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Sleep spindles may predict response to cognitive behavioral therapy for chronic insomnia


Sleep spindles may predict response to cognitive behavioral therapy for chronic insomnia

Dang-Vu, Thien Thanh, Hatch, Benjamin, Salimi, Ali, Mograss, Melodee A, Boucetta, Soufiane, O’byrne, Jordan, Brandewinder, Marie, Berthomier, Christian and Gouin, Jean-Philippe (2017) Sleep spindles may predict response to cognitive behavioral therapy for chronic insomnia. Sleep Medicine . ISSN 13899457 (In Press)

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Official URL: http://dx.doi.org/10.1016/j.sleep.2017.08.012


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 at determining whether sleep spindle density at pre-treatment predicts treatment response to cognitive behavioral therapy for insomnia.

Twenty-four participants with chronic primary insomnia took part in a 6-week cognitive behavioral therapy for insomnia performed in groups of 4 to 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 seven days and one overnight polysomnography, obtained at pre- and post-treatment. Spindle density during stages 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.

After adjusting for age, sex and education level, lower spindle density at pre-treatment predicted poorer response over the 12-months follow-up, as reflected by 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.

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.

Divisions:Concordia University > Faculty of Arts and Science > Exercise Science
Item Type:Article
Authors:Dang-Vu, Thien Thanh and Hatch, Benjamin and Salimi, Ali and Mograss, Melodee A and Boucetta, Soufiane and O’byrne, Jordan and Brandewinder, Marie and Berthomier, Christian and Gouin, Jean-Philippe
Journal or Publication:Sleep Medicine
Date:9 September 2017
  • Canadian Institutes of Health Research (CIHR, MOP 142191, PJT 153115)
  • Natural Sciences and Engineering Research Council of Canada (NSERC)
  • Fonds de Recherche du Québec – Santé (FRQ-S)
  • Canada Foundation for Innovation (CFI)
  • American Sleep Medicine Foundation (ASMF)
  • Pharmaprix
  • Centre de Recherches de l’Institut Universitaire de Gériatrie de Montréal
Digital Object Identifier (DOI):10.1016/j.sleep.2017.08.012
Keywords:electroencephalography; sleep spindles; neural oscillations; biomarkers; insomnia
ID Code:983025
Deposited By: Danielle Dennie
Deposited On:13 Sep 2017 19:52
Last Modified:01 Sep 2018 00:01


C.M. Morin, R. Benca Chronic insomnia Lancet, 379 (9821) (2012), pp. 1129–1141

R.R. Bootzin, D.R. Epstein Understanding and treating insomnia Annu Rev Clin Psychol, 7 (2011), pp. 435–458

C.M. Morin, et al. Epidemiology of insomnia: Prevalence, self-help treatments, consultations, and determinants of help-seeking behaviors Sleep Medicine, 7 (2) (2006), pp. 123–130

A.G. Harvey, et al. Comparative Efficacy of Behavior Therapy, Cognitive Therapy and Cognitive Behavior Therapy for Chronic Insomnia: A Randomized Controlled Trial Journal of consulting and clinical psychology, 82 (4) (2014), pp. 670–683

C.A. Espie, S.J. Inglis, L. Harvey Predicting clinically significant response to cognitive behavior therapy for chronic insomnia in general medical practice: analysis of outcome data at 12 months posttreatment Journal of consulting and clinical psychology, 69 (1) (2001), pp. 58–66

C.M. Morin, J.P. Culbert, S.M. Schwartz Nonpharmacological interventions for insomnia: a meta-analysis of treatment efficacy The American journal of psychiatry, 151 (8) (1994), pp. 1172–1180

L. Van Houdenhove, et al. Treating primary insomnia: clinical effectiveness and predictors of outcomes on sleep, daytime function and health-related quality of life Journal of clinical psychology in medical settings, 18 (3) (2011), pp. 312–321

P. Lacks, K. Powlishta Improvement following behavioral treatment for insomnia: Clinical significance, long term maintenance, and predictors of outcome Behavior therapy, 20 (1989), pp. 117–134

J. Lancee, et al. Baseline Depression Levels Do Not Affect Efficacy of Cognitive-Behavioral Self-Help Treatment for Insomnia Depression and Anxiety, 30 (2) (2013), pp. 149–156

J.D. Edinger, C.E. Carney, W.K. Wohlgemuth Pretherapy cognitive dispositions and treatment outcome in cognitive behavior therapy for insomnia Behavior therapy, 39 (4) (2008), pp. 406–416

C.J. Bathgate, J.D. Edinger, A.D. Krystal Insomnia Patients With Objective Short Sleep Duration Have a Blunted Response to Cognitive Behavioral Therapy for Insomnia Sleep, 40 (1) (2017), pp. 1–12

N. Lovato, L. Lack, D.J. Kennaway Comparing and contrasting therapeutic effects of cognitive-behavior therapy for older adults suffering from insomnia with short and long objective sleep duration Sleep Med, 22 (2016), pp. 4–12

A.D. Krystal, J.D. Edinger Sleep EEG Predictors and Correlates of the Response to Cognitive Behavioral Therapy for Insomnia Sleep, 33 (5) (2010), pp. 669–677

A. Lüthi Sleep Spindles: Where They Come From, What They Do The Neuroscientist, 20 (3) (2014), pp. 243–256

E. Werth, et al. Spindle frequency activity in the sleep EEG: individual differences and topographical distribution Electroencephalography and Clinical Neurophysiology, 103 (5) (1997), pp. 535–542

L. De Gennaro, et al. An electroencephalographic fingerprint of human sleep NeuroImage, 26 (1) (2005), pp. 114–122

L. De Gennaro, et al. The electroencephalographic fingerprint of sleep is genetically determined: a twin study Ann Neurol, 64 (4) (2008), pp. 455–460

M. Schabus, et al. Interindividual sleep spindle differences and their relation to learning-related enhancements Brain research, 1191 (2008), pp. 127–135

S.M. Fogel, C.T. Smith The function of the sleep spindle: A physiological index of intelligence and a mechanism for sleep-dependent memory consolidation Neuroscience & Biobehavioral Reviews, 35 (5) (2011), pp. 1154–1165

T.T. Dang-Vu, et al. Interplay between spontaneous and induced brain activity during human non-rapid eye movement sleep Proc Natl Acad Sci U S A, 108 (37) (2011), pp. 15438–15443

T.T. Dang-Vu, et al. Spontaneous brain rhythms predict sleep stability in the face of noise Curr Biol, 20 (15) (2010) R626-7

T.T. Dang-Vu, et al. Sleep spindles predict stress-related increases in sleep disturbances Frontiers in Human Neuroscience (2015), p. 9

C.H. Bastien, et al. Sleep spindles in chronic psychophysiological insomnia J Psychosom Res, 66 (1) (2009), pp. 59–65

J. Fernandez-Mendoza, et al. Sleep misperception and chronic insomnia in the general population: role of objective sleep duration and psychological profiles Psychosomatic Medicine, 73 (1) (2011), pp. 88–97

Morin, C.M. and C.A. Espie, Insomnia: a clinical guide to assessment and treatment. 2003, New York: Kluwer Academic/Plenum.

AASM, International Classification of Sleep Disorders, 3rd Edition. 2014, Darien, IL: American Academy of Sleep Medicine.

D.J. Buysse, et al. The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research Psychiatry Research, 28 (2) (1989), pp. 193–213

C.H. Bastien, A. Vallières, C.M. Morin Validation of the Insomnia Severity Index as an outcome measure for insomnia research Sleep Medicine, 2 (4) (2001), pp. 297–307

C.E. Carney, et al. The consensus sleep diary: standardizing prospective sleep self-monitoring Sleep, 35 (2) (2012), pp. 287–302

Iber, C., et al., The AASM Manual for the Scoring of Sleep and Associated Events. 2007, Westchester: American Academy of Sleep Medicine. 59.

L. De Gennaro, M. Ferrara, M. Bertini Topographical distribution of spindles: variations between and within nrem sleep cycles Sleep Res Online, 3 (4) (2000), pp. 155–160

C. Berthomier, et al. Automatic Analysis of Single-Channel Sleep EEG: Validation in Healthy Individuals Sleep, 30 (11) (2007), pp. 1587–1595

K. Cervena, et al. Effect of cognitive behavioural therapy for insomnia on sleep architecture and sleep EEG power spectra in psychophysiological insomnia J Sleep Res, 13 (4) (2004), pp. 385–393

J.M. Gaillard, R. Blois Spindle density in sleep of normal subjects Sleep, 4 (4) (1981), pp. 385–391

N. Martin, et al. Topography of age-related changes in sleep spindles Neurobiol Aging, 34 (2) (2013), pp. 468–476

H. Quené, H. van den Bergh On multi-level modeling of data from repeated measures designs: a tutorial Speech Communication, 43 (1–2) (2004), pp. 103–121

C.M. Morin, et al. Cognitive behavioral therapy, singly and combined with medication, for persistent insomnia: a randomized controlled trial JAMA : the journal of the American Medical Association, 301 (19) (2009), pp. 2005–2015

D. Riemann, et al. The hyperarousal model of insomnia: a review of the concept and its evidence Sleep Med Rev, 14 (1) (2010), pp. 19–31

C.L. Ehlers, D.J. Kupfer Effects of age on delta and REM sleep parameters Electroencephalogr Clin Neurophysiol, 72 (2) (1989), pp. 118–125

L. Parrino, M.G. Terzano Polysomnographic effects of hypnotic drugs A review. Psychopharmacology (Berl), 126 (1) (1996), pp. 1–16

L. Marshall, et al. Boosting slow oscillations during sleep potentiates memory Nature, 444 (7119) (2006), pp. 610–613

M. Schabus, et al. Enhancing sleep quality and memory in insomnia using instrumental sensorimotor rhythm conditioning Biological Psychology, 95 (2014), pp. 126–134
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