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[2021-Jun-02] Deciphering Complex Brain Networks with MR Neuroimaging and Deep Learning: Applications in Cognitive and Clinical Neuroscience

Institute of Information Systems and Applications

Speaker:

Dr. Shih-Yen Lin 林奭彥博士

Department of Computer Science, National Yang Ming Chiao Tung University 國立陽明交通大學資訊工程學系博士後研究員

Topic:

Deciphering Complex Brain Networks with MR Neuroimaging and Deep Learning: Applications in Cognitive and Clinical Neuroscience

Date:

13:30-15:00 Wednesday 02-Jun-2021

QR code:

 

Link:

https://meet.google.com/oxa-smvh-dmk

Hosted by:

Prof. Po-Chih Kuo

Abstract

Our cognitions are closely associated with the connections and interactions between brain regions. In recent decades, the development of magnetic resonance imaging (MRI) has enabled in vivo observations of the brain connections, and has given rise to many researches on brain connectivity. Many novel methods for brain network analysis have also emerged in recent years. Graph-theoretical analysis utilizes the knowledge in network science and describes high-level network attributes using various network measures. Graph signal processing aims to generalize conventional signal processing operations to the irregular graph domain. Inspired by the concept of graph signal processing, deep learning on graphs are deep learning models generalized to the irregular data domain and can better account for interregional interactions. However, these novel analysis methods have yet been widely applied to brain network analysis, and various open issues still exist. In this talk we will explore the potentials of the brain network analysis methods in the study of model-based and data-driven brain science. Studies covered will include: 1) graph theoretical analysis for investigating alterations the structural and functional brain network topology in patients with mild cognitive impairment (MCI) and Alzheimer’s Disease (AD); 2) investigation of functional brain network modulations under speech emotion by using graph-theoretical network analysis; and 3) an adaptive graph deep learning model for classification of brain activity.

All faculty and students are welcome to join.

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