Academic News
National Central University (NCU) and Taipei Veterans General Hospital (VGH) in Taiwan report a significant breakthrough in non-invasive brain stimulation for treatment-resistant depression. Supported by long-term funding from Taiwan’s National Science and Technology Council (NSTC) and Ministry of Education, the research represents an important step toward precision and personalized mental health care. The study identified several brainwave biomarkers that are strongly correlated with depressive symptom severity and predictive treatment outcomes. These findings, published in the International Journal of Clinical and Health Psychology, may establish the foundation for leveraging brainwave biomarkers to guide individualized depression treatment.
Repetitive transcranial magnetic stimulation (rTMS) is an FDA- and TFDA-approved treatment for major depressive disorder, using magnetic fields to induce weak electrical currents in the cerebral cortex and thereby modulate abnormal neural activity. However, clinical efficacy often varies significantly among patients due to individual differences. To address this variability, the research team employed Holo-Hilbert Spectral Analysis (HHSA), pioneered by Academician Norden E. Huang (Academia Sinica, Taiwan & U.S. National Academy of Engineering) during his tenure at NCU. HHSA enables the detection of subtle cross-frequency interactions invisible to conventional EEG analyses.
Using this innovative methodology, the NCU research team, led by Prof. Chi-Hung Juan (Director, Cognitive Intelligence and Precision Healthcare Center), Dr. Yi-Chun Tsai, and Prof. Cheng-Ta Li of Taipei Veterans General Hospital, found that high-frequency gamma oscillations, when modulated by slower alpha and beta rhythms, are strongly associated with the severity of depressive symptoms. Patients with lower baseline gamma activity demonstrated greater clinical responsiveness to rTMS, whereas those with elevated gamma activity exhibited attenuated therapeutic benefit. Importantly, these gamma abnormalities were detectable only through HHSA and remain difficult to observe using conventional EEG analysis.
To advance the field of neuropsychiatric treatment, Professors Juan and Li are leading the development of a real-time closed-loop electromagnetic stimulation system that integrates artificial intelligence, deep learning, and advanced computational modeling. The next-generation platform aims dynamically adapt stimulation parameters based on an individual’s brainwave state, with the goal of enhancing therapeutic efficacy for major depressive disorder and envisions a precision and personalized neuropsychiatric medicine.
Full-text: https://www.sciencedirect.com/science/article/pii/S1697260025000511
