Detection of Prenatal Alcohol Exposure Using Machine Learning Classification of Resting-State Functional Network Connectivity Data

Abstract

Introduction: Previous work utilizing resting state fMRI to measure functional network connectivity in rodents with moderate prenatal alcohol exposure (PAE) revealed several sex- and region-dependent alterations in FNC implicating FNC as potential biomarker for PAE. Given that FNC is sensitive to neurological and psychiatric conditions in humans, here, we explore the use of previously acquired FNC data and machine learning methods to detect PAE among a sample of rodents exposed to moderate PAE and controls exposed to a saccharin solution. Materials & Methods: We utilized previously acquired fMRI data from 48 adult rats, 24 PAE (12 male 12 female) and 24 saccharin exposed (SAC) controls (12 male and 12 female) for classification. The entire data sample was utilized to perform binary classification (SAC or PAE) of FNC data with multiple support vector machine (SVM) kernels and out-of-sample cross-validation to evaluate classification performance. Results: Results revealed accuracy rates of 62.5% for all samples, 58.3% for males, and 79.2% for females using a quadratic SVM kernel to classify moderate PAE from FNC data. In addition, brain networks localized to hippocampal and cortical regions contributed strongly to QSVM classifications. Conclusion: Our results suggest overall modest classification performance of a QSVM to detect moderate PAE from FNC data gathered from adult rats, yet good performance among females. Further developments and refinement of the technique hold promise for the detection of PAE in earlier developmental time periods that potentially offer additional tools for the non-invasive detection of PAE from FNC data.