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Abstract #2101

Automated Subject-Specific Seed Optimization Improves Dectection of Resting-state fMRI connectivity

KISHORE VAKAMUDI 1,2 , ELENA ACKLEY 2 , and STEFAN POSSE 1,2

1 DEPARTMENT OF PHYSICS AND ASTRONOMY, UNIVERSITY OF NEW MEXICO, ALBUQUERQUE, NEW MEXICO, United States, 2 DEPARTMENT OF NEUROLOGY, UNIVERSITY OF NEW MEXICO, ALBUQUERQUE, NEW MEXICO, United States

Seed-based connectivity analysis (SCA) is widely used to study functional connectivity. However, it suffers from variability inherent in investigator-specific and subject-specific seed selection dependencies. The current Automated Subject-Specific Seed Optimization (ASSSO) method uses an iterative brain atlas based approach identifies the optimal seed locations to maximize the detected RSN connectivity in individual subjects. This seed-selection will maximize the sensitivity for detecting RSN dynamics at short and long time scales across the entire brain in real-time. This methodology is expected to have important applications in presurgical mapping.

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