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Decoding the Brain’s Surface to Track Whole-Brain and Interior Brain Activity, Stephen LaConte, PhD
October 6, 2016 @ 11:15 am - 11:40 am
Stephen LaConte, PhD, Associate Professor, VTCRI and Associate Professor, Virginia Tech-Wake Forest School of Biomedical Engineering and Mechanics, College of Engineering —
Current approaches to surface measurements of brain activity such as functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) suffer from poor spatial resolution and an inability to directly observe interior brain regions and distributed brain networks. We have previously shown that activity in distinct regions of the brain can be predicted using multivariate supervised learning, using the brain activity from the whole brain. In this talk, we address the issues of spatial resolution and depth of penetration for methods such as fNIRS algorithmically by adapting our previous work to model regional and distributed network fluctuations from only the surface of the brain. Our results demonstrate the feasibility of surface-based predictive models to decode the activity of deeper brain regions and networks. Thus these algorithmic solutions could enable translating real-time fMRI experiments into fNIRS or other surface-based brain-computer-interfaces.