Introduction

Human immunodeficiency virus (HIV) infiltrates immune system cells and crosses the blood-brain barrier (BBB) shortly after seroconversion and leads to brain injury1. This infiltration triggers a cascade of effects, including axonal disruption, myelin loss, astrogliosis, and to a lesser extent, damage to white matter (WM) tracts2. Approximately 50% of people with HIV (PWH) may experience mild cognitive impairment. These deficits can affect cognitive domains such as executive function, attention, fine motor skills, and information processing speed2,3,4.

Despite the adoption of combination antiretroviral therapy (cART), chronic mild neuroinflammation is believed to be the primary reason for HIV-associated cognitive impairment. The key contributors to neuroinflammation are activated microglia and perivascular macrophages, with some involvement from astrocytes. An additional contribution is the transmigration to the central nervous system (CNS) of activated monocytes, which, after differentiation, increase the pool of perivascular macrophages1,5,6,7,8. As cART becomes more accessible, the understanding of brain abnormalities and cognitive deficits in HIV patients has become increasingly complex. Aging individuals receiving cART may develop comorbid medical conditions that independently lead to brain damage and cognitive changes. Furthermore, certain antiretroviral regimens have been associated with brain damage, complicating treatment strategies9. Hence, there is an urgent need for advanced techniques to deepen our understanding of the pathogenesis of tissue changes in the brain due to HIV infection. As a result, there is a growing interest in sensitive, reliable, readily accessible, and reproducible noninvasive imaging approaches for evaluating the brain injury.

Advanced MRI pulse sequences and post-processing methods provide novel quantitative measures reflective of brain injury10,11,12,13,14. Utilizing micrometer-scale displacement of tissue water, diffusion MRI (dMRI) can noninvasively detect microstructural abnormalities in the brain15,16,17,18. It provides excellent sensitivity to microstructural damage associated with HIV19,20,21,22. However, conventional dMRI approaches (such as diffusion tensor imaging, DTI) are significantly impacted by the dispersion of regional fiber orientations, such as crossing fibers, posing challenges in detecting regional pathology. For instance, the fractional anisotropy (FA) of white matter in DTI is closely linked to densely packed and myelinated axonal structures, as well as the presence of glial cells in disease23,24. However, interpreting the FA is challenging due to the blending of mesoscopic tissue features (e.g., fiber orientation dispersion and crossings) with microscopic features (e.g., axons, cells, and density). These complexities may result in FA changes misinterpreted as pathology25. Given that 90% of white matter voxels involve crossing fibers, the imperfect alignment of axonal fibers makes it nearly impossible to separate tissue microstructural anisotropy from macrostructure using FA26,27,28,29. Higher FA is usually attributed to more intact white matter tracts. However, increased FA may also occur in pathological processes. For example, in Alzheimer’s disease, increased FA may reflect pathological changes in fiber orientation dispersion30.31. However, advanced diffusion techniques like diffusion kurtosis imaging (DKI) offer higher sensitivity than DTI by capturing deviations from Gaussian water diffusivity32.

Tensor-valued diffusion encoding is a new technique which employs diffusion encoding in multiple directions. While encoding in a single direction – as is done for DTI – yields linear tensor encoding (LTE), encoding in all directions with equal sensitivity yields spherical tensor encoding (STE). By contrasting LTE and STE, additional information about the tissue microstructure can be obtained, such as the separation of microscopic anisotropy and orientation dispersion33. While a similar objective has been defined for many modelling methods using LTE, such methods are prone to bias due to modeling degeneracy34. Unlike DTI, where the interpretation of FA relies on both microscopic features and the bulk tract orientation dispersion, tensor-valued diffusion encoding separates these effects through diffusional variance decomposition35,36,37. This approach enables the assessment of axonal integrity by measuring microscopic fractional anisotropy (µFA) as well as isotropic and anisotropic diffusional variance (MKi and MKa) at the sub-voxel level. Thus, tensor-valued diffusion encoding measures may emerge as a sensitive biomarker for evaluating brain microstructure (both gray and white matter) in vivo38,39. To date, tensor-valued diffusion encoding has been used to assess microstructural abnormalities in several diseases36,40,41.

In addition to advanced MRI, cerebrospinal fluid (CSF) and plasma levels of neurofilament light (NFL) chain and glial fibrillary acid protein (GFAP) serve as fluid biomarkers of brain injury. NFL is released into the brain’s extracellular space (ECS) following axonal injury and subsequently detected in CSF and blood42,43. Elevated NFL levels occur in various neurological and neurodegenerative disorders, including HIV infection43,44,45,46. Furthermore, activated glial cells release microparticles expressing GFAP into circulation during brain injury47,48,49,50,51, and have been associated with cognitive impairment52, and viral infections such as HIV infection53,54.

In this study, we hypothesized that tensor-valued diffusion encoding metrics would offer greater sensitivity than conventional DTI metrics in detecting HIV-associated brain microstructural damage. Additionally, we posited that tensor-valued metrics would show stronger correlations with blood biomarkers of neuronal and glial injury, including NFL and GFAP, as well as with cognitive performance.

Methods and materials

Study subjects

Twenty-four PWH (age = 55±10 years, male/female = 17/7) and 31 matched healthy controls (HC) (age = 55±15 years, male/female = 24/7) were enrolled from Rochester NY, and vicinity area. The Institutional Research Subjects Review Board (RSRB) at the University of Rochester thoroughly reviewed and approved the study. All participants provided written informed consent prior to enrollment and underwent clinical, laboratory, neurocognitive, and brain MRI examinations. No participants were excluded from the analyses. All experiments were conducted in accordance with relevant guidelines and regulations. Detailed baseline demographics are presented in Table 1.

Table 1 Subject demographics.
Full size table

Our previous report55, provides detailed descriptions of the inclusion and exclusion criteria as well as all study procedures. To briefly summarize, PWH meeting inclusion criteria had stable cART for a minimum of 3 months before screening and were aged ≥ 18. Exclusions encompassed individuals with symptomatic cardiovascular diseases (angina, myocardial infarction, stroke, or other peripheral atherosclerotic disease) and uncontrolled vascular risk factors such as diabetes mellitus and hypertension. Additionally, those with severe premorbid or comorbid psychiatric disorders (schizophrenia, bipolar disorder, active depression), brain infections other than HIV-1, space-occupying brain lesions, dementia from any cause, and metallic implants were excluded. The control population differed from PWH based on HIV status, level of education and race.

Data acquisition

Blood sample

Whole blood (~ 40 ml) was drawn into sterile, acid-citrate-dextrose (ACD) Vacutainer® blood collection tubes. The plasma was then isolated and used for measuring specified markers. NFL and GFAP levels were measured using Single molecule array (Simoa™)56 kits by Quanterix on a Simoa HD-1 analyzer57,58,59.

Neuropsychological assessments

Assessments of neurocognitive and functional performance were performed in all subjects. Study coordinators trained and supervised by an experienced neuropsychologist, administered all neuropsychological tests. The test battery covered diverse cognitive domains, such as Attention/Working Memory (California Computerized Assessment Package, CalCAP; cognitive reflection test, CRT 4; CRT 14), Speed of Information Processing (Stroop Color Naming, Digit Symbol Modalities Test), Executive Function (Trail Making Test B, Stroop Interference Task), Language (letter and category fluency), Learning (Rey Auditory Verbal Learning Test Trials 1–5; Rey Complex Figure Test Immediate Recall), Memory (Rey Auditory Verbal Learning, RAVLT Trial 7; Rey Complex Figure Test (RCFT) and Recognition Trial, RCFT Delayed Recall), and Motor Skill (Grooved Pegboard). Assessment of premorbid intellectual functioning and English language fluency was limited to the baseline, utilizing the Wide Range Achievement Test (WRAT) 4-Reading subtest.

Before conducting analyses, raw cognitive scores from each test were converted to z-scores using normative data from the test manuals. Cognitive domain scores were then created by averaging the z-scores of the tests within each domain. The cognitive tests are normalized for age and education. A total cognitive score was calculated by summing the z-scores across six cognitive domains: Attention and Working Memory, Processing Speed, Executive Function, Fine Motor Skill, Verbal and Visual Learning, Verbal and Visual Memory, and Language. HAND diagnoses for each participant were determined using the Frascati criteria2.

Magnetic resonance imaging

MRI was performed on a 3T whole-body scanner (MAGNETOM Prisma Fit, Siemens, Erlangen, Germany, software version VE11C) equipped with a 64-channel head coil. The maximum gradient strength is 80 mT/m with a slew rate of 200 mT/m/s.

Anatomical imaging

The T1-weighted (T1w) images were acquired using a 3D magnetization prepared rapid acquisition gradient-echo (MPRAGE) sequence with inversion time (TI) = 926 ms, repetition time (TR) = 1840 ms; echo time (TE) = 2.45 ms; flip angle = 8˚; Field of View (FOV) = 256 × 256 × 192 mm3; GRAPPA = 2; number of slices = 192, voxel size = 1.0 × 1.0 × 1.0 mm3, and scan time = 4.16 min 3D T2-weighted FLAIR images were acquired with scan parameters: TI = 1,800 ms, TR = 5,000 ms, TE = 215 ms, FOV = 256 × 256 × 192 mm3; number of slices = 192, voxel size = 1.0 × 1.0 × 1.0 mm3, and scan time = 5.40 min.

Tensor-valued diffusion encoding

We employed a prototype pulse sequence that accommodates free waveform encoding (FWF version 1.13s), available at https://github.com/filip-szczepankiewicz/fwf_seq_resources, based on the diffusion-weighted single-shot spin echo sequence35,60 with encoding waveforms that were compensated for concomitant gradient effects. The imaging protocol involved acquiring 43 images with linear tensor-valued diffusion encoding (LTE) and 37 images with spherical tensor encoding (STE), spread across multiple b-values (b = 0, 100, 700, 1400, and 2000 ms/m2). The imaging parameters included TR = 4100 ms, TE = 91 ms, FOV = 224 × 224 × 30 mm3, partial-Fourier = 6/8, GRAPPA factor = 2, echo spacing (ESP) = 0.6 ms, number of volumes = 80, number of slices = 30, voxel size = 2 × 2 × 4 mm3 and scan time = 5:53 min. The images collected were axial, with phase encoding along the anterior-posterior direction.

Image analysis

Image analyses were performed using a combination of image processing tools, including FSL (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/)61, ANTs (http://stnava.github.io/ANTs/)62, and MATLAB (version 2021b).

All MR images underwent thorough inspection for artifacts, including motion, geometric distortion, and signal dropout. T1w images underwent structural segmentation using the anatomical processing script (fsl_anat) from FMRIB’s Software Library (FSL)63. The processing pipeline involved image reorientation and cropping, radio-frequency bias-field correction, linear and nonlinear registration to MNI 2 mm standard space through FLIRT and FNIRT, brain extraction via BET64, tissue segmentation using FAST, and subcortical structure segmentation employing the FIRST algorithm. White matter lesion segmentation was carried out using volBrain, an automated online MRI brain volumetry system65, based on T1w and FLAIR images. Due to the small size of the lesions, we did not employ lesion filling or exclude any subjects based on their presence.

All diffusion MRI metrics were generated and processed using the MATLAB-based multidimensional diffusion MRI framework66 available at https://github.com/markus-nilsson/md-dmri. Briefly, the diffusion-weighted images from each participant underwent a three-step processing approach: (1) Correction for eddy current-induced distortion and inter-volume subject motion was achieved by registering the images to an extrapolated reference67 using ElastiX (Version 5.0.0)68. The use of extrapolation-based references is crucial for accurate registration of high b-value images67. (2) Smoothing of the images was carried out using a 3D Gaussian kernel with a standard deviation of 0.4 voxels. (3) Voxel-by-voxel normalized anisotropic, isotropic, and total diffusional variance (MKa, MKi, MKt), as well as microscopic anisotropy (µFA), were obtained through linear least squares fitting of the log signal while correcting for heteroscedasticity via the “dtd_covarience” method. Using the same tensor-valued diffusion images, conventional DTI metrics (such as FA and MD) as well as Diffusional kurtosis imaging (DKI) metrics such as mean kurtosis (MK) were computed by employing linear least squares fitting using the same toolbox https://github.com/markus-nilsson/md-dmri69.

ROI analysis

For pre-specified regions of interest (ROIs), we calculated average ROI values for all MRI metrics (DTI – FA, MD; DKI - MK, tensor-valued diffusion encoding - µFA, MKi, MKa, MKt). We used Harvard-Oxford cortical and subcortical, and the Johns Hopkins University WM (JHU-WM) atlases available in FSL in standard MNI152-2 mm space for ROI extraction. Prior to this, all MRI metrics were registered to the high-resolution T1w images of the same individual using a 12-DOF linear registration (FLIRT tool in FSL). Then, individuals’ T1w images were spatially normalized to the MNI-T1-152 standard template using nonlinear registration (using ANTs)62,70. The transformation matrix and the warping field from these two steps were applied to DTI, DKI and tensor-valued diffusion encoding metrics. We then extracted mean values from the MRI metrics for the following: global white matter (GWM), cortical gray matter (CGM), subcortical gray matter (SGM) using the corresponding masks as ROIs, and four white matter tracts encompassing coherent, crossing, and fanning fibers: Genu of Corpus Callosum (GCC), Anterior Corona Radiata (ACR), Forceps Minor (FMin), Superior Fronto-Occipital Fasciculus (SFOF).

Statistical analyses

Statistical analyses were performed in Python (version 3.7.4). An unpaired t-test was used to compare the differences in ROIs between the two cohorts. Spearman correlation analyses were performed to find the associations between imaging metrics and blood markers and cognitive scores after controlling for age. A two-way analysis of variance (ANOVA) was performed to assess the effects of HIV status, MRI metrics, and their interactions on the cognitive status of the subjects. That is,

$${\text{Cognitive Score}} \sim {\text{MRI metrics + HIV Status + MRI metrics: HIV status}}$$

where “MRI metrics: HIV status” is the interaction of the variables. However, education level and age were not included as covariates in the analysis, as normative data for both were used to calculate total cognitive z-scores from the raw scores of each participant. We also performed similar ANOVA for blood markers to assess the effects of HIV status, MRI metrics and their interactions. A p-value of < 0.05 was considered statistically significant for a single hypothesis testing problem. For inferential problems that involved multiple hypotheses, the Benjamini–Hochberg multiple testing procedure was used to control the false discovery rate (FDR) at the < 0.05 level 71.

Fig. 1
figure 1

Example axial T1-wegithed image, tensor-valued diffusion encoding maps (µFA, MKi, MKa, MKt, ), DKI map (MK) as well as DTI maps (FA and MD) are presented from a 62-year-old individual with HIV. Intensity scale is also shown.

Full size image

Results

Figure 1 represents a T1-weighted anatomical image, voxel-by-voxel tensor-valued diffusion encoding, DKI and DTI maps from a PWH subject.

Our analysis revealed significant differences in tensor-valued diffusion metrics in several white matter ROIs, while no significant findings were observed for metrics MK and MD (Table 2). In Fig. 2, we illustrate the comparisons between PWH and HC cohorts across various white matter tracts, encompassing coherent, crossing, and fanning fibers. Notably, we observed a significant decrease in µFA (p = 0.042 for GCC, p = 0.002 for FMin, p = 0.007 for ACR, p = 0.042 for SFOF) and MKa (p = 0.042 for GCC, p = 0.006 for FMin, p = 0.007 for ACR, p = 0.049 for SFOF), along with a significant increase in MKi (p = 0.027 for SCC, p = 0.005 for FMin, p = 0.001 for ACR, p = 0.034 for SFOF) among PWH. In contrast, we observed a significant decrease in FA in the GCC (p = 0.032) and ACR (p = 0.025) in PWH compared to HC. While MD exhibited an increase in PWH, it did not reach statistical significance in any of these ROIs. The trend of changes in DTI metrics is consistent with previous works20,72,73,74,75,76. For example, several previous studies reported that PWH had a decreased FA in several brain regions, including genu and splenium of corpus callosum (GCC, SCC), and SFOF. Although PWH had a decreased µFA and MKa and increased MKi compared to healthy controls in global ROIs i.e., for GWM, CGM and SGM, none of the metrics exhibited significant difference.

Fig. 2
figure 2

Comparison of dMRI metrics. Tensor-valued diffusion encoding metrics show significant differences between PWH and HC cohorts in white matter regions with coherent, crossing, and fanning fibers. GCC: Genu of corpus callosum; ACR: anterior corona radiata; FMin: Forceps Minor; SFOF: superior fronto-occipital fasciculus; Significant p-values are shown as bold; mean diffusivity, MD values are expressed as x10− 3.

Full size image
Table 2 Diffusion MRI metrics for HIV and healthy controls in brain tissues bold font indicates correlations that were statistically significant after FDR correction.
Full size table

Participant characteristics

Detailed information about demographic, clinical, neurocognitive and MRI data of the study participants are presented in Table 1. The Welch’s Two Sample t-test did not reveal any statistically significant age difference between the HC and PWH (p = 0.947). Furthermore, in comparison to PWH, those who were HC exhibited significantly higher education levels and were more likely to be white.

Group comparisons of MRI metrics

Group comparisons of cognitive performance and blood markers

Welch’s two group t-test showed the total cognitive score was lower in the PWH cohort compared to the HC cohort (t = 2.22, p = 0.031). However, while the average concentrations of NFL and GFAP were slightly elevated in the PWH cohort compared to the HC cohort, these differences did not reach statistical significance.

Relationship between cognitive scores and imaging metrics

We investigated the correlation between total cognitive z-scores and tensor-valued diffusion encoding metrics (µFA, MKi, and MKa), DTI metrics (FA and MD) in global white matter (WM), subcortical gray matter (SGM), and cortical gray matter (CGM), among both PWH and HC individuals (see Fig. 3). Significant relationships were observed between total cognitive z-scores and tensor-valued diffusion metrics in the PWH cohort, while no statistical significance was found for HC subjects except for MKa in SGM. Additionally, FA showed no significant associations, except in GWM for PWH whereas MD exhibited significant correlations in both CGM and GWM in PWH. Correlations between total cognitive scores and tensor-valued diffusion encoding metrics in four white matter tracts, which involve coherent, crossing, and fanning fibers (GCC, ACR, FMin, and SFOF), are presented in Supplementary Fig. 1. Similar trends of changes were identified within those ROIs. Additionally, we compared the relationship between total cognitive scores and kurtosis measures MK and MKt (Supplementary Fig. 2). The results clearly indicate that b-tensor-based MKt demonstrates greater sensitivity than DKI-based MK in distinguishing PWH from HC, particularly in the ACR, a region with crossing fibers.

Fig. 3
figure 3

Relationships between total cognitive scores and tensor-valued diffusion encoding metrics in cortical gram matter (CGM), subcortical gray matter (SGM), global white matter. PWH: People with HIV, HC: Healthy controls.

Full size image

Further, we conducted correlation analyses between cognitive domain scores (i.e., Attention/Working Memory, Speed of Information Processing, Executive Function, Language, Learning, Memory, and Motor Skills) and tensor-valued diffusion metrics, as well as DTI metrics, specifically µFA and FA for global ROIs (see Supplementary Table 1). Our findings suggest that executive function, attention, and motor skills display increased sensitivity to microstructural tissue changes measured by tensor-valued diffusion encoding compared to DTI metrics. The trend of correlations aligns with previous studies involving DTI-derived FA and MD20,72,73,74,75,76.

We also performed two-way ANOVA to measure the effects of HIV status, MRI metrics of five ROIs (such as GWM, CGM and SGM, GCC and ACR) and their interactions on cognitive scores. Table 3 shows the representative results for total cognitive scores. We found a significant interaction between the total cognitive score and both µFA (p = 0.032) and MKa (p = 0.025) in the ACR. Additionally, HIV status showed a significant correlation with tensor-valued encoding metrics in the GCC. Supplementary Tables 2–4 shows two-way ANOVA between tensor-valued encoding metrics and cognitive scores in sub-domains (such as executive function, attention, and motor functions). No significant interactions were found between the individual cognitive domain scores and MRI metrics. Nevertheless, trends within the individual cognitive domains are similar to those of the total cognitive score, albeit less pronounced.

Table 3 Two-way ANOVA to measure the effects of imaging metrics, HIV status, and their interactions on total cognitive scores.
Full size table

Relationship between blood markers and MRI metrics

Figure 4 and Supplementary Fig. 3 illustrate the associations between average neurofilament light chain (NFL) concentrations, as well as GFAP with tensor-valued diffusion encoding, DKI and DTI metrics. NFL concentrations showed a negative correlation with µFA and MKa while being positively correlated in WM, and significance is mostly found in PWH subjects (p < 0.05). GFAP also shows similar trends. However, DTI and DKI metrics (FA, MD and MK) didn’t show any significant associations with blood markers. Additionally, no significant interactions were found between HIV status, MRI metrics with blood markers (not shown).

Fig. 4
figure 4

Relationship between blood markers (NFL and GFAP) and b-tensor metrics. Scatterplots of blood markers and MRI metrics (µFA, MKi and MKa; FA and MD) in global white matter (WM) for each cohort. Regression lines are drawn with 95% confidence intervals. Spearman correlation coefficients and corresponding p-values are displayed within each plot for each cohort.

Full size image

Discussion

This is the first study to apply diffusion MRI with tensor-valued diffusion encoding in the context of HIV-associated neuropathology to better understand the underlying brain tissue microstructure and to investigate the association between tensor-valued diffusion metrics and cognitive performance and blood markers of brain injury. Tensor-valued diffusion encoding proves valuable in unraveling orientation dispersion and sub-voxel anisotropy, surpassing the capabilities of conventional diffusion techniques like DTI, as it increases the amount of microstructure information encoded into the diffusion-weighted images34. Our hypothesis posits that axonal injury would associate with elevated plasma levels of NFL and GFAP and lower cognitive performance in PWH. Our findings reveal that (a) tensor-valued diffusion encoding metrics (µFA, MKa, MKi, MKt) demonstrate stronger sensitivity to microstructural abnormalities than DKI MK and DTI MD in PWH comparted to HC; (b) tensor-valued diffusion encoding metrics are significantly associated with cognitive scores in PWH but not with MK, FA and MD; and (c) tensor-valued diffusion encoding metrics in white matter are significantly associated with blood markers such as GFAP and NFL in PWH .

In alignment with observations in other neuroinflammatory and neurodegenerative disorders25,77,78,79, PWH exhibit a reduction in anisotropy-related metrics (FA, µFA, MKa) and an elevation in diffusivity-related metrics (MD, MKi) compared to their healthy counterparts. This indicates a widespread loss of tissue microstructural integrity and possible edema. The mean values for diffusion metrics (both DTI, DKI and tensor-valued diffusion encoding) align with previous studies involving both healthy and diseased subjects36,80,81.

Our findings indicate significant reductions by 10–15% in µFA values in various white matter regions, specifically in coherent (GCC), crossing (SFOF), and fanning (ACR) fibers, while FA showed 9–12% significant changes in GCC and ACR with no significant changes in MD, in PWH compared to healthy controls. This implies that microstructural changes, as measured by µFA, are predominantly due to the loss of local anisotropy rather than disruption of white matter fiber coherence in the HIV cohort. Since µFA is proposed as a measure of axonal integrity rather than myelin24,82, this decrease in µFA suggests widespread axonal damage resulting from HIV infection. Overall, tensor-valued metrics demonstrated more pronounced differences between PWH and healthy controls than DTI metrics across all ROIs. Furthermore, we observed a 17–24% decrease in MKa and up to a 4–10% decrease in MKt, along with a 10–21% increase in MKi, in white matter regions in PWH compared to controls. This is noteworthy, as conventional dMRI without tensor-valued diffusion encoding cannot separate MKi and MKa, as it can only detect MKt, which is the sum of the two. By using tensor-valued diffusion encoding to dissociate the two, larger differences between the groups were found.

The cognitive performance, as measured by the total cognitive Z-score, demonstrated a stronger correlation with tensor-valued diffusion encoding metrics compared to DTI metrics in PWH, underscoring the sensitivity of tensor-valued diffusion encoding metrics. Significantly, the interaction between tensor-valued diffusion metrics and HIV status was observed for the tensor-valued diffusion encoding-based anisotropy metrics (i.e., µFA and MKa in crossing and fanning fiber regions). This suggests that the cognitive effects in the HIV cohort are primarily linked to the loss of local anisotropy, impacting cognitive performance. Moreover, cognitive domain scores, particularly in executive function, attention, and motor functions, exhibited robust associations with anisotropy metrics, specifically µFA, compared to FA. However, trends in individual cognitive domains are similar to the total cognitive score, though weaker. The observed trend in correlations aligns with previous studies involving DTI-derived FA. Decreased FA has also been noted in various white matter regions, correlating with decreased memory and executive function in PWH exhibiting HIV-associated neurocognitive disorders, particularly in studies with larger sample size76,83,84.

In addition, this study unveils a notably stronger association between tensor-valued diffusion encoding metrics and both NFL and GFAP in white matter, compared to DTI and DKI metrics in PWH.

However, it is essential to acknowledge several limitations within this study. Firstly, despite the careful age matching between PWH and healthy controls, there exists an imbalance in the proportion of male and female participants. This discrepancy could introduce gender-related confounding factors. Despite concerted efforts to include female participants, the representation remains at a minimum of 25% in each cohort. Nevertheless, the proportion of males and females in both cohorts are not significantly different. However, the proportion of White s and African Americans was significantly imbalanced. Moreover, due to a lack of Simultaneous Multi-Slice (SMS) in the early implementation of the FWF sequence the collection of tensor-valued diffusion encoding metrics encountered limitations in image resolution (2 × 2 × 4 mm3). These limitations should be considered when interpreting the results and may warrant further investigation in future studies with larger and more diverse cohorts.

Conclusion

In this study, we investigated the effectiveness of tensor-valued diffusion encoding and associated analysis in delineating tissue microstructural degradation in PWH. Our findings indicate that metrics based on tensor-valued diffusion encoding demonstrate greater sensitivity in quantifying subtle changes associated with HIV infection. Moreover, we demonstrated a significant correlation between tensor-valued diffusion encoding metrics, cognitive scores, and plasma levels of NFL and GFAP in PWH. Therefore, the utilization of tensor-valued diffusion encoding offers a more comprehensive and clinically relevant insight into abnormalities in brain tissue microstructure related to HIV infection compared to the conventional DTI approach. Further studies with larger sample sizes and longitudinal designs are warranted.