Title of Paper:Gradient hyperalignment for multi-subject fMRI data alignment

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Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院

Journal:Lect. Notes Comput. Sci.

Abstract:Multi-subject fMRI data analysis is an interesting and challenging problem in human brain decoding studies. The inherent anatomical and functional variability across subjects make it necessary to do both anatomical and functional alignment before classification analysis. Besides, when it comes to big data, time complexity becomes a problem that cannot be ignored. This paper proposes Gradient Hyperalignment (Gradient-HA) as a gradient-based functional alignment method that is suitable for multi-subject fMRI datasets with large amounts of samples and voxels. The advantage of Gradient-HA is that it can solve independence and high dimension problems by using Independent Component Analysis (ICA) and Stochastic Gradient Ascent (SGA). Validation using multi-classification tasks on big data demonstrates that Gradient-HA method has less time complexity and better or comparable performance compared with other state-of-the-art functional alignment methods. © Springer Nature Switzerland AG 2018.

ISSN No.:0302-9743

Translation or Not:no

Date of Publication:2018-01-01

Co-author:Xu, Tonglin,zdq

Correspondence Author:YOUSEFNEZHAD MUHAMMAD