A neural mass model to predict electrical stimulation evoked responses in human and non-human primate brain

Ishita Basu, Britni Crocker, Kara Farnes, Madeline M. Robertson, Angelique C. Paulk, Deborah I. Vallejo, Darin D. Dougherty, Sydney S. Cash, Emad N. Eskandar, Mark M. Kramer, Alik S. Widge

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

OBJECTIVE: Deep brain stimulation (DBS) is a valuable tool for ameliorating drug resistant pathologies such as movement disorders and epilepsy. DBS is also being considered for complex neuro-psychiatric disorders, which are characterized by high variability in symptoms and slow responses that hinder DBS setting optimization. The objective of this work was to develop an in silico platform to examine the effects of electrical stimulation in regions neighboring a stimulated brain region. APPROACH: We used the Jansen-Rit neural mass model of single and coupled nodes to simulate the response to a train of electrical current pulses at different frequencies (10-160 Hz) of the local field potential recorded in the amygdala and cortical structures in human subjects and a non-human primate. RESULTS: We found that using a single node model, the evoked responses could be accurately modeled following a narrow range of stimulation frequencies. Including a second coupled node increased the range of stimulation frequencies whose evoked responses could be efficiently modeled. Furthermore, in a chronic recording from a non-human primate, features of the in vivo evoked response remained consistent for several weeks, suggesting that model re-parameterization for chronic stimulation protocols would be infrequent. SIGNIFICANCE: Using a model of neural population activity, we reproduced the evoked response to cortical and subcortical stimulation in human and non-human primate. This modeling framework provides an environment to explore, safely and rapidly, a wide range of stimulation settings not possible in human brain stimulation studies. The model can be trained on a limited dataset of stimulation responses to develop an optimal stimulation strategy for an individual patient.

Original languageEnglish (US)
Number of pages1
JournalJournal of Neural Engineering
Volume15
Issue number6
DOIs
StatePublished - Dec 1 2018

Fingerprint

Deep Brain Stimulation
Primates
Electric Stimulation
Brain
Movement Disorders
Computer Simulation
Psychiatry
Epilepsy
Pathology
Parameterization
Pharmaceutical Preparations
Population

ASJC Scopus subject areas

  • Biomedical Engineering
  • Cellular and Molecular Neuroscience

Cite this

Basu, I., Crocker, B., Farnes, K., Robertson, M. M., Paulk, A. C., Vallejo, D. I., ... Widge, A. S. (2018). A neural mass model to predict electrical stimulation evoked responses in human and non-human primate brain. Journal of Neural Engineering, 15(6). https://doi.org/10.1088/1741-2552/aae136

A neural mass model to predict electrical stimulation evoked responses in human and non-human primate brain. / Basu, Ishita; Crocker, Britni; Farnes, Kara; Robertson, Madeline M.; Paulk, Angelique C.; Vallejo, Deborah I.; Dougherty, Darin D.; Cash, Sydney S.; Eskandar, Emad N.; Kramer, Mark M.; Widge, Alik S.

In: Journal of Neural Engineering, Vol. 15, No. 6, 01.12.2018.

Research output: Contribution to journalArticle

Basu, I, Crocker, B, Farnes, K, Robertson, MM, Paulk, AC, Vallejo, DI, Dougherty, DD, Cash, SS, Eskandar, EN, Kramer, MM & Widge, AS 2018, 'A neural mass model to predict electrical stimulation evoked responses in human and non-human primate brain', Journal of Neural Engineering, vol. 15, no. 6. https://doi.org/10.1088/1741-2552/aae136
Basu, Ishita ; Crocker, Britni ; Farnes, Kara ; Robertson, Madeline M. ; Paulk, Angelique C. ; Vallejo, Deborah I. ; Dougherty, Darin D. ; Cash, Sydney S. ; Eskandar, Emad N. ; Kramer, Mark M. ; Widge, Alik S. / A neural mass model to predict electrical stimulation evoked responses in human and non-human primate brain. In: Journal of Neural Engineering. 2018 ; Vol. 15, No. 6.
@article{54e6f46897a94d7c9131ac8562b560f1,
title = "A neural mass model to predict electrical stimulation evoked responses in human and non-human primate brain",
abstract = "OBJECTIVE: Deep brain stimulation (DBS) is a valuable tool for ameliorating drug resistant pathologies such as movement disorders and epilepsy. DBS is also being considered for complex neuro-psychiatric disorders, which are characterized by high variability in symptoms and slow responses that hinder DBS setting optimization. The objective of this work was to develop an in silico platform to examine the effects of electrical stimulation in regions neighboring a stimulated brain region. APPROACH: We used the Jansen-Rit neural mass model of single and coupled nodes to simulate the response to a train of electrical current pulses at different frequencies (10-160 Hz) of the local field potential recorded in the amygdala and cortical structures in human subjects and a non-human primate. RESULTS: We found that using a single node model, the evoked responses could be accurately modeled following a narrow range of stimulation frequencies. Including a second coupled node increased the range of stimulation frequencies whose evoked responses could be efficiently modeled. Furthermore, in a chronic recording from a non-human primate, features of the in vivo evoked response remained consistent for several weeks, suggesting that model re-parameterization for chronic stimulation protocols would be infrequent. SIGNIFICANCE: Using a model of neural population activity, we reproduced the evoked response to cortical and subcortical stimulation in human and non-human primate. This modeling framework provides an environment to explore, safely and rapidly, a wide range of stimulation settings not possible in human brain stimulation studies. The model can be trained on a limited dataset of stimulation responses to develop an optimal stimulation strategy for an individual patient.",
author = "Ishita Basu and Britni Crocker and Kara Farnes and Robertson, {Madeline M.} and Paulk, {Angelique C.} and Vallejo, {Deborah I.} and Dougherty, {Darin D.} and Cash, {Sydney S.} and Eskandar, {Emad N.} and Kramer, {Mark M.} and Widge, {Alik S.}",
year = "2018",
month = "12",
day = "1",
doi = "10.1088/1741-2552/aae136",
language = "English (US)",
volume = "15",
journal = "Journal of Neural Engineering",
issn = "1741-2560",
publisher = "IOP Publishing Ltd.",
number = "6",

}

TY - JOUR

T1 - A neural mass model to predict electrical stimulation evoked responses in human and non-human primate brain

AU - Basu, Ishita

AU - Crocker, Britni

AU - Farnes, Kara

AU - Robertson, Madeline M.

AU - Paulk, Angelique C.

AU - Vallejo, Deborah I.

AU - Dougherty, Darin D.

AU - Cash, Sydney S.

AU - Eskandar, Emad N.

AU - Kramer, Mark M.

AU - Widge, Alik S.

PY - 2018/12/1

Y1 - 2018/12/1

N2 - OBJECTIVE: Deep brain stimulation (DBS) is a valuable tool for ameliorating drug resistant pathologies such as movement disorders and epilepsy. DBS is also being considered for complex neuro-psychiatric disorders, which are characterized by high variability in symptoms and slow responses that hinder DBS setting optimization. The objective of this work was to develop an in silico platform to examine the effects of electrical stimulation in regions neighboring a stimulated brain region. APPROACH: We used the Jansen-Rit neural mass model of single and coupled nodes to simulate the response to a train of electrical current pulses at different frequencies (10-160 Hz) of the local field potential recorded in the amygdala and cortical structures in human subjects and a non-human primate. RESULTS: We found that using a single node model, the evoked responses could be accurately modeled following a narrow range of stimulation frequencies. Including a second coupled node increased the range of stimulation frequencies whose evoked responses could be efficiently modeled. Furthermore, in a chronic recording from a non-human primate, features of the in vivo evoked response remained consistent for several weeks, suggesting that model re-parameterization for chronic stimulation protocols would be infrequent. SIGNIFICANCE: Using a model of neural population activity, we reproduced the evoked response to cortical and subcortical stimulation in human and non-human primate. This modeling framework provides an environment to explore, safely and rapidly, a wide range of stimulation settings not possible in human brain stimulation studies. The model can be trained on a limited dataset of stimulation responses to develop an optimal stimulation strategy for an individual patient.

AB - OBJECTIVE: Deep brain stimulation (DBS) is a valuable tool for ameliorating drug resistant pathologies such as movement disorders and epilepsy. DBS is also being considered for complex neuro-psychiatric disorders, which are characterized by high variability in symptoms and slow responses that hinder DBS setting optimization. The objective of this work was to develop an in silico platform to examine the effects of electrical stimulation in regions neighboring a stimulated brain region. APPROACH: We used the Jansen-Rit neural mass model of single and coupled nodes to simulate the response to a train of electrical current pulses at different frequencies (10-160 Hz) of the local field potential recorded in the amygdala and cortical structures in human subjects and a non-human primate. RESULTS: We found that using a single node model, the evoked responses could be accurately modeled following a narrow range of stimulation frequencies. Including a second coupled node increased the range of stimulation frequencies whose evoked responses could be efficiently modeled. Furthermore, in a chronic recording from a non-human primate, features of the in vivo evoked response remained consistent for several weeks, suggesting that model re-parameterization for chronic stimulation protocols would be infrequent. SIGNIFICANCE: Using a model of neural population activity, we reproduced the evoked response to cortical and subcortical stimulation in human and non-human primate. This modeling framework provides an environment to explore, safely and rapidly, a wide range of stimulation settings not possible in human brain stimulation studies. The model can be trained on a limited dataset of stimulation responses to develop an optimal stimulation strategy for an individual patient.

UR - http://www.scopus.com/inward/record.url?scp=85056668771&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85056668771&partnerID=8YFLogxK

U2 - 10.1088/1741-2552/aae136

DO - 10.1088/1741-2552/aae136

M3 - Article

VL - 15

JO - Journal of Neural Engineering

JF - Journal of Neural Engineering

SN - 1741-2560

IS - 6

ER -