Detection of prenatal alcohol exposure using machine learning classification of resting-state functional network connectivity data

• Published in: Alcohol (Fayetteville, N.Y.) Vol. 93; pp. 25 - 34
• Main Authors: Rodriguez, Carlos I., Vergara, Victor M., Davies, Suzy, Calhoun, Vince D., Savage, Daniel D., Hamilton, Derek A.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.06.2021; Elsevier Limited
• Subjects: Alcohol use; Algorithms; Animal cognition; Brain research; Classification; Females; Fetal alcohol spectrum disorder; Fetal alcohol syndrome; Functional magnetic resonance imaging; Functional network connectivity; Hippocampus; Learning algorithms; Machine learning; Magnetic resonance imaging; Males; Medical imaging; Neural networks; Prenatal alcohol exposure; Prenatal experience; Prenatal exposure; Psychiatric/Mental Health; Public health; Quantitative psychology; Saccharin; Variables; United States > US; Functional network connectivity; Fetal alcohol spectrum disorder; Prenatal alcohol exposure; Machine learning; Prenatal Alcohol Exposure; Fetal Alcohol Spectrum Disorder; Machine Learning; Functional Network Connectivity
• ISSN: 0741-8329; 1873-6823; 1873-6823
• DOI: 10.1016/j.alcohol.2021.03.001

Fetal Alcohol Spectrum Disorder (FASD), a wide range of physical and neurobehavioral abnormalities associated with prenatal alcohol exposure (PAE), is recognized as a significant public health concern. Advancements in the diagnosis of FASD have been hindered by a lack of consensus in diagnostic criteria and limited use of objective biomarkers. Previous research from our group utilized resting-state functional magnetic resonance imaging (fMRI) to measure functional network connectivity (FNC), which revealed several sex- and region-dependent alterations in FNC as a result of moderate PAE relative to controls. Considering that FNC is sensitive to moderate PAE, this study explored the use of FNC data and machine learning methods to detect PAE among a sample of rodents exposed to alcohol prenatally and controls. We utilized previously acquired resting state fMRI data collected from adult rats exposed to moderate levels of prenatal alcohol (PAE) or a saccharin control solution (SAC) to assess FNC of resting state networks extracted by spatial group independent component analysis (GICA). FNC data were subjected to binary classification using support vector machine (SVM) -based algorithms and leave-one-out-cross validation (LOOCV) in an aggregated sample of males and females (n = 48; 12 male PAE, 12 female PAE, 12 male SAC, 12 female SAC), a males-only sample (n = 24; 12 PAE, 12 SAC), and a females-only sample (n = 24; 12 PAE, 12 SAC). Results revealed that a quadratic SVM (QSVM) kernel was significantly effective for PAE detection in females. QSVM kernel-based classification resulted in accuracy rates of 62.5% for all animals, 58.3% for males, and 79.2% for females. Additionally, qualitative evaluation of QSVM weights implicates an overarching theme of several hippocampal and cortical networks in contributing to the formation of correct classification decisions by QSVM. Our results suggest that binary classification using QSVM and adult female FNC data is a potential candidate for the translational development of novel and non-invasive techniques for the identification of FASD. •Quadratic SVM classification of FNC patterns was effective in female rodents with moderate prenatal alcohol exposure.•Several hippocampal and cortical networks contribute to the formation of correct classification decisions.•The described approach may hold translational value for the identification of humans with prenatal alcohol exposure.•The described approach may hold translational value for developing novel ways of identifying prenatal alcohol exposure.