The two sides of Phobos: Gray and white matter abnormalities in phobic individuals.

Abstract

Small animal phobia (SAP) is a subtype of specific phobia characterized by an intense and irrational fear of small animals, which has been underexplored in the neuroscientific literature. Previous studies often faced limitations, such as small sample sizes, focusing on only one neuroimaging modality, and reliance on univariate analyses, which produced inconsistent findings. This study was designed to overcome these issues by using for the first time advanced multivariate machine-learning techniques to identify the neural mechanisms underlying SAP. Specifically, we relied on the multimodal Canonical Correlation Analysis approach combined with Independent Component Analysis (ICA) to decompose the structural magnetic resonance images from 122 participants into covarying gray and white matter networks. Stepwise logistic regression and boosted decision trees were then used to extract a predictive model of SAP. Our results indicate that four covarying gray and white matter networks, IC19, IC14, IC21, and IC13, were critical in classifying SAP individuals from control subjects. These networks included brain regions, such as the Middle Temporal Gyrus, Precuneus, Insula, and Anterior Cingulate Cortex-all known for their roles in emotional regulation, cognitive control, and sensory processing. To test the generalizability of our results, we additionally ran a supervised machine-learning model (boosted decision trees), which achieved an 83.3% classification accuracy, with AUC of 0.9, indicating good predictive power. These findings provide new insights into the neurobiological underpinnings of SAP and suggest potential biomarkers for diagnosing and treating this condition. The study offers a more nuanced understanding of SAP, with implications for future research and clinical applications in anxiety disorders.