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Abstract #2692

An Efficient FMRI Data Reduction Strategy Using Neighborhood Preserving Embedding Algorithm

Wei Zhao1, Huanjie Li1, Yunge Zhang1, Blaise B. Frederick 2, and Fengyu Cong1
1Biomedical Engineering, Dalian University of Technology, Dalian, China, 2Department of Psychiatry, Harvard Medical School, Boston, MA, United States

In neuroscience research, the group analysis using fMRI data for studying functional brain networks/connectivity in brain faces the challenge about information loss during fMRI data dimensionality reduction for increasing dimensionality. Proposed adapted the NPE (Neighborhood Preserving Embedding) stratagem on fMRI datasets, is an effective data reduction method that shows superior performance on efficient data reduction and sufficient information preservation. Our proposed method can strengthen useful group-sharing information and can avoid the limitation of selecting components based on variance of eigenvectors. Therefore, it has better performance on individual and group level outcomes, as well as improvements on the reliability and reproducibility.

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