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Wang R, Fu K, Zhao R, Wang D, Yang Z, Bin W, Gao Y, Ning X. Expanding the clinical application of OPM-MEG using an effective automatic suppression method for the dental brace metal artifact. Neuroimage 2024; 296:120661. [PMID: 38838840 DOI: 10.1016/j.neuroimage.2024.120661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 05/19/2024] [Accepted: 05/30/2024] [Indexed: 06/07/2024] Open
Abstract
Optically pumped magnetometer magnetoencephalography (OPM-MEG) holds significant promise for clinical functional brain imaging due to its superior spatiotemporal resolution. However, effectively suppressing metallic artifacts, particularly from devices such as orthodontic braces and vagal nerve stimulators remains a major challenge, hindering the wider clinical application of wearable OPM-MEG devices. A comprehensive analysis of metal artifact characteristics from time, frequency, and time-frequency perspectives was conducted for the first time using an OPM-MEG device in clinical medicine. This study focused on patients with metal orthodontics, examining the modulation of metal artifacts by breath and head movement, the incomplete regular sub-Gaussian distribution, and the high absolute power ratio in the 0.5-8 Hz band. The existing metal artifact suppression algorithms applied to SQUID-MEG, such as fast independent component analysis (FastICA), information maximization (Infomax), and algorithms for multiple unknown signal extraction (AMUSE), exhibit limited efficacy. Consequently, this study introduced the second-order blind identification (SOBI) algorithm, which utilized multiple time delays for the component separation of OPM-MEG measurement signals. We modified the time delays of the SOBI method to improve its efficacy in separating artifact components, particularly those in the ultralow frequency range. This approach employs the frequency-domain absolute power ratio, root mean square (RMS) value, and mutual information methods to automate the artifact component screening process. The effectiveness of this method was validated through simulation experiments involving four subjects in both resting and evoked experiments. In addition, the proposed method was also validated by the actual OPM-MEG evoked experiments of three subjects. Comparative analyses were conducted against the FastICA, Infomax, and AMUSE algorithms. Evaluation metrics included normalized mean square error, normalized delta band power error, RMS error, and signal-to-noise ratio, demonstrating that the proposed method provides optimal suppression of metal artifacts. This advancement holds promise for enhancing data quality and expanding the clinical applications of OPM-MEG.
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Affiliation(s)
- Ruonan Wang
- School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China; Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China; Institute of Large-scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Hangzhou 310051, China.
| | - Kaiwen Fu
- School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China; Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China; Institute of Large-scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Hangzhou 310051, China.
| | - Ruochen Zhao
- School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China; Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China; Institute of Large-scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Hangzhou 310051, China.
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan 250012, China; National Innovation Platform for industry-Education Integration in Medicine-Engineering Interdisciplinary, Shandong Key Laboratory for Magnetic Field-free Medicine and Functional Imaging, Shandong University, Research Institute of Shandong University, Jinan, 250014, China.
| | - Zhimin Yang
- State Key Laboratory of Traditional Chinese Medicine Syndrome/Health Construction Center, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China.
| | - Wei Bin
- State Key Laboratory of Traditional Chinese Medicine Syndrome/Health Construction Center, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China.
| | - Yang Gao
- Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China; Institute of Large-scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Hangzhou 310051, China; National Institute of Extremely-Weak Magnetic Field Infrastructure, Hangzhou 310051, China.
| | - Xiaolin Ning
- School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China; Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China; National Innovation Platform for industry-Education Integration in Medicine-Engineering Interdisciplinary, Shandong Key Laboratory for Magnetic Field-free Medicine and Functional Imaging, Shandong University, Research Institute of Shandong University, Jinan, 250014, China; National Institute of Extremely-Weak Magnetic Field Infrastructure, Hangzhou 310051, China.
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Askari P, Cardoso da Fonseca N, Pruitt T, Maldjian JA, Alick-Lindstrom S, Davenport EM. Magnetoencephalography (MEG) Data Processing in Epilepsy Patients with Implanted Responsive Neurostimulation (RNS) Devices. Brain Sci 2024; 14:173. [PMID: 38391747 PMCID: PMC10887328 DOI: 10.3390/brainsci14020173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 01/30/2024] [Accepted: 02/06/2024] [Indexed: 02/24/2024] Open
Abstract
Drug-resistant epilepsy (DRE) is often treated with surgery or neuromodulation. Specifically, responsive neurostimulation (RNS) is a widely used therapy that is programmed to detect abnormal brain activity and intervene with tailored stimulation. Despite the success of RNS, some patients require further interventions. However, having an RNS device in situ is a hindrance to the performance of neuroimaging techniques. Magnetoencephalography (MEG), a non-invasive neurophysiologic and functional imaging technique, aids epilepsy assessment and surgery planning. MEG performed post-RNS is complicated by signal distortions. This study proposes an independent component analysis (ICA)-based approach to enhance MEG signal quality, facilitating improved assessment for epilepsy patients with implanted RNS devices. Three epilepsy patients, two with RNS implants and one without, underwent MEG scans. Preprocessing included temporal signal space separation (tSSS) and an automated ICA-based approach with MNE-Python. Power spectral density (PSD) and signal-to-noise ratio (SNR) were analyzed, and MEG dipole analysis was conducted using single equivalent current dipole (SECD) modeling. The ICA-based noise removal preprocessing method substantially improved the signal-to-noise ratio (SNR) for MEG data from epilepsy patients with implanted RNS devices. Qualitative assessment confirmed enhanced signal readability and improved MEG dipole analysis. ICA-based processing markedly enhanced MEG data quality in RNS patients, emphasizing its clinical relevance.
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Affiliation(s)
- Pegah Askari
- Radiology Department, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- MEG Center of Excellence, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Biomedical Engineering Department, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Biomedical Engineering Department, The University of Texas at Arlington, Arlington, TX 76010, USA
| | - Natascha Cardoso da Fonseca
- Radiology Department, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- MEG Center of Excellence, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Tyrell Pruitt
- Radiology Department, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- MEG Center of Excellence, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Joseph A Maldjian
- Radiology Department, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- MEG Center of Excellence, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Biomedical Engineering Department, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Sasha Alick-Lindstrom
- Radiology Department, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- MEG Center of Excellence, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Neurology Department, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Elizabeth M Davenport
- Radiology Department, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- MEG Center of Excellence, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Biomedical Engineering Department, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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3
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Zheng L, Liao P, Wu X, Cao M, Cui W, Lu L, Xu H, Zhu L, Lyu B, Wang X, Teng P, Wang J, Vogrin S, Plummer C, Luan G, Gao JH. An artificial intelligence-based pipeline for automated detection and localisation of epileptic sources from magnetoencephalography. J Neural Eng 2023; 20:046036. [PMID: 37615416 DOI: 10.1088/1741-2552/acef92] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 08/10/2023] [Indexed: 08/25/2023]
Abstract
Objective.Magnetoencephalography (MEG) is a powerful non-invasive diagnostic modality for presurgical epilepsy evaluation. However, the clinical utility of MEG mapping for localising epileptic foci is limited by its low efficiency, high labour requirements, and considerable interoperator variability. To address these obstacles, we proposed a novel artificial intelligence-based automated magnetic source imaging (AMSI) pipeline for automated detection and localisation of epileptic sources from MEG data.Approach.To expedite the analysis of clinical MEG data from patients with epilepsy and reduce human bias, we developed an autolabelling method, a deep-learning model based on convolutional neural networks and a hierarchical clustering method based on a perceptual hash algorithm, to enable the coregistration of MEG and magnetic resonance imaging, the detection and clustering of epileptic activity, and the localisation of epileptic sources in a highly automated manner. We tested the capability of the AMSI pipeline by assessing MEG data from 48 epilepsy patients.Main results.The AMSI pipeline was able to rapidly detect interictal epileptiform discharges with 93.31% ± 3.87% precision based on a 35-patient dataset (with sevenfold patientwise cross-validation) and robustly rendered accurate localisation of epileptic activity with a lobar concordance of 87.18% against interictal and ictal stereo-electroencephalography findings in a 13-patient dataset. We also showed that the AMSI pipeline accomplishes the necessary processes and delivers objective results within a much shorter time frame (∼12 min) than traditional manual processes (∼4 h).Significance.The AMSI pipeline promises to facilitate increased utilisation of MEG data in the clinical analysis of patients with epilepsy.
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Affiliation(s)
- Li Zheng
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
- Changping Laboratory, Beijing, People's Republic of China
| | - Pan Liao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, People's Republic of China
| | - Xiuwen Wu
- Changping Laboratory, Beijing, People's Republic of China
- Center for Biomedical Engineering, University of Science and Technology of China, Anhui, People's Republic of China
| | - Miao Cao
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
- Changping Laboratory, Beijing, People's Republic of China
| | - Wei Cui
- Center for Biomedical Engineering, University of Science and Technology of China, Anhui, People's Republic of China
| | - Lingxi Lu
- Center for the Cognitive Science of Language, Beijing Language and Culture University, Beijing, People's Republic of China
| | - Hui Xu
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
| | - Linlin Zhu
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
| | - Bingjiang Lyu
- Changping Laboratory, Beijing, People's Republic of China
| | - Xiongfei Wang
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, People's Republic of China
- Beijing Key Laboratory of Epilepsy, Capital Medical University, Beijing, People's Republic of China
| | - Pengfei Teng
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Jing Wang
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Simon Vogrin
- Department of Neuroimaging, Swinburne University of Technology, Melbourne, Australia
| | - Chris Plummer
- Department of Neuroimaging, Swinburne University of Technology, Melbourne, Australia
| | - Guoming Luan
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, People's Republic of China
- Beijing Key Laboratory of Epilepsy, Capital Medical University, Beijing, People's Republic of China
| | - Jia-Hong Gao
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
- Changping Laboratory, Beijing, People's Republic of China
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, People's Republic of China
- McGovern Institute for Brain Research, Peking University, Beijing, People's Republic of China
- National Biomedical Imaging Center, Peking University, Beijing, People's Republic of China
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4
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Huang MX, Harrington DL, Angeles-Quinto A, Ji Z, Robb-Swan A, Huang CW, Shen Q, Hansen H, Baumgartner J, Hernandez-Lucas J, Nichols S, Jacobus J, Song T, Lerman I, Bazhenov M, Krishnan GP, Baker DG, Rao R, Lee RR. EMG-projected MEG High-Resolution Source Imaging of Human Motor Execution: Brain-Muscle Coupling above Movement Frequencies. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.23.23291825. [PMID: 37425691 PMCID: PMC10327237 DOI: 10.1101/2023.06.23.23291825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Magnetoencephalography (MEG) is a non-invasive functional imaging technique for pre-surgical mapping. However, movement-related MEG functional mapping of primary motor cortex (M1) has been challenging in presurgical patients with brain lesions and sensorimotor dysfunction due to the large numbers of trails needed to obtain adequate signal to noise. Moreover, it is not fully understood how effective the brain communication is with the muscles at frequencies above the movement frequency and its harmonics. We developed a novel Electromyography (EMG)-projected MEG source imaging technique for localizing M1 during ~1 minute recordings of left and right self-paced finger movements (~1 Hz). High-resolution MEG source images were obtained by projecting M1 activity towards the skin EMG signal without trial averaging. We studied delta (1-4 Hz), theta (4-7 Hz), alpha (8-12 Hz), beta (15-30 Hz), and gamma (30-90 Hz) bands in 13 healthy participants (26 datasets) and two presurgical patients with sensorimotor dysfunction. In healthy participants, EMG-projected MEG accurately localized M1 with high accuracy in delta (100.0%), theta (100.0%), and beta (76.9%) bands, but not alpha (34.6%) and gamma (0.0%) bands. Except for delta, all other frequency bands were above the movement frequency and its harmonics. In both presurgical patients, M1 activity in the affected hemisphere was also accurately localized, despite highly irregular EMG movement patterns in one patient. Altogether, our EMG-projected MEG imaging approach is highly accurate and feasible for M1 mapping in presurgical patients. The results also provide insight into movement related brain-muscle coupling above the movement frequency and its harmonics.
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Affiliation(s)
- Ming-Xiong Huang
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Radiology, University of California, San Diego, CA, USA
- Department of Electrical and Computer Engineering, University of California, San Diego, CA, USA
| | - Deborah L. Harrington
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Radiology, University of California, San Diego, CA, USA
| | | | - Zhengwei Ji
- Department of Radiology, University of California, San Diego, CA, USA
| | - Ashley Robb-Swan
- Department of Radiology, University of California, San Diego, CA, USA
| | - Charles W. Huang
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Qian Shen
- Department of Radiology, University of California, San Diego, CA, USA
| | - Hayden Hansen
- Department of Radiology, University of California, San Diego, CA, USA
| | - Jared Baumgartner
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA
| | | | - Sharon Nichols
- Department of Neurosciences, University of California, San Diego, CA, USA
| | - Joanna Jacobus
- Department of Psychiatry, University of California, San Diego, CA, USA
| | - Tao Song
- Department of Radiology, University of California, San Diego, CA, USA
| | - Imanuel Lerman
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA
| | - Maksim Bazhenov
- Department of Medicine, University of California, San Diego, CA, USA
| | - Giri P Krishnan
- Department of Medicine, University of California, San Diego, CA, USA
| | - Dewleen G. Baker
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Psychiatry, University of California, San Diego, CA, USA
- VA Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, USA
| | - Ramesh Rao
- Department of Electrical and Computer Engineering, University of California, San Diego, CA, USA
| | - Roland R. Lee
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Radiology, University of California, San Diego, CA, USA
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5
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Fred AL, Kumar SN, Kumar Haridhas A, Ghosh S, Purushothaman Bhuvana H, Sim WKJ, Vimalan V, Givo FAS, Jousmäki V, Padmanabhan P, Gulyás B. A Brief Introduction to Magnetoencephalography (MEG) and Its Clinical Applications. Brain Sci 2022; 12:brainsci12060788. [PMID: 35741673 PMCID: PMC9221302 DOI: 10.3390/brainsci12060788] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/01/2022] [Accepted: 06/08/2022] [Indexed: 11/30/2022] Open
Abstract
Magnetoencephalography (MEG) plays a pivotal role in the diagnosis of brain disorders. In this review, we have investigated potential MEG applications for analysing brain disorders. The signal-to-noise ratio (SNRMEG = 2.2 db, SNREEG < 1 db) and spatial resolution (SRMEG = 2−3 mm, SREEG = 7−10 mm) is higher for MEG than EEG, thus MEG potentially facilitates accurate monitoring of cortical activity. We found that the direct electrophysiological MEG signals reflected the physiological status of neurological disorders and play a vital role in disease diagnosis. Single-channel connectivity, as well as brain network analysis, using MEG data acquired during resting state and a given task has been used for the diagnosis of neurological disorders such as epilepsy, Alzheimer’s, Parkinsonism, autism, and schizophrenia. The workflow of MEG and its potential applications in the diagnosis of disease and therapeutic planning are also discussed. We forecast that computer-aided algorithms will play a prominent role in the diagnosis and prediction of neurological diseases in the future. The outcome of this narrative review will aid researchers to utilise MEG in diagnostics.
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Affiliation(s)
- Alfred Lenin Fred
- Department of CSE, Mar Ephraem College of Engineering and Technology, Marthandam 629171, Tamil Nadu, India; (A.L.F.); (F.A.S.G.)
| | | | - Ajay Kumar Haridhas
- Department of ECE, Mar Ephraem College of Engineering and Technology, Marthandam 629171, Tamil Nadu, India;
| | - Sayantan Ghosh
- Department of Integrative Biology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India;
| | - Harishita Purushothaman Bhuvana
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore; (H.P.B.); (W.K.J.S.); (V.V.); (V.J.)
| | - Wei Khang Jeremy Sim
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore; (H.P.B.); (W.K.J.S.); (V.V.); (V.J.)
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore
| | - Vijayaragavan Vimalan
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore; (H.P.B.); (W.K.J.S.); (V.V.); (V.J.)
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore
| | - Fredin Arun Sedly Givo
- Department of CSE, Mar Ephraem College of Engineering and Technology, Marthandam 629171, Tamil Nadu, India; (A.L.F.); (F.A.S.G.)
| | - Veikko Jousmäki
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore; (H.P.B.); (W.K.J.S.); (V.V.); (V.J.)
- Aalto NeuroImaging, Department of Neuroscience and Biomedical Engineering, Aalto University, 12200 Espoo, Finland
| | - Parasuraman Padmanabhan
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore; (H.P.B.); (W.K.J.S.); (V.V.); (V.J.)
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore
- Correspondence: (P.P.); (B.G.)
| | - Balázs Gulyás
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore; (H.P.B.); (W.K.J.S.); (V.V.); (V.J.)
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore
- Department of Clinical Neuroscience, Karolinska Institute, 17176 Stockholm, Sweden
- Correspondence: (P.P.); (B.G.)
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6
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de Lange P, Boto E, Holmes N, Hill RM, Bowtell R, Wens V, De Tiège X, Brookes MJ, Bourguignon M. Measuring the cortical tracking of speech with optically-pumped magnetometers. Neuroimage 2021; 233:117969. [PMID: 33744453 DOI: 10.1016/j.neuroimage.2021.117969] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 01/08/2021] [Accepted: 03/04/2021] [Indexed: 11/25/2022] Open
Abstract
During continuous speech listening, brain activity tracks speech rhythmicity at frequencies matching with the repetition rate of phrases (0.2-1.5 Hz), words (2-4 Hz) and syllables (4-8 Hz). Here, we evaluated the applicability of wearable MEG based on optically-pumped magnetometers (OPMs) to measure such cortical tracking of speech (CTS). Measuring CTS with OPMs is a priori challenging given the complications associated with OPM measurements at frequencies below 4 Hz, due to increased intrinsic interference and head movement artifacts. Still, this represents an important development as OPM-MEG provides lifespan compliance and substantially improved spatial resolution compared with classical MEG. In this study, four healthy right-handed adults listened to continuous speech for 9 min. The radial component of the magnetic field was recorded simultaneously with 45-46 OPMs evenly covering the scalp surface and fixed to an additively manufactured helmet which fitted all 4 participants. We estimated CTS with reconstruction accuracy and coherence, and determined the number of dominant principal components (PCs) to remove from the data (as a preprocessing step) for optimal estimation. We also identified the dominant source of CTS using a minimum norm estimate. CTS estimated with reconstruction accuracy and coherence was significant in all 4 participants at phrasal and word rates, and in 3 participants (reconstruction accuracy) or 2 (coherence) at syllabic rate. Overall, close-to-optimal CTS estimation was obtained when the 3 (reconstruction accuracy) or 10 (coherence) first PCs were removed from the data. Importantly, values of reconstruction accuracy (~0.4 for 0.2-1.5-Hz CTS and ~0.1 for 2-8-Hz CTS) were remarkably close to those previously reported in classical MEG studies. Finally, source reconstruction localized the main sources of CTS to bilateral auditory cortices. In conclusion, t his study demonstrates that OPMs can be used for the purpose of CTS assessment. This finding opens new research avenues to unravel the neural network involved in CTS across the lifespan and potential alterations in, e.g., language developmental disorders. Data also suggest that OPMs are generally suitable for recording neural activity at frequencies below 4 Hz provided PCA is used as a preprocessing step; 0.2-1.5-Hz being the lowest frequency range successfully investigated here.
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Affiliation(s)
- Paul de Lange
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), 808 Lennik Street, Brussels 1070, Belgium
| | - Elena Boto
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom
| | - Niall Holmes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom
| | - Ryan M Hill
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom
| | - Richard Bowtell
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom
| | - Vincent Wens
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), 808 Lennik Street, Brussels 1070, Belgium; Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Xavier De Tiège
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), 808 Lennik Street, Brussels 1070, Belgium; Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom
| | - Mathieu Bourguignon
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), 808 Lennik Street, Brussels 1070, Belgium; Laboratory of neurophysiology and movement biomechanics, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium; BCBL, Basque Center on Cognition, Brain and Language, San Sebastian 20009, Spain.
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7
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Sensorimotor Mapping With MEG: An Update on the Current State of Clinical Research and Practice With Considerations for Clinical Practice Guidelines. J Clin Neurophysiol 2021; 37:564-573. [PMID: 33165229 DOI: 10.1097/wnp.0000000000000481] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
In this article, we present the clinical indications and advances in the use of magnetoencephalography to map the primary sensorimotor (SM1) cortex in neurosurgical patients noninvasively. We emphasize the advantages of magnetoencephalography over sensorimotor mapping using functional magnetic resonance imaging. Recommendations to the referring physicians and the clinical magnetoencephalographers to achieve appropriate sensorimotor cortex mapping using magnetoencephalography are proposed. We finally provide some practical advice for the use of corticomuscular coherence, cortico-kinematic coherence, and mu rhythm suppression in this indication. Magnetoencephalography should now be considered as a method of reference for presurgical functional mapping of the sensorimotor cortex.
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8
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Lucena Gómez G, Peigneux P, Wens V, Bourguignon M. Localization accuracy of a common beamformer for the comparison of two conditions. Neuroimage 2021; 230:117793. [PMID: 33497769 DOI: 10.1016/j.neuroimage.2021.117793] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 11/03/2020] [Accepted: 01/15/2021] [Indexed: 11/25/2022] Open
Abstract
The linearly constrained minimum variance beamformer is frequently used to reconstruct sources underpinning neuromagnetic recordings. When reconstructions must be compared across conditions, it is considered good practice to use a single, "common" beamformer estimated from all the data at once. This is to ensure that differences between conditions are not ascribable to differences in beamformer weights. Here, we investigate the localization accuracy of such a common beamformer. Based on theoretical derivations, we first show that the common beamformer leads to localization errors in source reconstruction. We then turn to simulations in which we attempt to reconstruct a (genuine) source in a first condition, while considering a second condition in which there is an (interfering) source elsewhere in the brain. We estimate maps of mislocalization and assess statistically the difference between "standard" and "common" beamformers. We complement our findings with an application to experimental MEG data. The results show that the common beamformer may yield significant mislocalization. Specifically, the common beamformer may force the genuine source to be reconstructed closer to the interfering source than it really is. As the same applies to the reconstruction of the interfering source, both sources are pulled closer together than they are. This observation was further illustrated in experimental data. Thus, although the common beamformer allows for the comparison of conditions, in some circumstances it introduces localization inaccuracies. We recommend alternative approaches to the general problem of comparing conditions.
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Affiliation(s)
- Gustavo Lucena Gómez
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.
| | - Philippe Peigneux
- UR2NF - Neuropsychology and Functional Neuroimaging Research Unit at CRCN - Centre de Recherches Cognition et Neurosciences, and UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Vincent Wens
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium; Magnetoencephalography unit, Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Brussels, Belgium
| | - Mathieu Bourguignon
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium; BCBL, Basque Center on Cognition, Brain and Language, 20009 San Sebastian, Spain; Laboratoire Cognition Langage et Développement, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
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9
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Huang MX, Robb Swan A, Angeles Quinto A, Huang JW, De-la-Garza BG, Huang CW, Hesselink JR, Bigler ED, Wilde EA, Max JE. Resting-State Magnetoencephalography Source Imaging Pilot Study in Children with Mild Traumatic Brain Injury. J Neurotrauma 2019; 37:994-1001. [PMID: 31724480 DOI: 10.1089/neu.2019.6417] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Mild traumatic brain injury (mTBI) accounts for the vast majority of all pediatric TBI. An important minority of children who have suffered an mTBI have enduring cognitive and emotional symptoms. However, the mechanisms of chronic symptoms in children with pediatric mTBI are not fully understood. This is in part due to the limited sensitivity of conventional neuroimaging technologies. The present study examined resting-state magnetoencephalography (rs-MEG) source images in 12 children who had mTBI and 12 age-matched control children. The rs-MEG exams were performed in children with mTBI 6 months after injury when they reported no clinically significant post-injury psychiatric changes and few if any somatic sensorimotor symptoms but did report cognitive symptoms. MEG source magnitude images were obtained for different frequency bands in alpha (8-12 Hz), beta (15-30 Hz), gamma (30-90 Hz), and low-frequency (1-7 Hz) bands. In contrast to the control participants, rs-MEG source imaging in the children with mTBI showed: 1) hyperactivity from the bilateral insular cortices in alpha, beta, and low-frequency bands, from the left amygdala in alpha band, and from the left precuneus in beta band; 2) hypoactivity from the bilateral dorsolateral prefrontal cortices (dlPFC) in alpha and beta bands, from the ventromedial prefrontal cortex (vmPFC) in beta band, from the ventrolateral prefrontal cortex (vlPFC) in gamma band, from the anterior cingulate cortex (ACC) in alpha band, and from the right precuneus in alpha band. The present study showed that MEG source imaging technique revealed abnormalities in the resting-state electromagnetic signals from the children with mTBI.
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Affiliation(s)
- Ming-Xiong Huang
- Department of Radiology, University of California, San Diego, California.,Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, California
| | - Ashley Robb Swan
- Department of Radiology, University of California, San Diego, California.,Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, California
| | - Annemarie Angeles Quinto
- Department of Radiology, University of California, San Diego, California.,Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, California
| | - Jeffrey W Huang
- Department of Computer Sciences, Columbia University, New York, New York
| | | | - Charles W Huang
- Department of Bioengineering, Stanford University, Stanford, California
| | - John R Hesselink
- Department of Radiology, University of California, San Diego, California
| | - Erin D Bigler
- Department of Neurology, University of Utah, Salt Lake City, Utah
| | | | - Jeffrey E Max
- Department of Psychiatry, University of California, San Diego, California.,Department of Psychiatry, Rady Children's Hospital, San Diego, California
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10
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Chen YH, Howell B, Edgar JC, Huang M, Kochunov P, Hunter MA, Wootton C, Lu BY, Bustillo J, Sadek JR, Miller GA, Cañive JM. Associations and Heritability of Auditory Encoding, Gray Matter, and Attention in Schizophrenia. Schizophr Bull 2019; 45:859-870. [PMID: 30099543 PMCID: PMC6581123 DOI: 10.1093/schbul/sby111] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND Auditory encoding abnormalities, gray-matter loss, and cognitive deficits are all candidate schizophrenia (SZ) endophenotypes. This study evaluated associations between and heritability of auditory network attributes (function and structure) and attention in healthy controls (HC), SZ patients, and unaffected relatives (UR). METHODS Whole-brain maps of M100 auditory activity from magnetoencephalography recordings, cortical thickness (CT), and a measure of attention were obtained from 70 HC, 69 SZ patients, and 35 UR. Heritability estimates (h2r) were obtained for M100, CT at each group-difference region, and the attention measure. RESULTS SZ patients had weaker bilateral superior temporal gyrus (STG) M100 responses than HC and a weaker right frontal M100 response than UR. Abnormally large M100 responses in left superior frontal gyrus were observed in UR and SZ patients. SZ patients showed smaller CT in bilateral STG and right frontal regions. Interrelatedness between 3 putative SZ endophenotypes was demonstrated, although in the left STG the M100 and CT function-structure associations observed in HC and UR were absent in SZ patients. Heritability analyses also showed that right frontal M100 and bilateral STG CT measures are significantly heritable. CONCLUSIONS Present findings indicated that the 3 SZ endophenotypes examined are not isolated markers of pathology but instead are connected. The pattern of auditory encoding group differences and the pattern of brain function-structure associations differ as a function of brain region, indicating the need for regional specificity when studying these endophenotypes, and with the presence of left STG function-structure associations in HC and UR but not in SZ perhaps reflecting disease-associated damage to gray matter that disrupts function-structure relationships in SZ.
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Affiliation(s)
- Yu-Han Chen
- Department of Radiology, Lurie Family Foundations MEG Imaging Center, Children’s Hospital of Philadelphia, Philadelphia, PA,To whom correspondence should be addressed; Department of Radiology, The Children’s Hospital of Philadelphia, Seashore House 1F Room 116B, Philadelphia, PA 19104, USA; tel: +1(267)426-0959, fax: +1(267)425-2465, e-mail:
| | - Breannan Howell
- Department of Psychology, The University of New Mexico, Albuquerque, NM,Department of Psychiatry and Behavioral Sciences, Center for Psychiatric Research, The University of New Mexico, Albuquerque, NM
| | - J Christopher Edgar
- Department of Radiology, Lurie Family Foundations MEG Imaging Center, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Mingxiong Huang
- Department of Radiology, University of California, San Diego, San Diego, CA,Department of Radiology, VA San Diego Healthcare System, US Department of Veterans Affairs, San Diego, CA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, The University of Maryland, Baltimore, MD
| | - Michael A Hunter
- Department of Psychology, The University of New Mexico, Albuquerque, NM,Department of Psychiatry and Behavioral Sciences, Center for Psychiatric Research, The University of New Mexico, Albuquerque, NM
| | - Cassandra Wootton
- Department of Psychiatry and Behavioral Sciences, Center for Psychiatric Research, The University of New Mexico, Albuquerque, NM
| | - Brett Y Lu
- Department of Psychiatry, University of Hawaii at Manoa, Honolulu, HI
| | - Juan Bustillo
- Department of Psychiatry and Behavioral Sciences, Center for Psychiatric Research, The University of New Mexico, Albuquerque, NM
| | - Joseph R Sadek
- Psychiatry Research, New Mexico VA Health Care System, Raymond G. Murphy VA Medical Center, US Department of Veterans Affairs, Albuquerque, NM
| | - Gregory A Miller
- Department of Psychology, University of California, Los Angeles, CA,Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA
| | - José M Cañive
- Department of Psychiatry and Behavioral Sciences, Center for Psychiatric Research, The University of New Mexico, Albuquerque, NM,Psychiatry Research, New Mexico VA Health Care System, Raymond G. Murphy VA Medical Center, US Department of Veterans Affairs, Albuquerque, NM
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11
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Comparing the Performance of Popular MEG/EEG Artifact Correction Methods in an Evoked-Response Study. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2016; 2016:7489108. [PMID: 27524998 PMCID: PMC4972935 DOI: 10.1155/2016/7489108] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Accepted: 06/23/2016] [Indexed: 12/05/2022]
Abstract
We here compared results achieved by applying popular methods for reducing artifacts in magnetoencephalography (MEG) and electroencephalography (EEG) recordings of the auditory evoked Mismatch Negativity (MMN) responses in healthy adult subjects. We compared the Signal Space Separation (SSS) and temporal SSS (tSSS) methods for reducing noise from external and nearby sources. Our results showed that tSSS reduces the interference level more reliably than plain SSS, particularly for MEG gradiometers, also for healthy subjects not wearing strongly interfering magnetic material. Therefore, tSSS is recommended over SSS. Furthermore, we found that better artifact correction is achieved by applying Independent Component Analysis (ICA) in comparison to Signal Space Projection (SSP). Although SSP reduces the baseline noise level more than ICA, SSP also significantly reduces the signal—slightly more than it reduces the artifacts interfering with the signal. However, ICA also adds noise, or correction errors, to the waveform when the signal-to-noise ratio (SNR) in the original data is relatively low—in particular to EEG and to MEG magnetometer data. In conclusion, ICA is recommended over SSP, but one should be careful when applying ICA to reduce artifacts on neurophysiological data with relatively low SNR.
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12
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Resting state cortical oscillations of patients with Parkinson disease and with and without subthalamic deep brain stimulation: a magnetoencephalography study. J Clin Neurophysiol 2016; 32:109-18. [PMID: 25233246 DOI: 10.1097/wnp.0000000000000137] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
PURPOSE In this study, we investigate the modification to cortical oscillations of patients with Parkinson disease (PD) by subthalamic deep brain stimulation (STN-DBS). METHODS Spontaneous cortical oscillations of patients with PD were recorded with magnetoencephalography during on and off subthalamic nucleus deep brain stimulation states. Several features such as average frequency, average power, and relative subband power in regions of interest were extracted in the frequency domain, and these features were correlated with Unified Parkinson Disease Rating Scale III evaluation. The same features were also investigated in patients with PD without surgery and healthy controls. RESULTS Patients with Parkinson disease without surgery compared with healthy controls had a significantly lower average frequency and an increased average power in 1 to 48 Hz range in whole cortex. Higher relative power in theta and simultaneous decrease in beta and gamma over temporal and occipital were also observed in patients with PD. The Unified Parkinson Disease Rating Scale III rigidity score correlated with the average frequency and with the relative power of beta and gamma in frontal areas. During subthalamic nucleus deep brain stimulation, the average frequency increased significantly when stimulation was on compared with off state. In addition, the relative power dropped in delta, whereas it rose in beta over the whole cortex. Through the course of stimulation, the Unified Parkinson Disease Rating Scale III rigidity and tremor scores correlated with the relative power of alpha over left parietal. CONCLUSIONS Subthalamic nucleus deep brain stimulation improves the symptoms of PD by suppressing the synchronization of alpha rhythm in somatomotor region.
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13
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Migliorelli C, Alonso JF, Romero S, Mañanas MA, Nowak R, Russi A. Influence of metallic artifact filtering on MEG signals for source localization during interictal epileptiform activity. J Neural Eng 2016; 13:026029. [DOI: 10.1088/1741-2560/13/2/026029] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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14
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Chen YH, Stone-Howell B, Edgar JC, Huang M, Wootton C, Hunter MA, Lu BY, Sadek JR, Miller GA, Cañive JM. Frontal slow-wave activity as a predictor of negative symptoms, cognition and functional capacity in schizophrenia. Br J Psychiatry 2016; 208:160-7. [PMID: 26206861 PMCID: PMC4837382 DOI: 10.1192/bjp.bp.114.156075] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2014] [Revised: 11/04/2014] [Accepted: 11/08/2014] [Indexed: 12/30/2022]
Abstract
BACKGROUND Increased temporal and frontal slow-wave delta (1-4 Hz) and theta (4-7 Hz) activities are the most consistent resting-state neural abnormalities reported in schizophrenia. The frontal lobe is associated with negative symptoms and cognitive abilities such as attention, with negative symptoms and impaired attention associated with poor functional capacity. AIMS To establish whether frontal dysfunction, as indexed by slowing, would be associated with functional impairments. METHOD Eyes-closed magnetoencephalography data were collected in 41 participants with schizophrenia and 37 healthy controls, and frequency-domain source imaging localised delta and theta activity. RESULTS Elevated delta and theta activity in right frontal and right temporoparietal regions was observed in the schizophrenia v. CONTROL GROUP In schizophrenia, right-frontal delta activity was uniquely associated with negative but not positive symptoms. In the full sample, increased right-frontal delta activity predicted poorer attention and functional capacity. CONCLUSIONS Our findings suggest that treatment-associated decreases in slow-wave activity could be accompanied by improved functional outcome and thus better prognosis.
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Affiliation(s)
- Yu-Han Chen
- Yu-Han Chen, PhD, The Children's Hospital of Philadelphia and University of Pennsylvania, Department of Radiology, Philadelphia; Breannan Stone-Howell, MS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico; J. Christopher Edgar, PhD, The Children's Hospital of Philadelphia and University of Pennsylvania, Department of Radiology, Philadelphia; Mingxiong Huang, PhD, University of California, San Diego, Department of Radiology, and San Diego VA Healthcare System, Department of Radiology, San Diego, California; Cassandra Wootton, BS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico; Michael A. Hunter, BS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, The University of New Mexico School of Medicine, Department of Psychiatry and Department of Psychology, Albuquerque, New Mexico; Brett Y. Lu, MD, PhD, The University of Hawaii at Manoa, Department of Psychiatry, Honolulu, Hawaii; Joseph R. Sadek, PhD, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, New Mexico; Gregory A. Miller, PhD, University of California, Los Angeles, Department of Psychology, Los Angeles, California; José M. Canĩve, MD, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico, USA
| | - Breannan Stone-Howell
- Yu-Han Chen, PhD, The Children's Hospital of Philadelphia and University of Pennsylvania, Department of Radiology, Philadelphia; Breannan Stone-Howell, MS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico; J. Christopher Edgar, PhD, The Children's Hospital of Philadelphia and University of Pennsylvania, Department of Radiology, Philadelphia; Mingxiong Huang, PhD, University of California, San Diego, Department of Radiology, and San Diego VA Healthcare System, Department of Radiology, San Diego, California; Cassandra Wootton, BS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico; Michael A. Hunter, BS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, The University of New Mexico School of Medicine, Department of Psychiatry and Department of Psychology, Albuquerque, New Mexico; Brett Y. Lu, MD, PhD, The University of Hawaii at Manoa, Department of Psychiatry, Honolulu, Hawaii; Joseph R. Sadek, PhD, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, New Mexico; Gregory A. Miller, PhD, University of California, Los Angeles, Department of Psychology, Los Angeles, California; José M. Canĩve, MD, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico, USA
| | - J Christopher Edgar
- Yu-Han Chen, PhD, The Children's Hospital of Philadelphia and University of Pennsylvania, Department of Radiology, Philadelphia; Breannan Stone-Howell, MS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico; J. Christopher Edgar, PhD, The Children's Hospital of Philadelphia and University of Pennsylvania, Department of Radiology, Philadelphia; Mingxiong Huang, PhD, University of California, San Diego, Department of Radiology, and San Diego VA Healthcare System, Department of Radiology, San Diego, California; Cassandra Wootton, BS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico; Michael A. Hunter, BS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, The University of New Mexico School of Medicine, Department of Psychiatry and Department of Psychology, Albuquerque, New Mexico; Brett Y. Lu, MD, PhD, The University of Hawaii at Manoa, Department of Psychiatry, Honolulu, Hawaii; Joseph R. Sadek, PhD, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, New Mexico; Gregory A. Miller, PhD, University of California, Los Angeles, Department of Psychology, Los Angeles, California; José M. Canĩve, MD, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico, USA
| | - Mingxiong Huang
- Yu-Han Chen, PhD, The Children's Hospital of Philadelphia and University of Pennsylvania, Department of Radiology, Philadelphia; Breannan Stone-Howell, MS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico; J. Christopher Edgar, PhD, The Children's Hospital of Philadelphia and University of Pennsylvania, Department of Radiology, Philadelphia; Mingxiong Huang, PhD, University of California, San Diego, Department of Radiology, and San Diego VA Healthcare System, Department of Radiology, San Diego, California; Cassandra Wootton, BS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico; Michael A. Hunter, BS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, The University of New Mexico School of Medicine, Department of Psychiatry and Department of Psychology, Albuquerque, New Mexico; Brett Y. Lu, MD, PhD, The University of Hawaii at Manoa, Department of Psychiatry, Honolulu, Hawaii; Joseph R. Sadek, PhD, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, New Mexico; Gregory A. Miller, PhD, University of California, Los Angeles, Department of Psychology, Los Angeles, California; José M. Canĩve, MD, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico, USA
| | - Cassandra Wootton
- Yu-Han Chen, PhD, The Children's Hospital of Philadelphia and University of Pennsylvania, Department of Radiology, Philadelphia; Breannan Stone-Howell, MS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico; J. Christopher Edgar, PhD, The Children's Hospital of Philadelphia and University of Pennsylvania, Department of Radiology, Philadelphia; Mingxiong Huang, PhD, University of California, San Diego, Department of Radiology, and San Diego VA Healthcare System, Department of Radiology, San Diego, California; Cassandra Wootton, BS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico; Michael A. Hunter, BS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, The University of New Mexico School of Medicine, Department of Psychiatry and Department of Psychology, Albuquerque, New Mexico; Brett Y. Lu, MD, PhD, The University of Hawaii at Manoa, Department of Psychiatry, Honolulu, Hawaii; Joseph R. Sadek, PhD, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, New Mexico; Gregory A. Miller, PhD, University of California, Los Angeles, Department of Psychology, Los Angeles, California; José M. Canĩve, MD, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico, USA
| | - Michael A Hunter
- Yu-Han Chen, PhD, The Children's Hospital of Philadelphia and University of Pennsylvania, Department of Radiology, Philadelphia; Breannan Stone-Howell, MS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico; J. Christopher Edgar, PhD, The Children's Hospital of Philadelphia and University of Pennsylvania, Department of Radiology, Philadelphia; Mingxiong Huang, PhD, University of California, San Diego, Department of Radiology, and San Diego VA Healthcare System, Department of Radiology, San Diego, California; Cassandra Wootton, BS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico; Michael A. Hunter, BS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, The University of New Mexico School of Medicine, Department of Psychiatry and Department of Psychology, Albuquerque, New Mexico; Brett Y. Lu, MD, PhD, The University of Hawaii at Manoa, Department of Psychiatry, Honolulu, Hawaii; Joseph R. Sadek, PhD, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, New Mexico; Gregory A. Miller, PhD, University of California, Los Angeles, Department of Psychology, Los Angeles, California; José M. Canĩve, MD, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico, USA
| | - Brett Y Lu
- Yu-Han Chen, PhD, The Children's Hospital of Philadelphia and University of Pennsylvania, Department of Radiology, Philadelphia; Breannan Stone-Howell, MS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico; J. Christopher Edgar, PhD, The Children's Hospital of Philadelphia and University of Pennsylvania, Department of Radiology, Philadelphia; Mingxiong Huang, PhD, University of California, San Diego, Department of Radiology, and San Diego VA Healthcare System, Department of Radiology, San Diego, California; Cassandra Wootton, BS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico; Michael A. Hunter, BS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, The University of New Mexico School of Medicine, Department of Psychiatry and Department of Psychology, Albuquerque, New Mexico; Brett Y. Lu, MD, PhD, The University of Hawaii at Manoa, Department of Psychiatry, Honolulu, Hawaii; Joseph R. Sadek, PhD, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, New Mexico; Gregory A. Miller, PhD, University of California, Los Angeles, Department of Psychology, Los Angeles, California; José M. Canĩve, MD, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico, USA
| | - Joseph R Sadek
- Yu-Han Chen, PhD, The Children's Hospital of Philadelphia and University of Pennsylvania, Department of Radiology, Philadelphia; Breannan Stone-Howell, MS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico; J. Christopher Edgar, PhD, The Children's Hospital of Philadelphia and University of Pennsylvania, Department of Radiology, Philadelphia; Mingxiong Huang, PhD, University of California, San Diego, Department of Radiology, and San Diego VA Healthcare System, Department of Radiology, San Diego, California; Cassandra Wootton, BS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico; Michael A. Hunter, BS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, The University of New Mexico School of Medicine, Department of Psychiatry and Department of Psychology, Albuquerque, New Mexico; Brett Y. Lu, MD, PhD, The University of Hawaii at Manoa, Department of Psychiatry, Honolulu, Hawaii; Joseph R. Sadek, PhD, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, New Mexico; Gregory A. Miller, PhD, University of California, Los Angeles, Department of Psychology, Los Angeles, California; José M. Canĩve, MD, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico, USA
| | - Gregory A Miller
- Yu-Han Chen, PhD, The Children's Hospital of Philadelphia and University of Pennsylvania, Department of Radiology, Philadelphia; Breannan Stone-Howell, MS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico; J. Christopher Edgar, PhD, The Children's Hospital of Philadelphia and University of Pennsylvania, Department of Radiology, Philadelphia; Mingxiong Huang, PhD, University of California, San Diego, Department of Radiology, and San Diego VA Healthcare System, Department of Radiology, San Diego, California; Cassandra Wootton, BS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico; Michael A. Hunter, BS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, The University of New Mexico School of Medicine, Department of Psychiatry and Department of Psychology, Albuquerque, New Mexico; Brett Y. Lu, MD, PhD, The University of Hawaii at Manoa, Department of Psychiatry, Honolulu, Hawaii; Joseph R. Sadek, PhD, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, New Mexico; Gregory A. Miller, PhD, University of California, Los Angeles, Department of Psychology, Los Angeles, California; José M. Canĩve, MD, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico, USA
| | - José M Cañive
- Yu-Han Chen, PhD, The Children's Hospital of Philadelphia and University of Pennsylvania, Department of Radiology, Philadelphia; Breannan Stone-Howell, MS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico; J. Christopher Edgar, PhD, The Children's Hospital of Philadelphia and University of Pennsylvania, Department of Radiology, Philadelphia; Mingxiong Huang, PhD, University of California, San Diego, Department of Radiology, and San Diego VA Healthcare System, Department of Radiology, San Diego, California; Cassandra Wootton, BS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico; Michael A. Hunter, BS, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, The University of New Mexico School of Medicine, Department of Psychiatry and Department of Psychology, Albuquerque, New Mexico; Brett Y. Lu, MD, PhD, The University of Hawaii at Manoa, Department of Psychiatry, Honolulu, Hawaii; Joseph R. Sadek, PhD, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, New Mexico; Gregory A. Miller, PhD, University of California, Los Angeles, Department of Psychology, Los Angeles, California; José M. Canĩve, MD, New Mexico Raymond G. Murphy VA Healthcare System, Psychiatry Research, Albuquerque, and The University of New Mexico School of Medicine, Department of Psychiatry, Albuquerque, New Mexico, USA
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15
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Reliable recording and analysis of MEG-based corticokinematic coherence in the presence of strong magnetic artifacts. Clin Neurophysiol 2016; 127:1460-1469. [DOI: 10.1016/j.clinph.2015.07.030] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2015] [Revised: 07/08/2015] [Accepted: 07/25/2015] [Indexed: 11/21/2022]
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16
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Robb Swan A, Nichols S, Drake A, Angeles A, Diwakar M, Song T, Lee RR, Huang MX. Magnetoencephalography Slow-Wave Detection in Patients with Mild Traumatic Brain Injury and Ongoing Symptoms Correlated with Long-Term Neuropsychological Outcome. J Neurotrauma 2015; 32:1510-21. [PMID: 25808909 DOI: 10.1089/neu.2014.3654] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Mild traumatic brain injury (mTBI) is common in the United States, accounting for as many as 75-80% of all TBIs. It is recognized as a significant public health concern, but there are ongoing controversies regarding the etiology of persistent symptoms post-mTBI. This constellation of nonspecific symptoms is referred to as postconcussive syndrome (PCS). The present study combined results from magnetoencephalography (MEG) and cognitive assessment to examine group differences and relationships between brain activity and cognitive performance in 31 military and civilian individuals with a history of mTBI+PCS and 33 matched healthy control subjects. An operator-free analysis was used for MEG data to increase reliability of the technique. Subjects completed a comprehensive neuropsychological assessment, and measures of abnormal slow-wave activity from MEG were collected. Results demonstrated significant group differences on measures of executive functioning and processing speed. In addition, significant correlations between slow-wave activity on MEG and patterns of cognitive functioning were found in cortical areas, consistent with cognitive impairments on exams. Results provide more objective evidence that there may be subtle changes to the neurobiological integrity of the brain that can be detected by MEG. Further, these findings suggest that these abnormalities are associated with cognitive outcomes and may account, at least in part, for long-term PCS in those who have sustained an mTBI.
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Affiliation(s)
- Ashley Robb Swan
- 1 Research Services, VA San Diego Healthcare System , San Diego, California.,3 Department of Radiology, University of California , San Diego, San Diego, California
| | - Sharon Nichols
- 4 Department of Neuroscience, University of California , San Diego, San Diego, California
| | - Angela Drake
- 5 Department of Community Health, National University , San Diego, California
| | - AnneMarie Angeles
- 1 Research Services, VA San Diego Healthcare System , San Diego, California.,3 Department of Radiology, University of California , San Diego, San Diego, California
| | - Mithun Diwakar
- 3 Department of Radiology, University of California , San Diego, San Diego, California
| | - Tao Song
- 3 Department of Radiology, University of California , San Diego, San Diego, California
| | - Roland R Lee
- 1 Research Services, VA San Diego Healthcare System , San Diego, California.,2 Radiology Services, VA San Diego Healthcare System , San Diego, California.,3 Department of Radiology, University of California , San Diego, San Diego, California
| | - Ming-Xiong Huang
- 1 Research Services, VA San Diego Healthcare System , San Diego, California.,2 Radiology Services, VA San Diego Healthcare System , San Diego, California.,3 Department of Radiology, University of California , San Diego, San Diego, California
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17
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Migliorelli C, Alonso JF, Romero S, Mañanas MA, Nowak R, Russi A. Automatic BSS-based filtering of metallic interference in MEG recordings: definition and validation using simulated signals. J Neural Eng 2015; 12:046001. [DOI: 10.1088/1741-2560/12/4/046001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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18
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Gonzalez-Moreno A, Aurtenetxe S, Lopez-Garcia ME, del Pozo F, Maestu F, Nevado A. Signal-to-noise ratio of the MEG signal after preprocessing. J Neurosci Methods 2013; 222:56-61. [PMID: 24200506 DOI: 10.1016/j.jneumeth.2013.10.019] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2013] [Revised: 10/22/2013] [Accepted: 10/26/2013] [Indexed: 11/30/2022]
Abstract
BACKGROUND Magnetoencephalography (MEG) provides a direct measure of brain activity with high combined spatiotemporal resolution. Preprocessing is necessary to reduce contributions from environmental interference and biological noise. NEW METHOD The effect on the signal-to-noise ratio of different preprocessing techniques is evaluated. The signal-to-noise ratio (SNR) was defined as the ratio between the mean signal amplitude (evoked field) and the standard error of the mean over trials. RESULTS Recordings from 26 subjects obtained during and event-related visual paradigm with an Elekta MEG scanner were employed. Two methods were considered as first-step noise reduction: Signal Space Separation and temporal Signal Space Separation, which decompose the signal into components with origin inside and outside the head. Both algorithm increased the SNR by approximately 100%. Epoch-based methods, aimed at identifying and rejecting epochs containing eye blinks, muscular artifacts and sensor jumps provided an SNR improvement of 5-10%. Decomposition methods evaluated were independent component analysis (ICA) and second-order blind identification (SOBI). The increase in SNR was of about 36% with ICA and 33% with SOBI. COMPARISON WITH EXISTING METHODS No previous systematic evaluation of the effect of the typical preprocessing steps in the SNR of the MEG signal has been performed. CONCLUSIONS The application of either SSS or tSSS is mandatory in Elekta systems. No significant differences were found between the two. While epoch-based methods have been routinely applied the less often considered decomposition methods were clearly superior and therefore their use seems advisable.
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Affiliation(s)
- Alicia Gonzalez-Moreno
- Centre for Biomedical Technology, Technical University of Madrid, Campus de Montegancedo, 28223 Madrid, Spain.
| | - Sara Aurtenetxe
- Centre for Biomedical Technology, Technical University of Madrid, Campus de Montegancedo, 28223 Madrid, Spain.
| | - Maria-Eugenia Lopez-Garcia
- Centre for Biomedical Technology, Technical University of Madrid, Campus de Montegancedo, 28223 Madrid, Spain.
| | - Francisco del Pozo
- Centre for Biomedical Technology, Technical University of Madrid, Campus de Montegancedo, 28223 Madrid, Spain.
| | - Fernando Maestu
- Basic Psychology Department II, School of Psychology, Complutense University of Madrid, Campus de Somosaguas, 28223 Madrid, Spain; Centre for Biomedical Technology, Technical University of Madrid, Campus de Montegancedo, 28223 Madrid, Spain.
| | - Angel Nevado
- Basic Psychology Department II, School of Psychology, Complutense University of Madrid, Campus de Somosaguas, 28223 Madrid, Spain; Centre for Biomedical Technology, Technical University of Madrid, Campus de Montegancedo, 28223 Madrid, Spain.
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19
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Implanted medical devices or other strong sources of interference are not barriers to magnetoencephalographic recordings in epilepsy patients. Clin Neurophysiol 2013; 124:1283-9. [DOI: 10.1016/j.clinph.2013.04.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2012] [Revised: 03/20/2013] [Accepted: 04/04/2013] [Indexed: 11/21/2022]
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20
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Wang ZI, Alexopoulos AV, Nair D, Krishnan B, Mosher JC, Burgess RC, Kakisaka Y. Feasibility of magnetoencephalography recording in an epilepsy patient with implanted responsive cortical stimulation device. Clin Neurophysiol 2013; 124:1705-6. [PMID: 23474057 DOI: 10.1016/j.clinph.2013.01.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2012] [Revised: 01/24/2013] [Accepted: 01/31/2013] [Indexed: 11/15/2022]
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21
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Tal O, Diwakar M, Wong CW, Olafsson V, Lee R, Huang MX, Liu TT. Caffeine-Induced Global Reductions in Resting-State BOLD Connectivity Reflect Widespread Decreases in MEG Connectivity. Front Hum Neurosci 2013; 7:63. [PMID: 23459778 PMCID: PMC3586678 DOI: 10.3389/fnhum.2013.00063] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2012] [Accepted: 02/16/2013] [Indexed: 01/22/2023] Open
Abstract
In resting-state functional magnetic resonance imaging (fMRI), the temporal correlation between spontaneous fluctuations of the blood oxygenation level dependent (BOLD) signal from different brain regions is used to assess functional connectivity. However, because the BOLD signal is an indirect measure of neuronal activity, its complex hemodynamic nature can complicate the interpretation of differences in connectivity that are observed across conditions or subjects. For example, prior studies have shown that caffeine leads to widespread reductions in BOLD connectivity but were not able to determine if neural or vascular factors were primarily responsible for the observed decrease. In this study, we used source-localized magnetoencephalography (MEG) in conjunction with fMRI to further examine the origins of the caffeine-induced changes in BOLD connectivity. We observed widespread and significant (p < 0.01) reductions in both MEG and fMRI connectivity measures, suggesting that decreases in the connectivity of resting-state neuro-electric power fluctuations were primarily responsible for the observed BOLD connectivity changes. The MEG connectivity decreases were most pronounced in the beta band. By demonstrating the similarity in MEG and fMRI based connectivity changes, these results provide evidence for the neural basis of resting-state fMRI networks and further support the potential of MEG as a tool to characterize resting-state connectivity.
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Affiliation(s)
- Omer Tal
- Center for Functional MRI, University of California San Diego La Jolla, CA, USA ; Department of Bioengineering, University of California San Diego La Jolla, CA, USA
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22
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Van Poppel M, Wheless JW, Clarke DF, McGregor A, McManis MH, Perkins FF, Van Poppel K, Fulton S, Boop FA. Passive language mapping with magnetoencephalography in pediatric patients with epilepsy. J Neurosurg Pediatr 2012; 10:96-102. [PMID: 22725975 DOI: 10.3171/2012.4.peds11301] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
OBJECT Functional mapping is important for determining surgical candidacy and also in epilepsy surgery planning. However, in young children and uncooperative patients, language mapping has been particularly challenging despite the advances in performing noninvasive functional studies. In this study the authors review a series of children with epilepsy who underwent language mapping with magnetoencephalography (MEG) while sedated or sleeping, to determine receptive language localization for presurgical evaluation. METHODS The authors undertook a retrospective review of patients who underwent MEG between December 2007 and July 2009, and identified 15 individuals who underwent passive language testing as part of their presurgical evaluation because they were unable to participate in traditional language testing, such as Wada or functional MRI. Factors necessitating passive language testing included age and neurocognitive development. RESULTS Three of the 15 patients were deemed candidates for epilepsy surgery based on the results from standard preoperative testing, including video electroencephalography, MRI, and passive receptive language testing using MEG technology. The MEG studies were used successfully to localize language in all 3 patients, creating opportunities for seizure freedom through surgery that would not otherwise have been available. All 3 patients then underwent resective epilepsy surgery without experiencing postoperative language deficits. CONCLUSIONS This case series is the first to look at language mapping during sleep (passive language mapping) in which MEG was used and is the first to evaluate passive language testing in a patient population with intracranial pathological entities. This case series demonstrates that MEG can provide an alternative method for receptive language localization in patients with barriers to more traditional language testing, and in these 3 cases surgery was performed safely based on the results.
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Affiliation(s)
- Mark Van Poppel
- Department of Neurosurgery, University of Tennessee Health Science Center, Memphis, Tennessee, USA
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23
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Kakisaka Y, Mosher JC, Wang ZI, Jin K, Dubarry AS, Alexopoulos AV, Burgess RC. Utility of temporally-extended signal space separation algorithm for magnetic noise from vagal nerve stimulators. Clin Neurophysiol 2012; 124:1277-82. [PMID: 22727713 DOI: 10.1016/j.clinph.2012.03.082] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2011] [Revised: 02/18/2012] [Accepted: 03/10/2012] [Indexed: 11/19/2022]
Abstract
OBJECTIVE To evaluate the utility of a temporally-extended signal space separation algorithm (tSSS) for patients with vagal nerve stimulator (VNS). METHODS We evaluated median nerve somatosensory evoked responses (SER) of magnetoencephalography (MEG) in 27 VNS patients (48 sides) with/without tSSS processing. We classified SER dipoles as 'acceptable' if: (A) the location of the dipole was in the expected location in the central sulcus, and (B) the goodness of fit value (GOF) was greater than 80%. We evaluated (1) the number of sides which produced acceptable dipoles in each dataset (i.e. with/without tSSS processing), and in cases where the both data produced reliable dipoles, (2) compared their GOFs and the 95% confidence volumes (CV) (mm(3)). Statistical differences in the GOF and CV between with/without tSSS conditions were determined by paired t test. RESULTS Only 11 (23%) responses had reliable dipoles without tSSS processing, while all 48 (100%) had acceptable dipoles under tSSS processing. Additionally, the latter group had significantly higher GOF (increased by 7% on average) and lower CV (mean decrease of 200 mm(3)) than the former (p<0.01). CONCLUSIONS Processing with tSSS quantitatively improves dipole fitting of known sources in VNS patients. SIGNIFICANCE This algorithm permits satisfactory MEG testing in the relatively commonly encountered epilepsy patient with VNS.
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Affiliation(s)
- Yosuke Kakisaka
- Epilepsy Center, Department of Neurology, The Cleveland Clinic, Cleveland, OH 44195, USA
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24
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Diwakar M, Tal O, Liu TT, Harrington DL, Srinivasan R, Muzzatti L, Song T, Theilmann RJ, Lee RR, Huang MX. Accurate reconstruction of temporal correlation for neuronal sources using the enhanced dual-core MEG beamformer. Neuroimage 2011; 56:1918-28. [PMID: 21443954 DOI: 10.1016/j.neuroimage.2011.03.042] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2010] [Revised: 02/16/2011] [Accepted: 03/16/2011] [Indexed: 10/18/2022] Open
Abstract
Beamformer spatial filters are commonly used to explore the active neuronal sources underlying magnetoencephalography (MEG) recordings at low signal-to-noise ratio (SNR). Conventional beamformer techniques are successful in localizing uncorrelated neuronal sources under poor SNR conditions. However, the spatial and temporal features from conventional beamformer reconstructions suffer when sources are correlated, which is a common and important property of real neuronal networks. Dual-beamformer techniques, originally developed by Brookes et al. to deal with this limitation, successfully localize highly-correlated sources and determine their orientations and weightings, but their performance degrades at low correlations. They also lack the capability to produce individual time courses and therefore cannot quantify source correlation. In this paper, we present an enhanced formulation of our earlier dual-core beamformer (DCBF) approach that reconstructs individual source time courses and their correlations. Through computer simulations, we show that the enhanced DCBF (eDCBF) consistently and accurately models dual-source activity regardless of the correlation strength. Simulations also show that a multi-core extension of eDCBF effectively handles the presence of additional correlated sources. In a human auditory task, we further demonstrate that eDCBF accurately reconstructs left and right auditory temporal responses and their correlations. Spatial resolution and source localization strategies corresponding to different measures within the eDCBF framework are also discussed. In summary, eDCBF accurately reconstructs source spatio-temporal behavior, providing a means for characterizing complex neuronal networks and their communication.
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Affiliation(s)
- Mithun Diwakar
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
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25
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Carrette E, De Tiège X, De Beeck MO, De Herdt V, Meurs A, Legros B, Raedt R, Deblaere K, Van Roost D, Bourguignon M, Goldman S, Boon P, Van Bogaert P, Vonck K. Magnetoencephalography in epilepsy patients carrying a vagus nerve stimulator. Epilepsy Res 2011; 93:44-52. [DOI: 10.1016/j.eplepsyres.2010.10.011] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2010] [Revised: 10/11/2010] [Accepted: 10/24/2010] [Indexed: 11/29/2022]
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26
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Diwakar M, Huang MX, Srinivasan R, Harrington DL, Robb A, Angeles A, Muzzatti L, Pakdaman R, Song T, Theilmann RJ, Lee RR. Dual-Core Beamformer for obtaining highly correlated neuronal networks in MEG. Neuroimage 2011; 54:253-63. [DOI: 10.1016/j.neuroimage.2010.07.023] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2010] [Revised: 07/01/2010] [Accepted: 07/10/2010] [Indexed: 10/19/2022] Open
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