Poster No:
502
Submission Type:
Abstract Submission
Authors:
Hsin-Jung Tsai1, Hsin-Yu Kan1, Shih-Jen Tsai2
Institutions:
1Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan, 2Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
First Author:
Hsin-Jung Tsai, Ph.D
Institute of Brain Science, National Yang Ming Chiao Tung University
Taipei, Taiwan
Co-Author(s):
Hsin-Yu Kan, B.S.
Institute of Brain Science, National Yang Ming Chiao Tung University
Taipei, Taiwan
Introduction:
Depression is a leading cause of disability, imposing significant healthcare and socioeconomic burdens. Our previous study identified increased beta-band EEG activity in the central brain region as a predictor of poor antidepressant outcomes using machine learning (Tsai et al., 2023). This study prospectively applied EEG-based neurofeedback with a gamified brain-computer interface to explore whether modulating cortical activity enhances treatment efficacy. Theta-band power, with lower predictive accuracy, served as a control to address the potential placebo effects. We hypothesized that suppressing beta-frequency EEG activity would alleviate self-reported depressive symptoms and reduce clinician-assessed depression severity.
Methods:
Patients diagnosed with major depressive disorder (MDD) and without a history of brain stimulation therapy or psychotherapy within the past six months were enrolled. All patients were right-handed and undergoing antidepressant treatment at the time of the study. Participants were randomly assigned to either the beta-frequency inhibition group (experimental group) or the theta-frequency enhancement group (active control group). Both groups underwent 12 neurofeedback sessions over six weeks, with each session consisting of six 5-minute rounds. Prior to each session, a 5-minute resting-state EEG was recorded to establish baseline scores and training objectives. EEG relative power in the beta-band (15–30 Hz) or theta-band (5–8 Hz), normalized to the total EEG power (5–30 Hz), was calculated for the experimental and control groups, respectively. After signal preprocessing, real-time EEG power was computed every 10 seconds using a sliding window, and feedback was delivered with a 1-second resolution. During neurofeedback training, patients were instructed to move a character (representing real-time EEG power) toward a designated target without receiving prior guidance. Depression severity was assessed by an independent psychiatrist blinded to group allocation, with the Hamilton Depression Rating Scale-17 (HAMD-17) before, after the intervention, and at a 2-month follow-up. Self-reported depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9) and Beck Depression Inventory (BDI) at baseline, after 4, 8, and 12 neurofeedback sessions, and at follow-up. Remission was defined as a post-intervention HAMD-17 score ≤ 7.
Results:
Twenty patients with MDD were randomized to experimental (n = 10) or control (n = 10) groups. At baseline, no significant group differences were observed in demographics, duration of illness, or depression severity (all p > 0.05) were noted. Following 12 sessions, the experimental group demonstrated a significant reduction in depression severity (p = 0.03), with response and remission rates of 27.9% and 80%, respectively. At 2-month follow-up, response and remission rates further increased to 37.5% and 90%, compared to the post-intervention. The control group also showed a significant improvement (p = 0.04), with response and remission rates of 26% and 50%. However, a worsening of depression severity was noted at follow-up, with a response rate of -39.31%. Moreover, a significant time effect was observed in the experimental group for PHQ-9 (p = 0.01) and BDI (p = 0.03), but not in the control group. Post-hoc analysis showed initial response rates of 3.4% (PHQ-9) and 16.1% (BDI) in the experimental group, which increased to 30.9% and 29.9% after eight sessions. After intervention, cumulative response rates reached 27.30% for PHQ-9 and 44.66% for BDI, with a slight decline at follow-up.
Conclusions:
This active-control, assessor-blind study demonstrated the potential efficacy of suppressing high-frequency cortical activity in MDD. Specifically, neurofeedback targeting right-sided hyperactivation in the depressive brain appears to be a promising adjunct intervention to antidepressant treatment.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis
Motor Behavior:
Brain Machine Interface
Novel Imaging Acquisition Methods:
EEG 2
Keywords:
Affective Disorders
Electroencephaolography (EEG)
Emotions
Plasticity
Psychiatric Disorders
1|2Indicates the priority used for review
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
I accept
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information.
Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
I do not want to participate in the reproducibility challenge.
Please indicate below if your study was a "resting state" or "task-activation” study.
Task-activation
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
EEG/ERP
Behavior
Provide references using APA citation style.
Tsai, H.-J., Yang, W.-C., Tsai, S.-J., Lin, C.-H., & Yang, A. C. (2023). Right-side frontal-central cortical hyperactivation before the treatment predicts outcomes of antidepressant and electroconvulsive therapy responsivity in major depressive disorder. Journal of Psychiatric Research, 161, 377–385. https://doi.org/10.1016/j.jpsychires.2023.03.023
No