Structural Neuroimaging and Neuropsychologic Signatures in Children With Vertically Acquired HIV

Paul, Robert; Prasitsuebsai, Wasana; Jahanshad, Neda; Puthanakit, Thanyawee; Thompson, Paul M.; Aurpibul, Linda; Hansudewechakul, Rawiwan; Kosalaraksa, Pope; Kanjanavanit, Suparat; Ngampiyaskul, Chaiwat; Luesomboon, Wicharn; Lerdlum, Sukalaya; Pothisri, Mantana; Visrutaratna, Pannee; Valcour, Victor; Nir, Talia M.; Saremi, Arvin; Kerr, Stephen J.; Ananworanich, Jintanat

doi:10.1097/inf.0000000000001852

Cited by 15 publications

(19 citation statements)

References 32 publications

(31 reference statements)

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“…However, recent analyses indicate that the neurocognitive advantages attributed to initiation of ART during the immediate postnatal period are not sustained for many individuals in later childhood [5]. Similar findings have been reported in separate cohorts of older children with pHIV who initiated suppressive ART within weeks of birth [6][7], as well as subgroups of children who initiated ART after surviving the first year of life without significant immune compromise [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 65%

“…After birth, environmental pressures, including living with a chronic medical disease, moderate brain development and expression of brainbehavior phenotypes [35][36][37][38]. Many children infected with HIV also face chronic stress associated with poverty (food security and malnutrition, limited education, family and social support disruption; [6][7][8][9][10][11][12][13]). Stigma related to HIV and psychological challenges created by HIV disclosure among sexually active adolescents and young adults contribute to the burden of early life adversities in this vulnerable population [39].…”

Section: Discussionmentioning

confidence: 99%

“…A neuro substudy began one year after the start of the main PREDICT trial (MH#). Published outcomes report no differences in survival, AIDSdefining illnesses, or neurocognitive outcomes between the two treatment arms [8][9][10][11][12][13]. Most ).…”

Section: Methodsmentioning

confidence: 99%

“…The BVMI is a standardized test of graphomotor construction of 30 increasingly complex line drawings that are presented in sequential order. The test has been utilized extensively in international studies of pediatric neurodevelopment, including Thai and Cambodian children with pHIV [8][9][10][11][12][13]. The BVMI was developed for individuals from ages 2-90 years from diverse environmental, educational, and linguistic backgrounds [22].…”

Section: Assessment Of Neurocognitive Developmentmentioning

confidence: 99%

“…Caregivers of participants completed age-appropriate versions of the CBCL (age 2 to 5 years or age 6 years and over), translated into Thai and Khmer. Raw scores were converted to standardized scores (T scores) to allow comparison to published studies in this population [8][9][10][11][12][13].…”

Section: Candidate Predictors Of Neurocognitive Developmentmentioning

confidence: 99%

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Predicting neurodevelopmental outcomes in children with perinatal HIV using a novel machine learning algorithm

Paul

Cho

Mellins

et al. 2019

Preprint

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Background: A subset of children with perinatal HIV (pHIV) experience long-term neurocognitive symptoms despite treatment with antiretroviral therapy. However, predictors of neurocognitive outcomes remain elusive, particularly for children with pHIV residing in low-tomiddle income countries. The present study utilized a novel data analytic approach to identify clinically-relevant predictors of neurocognitive development in children with pHIV. Methods:Analyses were conducted on a large repository of longitudinal data from 285 children with pHIV in Thailand (n=170) and Cambodia (n=115). Participants were designated as neurocognitively resilient (i.e., positive slope; n=143) or at risk (i.e., negative slope; n=142) according to annual performances on the Beery-Buktenica Developmental Test of Visual-Motor Integration over an average of 5.4 years. Gradient-boosted multivariate regression (GBM) with 5-fold cross validation was utilized to identify the optimal combination of demographic, HIV disease, blood markers, and emotional health indices that predicted classification into the two neurocognitive subgroups. Model performance was assessed using Receiver Operator Curves and sensitivity/specificity. Results: The analytic approach distinguished neurocognitive subgroups with high accuracy (93%; sensitivity and specificity each > 90%). Dynamic change indices and interactions between mental health and biological indices emerged as key predictors. Conclusion: Machine learning-based regression defined a unique explanatory model of neurocognitive outcomes among children with pHIV. The predictive algorithm included a combination of HIV, physical health, and mental health indices extracted from readily available clinical measures. Studies are needed to explore the clinical relevance of the data-driven explanatory model, including potential to inform targeted interventions aimed at modifiable risk factors.

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Section: Introductionmentioning

confidence: 65%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Assessment Of Neurocognitive Developmentmentioning

confidence: 99%

Section: Candidate Predictors Of Neurocognitive Developmentmentioning

confidence: 99%

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Predicting neurodevelopmental outcomes in children with perinatal HIV using a novel machine learning algorithm

Paul

Cho

Mellins

et al. 2019

Preprint

Self Cite

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Assessing and Intervening with Children who are Chronically Ill

Colaluca¹

2022

Best Practices in School Neuropsychology

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Multimodal magnetic resonance neuroimaging measures characteristic of early cART‐treated pediatric HIV: A feature selection approach

Khobo

Jankiewicz

Holmes

et al. 2022

Human Brain Mapping

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Children with perinatally acquired HIV (CPHIV) have poor cognitive outcomes despite early combination antiretroviral therapy (cART). While CPHIV‐related brain alterations can be investigated separately using proton magnetic resonance spectroscopy ( 1 H‐MRS), structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and functional MRI (fMRI), a set of multimodal MRI measures characteristic of children on cART has not been previously identified. We used the embedded feature selection of a logistic elastic‐net (EN) regularization to select neuroimaging measures that distinguish CPHIV from controls and measured their classification performance via the area under the receiver operating characteristic curve (AUC) using repeated cross validation. We also wished to establish whether combining MRI modalities improved the models. In single modality analysis, sMRI volumes performed best followed by DTI, whereas individual EN models on spectroscopic, gyrification, and cortical thickness measures showed no class discrimination capability. Adding DTI and 1 H‐MRS in basal measures to sMRI volumes produced the highest classification performance . The best multimodal MRI set consisted of 22 DTI and sMRI volume features, which included reduced volumes of the bilateral globus pallidus and amygdala, as well as increased mean diffusivity (MD) and radial diffusivity (RD) in the right corticospinal tract in cART‐treated CPHIV. Consistent with previous studies of CPHIV, select subcortical volumes obtained from sMRI provide reasonable discrimination between CPHIV and controls. This may give insight into neuroimaging measures that are relevant in understanding the effects of HIV on the brain, thereby providing a starting point for evaluating their link with cognitive performance in CPHIV.

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Structural Neuroimaging and Neuropsychologic Signatures in Children With Vertically Acquired HIV

Abstract: Outcomes of the present study suggest abnormal brain maturation among HIV-infected pediatric survivors. Longitudinal studies of brain integrity and related resilience factors are needed to determine the impact of neuroimaging abnormalities on psychosocial function in pediatric HIV.

Cited by 15 publications

References 32 publications

Predicting neurodevelopmental outcomes in children with perinatal HIV using a novel machine learning algorithm

Predicting neurodevelopmental outcomes in children with perinatal HIV using a novel machine learning algorithm

Assessing and Intervening with Children who are Chronically Ill

Multimodal magnetic resonance neuroimaging measures characteristic of early cART‐treated pediatric HIV: A feature selection approach

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