T1 The phobic brain: Morphometric features correctly classify individuals with small animal phobia
Alessandro Scarano, Ascensión Fumero, Teresa Baggio, Francisco Rivero, Rosario J. Marrero, Teresa Olivares, Wenceslao Peñate, Yolanda Álvarez-Pérez, Juan Manuel Bethencourt, Alessandro Grecucci. Psychophysiology 2024
A1 Olivares Pérez, Teresa
A1 Scarano, Alessandro
A1 Fumero, Ascensión
A1 Baggio, Teresa
A1 Rivero, Francisco
A1 Marrero, Rosario J.
A1 Peñate, Wenceslao
A1 Álvarez-Pérez, Yolanda
A1 Bethencourt, Juan Manuel
A1 Grecucci, Alessandro
K1 affective neuroscience
K1 animal phobia
K1 anxiety
K1 machine learning
K1 support vector machine
AB 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.
LK http://riull.ull.es/xmlui/handle/915/41013
UL http://riull.ull.es/xmlui/handle/915/41013
LA en
NO ISSN: 1469-8986, 0048-5772https://doi.org/10.1111/psyp.14716
DS Repositorio institucional de la Universidad de La Laguna
RD 09-nov-2025