Abstract #0909

A unified ICA framework for identifying neuro-markers in functional connectivity among multiple different brain disorders

Yuhui Du^1,2, Zening Fu¹, Dongdong Lin¹, Mustafa Salman^1,3, Md Abdur Rahaman^1,3, Anees Abrol^1,3, Jing Sui^1,4, Shuang Gao⁴, Elizabeth A. Osuch^5,6, and Vince D. Calhoun^1,3

¹The Mind Research Network, Albuquerque, NM, United States, ²School of Computer and Information Technology, Shanxi University, Taiyuan, China, ³Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA, Albuquerque, NM, United States, ⁴Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, ⁵Lawson Health Research Institute, London Health Sciences Centre, London, ON, Canada, ⁶Department of Psychiatry, University of Western Ontario Schulich School of Medicine and Dentistry, London, ON, Canada

Functional network from ICA using fMRI data has been applied to identify biomarkers of brain disorders. However, the networks from ICA might be slightly different, making the comparison of results across different studies/diseases difficult. We propose a data-driven framework to estimate functional network maps and their inter-connectivity for linking neuromarkers among different disorders and studies. Our method is capable of computing functional networks which are optimized for independence based on each coming individual-subject data, and remaining their correspondence across different subjects by using unbiased templates. The results show this approach is an effective method for studying and classifying multiple-disorders.

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