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Obsessive-Compulsive Disorder

Obsessive-Compulsive Disorder

Obsessive-compulsive disorder (OCD) is a mental health condition characterized by recurrent obsessions, compulsions, or both, which consume significant time and interfere with daily functioning. Obsessions involve persistent, intrusive, and unwanted thoughts, images, or urges that evoke anxiety. Common examples include irrational fears of contamination or harm. Compulsions are repetitive behaviors or mental acts performed to reduce the anxiety caused by obsessions. For instance, individuals...

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Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

EEG microstate analysis and machine learning classification in patients with obsessive-compulsive disorder.

Mohan Ma¹, Bingxun Lu², Yumei Gong³

¹Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China.

Journal of Psychiatric Research

|January 16, 2025

Summary

Electroencephalogram (EEG) microstate analysis reveals altered brain network dynamics in obsessive-compulsive disorder (OCD). These changes correlate with symptom severity, suggesting potential for auxiliary diagnostic insights.

Keywords:

Disease classification EEG microstates Machine learning Obsessive thoughts Obsessive-compulsive disorder (OCD)

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Assessment and Communication for People with Disorders of Consciousness

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

Area of Science:

Neuroscience
Computational Neuroscience
Psychiatry

Background:

Electroencephalogram (EEG) microstate analysis offers a data-driven method to investigate brain network dynamics at a millisecond scale.
Obsessive-compulsive disorder (OCD) is associated with complex alterations in whole-brain functional networks.

Purpose of the Study:

To explore pathological changes in whole-brain functional networks in OCD patients using microstate analysis.
To assess the potential of microstate features as an auxiliary diagnostic index for OCD.

Main Methods:

Recruited 48 OCD patients and 52 healthy controls (HCs).
Recorded eyes-closed EEG using a 64-channel system.
Compared microstate features between groups, correlated them with clinical symptoms, and employed machine learning for classification.

Main Results:

OCD patients exhibited a significantly lower probability of transition from microstate B to C compared to HCs.
Obsessive thoughts correlated with microstate A duration, microstate B occurrence, and C-to-B transition probability.
Anxiety symptom scores (HAMA) negatively correlated with microstate C occurrence.
Machine learning models achieved AUCs of 70.43% for OCD vs. HC classification and 77.13% for differentiating anxiety severity within OCD patients.

Conclusions:

EEG microstate characteristics are altered in OCD and linked to obsessive thoughts and anxiety.
Machine learning models based on microstate features show limited but promising ability for OCD identification.
Further optimization of microstate-based classification approaches is warranted for clinical application.