Poster No:
502
Submission Type:
Abstract Submission
Authors:
Hsin-Jung Tsai1, Hsin-Yu Kan1, Shih-Jen Tsai2
Institutions:
1National Yang Ming Chiao Tung University, Taipei, Taiwan, 2Taipei Veterans General Hospital, Taipei, Taiwan
First Author:
Co-Author(s):
Introduction:
Depression is a leading cause of disability, imposing significant healthcare and socioeconomic burdens with an elevated suicide risk. 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 inhibiting 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 from the C4 electrode 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 power of EEG, 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 using the Hamilton Depression Rating Scale-17 (HAMD-17) before and after the intervention by an independent psychiatrist blinded to group allocation. Self-reported depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9) and Beck Depression Inventory (BDI) at baseline and after 4, 8, and 12 neurofeedback sessions. Remission was defined as a post-intervention HAMD-17 score ≤ 7. The response rate was calculated as [(post-intervention - pre-intervention)/pre-intervention; %]. Statistical significance was set at p < 0.025.
Results:
Sixteen patients with mild to moderate depression were randomly assigned to the experimental or control groups. There were no significant group differences in sex, age, age of onset, self-reported depressive symptoms (PHQ-9, p = 0.92; BDI, p = 0.84), or clinician-assessed depression severity (HAMD-17, p > 0.99) at baseline. In the experimental group, self-reported depressive symptoms decreased after four training sessions (PHQ-9, -13.33%; BDI, -17.07%) and showed further improvement at eight sessions (PHQ-9, -20.00%; BDI, -32.14%). After 12 training sessions, BDI scores showed significant reductions with a median response rate of -60.98%. HAMD-17 scores indicated significantly reduced depression severity (median score; pre vs. post = 10 vs. 5; z = -2.25, p = 0.0247) with a response rate of -55.56% and a remission rate of 88.9%. In contrast, the control group showed no significant improvements in self-reported depressive symptoms nor clinician-assessed severity (median score; pre vs. post = 10 vs. 9; z = -0.93, p = 0.35) with a median response rate of -10.0%.
Conclusions:
This active-control, assessor-blind pilot study reinforces our previous findings and further suggest that inhibiting high-frequency cortical activity enhances treatment efficacy, as demonstrated by improved remission and response rates. Neurofeedback targeting beta-band EEG inhibition may serve as an effective adjunctive approach for patients with major depression.
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
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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):
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Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Were any animal research approved by the relevant IACUC or other animal research panel?
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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
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