Keyword search (4,163 papers available)

"Electroencephalography" Keyword-tagged Publications:

Title Authors PubMed ID
1 Sound degradation type differentially affects neural indicators of cognitive workload and speech tracking Gagné N; Greenlaw KM; Coffey EBJ; 40412301
PSYCHOLOGY
2 Phase-Amplitude Coupling of NREM Sleep Oscillations Shows Between-Night Stability and is Related to Overnight Memory Gains Cross N; O' Byrne J; Weiner OM; Giraud J; Perrault AA; Dang-Vu TT; 40214027
PERFORM
3 PreVISE: an efficient virtual reality system for SEEG surgical planning Spiegler P; Abdelsalam H; Hellum O; Hadjinicolaou A; Weil AG; Xiao Y; 39735694
ENCS
4 Metrics for evaluation of automatic epileptogenic zone localization in intracranial electrophysiology Hrtonova V; Nejedly P; Travnicek V; Cimbalnik J; Matouskova B; Pail M; Peter-Derex L; Grova C; Gotman J; Halamek J; Jurak P; Brazdil M; Klimes P; Frauscher B; 39608298
SOH
5 A protocol for trustworthy EEG decoding with neural networks Borra D; Magosso E; Ravanelli M; 39549492
ENCS
6 SpeechBrain-MOABB: An open-source Python library for benchmarking deep neural networks applied to EEG signals Borra D; Paissan F; Ravanelli M; 39265481
ENCS
7 The neurophysiology of closed-loop auditory stimulation in sleep: A magnetoencephalography study Jourde HR; Merlo R; Brooks M; Rowe M; Coffey EBJ; 37675803
CONCORDIA
8 Dynamic networks differentiate the language ability of children with cochlear implants Koirala N; Deroche MLD; Wolfe J; Neumann S; Bien AG; Doan D; Goldbeck M; Muthuraman M; Gracco VL; 37409105
PSYCHOLOGY
9 Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data Thölke P; Mantilla-Ramos YJ; Abdelhedi H; Maschke C; Dehgan A; Harel Y; Kemtur A; Mekki Berrada L; Sahraoui M; Young T; Bellemare Pépin A; El Khantour C; Landry M; Pascarella A; Hadid V; Combrisson E; O' Byrne J; Jerbi K; 37385392
IMAGING
10 Neurophysiology, Neuropsychology, and Epilepsy, in 2022: Hills We Have Climbed and Hills Ahead. Neurophysiology in epilepsy Frauscher B; Bénar CG; Engel JJ; Grova C; Jacobs J; Kahane P; Wiebe S; Zjilmans M; Dubeau F; 37119580
PERFORM
11 Electroencephalographic characteristics of children and adolescents with chronic musculoskeletal pain Ocay DD; Teel EF; Luo OD; Savignac C; Mahdid Y; Blain-Moraes S; Ferland CE; 36601627
HKAP
12 Alpha and beta neural oscillations differentially reflect age-related differences in bilateral coordination Shih PC; Steele CJ; Nikulin VV; Gundlach C; Kruse J; Villringer A; Sehm B; 33979705
PSYCHOLOGY
13 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
14 PASS: A Multimodal Database of Physical Activity and Stress for Mobile Passive Body/ Brain-Computer Interface Research Parent M; Albuquerque I; Tiwari A; Cassani R; Gagnon JF; Lafond D; Tremblay S; Falk TH; 33363449
PERFORM
15 Source imaging of deep-brain activity using the regional spatiotemporal Kalman filter Hamid L; Habboush N; Stern P; Japaridze N; Aydin Ü; Wolters CH; Claussen JC; Heute U; Stephani U; Galka A; Siniatchkin M; 33250282
PERFORM
16 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
17 Sleep spindles may predict response to cognitive-behavioral therapy for chronic insomnia Dang-Vu TT; Hatch B; Salimi A; Mograss M; Boucetta S; O' Byrne J; Brandewinder M; Berthomier C; Gouin JP; 29157588
PERFORM

 

Title:Sleep spindles may predict response to cognitive-behavioral therapy for chronic insomnia
Authors:Dang-Vu TTHatch BSalimi AMograss MBoucetta SO'Byrne JBrandewinder MBerthomier CGouin JP
Link:https://pubmed.ncbi.nlm.nih.gov/29157588/
DOI:10.1016/j.sleep.2017.08.012
Publication:Sleep medicine
Keywords:BiomarkersElectroencephalographyInsomniaNeural oscillationsSleep spindles
PMID:29157588 Category:Sleep Med Date Added:2019-05-31
Dept Affiliation: PERFORM
1 Department of Exercise Science, Concordia University, Montréal, QC, Canada; Department of Psychology, Concordia University, Montréal, QC, Canada; Center for Studies in Behavioral Neurobiology, Concordia University, Montréal, QC, Canada; PERFORM Center, Concordia University, Montréal, QC, Canada; Center for Clinical Research in Health, Concordia University, Montréal, QC, Canada; Centre de Recherches de l'Institut Universitaire de Gériatrie de Montréal, Montréal, QC, Canada; Department of Neurosciences, Université de Montréal, Montréal, QC, Canada. Electronic address: tt.dangvu@concordia.ca.
2 Department of Psychology, Concordia University, Montréal, QC, Canada; Center for Studies in Behavioral Neurobiology, Concordia University, Montréal, QC, Canada; PERFORM Center, Concordia University, Montréal, QC, Canada.
3 Department of Exercise Science, Concordia University, Montréal, QC, Canada; Center for Studies in Behavioral Neurobiology, Concordia University, Montréal, QC, Canada; PERFORM Center, Concordia University, Montréal, QC, Canada; Centre de Recherches de l'Institut Universitaire de Gériatrie de Montréal, Montréal, QC, Canada.
4 Department of Exercise Science, Concordia University, Montréal, QC, Canada; Department of Psychology, Concordia University, Montréal, QC, Canada; PERFORM Center, Concordia University, Montréal, QC, Canada; Centre de Recherches de l'Institut Universitaire de Gériatrie de Montréal, Montréal, QC, Canada.
5 Physip SA, Paris, France.
6 Department of Psychology, Concordia University, Montréal, QC, Canada; PERFORM Center, Concordia University, Montréal, QC, Canada; Center for Clinical Research in Health, Concordia University, Montréal, QC, Canada.

Description:

Background: While cognitive-behavioral therapy for insomnia constitutes the first-line treatment for chronic insomnia, only few reports have investigated how sleep architecture relates to response to this treatment. In this pilot study, we aimed to determine whether pre-treatment sleep spindle density predicts treatment response to cognitive-behavioral therapy for insomnia.

Methods: Twenty-four participants with chronic primary insomnia participated in a 6-week cognitive-behavioral therapy for insomnia performed in groups of 4-6 participants. Treatment response was assessed using the Pittsburgh Sleep Quality Index and the Insomnia Severity Index measured at pre- and post-treatment, and at 3- and 12-months' follow-up assessments. Secondary outcome measures were extracted from sleep diaries over 7 days and overnight polysomnography, obtained at pre- and post-treatment. Spindle density during stage N2-N3 sleep was extracted from polysomnography at pre-treatment. Hierarchical linear modeling analysis assessed whether sleep spindle density predicted response to cognitive-behavioral therapy.

Results: After adjusting for age, sex, and education level, lower spindle density at pre-treatment predicted poorer response over the 12-month follow-up, as reflected by a smaller reduction in Pittsburgh Sleep Quality Index over time. Reduced spindle density also predicted lower improvements in sleep diary sleep efficiency and wake after sleep onset immediately after treatment. There were no significant associations between spindle density and changes in the Insomnia Severity Index or polysomnography variables over time.

Conclusion: These preliminary results suggest that inter-individual differences in sleep spindle density in insomnia may represent an endogenous biomarker predicting responsiveness to cognitive-behavioral therapy. Insomnia with altered spindle activity might constitute an insomnia subtype characterized by a neurophysiological vulnerability to sleep disruption associated with impaired responsiveness to cognitive-behavioral therapy.





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