliminarily considered by group testing by CSVD status the mean region of interest metric values in the HIV-infected (HIV+) and HIV-uninfected (HIV-) subjects separately.


The presence of CSVD was associated with changes in diffusion metrics (Figure 1). In the cerebellar peduncles, FA was decreased while ODI was increased. AD and RK showed opposite and asymmetric changes. Diffusion metric changes due to the effects of HIV were more global and pronounced. As shown in figure 2, both AD and FA were decreased across much of the white matter, possibly indicating a greater contribution of injury via axonal damage. ODI is increased in many of the areas of decreased FA, as expected.7 Effects due to aging were present in nearly every metric and reflect changes found in previous studies.8 Significant diffusion metric differences due to CSVD were identical in both the HIV+ and HIV- populations, indicating that the impact CSVD alone is similar irrespective of HIV status.

Figure 1: TBSS results for CSVD group difference. Blue indicates decreased metric values compared to controls. Red indicates increased metric values compared to controls. P-values less than 0.1 were considered significant.
Figure 2: TBSS results for HIV covariate analysis. Blue indicates decreased metric values given HIV-infection. Red indicates increased metric values given HIV-infection. P-values less than 0.1 were considered significant.


The presence of CSVD was associated with few areas of altered diffusion. However, HIV status showed a diffuse global effect on diffusion metrics, irrespective of CSVD. As expected, we confirmed that aging itself can affect diffusion.


Kyle Murray,¹ Arun Venkataraman,¹ Abrar Faiyaz,¹ Yuchuan Zhuang,¹ Md Nasir Uddin,¹ Madalina Tivarus,¹ Henry Wang,¹ Bogachan Sahin,¹ Lu Wang,¹ Xing Qiu,¹ Jianhui Zhong,¹ Sanjay B Maggirwar,² and Giovanni Schifitto¹

University of Rochester¹, George Washington University²


  1. Assaf Y. and Pasternak O (2008), ‘Diffusion tensor imaging (DTI)-based white matter mapping in brain research: a review.’ Journal of Molecular Neuroscience, vol. 34, no. 1, pp.51-61.
  2. Steven A., Zhuo J., and Melhelm E (2014), ‘Diffusion Kurtosis Imaging: And Emerging Technique for Evaluating the Microstructural Environment of the Brain.’ American Journal of Roentgenology, vol. 202, no. 1, pp. W26-W33.
  3. Grussu F., Schneider T., Tur C., Yates R., Tachrount M., Ianus A., Yiannakas M., Newcombe J., Zhang H., Alexander D., DeLuca G., and Gandini Wheeler-Kingshott C. (2017), ‘Neurite dispersion: a new marker of multiple sclerosis spinal cord pathology?’ Annals of Clinical and Translational Neurology, vol. 4, no. 9, pp. 663-679.
  4. Moulignier A., et al (2018), ‘Silent Cerebral Small-Vessel Disease Is Twice as Prevalent in Middle-Aged Individuals With Well-Controlled, Combination Antiretroviral Therapy-Treated Human Immunodeficieny Virus (HIV) Than in HIV-Uninfected Individuals.’ Clinical Infectious Diseases, vol. 66, no. 11, pp. 1762-1769.
  5. Woolrich M.W., Jbabdi S., Patenaude P., Chappell M., Makni S., Behrens T., Beckmann C., Jenkinson M., Smith S.M. (2009), ‘Bayesian analysis of neuroimaging data in FSL.’ NeuroImage, vol. 45, pp. S173-186.
  6. Garyfallidis E., Brett M., Amirbekian B., Rokem A., van der Walt S., Descoteaux M., Nimmo-Smith I. and Dipy Contributors (2014), ‘DIPY, a library for the analysis of diffusion MRI data.’  Frontiers in Neuroinformatics, vol. 8, no. 8.
  7. Chang Y.S., Owen J.P., Pojman N.J., Thieu T., Bukshpun P., et al. (2015), ‘White Matter Changes of Neurite Density and Fiber Orientation Dispersion during Human Brain Maturation,’ PLOS ONE vol. 10, no. 6.
  8. Kodiweera C., Alexander A. L., Harezlak J., McAllister T. W., & Wu Y. C. (2016), ‘Age effects and sex differences in human brain white matter of young to middle-aged adults: A DTI, NODDI, and q-space study,’ NeuroImagevol. 128, pp. 180–192.

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