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A Gaussian process model of human electrocorticographic data

Lucy L. W. Owen, Andrew C. Heusser, Jeremy R. Manning
doi: https://doi.org/10.1101/121020
Lucy L. W. Owen
1Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA
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Andrew C. Heusser
1Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA
2Akili Interactive, Boston, MA 02110, USA
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Jeremy R. Manning
1Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA
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Abstract

We present a model-based method for inferring full-brain neural activity at millimeter-scale spatial resolutions and millisecond-scale temporal resolutions using standard human intracranial recordings. Our approach assumes that different people’s brains exhibit similar correlational structure, and that activity and correlation patterns vary smoothly over space. One can then ask, for an arbitrary individual’s brain: given recordings from a limited set of locations in that individual’s brain, along with the observed spatial correlations learned from other people’s recordings, how much can be inferred about ongoing activity at other locations throughout that individual’s brain? We show that our approach generalizes across people and tasks, thereby providing a person- and task-general means of inferring high spatiotemporal resolution full-brain neural dynamics from standard low-density intracranial recordings.

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Posted October 12, 2018.
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A Gaussian process model of human electrocorticographic data
Lucy L. W. Owen, Andrew C. Heusser, Jeremy R. Manning
bioRxiv 121020; doi: https://doi.org/10.1101/121020
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A Gaussian process model of human electrocorticographic data
Lucy L. W. Owen, Andrew C. Heusser, Jeremy R. Manning
bioRxiv 121020; doi: https://doi.org/10.1101/121020

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