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

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          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.

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          Most cited references66

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          Parallel organization of functionally segregated circuits linking basal ganglia and cortex.

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            Theta oscillations in the hippocampus.

            Theta oscillations represent the "on-line" state of the hippocampus. The extracellular currents underlying theta waves are generated mainly by the entorhinal input, CA3 (Schaffer) collaterals, and voltage-dependent Ca(2+) currents in pyramidal cell dendrites. The rhythm is believed to be critical for temporal coding/decoding of active neuronal ensembles and the modification of synaptic weights. Nevertheless, numerous critical issues regarding both the generation of theta oscillations and their functional significance remain challenges for future research.
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              CoSaMP: Iterative signal recovery from incomplete and inaccurate samples

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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                28 November 2017
                14 November 2017
                14 November 2017
                : 114
                : 48
                : E10465-E10474
                Affiliations
                [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
                Author notes
                2To whom correspondence may be addressed. Email: patrickp@ 123456nmr.mgh.harvard.edu or msh@ 123456nmr.mgh.harvard.edu .

                Edited by Robert Desimone, Massachusetts Institute of Technology, Cambridge, MA, and approved September 18, 2017 (received for review March 31, 2017)

                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.

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

                Author information
                http://orcid.org/0000-0002-9856-006X
                Article
                201705414
                10.1073/pnas.1705414114
                5715738
                29138310
                428a7917-a17d-4600-8dea-466226cb7c30
                Copyright © 2017 the Author(s). Published by PNAS.

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

                History
                Page count
                Pages: 10
                Funding
                Funded by: HHS | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB) 100000070
                Award ID: P41-EB015896
                Funded by: HHS | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB) 100000070
                Award ID: R01-EB009048
                Funded by: HHS | NIH | National Center for Research Resources (NCRR) 100000097
                Award ID: S10-RR031599
                Funded by: HHS | NIH | NIH Office of the Director (OD) 100000052
                Award ID: DP2-OD006454
                Categories
                PNAS Plus
                Biological Sciences
                Neuroscience
                Physical Sciences
                Engineering
                PNAS Plus

                meg,eeg,subcortical structures,source localization,sparsity
                meg, eeg, subcortical structures, source localization, sparsity

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