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

Abstract

IntroductionPrevious 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 & MethodsWe 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. ResultsResults 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. ConclusionOur 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. IMPACT STATEMENTThe diagnosis of fetal alcohol spectrum disorders (FASDs) can be challenging in individuals who lack the hallmark facial dysmorphologies associated with heavy prenatal alcohol exposure (PAE). The absence of a diagnosis prevents individuals with PAE from receiving the treatment and services that improves quality of life outcomes. This research explores the use of preclinical functional network connectivity data and machine learning techniques as a novel and non-invasive means of detecting PAE. Our aim is to contribute basic science towards improving diagnostic strategies that can lead to securing timely and appropriate support for individuals with FASD and their caregivers.