TY - JOUR
T1 - A biophysically constrained brain connectivity model based on stimulation-evoked potentials.
AU - Schmid, William
AU - Danstrom, Isabel A.
AU - Crespo Echevarria, Maria
AU - Adkinson, Joshua
AU - Mattar, Layth
AU - Banks, Garrett P.
AU - Sheth, Sameer A.
AU - Watrous, Andrew J.
AU - Heilbronner, Sarah R.
AU - Bijanki, Kelly R.
AU - Alabastri, Alessandro
AU - Bartoli, Eleonora
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/5
Y1 - 2024/5
N2 - Background: 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.
AB - Background: 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.
KW - 3D conductivity model
KW - Brain connectivity
KW - Intracranial recordings
KW - Pulse-evoked potentials
KW - Single-pulse electrical stimulation
KW - Tractography
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U2 - 10.1016/j.jneumeth.2024.110106
DO - 10.1016/j.jneumeth.2024.110106
M3 - Article
C2 - 38453060
AN - SCOPUS:85188061271
SN - 0165-0270
VL - 405
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
M1 - 110106
ER -