Abstract #1692

Resting-state Functional Network Connectivity Pattern as a Cognitive Marker for Task Performance

Hua Xie¹, Javier Gonzalez-Castillo², Eswar Damaraju^3,4, Peter Bandettini^2,5, Vince Calhoun^3,4, and Sunanda Mitra¹

¹Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, United States, ²Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States, ³The Mind Research Network, Albuquerque, NM, United States, ⁴Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States, ⁵Functional MRI Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States

Attentional lapses have been shown to be associated with an altered connectivity and activation pattern of the default-mode network. To further our understanding of the relationship between resting-state connectivity pattern and task performance, we analyzed a multitask dataset including four mental tasks (rest, memory, video, and math). We computed whole-brain connectivity patterns using all volumes during rest (rs-FNC), and the dynamic functional network connectivity (dFNC) patterns during tasks with a sliding window method. We compared similarity between the rs-FNC pattern and dFNCs, which was correlated to the task performance and thus might be used as a cognitive biomarker.

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