fMRI-Based Machine Learning Analysis of Neural Substrates of Pediatric Anxiety: Temporal Pole and Emotional Face-Responses

Abstract

A prominent cognitive aspect of anxiety is dysregulation of emotional interpretation of facial expressions, associated with neural activity from the amygdala and prefrontal cortex. We report machine learning analysis of fMRI results supporting a key role for a third area, the temporal pole (TP) for childhood anxiety in this context. This finding is based on differential fMRI responses to emotional faces (e.g. angry versus fearful faces) in children with one or more of generalized anxiety, separation anxiety, and social phobia (n = 22) compared with matched controls (n = 23). In our machine learning model, the right TP distinguished anxious from control children (accuracy = 81%). Involvement of the TP as significant for neurocognitive aspects of pediatric anxiety is a novel finding worthy of further investigation.