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Abstract #5379

Pseudo-bootstrap network analysis - an application in functional connectivity fingerprinting

Hu Cheng1, Ao Li1, Andrea Avena-Koenigsberger1, Chunfeng Huang1, and Sharlene Newman1

1Indiana University, Bloomington, IN, United States

As an alternative to template based brain parcellation in functional connectivity analysis, nearly equal-sized random parcellations are applied to individual subjects multiple times to obtain a pseudo-bootstrap sample of the functional network. As one application, the method was applied on the HCP resting state dataset to identify individuals across scan sessions based on the mean functional connectivity. With a parcellation number of 278 and bootstrap sample size of 400, an accuracy rate of ~90% was achieved by simply finding the maximum correlation of mean functional connectivity of pseudo-bootstrap samples between two scan sessions.

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