ER-detect: a pipeline for robust detection of early evoked responses in BIDS-iEEG electrical stimulation data.

Autor:	van den Boom MA; Department of Physiology and Biomedical Engineering, Mayo Clinic; Rochester, MN, USA.; Department of Neurosurgery, Mayo Clinic; Rochester, MN, USA., Gregg NM; Department of Neurology, Mayo Clinic, Rochester, MN; USA., Valencia GO; Department of Physiology and Biomedical Engineering, Mayo Clinic; Rochester, MN, USA., Lundstrom BN; Department of Neurology, Mayo Clinic, Rochester, MN; USA., Miller KJ; Department of Neurosurgery, Mayo Clinic; Rochester, MN, USA., van Blooijs D; Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht; Utrecht, NL.; Stichting Epilepsie Instellingen Nederland (SEIN); Zwolle, The Netherlands., Huiskamp GJM; Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht; Utrecht, NL., Leijten FSS; Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht; Utrecht, NL., Worrell GA; Department of Physiology and Biomedical Engineering, Mayo Clinic; Rochester, MN, USA.; Department of Neurology, Mayo Clinic, Rochester, MN; USA., Hermes D; Department of Physiology and Biomedical Engineering, Mayo Clinic; Rochester, MN, USA.
Jazyk:	angličtina
Zdroj:	BioRxiv : the preprint server for biology [bioRxiv] 2024 Jan 11. Date of Electronic Publication: 2024 Jan 11.
DOI:	10.1101/2024.01.09.574915
Abstrakt:	Human brain connectivity can be measured in different ways. Intracranial EEG (iEEG) measurements during single pulse electrical stimulation provide a unique way to assess the spread of electrical information with millisecond precision. To provide a robust workflow to process these cortico-cortical evoked potential (CCEP) data and detect early evoked responses in a fully automated and reproducible fashion, we developed Early Response (ER)-detect. ER-detect is an open-source Python package and Docker application to preprocess BIDS structured iEEG data and detect early evoked CCEP responses. ER-detect can use three response detection methods, which were validated against 14-manually annotated CCEP datasets from two different sites by four independent raters. Results showed that ER-detect's automated detection performed on par with the inter-rater reliability (Cohen's Kappa of ~ 0.6). Moreover, ER-detect was optimized for processing large CCEP datasets, to be used in conjunction with other connectomic investigations. ER-detect provides a highly efficient standardized workflow such that iEEG-BIDS data can be processed in a consistent manner and enhance the reproducibility of CCEP based connectivity results. Competing Interests: Research reported in this publication was supported by the National Institute of Mental Health of the National Institutes of Health under Award Number R01MH122258 (DH, MvdB, GOV, FSSL, GAW, KJM; the content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health), the Mayo Clinic DERIVE Office and Center for Biomedical Discovery support (DH, KJM, MvdB, GAW), and the Epilepsy Foundation of the Netherlands under Award Number NEF17-07 (DvB). BNL has no personal financial interests, but declares intellectual property licensed to Cadence Neuroscience Inc (contractual rights waived; all funds to Mayo Clinic) and Seer Medical Inc (contractual rights waived; all funds to Mayo Clinic), site investigator (Medtronic EPAS, Neuroelectrics tDCS for Epilepsy), industry consultant (Epiminder, Medtronic, Neuropace, Philips Neuro; all funds to Mayo Clinic), and educational support (Dixi Medical). NMG declares industry consultant for NeuroOne Inc., funds to Mayo Clinic. GAW was supported by National Institutes of Health Grant UH2&3 NS095495 and unrelated to this research has licensed intellectual property developed at Mayo Clinic to Cadence Neuroscience Inc. and NeuroOne Inc.
Databáze:	MEDLINE
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