Keyword search (3,619 papers available)


Arcuate fasciculus architecture is associated with individual differences in pre-attentive detection of unpredicted music changes

Author(s): Vaquero L; Ramos-Escobar N; Cucurell D; François C; Putkinen V; Segura E; Huotilainen M; Penhune V; Rodríguez-Fornells A;...

The mismatch negativity (MMN) is an event related brain potential (ERP) elicited by unpredicted sounds presented in a sequence of repeated auditory stimuli. The neural sources of the MMN have been ...

Article GUID: 33454403

Cortical gradients of functional connectivity are robust to state-dependent changes following sleep deprivation.

Author(s): Cross N; Paquola C; Pomares FB; Perrault AA; Jegou A; Nguyen A; Aydin U; Bernhardt BC; Grova C; Dang-Vu TT;...

Sleep deprivation leads to significant impairments in cognitive performance and changes to the interactions between large scale cortical networks, yet the hierarchical organisation of cortical acti...

Article GUID: 33186718

The sensation of groove engages motor and reward networks.

Author(s): Matthews TE, Witek MAG, Lund T, Vuust P, Penhune VB

Neuroimage. 2020 Mar 23;:116768 Authors: Matthews TE, Witek MAG, Lund T, Vuust P, Penhune VB

Article GUID: 32217163

What you learn & when you learn it: Impact of early bilingual & music experience on the structural characteristics of auditory-motor pathways

Author(s): Vaquero L; Rousseau PN; Vozian D; Klein D; Penhune V;

Music and language engage the dorsal auditory pathway, linked by the arcuate fasciculus (AF). Sustained practice in these activities can modify brain structure, depending on length of experience but also age of onset (AoO). To study the impact of early expe...

Article GUID: 32119984

Investigating microstructural variation in the human hippocampus using non-negative matrix factorization.

Author(s): Patel R, Steele CJ, Chen A, Patel S, Devenyi GA, Germann J, Tardif CL, Chakravarty MM

Neuroimage. 2019 Nov 09;:116348 Authors: Patel R, Steele CJ, Chen A, Patel S, Devenyi GA, Germann J, Tardif CL, Chakravarty MM

Article GUID: 31715254

BOLD signal physiology: Models and applications.

Author(s): Gauthier CJ, Fan AP

Neuroimage. 2019 02 15;187:116-127 Authors: Gauthier CJ, Fan AP

Article GUID: 29544818

ERP evidence of adaptive changes in error processing and attentional control during rhythm synchronization learning

Author(s): Padrão G; Penhune V; de Diego-Balaguer R; Marco-Pallares J; Rodriguez-Fornells A;

The ability to detect and use information from errors is essential during the acquisition of new skills. There is now a wealth of evidence about the brain mechanisms involved in error processing. However, the extent to which those mechanisms are engaged dur...

Article GUID: 24956067

White-matter structural connectivity predicts short-term melody and rhythm learning in non-musicians

Author(s): Vaquero L; Ramos-Escobar N; François C; Penhune V; Rodríguez-Fornells A;

Music learning has received increasing attention in the last decades due to the variety of functions and brain plasticity effects involved during its practice. Most previous reports interpreted the differences between music experts and laymen as the result ...

Article GUID: 29929006

Regional cerebellar volumes are related to early musical training and finger tapping performance.

Author(s): Baer LH, Park MT, Bailey JA, Chakravarty MM, Li KZ, Penhune VB

Neuroimage. 2015 Apr 01;109:130-9 Authors: Baer LH, Park MT, Bailey JA, Chakravarty MM, Li KZ, Penhune VB

Article GUID: 25583606

Advanced MRI techniques to improve our understanding of experience-induced neuroplasticity.

Author(s): Tardif CL, Gauthier CJ, Steele CJ, Bazin PL, Schäfer A, Schaefer A, Turner R, Villringer A

Neuroimage. 2016 05 01;131:55-72 Authors: Tardif CL, Gauthier CJ, Steele CJ, Bazin PL, Schäfer A, Schaefer A, Turner R, Villringer A

Article GUID: 26318050

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

L-DOPA reduces model-free control of behavior by attenuating the transfer of value to action.

Author(s): Kroemer NB, Lee Y, Pooseh S, Eppinger B, Goschke T, Smolka MN

Neuroimage. 2019 02 01;186:113-125 Authors: Kroemer NB, Lee Y, Pooseh S, Eppinger B, Goschke T, Smolka MN

Article GUID: 30381245

Tracking the microstructural properties of the main white matter pathways underlying speech processing in simultaneous interpreters

Author(s): Elmer S; Hänggi J; Vaquero L; Cadena GO; François C; Rodríguez-Fornells A;

Due to the high linguistic and cognitive demands placed on real-time language translation, professional simultaneous interpreters (SIs) have previously been proposed to serve as a reasonable model for evaluating experience-dependent brain properties. Howeve...

Article GUID: 30831314

Cortical reactivations during sleep spindles following declarative learning.

Author(s): Jegou A, Schabus M, Gosseries O, Dahmen B, Albouy G, Desseilles M, Sterpenich V, Phillips C, Maquet P, Grova C, Dang-Vu TT

Neuroimage. 2019 Jul 15;195:104-112 Authors: Jegou A, Schabus M, Gosseries O, Dahmen B, Albouy G, Desseilles M, Sterpenich V, Phillips C, Maquet P, Grova C, Dang-Vu TT

Article GUID: 30928690

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

Investigation of the confounding effects of vasculature and metabolism on computational anatomy studies.

Author(s): Tardif CL, Steele CJ, Lampe L, Bazin PL, Ragert P, Villringer A, Gauthier CJ

Neuroimage. 2017 04 01;149:233-243 Authors: Tardif CL, Steele CJ, Lampe L, Bazin PL, Ragert P, Villringer A, Gauthier CJ

Article GUID: 28159689

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


Title: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.
Authors:Chowdhury RAMerlet IBirot GKobayashi ENica ABiraben AWendling FLina JMAlbera LGrova C
Link:www.ncbi.nlm.nih.gov/pubmed/27561712?dopt=Abstract
Category:Neuroimage
PMID:27561712
Dept Affiliation: PERFORM
1 Multimodal Functional Imaging Lab, Biomedical Engineering Dpt., McGill University , Montreal, Canada. Electronic address: rasheda.chowdhury@mail.mcgill.ca.
2 INSERM, U1099, 35000 Rennes, France; Université de Rennes 1, Laboratoire de Traitement du Signal et de l'Image, 35000 Rennes, France.
3 Department of Fundamental and Clinical Neurosciences, University of Geneva, Switzerland.
4 Neurology and Neurosurgery Department, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada.
5 INSERM, U1099, 35000 Rennes, France; Université de Rennes 1, Laboratoire de Traitement du Signal et de l'Image, 35000 Rennes, France; Neurology Department, CHU de Rennes, France.
6 Département de Genie Electrique, Ecole de Technologie Supérieure, Canada.
7 INSERM, U1099, 35000 Rennes, France; Université de Rennes 1, Laboratoire de Traitement du Signal et de l'Image, 35000 Rennes, France; INRIA, Centre Inria Rennes - Bretagne Atlantique, 35042 Rennes Cedex, France.
8 Multimodal Functional Imaging Lab, Biomedical Engineering Dpt., McGill University , Montreal, Canada; Neurology and Neurosurgery Department, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada; Physics Dpt., PERFORM Centre, Concordia University, Canada.

Description:

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 Dec;143:175-195

Authors: Chowdhury RA, Merlet I, Birot G, Kobayashi E, Nica A, Biraben A, Wendling F, Lina JM, Albera L, Grova C

Abstract

Electric Source Imaging (ESI) and Magnetic Source Imaging (MSI) of EEG and MEG signals are widely used to determine the origin of interictal epileptic discharges during the pre-surgical evaluation of patients with epilepsy. Epileptic discharges are detectable on EEG/MEG scalp recordings only when associated with a spatially extended cortical generator of several square centimeters, therefore it is essential to assess the ability of source localization methods to recover such spatial extent. In this study we evaluated two source localization methods that have been developed for localizing spatially extended sources using EEG/MEG data: coherent Maximum Entropy on the Mean (cMEM) and 4th order Extended Source Multiple Signal Classification (4-ExSo-MUSIC). In order to propose a fair comparison of the performances of the two methods in MEG versus EEG, this study considered realistic simulations of simultaneous EEG/MEG acquisitions taking into account an equivalent number of channels in EEG (257 electrodes) and MEG (275 sensors), involving a biophysical computational neural mass model of neuronal discharges and realistically shaped head models. cMEM and 4-ExSo-MUSIC were evaluated for their sensitivity to localize complex patterns of epileptic discharges which includes (a) different locations and spatial extents of multiple synchronous sources, and (b) propagation patterns exhibited by epileptic discharges. Performance of the source localization methods was assessed using a detection accuracy index (Area Under receiver operating characteristic Curve, AUC) and a Spatial Dispersion (SD) metric. Finally, we also presented two examples illustrating the performance of cMEM and 4-ExSo-MUSIC on clinical data recorded using high resolution EEG and MEG. When simulating single sources at different locations, both 4-ExSo-MUSIC and cMEM exhibited excellent performance (median AUC significantly larger than 0.8 for EEG and MEG), whereas, only for EEG, 4-ExSo-MUSIC showed significantly larger AUC values than cMEM. On the other hand, cMEM showed significantly lower SD values than 4-ExSo-MUSIC for both EEG and MEG. When assessing the impact of the source spatial extent, both methods provided consistent and reliable detection accuracy for a wide range of source spatial extents (source sizes ranging from 3 to 20cm2 for MEG and 3 to 30cm2 for EEG). For both EEG and MEG, 4-ExSo-MUSIC localized single source of large signal-to-noise ratio better than cMEM. In the presence of two synchronous sources, cMEM was able to distinguish well the two sources (their location and spatial extent), while 4-ExSo-MUSIC only retrieved one of them. cMEM was able to detect the spatio-temporal propagation patterns of two synchronous activities while 4-ExSo-MUSIC favored the strongest source activity. Overall, in the context of localizing sources of epileptic discharges from EEG and MEG data, 4-ExSo-MUSIC and cMEM were found accurately sensitive to the location and spatial extent of the sources, with some complementarities. Therefore, they are both eligible for application on clinical data.

PMID: 27561712 [PubMed