Keyword search (4,163 papers available)

"Lina JM" Authored Publications:

Title Authors PubMed ID
1 How vigilance states influence source imaging of physiological brain oscillations: evidence from intracranial EEG Wei X; Afnan J; Avigdor T; von Ellenrieder N; Delaire É; Royer J; Ho A; Minato E; Schiller K; Jaber K; Wang YL; Moye M; Bernhardt BC; Lina JM; Grova C; Frauscher B; 41687693
SOH
2 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
3 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
4 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
5 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
6 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
7 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
8 Data-driven beamforming technique to attenuate ballistocardiogram artefacts in electroencephalography-functional magnetic resonance imaging without detecting cardiac pulses in electrocardiography recordings Uji M; Cross N; Pomares FB; Perrault AA; Jegou A; Nguyen A; Aydin U; Lina JM; Dang-Vu TT; Grova C; 34101939
PERFORM
9 How cerebral cortex protects itself from interictal spikes: The alpha/beta inhibition mechanism Pellegrino G; Hedrich T; Sziklas V; Lina JM; Grova C; Kobayashi E; 34002916
PERFORM
10 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
11 Effects of Independent Component Analysis on Magnetoencephalography Source Localization in Pre-surgical Frontal Lobe Epilepsy Patients Pellegrino G, Xu M, Alkuwaiti A, Porras-Bettancourt M, Abbas G, Lina JM, Grova C, Kobayashi E, 32582009
PERFORM
12 Accuracy and spatial properties of distributed magnetic source imaging techniques in the investigation of focal epilepsy patients. Pellegrino G, Hedrich T, Porras-Bettancourt M, Lina JM, Aydin Ü, Hall J, Grova C, Kobayashi E 32386115
PERFORM
13 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
14 Differences in MEG and EEG power-law scaling explained by a coupling between spatial coherence and frequency: a simulation study. Bénar CG, Grova C, Jirsa VK, Lina JM 31292816
PERFORM
15 Localization Accuracy of Distributed Inverse Solutions for Electric and Magnetic Source Imaging of Interictal Epileptic Discharges in Patients with Focal Epilepsy. Heers M, Chowdhury RA, Hedrich T, Dubeau F, Hall JA, Lina JM, Grova C, Kobayashi E 25609211
PERFORM
16 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
17 Detection and Magnetic Source Imaging of Fast Oscillations (40-160 Hz) Recorded with Magnetoencephalography in Focal Epilepsy Patients. von Ellenrieder N, Pellegrino G, Hedrich T, Gotman J, Lina JM, Grova C, Kobayashi E 26830767
PERFORM
18 Intracranial EEG potentials estimated from MEG sources: A new approach to correlate MEG and iEEG data in epilepsy. Grova C, Aiguabella M, Zelmann R, Lina JM, Hall JA, Kobayashi E 26931511
PERFORM
19 SPARK: Sparsity-based analysis of reliable k-hubness and overlapping network structure in brain functional connectivity. Lee K, Lina JM, Gotman J, Grova C 27046111
PERFORM
20 Source localization of the seizure onset zone from ictal EEG/MEG data. Pellegrino G, Hedrich T, Chowdhury R, Hall JA, Lina JM, Dubeau F, Kobayashi E, Grova C 27059157
PERFORM
21 Clinical yield of magnetoencephalography distributed source imaging in epilepsy: A comparison with equivalent current dipole method. Pellegrino G, Hedrich T, Chowdhury RA, Hall JA, Dubeau F, Lina JM, Kobayashi E, Grova C 29024165
PERFORM
22 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
23 Disruption, emergence and lateralization of brain network hubs in mesial temporal lobe epilepsy. Lee K, Khoo HM, Lina JM, Dubeau F, Gotman J, Grova C 30094158
PERFORM
24 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
25 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. Chowdhury RA, Merlet I, Birot G, Kobayashi E, Nica A, Biraben A, Wendling F, Lina JM, Albera L, Grova C 27561712
PERFORM
26 Comparison of the spatial resolution of source imaging techniques in high-density EEG and MEG. Hedrich T, Pellegrino G, Kobayashi E, Lina JM, Grova C 28619655
PERFORM
27 Beyond spindles: interactions between sleep spindles and boundary frequencies during cued reactivation of motor memory representations. Laventure S, Pinsard B, Lungu O, Carrier J, Fogel S, Benali H, Lina JM, Boutin A, Doyon J 30137521
PERFORM

 

Title:Deconvolution of hemodynamic responses along the cortical surface using personalized functional near infrared spectroscopy
Authors:Machado ACai ZVincent TPellegrino GLina JMKobayashi EGrova C
Link:https://pubmed.ncbi.nlm.nih.gov/33727581/
DOI:10.1038/s41598-021-85386-0
Publication:Scientific reports
Keywords:
PMID:33727581 Category: Date Added:2021-03-17
Dept Affiliation: PERFORM
1 Multimodal Functional Imaging Laboratory, Biomedical Engineering Department, McGill University, 3801 Rue University 751, Montreal, QC, H3A2B4, Canada. alexis.machado@mail.mcgill.ca.
2 Multimodal Functional Imaging Laboratory, Biomedical Engineering Department, McGill University, 3801 Rue University 751, Montreal, QC, H3A2B4, Canada.
3 Department of Physics and PERFORM center, Concordia University, Montreal, Canada.
4 Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada.
5 École de technologie supérieure de l'Université du Québec, Montreal, Canada.
6 Centre de Recherches en Mathématiques, Montreal, QC, Canada.
7 Centre d'Etudes Avancées en Médecine Du Sommeil, Centre de Recherche de l'Hopital Sacré-Coeur De Mo

Description:

In functional near infrared spectroscopy (fNIRS), deconvolution analysis of oxy and deoxy-hemoglobin concentration changes allows estimating specific hemodynamic response functions (HRF) elicited by neuronal activity, taking advantage of the fNIRS excellent temporal resolution. Diffuse optical tomography (DOT) is also becoming the new standard reconstruction procedure as it is more accurate than the modified Beer Lambert law approach at the sensor level. The objective of this study was to assess the relevance of HRF deconvolution after DOT constrained along the cortical surface. We used local personalized fNIRS montages which consists in optimizing the position of fNIRS optodes to ensure maximal sensitivity to subject specific target brain regions. We carefully evaluated the accuracy of deconvolution when applied after DOT, using realistic simulations involving several HRF models at different signal to noise ratio (SNR) levels and on real data related to motor and visual tasks in healthy subjects and from spontaneous pathological activity in one patient with epilepsy. We demonstrated that DOT followed by deconvolution was able to accurately recover a large variability of HRFs over a large range of SNRs. We found good performances of deconvolution analysis for SNR levels usually encountered in our applications and we were able to reconstruct accurately the temporal dynamics of HRFs in real conditions.





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