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The following is a summary of “Abnormal nonlinear features of EEG microstate sequence in obsessive–compulsive disorder,” published in the December 2024 issue of Psychiatry by Ren et al.
Researchers conducted a retrospective study on electroencephalography (EEG) microstates in obsessive-compulsive disorder (OCD). They found that nonlinear features of EEG microstates could serve as potential biomarkers for OCD.
They collected resting-state EEG data from 48 patients with OCD and 48 healthy controls (HC). EEG microstate analysis was performed to extract temporal parameters (duration, occurrence, coverage) and nonlinear features (sample entropy, Lempel–Ziv complexity, Hurst index). These parameters were then input into 3 machine-learning models to classify patients with OCD.
The results showed that both groups had 4 typical EEG microstate topographies. The duration of microstates A, B, and C in patients with OCD decreased significantly, while microstate D occurrence increased significantly. Sample entropy and Lempel–Ziv complexity increased significantly in patients with OCD, while the Hurst index decreased. Classification accuracy using nonlinear features of microstate sequences reached 85%, significantly higher than temporal parameter models.
Investigators provided additional insights into EEG microstates in patients suffering from OCD with a larger sample size and concluded that nonlinear features of EEG microstate sequences could serve as potential biomarkers for distinguishing patients with OCD.
Source: bmcpsychiatry.biomedcentral.com/articles/10.1186/s12888-024-06334-6
