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 by comparing model predictions 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 and computational analysis frameworks, while also considering the model predictions from a perspective of possible personalized treatment. The reviewed computational approaches used dynamical systems frameworks or machine learning methods for modeling, analyzing and classifying patient data. 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.
Highlights
In this review, we assessed the evolution of the computational modeling efforts that aim to study some aspects of obsessive compulsive disorder (OCD) pathophysiology
Intermittent dynamical instability, heteroclinic cycles Optimal sub-thalamic nucleus (STN)-deep brain stimulation (DBS) in treatment-refractory OCD Identified 4 predictors for suicide attempt Identified 24 most predictive items for remission Pediatric OCD treatment (ICBT) outcome Identified brain regions and discriminating structural MRI (sMRI) patterns Patients with/without sensory phenomena White matter abnormalities Identified 9 predictive variables for severity Severity from mOFC, left putamen gray matter volumes Identified 4 trans-diagnostic data-driven groups Pathological activation in orbito-striato-thalamo-orbital network ↑ θ power in qEEG → effect of right frontal rTMS↑ Identified 4 compulsive/impulsive subgroups indicating severity CSTC connections ↑ posterior cerebellar connections ↓ Exaggerated cingulate error signals, learning rates ↓ Sensitivity to outcome devaluation ↑ Goal-directed deficits associated with compulsivity, intrusive thought
Model-free habit formation ↑ model-based control ↓ mOFC, caudate gray matter volumes ↓ With higher presynaptic dopamine in ventral striatum: → model-based coding in lateral prefrontal cortex (PFC) ↑ → model-free coding in ventral striatum ↓ Stimulus-bound preservation ↓ punishment-driven learning ↑ D2/3 agonists & antagonists → punishment-driven learning ↑ Treatment strategy when risk of adverse drug effects Error control ↑ fronto-cingulate cortex ↑ dorsal anterior cingular cortex (dACC) → left-DLPFC effective connectivity ↑ State transition uncertainty ↑ over-exploratory, over-flexibility Information gathering ↑ decision threshold ↑ delayed urgency signal Dissociation between confidence and action, abandonment of historical information, reliance on prediction errors ↑ 4 symptom dimensions in OCD: Incompleteness, taboo thoughts, responsibility, contamination Impaired transfer across repeated decision episodes Driven by implicit memory On verbal recognition memory, discriminability ↓ between old and new stimuli
Summary
We assessed the evolution of the computational modeling efforts that aim to study some aspects of obsessive compulsive disorder (OCD) pathophysiology. The computational and theoretical investigations support the move from the currently used nosological classification toward trans-dimensional approaches [1]. This trend is motivated by a necessity to gain a deeper and more biologically grounded understanding of the disease in order to develop personalized interventions. A plethora of reinforcement learning (RL) articles and several diverse computational analysis studies are reviewed [14,15,16,17,18]. Both model-based and model-free RL are utilized to examine pathological aspects of goal-directed and habitual systems in OCD.
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