Keyword search (4,163 papers available)

"Delaire É" 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 Spectral and network investigation reveals distinct power and connectivity patterns between phasic and tonic REM sleep Avigdor T; Peter-Derex L; Ho A; Schiller K; Wang Y; Abdallah C; Delaire E; Jaber K; Travnicek V; Grova C; Frauscher B; 40394955
SOH
4 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
5 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
6 Introduction to the shared near infrared spectroscopy format Tucker S; Dubb J; Kura S; von Lühmann A; Franke R; Horschig JM; Powell S; Oostenveld R; Lührs M; Delaire É; Aghajan ZM; Yun H; Yücel MA; Fang Q; Huppert TJ; Frederick BB; Pollonini L; Boas D; Luke R; 36507152
ENCS
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

 

Title:Spectral and network investigation reveals distinct power and connectivity patterns between phasic and tonic REM sleep
Authors:Avigdor TPeter-Derex LHo ASchiller KWang YAbdallah CDelaire EJaber KTravnicek VGrova CFrauscher B
Link:https://pubmed.ncbi.nlm.nih.gov/40394955/
DOI:10.1093/sleep/zsaf133
Publication:Sleep
Keywords:ConnectivityMicrostateREMSpectrumTonic REMphasic REM
PMID:40394955 Category: Date Added:2025-05-21
Dept Affiliation: SOH
1 Analytical Neurophysiology Lab, McGill University, Montreal, Quebec, Canada.
2 Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montreal, Quebec, Canada.
3 Center for Sleep Medicine, Croix-Rousse Hospital, Hospices Civils de Lyon, Lyon, France; Lyon Neuroscience Research Center, PAM Team, INSERM U1028 / CNRS UMR 5292 / Lyon 1 University, Lyon, France.
4 Analytical Neurophysiological Lab, Department of Neurology, Duke University, Durham, North Carolina, USA.
5 Multimodal Functional Imaging Lab, Department of Physics, PERFORM Center / School of Health, Concordia University, Montreal, Quebec, Canada.
6 Department of Biomedical Engineering. Duke Pratt School of Engineering, Durham, North Carolina, USA.
7 Institute of Scientific Instruments, Czech Academy of Sciences, Brno, Czech Republic.
8 International Clinical Research Center, St Anne's University Hospital, Brno, Czech Republic.

Description:

Although rapid eye movement (REM) sleep is often thought of as a singular state, it consists of two substates, phasic and tonic REM, defined by the presence (respectively absence) of bursts of rapid eye movements. These two substates have distinct EEG signatures and functional properties. However, whether they exhibit regional specificities remains unknown. Using intracranial EEG recordings from 31 patients, we analyzed expert labeled segments from tonic and phasic REM and contrasted them with wakefulness segments. We assessed the spectral and connectivity content of these segments using Welch's method to estimate power spectral density and the phase locking value to assess functional connectivity. Overall, we found a widespread power gradient between low and high frequencies (p < 0.05, Cohen's d = 0.17± 0.20), with tonic REM being dominated by lower frequencies (p < 0.01, d = 0.18 ± 0.08), and phasic REM by higher frequencies (p < 0.01, d = 0.18 ± 0.19). However, some regions such as the occipito-temporal areas as well as medial frontal regions exhibit opposite trends. Connectivity was overall higher in all bands except in the low and high ripple frequency band in most networks during tonic REM (p < 0.01, d = 0.08 ± 0.09) compared to phasic REM. Yet, functional connections involving the visual network were always stronger during phasic REM when compared to tonic REM. These findings highlight the spatiotemporal heterogeneity of REM sleep which is consistent with the concept of focal sleep in humans.





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