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Grant: Discovery Grants Program - Individual

Université de Montréal

$2,500.00

Development of tools and standards for dissecting the temporal properties of EEG events during sleep

FY 2020-21

Summary
Sleep is essential for the function of the brain. Learning and memory, cognitive performance, emotional stability, reaction time and judgement are all compromised by poor sleep and sleep deprivation. During sleep the brain performs unique and repetitive patterns of activity that can be measured using electroencephalography (EEG). Many of these distinct patterns, such as 'sleep-spindles' can be described as an EEG fingerprint that is unique to each individual. The study of sleep spindles has gained tremendous interest in recent years. They play an important role in learning and memory, and are altered by aging, psychiatric and neurological disorders. However, limitations in the current tools to study sleep spindles are inhibiting progress in this field. The goal of our research program is to develop tools and standards for the study of sleep spindles, and to provide these open-source resources to the research community. Expanding on our previous work, we have now developed a crowdsourcing platform to gather a large database of spindles from human expert spindle scorers. We use this database to develop and test computer algorithms that analyze the EEG signal and detect sleep spindles. We also propose to develop new analytic tools that measure not just the number of spindles, but characterize important spindle characteristics such as pattern of spindles over time, and the relationship of spindles to other wave patterns seen in the EEG fingerprint. Emerging data suggests that the relationship between spindles and slow waves may play an important role in the consolidation of learning and memory during sleep. Using existing datasets such as the Montreal Archive of Sleep Studies (MASS), we will test whether spindle characteristics and timing influence cognitive performance, learning and memory. Understanding these important events in the EEG fingerprint will help us understand the mysterious process of sleep, and the specific mechanisms that support the function of the brain.
Program
Discovery Grants Program - Individual
Location
Quebec, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
RGPIN-2019-05107
Name nom
Warby, Simon
Department département
Psychiatry
Organization id
63
Institution établissement
Université de Montréal
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2020
Competition year année de concours
2019
Award amount
2500
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
1502
Committee name en
Biological Systems and Functions
Committee name fr
Systèmes et fonctions biologiques
Area of application code
1101
Area of application group en
Health, education and social services
Area of application group fr
Santé publique, éducation et services sociaux
Area of application en
Human health (including medically-related psychological research)
Area of application fr
Santé publique (y compris recherche psychologique liée à la médecine)
Research subject code
9000
Research subject group en
Medical sciences
Research subject group fr
Sciences médicales
Research subject en
Medical sciences
Research subject fr
Sciences médicales
Installment
inst:2-5
Partie
4
Nb partie
5
Application title
Development of tools and standards for dissecting the temporal properties of EEG events during sleep
Keyword
NSERC
Application summary
Sleep is essential for the function of the brain. Learning and memory, cognitive performance, emotional stability, reaction time and judgement are all compromised by poor sleep and sleep deprivation. During sleep the brain performs unique and repetitive patterns of activity that can be measured using electroencephalography (EEG). Many of these distinct patterns, such as 'sleep-spindles' can be described as an EEG fingerprint that is unique to each individual. The study of sleep spindles has gained tremendous interest in recent years. They play an important role in learning and memory, and are altered by aging, psychiatric and neurological disorders. However, limitations in the current tools to study sleep spindles are inhibiting progress in this field. The goal of our research program is to develop tools and standards for the study of sleep spindles, and to provide these open-source resources to the research community. Expanding on our previous work, we have now developed a crowdsourcing platform to gather a large database of spindles from human expert spindle scorers. We use this database to develop and test computer algorithms that analyze the EEG signal and detect sleep spindles. We also propose to develop new analytic tools that measure not just the number of spindles, but characterize important spindle characteristics such as pattern of spindles over time, and the relationship of spindles to other wave patterns seen in the EEG fingerprint. Emerging data suggests that the relationship between spindles and slow waves may play an important role in the consolidation of learning and memory during sleep. Using existing datasets such as the Montreal Archive of Sleep Studies (MASS), we will test whether spindle characteristics and timing influence cognitive performance, learning and memory. Understanding these important events in the EEG fingerprint will help us understand the mysterious process of sleep, and the specific mechanisms that support the function of the brain.