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

Research output: Contribution to journalArticle


  • Pavitra Krishnaswamy
  • Gabriel Obregon-Henao
  • Jyrki Ahveninen
  • Sheraz Khan
  • Behtash Babadi
  • Juan Eugenio Iglesias
  • Matti S. Hämäläinen
  • Patrick L. Purdon

Research units

  • Harvard University
  • Massachusetts Institute of Technology
  • Agency for Science, Technology and Research
  • University of Maryland, College Park
  • Athinoula A. Martinos Center for Biomedical Imaging
  • Karolinska Institutet


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.


Original languageEnglish
Pages (from-to)E10465-E10474
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number48
Publication statusPublished - 28 Nov 2017
MoE publication typeA1 Journal article-refereed

    Research areas

  • EEG, MEG, Source localization, Sparsity, Subcortical structures

ID: 16605081