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Abstract #3144

Optimizing connectome-based real-time neurofeedback for improved attentional control

Xiangrui Li1, Oyetunde Gbadeyan 1, and Ruchika Shaurya Prakash1
1Department of Psychology and Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, OH, United States

In this proof-of-concept study, we conducted an optimization assessment of predictive models for use with real-time functional magnetic resonance imaging. Here, we utilized two existing connectome-based models of sustained attention and mind-wandering derived in independent datasets, with each model comprising connections predictive of positive or negative associations with target behavior. Both models showed significant networks strengths across the blocks of the sustained attention task, with the combined model showing significant network strengths and capturing vigilance decrements. Our results suggest that our two models are representative of high and low attentional states, thus making them appropriate targets for neurofeedback.

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