Objective

Inflammation and infection within the central nervous system is initiated during primary HIV infection (PHI), but the association of these processes with the integrity of brain white matter during PHI is unknown.

Design

We used diffusion tensor imaging (DTI) in this prospective cross-sectional neuroimaging study to determine the extent of white matter involvement in early HIV infection.

Methods

Antiretroviral-naive PHI (defined as <1 year after infection, n = 62), chronic HIV infection (CHI, n = 16), and HIV-uninfected (n = 19) participants had DTI, laboratory, and neuropsychometric performance assessments. DTI metrics were examined using region of interest and whole brain voxelwise analyses. Linear mixed-effects models assessed correlations between DTI measures and laboratory and neuropsychometric performance values.

Results

PHI participants were assessed at a median 4.1 months after estimated infection, and had median CD4 cell count of 573 cells/μl, and HIV-1 RNA viral load of 4.5 log10 copies/ml in plasma and 2.6 log10 copies/ml in cerebrospinal fluid (CSF). DTI metrics in PHI individuals were similar to HIV- participants and correlated with disruptions in the blood-brain barrier (indicated by CSF/plasma albumin ratio and CSF protein). CHI participants had significant loss of white matter integrity that correlated with biomarkers of infection and inflammation (blood viral load, CD4 T-cell count, and neopterin, and CSF white blood cell). Within the PHI group, DTI metrics inversely correlated with increasing days since infection.

Conclusion

In individuals assessed during PHI, group DTI measures suggested relative preservation of white matter microstructural integrity, but were associated with disruption of the blood-brain barrier and estimated duration of infection.

We describe and apply novel methodology for whole-brain analysis of resting state fMRI functional connectivity data, combining conventional multi-channel Pearson correlation with covariance analysis. Unlike correlation, covariance analysis preserves signal amplitude information, which feature of fMRI time series may carry physiological significance. Additionally, we demonstrate that dimensionality reduction of the fMRI data offers several computational advantages including projection onto a space of manageable dimension, enabling linear operations on functional connectivity measures and exclusion of variance unrelated to resting state network structure. We show that group-averaged, dimensionality reduced, covariance and correlation matrices are related, to reasonable approximation, by a single scalar factor. We apply this methodology to the analysis of a large, resting state fMRI data set acquired in a prospective, controlled study of mindfulness training and exercise in older, sedentary participants at risk for developing cognitive decline. Results show marginally significant effects of both mindfulness training and exercise in both covariance and correlation measures of functional connectivity.

Diffusion imaging aims to non-invasively characterize the anatomy and integrity of the brain's white matter fibers. We evaluated the accuracy and reliability of commonly used diffusion imaging methods as a function of data quantity and analysis method, using both simulations and highly sampled individual-specific data (927-1442 diffusion weighted images [DWIs] per individual). Diffusion imaging methods that allow for crossing fibers (FSL's BedpostX [BPX], DSI Studio's Constant Solid Angle Q-Ball Imaging [CSA-QBI], MRtrix3's Constrained Spherical Deconvolution [CSD]) estimated excess fibers when insufficient data were present and/or when the data did not match the model priors. To reduce such overfitting, we developed a novel Bayesian Multi-tensor Model-selection (BaMM) method and applied it to the popular ball-and-stick model used in BedpostX within the FSL software package. BaMM was robust to overfitting and showed high reliability and the relatively best crossing-fiber accuracy with increasing amounts of diffusion data. Thus, sufficient data and an overfitting resistant analysis method enhance precision diffusion imaging. For potential clinical applications of diffusion imaging, such as neurosurgical planning and deep brain stimulation (DBS), the quantities of data required to achieve diffusion imaging reliability are lower than those needed for functional MRI.