Keyword search (4,163 papers available)

"entropy" Keyword-tagged 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 Effect of Microstructure on Oxidation Resistance and TGO Formation in FeCoNiCrAl HEA Coatings Deposited by Low-Temperature HVAF Spraying Shahbazi H; Lima RS; Stoyanov P; Moreau C; 40271745
ENCS
3 EEG complexity during mind wandering: A multiscale entropy investigation Cnudde K; Kim G; Murch WS; Handy TC; Protzner AB; Kam JWY; 36621593
CONCORDIA
4 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
5 Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures Bourouis S; Pawar Y; Bouguila N; 35009726
ENCS
6 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
7 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
8 Randomness, Informational Entropy, and Volatility Interdependencies among the Major World Markets: The Role of the COVID-19 Pandemic Lahmiri S; Bekiros S; 33286604
JMSB
9 Renyi entropy and mutual information measurement of market expectations and investor fear during the COVID-19 pandemic Lahmiri S; Bekiros S; 32834621
JMSB
10 The impact of COVID-19 pandemic upon stability and sequential irregularity of equity and cryptocurrency markets Lahmiri S; Bekiros S; 32501379
JMSB
11 MEG-EEG Information Fusion and Electromagnetic Source Imaging: From Theory to Clinical Application in Epilepsy. Chowdhury RA, Zerouali Y, Hedrich T, Heers M, Kobayashi E, Lina JM, Grova C 26016950
PERFORM
12 Reproducibility of EEG-MEG fusion source analysis of interictal spikes: Relevance in presurgical evaluation of epilepsy. Chowdhury RA, Pellegrino G, Aydin Ü, Lina JM, Dubeau F, Kobayashi E, Grova C 29164737
PERFORM

 

Title:Reproducibility of EEG-MEG fusion source analysis of interictal spikes: Relevance in presurgical evaluation of epilepsy.
Authors:Chowdhury RA,  Pellegrino G,  Aydin Ü,  Lina JM,  Dubeau F,  Kobayashi E,  Grova C
Link:https://www.ncbi.nlm.nih.gov/pubmed/29164737?dopt=Abstract
DOI:10.1002/hbm.23889
Publication:Human brain mapping
Keywords:coherent maximum entropy on the mean;  fusion of EEG and MEG;  interictal epileptic spikes;  presurgical evaluation of epilepsy;  reproducibility;  single trial localization; 
PMID:29164737 Category:Hum Brain Mapp Date Added:2019-06-04
Dept Affiliation: PERFORM
1 Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montreal, Québec, Canada.
2 San Camillo Hospital IRCCS, 80 Via Alberoni, Venice, 30126, Italy.
3 Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montreal, Québec, Canada.
4 Ecole de Technologie Supérieure, Montréal, Québec, Canada.
5 Centre de Recherches Mathématiques, Université de Montréal, Montréal, Québec, Canada.
6 Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.

Description:

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

Hum Brain Mapp. 2018 02;39(2):880-901

Authors: Chowdhury RA, Pellegrino G, Aydin Ü, Lina JM, Dubeau F, Kobayashi E, Grova C

Abstract

Fusion of electroencephalography (EEG) and magnetoencephalography (MEG) data using maximum entropy on the mean method (MEM-fusion) takes advantage of the complementarities between EEG and MEG to improve localization accuracy. Simulation studies demonstrated MEM-fusion to be robust especially in noisy conditions such as single spike source localizations (SSSL). Our objective was to assess the reliability of SSSL using MEM-fusion on clinical data. We proposed to cluster SSSL results to find the most reliable and consistent source map from the reconstructed sources, the so-called consensus map. Thirty-four types of interictal epileptic discharges (IEDs) were analyzed from 26 patients with well-defined epileptogenic focus. SSSLs were performed on EEG, MEG, and fusion data and consensus maps were estimated using hierarchical clustering. Qualitative (spike-to-spike reproducibility rate, SSR) and quantitative (localization error and spatial dispersion) assessments were performed using the epileptogenic focus as clinical reference. Fusion SSSL provided significantly better results than EEG or MEG alone. Fusion found at least one cluster concordant with the clinical reference in all cases. This concordant cluster was always the one involving the highest number of spikes. Fusion yielded highest reproducibility (SSR EEG?=?55%, MEG?=?71%, fusion?=?90%) and lowest localization error. Also, using only few channels from either modality (21EEG + 272MEG or 54EEG + 25MEG) was sufficient to reach accurate fusion. MEM-fusion with consensus map approach provides an objective way of finding the most reliable and concordant generators of IEDs. We, therefore, suggest the pertinence of SSSL using MEM-fusion as a valuable clinical tool for presurgical evaluation of epilepsy.

PMID: 29164737 [PubMed - indexed for MEDLINE]




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