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

Autor:	Bernabei JM; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.; Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA., Arnold TC; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.; Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA., Shah P; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.; Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA., Revell A; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.; Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA., Ong IZ; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.; Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA., Kini LG; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.; Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA., Stein JM; Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA., Shinohara RT; Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA.; Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA 19104, USA.; Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA 19104, USA., Lucas TH; Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA., Davis KA; Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.; Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA., Bassett DS; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.; Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA.; Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA.; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.; Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA.; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA.; The Santa Fe Institute, Santa Fe, NM 87501, USA., Litt B; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.; Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.; Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA.; Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA.
Jazyk:	angličtina
Zdroj:	Brain communications [Brain Commun] 2021 Jul 11; Vol. 3 (3), pp. fcab156. Date of Electronic Publication: 2021 Jul 11 (Print Publication: 2021).
DOI:	10.1093/braincomms/fcab156
Abstrakt:	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. (© The Author(s) (2021). Published by Oxford University Press on behalf of the Guarantors of Brain.)
Databáze:	MEDLINE
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