首页    期刊浏览 2025年06月30日 星期一
登录注册

文章基本信息

  • 标题:Sparsity enables estimation of both subcortical and cortical activity from MEG and EEG
  • 本地全文:下载
  • 作者:Pavitra Krishnaswamy ; Gabriel Obregon-Henao ; Jyrki Ahveninen
  • 期刊名称:Proceedings of the National Academy of Sciences
  • 印刷版ISSN:0027-8424
  • 电子版ISSN:1091-6490
  • 出版年度:2017
  • 卷号:114
  • 期号:48
  • 页码:E10465-E10474
  • DOI:10.1073/pnas.1705414114
  • 语种:English
  • 出版社:The National Academy of Sciences of the United States of America
  • 摘要: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.
  • 关键词:MEG ; EEG ; subcortical structures ; source localization ; sparsity
国家哲学社会科学文献中心版权所有