Electrocorticography and stereo EEG provide distinct measures of brain connectivity: implications for network models

Author:

Bernabei John M12ORCID,Arnold T Campbell12ORCID,Shah Preya12,Revell Andrew12ORCID,Ong Ian Z12ORCID,Kini Lohith G12,Stein Joel M3,Shinohara Russell T456,Lucas Timothy H7,Davis Kathryn A28,Bassett Danielle S178910ORCID,Litt Brian1278

Affiliation:

1. Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA

2. Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA

3. Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA

4. Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA

5. Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA 19104, USA

6. Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA 19104, USA

7. Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA

8. Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA

9. Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA

10. Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA

Abstract

Abstract Brain network models derived from graph theory have the potential to guide functional neurosurgery, and to improve rates of post-operative seizure freedom for patients with epilepsy. A barrier to applying these models clinically is that intracranial EEG electrode implantation strategies vary by centre, region and country, from cortical grid & strip electrodes (Electrocorticography), to purely stereotactic depth electrodes (Stereo EEG), to a mixture of both. To determine whether models derived from one type of study are broadly applicable to others, we investigate the differences in brain networks mapped by electrocorticography and stereo EEG in a cohort of patients who underwent surgery for temporal lobe epilepsy and achieved a favourable outcome. We show that networks derived from electrocorticography and stereo EEG define distinct relationships between resected and spared tissue, which may be driven by sampling bias of temporal depth electrodes in patients with predominantly cortical grids. We propose a method of correcting for the effect of internodal distance that is specific to electrode type and explore how additional methods for spatially correcting for sampling bias affect network models. Ultimately, we find that smaller surgical targets tend to have lower connectivity with respect to the surrounding network, challenging notions that abnormal connectivity in the epileptogenic zone is typically high. Our findings suggest that effectively applying computational models to localize epileptic networks requires accounting for the effects of spatial sampling, particularly when analysing both electrocorticography and stereo EEG recordings in the same cohort, and that future network studies of epilepsy surgery should also account for differences in focality between resection and ablation. We propose that these findings are broadly relevant to intracranial EEG network modelling in epilepsy and an important step in translating them clinically into patient care.

Funder

National Institutes of Health

Publisher

Oxford University Press (OUP)

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference43 articles.

1. Definition and localization of the epileptogenic zone. The epileptogenic zone: General principles;Lüders;Epileptic Disord,2006

2. The history of invasive EEG evaluation in epilepsy patients;Reif;Seizure,2016

3. The epileptogenic zone: Concept and definition;Jehi;Epilepsy Curr,2018

4. Stereoelectroencephalography in epilepsy, cognitive neurophysiology, and psychiatric disease: Safety, efficacy, and place in therapy;Youngerman;Neuropsychiatr Dis Treat,2019

5. Human intracranial EEG: Promises and limitations;Parvizi;Nat Neurosci,2018

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3