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A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
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A biophysically constrained brain connectivity model based on stimulation-evoked potentials.

William Schmid1, Isabel A Danstrom2, Maria Crespo Echevarria2

  • 1Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston, TX 77005, USA.

Journal of Neuroscience Methods
|March 7, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method using single-pulse electrical stimulation (SPES) and topology optimization to map brain connectivity. The approach successfully models neural pathways from electrical data, offering insights beyond traditional imaging methods.

Keywords:
3D conductivity modelBrain connectivityIntracranial recordingsPulse-evoked potentialsSingle-pulse electrical stimulationTractography

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Area of Science:

  • Neuroscience
  • Computational modeling
  • Epilepsy research

Background:

  • Single-pulse electrical stimulation (SPES) is used to map functional effective connectivity in epilepsy patients.
  • Current methods integrating structural connectivity with SPES data have limitations.
  • Advanced modeling is needed to improve understanding of SPES-derived connectivity.

Purpose of the Study:

  • To develop and validate a novel topology optimization method for inferring neural pathways from SPES data.
  • To model 3D electrical conductivity using early response components (C1) from pulse-evoked potentials (PEPs).
  • To compare PEP-derived connectivity with structural connectivity (tractography) and anatomical features.

Main Methods:

  • Utilized intracranial electrophysiology data from stereo-electroencephalography (sEEG).
  • Employed an automated detection method for C1 components in PEPs.
  • Applied a novel topology optimization method to model 3D electrical conductivity and infer neural pathways.

Main Results:

  • The optimization model accurately resolved conductivity paths with low error.
  • High error in specific electrode contacts correlated with anatomical complexities.
  • The C1 component showed strong correlation with other PEP features and weak correlation with tractography.

Conclusions:

  • Informing topology optimization with human intracranial SPES data enables 3D conductivity mapping of functional neural ensembles.
  • PEP-estimated effective connectivity is correlated with, yet distinct from, structural connectivity.
  • Modeled conductivity can resolve neural pathways without relying on anatomical priors.