Precision · Data · Psychiatry
We develop novel techniques for identifying objective biomarkers of psychiatric disorders, and for understanding the pathophysiology of mental illness.
School of Medicine and Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University · Taipei Veterans General Hospital
Research
Genome-wide and nationwide-cohort studies linking genetic variation, environmental exposure and health-service data to psychiatric risk across the Taiwanese population.
Multiscale entropy, empirical mode decomposition and information-based similarity applied to physiological time series — the methodological core the laboratory was built on.
Structural, functional and diffusion MRI of psychiatric illness, including brain-age modelling of progressive change across the course of schizophrenia and mood disorders.
Cardiopulmonary coupling, sleep-stage instability and wearable-derived measures of sleep quality in psychiatric and neurodegenerative disease.
Heart-rate dynamics as a window on autonomic regulation — from complexity loss in ageing to autonomic signatures of psychiatric illness.
Interpretable deep learning on brain images and physiological signals, and the digital-medicine infrastructure that turns those models into usable clinical research tools.
Deep brain stimulation and repetitive transcranial magnetic stimulation as precision interventions — the laboratory’s newest direction, currently supported by its largest grant.
Selected recent work
A review mapping how digital medicine — wearables, AI and big data — is reshaping neuropsychiatry and enabling an emerging framework of precision psychiatry.
HHSA revealed two dominant cross-frequency interactions within the alpha-carrier band (8-16 Hz): one modulated by 3-6-Hz (high-delta to theta) and another by 1-2-Hz (low-delta) oscillations, indicating layered modulation under anesthesia.
Our findings suggested active cognitive rehabilitation to patients with schizophrenia throughout the course of illness may be warranted.
Our findings elucidate the complex relationships between IR surrogate markers, genetic predisposition, and disease phenotypes in the Taiwanese population, contributing valuable insights to the field of metabolic research.
