Using network analysis to localize the epileptogenic zone from invasive EEG recordings in intractable focal epilepsy

Adam Li; Bhaskar Chennuri; Sandya Subramanian; Robert Yaffe; Steve Gliske; William Stacey; Robert Norton; Austin Jordan; Kareem A. Zaghloul; Sara K. Inati; Shubhi Agrawal; Jennifer J. Haagensen; Jennifer Hopp; Chalita Atallah; Emily Johnson; Nathan Crone; William S. Anderson; Zach Fitzgerald; Juan Bulacio; John T. Gale; Sridevi V. Sarma; Jorge Gonzalez-Martinez

doi:10.1162/netn_a_00043

Network Neuroscience (Jun 2018)

Using network analysis to localize the epileptogenic zone from invasive EEG recordings in intractable focal epilepsy

Adam Li,
Bhaskar Chennuri,
Sandya Subramanian,
Robert Yaffe,
Steve Gliske,
William Stacey,
Robert Norton,
Austin Jordan,
Kareem A. Zaghloul,
Sara K. Inati,
Shubhi Agrawal,
Jennifer J. Haagensen,
Jennifer Hopp,
Chalita Atallah,
Emily Johnson,
Nathan Crone,
William S. Anderson,
Zach Fitzgerald,
Juan Bulacio,
John T. Gale,
Sridevi V. Sarma,
Jorge Gonzalez-Martinez

Affiliations

Adam Li: Institute for Computational Medicine, Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
Bhaskar Chennuri: Institute for Computational Medicine, Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
Sandya Subramanian: Institute for Computational Medicine, Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
Robert Yaffe: Institute for Computational Medicine, Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
Steve Gliske: University of Michigan, Ann Arbor, USA
William Stacey: University of Michigan, Ann Arbor, USA
Robert Norton: Uptake Technologies Inc., Chicago, IL, USA
Austin Jordan: Institute for Computational Medicine, Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
Kareem A. Zaghloul: Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA
Sara K. Inati: Office of the Clinical Director, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA
Shubhi Agrawal: Office of the Clinical Director, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA
Jennifer J. Haagensen: Neurology, University of Maryland Medical Center, Baltimore, MD, USA
Jennifer Hopp: Neurology, University of Maryland Medical Center, Baltimore, MD, USA
Chalita Atallah: Neurology, University of Maryland Medical Center, Baltimore, MD, USA
Emily Johnson: Neurology, Johns Hopkins Hospital, Baltimore, MD, USA
Nathan Crone: Neurosurgery, Johns Hopkins Hospital, Baltimore, MD, USA
William S. Anderson: Neurosurgery, Johns Hopkins Hospital, Baltimore, MD, USA
Zach Fitzgerald: Neurosurgery, Cleveland Clinic, Cleveland, OH, USA
Juan Bulacio: Neurosurgery, Cleveland Clinic, Cleveland, OH, USA
John T. Gale: Neurosurgery, Cleveland Clinic, Cleveland, OH, USA
Sridevi V. Sarma: Institute for Computational Medicine, Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
Jorge Gonzalez-Martinez: Neurosurgery, Cleveland Clinic, Cleveland, OH, USA

DOI: https://doi.org/10.1162/netn_a_00043
Journal volume & issue: Vol. 02, no. 02
pp. 218 – 240

Abstract

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Treatment of medically intractable focal epilepsy (MIFE) by surgical resection of the epileptogenic zone (EZ) is often effective provided the EZ can be reliably identified. Even with the use of invasive recordings, the clinical differentiation between the EZ and normal brain areas can be quite challenging, mainly in patients without MRI detectable lesions. Consequently, despite relatively large brain regions being removed, surgical success rates barely reach 60–65%. Such variable and unfavorable outcomes associated with high morbidity rates are often caused by imprecise and/or inaccurate EZ localization. We developed a localization algorithm that uses network-based data analytics to process invasive EEG recordings. This network algorithm analyzes the centrality signatures of every contact electrode within the recording network and characterizes contacts into susceptible EZ based on the centrality trends over time. The algorithm was tested in a retrospective study that included 42 patients from four epilepsy centers. Our algorithm had higher agreement with EZ regions identified by clinicians for patients with successful surgical outcomes and less agreement for patients with failed outcomes. These findings suggest that network analytics and a network systems perspective of epilepsy may be useful in assisting clinicians in more accurately localizing the EZ. Epilepsy is a disease that results in abnormal firing patterns in parts of the brain that comprise the epileptogenic network, known as the epileptogenic zone (EZ). Current methods to localize the EZ for surgical treatment often require observations of hundreds of thousands of EEG data points measured from many electrodes implanted in a patient’s brain. In this paper, we used network science to show that EZ regions may exhibit specific network signatures before, during, and after seizure events. Our algorithm computes the likelihood of each electrode being in the EZ and tends to agree more with clinicians during successful resections and less during failed surgeries. These results suggest that a networked analysis approach to EZ localization may be valuable in a clinical setting.

Published in Network Neuroscience

ISSN: 2472-1751 (Online)
Publisher: The MIT Press
Country of publisher: United States
LCC subjects: Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry
Website: https://direct.mit.edu/netn

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