The phobic brain: Morphometric features correctly classify individuals with small animal phobia - Scarano, Alessandro; Fumero, Ascensión; Baggio, Teresa; Rivero, Francisco; Marrero, Rosario J.; Olivares, Teresa; Peñate, Wenceslao; Álvarez‐Pérez, Yolanda; Bethencourt, Juan Manuel; Grecucci, Alessandro (Psychophysiology, 2025)
Specific phobia represents an anxiety disorder category characterized by intense fear generated by specific stimuli. Among specific phobias, small animal phobia (SAP) denotes a particular condition that has been poorly investigated in the neuroscientific literature. Moreover, the few previous studies on this topic have mostly employed univariate analyses, with limited and unbalanced samples, leading to inconsistent results. To overcome these limitations, and to characterize the neural underpinnings of SAP, this study aims to develop a classification model of individuals with SAP based on gray matter features, by using a machine learning method known as the binary support vector machine. Moreover, the contribution of specific structural macro‐networks, such as the default mode, the salience, the executive, and the affective networks, in separating phobic subjects from controls was assessed. Thirty‐two subjects with SAP and 90 matched healthy controls were tested to this aim. At a whole‐brain level, we found a significant predictive model including brain structures related to emotional regulation, cognitive control, and sensory integration, such as the cerebellum, the temporal pole, the frontal cortex, temporal lobes, the amygdala and the thalamus. Instead, when considering macro‐networks analysis, we found the Default, the Affective, and partially the Central Executive and the Sensorimotor networks, to significantly outperform the other networks in classifying SAP individuals. In conclusion, this study expands knowledge about the neural basis of SAP, proposing new research directions and potential diagnostic strategies. Small animal phobia (SAP) is under‐researched, with previous studies using limited, unbalanced samples and univariate analyses. This study employs for the first time a machine learning method to classify 32 SAP individuals based on structural MRI versus 90 matched controls. Key brain structures included frontal and temporal regions, as well as the amygdala and thalamus. In further analyses we showed that The default mode and the affective networks were among the most predictive networks.