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Vitali H, Campus C, Signorini S, De Giorgis V, Morelli F, Varesio C, Pasca L, Sammartano A, Gori M. Blindness affects the developmental trajectory of the sleeping brain. Neuroimage 2024; 286:120508. [PMID: 38181867 DOI: 10.1016/j.neuroimage.2024.120508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 12/12/2023] [Accepted: 01/03/2024] [Indexed: 01/07/2024] Open
Abstract
Sleep plays a crucial role in brain development, sensory information processing, and consolidation. Sleep spindles are markers of these mechanisms as they mirror the activity of the thalamocortical circuits. Spindles can be subdivided into two groups, slow (10-13 Hz) and fast (13-16 Hz), which are each associated with different functions. Specifically, fast spindles oscillate in the high-sigma band and are associated with sensorimotor processing, which is affected by visual deprivation. However, how blindness influences spindle development has not yet been investigated. We recorded nap video-EEG of 50 blind/severely visually impaired (BSI) and 64 sighted children aged 5 months to 6 years old. We considered aspects of both macro- and micro-structural spindles. The BSI children lacked the evolution of developmental spindles within the central area. Specifically, young BSI children presented low central high-sigma and high-beta (25-30 Hz) event-related spectral perturbation and showed no signs of maturational decrease. High-sigma and high-beta activity in the BSI group correlated with clinical indices predicting perceptual and motor disorders. Our findings suggest that fast spindles are pivotal biomarkers for identifying an early developmental deviation in BSI children. These findings are critical for initial therapeutic intervention.
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Affiliation(s)
- Helene Vitali
- Unit for Visually Impaired People, Center for Human Technologies, Fondazione Istituto Italiano di Tecnologia, Istituto Italiano di Tecnologia, Via Enrico Melen 83, Building B, Genoa 16152, Italy; DIBRIS, University of Genova, Genoa 16145, Italy
| | - Claudio Campus
- Unit for Visually Impaired People, Center for Human Technologies, Fondazione Istituto Italiano di Tecnologia, Istituto Italiano di Tecnologia, Via Enrico Melen 83, Building B, Genoa 16152, Italy
| | - Sabrina Signorini
- Department of Child Neurology and Psychiatry, IRCCS Mondino Foundation, Pavia 27100, Italy
| | - Valentina De Giorgis
- Department of Child Neurology and Psychiatry, IRCCS Mondino Foundation, Pavia 27100, Italy; Department of Brain and Behavioural Sciences, University of Pavia, Pavia 27100, Italy; Member of European Reference Network for Rare and Complex Epilepsies, EpiCARE, Italy
| | - Federica Morelli
- Department of Child Neurology and Psychiatry, IRCCS Mondino Foundation, Pavia 27100, Italy; Department of Brain and Behavioural Sciences, University of Pavia, Pavia 27100, Italy
| | - Costanza Varesio
- Department of Child Neurology and Psychiatry, IRCCS Mondino Foundation, Pavia 27100, Italy; Department of Brain and Behavioural Sciences, University of Pavia, Pavia 27100, Italy; Member of European Reference Network for Rare and Complex Epilepsies, EpiCARE, Italy
| | - Ludovica Pasca
- Department of Child Neurology and Psychiatry, IRCCS Mondino Foundation, Pavia 27100, Italy; Department of Brain and Behavioural Sciences, University of Pavia, Pavia 27100, Italy; Member of European Reference Network for Rare and Complex Epilepsies, EpiCARE, Italy
| | - Alessia Sammartano
- Department of Child Neurology and Psychiatry, IRCCS Mondino Foundation, Pavia 27100, Italy; Member of European Reference Network for Rare and Complex Epilepsies, EpiCARE, Italy
| | - Monica Gori
- Unit for Visually Impaired People, Center for Human Technologies, Fondazione Istituto Italiano di Tecnologia, Istituto Italiano di Tecnologia, Via Enrico Melen 83, Building B, Genoa 16152, Italy.
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2
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Baena D, Fang Z, Gibbings A, Smith D, Ray LB, Doyon J, Owen AM, Fogel SM. Functional differences in cerebral activation between slow wave-coupled and uncoupled sleep spindles. Front Neurosci 2023; 16:1090045. [PMID: 36741053 PMCID: PMC9889560 DOI: 10.3389/fnins.2022.1090045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 12/28/2022] [Indexed: 01/20/2023] Open
Abstract
Spindles are often temporally coupled to slow waves (SW). These SW-spindle complexes have been implicated in memory consolidation that involves transfer of information from the hippocampus to the neocortex. However, spindles and SW, which are characteristic of NREM sleep, can occur as part of this complex, or in isolation. It is not clear whether dissociable parts of the brain are recruited when coupled to SW vs. when spindles or SW occur in isolation. Here, we tested differences in cerebral activation time-locked to uncoupled spindles, uncoupled SW and coupled SW-spindle complexes using simultaneous EEG-fMRI. Consistent with the "active system model," we hypothesized that brain activations time-locked to coupled SW-spindles would preferentially occur in brain areas known to be critical for sleep-dependent memory consolidation. Our results show that coupled spindles and uncoupled spindles recruit distinct parts of the brain. Specifically, we found that hippocampal activation during sleep is not uniquely related to spindles. Rather, this process is primarily driven by SWs and SW-spindle coupling. In addition, we show that SW-spindle coupling is critical in the activation of the putamen. Importantly, SW-spindle coupling specifically recruited frontal areas in comparison to uncoupled spindles, which may be critical for the hippocampal-neocortical dialogue that preferentially occurs during sleep.
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Affiliation(s)
- Daniel Baena
- Sleep Unit, University of Ottawa Institute of Mental Health Research at The Royal, Ottawa, ON, Canada
| | - Zhuo Fang
- School of Psychology, University of Ottawa, Ottawa, ON, Canada
| | - Aaron Gibbings
- Sleep Unit, University of Ottawa Institute of Mental Health Research at The Royal, Ottawa, ON, Canada
| | - Dylan Smith
- Sleep Unit, University of Ottawa Institute of Mental Health Research at The Royal, Ottawa, ON, Canada
| | - Laura B. Ray
- School of Psychology, University of Ottawa, Ottawa, ON, Canada
| | - Julien Doyon
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
| | - Adrian M. Owen
- The Brain and Mind Institute, Western University, London, ON, Canada,Department of Physiology and Pharmacology, Western University, London, ON, Canada
| | - Stuart M. Fogel
- Sleep Unit, University of Ottawa Institute of Mental Health Research at The Royal, Ottawa, ON, Canada,School of Psychology, University of Ottawa, Ottawa, ON, Canada,The Brain and Mind Institute, Western University, London, ON, Canada,Department of Physiology and Pharmacology, Western University, London, ON, Canada,University of Ottawa Brain and Mind Research Institute, Ottawa, ON, Canada,*Correspondence: Stuart M. Fogel,
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3
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Toor B, van den Berg N, Ray LB, Fogel SM. Sleep spindles and slow waves are physiological markers for age-related changes in gray matter in brain regions supporting problem-solving skills. Learn Mem 2023; 30:12-24. [PMID: 36564151 PMCID: PMC9872192 DOI: 10.1101/lm.053649.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 11/29/2022] [Indexed: 12/25/2022]
Abstract
As we age, the added benefit of sleep for memory consolidation is lost. One of the hallmark age-related changes in sleep is the reduction of sleep spindles and slow waves. Gray matter neurodegeneration is related to both age-related changes in sleep and age-related changes in memory, including memory for problem-solving skills. Here, we investigated whether spindles and slow waves might serve as biological markers for neurodegeneration of gray matter and for the related memory consolidation deficits in older adults. Forty healthy young adults (20-35 yr) and 30 healthy older adults (60-85 yr) were assigned to either nap or wake conditions. Participants were trained on the Tower of Hanoi in the morning, followed by either a 90-min nap opportunity or period of wakefulness, and were retested afterward. We found that age-related changes in sleep spindles and slow waves were differentially related to gray matter intensity in young and older adults in brain regions that support sleep-dependent memory consolidation for problem-solving skills. Specifically, we found that spindles were related to gray matter in neocortical areas (e.g., somatosensory and parietal cortex), and slow waves were related to gray matter in the anterior cingulate, hippocampus, and caudate, all areas known to support problem-solving skills. These results suggest that both sleep spindles and slow waves may serve as biological markers of age-related neurodegeneration of gray matter and the associated reduced benefit of sleep for memory consolidation in older adults.
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Affiliation(s)
- Balmeet Toor
- School of Psychology, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| | | | - Laura B Ray
- School of Psychology, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| | - Stuart M Fogel
- School of Psychology, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
- Sleep Unit, The Royal's Institute of Mental Health Research, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
- University of Ottawa Brain and Mind Institute, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
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4
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Gibbings A, Ray LB, Gagnon S, Collin CA, Robillard R, Fogel SM. The EEG correlates and dangerous behavioral consequences of drowsy driving after a single night of mild sleep deprivation. Physiol Behav 2022; 252:113822. [PMID: 35469778 DOI: 10.1016/j.physbeh.2022.113822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 04/12/2022] [Accepted: 04/21/2022] [Indexed: 10/18/2022]
Abstract
OBJECTIVE Here, we investigated the behavioral, cognitive, and electrophysiological impact of mild, acute sleep loss via simultaneously recorded behavioral and electrophysiological measures of vigilance during a "real-world", simulated driving task. METHODS Participants (N = 34) visited the lab for two testing days where their brain activity and vigilance were simultaneously recorded during a driving simulator task. The driving task lasted approximately 70 mins and consisted of tailgating the lead car at high speed, which braked randomly, requiring participants to react quickly to avoid crashing. The night before testing, participants either slept from 12am-9am (Normally Rested), or 1am-6am (Sleep Restriction). RESULTS After a single night of mild sleep restriction, sleepiness was increased, participants took longer to brake, missed more braking events, and crashed more often. Brain activity showed more intense alpha burst activity and significant changes in EEG spectral power frequencies related to arousal (e.g., delta, theta, alpha). Importantly, increases in amplitude and number of alpha bursts predicted delays in reaction time when braking. CONCLUSIONS The findings of this study suggest that a single night of mild sleep loss has significant, negative consequences on driving performance and vigilance, and a clear impact on the physiology of the brain in ways that reflect reduced arousal. SIGNIFICANCE Understanding neural and cognitive changes associated with sleep loss may lead to important advancements in identifying and preventing potentially dangerous sleep-related lapses in vigilance.
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Affiliation(s)
- A Gibbings
- Sleep Research Unit, The University of Ottawa's Institute of Mental Health Research at The Royal, Ottawa, K1Z 7K4, Canada; School of Psychology, University of Ottawa, Ottawa, K1N 6N5, Canada
| | - L B Ray
- Sleep Research Unit, The University of Ottawa's Institute of Mental Health Research at The Royal, Ottawa, K1Z 7K4, Canada
| | - S Gagnon
- School of Psychology, University of Ottawa, Ottawa, K1N 6N5, Canada
| | - C A Collin
- School of Psychology, University of Ottawa, Ottawa, K1N 6N5, Canada
| | - R Robillard
- Sleep Research Unit, The University of Ottawa's Institute of Mental Health Research at The Royal, Ottawa, K1Z 7K4, Canada; School of Psychology, University of Ottawa, Ottawa, K1N 6N5, Canada
| | - S M Fogel
- Sleep Research Unit, The University of Ottawa's Institute of Mental Health Research at The Royal, Ottawa, K1Z 7K4, Canada; School of Psychology, University of Ottawa, Ottawa, K1N 6N5, Canada; University of Ottawa Brain & Mind Research Institute, Ottawa, K1H 8M5, Canada.
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5
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Jones MR, Brandner AJ, Vendruscolo LF, Vendruscolo JCM, Koob GF, Schmeichel BE. Effects of Alcohol Withdrawal on Sleep Macroarchitecture and Microarchitecture in Female and Male Rats. Front Neurosci 2022; 16:838486. [PMID: 35757544 PMCID: PMC9226367 DOI: 10.3389/fnins.2022.838486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 02/22/2022] [Indexed: 11/17/2022] Open
Abstract
The prevalence of sleep disruptions is higher among people with alcohol use disorder (AUD), particularly during alcohol withdrawal, compared to non-AUD individuals. Although women generally have a higher risk of developing sleep disorders, few studies have investigated sex differences in sleep disruptions following chronic alcohol exposure. The present study examined sleep macroarchitecture (time spent asleep or awake and sleep onset latency) and microarchitecture (bout rate and duration and sleep spindle characterization) prior to alcohol vapor exposure (baseline), during acute withdrawal, and through protracted abstinence in female and male rats. Females and males showed reduced time in rapid eye movement (REM) sleep during acute withdrawal, which returned to baseline levels during protracted abstinence. REM sleep onset latency was decreased during protracted abstinence in females only. Furthermore, there was a sex difference observed in overall REM sleep bout rate. Although there were no changes in non-REM sleep time, or to non-REM sleep bout rate or duration, there was an increase in non-REM sleep intra-spindle frequency during acute withdrawal in both females and males. Finally, there was increased wakefulness time and bout duration during acute withdrawal in both females and males. The results demonstrate both macroarchitectural and microarchitectural changes in sleep following chronic alcohol exposure, particularly during acute withdrawal, suggesting the need for therapeutic interventions for sleep disturbances during withdrawal in individuals with AUD. Furthermore, sex differences were observed in REM sleep, highlighting the importance of including both sexes in future alcohol-related sleep studies.
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Affiliation(s)
- Marissa R Jones
- Department of Biomedical Sciences, Quillen College of Medicine, East Tennessee State University, Johnson City, TN, United States
| | - Adam J Brandner
- Neurobiology of Addiction Section, Integrative Neuroscience Research Branch, National Institute on Drug Abuse, Intramural Research Program, Baltimore, MD, United States
| | - Leandro F Vendruscolo
- Neurobiology of Addiction Section, Integrative Neuroscience Research Branch, National Institute on Drug Abuse, Intramural Research Program, Baltimore, MD, United States
| | - Janaina C M Vendruscolo
- Neurobiology of Addiction Section, Integrative Neuroscience Research Branch, National Institute on Drug Abuse, Intramural Research Program, Baltimore, MD, United States
| | - George F Koob
- Neurobiology of Addiction Section, Integrative Neuroscience Research Branch, National Institute on Drug Abuse, Intramural Research Program, Baltimore, MD, United States
| | - Brooke E Schmeichel
- Department of Biomedical Sciences, Quillen College of Medicine, East Tennessee State University, Johnson City, TN, United States.,Neurobiology of Addiction Section, Integrative Neuroscience Research Branch, National Institute on Drug Abuse, Intramural Research Program, Baltimore, MD, United States
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6
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Baena D, Cantero JL, Atienza M. Stability of neural encoding moderates the contribution of sleep and repeated testing to memory consolidation. Neurobiol Learn Mem 2021; 185:107529. [PMID: 34597816 DOI: 10.1016/j.nlm.2021.107529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 09/03/2021] [Accepted: 09/24/2021] [Indexed: 10/20/2022]
Abstract
There is evidence suggesting that online consolidation during retrieval-mediated learning interacts with offline consolidation during subsequent sleep to transform memory. Here we investigate whether this interaction persists when retrieval-mediated learning follows post-training sleep and whether the direction of this interaction is conditioned by the quality of encoding resulting from manipulation of the amount of sleep on the previous night. The quality of encoding was determined by computing the degree of similarity between EEG-activity patterns across restudy of face pairs in two groups of young participants, one who slept the last 4 h of the pre-training night, and another who slept 8 h. The offline consolidation was assessed by computing the degree of coupling between slow oscillations (SOs) and spindles (SPs) during post-training sleep, while the online consolidation was evaluated by determining the degree of similarity between EEG-activity patterns recorded during the study phase and during repeated recognition of either the same face pair (i.e., specific similarity) or face pairs sharing sex and profession (i.e., categorical similarity) to evaluate differentiation and generalization, respectively. The study and recognition phases were separated by a night of normal sleep duration. Mixed-effects models revealed that the stability of neural encoding moderated the relationship between sleep- and retrieval-mediated consolidation processes over left frontal regions. For memories showing lower encoding stability, the enhanced SO-SP coupling was associated with increased reinstatement of category-specific encoding-related activity at the expense of content-specific activity, whilst the opposite occurred for memories showing greater encoding stability. Overall, these results suggest that offline consolidation during post-training sleep interacts with online consolidation during retrieval the next day to favor the reorganization of memory contents, by increasing specificity of stronger memories and generalization of the weaker ones.
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Affiliation(s)
- Daniel Baena
- Laboratory of Functional Neuroscience, Universidad Pablo de Olavide, Seville 41013, Spain
| | - Jose L Cantero
- Laboratory of Functional Neuroscience, Universidad Pablo de Olavide, Seville 41013, Spain; CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, Spain
| | - Mercedes Atienza
- Laboratory of Functional Neuroscience, Universidad Pablo de Olavide, Seville 41013, Spain; CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, Spain.
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7
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Porteous M, Fogel S, Ray L, Hickie IB, Carpenter JS, Louati K, Robillard R. Increased spindle density correlates with sleep continuity improvements following an eight-week course of a melatonin agonist in people with depression: A proof-of-concept study with agomelatine. Eur J Neurosci 2021; 54:5112-5119. [PMID: 34089546 DOI: 10.1111/ejn.15340] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 04/27/2021] [Accepted: 05/23/2021] [Indexed: 11/27/2022]
Abstract
Sleep fragmentation and reductions in sleep spindles have been observed in individuals with depression. Sleep spindles are known to play a protective role for sleep, and there are indications that melatonin agents can enhance spindles in healthy people. Whether agomelatine, a melatonin agonist indicated for the treatment of depression, may increase spindle density sufficiently to impact sleep continuity in people with depression remains unknown. This proof-of-concept study investigated changes in spindles following agomelatine intake in young adults with depression and assessed how they may relate to potential changes in sleep continuity and depressive symptoms. This study was based on an open-label design. Fifteen participants between 17 and 28 years of age (mean = 22.2; standard deviation [SD] = 3.4) with a diagnosis of a depressive disorder underwent polysomnography before and after an intervention including a 1 hr psychoeducation session centered on sleep and circadian rhythms, and an 8-week course of agomelatine (25-50 mg) with a guided sleep phase advance. Fast spindle density significantly increased from pre- to post-intervention. This increase in spindle density significantly correlated with a reduction in wake after sleep onset, and a similar trend was found with increased sleep efficiency. There was no significant correlation between spindle parameters and depressive symptoms. These findings suggest that agomelatine may contribute to enhanced sleep consolidation, possibly in part through the modulation of spindle production. This should be confirmed by larger randomized control trials.
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Affiliation(s)
- Meggan Porteous
- Sleep Research Unit, University of Ottawa Institute of Mental Health Research at The Royal, Ottawa, ON, Canada.,Faculty of Social Sciences, School of Psychology, University of Ottawa, Ottawa, ON, Canada
| | - Stuart Fogel
- Sleep Research Unit, University of Ottawa Institute of Mental Health Research at The Royal, Ottawa, ON, Canada.,Faculty of Social Sciences, School of Psychology, University of Ottawa, Ottawa, ON, Canada
| | - Laura Ray
- Faculty of Social Sciences, School of Psychology, University of Ottawa, Ottawa, ON, Canada
| | - Ian B Hickie
- Brain and Mind Centre, The University of Sydney, Camperdown, NSW, Australia
| | - Joanne S Carpenter
- Brain and Mind Centre, The University of Sydney, Camperdown, NSW, Australia
| | - Khaoula Louati
- Faculty of Social Sciences, School of Psychology, University of Ottawa, Ottawa, ON, Canada
| | - Rebecca Robillard
- Sleep Research Unit, University of Ottawa Institute of Mental Health Research at The Royal, Ottawa, ON, Canada.,Faculty of Social Sciences, School of Psychology, University of Ottawa, Ottawa, ON, Canada
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8
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Goldschmied JR, Lacourse K, Maislin G, Delfrate J, Gehrman P, Pack FM, Staley B, Pack AI, Younes M, Kuna ST, Warby SC. Spindles are highly heritable as identified by different spindle detectors. Sleep 2021; 44:5963958. [PMID: 33165618 DOI: 10.1093/sleep/zsaa230] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 09/25/2020] [Indexed: 11/13/2022] Open
Abstract
STUDY OBJECTIVES Sleep spindles, a defining feature of stage N2 sleep, are maximal at central electrodes and are found in the frequency range of the electroencephalogram (EEG) (sigma 11-16 Hz) that is known to be heritable. However, relatively little is known about the heritability of spindles. Two recent studies investigating the heritability of spindles reported moderate heritability, but with conflicting results depending on scalp location and spindle type. The present study aimed to definitively assess the heritability of sleep spindle characteristics. METHODS We utilized the polysomnography data of 58 monozygotic and 40 dizygotic same-sex twin pairs to identify heritable characteristics of spindles at C3/C4 in stage N2 sleep including density, duration, peak-to-peak amplitude, and oscillation frequency. We implemented and tested a variety of spindle detection algorithms and used two complementary methods of estimating trait heritability. RESULTS We found robust evidence to support strong heritability of spindles regardless of detector method (h2 > 0.8). However not all spindle characteristics were equally heritable, and each spindle detection method produced a different pattern of results. CONCLUSIONS The sleep spindle in stage N2 sleep is highly heritable, but the heritability differs for individual spindle characteristics and depends on the spindle detector used for analysis.
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Affiliation(s)
| | - Karine Lacourse
- Center for Advanced Research in Sleep Medicine, Centre de Recherche de l'Hôpital du Sacré-Cœur de Montréal, QC, Canada
| | - Greg Maislin
- Division of Sleep Medicine/Department of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Jacques Delfrate
- Center for Advanced Research in Sleep Medicine, Centre de Recherche de l'Hôpital du Sacré-Cœur de Montréal, QC, Canada
| | - Philip Gehrman
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA
| | - Frances M Pack
- Division of Sleep Medicine/Department of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Bethany Staley
- Division of Sleep Medicine/Department of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Allan I Pack
- Division of Sleep Medicine/Department of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Magdy Younes
- YRT Ltd, Winnipeg, Manitoba, Canada.,Sleep Disorders Centre, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Samuel T Kuna
- Division of Sleep Medicine/Department of Medicine, University of Pennsylvania, Philadelphia, PA.,Department of Medicine, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA
| | - Simon C Warby
- Center for Advanced Research in Sleep Medicine, Centre de Recherche de l'Hôpital du Sacré-Cœur de Montréal, QC, Canada
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9
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Gomez-Pilar J, Gutiérrez-Tobal GC, Poza J, Fogel S, Doyon J, Northoff G, Hornero R. Spectral and temporal characterization of sleep spindles-methodological implications. J Neural Eng 2021; 18. [PMID: 33618345 DOI: 10.1088/1741-2552/abe8ad] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 02/22/2021] [Indexed: 11/12/2022]
Abstract
Objective. Nested into slow oscillations (SOs) and modulated by their up-states, spindles are electrophysiological hallmarks of N2 sleep stage that present a complex hierarchical architecture. However, most studies have only described spindles in basic statistical terms, which were limited to the spindle itself without analyzing the characteristics of the pre-spindle moments in which the SOs are originated. The aim of this study was twofold: (a) to apply spectral and temporal measures to the pre-spindle and spindle periods, as well as analyze the correlation between them, and (b) to evaluate the potential of these spectral and temporal measures in future automatic detection algorithms.Approach. An automatic spindle detection algorithm was applied to the overnight electroencephalographic recordings of 26 subjects. Ten complementary features (five spectral and five temporal parameters) were computed in the pre-spindle and spindle periods after their segmentation. These features were computed independently in each period and in a time-resolved way (sliding window). After the statistical comparison of both periods, a correlation analysis was used to assess their interrelationships. Finally, a receiver operating-characteristic (ROC) analysis along with a bootstrap procedure was conducted to further evaluate the degree of separability between the pre-spindle and spindle periods.Main results. The results show important time-varying changes in spectral and temporal parameters. The features calculated in pre-spindle and spindle periods are strongly and significantly correlated, demonstrating the association between the pre-spindle characteristics and the subsequent spindle. The ROC analysis exposes that the typical feature used in automatic spindle detectors, i.e. the power in the sigma band, is outperformed by other features, such as the spectral entropy in this frequency range.Significance. The novel features applied here demonstrate their utility as predictors of spindles that could be incorporated into novel algorithms of automatic spindle detectors, in which the analysis of the pre-spindle period becomes relevant for improving their performance. From the clinical point of view, these features may serve as novel precision therapeutic targets to enhance spindle production with the aim of improving memory, cognition, and sleep quality in healthy and clinical populations. The results evidence the need for characterizing spindles in terms beyond power and the spindle period itself to more dynamic measures and the pre-spindle period. Physiologically, these findings suggest that spindles are more than simple oscillations, but nonstable oscillatory bursts embedded in the complex pre-spindle dynamics.
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Affiliation(s)
- Javier Gomez-Pilar
- Biomedical Engineering Group, University of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Valladolid, Spain
| | - Gonzalo C Gutiérrez-Tobal
- Biomedical Engineering Group, University of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Valladolid, Spain
| | - Jesús Poza
- Biomedical Engineering Group, University of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Valladolid, Spain.,IMUVA, Mathematics Research Institute, University of Valladolid, Valladolid, Spain
| | - Stuart Fogel
- School of Psychology, University of Ottawa, Ottawa, Canada.,Mind, Brain Imaging and Neuroethics, Institute of Mental Health Research, University of Ottawa, Ottawa, Canada
| | - Julien Doyon
- Functional Neuroimaging Unit, Centre de Recherche de l'institut Universitaire de Gériatrie de 8 Montréal, Montreal, Canada.,McConnell Brain Imaging Centre and Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | - Georg Northoff
- Mind, Brain Imaging and Neuroethics, Institute of Mental Health Research, University of Ottawa, Ottawa, Canada.,Mental Health Center, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People's Republic of China
| | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Valladolid, Spain.,IMUVA, Mathematics Research Institute, University of Valladolid, Valladolid, Spain
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10
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Thompson K, Gibbings A, Shaw J, Ray L, Hébert G, De Koninck J, Fogel S. Sleep and Second-Language Acquisition Revisited: The Role of Sleep Spindles and Rapid Eye Movements. Nat Sci Sleep 2021; 13:1887-1902. [PMID: 34703346 PMCID: PMC8536881 DOI: 10.2147/nss.s326151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 09/01/2021] [Indexed: 11/30/2022] Open
Abstract
INTRODUCTION Second-language learning (SLL) depends on distinct functional-neuroanatomical systems including procedural and declarative long-term memory. Characteristic features of rapid eye movement (REM) and non-REM sleep such as rapid eye movements and sleep spindles are electrophysiological markers of cognitively complex procedural and declarative memory consolidation, respectively. In adults, grammatical learning depends at first on declarative memory ("early SLL") then shifts to procedural memory with experience ("late SLL"). However, it is unknown if the shift from declarative to procedural memory in early vs late SLL is supported by sleep. Here, we hypothesized that increases in sleep spindle characteristics would be associated with early SLL, whereas increases in REM activity (eg, density and EEG theta-band activity time-locked to rapid eye movements) would be associated with late SLL. METHODS Eight Anglophone (English first language) participants completed four polysomnographic recordings throughout an intensive 6-week French immersion course. Sleep spindle data and electroencephalographic spectral power time-locked to rapid eye movements were extracted from parietal temporal electrodes. RESULTS As predicted, improvements in French proficiency were associated with changes in spindles during early SLL. Furthermore, we observed increased event-related theta power time-locked to rapid eye movements during late SLL compared with early SLL. The increases in theta power were significantly correlated with improvements in French proficiency. DISCUSSION This supports the notion that sleep spindles are involved in early SLL when grammar depends on declarative memory, whereas cortical theta activity time-locked to rapid eye movements is involved in late SLL when grammar depends on procedural memory.
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Affiliation(s)
| | - Aaron Gibbings
- School of Psychology, University of Ottawa, Ottawa, ON, Canada.,Sleep Unit, University of Ottawa Institute of Mental Health at the Royal, Ottawa, ON, Canada
| | - James Shaw
- School of Psychology, University of Ottawa, Ottawa, ON, Canada
| | - Laura Ray
- School of Psychology, University of Ottawa, Ottawa, ON, Canada
| | - Gilles Hébert
- School of Psychology, University of Ottawa, Ottawa, ON, Canada
| | - Joseph De Koninck
- School of Psychology, University of Ottawa, Ottawa, ON, Canada.,University of Ottawa Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada
| | - Stuart Fogel
- School of Psychology, University of Ottawa, Ottawa, ON, Canada.,Sleep Unit, University of Ottawa Institute of Mental Health at the Royal, Ottawa, ON, Canada.,University of Ottawa Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada
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11
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Gibbings A, Ray LB, Berberian N, Nguyen T, Shahidi Zandi A, Owen AM, Comeau FJE, Fogel SM. EEG and behavioural correlates of mild sleep deprivation and vigilance. Clin Neurophysiol 2020; 132:45-55. [PMID: 33248433 DOI: 10.1016/j.clinph.2020.10.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 09/15/2020] [Accepted: 10/16/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVE The current study investigated the behavioral, cognitive, and electrophysiological impact of mild (only a few hours) and acute (one night) sleep loss via simultaneously recorded behavioural and physiological measures of vigilance. METHODS Participants (N = 23) came into the lab for two testing days where their brain activity and vigilance were recorded and assessed. The night before the testing session, participants either slept from 12am to 9am (Normally Rested), or from 1am to 6am (Sleep Restriction). RESULTS Vigilance was reduced and sleepiness was increased in the Sleep Restricted vs. Normally Rested condition, and this was exacerbated over the course of performing the vigilance task. As well, sleep restriction resulted in more intense alpha bursts. Lastly, EEG spectral power differed in Sleep Restricted vs. Normally Rested conditions as sleep onset progressed, particularly for frequencies reflecting arousal (e.g., delta, alpha, beta). CONCLUSIONS The findings of this study suggest that only one night of mild sleep loss significantly increases sleepiness and, importantly, reduces vigilance. In addition, this sleep loss has a clear impact on the physiology of the brain in ways that reflect reduced arousal. SIGNIFICANCE Understanding the neural correlates and cognitive processes associated with loss of sleep may lead to important advancements in identifying and preventing deleterious or potentially dangerous, sleep-related lapses in vigilance.
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Affiliation(s)
- A Gibbings
- Sleep Research Unit, The Royal's Institute of Mental Health Research, Ottawa K1Z 7K4, Canada; School of Psychology, University of Ottawa, Ottawa K1N 6N5, Canada
| | - L B Ray
- Sleep Research Unit, The Royal's Institute of Mental Health Research, Ottawa K1Z 7K4, Canada
| | - N Berberian
- School of Psychology, University of Ottawa, Ottawa K1N 6N5, Canada
| | - T Nguyen
- Sleep Research Unit, The Royal's Institute of Mental Health Research, Ottawa K1Z 7K4, Canada
| | - A Shahidi Zandi
- Alcohol Countermeasures Systems Corp (ACS), Toronto M9W 6J2, Canada
| | - A M Owen
- The Brain & Mind Institute, Western University, London N6A 5B7, Canada
| | - F J E Comeau
- Alcohol Countermeasures Systems Corp (ACS), Toronto M9W 6J2, Canada
| | - S M Fogel
- Sleep Research Unit, The Royal's Institute of Mental Health Research, Ottawa K1Z 7K4, Canada; School of Psychology, University of Ottawa, Ottawa K1N 6N5, Canada; The Brain & Mind Institute, Western University, London N6A 5B7, Canada; University of Ottawa Brain & Mind Research Institute, Ottawa K1H 8M5, Canada.
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12
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Gomez-Pilar J, Northoff G, Vaquerizo-Villar F, Poza J, Gutierrez-Tobal GC, Hornero R. Intraindividual Characterization of the Sleep Spindle Variability in Healthy Subjects. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:3473-3476. [PMID: 33018751 DOI: 10.1109/embc44109.2020.9176315] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Spatial and frequency characterization of sleep spindles have been extensively addressed using M/EEG or fMRI recordings. However, its intraindividual variability across time has not been addressed. Here we propose to assess the intraindividual variability of sleep spindles in a time-resolved way by means of a trial-to-trial-variability (TTV) measure. For that purpose, the EEG of 26 healthy subjects were recorded overnight. After an exhaustive preprocessing pipeline to remove artifacts, spindles were automatically detected using a complex demodulation-based method. Then, the Wavelet Scalogram was estimated to validate it. Spindle TTV of each participant was also computed for all the conventional EEG frequency bands. Root mean square (RMS) of each TTV signal was calculated as a measure of the total variability of each spindle. Results showed significant differences in the variability between frequencies. Specifically, RMS in the beta-1 frequency band showed higher values as compared to all the other frequency bands (p<0.001). TTV curves showed a dichotomic trend, with lower frequencies showing an increase in the variability before the spindle onset, and higher frequencies showing such increase after the onset. The dependence of the spindle variability with the frequency could be explained by the influence of the multiple cortical generators involved.Clinical Relevance- Sleep spindles are similarly affected in different cognitive-related disorders, which supports the relevance of assessing abnormal sleep patterns as a possible cause for such cognitive deficits.
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Lacourse K, Yetton B, Mednick S, Warby SC. Massive online data annotation, crowdsourcing to generate high quality sleep spindle annotations from EEG data. Sci Data 2020; 7:190. [PMID: 32561751 PMCID: PMC7305234 DOI: 10.1038/s41597-020-0533-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 05/13/2020] [Indexed: 12/18/2022] Open
Abstract
Spindle event detection is a key component in analyzing human sleep. However, detection of these oscillatory patterns by experts is time consuming and costly. Automated detection algorithms are cost efficient and reproducible but require robust datasets to be trained and validated. Using the MODA (Massive Online Data Annotation) platform, we used crowdsourcing to produce a large open-source dataset of high quality, human-scored sleep spindles (5342 spindles, from 180 subjects). We evaluated the performance of three subtype scorers: “experts, researchers and non-experts”, as well as 7 previously published spindle detection algorithms. Our findings show that only two algorithms had performance scores similar to human experts. Furthermore, the human scorers agreed on the average spindle characteristics (density, duration and amplitude), but there were significant age and sex differences (also observed in the set of detected spindles). This study demonstrates how the MODA platform can be used to generate a highly valid open source standardized dataset for researchers to train, validate and compare automated detectors of biological signals such as the EEG.
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Affiliation(s)
- Karine Lacourse
- Centre d'études avancées en médecine du sommeil, Montréal, Canada.
| | - Ben Yetton
- Department of Cognitive Science, University of California, Irvine, CA, USA
| | - Sara Mednick
- Department of Cognitive Science, University of California, Irvine, CA, USA
| | - Simon C Warby
- Centre d'études avancées en médecine du sommeil, Montréal, Canada.,Department of Psychiatry, Université de Montréal, Montréal, Canada
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14
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Ujma PP, Bódizs R, Dresler M. Sleep and intelligence: critical review and future directions. Curr Opin Behav Sci 2020. [DOI: 10.1016/j.cobeha.2020.01.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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15
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Uygun DS, Katsuki F, Bolortuya Y, Aguilar DD, McKenna JT, Thankachan S, McCarley RW, Basheer R, Brown RE, Strecker RE, McNally JM. Validation of an automated sleep spindle detection method for mouse electroencephalography. Sleep 2020; 42:5185635. [PMID: 30476300 DOI: 10.1093/sleep/zsy218] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Indexed: 11/12/2022] Open
Abstract
Study Objectives Sleep spindles are abnormal in several neuropsychiatric conditions and have been implicated in associated cognitive symptoms. Accordingly, there is growing interest in elucidating the pathophysiology behind spindle abnormalities using rodent models of such disorders. However, whether sleep spindles can reliably be detected in mouse electroencephalography (EEG) is controversial necessitating careful validation of spindle detection and analysis techniques. Methods Manual spindle detection procedures were developed and optimized to generate an algorithm for automated detection of events from mouse cortical EEG. Accuracy and external validity of this algorithm were then assayed via comparison to sigma band (10-15 Hz) power analysis, a proxy for sleep spindles, and pharmacological manipulations. Results We found manual spindle identification in raw mouse EEG unreliable, leading to low agreement between human scorers as determined by F1-score (0.26 ± 0.07). Thus, we concluded it is not possible to reliably score mouse spindles manually using unprocessed EEG data. Manual scoring from processed EEG data (filtered, cubed root-mean-squared), enabled reliable detection between human scorers, and between human scorers and algorithm (F1-score > 0.95). Algorithmically detected spindles correlated with changes in sigma-power and were altered by the following conditions: sleep-wake state changes, transitions between NREM and REM sleep, and application of the hypnotic drug zolpidem (10 mg/kg, intraperitoneal). Conclusions Here we describe and validate an automated paradigm for rapid and reliable detection of spindles from mouse EEG recordings. This technique provides a powerful tool to facilitate investigations of the mechanisms of spindle generation, as well as spindle alterations evident in mouse models of neuropsychiatric disorders.
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Affiliation(s)
- David S Uygun
- Department of Psychiatry, VA Boston Healthcare System and Harvard Medical School, West Roxbury, MA
| | - Fumi Katsuki
- Department of Psychiatry, VA Boston Healthcare System and Harvard Medical School, West Roxbury, MA
| | - Yunren Bolortuya
- Department of Psychiatry, VA Boston Healthcare System and Harvard Medical School, West Roxbury, MA
| | - David D Aguilar
- Department of Psychiatry, VA Boston Healthcare System and Harvard Medical School, West Roxbury, MA
| | - James T McKenna
- Department of Psychiatry, VA Boston Healthcare System and Harvard Medical School, West Roxbury, MA
| | - Stephen Thankachan
- Department of Psychiatry, VA Boston Healthcare System and Harvard Medical School, West Roxbury, MA
| | - Robert W McCarley
- Department of Psychiatry, VA Boston Healthcare System and Harvard Medical School, West Roxbury, MA
| | - Radhika Basheer
- Department of Psychiatry, VA Boston Healthcare System and Harvard Medical School, West Roxbury, MA
| | - Ritchie E Brown
- Department of Psychiatry, VA Boston Healthcare System and Harvard Medical School, West Roxbury, MA
| | - Robert E Strecker
- Department of Psychiatry, VA Boston Healthcare System and Harvard Medical School, West Roxbury, MA
| | - James M McNally
- Department of Psychiatry, VA Boston Healthcare System and Harvard Medical School, West Roxbury, MA
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16
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Kinoshita T, Fujiwara K, Kano M, Ogawa K, Sumi Y, Matsuo M, Kadotani H. Sleep Spindle Detection Using RUSBoost and Synchrosqueezed Wavelet Transform. IEEE Trans Neural Syst Rehabil Eng 2020; 28:390-398. [PMID: 31944960 DOI: 10.1109/tnsre.2020.2964597] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Sleep spindles are important electroencephalographic (EEG) waveforms in sleep medicine; however, it is burdensome even for experts to detect spindles, so automatic spindle detection methodologies have been investigated. Conventional methods utilize waveforms template matching or machine learning for detecting spindles. In the former approach, it is necessary to tune thresholds for individual adaptation, while the latter approach has the problem of imbalanced data because the amount of sleep spindles is small compared with the entire EEG data. The present work proposes a sleep spindle detection method that combines wavelet synchrosqueezed transform (SST) and random under-sampling boosting (RUSBoost). SST is a time-frequency analysis method suitable for extracting features of spindle waveforms. RUSBoost is a framework for coping with the imbalanced data problem. The proposed SST-RUS can deal with the imbalanced data in spindle detection and does not require threshold tuning because RUSBoost uses majority voting of weak classifiers for discrimination. The performance of SST-RUS was validated using an open-access database called the Montreal archives of sleep studies cohort 1 (MASS-C1), which showed an F-measure of 0.70 with a sensitivity of 76.9% and a positive predictive value of 61.2%. The proposed method can reduce the burden of PSG scoring.
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17
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Abstract
Sleep spindles are burstlike signals in the electroencephalogram (EEG) of the sleeping mammalian brain and electrical surface correlates of neuronal oscillations in thalamus. As one of the most inheritable sleep EEG signatures, sleep spindles probably reflect the strength and malleability of thalamocortical circuits that underlie individual cognitive profiles. We review the characteristics, organization, regulation, and origins of sleep spindles and their implication in non-rapid-eye-movement sleep (NREMS) and its functions, focusing on human and rodent. Spatially, sleep spindle-related neuronal activity appears on scales ranging from small thalamic circuits to functional cortical areas, and generates a cortical state favoring intracortical plasticity while limiting cortical output. Temporally, sleep spindles are discrete events, part of a continuous power band, and elements grouped on an infraslow time scale over which NREMS alternates between continuity and fragility. We synthesize diverse and seemingly unlinked functions of sleep spindles for sleep architecture, sensory processing, synaptic plasticity, memory formation, and cognitive abilities into a unifying sleep spindle concept, according to which sleep spindles 1) generate neural conditions of large-scale functional connectivity and plasticity that outlast their appearance as discrete EEG events, 2) appear preferentially in thalamic circuits engaged in learning and attention-based experience during wakefulness, and 3) enable a selective reactivation and routing of wake-instated neuronal traces between brain areas such as hippocampus and cortex. Their fine spatiotemporal organization reflects NREMS as a physiological state coordinated over brain and body and may indicate, if not anticipate and ultimately differentiate, pathologies in sleep and neurodevelopmental, -degenerative, and -psychiatric conditions.
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Affiliation(s)
- Laura M J Fernandez
- Department of Fundamental Neurosciences, University of Lausanne, Lausanne, Switzerland
| | - Anita Lüthi
- Department of Fundamental Neurosciences, University of Lausanne, Lausanne, Switzerland
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18
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Fang Z, Ray LB, Houldin E, Smith D, Owen AM, Fogel SM. Sleep Spindle-dependent Functional Connectivity Correlates with Cognitive Abilities. J Cogn Neurosci 2019; 32:446-466. [PMID: 31659927 DOI: 10.1162/jocn_a_01488] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
EEG studies have shown that interindividual differences in the electrophysiological properties of sleep spindles (e.g., density, amplitude, duration) are highly correlated with trait-like "reasoning" abilities (i.e., "fluid intelligence"; problem-solving skills; the ability to employ logic or identify complex patterns), but not interindividual differences in STM or "verbal" intellectual abilities. Previous simultaneous EEG-fMRI studies revealed brain activations time-locked to spindles. Our group has recently demonstrated that the extent of activation in a subset of these regions was related to interindividual differences in reasoning intellectual abilities, specifically. However, spindles reflect communication between spatially distant and functionally distinct brain areas. The functional communication among brain regions related to spindles and their relationship to reasoning abilities have yet to be investigated. Using simultaneous EEG-fMRI sleep recordings and psychophysiological interaction analysis, we identified spindle-related functional communication among brain regions in the thalamo-cortical-BG system, the salience network, and the default mode network. Furthermore, the extent of the functional connectivity of the cortical-striatal circuitry and the thalamo-cortical circuitry was specifically related to reasoning abilities but was unrelated to STM or verbal abilities, thus suggesting that individuals with higher fluid intelligence have stronger functional coupling among these brain areas during spontaneous spindle events. This may serve as a first step in further understanding the function of sleep spindles and the brain network functional communication, which support the capacity for fluid intelligence.
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Affiliation(s)
- Zhuo Fang
- Brain & Mind Institute, Western University, London, Canada.,University of Ottawa Brain and Mind Research Institute, Ottawa, Canada
| | - Laura B Ray
- Brain & Mind Institute, Western University, London, Canada.,Sleep Unit, the Royal's Institute for Mental Health Research, University of Ottawa, Ottawa, Canada
| | - Evan Houldin
- Brain & Mind Institute, Western University, London, Canada.,Western University, London, Canada
| | - Dylan Smith
- University of Ottawa, Ottawa, Canada.,Sleep Unit, the Royal's Institute for Mental Health Research, University of Ottawa, Ottawa, Canada
| | - Adrian M Owen
- Brain & Mind Institute, Western University, London, Canada.,Western University, London, Canada
| | - Stuart M Fogel
- Brain & Mind Institute, Western University, London, Canada.,Western University, London, Canada.,University of Ottawa, Ottawa, Canada.,Sleep Unit, the Royal's Institute for Mental Health Research, University of Ottawa, Ottawa, Canada.,University of Ottawa Brain and Mind Research Institute, Ottawa, Canada
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19
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DOSED: A deep learning approach to detect multiple sleep micro-events in EEG signal. J Neurosci Methods 2019; 321:64-78. [PMID: 30946878 DOI: 10.1016/j.jneumeth.2019.03.017] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 03/21/2019] [Accepted: 03/29/2019] [Indexed: 12/30/2022]
Abstract
BACKGROUND Electroencephalography (EEG) monitors brain activity during sleep and is used to identify sleep disorders. In sleep medicine, clinicians interpret raw EEG signals in so-called sleep stages, which are assigned by experts to every 30s window of signal. For diagnosis, they also rely on shorter prototypical micro-architecture events which exhibit variable durations and shapes, such as spindles, K-complexes or arousals. Annotating such events is traditionally performed by a trained sleep expert, making the process time consuming, tedious and subject to inter-scorer variability. To automate this procedure, various methods have been developed, yet these are event-specific and rely on the extraction of hand-crafted features. NEW METHOD We propose a novel deep learning architecture called Dreem One Shot Event Detector (DOSED). DOSED jointly predicts locations, durations and types of events in EEG time series. The proposed approach, applied here on sleep related micro-architecture events, is inspired by object detectors developed for computer vision such as YOLO and SSD. It relies on a convolutional neural network that builds a feature representation from raw EEG signals, as well as two modules performing localization and classification respectively. RESULTS AND COMPARISON WITH OTHER METHODS The proposed approach is tested on 4 datasets and 3 types of events (spindles, K-complexes, arousals) and compared to the current state-of-the-art detection algorithms. CONCLUSIONS Results demonstrate the versatility of this new approach and improved performance compared to the current state-of-the-art detection methods.
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20
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Wang X, Gong G, Li N, Qiu S. Detection Analysis of Epileptic EEG Using a Novel Random Forest Model Combined With Grid Search Optimization. Front Hum Neurosci 2019; 13:52. [PMID: 30846934 PMCID: PMC6393755 DOI: 10.3389/fnhum.2019.00052] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Accepted: 01/30/2019] [Indexed: 01/21/2023] Open
Abstract
In the automatic detection of epileptic seizures, the monitoring of critically ill patients with time varying EEG signals is an essential procedure in intensive care units. There is an increasing interest in using EEG analysis to detect seizure, and in this study we aim to get a better understanding of how to visualize the information in the EEG time-frequency feature, and design and train a novel random forest algorithm for EEG decoding, especially for multiple-levels of illness. Here, we propose an automatic detection framework for epileptic seizure based on multiple time-frequency analysis approaches; it involves a novel random forest model combined with grid search optimization. The short-time Fourier transformation visualizes seizure features after normalization. The dimensionality of features is reduced through principal component analysis before feeding them into the classification model. The training parameters are optimized using grid search optimization to improve detection performance and diagnostic accuracy by in the recognition of three different levels epileptic of conditions (healthy subjects, seizure-free intervals, seizure activity). Our proposed model was used to classify 500 samples of raw EEG data, and multiple cross-validations were adopted to boost the modeling accuracy. Experimental results were evaluated by an accuracy, a confusion matrix, a receiver operating characteristic curve, and an area under the curve. The evaluations indicated that our model achieved the more effective classification than some previous typical methods. Such a scheme for computer-assisted clinical diagnosis of seizures has a potential guiding significance, which not only relieves the suffering of patient with epilepsy to improve quality of life, but also helps neurologists reduce their workload.
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Affiliation(s)
- Xiashuang Wang
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China.,Automation Science and Electrical Engineering, Beihang University, Beijing, China
| | - Guanghong Gong
- Automation Science and Electrical Engineering, Beihang University, Beijing, China
| | - Ni Li
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China.,Automation Science and Electrical Engineering, Beihang University, Beijing, China
| | - Shi Qiu
- Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China
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21
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Fang Z, Ray LB, Owen AM, Fogel SM. Brain Activation Time-Locked to Sleep Spindles Associated With Human Cognitive Abilities. Front Neurosci 2019; 13:46. [PMID: 30787863 PMCID: PMC6372948 DOI: 10.3389/fnins.2019.00046] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Accepted: 01/17/2019] [Indexed: 12/21/2022] Open
Abstract
Simultaneous electroencephalography and functional magnetic resonance imaging (EEG–fMRI) studies have revealed brain activations time-locked to spindles. Yet, the functional significance of these spindle-related brain activations is not understood. EEG studies have shown that inter-individual differences in the electrophysiological characteristics of spindles (e.g., density, amplitude, duration) are highly correlated with “Reasoning” abilities (i.e., “fluid intelligence”; problem solving skills, the ability to employ logic, identify complex patterns), but not short-term memory (STM) or verbal abilities. Spindle-dependent reactivation of brain areas recruited during new learning suggests night-to-night variations reflect offline memory processing. However, the functional significance of stable, trait-like inter-individual differences in brain activations recruited during spindle events is unknown. Using EEG–fMRI sleep recordings, we found that a subset of brain activations time-locked to spindles were specifically related to Reasoning abilities but were unrelated to STM or verbal abilities. Thus, suggesting that individuals with higher fluid intelligence have greater activation of brain regions recruited during spontaneous spindle events. This may serve as a first step to further understand the function of sleep spindles and the brain activity which supports the capacity for Reasoning.
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Affiliation(s)
- Zhuo Fang
- Brain and Mind Institute, Western University, London, ON, Canada.,School of Psychology, University of Ottawa, Ottawa, ON, Canada
| | - Laura B Ray
- Brain and Mind Institute, Western University, London, ON, Canada.,Sleep Unit, The Royal's Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada
| | - Adrian M Owen
- Brain and Mind Institute, Western University, London, ON, Canada.,Department of Psychology, Western University, London, ON, Canada
| | - Stuart M Fogel
- Brain and Mind Institute, Western University, London, ON, Canada.,School of Psychology, University of Ottawa, Ottawa, ON, Canada.,Sleep Unit, The Royal's Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada.,Department of Psychology, Western University, London, ON, Canada.,University of Ottawa Brain and Mind Research Institute, Ottawa, ON, Canada
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22
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Affiliation(s)
- Péter Przemyslaw Ujma
- Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary
- National Institute of Clinical Neuroscience, Budapest, Hungary
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23
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Viczko J, Sergeeva V, Ray LB, Owen AM, Fogel SM. Does sleep facilitate the consolidation of allocentric or egocentric representations of implicitly learned visual-motor sequence learning? ACTA ACUST UNITED AC 2018; 25:67-77. [PMID: 29339558 PMCID: PMC5772393 DOI: 10.1101/lm.044719.116] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2016] [Accepted: 10/03/2017] [Indexed: 11/25/2022]
Abstract
Sleep facilitates the consolidation (i.e., enhancement) of simple, explicit (i.e., conscious) motor sequence learning (MSL). MSL can be dissociated into egocentric (i.e., motor) or allocentric (i.e., spatial) frames of reference. The consolidation of the allocentric memory representation is sleep-dependent, whereas the egocentric consolidation process is independent of sleep or wake for explicit MSL. However, it remains unclear the extent to which sleep contributes to the consolidation of implicit (i.e., unconscious) MSL, nor is it known what aspects of the memory representation (egocentric, allocentric) are consolidated by sleep. Here, we investigated the extent to which sleep is involved in consolidating implicit MSL, specifically, whether the egocentric or the allocentric cognitive representations of a learned sequence are enhanced by sleep, and whether these changes support the development of explicit sequence knowledge across sleep but not wake. Our results indicate that egocentric and allocentric representations can be behaviorally dissociated for implicit MSL. Neither representation was preferentially enhanced across sleep nor were developments of explicit awareness observed. However, after a 1-wk interval performance enhancement was observed in the egocentric representation. Taken together, these results suggest that like explicit MSL, implicit MSL has dissociable allocentric and egocentric representations, but unlike explicit sequence learning, implicit egocentric and allocentric memory consolidation is independent of sleep, and the time-course of consolidation differs significantly.
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Affiliation(s)
- Jeremy Viczko
- The Brain & Mind Institute, Western University, London, Ontario N6A 5B7, Canada.,Department of Psychology, Western University, London, Ontario N6A 5C2, Canada
| | - Valya Sergeeva
- The Brain & Mind Institute, Western University, London, Ontario N6A 5B7, Canada.,Department of Psychology, Western University, London, Ontario N6A 5C2, Canada
| | - Laura B Ray
- The Brain & Mind Institute, Western University, London, Ontario N6A 5B7, Canada
| | - Adrian M Owen
- The Brain & Mind Institute, Western University, London, Ontario N6A 5B7, Canada.,Department of Psychology, Western University, London, Ontario N6A 5C2, Canada
| | - Stuart M Fogel
- The Brain & Mind Institute, Western University, London, Ontario N6A 5B7, Canada.,Department of Psychology, Western University, London, Ontario N6A 5C2, Canada.,School of Psychology, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada.,The Royal's Institute for Mental Health Research, Ottawa, Ontario K1Z 7K5, Canada.,University of Ottawa Brain and Mind Research Institute, Ottawa, Ontario K1H 8M5, Canada
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24
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Patti CR, Penzel T, Cvetkovic D. Sleep spindle detection using multivariate Gaussian mixture models. J Sleep Res 2017; 27:e12614. [DOI: 10.1111/jsr.12614] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Accepted: 08/31/2017] [Indexed: 11/28/2022]
Affiliation(s)
| | - Thomas Penzel
- Interdisciplinary Sleep Centre at Charite Universitaetsmedizin Berlin; Berlin Germany
- International Clinical Research Center; St Anne's University Hospital Brno; Brno Czech Republic
| | - Dean Cvetkovic
- School of Engineering; RMIT University; Melbourne Vic. Australia
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Sleep spindle detection based on non-experts: A validation study. PLoS One 2017; 12:e0177437. [PMID: 28493938 PMCID: PMC5426701 DOI: 10.1371/journal.pone.0177437] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2016] [Accepted: 04/27/2017] [Indexed: 11/30/2022] Open
Abstract
Accurate and efficient detection of sleep spindles is a methodological challenge. The present study describes a method of using non-experts for manual detection of sleep spindles. We recruited five experts and 168 non-experts to manually identify spindles in stage N2 and stage N3 sleep data using a MATLAB interface. Scorers classified each spindle into definite and indefinite spindle (with weights of 1 and 0.5, respectively). First, a method of optimizing the thresholds of the expert/non-expert group consensus according to the results of experts and non-experts themselves is described. Using this method, we established expert and non-expert group standards from expert and non-expert scorers, respectively, and evaluated the performance of the non-expert group standards by compared with the expert group standard (termed EGS). The results indicated that the highest performance was the non-expert group standard when definite spindles were only considered (termed nEGS-1; F1 score = 0.78 for N2; 0.68 for N3). Second, four automatic spindle detection methods were compared with the EGS. We found that the performance of nEGS-1 versus EGS was higher than that of the four automated methods. Our results also showed positive correlation between the mean F1 score of individual expert in EGS and the F1 score of nEGS-1 versus EGS across 30 segments of stage N2 data (r = 0.61, P < 0.001). Further, we found that six and nine non-experts were needed to manually identify spindles in stages N2 and N3, respectively, while maintaining acceptable performance of nEGS-1 versus EGS (F1 score = 0.79 for N2; 0.64 for N3). In conclusion, this study establishes a detailed process for detection of sleep spindles by non-experts in a crowdsourcing scheme.
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Fogel S, Albouy G, King BR, Lungu O, Vien C, Bore A, Pinsard B, Benali H, Carrier J, Doyon J. Reactivation or transformation? Motor memory consolidation associated with cerebral activation time-locked to sleep spindles. PLoS One 2017; 12:e0174755. [PMID: 28422976 PMCID: PMC5396873 DOI: 10.1371/journal.pone.0174755] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Accepted: 03/14/2017] [Indexed: 01/03/2023] Open
Abstract
Motor memory consolidation is thought to depend on sleep-dependent reactivation of brain areas recruited during learning. However, up to this point, there has been no direct evidence to support this assertion in humans, and the physiological processes supporting such reactivation are unknown. Here, simultaneous electroencephalographic and functional magnetic resonance imaging (EEG-fMRI) recordings were conducted during post-learning sleep to directly investigate the spindle-related reactivation of a memory trace formed during motor sequence learning (MSL), and its relationship to overnight enhancement in performance (reflecting consolidation). We show that brain regions within the striato-cerebello-cortical network recruited during training on the MSL task, and in particular the striatum, were also activated during sleep, time-locked to spindles. Interestingly, the consolidated trace in the striatum was not simply strengthened, but was transformed/reorganized from rostrodorsal (associative) to caudoventral (sensorimotor) subregions. Moreover, the degree of the reactivation was correlated with overnight improvements in performance. Altogether, the present findings demonstrate that striatal reactivation linked to sleep spindles in the post-learning night, is related to motor memory consolidation.
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Affiliation(s)
- Stuart Fogel
- Functional Neuroimaging Unit, Centre de Recherche de l’institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
- Department of Psychology, University of Montreal, Montreal, Quebec, Canada
- School of Psychology, University of Ottawa, Ottawa, Ontario, Canada
- University of Ottawa Institute of Mental Health Research, University of Ottawa, Ottawa, Ontario, Canada
- University of Ottawa Brain & Mind Research Institute, University of Ottawa, Ottawa, Ontario, Canada
| | - Genevieve Albouy
- Functional Neuroimaging Unit, Centre de Recherche de l’institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
- Department of Psychology, University of Montreal, Montreal, Quebec, Canada
| | - Bradley R. King
- Functional Neuroimaging Unit, Centre de Recherche de l’institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
- Department of Psychology, University of Montreal, Montreal, Quebec, Canada
| | - Ovidiu Lungu
- Functional Neuroimaging Unit, Centre de Recherche de l’institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
| | - Catherine Vien
- Functional Neuroimaging Unit, Centre de Recherche de l’institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
- Department of Psychology, University of Montreal, Montreal, Quebec, Canada
| | - Arnaud Bore
- Functional Neuroimaging Unit, Centre de Recherche de l’institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
| | - Basile Pinsard
- Functional Neuroimaging Unit, Centre de Recherche de l’institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
- Department of Psychology, University of Montreal, Montreal, Quebec, Canada
| | - Habib Benali
- Functional Neuroimaging Unit, Centre de Recherche de l’institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
- Functional Neuroimaging Laboratory, INSERM, Paris, France
| | - Julie Carrier
- Functional Neuroimaging Unit, Centre de Recherche de l’institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
- Centre D’études Avancées en Médecine du Sommeil, Hôpital du Sacré-Cœur de Montréal, Montréal, Quebec, Canada
| | - Julien Doyon
- Functional Neuroimaging Unit, Centre de Recherche de l’institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
- Department of Psychology, University of Montreal, Montreal, Quebec, Canada
- * E-mail:
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Lajnef T, O’Reilly C, Combrisson E, Chaibi S, Eichenlaub JB, Ruby PM, Aguera PE, Samet M, Kachouri A, Frenette S, Carrier J, Jerbi K. Meet Spinky: An Open-Source Spindle and K-Complex Detection Toolbox Validated on the Open-Access Montreal Archive of Sleep Studies (MASS). Front Neuroinform 2017; 11:15. [PMID: 28303099 PMCID: PMC5332402 DOI: 10.3389/fninf.2017.00015] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2016] [Accepted: 02/01/2017] [Indexed: 12/02/2022] Open
Abstract
Sleep spindles and K-complexes are among the most prominent micro-events observed in electroencephalographic (EEG) recordings during sleep. These EEG microstructures are thought to be hallmarks of sleep-related cognitive processes. Although tedious and time-consuming, their identification and quantification is important for sleep studies in both healthy subjects and patients with sleep disorders. Therefore, procedures for automatic detection of spindles and K-complexes could provide valuable assistance to researchers and clinicians in the field. Recently, we proposed a framework for joint spindle and K-complex detection (Lajnef et al., 2015a) based on a Tunable Q-factor Wavelet Transform (TQWT; Selesnick, 2011a) and morphological component analysis (MCA). Using a wide range of performance metrics, the present article provides critical validation and benchmarking of the proposed approach by applying it to open-access EEG data from the Montreal Archive of Sleep Studies (MASS; O'Reilly et al., 2014). Importantly, the obtained scores were compared to alternative methods that were previously tested on the same database. With respect to spindle detection, our method achieved higher performance than most of the alternative methods. This was corroborated with statistic tests that took into account both sensitivity and precision (i.e., Matthew's coefficient of correlation (MCC), F1, Cohen κ). Our proposed method has been made available to the community via an open-source tool named Spinky (for spindle and K-complex detection). Thanks to a GUI implementation and access to Matlab and Python resources, Spinky is expected to contribute to an open-science approach that will enhance replicability and reliable comparisons of classifier performances for the detection of sleep EEG microstructure in both healthy and patient populations.
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Affiliation(s)
- Tarek Lajnef
- Psychology Department, University of MontrealMontreal, QC, Canada
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de MontréalMontreal, QC, Canada
| | - Christian O’Reilly
- Blue Brain Project, École Polytechnique Fédérale de LausanneGeneve, Switzerland
| | - Etienne Combrisson
- Psychology Department, University of MontrealMontreal, QC, Canada
- Inter-University Laboratory of Human Movement Biology, University Claude Bernard Lyon 1Villeurbanne, France
- DYCOG Lab, Lyon Neuroscience Research Center, INSERM U1028, UMR 5292, University Lyon ILyon, France
| | - Sahbi Chaibi
- LETI Lab Sfax National Engineering School (ENIS), University of SfaxSfax, Tunisia
| | - Jean-Baptiste Eichenlaub
- Department of Neurology, Massachusetts General Hospital (MGH), Harvard Medical SchoolBoston, MA, USA
| | - Perrine M. Ruby
- DYCOG Lab, Lyon Neuroscience Research Center, INSERM U1028, UMR 5292, University Lyon ILyon, France
| | - Pierre-Emmanuel Aguera
- DYCOG Lab, Lyon Neuroscience Research Center, INSERM U1028, UMR 5292, University Lyon ILyon, France
| | - Mounir Samet
- LETI Lab Sfax National Engineering School (ENIS), University of SfaxSfax, Tunisia
| | - Abdennaceur Kachouri
- LETI Lab Sfax National Engineering School (ENIS), University of SfaxSfax, Tunisia
| | - Sonia Frenette
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de MontréalMontreal, QC, Canada
| | - Julie Carrier
- Psychology Department, University of MontrealMontreal, QC, Canada
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de MontréalMontreal, QC, Canada
| | - Karim Jerbi
- Psychology Department, University of MontrealMontreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal (CRIUGM)Montréal, QC, Canada
- Centre de Recherche En Neuropsychologie Et Cognition (CERNEC), Psychology Department, Université de MontréalMontréal, QC, Canada
- BRAMS, International Laboratory for Research on Brain, Music, and SoundMontreal, QC, Canada
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Fang Z, Sergeeva V, Ray LB, Viczko J, Owen AM, Fogel SM. Sleep Spindles and Intellectual Ability: Epiphenomenon or Directly Related? J Cogn Neurosci 2017; 29:167-182. [DOI: 10.1162/jocn_a_01034] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Abstract
Sleep spindles—short, phasic, oscillatory bursts of activity that characterize non-rapid eye movement sleep—are one of the only electrophysiological oscillations identified as a biological marker of human intelligence (e.g., cognitive abilities commonly assessed using intelligence quotient tests). However, spindles are also important for sleep maintenance and are modulated by circadian factors. Thus, the possibility remains that the relationship between spindles and intelligence quotient may be an epiphenomenon of a putative relationship between good quality sleep and cognitive ability or perhaps modulated by circadian factors such as morningness–eveningness tendencies. We sought to ascertain whether spindles are directly or indirectly related to cognitive abilities using mediation analysis. Here, we show that fast (13.5–16 Hz) parietal but not slow (11–13.5 Hz) frontal spindles in both non-rapid eye movement stage 2 sleep and slow wave sleep are directly related to reasoning abilities (i.e., cognitive abilities that support “fluid intelligence,” such as the capacity to identify complex patterns and relationships and the use of logic to solve novel problems) but not verbal abilities (i.e., cognitive abilities that support “crystalized intelligence”; accumulated knowledge and experience) or cognitive abilities that support STM (i.e., the capacity to briefly maintain information in an available state). The relationship between fast spindles and reasoning abilities is independent of the indicators of sleep maintenance and circadian chronotype, thus suggesting that spindles are indeed a biological marker of cognitive abilities and can serve as a window to further explore the physiological and biological substrates that give rise to human intelligence.
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Affiliation(s)
- Zhuo Fang
- 1Western University, London, Ontario, Canada
| | | | | | | | | | - Stuart M. Fogel
- 1Western University, London, Ontario, Canada
- 2University of Ottawa, Ontario Canada
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Younes M, Hanly PJ. Minimizing Interrater Variability in Staging Sleep by Use of Computer-Derived Features. J Clin Sleep Med 2016; 12:1347-1356. [PMID: 27448418 DOI: 10.5664/jcsm.6186] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Accepted: 06/06/2016] [Indexed: 01/16/2023]
Abstract
STUDY OBJECTIVES Inter-scorer variability in sleep staging of polysomnograms (PSGs) results primarily from difficulty in determining whether: (1) an electroencephalogram pattern of wakefulness spans > 15 sec in transitional epochs, (2) spindles or K complexes are present, and (3) duration of delta waves exceeds 6 sec in a 30-sec epoch. We hypothesized that providing digitally derived information about these variables to PSG scorers may reduce inter-scorer variability. METHODS Fifty-six PSGs were scored (five-stage) by two experienced technologists, (first manual, M1). Months later, the technologists edited their own scoring (second manual, M2). PSGs were then scored with an automatic system and the same two technologists and an additional experienced technologist edited them, epoch-by-epoch (Edited-Auto). This resulted in seven manual scores for each PSG. The two M2 scores were then independently modified using digitally obtained values for sleep depth and delta duration and digitally identified spindles and K complexes. RESULTS Percent agreement between scorers in M2 was 78.9 ± 9.0% before modification and 96.5 ± 2.6% after. Errors of this approach were defined as a change in a manual score to a stage that was not assigned by any scorer during the seven manual scoring sessions. Total errors averaged 7.1 ± 3.7% and 6.9 ± 3.8% of epochs for scorers 1 and 2, respectively, and there was excellent agreement between the modified score and the initial manual score of each technologist. CONCLUSIONS Providing digitally obtained information about sleep depth, delta duration, spindles and K complexes during manual scoring can greatly reduce interrater variability in sleep staging by eliminating the guesswork in scoring epochs with equivocal features.
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Affiliation(s)
- Magdy Younes
- YRT Ltd, Winnipeg, MB, Canada.,Sleep Centre, Foothills Medical Centre, Calgary, Alberta, Canada.,Sleep Disorders Centre, Winnipeg, Manitoba, Canada
| | - Patrick J Hanly
- Sleep Centre, Foothills Medical Centre, Calgary, Alberta, Canada
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30
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Fogel S, Vien C, Karni A, Benali H, Carrier J, Doyon J. Sleep spindles: a physiological marker of age-related changes in gray matter in brain regions supporting motor skill memory consolidation. Neurobiol Aging 2016; 49:154-164. [PMID: 27815989 DOI: 10.1016/j.neurobiolaging.2016.10.009] [Citation(s) in RCA: 71] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Revised: 09/08/2016] [Accepted: 10/03/2016] [Indexed: 12/21/2022]
Abstract
Sleep is necessary for the optimal consolidation of procedural learning, and in particular, for motor sequential skills. Motor sequence learning remains intact with age, but sleep-dependent consolidation is impaired, suggesting that memory deficits for procedural skills are specifically impacted by age-related changes in sleep. Age-related changes in spindles may be responsible for impaired motor sequence learning consolidation, but the morphological basis for this deficit is unknown. Here, we found that gray matter in the hippocampus and cerebellum was positively correlated with both sleep spindles and offline improvements in performance in young participants but not in older participants. These results suggest that age-related changes in gray matter in the hippocampus relate to spindles and may underlie age-related deficits in sleep-related motor sequence memory consolidation. In this way, spindles can serve as a biological marker for structural brain changes and the related memory deficits in older adults.
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Affiliation(s)
- Stuart Fogel
- Functional Neuroimaging Unit, Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montreal, Canada; Department of Psychology, University of Montreal, Montreal, Canada; School of Psychology, University of Ottawa, Ottawa, Canada; University of Ottawa Institute of Mental Health Research, Ottawa, Canada; University of Ottawa Brain and Mind Research Institute, Ottawa, Canada
| | - Catherine Vien
- Functional Neuroimaging Unit, Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montreal, Canada; Department of Psychology, University of Montreal, Montreal, Canada
| | - Avi Karni
- Laboratory for Human Brain & Learning, Sagol Department of Neurobiology & the E.J. Safra Brain Research Center, University of Haifa, Haifa, Israel
| | - Habib Benali
- Functional Neuroimaging Unit, Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montreal, Canada; Functional Neuroimaging Laboratory, INSERM, Paris, France
| | - Julie Carrier
- Functional Neuroimaging Unit, Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montreal, Canada; Department of Psychology, University of Montreal, Montreal, Canada; Centre d'études Avancées en Médecine du Sommeil, Hôpital du Sacré-Cœur de Montréal, Montreal, Canada
| | - Julien Doyon
- Functional Neuroimaging Unit, Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montreal, Canada; Department of Psychology, University of Montreal, Montreal, Canada.
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31
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Shelgikar AV, Anderson PF, Stephens MR. Sleep Tracking, Wearable Technology, and Opportunities for Research and Clinical Care. Chest 2016; 150:732-43. [PMID: 27132701 DOI: 10.1016/j.chest.2016.04.016] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Revised: 03/21/2016] [Accepted: 04/12/2016] [Indexed: 10/21/2022] Open
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32
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Sleep Spindles as an Electrographic Element: Description and Automatic Detection Methods. Neural Plast 2016; 2016:6783812. [PMID: 27478649 PMCID: PMC4958487 DOI: 10.1155/2016/6783812] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Accepted: 04/27/2016] [Indexed: 12/16/2022] Open
Abstract
Sleep spindle is a peculiar oscillatory brain pattern which has been associated with a number of sleep (isolation from exteroceptive stimuli, memory consolidation) and individual characteristics (intellectual quotient). Oddly enough, the definition of a spindle is both incomplete and restrictive. In consequence, there is no consensus about how to detect spindles. Visual scoring is cumbersome and user dependent. To analyze spindle activity in a more robust way, automatic sleep spindle detection methods are essential. Various algorithms were developed, depending on individual research interest, which hampers direct comparisons and meta-analyses. In this review, sleep spindle is first defined physically and topographically. From this general description, we tentatively extract the main characteristics to be detected and analyzed. A nonexhaustive list of automatic spindle detection methods is provided along with a description of their main processing principles. Finally, we propose a technique to assess the detection methods in a robust and comparable way.
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33
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Younes M, Raneri J, Hanly P. Staging Sleep in Polysomnograms: Analysis of Inter-Scorer Variability. J Clin Sleep Med 2016; 12:885-94. [PMID: 27070243 DOI: 10.5664/jcsm.5894] [Citation(s) in RCA: 71] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2016] [Accepted: 02/22/2016] [Indexed: 01/13/2023]
Abstract
STUDY OBJECTIVES To determine the reasons for inter-scorer variability in sleep staging of polysomnograms (PSGs). METHODS Fifty-six PSGs were scored (5-stage sleep scoring) by 2 experienced technologists, (first manual, M1). Months later, the technologists edited their own scoring (second manual, M2) based upon feedback from the investigators that highlighted differences between their scoring. The PSGs were then scored with an automatic system (Auto) and the technologists edited them, epoch-by-epoch (Edited-Auto). This resulted in 6 different manual scores for each PSG. Epochs were classified as scorer errors (one M1 score differed from the other 5 scores), scorer bias (all 3 scores of each technologist were similar, but differed from the other technologist) and equivocal (sleep scoring was inconsistent within and between technologists). RESULTS Percent agreement after M1 was 78.9% ± 9.0% and was unchanged after M2 (78.1% ± 9.7%) despite numerous edits (≈40/PSG) by the scorers. Agreement in Edited-Auto was higher (86.5% ± 6.4%, p < 1E-9). Scorer errors (< 2% of epochs) and scorer bias (3.5% ± 2.3% of epochs) together accounted for < 20% of M1 disagreements. A large number of epochs (92 ± 44/PSG) with scoring agreement in M1 were subsequently changed in M2 and/or Edited-Auto. Equivocal epochs, which showed scoring inconsistency, accounted for 28% ± 12% of all epochs, and up to 76% of all epochs in individual patients. Disagreements were largely between awake/NREM, N1/N2, and N2/N3 sleep. CONCLUSION Inter-scorer variability is largely due to epochs that are difficult to classify. Availability of digitally identified events (e.g., spindles) or calculated variables (e.g., depth of sleep, delta wave duration) during scoring may greatly reduce scoring variability.
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Affiliation(s)
- Magdy Younes
- YRT Ltd, Winnipeg, MB, Canada.,Sleep Centre, Foothills Medical Centre, Calgary, Alberta, Canada.,Sleep Disorders Centre, Winnipeg, Manitoba, Canada
| | - Jill Raneri
- Sleep Centre, Foothills Medical Centre, Calgary, Alberta, Canada
| | - Patrick Hanly
- Sleep Centre, Foothills Medical Centre, Calgary, Alberta, Canada
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