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Abstract #1294

Investigating Brain Connectomic Alterations in Autism using Reproducibility of Independent Components derived from Resting State fMRI

Mohammed Syed 1 , Zhi Yang 2 , and Gopikrishna Deshpande 3,4

1 Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, United States, 2 Key Laboratory of Behavioral Sciences, Institute of Psychology, Chinese Academy of Sciences, Beijing, China, 3 Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States, 4 Department of Psychology, Auburn University, Auburn, AL, United States

Autism is a heterogeneous spectrum disorder, hence fMRI connectivity metrics derived from the autism group may not be highly reproducible within that group, leading to poor generalizability which in turn leads to lower classification accuracies. We hypothesize that functional brain networks that are most reproducible within autism and healthy control groups separately, but not when the two groups are merged, may possess the ability to distinguish effectively between the groups. We characterize reproducibility of networks using generalized Ranking and Averaging Independent Component Analysis by Reproducibility (gRAICAR) algorithm and provide evidence in support of the above hypothesis.

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