Currently, extensive research is ongoing to perform classification between healthy controls (HC) and patients by extracting features from resting state fMRI based dynamic connectivity states where these states are typically identified by applying different clustering algorithm. However, for classification purposes, the information captured by all dynamic states may not be significant. In this work, we propose a brute force (BF) approach where we consider a subset of these states to perform classification. Our results indicate that in most of the cases, there exists a subset of states which provides better accuracy instead of utilizing information from all of the states.
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