A biophysically constrained brain connectivity model based on stimulation-evoked potentials.

Simple item page
dc.citation.articleNumber110106
dc.citation.journalTitleJournal of Neuroscience Methods
dc.citation.volumeNumber405
dc.contributor.authorSchmid, William
dc.contributor.authorDanstrom, Isabel A.
dc.contributor.authorCrespo Echevarria, Maria
dc.contributor.authorAdkinson, Joshua
dc.contributor.authorMattar, Layth
dc.contributor.authorBanks, Garrett P.
dc.contributor.authorSheth, Sameer A.
dc.contributor.authorWatrous, Andrew J.
dc.contributor.authorHeilbronner, Sarah R.
dc.contributor.authorBijanki, Kelly R.
dc.contributor.authorAlabastri, Alessandro
dc.contributor.authorBartoli, Eleonora
dc.date.accessioned2024-08-02T13:32:06Z
dc.date.available2024-08-02T13:32:06Z
dc.date.issued2024
dc.description.abstractBackground Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies. New method Using intracranial electrophysiology data recorded from a single patient undergoing stereo-electroencephalography (sEEG) evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D electrical conductivity to infer neural pathways from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features. Results The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlated with additional PEP features and displayed stable, weak correlations with tractography measures. Comparison with existing method Existing methods for estimating neural signal pathways are imaging-based and thus rely on anatomical inferences. Conclusions These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.
dc.identifier.citationSchmid, W., Danstrom, I. A., Crespo Echevarria, M., Adkinson, J., Mattar, L., Banks, G. P., Sheth, S. A., Watrous, A. J., Heilbronner, S. R., Bijanki, K. R., Alabastri, A., & Bartoli, E. (2024). A biophysically constrained brain connectivity model based on stimulation-evoked potentials. Journal of Neuroscience Methods, 405, 110106. https://doi.org/10.1016/j.jneumeth.2024.110106
dc.identifier.digital1-s20-S0165027024000517-main
dc.identifier.doihttps://doi.org/10.1016/j.jneumeth.2024.110106
dc.identifier.urihttps://hdl.handle.net/1911/117549
dc.language.isoeng
dc.publisherElsevier
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution-NonCommercial (CC BY-NC) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.titleA biophysically constrained brain connectivity model based on stimulation-evoked potentials.
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
1-s20-S0165027024000517-main.pdf
Size:
17.62 MB
Format:
Adobe Portable Document Format
Download
Collections
Faculty Publications