Connectome-based neurofeedback: A pilot study to improve sustained attention
Dustin Scheinost,
Tiffany W. Hsu,
Emily W. Avery,
Michelle Hampson,
R. Todd Constable,
Marvin M. Chun,
Monica D. Rosenberg
Affiliations
Dustin Scheinost
Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Child Study Center, Yale School of Medicine, New Haven, CT, USA; Department of Statistics and Data Science, Yale University, New Haven, CT, USA; Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA; Corresponding author. Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
Tiffany W. Hsu
Department of Psychology, Stanford University, Stanford, CA, USA
Emily W. Avery
Department of Psychology, Yale University, New Haven, CT, USA
Michelle Hampson
Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Child Study Center, Yale School of Medicine, New Haven, CT, USA; Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
R. Todd Constable
Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA; Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
Marvin M. Chun
Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA; Department of Psychology, Yale University, New Haven, CT, USA; Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA
Monica D. Rosenberg
Department of Psychology, Yale University, New Haven, CT, USA; Department of Psychology, University of Chicago, Chicago, IL, USA; Corresponding author. Department of Psychology, University of Chicago, Chicago, IL, USA.
Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback is a non-invasive, non-pharmacological therapeutic tool that may be useful for training behavior and alleviating clinical symptoms. Although previous work has used rt-fMRI to target brain activity in or functional connectivity between a small number of brain regions, there is growing evidence that symptoms and behavior emerge from interactions between a number of distinct brain areas. Here, we propose a new method for rt-fMRI, connectome-based neurofeedback, in which intermittent feedback is based on the strength of complex functional networks spanning hundreds of regions and thousands of functional connections. We first demonstrate the technical feasibility of calculating whole-brain functional connectivity in real-time and provide resources for implementing connectome-based neurofeedback. We next show that this approach can be used to provide accurate feedback about the strength of a previously defined connectome-based model of sustained attention, the saCPM, during task performance. Although, in our initial pilot sample, neurofeedback based on saCPM strength did not improve performance on out-of-scanner attention tasks, future work characterizing effects of network target, training duration, and amount of feedback on the efficacy of rt-fMRI can inform experimental or clinical trial designs.
