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Abstract #1244

Estimation of stable whole-brain effective-connectivity characterization of mental disorders

Lipeng Ning1,2 and Yogesh Rathi1,2
1Brigham and Women's Hospital, Boston, MA, United States, 2Harvard Medical School, Boston, MA, United States

We propose an algorithm to estimate whole-brain effective connectivity measures by integrating structural connectivity matrix between brain regions and resting-state functional MRI data. Our algorithm first uses the Lyapunov inequality from control theory to ensure that the estimated whole-brain dynamic system is stable and physically meaningful. Then, the effective connectivity measure is characterized by a novel conditional causality measure. We applied the proposed algorithm to a public dataset which consisted of healthy controls (n=94), patients with schizophrenia (n=45), bipolar (n=44) and ADHD (n=37). Our results show that the proposed approach provides reliable estimation brain-network features of these brain disorders.

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