Meeting Banner
Abstract #3287

Support Vector Machine Classification of Spontaneous Cognition Using Whole-Brain Resting-State Functional Connectivity

Ying-Hui Chou1, Pooja Gaur2, Carol P. Weingarten1, Mei-Lan Chu1, David Madden1, Allen W. Song1, Nan-Kuei Chen3

1Duke University Medical Center, Durham, NC, United States; 2Vanderbilt University, Nashville, TN, United States; 3Duke University, Durham, NC, United States

In this study, we demonstrated that the behavior-based connectivity analysis and support vector machine methods can be used to decode the whole-brain resting-state functional connectivity patterns and classify individuals who reported inner language as the dominant mental activity during resting-state fMRI scan from those who did not with a sensitivity of 0.88 and a specificity of 0.9. Our findings can lead to a better understanding of variations in resting-state fMRI signals and their dependence on the spontaneous cognition/mind wandering.