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PubMed Publications| Keyword search (4,163 papers available) |  |
"Frauscher B" Authored Publications:
| Title | Authors | PubMed ID | |
|---|---|---|---|
| 1 | How vigilance states influence source imaging of physiological brain oscillations: evidence from intracranial EEG | Wei X; Afnan J; Avigdor T; von Ellenrieder N; Delaire É; Royer J; Ho A; Minato E; Schiller K; Jaber K; Wang YL; Moye M; Bernhardt BC; Lina JM; Grova C; Frauscher B; | 41687693 SOH |
| 2 | Personalized biomarkers of multiscale functional alterations in temporal lobe epilepsy | Xie K; Sahlas E; Ngo A; Chen J; Arafat T; Royer J; Zhou Y; Rodríguez-Cruces R; Dascal A; Caldairou B; Fadaie F; Barnett A; Audrain S; Larivière S; Caciagli L; Pana R; Weil AG; Grova C; Frauscher B; Schrader DV; Zhang Z; Concha L; Bernasconi A; Bernasconi N; Bernhardt BC; | 41258102 SOH |
| 3 | Visual Features in Stereo-Electroencephalography to Predict Surgical Outcome: A Multicenter Study | Abdallah C; Thomas J; Aron O; Avigdor T; Jaber K; Doležalová I; Mansilla D; Nevalainen P; Parikh P; Singh J; Beniczky S; Kahane P; Minotti L; Chabardes S; Colnat-Coulbois S; Maillard L; Hall J; Dubeau F; Gotman J; Grova C; Frauscher B; | 40519108 SOH |
| 4 | Spectral and network investigation reveals distinct power and connectivity patterns between phasic and tonic REM sleep | Avigdor T; Peter-Derex L; Ho A; Schiller K; Wang Y; Abdallah C; Delaire E; Jaber K; Travnicek V; Grova C; Frauscher B; | 40394955 SOH |
| 5 | The Awakening Brain is Characterized by a Widespread and Spatiotemporally Heterogeneous Increase in High Frequencies | Avigdor T; Ren G; Abdallah C; Dubeau F; Grova C; Frauscher B; | 40126936 PERFORM |
| 6 | Metrics for evaluation of automatic epileptogenic zone localization in intracranial electrophysiology | Hrtonova V; Nejedly P; Travnicek V; Cimbalnik J; Matouskova B; Pail M; Peter-Derex L; Grova C; Gotman J; Halamek J; Jurak P; Brazdil M; Klimes P; Frauscher B; | 39608298 SOH |
| 7 | NREM sleep brain networks modulate cognitive recovery from sleep deprivation | Lee K; Wang Y; Cross NE; Jegou A; Razavipour F; Pomares FB; Perrault AA; Nguyen A; Aydin Ü; Uji M; Abdallah C; Anticevic A; Frauscher B; Benali H; Dang-Vu TT; Grova C; | 39005401 PERFORM |
| 8 | EEG/MEG source imaging of deep brain activity within the maximum entropy on the mean framework: Simulations and validation in epilepsy | Afnan J; Cai Z; Lina JM; Abdallah C; Delaire E; Avigdor T; Ros V; Hedrich T; von Ellenrieder N; Kobayashi E; Frauscher B; Gotman J; Grova C; | 38994740 SOH |
| 9 | A spatial perturbation framework to validate implantation of the epileptogenic zone | Jaber K; Avigdor T; Mansilla D; Ho A; Thomas J; Abdallah C; Chabardes S; Hall J; Minotti L; Kahane P; Grova C; Gotman J; Frauscher B; | 38897997 SOH |
| 10 | Systematic review of seizure-onset patterns in stereo-electroencephalography: Current state and future directions | Abdallah C; Mansilla D; Minato E; Grova C; Beniczky S; Frauscher B; | 38733701 PERFORM |
| 11 | Consistency of electrical source imaging in presurgical evaluation of epilepsy across different vigilance states | Avigdor T; Abdallah C; Afnan J; Cai Z; Rammal S; Grova C; Frauscher B; | 38217279 PERFORM |
| 12 | Targeted density electrode placement achieves high concordance with traditional high-density EEG for electrical source imaging in epilepsy | Horrillo-Maysonnial A; Avigdor T; Abdallah C; Mansilla D; Thomas J; von Ellenrieder N; Royer J; Bernhardt B; Grova C; Gotman J; Frauscher B; | 37704552 PERFORM |
| 13 | Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas | Afnan J; von Ellenrieder N; Lina JM; Pellegrino G; Arcara G; Cai Z; Hedrich T; Abdallah C; Khajehpour H; Frauscher B; Gotman J; Grova C; | 37149236 PERFORM |
| 14 | Neurophysiology, Neuropsychology, and Epilepsy, in 2022: Hills We Have Climbed and Hills Ahead. Neurophysiology in epilepsy | Frauscher B; Bénar CG; Engel JJ; Grova C; Jacobs J; Kahane P; Wiebe S; Zjilmans M; Dubeau F; | 37119580 PERFORM |
| 15 | Clinical Yield of Electromagnetic Source Imaging and Hemodynamic Responses in Epilepsy: Validation With Intracerebral Data | Abdallah C; Hedrich T; Koupparis A; Afnan J; Hall JA; Gotman J; Dubeau F; von Ellenrieder N; Frauscher B; Kobayashi E; Grova C; | 35473762 PERFORM |
| 16 | Fast oscillations >40 Hz localize the epileptogenic zone: An electrical source imaging study using high-density electroencephalography. | Avigdor T, Abdallah C, von Ellenrieder N, Hedrich T, Rubino A, Lo Russo G, Bernhardt B, Nobili L, Grova C, Frauscher B | 33450578 PERFORM |
| Title: | Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas | ||||
| Authors: | Afnan J, von Ellenrieder N, Lina JM, Pellegrino G, Arcara G, Cai Z, Hedrich T, Abdallah C, Khajehpour H, Frauscher B, Gotman J, Grova C | ||||
| Link: | https://pubmed.ncbi.nlm.nih.gov/37149236/ | ||||
| DOI: | 10.1016/j.neuroimage.2023.120158 | ||||
| Publication: | NeuroImage | ||||
| Keywords: | Intracranial EEG; Magnetoencephalography; Resting state; Source imaging; Spectral analysis; Validation; | ||||
| PMID: | 37149236 | Category: | Date Added: | 2023-05-07 | |
| Dept Affiliation: |
PERFORM
1 Integrated Program in Neuroscience, McGill University, Montréal, Québec H3A 1A1, Canada; Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec H3A 2B4, Canada; Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, Québec H3A 2B4, Canada. Electronic address: jawata.afnan@mail.mcgill.ca. 2 Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, Québec H3A 2B4, Canada. 3 Centre De Recherches Mathématiques, Montréal, Québec H3C 3J7, Canada; Electrical Engineering Department, École De Technologie Supérieure, Montréal, Québec H3C 1K3, Canada; Center for Advanced Research in Sleep Medicine, Sacré-Coeur Hospital, Montréal, Québec, Canada. 4 Epilepsy program, Schulich School of Medicine and Dentistry, Western University, London, Ontario N6A 5C1, Canada. 5 Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy. 6 Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec H3A 2B4, Canada. 7 Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec H3A 2B4, Canada; Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Québec, Canada. 8 Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec H3A 2B4, Canada; Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montréal, Québec H4B 1R6, Canada. 9 Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, Québec H3A 2B4, Canada; Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Québec, Canada. 10 Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, Québec H3A 2B4, Canada. Electronic address: jean.gotman@mcgill.ca. 11 Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec H3A 2B4, Canada; Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, Québec H3A 2B4, Canada; Centre De Recherches Mathématiques, Montréal, Québec H3C 3J7, Canada; Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montréal, Québec H4B 1R6, Canada. Electronic address: christophe.grova@concordia.ca. |
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Description: |
Background: Magnetoencephalography (MEG) is a widely used non-invasive tool to estimate brain activity with high temporal resolution. However, due to the ill-posed nature of the MEG source imaging (MSI) problem, the ability of MSI to identify accurately underlying brain sources along the cortical surface is still uncertain and requires validation. Method: We validated the ability of MSI to estimate the background resting state activity of 45 healthy participants by comparing it to the intracranial EEG (iEEG) atlas (https://mni-open-ieegatlas. Research: mcgill.ca/). First, we applied wavelet-based Maximum Entropy on the Mean (wMEM) as an MSI technique. Next, we converted MEG source maps into intracranial space by applying a forward model to the MEG-reconstructed source maps, and estimated virtual iEEG (ViEEG) potentials on each iEEG channel location; we finally quantitatively compared those with actual iEEG signals from the atlas for 38 regions of interest in the canonical frequency bands. Results: The MEG spectra were more accurately estimated in the lateral regions compared to the medial regions. The regions with higher amplitude in the ViEEG than in the iEEG were more accurately recovered. In the deep regions, MEG-estimated amplitudes were largely underestimated and the spectra were poorly recovered. Overall, our wMEM results were similar to those obtained with minimum norm or beamformer source localization. Moreover, the MEG largely overestimated oscillatory peaks in the alpha band, especially in the anterior and deep regions. This is possibly due to higher phase synchronization of alpha oscillations over extended regions, exceeding the spatial sensitivity of iEEG but detected by MEG. Importantly, we found that MEG-estimated spectra were more comparable to spectra from the iEEG atlas after the aperiodic components were removed. Conclusion: This study identifies brain regions and frequencies for which MEG source analysis is likely to be reliable, a promising step towards resolving the uncertainty in recovering intracerebral activity from non-invasive MEG studies. |
