An attractor hypothesis of obsessive-compulsive disorder

Area of Science:

• Neuroscience
• Computational Psychiatry
• Systems Neuroscience

Background:

• Obsessive-compulsive disorder (OCD) presents with diverse symptoms, including cognitive inflexibility and repetitive behaviors.
• Current understanding of OCD pathophysiology lacks a unifying dynamical framework.
• Attractor network models are used to understand neural dynamics and cognitive processes.

Purpose of the Study:

• To propose a statistical dynamical framework explaining OCD symptoms.
• To link symptom heterogeneity in OCD to attractor network dynamics in specific brain regions.
• To investigate the role of receptor conductances in modulating network stability.

Main Methods:

• Utilized a top-down statistical dynamical approach.
• Developed integrate-and-fire network simulations.
• Investigated the effects of N-methyl-d-aspartate (NMDA) and alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor conductances.
• Simulated the impact of gamma-aminobutyric acid (GABA)-receptor activated currents.

Main Results:

• Increased attractor basin depth leads to excessive network stability, hindering state transitions.
• Simulations demonstrated that elevated NMDA and/or AMPA receptor conductances increase network stability.
• Increased glutamatergic activity correlates with overstable attractor networks, potentially explaining OCD symptoms.
• GABAergic currents can partially reverse this overstability.

Conclusions:

• OCD symptoms may arise from abnormally stable attractor networks in specific brain regions.
• Dysregulated glutamatergic activity, leading to network overstability, is a plausible mechanism for OCD.
• The proposed framework offers a potential explanation for cognitive and motor symptoms in OCD.
• Pharmacological modulation of receptor systems could be a therapeutic target for OCD.