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NCTS Data Sciences Forum
 
10:00 - 12:30, October 27, 2016 (Thursday)
Room 440, Astronomy-Mathematics Building, NTU
(台灣大學天文數學館 440室)
Precision Psychiatry: How Big Neuroimaging Data Can Shape the Future of Psychiatric Medicine
Albert C. Yang (Taipei Veterans General Hospital & National Yang-Ming University)

楊智傑(台北榮民總醫院精神部醫師;國立陽明大學精神學科、腦科副教授)

 
Organizer
Jungkai  Chen (NTU)
Weichung  Wang (NTU)
Hau-Tieng  Wu (University of Toronto)

Abstract

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 (RDoC) 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, such as Human Connectome Project, Autism Brain Imaging Data Exchange, Alzheimer’s disease neuroimaging initiative, or Bipolar & Schizophrenia Network on Intermediate Phenotypes. To quantify the complex brain signal data, an approach that integrates mathematics, physics, and computational neuroscience is required. Nonlinear dynamical approaches to brain signal 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 dynamical complexity measures in both temporal and spatial dimensions as objective brain biomarkers to investigate the classification of neuroimaging data in aging and mental illness.

Anyone who are interesting in relative topics are welcome to join.People who are far away from the NCTS are also welcome to join the forum via Skype. Please contact Ms. Ejan  Chen (02)33668816  ejanchen@ncts.ntu.edu.tw for participation via Skype.

 

 

 

 

 

 

 

 



 

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