Abstract #5484

Multivariate Pattern Analysis of fMRI Data using Deep Neural Network

Yi-Cheng Wang¹, Jia-Ren Chang², Chia-Lin Chen¹, Ching-Ju Yang³, Wei-Chi Li³, Jen-Chuen Hsieh^3,4, Li-Fen Chen^3,4,5, and Yong-Sheng Chen²

¹Institute of Biomedical Engineering, National Chiao Tung University, Hsinchu city, Taiwan, ²Department of Computer Science, National Chiao Tung University, Hsinchu city, Taiwan, ³Institute of Brain Science, National Yang-Ming University, Taipei city, Taiwan, ⁴Integrated Brain Research Unit, Department of Medical Research, Taipei Veterans General Hospital, Taipei city, Taiwan, ⁵Institute of Biomedical Informatics, National Yang-Ming University, Taipei city, Taiwan

This paper presents a novel MVPA method based on deep neural networks, which can identify a group of voxels with their pattern of activity capable of differentiating experimental conditions. Through the forward inference procedure, the proposed deep neural network can also be applied to distinguish brain imaging data of different experimental conditions. Our experimental results suggest that deep neural networks are of great potential as an MVPA tool for functional brain mapping.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.