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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