Obsessive-Compulsive Disorder (OCD) is a diverse mental health condition that leads to substantial impairment and currently has limited success in treatment outcomes. The aim of the current study was to examine the ratio of electroencephalographic (EEG) band power within the Autogenous-Reactive (AO-RO) taxonomy of OCD during inhibition to improve our understanding of the disorder. Inhibition was measured broadly using interference and action cancellation tasks while EEG data was recorded from 61 undergraduate students. EEG band power was computed from frontal-central electrodes Fz and Cz for theta and beta frequency bands. Event-related spectral perturbations (ERSPs) were used to measure EEG band power during inhibitory task performance to calculate the Theta/Beta ratio (TBR). The relationship between AO-RO severity and the TBR at each electrode was statistically analyzed using two hierarchical linear regressions. TBR at electrode Fz during the stop-signal task was the only significant EEG predictor of AO severity. TBR predictors were not significant for RO severity. These results suggest that AO is more strongly associated with a neural correlate of inefficient and excessive cognitive and attentional control than RO. Further research is required for determining the utility of TBR for characterizing the heterogeneity within OCD.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.pscychresns.2025.111966DOI Listing

Publication Analysis

Top Keywords

eeg band
12
band power
12
theta/beta ratio
8
severity tbr
8
tbr electrode
8
eeg
5
tbr
5
relationship eeg
4
eeg theta/beta
4
ratio response
4

Similar Publications

Phase slips extracted from derivatives of EEG data provide a deeper insight into the formation of cortical phase transitions.

Front Integr Neurosci

February 2025

Department of Engineering, Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland.

The phase slips are generally extracted from the EEG using Hilbert transforms but could also be extracted from the derivatives of EEG, providing additional information about the formation of cortical phase transitions. We examined this from the 30 s long, 256-channel resting state, eyes open EEG data of a 30-year-old male subject. The phase slip rates, PSR1 from EEG, PSR2 from the first-order derivative of EEG, and PSR3 from the second-order derivative of EEG, respectively, were extracted.

View Article and Find Full Text PDF

Anterior-posterior interactions in the alpha band (8-12 Hz) have been implicated in a variety of functions including perception, attention, and working memory. The underlying neural communication can be flexibly controlled by adjusting phase relations when activities across anterior-posterior regions oscillate at a matched frequency. We thus investigated how alpha oscillation frequencies spontaneously converged along anterior-posterior regions by tracking oscillatory EEG activity while participants rested.

View Article and Find Full Text PDF

An Investigation of Pre-stimulus EEG for Prediction of Driver Reaction Time.

Biomed Phys Eng Express

March 2025

Faculty of Engineering and Computing, Dublin City University, Dublin 9, Dublin, IRELAND.

Driver drowsiness significantly contributes to road accidents worldwide, and timely prediction of driver reaction time is crucial for developing effective advanced driver assistance systems. In this paper, we present an EEG-based prediction framework that investigates the impact of different pre-stimulus time windows, frequency band combinations, and channel groups on driver reaction time estimation using data from a 90-minute sustained attention driving task. Our systematic evaluation using a publicly available dataset of 24 drivers reveals that a 2-second pre-stimulus window yields the lowest prediction error.

View Article and Find Full Text PDF

Brain-computer interfaces (BCIs) based on electroencephalogram (EEG) enable direct interactions between the brain and external environments, with applications in medical rehabilitation, motor substitution, gaming, and entertainment. Traditional methods that model the non-Euclidean characteristics of EEG signals demonstrate robustness and high performance, but they suffer from significant computational costs and are typically restricted to a single BCI paradigm. This article addresses these limitations by utilizing a diffeomorphism from Riemannian manifolds to the Cholesky space, which simplifies the solution process and enables application across multiple BCI paradigms.

View Article and Find Full Text PDF

Neural correlates of fatigue after traumatic brain injury.

Brain Commun

February 2025

Division of Neurorehabilitation, Department of Clinical Neurosciences, University Hospitals of Geneva, Geneva 14 1211, Switzerland.

Fatigue is the main cause of disability after traumatic brain injury and has negative impact on social, physical and cognitive functions, participation in daily activities, and ability to work. Since the neural underpinnings are largely unknown, few causal treatments are currently available. This study therefore aimed to investigate the neural correlates of subjective fatigue after traumatic brain injury, controlling for differences in cognitive performance, motor performance and subjective psychological covariates such as depression, anxiety and apathy.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!