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Sparsity enables estimation of both subcortical and cortical activity from MEG and EEG

  1. Patrick L. Purdond,e,1,2
  1. aAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129;
  2. bHarvard–Massachusetts Institute of Technology Division of Health Sciences and Technology, Cambridge, MA 02139;
  3. cInstitute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore 138632, Singapore;
  4. dDepartment of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114;
  5. eHarvard Medical School, Boston, MA 02115;
  6. fDepartment of Neurology, Massachusetts General Hospital, Charlestown, MA 02129;
  7. gDepartment of Electrical & Computer Engineering, University of Maryland, College Park, MD 20742;
  8. hDepartment of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo 02150, Finland;
  9. iThe Swedish National Facility for Magnetoencephalography (NatMEG), Department of Clinical Neuroscience, Karolinska Institute, Stockholm 17177, Sweden
  1. 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.

Footnotes

  • ?1M.S.H. and P.L.P. contributed equally to this work.

  • ?2To whom correspondence may be addressed. Email: patrickp{at}nmr.mgh.harvard.edu or msh{at}nmr.mgh.harvard.edu.
  • Author contributions: P.K., B.B., M.S.H., and P.L.P. designed research; P.K., G.O.-H., J.A., S.K., and J.E.I. performed research; P.K., G.O.-H., J.A., S.K., B.B., M.S.H., and P.L.P. analyzed data; and P.K., M.S.H, and P.L.P. wrote the paper.

  • The authors declare no conflict of interest.

  • This article is a PNAS Direct Submission.

  • This article contains supporting information online at www.danielhellerman.com/lookup/suppl/doi:10.1073/pnas.1705414114/-/DCSupplemental.

This is an open access article distributed under the PNAS license.

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