Distributed source modeling of intracranial stereoelectro-encephalographic measurements

Fa Hsuan Lin, Hsin Ju Lee, Jyrki Ahveninen, Iiro P. Jääskeläinen, Hsiang Yu Yu, Cheng Chia Lee, Chien Chen Chou, Wen Jui Kuo*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)
226 Downloads (Pure)

Abstract

Intracranial stereoelectroencephalography (sEEG) provides unsurpassed sensitivity and specificity for human neurophysiology. However, functional mapping of brain functions has been limited because the implantations have sparse coverage and differ greatly across individuals. Here, we developed a distributed, anatomically realistic sEEG source-modeling approach for within- and between-subject analyses. In addition to intracranial event-related potentials (iERP), we estimated the sources of high broadband gamma activity (HBBG), a putative correlate of local neural firing. Our novel approach accounted for a significant portion of the variance of the sEEG measurements in leave-one-out cross-validation. After logarithmic transformations, the sensitivity and signal-to-noise ratio were linearly inversely related to the minimal distance between the brain location and electrode contacts (slope≈−3.6). The signa-to-noise ratio and sensitivity in the thalamus and brain stem were comparable to those locations at the vicinity of electrode contact implantation. The HGGB source estimates were remarkably consistent with analyses of intracranial-contact data. In conclusion, distributed sEEG source modeling provides a powerful neuroimaging tool, which facilitates anatomically-normalized functional mapping of human brain using both iERP and HBBG data.

Original languageEnglish
Article number117746
Number of pages15
JournalNeuroImage
Volume230
DOIs
Publication statusPublished - 15 Apr 2021
MoE publication typeA1 Journal article-refereed

Funding

This study was supported by Natural Sciences and Engineering Research Council of Canada ( RGPIN-2020-05927 ), Ministry of Science and Technology, Taiwan ( 103-2628-B-002-002-MY3 , 105-2221-E-002-104 , 108-2410-H-010 -006 -MY2 , 109-2314-B-075-053 ), National Health Research Institutes ( NHRI-EX108-10727EI , NHRI-EX109-10905NI ), the Academy of Finland (project numbers 138145 and 276643 to IPJ and 298131 to FHL), aivoAALTO project funding of the Aalto University, National Institute on Deafness and other Communication Disorders ( R01DC016765 supported JA, FHL, WJK; R01DC017991 supported JA, IPJ; R01DC016915 supported JA) and Russian Federation Government grant ag. 075-15-2019-1930 to I.P.J.

Keywords

  • Brainstem
  • Cross-validation
  • Dynamic statistical parametric maps
  • Invasive
  • L2-norm
  • MNE
  • Thalamus

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