Advances in highly active antiretroviral therapy (HAART) have prolonged the lifespan for HIV-infected (HIV+) patients.1 This has increased the incidence of older (>50 years old) HIV+ individuals in the United States.2 Most HIV+ individuals in the United States have access to HAART and a majority virologically well controlled.3 Despite these advances, cognitive impairment and systemic inflammation persist in the HAART era.4,5
Currently, HIV-associated cognitive impairment is quantified by neuropsychological performance (NP) testing and ability to perform activities of daily living.6 However, NP may be insensitive to neurodegenerative changes associated with chronic low-level neuroinflammation that persist in virologically well-controlled HIV+ patients.7,8 Systemic inflammation can exacerbate immune dysfunction and lead to conformational changes in cellular expression that are similar to “premature aging.”9
The advent of HAART has led to increased focus on mechanisms that reduce chronic systemic inflammation and improve prognostic outcome.10 Peripheral blood measures and/or cerebrospinal fluid (CSF) markers have traditionally been used to quantify residual inflammation. Peripheral markers of inflammation (eg, neopterin, C-reactive protein, sCD163, sCD14, and subpopulations of monocytes and CD4 and CD8 T lymphocytes) are often elevated before initiation of HAART and remain abnormal even after viral suppression with HAART.5,7 These measures are regarded as proxies for disease severity and immune activity.11,12 The focus of ongoing studies has shifted to diminishing residual inflammation that remains in virologically well-controlled individuals receiving HAART.10 However, current investigations have not focused on the spatial extent of inflammatory changes that continue to persist in the brain. Neuroimaging of neuroinflammation may provide important localization of residual inflammatory changes in HIV+ patients receiving HAART.
Diffusion tensor imaging (DTI) is a noninvasive magnetic resonance imaging method that detects changes in white matter microstructural integrity.13,14 DTI, which assesses the diffusion of water molecules, has become increasingly popular for assessing HIV-associated white matter changes within HIV+ patients with detectable15–17 and undetectable viral loads.18 However, DTI is incapable of distinguishing underlying pathophysiological changes beyond axonal integrity19 and can be confounded by inflammation.20
Diffusion basis spectral imaging (DBSI) uses a novel data-driven multiple-tensor modeling approach to disentangle biological entities within a particular voxel.20 This method preserves the characteristics of DTI by assuming Gaussian diffusion but avoids using high b-values.21 In DBSI, the apparent diffusion coefficient is further differentiated into anisotropic (representing fiber tracts) and isotropic [representing infiltrating cells (as measured by cellularity), edema, and tissue loss] components. Thus, DBSI provides additional information concerning underlying white matter dysfunction and surrounding pathological changes.20–22
Here, we compared DBSI-derived cellularity, a correlate of neuroinflammation, in a cohort of aviremic HIV+ patients and HIV− controls. We predict that cellularity will be higher in virologically well-controlled HIV+ patients compared with HIV− controls, as this metric will reflect subtle inflammation that may continue to persist after viral suppression is attained with HAART. First, we compared standard DTI measures to DBSI metrics for both groups. Next, we focused on changes in cellularity with respect to aging and NP testing. Finally, we studied associations between cellularity and laboratory variables within the HIV+ cohort.
MATERIALS AND METHODS
Participants were recruited from the Washington University School of Medicine (WUSM) Infectious Disease Clinic, the WUSM AIDS Clinical Trial Unit (ACTU), and the Supportive Positive Opportunities with Teens (SPOT). Participants provided informed written consent that was approved by the Institutional Review Board at WUSM. Each participant was screened for the following exclusion criteria: current or history of confounding neurological disorders, ≥29 on the Beck Depression Inventory-II,23 current alcohol or substance abuse, head injury with loss of consciousness greater than 30 minutes, claustrophobia or seizures, or fewer than 8 years of education. Current alcohol and substance misuse information were collected through self-report for the past month before NP testing. Confirmation of serologic status was performed for HIV− participants using a rapid oral HIV test. All HIV+ individuals were virally suppressed (<20 copies/mL) and on a stable HAART regiment for at least 3 months before the time of assessment. Demographic information on both groups is provided in Table 1. For the HIV+ cohort, clinical disease variables (eg, duration of infection, medication status, etc.) were obtained from medical records or self-reported when records were not available. All HIV+ participants had laboratory evaluations (peripheral blood CD4 cell count and HIV RNA viral load) within 1 year of neuroimaging.
Cognition was evaluated across a standard battery that encompasses 3 domains: executive, motor, and verbal learning memory.24 Raw NP scores were converted to standardized T scores that were adjusted for age, sex, and education by subtracting the appropriate normative mean from the raw score and then dividing by the normative SD. The 3 domains tested included the following:
- Executive function—Letter Number Sequencing, Trail Making Test-B,25 and Verbal Fluency26
- Verbal learning and memory—Hopkins Verbal Learning Test (HVLT) learning and recall.27
- Psychomotor speed—Grooved Peg Board28 and Trail Making Test-A.25
NP scores were categorized into their respective domains and averaged to yield a domain-specific value. A composite neuropsychological summary T score was calculated by averaging T scores across domains.29–31 These NP tests have previously been shown to be affected by HIV.32
Imaging was performed on the same 3T Siemens Tim TRIO for all participants (Siemens AG, Erlangen, Germany). High-resolution 3D magnetization-prepared rapid acquisition of gradient echo images were collected in the sagittal plane using a 12-channel head coil. A total of 176 slices, 1.0-mm slice thickness, and voxel dimensions of 1.0 × 1.0 × 1.0 mm were acquired. Two sequential diffusion-weighted scans were obtained (2 × 2 × 2 mm voxels, TR = 9900 ms, TE = 102 ms, flip angle = 90 degree, 23 directions, b-values ranging from 0 to 1400 s/mm2) and 1 nondiffusion–weighted image.33
Image Preprocessing for DTI and DBSI
Preprocessing included correction for motion and eddy current distortions followed by skull stripping using FMRIB software library (FSL) 188.8.131.52 Rigorous motion inspection was applied after eddy current correction. Subjects who moved more than 3.5 mm were excluded. Tensor calculation for fractional anisotropy (FA) was derived using FSL for preparation of tract-based spatial statistics.35
FA measurements were generated in FSL with DTIFIT.36,37 This single diffusion model represents the overall displacement of water molecules primarily oriented by the hydrophobic properties of myelin. This model depicts axonal projections but can be insensitive to subtle patterns of diffusion that reflect underlying pathological changes due to persistent inflammation (Jones et al, 2013). In single diffusion models, FA values reflect the diffusion signal (Fig. 1 adapted from Chiang et al, 2014) but do not dissociate anisotropic from isotropic diffusion.
The multiple-tensor model that is used by DBSI allows for increased sensitivity at detecting underlying biological changes that can disrupt standard DTI metrics. Inflammation, as detected by DBSI, has been shown to cause spurious results regarding white matter damage.21 Extracting the cellularity component from traditional measures of structural integrity (FA) could clarify whether HIV+ pathology can also compromise structural integrity. We expected an increase in cellularity in virologically well-controlled HIV+ patients compared with HIV− controls as this metric signifies subtle inflammation even in virally suppressed HIV+ individuals receiving HAART.
DBSI metrics were generated using in-house software scripted in MATLAB.20 The overall diffusion signal (Sk) measured by DBSI comprises both anisotropic (Ak) and isotropic components (Ik; Equation 1). The anisotropic signal depicts elliptical diffusion but does not quantify the degree of anisotropy (red wedge Fig. 1). DBSI-derived FA is generated on the isolated anisotropic signal, which excludes isotropic diffusion (blue and green wedges Fig. 1). For DTI, FA quantifies the degree of anisotropy for the entire signal, whereas DBSI estimates this metric only for anisotropic patterns of diffusion (red wedge Fig. 1).
In Equation 2, the anisotropic component is derived from the axial (λ‖_i) diffusivity, radial diffusivity (λ⊥_i), and the angle (ψik) between the kth diffusion gradient and the principal direction of the ith anisotropic tensor. The pure anisotropic signal reflects water diffusion in and around the axons. FA was derived from Ak using previously described methods.20
Isotropic diffusion comprises both restricted diffusion (cellularity) and nonrestricted isotropic diffusion patterns (extracellular space). In Equation 3, the integral, demarcated by a and b represent the boundaries for the isotropic diffusion spectrum. Cellularity is defined by an upper boundary (b) of 0.3 μm2/ms, whereas extracellular diffusion is calculated when the boundary is greater than 0.3 μm2/ms.20–22
Postprocessing of DTI and DBSI was conducted with tract-based spatial statistics.35 Tract-based spatial statistics alleviates registration artifacts and isolates voxels within the white matter.35 Briefly, all postprocessed images were warped into common space using a combination of linear and nonlinear alignments.35 White matter skeletons were generated from voxels within core white matter by searching for the local FA maximum in each voxel.35
Demographic and Clinical Measures
NP differences in cognitive domains and composite scores were compared between HIV− and HIV+ participants. Correlations were also performed between NP values (domain and composite scores) and cellularity for both HIV+ and HIV− controls. In addition, associations were performed between clinical variables (current CD4, nadir CD4, duration of infection, and duration of treatment) and cellularity for HIV+ participants after adjusting for sex and age.
A voxel-wise analysis of both DBSI and DTI data was performed using Randomise, a statistical package in FSL.38 A threshold-free cluster enhancement approach was used to correct for multiple comparisons with a family-wise error rate derived from 5000 Monte Carlo permutations.39 For the preceding voxel-wise analyses only, residual maps were created after adjusting for age and sex at the voxel level using a general linear model in FSL.
Because microglial activity can occur throughout the brain in HIV,40 an average skeletal value for cellularity was derived and used for subsequent analyses. The effect of age on cellularity was assessed for HIV+ patients and HIV− controls. Linear regression models assessed the relationship between cellularity and age for both HIV+ and HIV− groups separately, and jointly. For this regression model, sex was treated as a covariate at the voxel level before quantifying a cellularity value. The potential interaction between age and HIV status on cellularity was also evaluated. For the HIV+ group, we performed a multiple linear regression of cellularity as a function of age after adjusting for duration of infection and duration of treatment. Residuals were calculated in the HIV+ cohort for age and cellularity after adjusting for either duration of infection or duration of treatment. These residuals were subsequently compared with the HIV− cohort to determine whether an interaction between age and HIV status was present.
Demographic and Clinical Variables
Overall, the HIV+ group (n = 92) was significantly older (P < 0.001) and had a higher proportion of men (P = 0.003) compared with the HIV− cohort (n = 66). The 2 groups were similar regarding education. No differences were seen between cohorts for substance misuse including cannabis (P = 0.68), cocaine (P = 0.52), opiates (P = 0.68), methamphetamine (P = 0.93), barbiturates (P = 0.56), benzodiazepines (P = 0.56), phencyclidine (P = 0.56), alcohol (P = 0.29), or smoking (P = 0.77). Observed differences in age and sex were included within subsequent analyses.
Voxel-Wise Comparison for DTI and DBSI
DTI-derived FA, which does not exclude isotropic patterns of diffusion, was significantly different between HIV+ and HIV− individuals. Differences were predominantly seen within the left inferior longitudinal fasciculus, parietal regions, external capsule, anterior corona radiate, and bilateral superior temporal regions (Supplemental Digital Content Fig. 1, http://links.lww.com/QAI/B71). However, FA derived from DBSI was not significantly different between the 2 groups. DBSI FA exclusively evaluates the anisotropic component (red wedge; Fig. 1) independently from isotropic diffusion.
Cellularity was the only isotropic measure that was significantly different between the 2 groups (P < 0.05 corrected). Differences in cellularity were seen diffusely throughout the brain (Fig. 2). Because of the large age difference between our 2 populations, we repeated this voxel-wise analysis on a smaller subset of 40 HIV− and 40 HIV+ individuals that were age–sex matched (Supplemental Digital Content Fig. 2, http://links.lww.com/QAI/B71). Similar diffuse changes were seen with HIV+ participants having higher cellularity than HIV− individuals. Observed differences between the 2 diffusion methods may reflect that DTI measures are confounded by isotropic patterns of diffusion that appear as a loss in white matter integrity.
Skeletal Average Comparison for DTI and DBSI
Because of the disperse cellularity findings, skeletal averages were acquired for subsequent analyses. Skeletal averages revealed similar findings to those observed using a voxel-wise analysis (Fig. 3). In particular, FA derived from DTI was significantly diminished for HIV+ patients compared with HIV− controls. By contrast, significant differences were seen for average cellularity, but not FA, for DBSI.
Relationships Between DBSI and NP or Clinical Variables
NP was not significantly different between HIV+ and HIV− participants for either composite (P = 0.76) or domain-specific scores (P = 0.23 Memory; P = 0.88 Psychomotor Speed; P = 0.47 Executive). Cellularity did not correlate with cognitive measures (data not shown). Cellularity also did not correlate with laboratory measures [including log nadir CD4 (r = 0.218; P = 0.087), log current CD4 (r = 0.083; P = 0.48)], or clinical measures [duration of infection (r = −0.137, P = 0.23)]. A trend toward a negative association was observed between years on medication and cellularity (r = −0.202; P = 0.072).
Effects of Aging on Cellularity in HIV+ and HIV− Individuals
In an analyses that included all participants, a significant interaction was observed between age and HIV status for cellularity (P = 0.025). This negative association was primarily driven by HIV+ (r = −0.310; P = 0.003) and not HIV− controls (r < 0.001; P = 0.99) (Fig. 4). When duration of infection was included as a covariate for the HIV+ group, the association between age and cellularity remained unchanged (r = −0.240; P = 0.035), but the interaction between HIV status and age was only at a trend level (P = 0.076). After adjusting for duration on HAART, neither the relationship between cellularity and age (r = −0.197; P = 0.0785; Fig. 4) nor the interaction with HIV status was significant (P = 0.154).
DBSI has been previously used to assess inflammation in other neurological diseases (eg, multiple sclerosis).22 We extended its application to virologically well-controlled HIV+ individuals. Our data show that typical DTI measurement may be confounded by the presence of inflammation, which is not the case for DBSI. Diffuse increases in cellularity were seen in HIV+ individuals compared with HIV− controls. In particular, cellularity was greatest in younger HIV+ individuals and was modulated by duration on HAART. Observed changes in cellularity did not correlate with clinical laboratory variables or degree of cognitive impairment.
NP has often been used to measure the effects of HIV in the brain.6 NP testing may be insensitive to subtle inflammation that persists in virologically well-controlled HIV+ individuals in the HAART era.7 Additional neuroimaging metrics that measure inflammation may provide important information concerning residual reservoirs.41 Our results suggest that diffuse inflammation, as measured by cellularity, may still occur in HIV+ patients regardless of their degree of impairment. Additional adjunctive measures to reduce inflammation may be needed for virologically well-controlled HIV+ individuals on HAART.
Advanced noninvasive neuroimaging modalities have focused on HIV-associated changes in the white matter. In particular, diffusion-weighted imaging has increasingly been used within HIV+ individuals.42 Often decreases in FA have been seen in HIV+ individuals compared with HIV− controls.15,18,33,43–46 Although we observed a decrease in FA for HIV+ individuals using DTI, DBSI revealed that observed changes in DTI may in fact reflect the continued presence of inflammation. Inflammation can appear as axonal loss from reduced diffusion along the primary direction yielding a concurrent reduction in FA.20 When FA was derived independently from other isotropic factors using DBSI, no difference was seen between HIV+ and HIV− individuals. These results suggest a caution when investigating typical DTI measures, such as FA in this population.
Cellularity is sensitive to cellular presence and may reflect microglial activity in the brain.22 Our results complement previous pathological findings that show increased level of microglial activation in HIV+ individuals.47 In addition, a recent positron emission tomography study also revealed similar diffuse spatial patterns of microglial activation in aviremic cognitively normal HIV+ patients compared with HIV− controls.40 Our current results using DBSI-derived metric of cellularity nicely complement the aforementioned study. What is unique about DBSI is that this technique can be performed on most conventional magnetic resonance imaging, does not require the synthesis of a tracer, and does not require genetic studies to confirm receptor-binding capacity.
An increasing older HIV+ population is now present as the HIV infection has become a more chronic condition. We observed a negative relationship between aging and cellularity for the HIV+ group with younger HIV+ individuals having the greatest cellularity. By contrast, cellularity was constant across a spectrum of ages for HIV− controls. This unique aging relationship for HIV+ individuals was influenced by HAART. After accounting for duration of HAART, a decrease in inflammation was seen with long-term treatment. This implies that persistent inflammation can partially recede with aging, which is in agreement with previous studies that monitor peripheral markers of immune activation.10,48
Inflammation is present even after HAART using in vivo neuroimaging. A reduction of inflammation is possible but may require protracted HAART administration before normalization. Neuroimaging markers paired with other markers of inflammation could therefore assist in better characterizing a patient's treatment trajectory. Viral reservoirs are the proposed instigators of subtle inflammation, and cellularity may directly monitor spatial topography of viral reservoir activity or indirectly by a secondary inflammatory process. These results would suggest that early initiation of HAART with continued maintenance is important for reducing inflammation associated with HIV. However, our current study is cross sectional and longitudinal inquiries are needed.
Microglial and macrophages assist in central nervous system injury repair and are the main sources of proinflammatory cytokines.49 Initiation of HAART can reduce immune-mediated changes, but microglial and macrophage activation remains elevated.4,12,50 In addition, latent HIV viral antigen can instigate further immune activation leading to a proliferation of microglia and reactivation of virus.51 Cellularity is sensitive to cellular proliferation and may reflect prominent microglial and macrophage-induced inflammation that occurs in response to injury.20 This would explain the lack of a relationship with absolute and nadir CD4 T cells that was observed in this study. Our current study focused on peripheral cellular markers that are routinely acquired in the clinic. We did not investigate soluble plasma and CSF inflammatory markers or specific subpopulations of peripheral cells that could provide additional insight on cellular and immunologic correlates of cellularity. Further studies are needed to determine whether there is an association between cellularity with naive, effector, central or effector memory T cells, subpopulations of monocytes and soluble plasma, and CSF inflammatory markers.
In summary, our data demonstrate a novel neuroimaging approach to detect residual inflammation in virologically well-controlled HIV+ individuals. DBSI has the potential to distinguish different etiologies that influence the patterns of diffusion within an HIV+ cohort. Elevated levels of cellularity were diffusely seen in HIV+ individuals compared with HIV− controls, with younger HIV+ patients having the greatest cellularity changes. After adjusting for duration of HAART, the association between age and cellularity in HIV+ individuals was no longer significant suggesting that prolonged treatment may reduce inflammation. These studies suggest a potential noninvasive method for assessing potential brain viral reservoirs in HIV+ individuals on HAART.
The authors express their sincerest gratitude to their study participants who helped make this work possible.
1. Lohse N, Hanse AE, Gerstoft J, et al. Improved survival in HIV
-infected persons: consequences and perspectives. J Antimicrob Chemother. 2007;3:461–463.
2. Luther VP, Wilkin AM. HIV
diseases in older adults. Clin Geriatr Med. 2007;23:567–583.
3. Kaplan JE, Hanson D, Dworkin MS, et al. Epidemiology of human immunodeficiency virus-associated opportunistic infections in the United States in the era of highly active antiretroviral therapy. Clin Infect Dis. 2000;30:S5–S14.
4. Heaton RK, Franklin DR, Ellis RJ, et al. HIV
-associated neurocognitive disorders before and during the era of combination antiretroviral therapy: differences in rates, nature, and predictors. J Neurovirol. 2011;17:3–16.
5. Burdo TH, Lentz MR, Autisser P, et al. Soluble CD163 made by monocyte/macrophages is a novel marker of HIV
activity in early and chronic infection prior to and after anti-retroviral therapy. J Infect Dis. 2011;204:154–163.
6. Antinori A, Marcotullio S, Andreoni M, et al. Italian guidelines for the use of antiretroviral agents and the diagnostic-clinical management of HIV
-1 infected persons. Update December 2014. New Microbiol. 2015;38:299–328.
7. Harezlak J, Buchthal S, Taylor M, et al. Persistence of HIV
- associated cognitive impairment, inflammation
and neuronal injury in era of highly active antiretroviral treatment. AIDS. 2011;25:625–633.
8. Lichtfuss GF, Cheng W, Farsakoglu Y, et al. Virologically suppressed HIV
patients show activation of NK cells and persistent innate immune activation. J Immunol. 2012;189:1491–1499.
9. Nasi M, Pinti M, Biasi SD, et al. Aging with HIV
infection: a journey to the center of inflammAIDS, immunosenescence and neuroHIV. Immunol Lett. 2014;162:329–333.
10. Funderburg NT, Boucher M, Sattar A, et al. Rosuvastatin decreases intestinal fatty acid binding protein (I-FABP), but does not alter zonulin or lipopolysaccharide binding protein (LBP) levels, in HIV
-infected subjects on antiretroviral therapy. Pathog Immun. 2016;1:118–128.
11. Schuler PJ, Macatangay BJC, Saze Z, et al. CD4+CD73+ T cells are associated with lower T-cell activation and C reactive protein levels and are depleted in HIV
-1 infection regardless of viral suppression. AIDS. 2013;27:1545–1555.
12. Burdo TH, Weiffenbach A, Woods SP, et al. Elevated sCD163 in plasma but not cerebrospinal fluid is a marker of neurocognitive impairment in HIV
infection. AIDS. 2013;27:1387–1395.
13. Basser PJ, Mattiello J, LeBihan D. MR diffusion tensor spectroscopy and imaging. Biophys J. 1994;1:259–267.
14. Pierpaoli C, Jezzard P, Basser PJ, et al. Diffusion tensor MR imaging of the human brain. Radiology. 1996;201:637–648.
15. 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.
16. 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.
17. Hoare J, Jean-Paul F, Nicole P, et al. White matter micro-structural changes in ART-naive and ART-treated children and adolescents infected with HIV
in South Africa. AIDS. 2015;29:1793–1801.
18. Su T, Caan MWA, Wit FWNM, et al. White matter structure alterations in HIV
-1-infected men with sustained suppression of viraemia on treatment. AIDS. 2016;30:311–322.
19. Jones DK, Knosche TR, Turner R. White matter integrity, fiber count, and other fallacies: the do's and dont's of diffusion MRI. Neuroimage. 2013;73:239–254.
20. Wang Y, Wang Q, Haldar JP, et al. Quantification of increased cellularity
during inflammatory demyelination. Brain. 2011;134:3590–3601.
21. Chiang C, Wang Y, Sun P, et al. Quantifying white matter tract diffusion parameters in the presence of increased extra-fiber cellularity
and vasogenic edema. Neuroimage. 2014;101:310–319.
22. Wang Y, Sun P, Wang Q, et al. Differentiation and quantification of inflammation
, demyelination and axon injury or loss in multiple sclerosis. Brain. 2015;138:1223–1238.
23. Beck AT, Steer RA, Ball R, et al. Comparison of beck depression inventories-IA and –II in psychiatric out-patients. J Pers Assess. 1996;67:588–597.
24. Baker LM, Robert PH, Heap-Woodruff JM, et al. The effect of central nervous system penetration effectiveness of highly active antiretroviral therapy on neuropsychological performance and neuroimaging in HIV
infected individuals. J Neuroimmune Pharmacol. 2015;10:487–492.
25. Reitan RM. Validity of the trail making test as an indicator of organic brain damage. Percept Mot Skill. 1958;8:271–276.
26. Benton AL, Hamsher K. Multilingual Aphasia Examination: Manual of Instruction. Iowa City, IA: University of Iowa; 1976.
27. Brandt J. The Hopkins verbal learning test: development of a new memory test with six equivalent forms. Clin Neuropsychol. 1991;5:125–142.
28. Baser CN, Ruff RM. Construct validity of the San Diego neuropsychological test battery. Arch Clin Neuropsychol. 1987;2:13–32.
29. De Santi S, Pirraglia E, Barr W, et al. Robust and conventional neuropsychological norms: diagnosis and prediction of age-related cognitive decline. Neuropsychol. 2008;22:469–484.
30. Gladsjo JA, Schuman CC, Evans JD, et al. Norms for letter and category fluency: demographic corrections for age, education, and ethnicity. Assessment. 1999;6:147–178.
31. Ruff RM, Parker SB. Gender-and age-specific changes in motor speed and eye-hand coordination in adults: normative values for the finger tapping and grooved pegboard tests. Percept Mot Skills. 1993;76:1219–1230.
32. Woods SP, Moore DJ, Weber E, et al. Cognitive neuropsychology of HIV
-associated neurocognitive disorders. Neuropsychol Rev. 2009;19:152–168.
33. Wright PW, Vaida FF, Fernandez RJ, et al. Cerebral white matter integrity during primary HIV
infection. AIDS. 2015;29:433–442.
34. Smith SM, Jenkinson M, Woolrich MW, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004;23:S208–S219.
35. Smith SM, Jenkinson M, Johansen-Berg H, et al. Tract-based spatial statistics: voxelwise analysis of multisubject diffusion data. Neuroimage. 2006;31:1487–1505.
36. Hrabe J, Kaur G, Guilfoyle DN. Principles and limitations of NMR diffusion measurements. J Med Phys. 2007;32:34–42.
37. Behrens TEJ, Woolrigh MW, Jenkinson M, et al. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn Reson Med. 2003;50:1077–1088.
38. Winkler AM, Ridgway GR, Webster MA, et al. Permutation inference for the general linear model. Neuroimage. 2014;92:381–397.
39. Nichols TE, Holmes AP. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp. 2002;15:1–25.
40. Vera JH, Guo Q, Cole JH, et al. Neuroinflammation in treated HIV
-positive individuals. Neurology. 2016;86:1425–1432.
41. Clifford DB, Ances BM. HIV
-associated neurocognitive disorder. Lancet Infect Dis. 2013;13:907.
42. Masters MC, Ances BM. Role of neuroimaging in HIV
-associated neurocognitive disorders. Semin Neurol. 2014;34:89–102.
43. Ragin AB, Wu Y, Gao Y, et al. Brain alterations within the first 100 days of HIV
infection. Ann Clin Transl Neurol. 2015;2:12–21.
44. 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.
45. Du H, Wu Y, Ochs R, et al. A comparative evaluation of quantitative neuroimaging measurements of brain status in HIV
infection. Psychiatry Res. 2012;203:95–99.
46. Chen Y, An H, Zhu H, et al. White matter abnormalities revealed by diffusion tensor imaging
in non-demented and demented HIV
+ patients. Neuroimage. 2009;47:1154–1162.
47. Tauber SC, Staszewki O, Prinz M, et al. HIV
encephalopathy: glial activation and hippocampal neuronal apoptosis, but limited neural repair. HIV
48. Churchill MJ, Deeks SG, Margolis DM, et al. HIV
reservoirs: what, where and how to target them. Nat Rev Microbiol. 2016;14:55–60.
49. Colonna M, Butovsky O. Microglia function in the central nervous system during health and neurodegeneration. Annu Rev Immunol. 2017;35:441–468.
50. Sereti I, Krebs SJ, Phanuphak N, et al. Persistent, albeit reduced, chronic inflammation
in persons starting antiretroviral therapy in acute HIV
infection. Clin Infect Dis. 2017;64:124–131.
51. Tompkins L, Dukhovlinova E, Swanstrom R. “HIV
reservoirs in the central nervous system”. In: Encyclopedia of AIDS. New York, NY: Springer Science + Business Media New York; 2016.