EEG microstate and functional connectivity analyses of suicide attempt and ideation in depression
Poster No:
452
Submission Type:
Abstract Submission
Authors:
HYEONHO HWANG1, Ji Sun Kim2, Sungkean Kim1
Institutions:
1Hanyang University, Ansan, Gyeonggi-do, 2Soonchunhyang University, Cheonan, Chungcheongnam-do
First Author:
Co-Author(s):
Introduction:
Suicide is a pressing global health crisis, claiming over 720,000 lives annually worldwide. The complex nature of suicidal behavior involving biological, psychological, social, and cultural factors necessitates a comprehensive approach to prevention. Current clinical practices for assessing suicide risk, primarily relying on interviews and self-report questionnaires, have limitations, highlighting the need for objective biological markers. Electroencephalography (EEG), known for its high temporal resolution and accessibility, has emerged as a promising tool for elucidating brain activity patterns associated with suicidal behavior. Traditional EEG analyses may not fully capture the dynamic nature of brain activity related to suicidal behaviors, as they rely on averaged measures over extended periods. This study introduces two analytical approaches: microstate analysis and microstate-based functional connectivity (FC) analysis. Microstate analysis segments the continuous EEG signal into quasi-stable states, while microstate-based FC analysis enables studying connectivity changes associated with specific brain states. This research aimed to identify objective neurobiological markers for suicide risk assessment by comparing patients with suicide attempt (SA) and suicidal ideation (SI) in major depressive disorder (MDD).
Methods:
The study included 130 drug-naïve MDD patients (68 SA, 62 SI). Patients with SA were evaluated within seven days of their attempts, while SI patients had no history of suicide attempts. We recorded resting-state EEG data for five minutes during an eyes-open state. The EEG data underwent preprocessing steps, including down-sampling to 128 Hz, bandpass filtering (1–40 Hz), wavelet transformation, and artifact removal. For microstate analysis, we utilized the MICROSTATELAB toolbox on 40-second artifact-free segments [1]. Individual microstate maps were identified using the Atomize-Agglomerate Hierarchical Clustering algorithm, exploring cluster solutions ranging from four to seven classes. Mean microstate maps were calculated separately for both groups, and grand mean maps were computed across groups. The temporal parameters for each microstate class were extracted, including average duration, frequency of occurrence, and coverage percentage. For microstate-based FC analysis [2], we employed the phase-lag index (PLI) to assess connectivity across channel pairs within theta (4–8 Hz), alpha (8–12 Hz), and beta (12–30 Hz) bands [3]. The PLI was calculated using 50 randomly sampled 2-second epochs for each participant and microstate class. Statistical analyses included Welch's t-tests with false discovery rate correction and correlation analyses between EEG features and psychological measures.
Results:
Microstate analysis revealed marginally higher occurrences of microstates A and B in the SI group. Microstate-based PLI analysis showed significantly higher alpha band connectivity in the SA group, particularly during microstate E (Fig. 1). In the SI group, alpha band connectivity during microstate E was positively correlated with difficulties in emotion regulation (Fig. 2). These findings suggest distinct patterns of brain network organization between SA and SI groups.
Conclusions:
This study identified distinct EEG microstate and microstate-based FC patterns between SI and SA groups in MDD. The SA group showed significantly higher alpha band connectivity during microstate E, while the SI group exhibited marginally higher occurrences of microstates A and B. These neurobiological differences may reflect distinct cognitive and emotional processing patterns associated with different types of suicidal behaviors. Our findings demonstrate the potential of combining microstate and connectivity analyses for understanding suicidal behavior and developing EEG-based biomarkers for objective suicide risk assessment.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
EEG/MEG Modeling and Analysis
Novel Imaging Acquisition Methods:
EEG 2
Keywords:
ADULTS
Affective Disorders
Computing
Data analysis
Electroencephaolography (EEG)
ELECTROPHYSIOLOGY
Emotions
Psychiatric Disorders
Statistical Methods
Systems
1|2Indicates the priority used for review
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[2] Tait, L., & Zhang, J. (2022). MEG cortical microstates: spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses. NeuroImage, 251, 119006.
[3] Stam, C. J., Nolte, G., & Daffertshofer, A. (2007). Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Human brain mapping, 28(11), 1178-1193.