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Abstract #0360

Within- And Between-Subject Reproducibility of Matrix-Based Analysis of Resting-State Functional Connectivity Network

Ying-hui Chou1, Lawrence P. Panych2, Chandlee C. Dickey3, Nan-kuei Chen4

1Fu-Jen Catholic University, Hsin-chung, Taipei, Taiwan; 2Brigham and Womens Hospital and Harvard Medical School, Boston, MA, United States; 3VA Boston Healthcare System and Harvard Medical School, Boston, MA, United States; 4Duke University, Durham, NC, United States

In this study, we assessed the within- and between-subject reproducibility of resting-state functional connectivity measured by a matrix-based analysis (MBA) in six healthy volunteers. The MBA can quantify connectivity strength for the whole brain without a priori model, and can be applied to dissociate clinical populations. Each participant was scanned nine times for more than a one-year period. Our results show that 1) the functional networks measured by the MBA are highly reproducible across nine sessions; and 2) there exists measurable between-subject variance. The MBA-based connectivity mapping should prove useful for monitoring long-term changes in functional networks.