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The successful implementation of prevention programs for mother-To-child human immunodeficiency virus (HIV) transmission has dramatically reduced the prevalence of infants infected with HIV while increasing that of HIV-exposed uninfected (HEU) children. Neuropsychological assessments indicate that HEU children may exhibit differences in neurodevelopment compared to unexposed children (HUU). Pathological mechanisms leading to such neurodevelopmental delays are not clear. In this observational birth cohort study we explored the integrity of regional white matter microstructure in HEU infants, shortly after birth. Microstructural changes in white matter associated with prenatal HIV exposure were evaluated in HEU infants (n=15) and matched controls (n=22) using diffusion tensor imaging and tract-based spatial statistics. Additionally, diffusion values were extracted and compared for white matter tracts of interest, and associations with clinical outcomes from the Dubowitz neonatal neurobehavioral tool were investigated. Higher fractional anisotropy in the middle cerebellar peduncles of HEU compared to HUU neonates was found after correction for age and gender. Scores on the Dubowitz abnormal neurological signs subscale were positively correlated with FA (r=0.58, P=0.038) in the left uncinate fasciculus in HEU infants. This is the first study to present data suggesting that prenatal HIV exposure without infection is associated with altered white matter microstructural integrity in the neonatal period. Longitudinal studies of HEU infants as their brains mature are necessary to understand further the significance of prenatal HIV and antiretroviral treatment exposure on white matter integrity and neurodevelopmental outcomes.
This is a nested observational substudy of infants enrolled in a larger population-based birth cohort study, the Drakenstein Child Health Study (DCHS).16,17 The DCHS is located in the Drakenstein area in Paarl, a peri-urban area in the Western Cape province of South Africa. The local community of ∼200,000 people is of low socioeconomic status, live in informal housing or crowded conditions, and have high levels of unemployment. Infectious diseases including pneumonia, HIV, and tuberculosis are common. The area has a well-established free healthcare system, where ∼90% of women access public sector antenatal care and child health services. The DCHS recruited >1000 pregnant women in their second trimester attending 2 primary health clinics serving different populations—TC Newman (serving a mixed race population) and Mbekweni (serving a black African population).16,17 We recruited pregnant women at 20 to 24 weeks gestation, obtained written informed consent, and collected background data as per the DCHS protocol.16,17 We confirmed the HIV status of mothers antenatally utilizing standard HIV testing algorithms and available laboratory tests.18 We confirmed the HIV status of the infants by testing postnatally using the qualitative polymerase chain reaction (PCR) technique. Infants who tested positive on the PCR testing had their HIV status confirmed using quantitative HIV viral load testing. The study collected measures of potential confounding factors. The Alcohol, Smoking and Substance Screening Test (ASSIST) was used to assess maternal substance abuse.17–20 Following this initial screen, prenatal alcohol exposure was further defined by a history of moderate-severe alcohol use in any of the pregnancy trimesters. Maternal cigarette smoking status was further documented using the Fagerström Test for Nicotine Dependence.21 Objective measures of maternal substance use were also included.16,17 Maternal urine cotinine was measured antenatally and at the time of birth to detect and quantify current smoking status. In addition, maternal urine samples were tested antenatally with rapid urine dipstick testing for recent use of common illicit substances, including methamphetamines, cocaine, cannabis, methaqualone, and opiates. Following birth, mother–child pairs identified through the HIV-testing approach were included for study unless mothers had a positive urine screen for illicit drugs of abuse (any group), the infants were premature (<36 weeks), or had low APGAR scores (<7 at 5 minutes), and/or history of neonatal ICU admission for hypoxic ischemic encephalopathy or other significant neonatal complications. Infants were also excluded from this study if they had an identified genetic syndrome or congenital abnormality. In this nested substudy, 39 infants were assessed: 15 HEU infants and 24 matched HUU controls. No infants who were identified in the antenatal visit for inclusion were lost by the time of scanning and none refused consent for scanning. In this study we imaged 2 to 4-week-old infants during natural (ie unsedated) sleep. Earplugs and mini-muffs were used for ear protection, a pulse oximeter was used to monitor pulse and oxygenation, and a qualified neonatal nurse or pediatrician was present with the infant during the imaging session. At the time of scanning, basic infant anthropometry was acquired, including length, weight, and occipito-frontal head circumference. The Dubowitz neurobehavioral scale, a measure of neonatal neuromotor and neurobehavior status, was used to study early neurological and neurobehavioral changes and to identify potential associations with neuroimaging findings.22 The score is based on the distribution of the scores for each item in the population of low-risk term infants, and the optimality score is obtained by summating the optimality scores of individual items. Together, the examination can be used to detect abnormal neurological signs associated with specific patterns of lesions observed on brain imaging. For this study, the “behavior” cluster, which includes items scoring irritability, cry, consolability, alertness, visual and auditory orientation and eye movements, and the “abnormal signs” cluster, which focuses on posture, tremor, and startle items, were of particular interest. The DCHS was approved by the Faculty of Health Sciences human research ethics committees of the University of Cape Town and Stellenbosch University in South Africa, as well as by the Western Cape Department of Health Provincial Research Committee. This substudy was independently reviewed and approved as HREC 525/2012. As above, mothers provided written informed consent for participation in the study. White matter microstructure can be characterized in vivo with diffusion tensor imaging (DTI), a noninvasive technique that utilizes the intrinsic directionality of water diffusion along fiber pathways to provide highly specific anatomical information.23 The most widely used index is fractional anisotropy (FA). This represents orientation-dependent variation in the diffusivity of water and reflects a number of microstructural properties such as degree of myelination, axon diameter, fiber coherence, and fiber tracking density.24,25 Other reported indices include mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). MD represents a measure of average diffusivity. Alterations in MD may indicate a decrease in cellular density, myelin degradation, or an increase in the extra- and/or intracellular volumes.24 Organized neural microstructure associated with improved cognition and behavior are typically associated with higher FA values and lower MD values. White matter microstructural pathology, however, is more generally associated with lower FA and higher MD values.23,24 AD represents diffusivity along the axonal structure, typically reflecting axonal membrane integrity and fiber coherence.26 AD may be higher when there is damage to the neurofilaments or axons.27,28 RD typically reflects average perpendicular diffusion and indicates degree of myelination; RD is generally higher with myelin damage or reduced myelination.28,29 Diffusion weighted images were acquired in the transverse plane on a Siemens Magnetom 3T Allegra MRI system using a spin-echo, echo-planar sequence along 45 noncollinear diffusion directions (b-values 0 s/mm2 and 1000 s/mm2, TR/TE = 7900/90 ms, slice thickness 1.6 mm, FOV 160 mm, voxel size 1.3 × 1.3 × 1.6 mm3, 2 averages in anterior-posterior and posterior-anterior orientation, scanning time 6.27 min per average). Diffusion imaging techniques are highly sensitive to the motion of subjects during scan acquisition. As a result, acquiring diffusion imaging data in infants offers particular logistical and technical challenges. Initially, manual quality control of individual subject data was applied. Only subjects with a minimum of 12 acquisition volumes that were artifact-free were allowed through the data preprocessing step. Subsequently, FMRIB's Diffusion Toolbox and processing streams from Tract-Based Spatial Statistics (TBSS) were used to perform a whole-brain analysis. Diffusion weighted images from individual subjects were registered to a corresponding b = 0 image. This step was performed in order to correct for distortions resulting from eddy currents as well as movement. Estimation of susceptibility-induced off-resonance field was performed using the FSL top-up tool. Subsequently, a single corrected image was created using the combination of the 2 images. The FSL Brain Extraction Tool was then used for the brain-extraction of images and following this the calculation of diffusion tensors was performed at each voxel. Values for each subject for FA, MD, AD, and RD were then obtained for between group analyses. Diffusion values by regions of interest (ROIs) were extracted, exported, and compared by group using standard SPSS statistical packages as below. Statistical analyses controlled for gender and postnatal age at the time of the scan. Age (in days) was considered particularly critical due to the rapid pace at which white matter maturation evolves in early neonatal life.30 The standard pipeline for TBSS analysis was applied for statistical analysis. In this study, the FMRIB FA template for adults, provided by FSL was not considered appropriate for neonatal DTI analysis. Thus, each subject was registered to a representative target that was preselected from the control cohort. The subject with the lowest mean warp coefficient from the control cohort was chosen as the target image. Each FA image was aligned into a standard space and upsampled to 1 × 1 × 1 mm3 voxel size. Next, the average FA image was created and thinned to create a skeletonized mean FA image, which represents the center of all white matter tracts common to the study group. An FA value of 0.2 was used as a threshold for the skeleton. This study was explorative and so we applied a more stringent threshold compared to that of some previous studies of this age group that used a threshold FA value of 0.15. Subsequently, we projected diffusion data onto this skeleton for the statistical analysis. The FSL's Randomize tool was used to assess voxelwise differences in DTI metrics among the study groups. Specifically, between group variations were investigated with unpaired t tests and correlational analyses, and statistical analyses were corrected for multiple comparisons with threshold-free cluster enhancement. We considered results with a P value of <0.05 as statistically significant. Following FSL preprocessing, we examined group main effects using extracted data on different diffusion indices by ROI. We applied affine-registration to each brain and a standard FMRIB58_FA template. The white-matter atlas from Johns Hopkins University was used for the extraction of MD values for each subject for the ROIs. ROIs included associative bundles, commissural bundles, projection bundles, and large white matter tracts of the brainstem and cerebellum. Generalized linear models were used to evaluate group differences in diffusion parameters, with neonatal postnatal age (days) at scan time as well as gender as covariates. Results were Bonferroni corrected.
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