Keyword search (4,163 papers available)

"Cai Z" 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 NIRSTORM: a Brainstorm extension dedicated to functional near-infrared spectroscopy data analysis, advanced 3D reconstructions, and optimal probe design Delaire É; Vincent T; Cai Z; Machado A; Hugueville L; Schwartz D; Tadel F; Cassani R; Bherer L; Lina JM; Pélégrini-Issac M; Grova C; 40375973
SOH
3 Combating childhood overweight and obesity: The role of Olympic Movement and bodily movement Tam BT; Wan K; Santosa S; Cai Z; 39991475
SOH
4 Alzheimer's Imaging Consortium Soucy JP; Belasso CJ; Cai Z; Bezgin G; Stevenson J; Rahmouni N; Tissot C; Lussier FZ; Rosa-Neto P; Rivaz HJ; Benali H; 39782975
CONCORDIA
5 Biomarkers Soucy JP; Belasso CJ; Cai Z; Bezgin G; Stevenson J; Rahmouni N; Tissot C; Lussier FZ; Rosa-Neto P; Rivaz HJ; Benali H; 39784152
CONCORDIA
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 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
8 Bayesian workflow for the investigation of hierarchical classification models from tau-PET and structural MRI data across the Alzheimer's disease spectrum Belasso CJ; Cai Z; Bezgin G; Pascoal T; Stevenson J; Rahmouni N; Tissot C; Lussier F; Rosa-Neto P; Soucy JP; Rivaz H; Benali H; 37920382
PERFORM
9 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
10 Hierarchical Bayesian modeling of the relationship between task-related hemodynamic responses and cortical excitability Cai Z; Pellegrino G; Lina JM; Benali H; Grova C; 36250709
PERFORM
11 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
12 Deconvolution of hemodynamic responses along the cortical surface using personalized functional near infrared spectroscopy Machado A; Cai Z; Vincent T; Pellegrino G; Lina JM; Kobayashi E; Grova C; 33727581
PERFORM
13 The movement time analyser task investigated with functional near infrared spectroscopy: an ecologic approach for measuring hemodynamic response in the motor system. Vasta R, Cerasa A, Gramigna V, Augimeri A, Olivadese G, Pellegrino G, Martino I, Machado A, Cai Z, Caracciolo M, Grova C, Quattrone A 27055849
PERFORM
14 Optimal positioning of optodes on the scalp for personalized functional near-infrared spectroscopy investigations. Machado A, Cai Z, Pellegrino G, Marcotte O, Vincent T, Lina JM, Kobayashi E, Grova C 30107210
PERFORM

 

Title:EEG/MEG source imaging of deep brain activity within the maximum entropy on the mean framework: Simulations and validation in epilepsy
Authors:Afnan JCai ZLina JMAbdallah CDelaire EAvigdor TRos VHedrich Tvon Ellenrieder NKobayashi EFrauscher BGotman JGrova C
Link:https://pubmed.ncbi.nlm.nih.gov/38994740/
DOI:10.1002/hbm.26720
Publication:Human brain mapping
Keywords:HD-EEGMEGdeep brain activityepilepsysource imaging
PMID:38994740 Category: Date Added:2024-07-12
Dept Affiliation: SOH
1 Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, Canada.
2 Integrated Program in Neuroscience, McGill University, Montréal, Québec, Canada.
3 Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada.
4 Physnum Team, Centre De Recherches Mathématiques, Montréal, Québec, Canada.
5 Electrical Engineering Department, École De Technologie Supérieure, Montréal, Québec, Canada.
6 Center for Advanced Research in Sleep Medicine, Sacré-Coeur Hospital, Montréal, Québec, Canada.
7 Analytical Neurophysiology Lab, Department of Neurology, Duke University School of Medicine, Durham, North Carolina, USA.
8 Multimodal Functional Imaging Lab, Department of Physics and Concordia School of Health, Concordia University, Montréal, Québec, Canada.

Description:

Electro/Magneto-EncephaloGraphy (EEG/MEG) source imaging (EMSI) of epileptic activity from deep generators is often challenging due to the higher sensitivity of EEG/MEG to superficial regions and to the spatial configuration of subcortical structures. We previously demonstrated the ability of the coherent Maximum Entropy on the Mean (cMEM) method to accurately localize the superficial cortical generators and their spatial extent. Here, we propose a depth-weighted adaptation of cMEM to localize deep generators more accurately. These methods were evaluated using realistic MEG/high-density EEG (HD-EEG) simulations of epileptic activity and actual MEG/HD-EEG recordings from patients with focal epilepsy. We incorporated depth-weighting within the MEM framework to compensate for its preference for superficial generators. We also included a mesh of both hippocampi, as an additional deep structure in the source model. We generated 5400 realistic simulations of interictal epileptic discharges for MEG and HD-EEG involving a wide range of spatial extents and signal-to-noise ratio (SNR) levels, before investigating EMSI on clinical HD-EEG in 16 patients and MEG in 14 patients. Clinical interictal epileptic discharges were marked by visual inspection. We applied three EMSI methods: cMEM, depth-weighted cMEM and depth-weighted minimum norm estimate (MNE). The ground truth was defined as the true simulated generator or as a drawn region based on clinical information available for patients. For deep sources, depth-weighted cMEM improved the localization when compared to cMEM and depth-weighted MNE, whereas depth-weighted cMEM did not deteriorate localization accuracy for superficial regions. For patients' data, we observed improvement in localization for deep sources, especially for the patients with mesial temporal epilepsy, for which cMEM failed to reconstruct the initial generator in the hippocampus. Depth weighting was more crucial for MEG (gradiometers) than for HD-EEG. Similar findings were found when considering depth weighting for the wavelet extension of MEM. In conclusion, depth-weighted cMEM improved the localization of deep sources without or with minimal deterioration of the localization of the superficial sources. This was demonstrated using extensive simulations with MEG and HD-EEG and clinical MEG and HD-EEG for epilepsy patients.





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