Machine Learning to Generate a Multivariate Model of Brain Injury in HIV Patients

点击翻译按钮获取中文摘要

This R21 application responds to RFA-MH-18-611 “Altered neuronal circuits, receptors and networks in HIV-induced Central Nervous System (CNS) dysfunction (R21)”. Despite effective viral suppression from combined antiretroviral therapy, up to 50% of the patients continue to have HIV-associated neurocognitive disorders (HAND). The cognitive deficits or impairment in HIV patients may be due to legacy effects from early stages of the infection, residual viral reservoirs with ongoing neuroinflammation, and potential neurotoxicity from some of the antiretroviral medications. Furthermore, co-morbid disorders associated with the aging HIV+ population, the high prevalence of substance use, and host characteristics, may further increase the risk and exacerbate the severity of HAND. Optimal care for patients with HAND requires efficient and appropriate diagnosis that can guide effective treatments. However, the current diagnostic approach for HAND requires lengthy and specialized cognitive tests and involves subjective components. Our overall goal is to develop an unsupervised machine learning (ML) algorithm to assess brain pathology, using objective measures such as alterations in structural connectivity on DTI, augmented with other neuroimaging and clinical variables. Ultimately, this may lead to a robust approach to classify subtypes and to quantify brain injury in HIV-infected individuals. This exploratory project has three specific aims (SA): SA1: Employ an automated unsupervised ML algorithm to detect subgroups of HIV-infected subjects, based solely on DTI tractography (structural connectivity). SA2: Add objective demographic, genetic, clinical, and non-DTI MR variables to the training DTI tractography data set, and determine their effects on predicting HAND and cognitive performance. SA3: Evaluate the optimized model for stability to undersampling, and determine whether it can be generalized to other data sets, including those from multi-center studies (i.e. Multicenter AIDS Cohort Study). Our optimized ML model has the potential to provide efficient, objective and reproducible biomarkers to identify individuals with or at risk for HAND, to guide prevention and treatment for HAND, and thereby ameliorate the burden of HIV infection and dementias.