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Effects of Independent Component Analysis on Magnetoencephalography Source Localization in Pre-surgical Frontal Lobe Epilepsy Patients

Author(s): Pellegrino G, Xu M, Alkuwaiti A, Porras-Bettancourt M, Abbas G, Lina JM, Grova C, Kobayashi E,...

Objective: Magnetoencephalography source imaging (MSI) of interictal epileptiform discharges (IED) is a useful presurgical tool in the evaluation of drug-resistant frontal lobe epilepsy (FLE) patie...

Article GUID: 32582009
Accuracy and spatial properties of distributed magnetic source imaging techniques in the investigation of focal epilepsy patients.

Author(s): Pellegrino G, Hedrich T, Porras-Bettancourt M, Lina JM, Aydin Ü, Hall J, Grova C, Kobayashi E

Hum Brain Mapp. 2020 May 09;: Authors: Pellegrino G, Hedrich T, Porras-Bettancourt M, Lina JM, Aydin Ü, Hall J, Grova C, Kobayashi E

Article GUID: 32386115
Magnetoencephalography resting state connectivity patterns as indicatives of surgical outcome in epilepsy patients.

Author(s): Aydin Ü, Pellegrino G, Bin Ka'b Ali O, Abdallah C, Dubeau F, Lina JM, Kobayashi E, Grova C

J Neural Eng. 2020 Mar 18;: Authors: Aydin Ü, Pellegrino G, Bin Ka'b Ali O, Abdallah C, Dubeau F, Lina JM, Kobayashi E, Grova C

Article GUID: 32191632
Differences in MEG and EEG power-law scaling explained by a coupling between spatial coherence and frequency: a simulation study.

Author(s): Bénar CG, Grova C, Jirsa VK, Lina JM

J Comput Neurosci. 2019 Jul 11;: Authors: Bénar CG, Grova C, Jirsa VK, Lina JM

Article GUID: 31292816
Localization Accuracy of Distributed Inverse Solutions for Electric and Magnetic Source Imaging of Interictal Epileptic Discharges in Patients with Focal Epilepsy.

Author(s): Heers M, Chowdhury RA, Hedrich T, Dubeau F, Hall JA, Lina JM, Grova C, Kobayashi E

Brain Topogr. 2016 Jan;29(1):162-81 Authors: Heers M, Chowdhury RA, Hedrich T, Dubeau F, Hall JA, Lina JM, Grova C, Kobayashi E

Article GUID: 25609211
MEG-EEG Information Fusion and Electromagnetic Source Imaging: From Theory to Clinical Application in Epilepsy.

Author(s): Chowdhury RA, Zerouali Y, Hedrich T, Heers M, Kobayashi E, Lina JM, Grova C

Brain Topogr. 2015 Nov;28(6):785-812 Authors: Chowdhury RA, Zerouali Y, Hedrich T, Heers M, Kobayashi E, Lina JM, Grova C

Article GUID: 26016950
Detection and Magnetic Source Imaging of Fast Oscillations (40-160 Hz) Recorded with Magnetoencephalography in Focal Epilepsy Patients.

Author(s): von Ellenrieder N, Pellegrino G, Hedrich T, Gotman J, Lina JM, Grova C, Kobayashi E

Brain Topogr. 2016 Mar;29(2):218-31 Authors: von Ellenrieder N, Pellegrino G, Hedrich T, Gotman J, Lina JM, Grova C, Kobayashi E

Article GUID: 26830767
Intracranial EEG potentials estimated from MEG sources: A new approach to correlate MEG and iEEG data in epilepsy.

Author(s): Grova C, Aiguabella M, Zelmann R, Lina JM, Hall JA, Kobayashi E

Hum Brain Mapp. 2016 May;37(5):1661-83 Authors: Grova C, Aiguabella M, Zelmann R, Lina JM, Hall JA, Kobayashi E

Article GUID: 26931511
SPARK: Sparsity-based analysis of reliable k-hubness and overlapping network structure in brain functional connectivity.

Author(s): Lee K, Lina JM, Gotman J, Grova C

Neuroimage. 2016 07 01;134:434-449 Authors: Lee K, Lina JM, Gotman J, Grova C

Article GUID: 27046111
Source localization of the seizure onset zone from ictal EEG/MEG data.

Author(s): Pellegrino G, Hedrich T, Chowdhury R, Hall JA, Lina JM, Dubeau F, Kobayashi E, Grova C

Hum Brain Mapp. 2016 07;37(7):2528-46 Authors: Pellegrino G, Hedrich T, Chowdhury R, Hall JA, Lina JM, Dubeau F, Kobayashi E, Grova C

Article GUID: 27059157
Clinical yield of magnetoencephalography distributed source imaging in epilepsy: A comparison with equivalent current dipole method.

Author(s): Pellegrino G, Hedrich T, Chowdhury RA, Hall JA, Dubeau F, Lina JM, Kobayashi E, Grova C

Hum Brain Mapp. 2018 01;39(1):218-231 Authors: Pellegrino G, Hedrich T, Chowdhury RA, Hall JA, Dubeau F, Lina JM, Kobayashi E, Grova C

Article GUID: 29024165
Reproducibility of EEG-MEG fusion source analysis of interictal spikes: Relevance in presurgical evaluation of epilepsy.

Author(s): Chowdhury RA, Pellegrino G, Aydin Ü, Lina JM, Dubeau F, Kobayashi E, Grova C

Hum Brain Mapp. 2018 02;39(2):880-901 Authors: Chowdhury RA, Pellegrino G, Aydin Ü, Lina JM, Dubeau F, Kobayashi E, Grova C

Article GUID: 29164737
Disruption, emergence and lateralization of brain network hubs in mesial temporal lobe epilepsy.

Author(s): Lee K, Khoo HM, Lina JM, Dubeau F, Gotman J, Grova C

Neuroimage Clin. 2018;20:71-84 Authors: Lee K, Khoo HM, Lina JM, Dubeau F, Gotman J, Grova C

Article GUID: 30094158
Optimal positioning of optodes on the scalp for personalized functional near-infrared spectroscopy investigations.

Author(s): Machado A, Cai Z, Pellegrino G, Marcotte O, Vincent T, Lina JM, Kobayashi E, Grova C

J Neurosci Methods. 2018 Nov 01;309:91-108 Authors: Machado A, Cai Z, Pellegrino G, Marcotte O, Vincent T, Lina JM, Kobayashi E, Grova C

Article GUID: 30107210
Complex patterns of spatially extended generators of epileptic activity: Comparison of source localization methods cMEM and 4-ExSo-MUSIC on high resolution EEG and MEG data.

Author(s): Chowdhury RA, Merlet I, Birot G, Kobayashi E, Nica A, Biraben A, Wendling F, Lina JM, Albera L, Grova C...

Complex patterns of spatially extended generators of epileptic activity: Comparison of source localization methods cMEM and 4-ExSo-MUSIC on high resolution EEG and MEG data.
Neuroimage. 2016 De...

Article GUID: 27561712
Comparison of the spatial resolution of source imaging techniques in high-density EEG and MEG.

Author(s): Hedrich T, Pellegrino G, Kobayashi E, Lina JM, Grova C

Neuroimage. 2017 08 15;157:531-544 Authors: Hedrich T, Pellegrino G, Kobayashi E, Lina JM, Grova C

Article GUID: 28619655
Beyond spindles: interactions between sleep spindles and boundary frequencies during cued reactivation of motor memory representations.

Author(s): Laventure S, Pinsard B, Lungu O, Carrier J, Fogel S, Benali H, Lina JM, Boutin A, Doyon J

Sleep. 2018 Sep 01;41(9): Authors: Laventure S, Pinsard B, Lungu O, Carrier J, Fogel S, Benali H, Lina JM, Boutin A, Doyon J

Article GUID: 30137521
Title:Differences in MEG and EEG power-law scaling explained by a coupling between spatial coherence and frequency: a simulation study.
Authors:Bénar CG,  Grova C,  Jirsa VK,  Lina JM
Link:https://www.ncbi.nlm.nih.gov/pubmed/31292816?dopt=Abstract
DOI:10.1007/s10827-019-00721-9
Category:J Comput Neurosci
PMID:31292816
Dept Affiliation: PERFORM
1 Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France. christian.benar@univ-amu.fr.
2 PERFORM Centre and Physics Department, Concordia University, Montreal, QC, Canada.
3 Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
4 Multimodal Functional Imaging Laboratory, Biomedical Engineering Department, McGill University, Montreal, QC, Canada.
5 Centre de Recherches Mathématiques, Montreal, QC, Canada.
6 Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.
7 Département de Génie Électrique, École de Technologie Supérieure, Montreal, QC, Canada.
8 Centre d'Etudes Avancées en Médecine du Sommeil, Hôpital Sacré Cœur, Montreal, QC, Canada.

Description:

Differences in MEG and EEG power-law scaling explained by a coupling between spatial coherence and frequency: a simulation study.

J Comput Neurosci. 2019 Jul 11;:

Authors: Bénar CG, Grova C, Jirsa VK, Lina JM

Abstract

Electrophysiological signals (electroencephalography, EEG, and magnetoencephalography, MEG), as many natural processes, exhibit scale-invariance properties resulting in a power-law (1/f) spectrum. Interestingly, EEG and MEG differ in their slopes, which could be explained by several mechanisms, including non-resistive properties of tissues. Our goal in the present study is to estimate the impact of space/frequency structure of source signals as a putative mechanism to explain spectral scaling properties of neuroimaging signals. We performed simulations based on the summed contribution of cortical patches with different sizes (ranging from 0.4 to 104.2 cm2). Small patches were attributed signals of high frequencies, whereas large patches were associated with signals of low frequencies, on a logarithmic scale. The tested parameters included i) the space/frequency structure (range of patch sizes and frequencies) and ii) the amplitude factor c parametrizing the spatial scale ratios. We found that the space/frequency structure may cause differences between EEG and MEG scale-free spectra that are compatible with real data findings reported in previous studies. We also found that below a certain spatial scale, there were no more differences between EEG and MEG, suggesting a limit for the resolution of both methods.Our work provides an explanation of experimental findings. This does not rule out other mechanisms for differences between EEG and MEG, but suggests an important role of spatio-temporal structure of neural dynamics. This can help the analysis and interpretation of power-law measures in EEG and MEG, and we believe our results can also impact computational modeling of brain dynamics, where different local connectivity structures could be used at different frequencies.

PMID: 31292816 [PubMed - as supplied by publisher]