Keyword search (4,163 papers available)

"Abdallah C" Authored Publications:

Title Authors PubMed ID
1 Hemodynamic correlates of fluctuations in neuronal excitability: A simultaneous Paired Associative Stimulation (PAS) and functional near infra-red spectroscopy (fNIRS) study Cai Z; Pellegrino G; Spilkin A; Delaire E; Uji M; Abdallah C; Lina JM; Fecteau S; Grova C; 40567300
PERFORM
2 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
3 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
4 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
5 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
6 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
7 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
8 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
9 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
10 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
11 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
12 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
13 Evaluation of a personalized functional near infra-red optical tomography workflow using maximum entropy on the mean Cai Z; Uji M; Aydin Ü; Pellegrino G; Spilkin A; Delaire É; Abdallah C; Lina JM; Grova C; 34342073
PERFORM
14 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
15 Magnetoencephalography resting state connectivity patterns as indicatives of surgical outcome in epilepsy patients. Aydin Ü, Pellegrino G, Bin Ka'b Ali O, Abdallah C, Dubeau F, Lina JM, Kobayashi E, Grova C 32191632
PERFORM

 

Title:Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas
Authors:Afnan Jvon Ellenrieder NLina JMPellegrino GArcara GCai ZHedrich TAbdallah CKhajehpour HFrauscher BGotman JGrova C
Link:https://pubmed.ncbi.nlm.nih.gov/37149236/
DOI:10.1016/j.neuroimage.2023.120158
Publication:NeuroImage
Keywords:Intracranial EEGMagnetoencephalographyResting stateSource imagingSpectral analysisValidation
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.

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.





BookR developed by Sriram Narayanan
for the Concordia University School of Health
Copyright © 2011-2026
Cookie settings
Concordia University