AUTHOR=Szalisznyó Krisztina , Silverstein David N. 

TITLE=Computational Predictions for OCD Pathophysiology and Treatment: A Review

JOURNAL=Frontiers in Psychiatry

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2021.687062

DOI=10.3389/fpsyt.2021.687062

ISSN=1664-0640

ABSTRACT=Obsessive compulsive disorder (OCD) can manifest as a debilitating disease with high degrees of co-morbidity as well as clinical and etiological heterogenity. However, the underlying pathophysiology is
not clearly understood. Computational psychiatry is an emerging field in which behavior and its neural correlates are quantitatively analyzed and computational models are developed to improve
understanding of disorders and comparing model evidence to observations. The aim is to more precisely understand psychiatric illnesses. Such computational and theoretical approaches may also
enable more personalized treatments. Yet, these methodological approaches are not self-evident for clinicians with a traditional medical background. In this mini-review, we summarize a selection of
computational OCD models, computational analysis frameworks and  also review the model predictions from a perspective of possible personalized treatment. The reviewed computational approaches used dynamical systems frameworks, machine learning methods for analyzing and classifying patient data and Bayesian interpretations  of probability for model selection were also included. The computational dissection of the underlying pathology is expected to narrow the explanatory gap between the phenomenological nosology and the neuropathophysiological background of this heterogeneous disorder. It may also contribute to develop biologically grounded and more informed dimensional taxonomies of psychopathology.