Despite effective viral suppression, youth with perinatal HIV (PHIVY) often demonstrate long-term cognitive deficits. We measured grey matter cortical thickness as a measure of brain structural integrity in 11 PHIVY receiving long term cART compared to 16 age-matched controls and assessed neurocognitive performance. The PHIVY group performed significantly worse than controls. Regions of significantly thinner and thicker cortex in PHIVY were observed which may contribute to these deficits in neurocognitive function. Cortical thickness in PHIVY was correlated with current CD4 count and neurocognitive performance. Our findings suggest the potential importance of continued monitoring of PHIVY.
We investigated eleven PHIVY (age 22.50±2.9 years) and sixteen healthy controls (HC) (age 22.45±3.0 years). All MRI studies were performed on a Siemens 3T Prisma MRI scanner using a 16-channel phased-array head ‘receive’ coil. High-resolution T1-weighted images were acquired using a MPRAGE sequence with TR/TE=2200/2.41 ms, inversion time=900 ms, flip angle=9°, matrix size=320×320, FOV=230×256 mm2, slice thickness=0.9 mm, 192 slices. For PHIVY subjects, the following additional data were collected: age at first treatment, HIV viral load, highest viral load, CD4 T-cell counts, lowest CD4, lowest CD4%, current antiretroviral therapy, and presence of HIV encephalopathy. In addition, a comprehensive neuropsychological (NP) assessment battery was also administered to all participants and grouped into 13 cognitive domains. Raw data and Z-scores were transformed into T-scores by utilizing established normative data. Domain T-scores were calculated by averaging the T-score of the individual tests comprising the neurocognitive domain.
Demographic and neurocognitive performance were assessed by independent samples t-tests and MANCOVA (age, sex covariates) respectively. We used the FreeSurfer and MATLAB-based SPM12 software packages for data processing and analysis as described in detail elsewhere12-14. The FreeSurfer processing stream was followed to generate cortical thickness across the brain, except for cerebellar areas using a general linear model with condition (PHIVY/HC) and sex as independent variables. We also assessed correlations of cortical thickness with cognitive domain T-scores and clinical variables. Each hemisphere was analyzed separately, with 10 mm smoothing. We used a threshold of P ≤ 0.05 and the areas of significant difference were overlaid onto the inflated cortical surface.
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