Sparsity enables estimation of both subcortical and cortical activity from MEG and EEG
Edited by Robert Desimone, Massachusetts Institute of Technology, Cambridge, MA, and approved September 18, 2017 (received for review March 31, 2017)
Significance
Subcortical structures play a critical role in brain functions such as sensory perception, memory, emotion, and consciousness. There are limited options for assessing neuronal dynamics within subcortical structures in humans. Magnetoencephalography and electroencephalography can measure electromagnetic fields generated by subcortical activity. But localizing the sources of these fields is very difficult, because the fields generated by subcortical structures are small and cannot be distinguished from distributed cortical activity. We show that cortical and subcortical fields can be distinguished if the cortical sources are sparse. We then describe an algorithm that uses sparsity in a hierarchical fashion to jointly localize cortical and subcortical sources. Our work offers alternative perspectives and tools for assessing subcortical brain dynamics in humans.
Abstract
Subcortical structures play a critical role in brain function. However, options for assessing electrophysiological activity in these structures are limited. Electromagnetic fields generated by neuronal activity in subcortical structures can be recorded noninvasively, using magnetoencephalography (MEG) and electroencephalography (EEG). However, these subcortical signals are much weaker than those generated by cortical activity. In addition, we show here that it is difficult to resolve subcortical sources because distributed cortical activity can explain the MEG and EEG patterns generated by deep sources. We then demonstrate that if the cortical activity is spatially sparse, both cortical and subcortical sources can be resolved with M/EEG. Building on this insight, we develop a hierarchical sparse inverse solution for M/EEG. We assess the performance of this algorithm on realistic simulations and auditory evoked response data, and show that thalamic and brainstem sources can be correctly estimated in the presence of cortical activity. Our work provides alternative perspectives and tools for characterizing electrophysiological activity in subcortical structures in the human brain.
Acknowledgments
We acknowledge data collection assistance from Samantha Huang, Stephanie Rossi, and Tommi Raij; helpful discussions on source space construction with Koen Van Leemput, Imam Aganj, Doug Greve, and Bruce Fischl; and helpful discussions on canonical correlations and sparsity with Demba Ba and Emery N. Brown. This work was supported by NIH Grants P41-EB015896, 5R01EB022889, and 5R01-EB009048, NIH Grant 1S10RR031599-01 (to M.S.H.), and NIH Grant DP2-OD006454 (to P.L.P.).
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Copyright © 2017 the Author(s). Published by PNAS. This is an open access article distributed under the PNAS license.
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Published online: November 14, 2017
Published in issue: November 28, 2017
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Acknowledgments
We acknowledge data collection assistance from Samantha Huang, Stephanie Rossi, and Tommi Raij; helpful discussions on source space construction with Koen Van Leemput, Imam Aganj, Doug Greve, and Bruce Fischl; and helpful discussions on canonical correlations and sparsity with Demba Ba and Emery N. Brown. This work was supported by NIH Grants P41-EB015896, 5R01EB022889, and 5R01-EB009048, NIH Grant 1S10RR031599-01 (to M.S.H.), and NIH Grant DP2-OD006454 (to P.L.P.).
Notes
This article is a PNAS Direct Submission.
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The authors declare no conflict of interest.
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