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Unsupervised machine-learning classification of electrophysiologically active electrodes during human cognitive task performance

KV. Saboo, Y. Varatharajah, BM. Berry, V. Kremen, MR. Sperling, KA. Davis, BC. Jobst, RE. Gross, B. Lega, SA. Sheth, GA. Worrell, RK. Iyer, MT. Kucewicz,

. 2019 ; 9 (1) : 17390. [pub] 20191122

Jazyk angličtina Země Velká Británie

Typ dokumentu časopisecké články, práce podpořená grantem, Research Support, U.S. Gov't, Non-P.H.S., validační studie

Perzistentní odkaz   https://www.medvik.cz/link/bmc20028745

Identification of active electrodes that record task-relevant neurophysiological activity is needed for clinical and industrial applications as well as for investigating brain functions. We developed an unsupervised, fully automated approach to classify active electrodes showing event-related intracranial EEG (iEEG) responses from 115 patients performing a free recall verbal memory task. Our approach employed new interpretable metrics that quantify spectral characteristics of the normalized iEEG signal based on power-in-band and synchrony measures. Unsupervised clustering of the metrics identified distinct sets of active electrodes across different subjects. In the total population of 11,869 electrodes, our method achieved 97% sensitivity and 92.9% specificity with the most efficient metric. We validated our results with anatomical localization revealing significantly greater distribution of active electrodes in brain regions that support verbal memory processing. We propose our machine-learning framework for objective and efficient classification and interpretation of electrophysiological signals of brain activities supporting memory and cognition.

Citace poskytuje Crossref.org

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$a Identification of active electrodes that record task-relevant neurophysiological activity is needed for clinical and industrial applications as well as for investigating brain functions. We developed an unsupervised, fully automated approach to classify active electrodes showing event-related intracranial EEG (iEEG) responses from 115 patients performing a free recall verbal memory task. Our approach employed new interpretable metrics that quantify spectral characteristics of the normalized iEEG signal based on power-in-band and synchrony measures. Unsupervised clustering of the metrics identified distinct sets of active electrodes across different subjects. In the total population of 11,869 electrodes, our method achieved 97% sensitivity and 92.9% specificity with the most efficient metric. We validated our results with anatomical localization revealing significantly greater distribution of active electrodes in brain regions that support verbal memory processing. We propose our machine-learning framework for objective and efficient classification and interpretation of electrophysiological signals of brain activities supporting memory and cognition.
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$a Varatharajah, Yogatheesan $u University of Illinois, Dept. of Electrical and Computer Engineering, Urbana-Champaign, IL, USA.
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$a Berry, Brent M $u Mayo Clinic, Dept. of Neurology, Rochester, MN, USA. Mayo Clinic, Dept. of Physiology & Biomedical Engineering, Rochester, MN, USA.
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$a Kremen, Vaclav $u Mayo Clinic, Dept. of Neurology, Rochester, MN, USA. Mayo Clinic, Dept. of Physiology & Biomedical Engineering, Rochester, MN, USA. Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic.
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$a Sperling, Michael R $u Thomas Jefferson University Hospital, Dept. of Neurology, Philadelphia, PA, USA.
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$a Jobst, Barbara C $u Dartmouth-Hitchcock Medical Center, Dept. of Neurology, Lebanon, NH, USA.
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$a Gross, Robert E $u Emory University, Dept. of Neurosurgery, Atlanta, GA, USA.
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$a Lega, Bradley $u UT Southwestern Medical Center, Dept. of Neurosurgery, Dallas, TX, USA.
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$a Sheth, Sameer A $u Baylor College of Medicine, Dept. of Neurosurgery, Houston, TX, USA.
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$a Worrell, Gregory A $u Mayo Clinic, Dept. of Neurology, Rochester, MN, USA. Mayo Clinic, Dept. of Physiology & Biomedical Engineering, Rochester, MN, USA.
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$a Iyer, Ravishankar K $u University of Illinois, Dept. of Electrical and Computer Engineering, Urbana-Champaign, IL, USA.
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$a Kucewicz, Michal T $u Mayo Clinic, Dept. of Neurology, Rochester, MN, USA. Kucewicz.Michal@mayo.edu. Mayo Clinic, Dept. of Physiology & Biomedical Engineering, Rochester, MN, USA. Kucewicz.Michal@mayo.edu. Gdansk University of Technology, Faculty of Electronics, Telecommunications and Informatics, Multimedia Systems Department, Gdansk, Poland. Kucewicz.Michal@mayo.edu.
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