| 活動說明 |
講者：楊智傑 副教授
陽明大學 腦科學研究所
講題：
Precision Psychiatry:Search for Dynamical Brain Marker for Neuropsychiatric Diseases
摘要：
Despite substantial efforts, the causes of most psychiatric disorders remain unclear; even categorizing such disorders precisely has been difficult. The diagnostic systems in psychiatry have mostly relied on descriptive phenomenology that does not fully consider the heterogeneous symptoms or their biological mechanisms, etiology, and genotypes. Recent approaches to psychiatric classification such as Research Domain Criteria have moved toward characterization of biomarkers that cut across symptom-based diagnoses but map on to translational domains from cellular to circuitry and behavioral levels. Increasing amount of neuroimaging data has been established in recent years to seek for understanding the complex brain functions in both healthy and pathological mental conditions. To quantify the complex brain signal data, an approach that integrates mathematics, physics, and computational neuroscience is required. The analysis of neuroimaging data may have the potential to develop useful markers to extract fundamental features from large nonlinear, spatio-temporal neuroimaging data at multiple levels. Furthermore, one of the major challenges of brain imaging and neuroscience is the classification of human brain data. Recently, deep learning or related neural-network methods are breaking records of the classification accuracy in the areas of speech, signal, image, video and text mining and recognition. Therefore, this talk will introduce how brain signal analysis in neuroimaging could help to understand the pathophysiology involved in aging and mental illness and how machine learning could help to validate the use of neuroimaging markers to investigate the classification of mental illness.
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