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.