Evaluation of a dual signal subspace projection algorithm in magnetoencephalographic recordings from patients with intractable epilepsy and vagus nerve stimulators

Magnetoencephalography (MEG) data is subject to many sources of environmental noise, and interference rejection is a necessary step in the processing of MEG data. Large amplitude interference caused by sources near the brain have been common in clinical settings and are difficult to reject. Artifact...

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Veröffentlicht in:	NeuroImage (Orlando, Fla.) Jg. 188; S. 161 - 170
Hauptverfasser:	Cai, Chang, Xu, Jiajing, Velmurugan, Jayabal, Knowlton, Robert, Sekihara, Kensuke, Nagarajan, Srikantan S., Kirsch, Heidi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Inc 01.03.2019 Elsevier Limited
Schlagworte:	Adolescent Adult Algorithms Bayesian analysis Brain mapping Champagne Convulsions & seizures Data processing Drug Resistant Epilepsy - physiopathology Drug Resistant Epilepsy - therapy DSSP Epilepsy Female Firing pattern Humans Intractable epilepsy Localization Magnetoencephalography Magnetoencephalography - methods Male Methods Principal components analysis Transplants & implants Vagus nerve Vagus Nerve Stimulation Vagus nerve stimulators Young Adult Vagus nerve stimulators Intractable epilepsy DSSP Magnetoencephalography Brain mapping
ISSN:	1053-8119, 1095-9572, 1095-9572
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Abstract	Magnetoencephalography (MEG) data is subject to many sources of environmental noise, and interference rejection is a necessary step in the processing of MEG data. Large amplitude interference caused by sources near the brain have been common in clinical settings and are difficult to reject. Artifact from vagal nerve stimulators (VNS) is a prototypical example. In this study, we describe a novel MEG interference rejection algorithm called dual signal subspace projection (DSSP), and evaluate its performance in clinical MEG data from people with epilepsy and implanted VNS. The performance of DSSP was evaluated in a retrospective cohort study of patients with epilepsy and VNS who had MEG scans for source localization of interictal epileptiform discharges. DSSP was applied to the MEG data and compared with benchmark for performance. We evaluated the clinical impact of interference rejection based on human expert detection and estimation of the location and time-course of interictal spikes, using an empirical Bayesian source reconstruction algorithm (Champagne). Clinical recordings, after DSSP processing, became more readable and a greater number of interictal epileptic spikes could be clearly identified. Source localization results of interictal spikes also significantly improved from those achieved before DSSP processing, including meaningful estimates of activity time courses. Therefore, DSSP is a valuable novel interference rejection algorithm that can be successfully deployed for the removal of strong artifacts and interferences in MEG. •Performance evaluation of a novel MEG interference rejection algorithm called dual signal subspace projection (DSSP) in clinical MEG data from patients with epilepsy and implanted vagus nerve stimulators (VNS).•DSSP shows significant low-frequency reduction of artifacts from VNS, and increases the detection and identification of interictal epileptic spikes.•DSSP improved the timing and localization of identified epileptic spikes.•DSSP is a valuable novel interference rejection algorithm that can be successfully deployed for the removal of strong artifacts and interferences in MEG.
AbstractList	Magnetoencephalography (MEG) data is subject to many sources of environmental noise, and interference rejection is a necessary step in the processing of MEG data. Large amplitude interference caused by sources near the brain have been common in clinical settings and are difficult to reject. Artifact from vagal nerve stimulators (VNS) is a prototypical example. In this study, we describe a novel MEG interference rejection algorithm called dual signal subspace projection (DSSP), and evaluate its performance in clinical MEG data from people with epilepsy and implanted VNS. The performance of DSSP was evaluated in a retrospective cohort study of patients with epilepsy and VNS who had MEG scans for source localization of interictal epileptiform discharges. DSSP was applied to the MEG data and compared with benchmark for performance. We evaluated the clinical impact of interference rejection based on human expert detection and estimation of the location and time-course of interictal spikes, using an empirical Bayesian source reconstruction algorithm (Champagne). Clinical recordings, after DSSP processing, became more readable and a greater number of interictal epileptic spikes could be clearly identified. Source localization results of interictal spikes also significantly improved from those achieved before DSSP processing, including meaningful estimates of activity time courses. Therefore, DSSP is a valuable novel interference rejection algorithm that can be successfully deployed for the removal of strong artifacts and interferences in MEG. Magnetoencephalography (MEG) data is subject to many sources of environmental noise, and interference rejection is a necessary step in the processing of MEG data. Large amplitude interference caused by sources near the brain have been common in clinical settings and are difficult to reject. Artifact from vagal nerve stimulators (VNS) is a prototypical example. In this study, we describe a novel MEG interference rejection algorithm called dual signal subspace projection (DSSP), and evaluate its performance in clinical MEG data from people with epilepsy and implanted VNS. The performance of DSSP was evaluated in a retrospective cohort study of patients with epilepsy and VNS who had MEG scans for source localization of interictal epileptiform discharges. DSSP was applied to the MEG data and compared with benchmark for performance. We evaluated the clinical impact of interference rejection based on human expert detection and estimation of the location and time-course of interictal spikes, using an empirical Bayesian source reconstruction algorithm (Champagne). Clinical recordings, after DSSP processing, became more readable and a greater number of interictal epileptic spikes could be clearly identified. Source localization results of interictal spikes also significantly improved from those achieved before DSSP processing, including meaningful estimates of activity time courses. Therefore, DSSP is a valuable novel interference rejection algorithm that can be successfully deployed for the removal of strong artifacts and interferences in MEG. •Performance evaluation of a novel MEG interference rejection algorithm called dual signal subspace projection (DSSP) in clinical MEG data from patients with epilepsy and implanted vagus nerve stimulators (VNS).•DSSP shows significant low-frequency reduction of artifacts from VNS, and increases the detection and identification of interictal epileptic spikes.•DSSP improved the timing and localization of identified epileptic spikes.•DSSP is a valuable novel interference rejection algorithm that can be successfully deployed for the removal of strong artifacts and interferences in MEG. Magnetoencephalography (MEG) data is subject to many sources of environmental noise, and interference rejection is a necessary step in the processing of MEG data. Large amplitude interference caused by sources near the brain have been common in clinical settings and are difficult to reject. Artifact from vagal nerve stimulators (VNS) is a prototypical example. In this study, we describe a novel MEG interference rejection algorithm called dual signal subspace projection (DSSP), and evaluate its performance in clinical MEG data from people with epilepsy and implanted VNS. The performance of DSSP was evaluated in a retrospective cohort study of patients with epilepsy and VNS who had MEG scans for source localization of interictal epileptiform discharges. DSSP was applied to the MEG data and compared with benchmark for performance. We evaluated the clinical impact of interference rejection based on human expert detection and estimation of the location and time-course of interictal spikes, using an empirical Bayesian source reconstruction algorithm (Champagne). Clinical recordings, after DSSP processing, became more readable and a greater number of interictal epileptic spikes could be clearly identified. Source localization results of interictal spikes also significantly improved from those achieved before DSSP processing, including meaningful estimates of activity time courses. Therefore, DSSP is a valuable novel interference rejection algorithm that can be successfully deployed for the removal of strong artifacts and interferences in MEG.Magnetoencephalography (MEG) data is subject to many sources of environmental noise, and interference rejection is a necessary step in the processing of MEG data. Large amplitude interference caused by sources near the brain have been common in clinical settings and are difficult to reject. Artifact from vagal nerve stimulators (VNS) is a prototypical example. In this study, we describe a novel MEG interference rejection algorithm called dual signal subspace projection (DSSP), and evaluate its performance in clinical MEG data from people with epilepsy and implanted VNS. The performance of DSSP was evaluated in a retrospective cohort study of patients with epilepsy and VNS who had MEG scans for source localization of interictal epileptiform discharges. DSSP was applied to the MEG data and compared with benchmark for performance. We evaluated the clinical impact of interference rejection based on human expert detection and estimation of the location and time-course of interictal spikes, using an empirical Bayesian source reconstruction algorithm (Champagne). Clinical recordings, after DSSP processing, became more readable and a greater number of interictal epileptic spikes could be clearly identified. Source localization results of interictal spikes also significantly improved from those achieved before DSSP processing, including meaningful estimates of activity time courses. Therefore, DSSP is a valuable novel interference rejection algorithm that can be successfully deployed for the removal of strong artifacts and interferences in MEG.
Author	Xu, Jiajing Kirsch, Heidi Velmurugan, Jayabal Knowlton, Robert Nagarajan, Srikantan S. Cai, Chang Sekihara, Kensuke
Author_xml	– sequence: 1 givenname: Chang surname: Cai fullname: Cai, Chang organization: Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, 94143-0628, USA – sequence: 2 givenname: Jiajing surname: Xu fullname: Xu, Jiajing organization: Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, 94143-0628, USA – sequence: 3 givenname: Jayabal surname: Velmurugan fullname: Velmurugan, Jayabal organization: Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, 94143-0628, USA – sequence: 4 givenname: Robert surname: Knowlton fullname: Knowlton, Robert organization: Department of Neurology, University of California, San Francisco, CA 94143-0628, USA – sequence: 5 givenname: Kensuke surname: Sekihara fullname: Sekihara, Kensuke organization: Department of Advanced Technology in Medicine, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8519, Japan – sequence: 6 givenname: Srikantan S. surname: Nagarajan fullname: Nagarajan, Srikantan S. email: sri@ucsf.edu organization: Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, 94143-0628, USA – sequence: 7 givenname: Heidi surname: Kirsch fullname: Kirsch, Heidi email: Heidi.Kirsch@ucsf.edu organization: Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, 94143-0628, USA
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Cites_doi	10.1002/sim.2941 10.1109/PROC.1975.10036 10.1016/j.clinph.2003.10.036 10.1016/j.neuroimage.2014.05.068 10.1088/0031-9155/51/7/008 10.1002/hbm.20627 10.1016/j.neuroimage.2008.02.059 10.1109/77.919433 10.1088/1741-2560/13/3/036007 10.1088/1741-2552/aa7693 10.1016/j.neuroimage.2011.12.027 10.1016/j.neuroimage.2005.09.055 10.1016/j.neuroimage.2009.06.083 10.1016/j.neuroimage.2008.02.006
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Keywords	Vagus nerve stimulators Intractable epilepsy DSSP Magnetoencephalography Brain mapping
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SubjectTerms	Adolescent Adult Algorithms Bayesian analysis Brain mapping Champagne Convulsions & seizures Data processing Drug Resistant Epilepsy - physiopathology Drug Resistant Epilepsy - therapy DSSP Epilepsy Female Firing pattern Humans Intractable epilepsy Localization Magnetoencephalography Magnetoencephalography - methods Male Methods Principal components analysis Transplants & implants Vagus nerve Vagus Nerve Stimulation Vagus nerve stimulators Young Adult
Title	Evaluation of a dual signal subspace projection algorithm in magnetoencephalographic recordings from patients with intractable epilepsy and vagus nerve stimulators
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