Predicting the phase distribution during multi-channel transcranial alternating current stimulation in silico and in vivo.

Finite element method Nonhuman primate experiment Phasor analysis Transcranial alternating current stimulation

Journal

Computers in biology and medicine
ISSN: 1879-0534
Titre abrégé: Comput Biol Med
Pays: United States
ID NLM: 1250250

Informations de publication

Date de publication:
20 Sep 2023
Historique:
received: 15 06 2023
revised: 22 08 2023
accepted: 19 09 2023
medline: 29 9 2023
pubmed: 29 9 2023
entrez: 28 9 2023
Statut: aheadofprint

Résumé

Transcranial alternating current stimulation (tACS) is a widely used noninvasive brain stimulation (NIBS) technique to affect neural activity. TACS experiments have been coupled with computational simulations to predict the electromagnetic fields within the brain. However, existing simulations are focused on the magnitude of the field. As the possibility of inducing the phase gradient in the brain using multiple tACS electrodes arises, a simulation framework is necessary to investigate and predict the phase gradient of electric fields during multi-channel tACS. Here, we develop such a framework for phasor simulation using phasor algebra and evaluate its accuracy using in vivo recordings in monkeys. We extract the phase and amplitude of electric fields from intracranial recordings in two monkeys during multi-channel tACS and compare them to those calculated by phasor analysis using finite element models. Our findings demonstrate that simulated phases correspond well to measured phases (r = 0.9). Further, we systematically evaluated the impact of accurate electrode placement on modeling and data agreement. Finally, our framework can predict the amplitude distribution in measurements given calibrated tissues' conductivity. Our validated general framework for simulating multi-phase, multi-electrode tACS provides a streamlined tool for principled planning of multi-channel tACS experiments.

Sections du résumé

BACKGROUND BACKGROUND
Transcranial alternating current stimulation (tACS) is a widely used noninvasive brain stimulation (NIBS) technique to affect neural activity. TACS experiments have been coupled with computational simulations to predict the electromagnetic fields within the brain. However, existing simulations are focused on the magnitude of the field. As the possibility of inducing the phase gradient in the brain using multiple tACS electrodes arises, a simulation framework is necessary to investigate and predict the phase gradient of electric fields during multi-channel tACS.
OBJECTIVE OBJECTIVE
Here, we develop such a framework for phasor simulation using phasor algebra and evaluate its accuracy using in vivo recordings in monkeys.
METHODS METHODS
We extract the phase and amplitude of electric fields from intracranial recordings in two monkeys during multi-channel tACS and compare them to those calculated by phasor analysis using finite element models.
RESULTS RESULTS
Our findings demonstrate that simulated phases correspond well to measured phases (r = 0.9). Further, we systematically evaluated the impact of accurate electrode placement on modeling and data agreement. Finally, our framework can predict the amplitude distribution in measurements given calibrated tissues' conductivity.
CONCLUSIONS CONCLUSIONS
Our validated general framework for simulating multi-phase, multi-electrode tACS provides a streamlined tool for principled planning of multi-channel tACS experiments.

Identifiants

pubmed: 37769460
pii: S0010-4825(23)00981-2
doi: 10.1016/j.compbiomed.2023.107516
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

107516

Subventions

Organisme : NIMH NIH HHS
ID : RF1 MH124909
Pays : United States

Commentaires et corrections

Type : UpdateOf

Informations de copyright

Copyright © 2023. Published by Elsevier Ltd.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no competing interests.

Auteurs

Sangjun Lee (S)

Department of Biomedical Engineering, University of Minnesota, MN, USA. Electronic address: lee03936@umn.edu.

Sina Shirinpour (S)

Department of Biomedical Engineering, University of Minnesota, MN, USA.

Ivan Alekseichuk (I)

Department of Biomedical Engineering, University of Minnesota, MN, USA.

Nipun Perera (N)

Department of Biomedical Engineering, University of Minnesota, MN, USA.

Gary Linn (G)

Translational Neuroscience Lab Division, Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department of Psychiatry NYU Grossman School of Medicine, New York City, NY, USA.

Charles E Schroeder (CE)

Translational Neuroscience Lab Division, Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Departments of Neurological Surgery and Psychiatry, Columbia University College of Physicians and Surgeons, NY, USA.

Arnaud Y Falchier (AY)

Translational Neuroscience Lab Division, Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department of Psychiatry NYU Grossman School of Medicine, New York City, NY, USA.

Alexander Opitz (A)

Department of Biomedical Engineering, University of Minnesota, MN, USA. Electronic address: aopitz@umn.edu.

Classifications MeSH