Resting state connectivity in neocortical epilepsy

The epilepsy network as a patient-specific biomarker

Alexandria C. Marino, Genevieve J. Yang, Evgeniya Tyrtova, Kun Wu, Hitten P. Zaveri, Pue Farooque, Dennis D. Spencer, Sarah Kathleen Bandt*

*Corresponding author for this work

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Objective: Localization related epilepsy (LRE) is increasingly accepted as a network disorder. To better understand the network specific characteristics of LRE, we defined individual epilepsy networks and compared them across patients. Methods: The epilepsy network was defined in the slow cortical potential frequency band in 10 patients using intracranial EEG data obtained during interictal periods. Cortical regions were included in the epilepsy network if their connectivity pattern was similar to the connectivity pattern of the seizure onset electrode contact. Patients were subdivided into frontal, temporal, and posterior quadrant cohorts according to the anatomic location of seizure onset. Jaccard similarity was calculated within each cohort to assess for similarity of the epilepsy network between patients within each cohort. Results: All patients exhibited an epilepsy network in the slow cortical potential frequency band. The topographic distribution of this correlated network activity was found to be unique at the single subject level. Conclusions: The epilepsy network was unique at the single patient level, even between patients with similar seizure onset locations. Significance: We demonstrated that the epilepsy network is patient-specific. This is in keeping with our current understanding of brain networks and identifies the patient-specific epilepsy network as a possible biomarker in LRE.

Original languageEnglish (US)
Pages (from-to)280-288
Number of pages9
JournalClinical Neurophysiology
Volume130
Issue number2
DOIs
StatePublished - Feb 1 2019

Fingerprint

Epilepsy
Biomarkers
Partial Epilepsy
Seizures
Electrodes
Brain

Keywords

  • Epilepsy
  • Epilepsy network
  • Intracranial EEG

ASJC Scopus subject areas

  • Sensory Systems
  • Neurology
  • Clinical Neurology
  • Physiology (medical)

Cite this

Marino, Alexandria C. ; Yang, Genevieve J. ; Tyrtova, Evgeniya ; Wu, Kun ; Zaveri, Hitten P. ; Farooque, Pue ; Spencer, Dennis D. ; Bandt, Sarah Kathleen. / Resting state connectivity in neocortical epilepsy : The epilepsy network as a patient-specific biomarker. In: Clinical Neurophysiology. 2019 ; Vol. 130, No. 2. pp. 280-288.
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Resting state connectivity in neocortical epilepsy : The epilepsy network as a patient-specific biomarker. / Marino, Alexandria C.; Yang, Genevieve J.; Tyrtova, Evgeniya; Wu, Kun; Zaveri, Hitten P.; Farooque, Pue; Spencer, Dennis D.; Bandt, Sarah Kathleen.

In: Clinical Neurophysiology, Vol. 130, No. 2, 01.02.2019, p. 280-288.

Research output: Contribution to journalArticle

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AU - Marino, Alexandria C.

AU - Yang, Genevieve J.

AU - Tyrtova, Evgeniya

AU - Wu, Kun

AU - Zaveri, Hitten P.

AU - Farooque, Pue

AU - Spencer, Dennis D.

AU - Bandt, Sarah Kathleen

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N2 - Objective: Localization related epilepsy (LRE) is increasingly accepted as a network disorder. To better understand the network specific characteristics of LRE, we defined individual epilepsy networks and compared them across patients. Methods: The epilepsy network was defined in the slow cortical potential frequency band in 10 patients using intracranial EEG data obtained during interictal periods. Cortical regions were included in the epilepsy network if their connectivity pattern was similar to the connectivity pattern of the seizure onset electrode contact. Patients were subdivided into frontal, temporal, and posterior quadrant cohorts according to the anatomic location of seizure onset. Jaccard similarity was calculated within each cohort to assess for similarity of the epilepsy network between patients within each cohort. Results: All patients exhibited an epilepsy network in the slow cortical potential frequency band. The topographic distribution of this correlated network activity was found to be unique at the single subject level. Conclusions: The epilepsy network was unique at the single patient level, even between patients with similar seizure onset locations. Significance: We demonstrated that the epilepsy network is patient-specific. This is in keeping with our current understanding of brain networks and identifies the patient-specific epilepsy network as a possible biomarker in LRE.

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