Electrocortical source imaging of intracranial EEG data in epilepsy

Zeynep Akalin Acar, Jason Palmer, Gregory Alan Worrell, Scott Makeig

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Here we report first results of numerical methods for modeling the dynamic structure and evolution of epileptic seizure activity in an intracranial subdural electrode recording from a patient with partial refractory epilepsy. A 16-min dataset containing two seizures was decomposed using up to five competing adaptive mixture independent component analysis (AMICA) models. Multiple models modeled early or late ictal, or pre- or post-ictal periods in the data, respectively. To localize sources, a realistic Boundary Element Method (BEM) head model was constructed for the patient with custom open skull and plastic (non-conductive) electrode holder features. Source localization was performed using Sparse Bayesian Learning (SBL) on a dictionary of overlapping multi-scale cortical patches constructed from 80,130 dipoles in gray matter perpendicular to the cortical surface. Remaining mutual information among seizure-model AMICA components was dominated by two dependent component subspaces with largely contiguous source domains localized to superior frontal gyrus and precen-tral gyrus; these accounted for most of the ictal activity. Similar though much weaker dependent subspaces were also revealed in pre-ictal data by the associated AMICA model. Electrocortical source imaging appears promising both for clinical epilepsy research and for basic cognitive neuroscience research using volunteer patients who must undergo invasive monitoring for medical purposes.

Original languageEnglish (US)
Title of host publicationProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Pages3909-3912
Number of pages4
DOIs
StatePublished - 2011
Event33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 - Boston, MA, United States
Duration: Aug 30 2011Sep 3 2011

Other

Other33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
CountryUnited States
CityBoston, MA
Period8/30/119/3/11

Fingerprint

Electroencephalography
Epilepsy
Stroke
Independent component analysis
Imaging techniques
Electrodes
Seizures
Insulator Elements
Partial Epilepsy
Glossaries
Boundary element method
Prefrontal Cortex
Research
Skull
Refractory materials
Plastics
Volunteers
Numerical methods
Head
Electrocorticography

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

Acar, Z. A., Palmer, J., Worrell, G. A., & Makeig, S. (2011). Electrocortical source imaging of intracranial EEG data in epilepsy. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (pp. 3909-3912). [6090971] https://doi.org/10.1109/IEMBS.2011.6090971

Electrocortical source imaging of intracranial EEG data in epilepsy. / Acar, Zeynep Akalin; Palmer, Jason; Worrell, Gregory Alan; Makeig, Scott.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 2011. p. 3909-3912 6090971.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Acar, ZA, Palmer, J, Worrell, GA & Makeig, S 2011, Electrocortical source imaging of intracranial EEG data in epilepsy. in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS., 6090971, pp. 3909-3912, 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011, Boston, MA, United States, 8/30/11. https://doi.org/10.1109/IEMBS.2011.6090971
Acar ZA, Palmer J, Worrell GA, Makeig S. Electrocortical source imaging of intracranial EEG data in epilepsy. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 2011. p. 3909-3912. 6090971 https://doi.org/10.1109/IEMBS.2011.6090971
Acar, Zeynep Akalin ; Palmer, Jason ; Worrell, Gregory Alan ; Makeig, Scott. / Electrocortical source imaging of intracranial EEG data in epilepsy. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 2011. pp. 3909-3912
@inproceedings{9bf435ebd738424eb1bcb3399ce189fa,
title = "Electrocortical source imaging of intracranial EEG data in epilepsy",
abstract = "Here we report first results of numerical methods for modeling the dynamic structure and evolution of epileptic seizure activity in an intracranial subdural electrode recording from a patient with partial refractory epilepsy. A 16-min dataset containing two seizures was decomposed using up to five competing adaptive mixture independent component analysis (AMICA) models. Multiple models modeled early or late ictal, or pre- or post-ictal periods in the data, respectively. To localize sources, a realistic Boundary Element Method (BEM) head model was constructed for the patient with custom open skull and plastic (non-conductive) electrode holder features. Source localization was performed using Sparse Bayesian Learning (SBL) on a dictionary of overlapping multi-scale cortical patches constructed from 80,130 dipoles in gray matter perpendicular to the cortical surface. Remaining mutual information among seizure-model AMICA components was dominated by two dependent component subspaces with largely contiguous source domains localized to superior frontal gyrus and precen-tral gyrus; these accounted for most of the ictal activity. Similar though much weaker dependent subspaces were also revealed in pre-ictal data by the associated AMICA model. Electrocortical source imaging appears promising both for clinical epilepsy research and for basic cognitive neuroscience research using volunteer patients who must undergo invasive monitoring for medical purposes.",
author = "Acar, {Zeynep Akalin} and Jason Palmer and Worrell, {Gregory Alan} and Scott Makeig",
year = "2011",
doi = "10.1109/IEMBS.2011.6090971",
language = "English (US)",
isbn = "9781424441211",
pages = "3909--3912",
booktitle = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

}

TY - GEN

T1 - Electrocortical source imaging of intracranial EEG data in epilepsy

AU - Acar, Zeynep Akalin

AU - Palmer, Jason

AU - Worrell, Gregory Alan

AU - Makeig, Scott

PY - 2011

Y1 - 2011

N2 - Here we report first results of numerical methods for modeling the dynamic structure and evolution of epileptic seizure activity in an intracranial subdural electrode recording from a patient with partial refractory epilepsy. A 16-min dataset containing two seizures was decomposed using up to five competing adaptive mixture independent component analysis (AMICA) models. Multiple models modeled early or late ictal, or pre- or post-ictal periods in the data, respectively. To localize sources, a realistic Boundary Element Method (BEM) head model was constructed for the patient with custom open skull and plastic (non-conductive) electrode holder features. Source localization was performed using Sparse Bayesian Learning (SBL) on a dictionary of overlapping multi-scale cortical patches constructed from 80,130 dipoles in gray matter perpendicular to the cortical surface. Remaining mutual information among seizure-model AMICA components was dominated by two dependent component subspaces with largely contiguous source domains localized to superior frontal gyrus and precen-tral gyrus; these accounted for most of the ictal activity. Similar though much weaker dependent subspaces were also revealed in pre-ictal data by the associated AMICA model. Electrocortical source imaging appears promising both for clinical epilepsy research and for basic cognitive neuroscience research using volunteer patients who must undergo invasive monitoring for medical purposes.

AB - Here we report first results of numerical methods for modeling the dynamic structure and evolution of epileptic seizure activity in an intracranial subdural electrode recording from a patient with partial refractory epilepsy. A 16-min dataset containing two seizures was decomposed using up to five competing adaptive mixture independent component analysis (AMICA) models. Multiple models modeled early or late ictal, or pre- or post-ictal periods in the data, respectively. To localize sources, a realistic Boundary Element Method (BEM) head model was constructed for the patient with custom open skull and plastic (non-conductive) electrode holder features. Source localization was performed using Sparse Bayesian Learning (SBL) on a dictionary of overlapping multi-scale cortical patches constructed from 80,130 dipoles in gray matter perpendicular to the cortical surface. Remaining mutual information among seizure-model AMICA components was dominated by two dependent component subspaces with largely contiguous source domains localized to superior frontal gyrus and precen-tral gyrus; these accounted for most of the ictal activity. Similar though much weaker dependent subspaces were also revealed in pre-ictal data by the associated AMICA model. Electrocortical source imaging appears promising both for clinical epilepsy research and for basic cognitive neuroscience research using volunteer patients who must undergo invasive monitoring for medical purposes.

UR - http://www.scopus.com/inward/record.url?scp=84055217825&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84055217825&partnerID=8YFLogxK

U2 - 10.1109/IEMBS.2011.6090971

DO - 10.1109/IEMBS.2011.6090971

M3 - Conference contribution

SN - 9781424441211

SP - 3909

EP - 3912

BT - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

ER -