Genetic algorithm-based fMRI group-constrained independent component analysis

Zishuo Wang; Yuhu Shi

doi:10.1117/12.3032794

5 July 2024 Genetic algorithm-based fMRI group-constrained independent component analysis

Zishuo Wang, Yuhu Shi

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 1318436 (2024) https://doi.org/10.1117/12.3032794
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

In this paper, based on the multi-objective optimization framework, we use genetic algorithms to solve the multi-objective GCICA model to explore the commonality of group functional connectivity among group subjects (multi-subjects), and propose a group analysis method named GAGCICA, which is very effective in solving the deficiencies of the previous group analysis methods while maintaining the superior group functional network detection capability. The experimental results show the superior performance of brain network retesting reproduction at the group level obtained from the modal resting state data. It is demonstrated that the genetic algorithm has better signal recovery performance in solving the multi-objective group ICA problem and better reflects the commonality of subjects in the group.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zishuo Wang and Yuhu Shi "Genetic algorithm-based fMRI group-constrained independent component analysis", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 1318436 (5 July 2024); https://doi.org/10.1117/12.3032794

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