Sleep Spindles: Breaking the Methodological Wall

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Editorial

18 January 2017

Editorial: Sleep Spindles: Breaking the Methodological Wall

Christian O'Reilly

,

Simon C. Warby

and

Tore Nielsen

3,317 views

6 citations

Editors

3

Christian O'Reilly

University of South Carolina

Montreal University

Stanford University

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In the last decade, sleep spindles have attracted steadily increasing attention. This interest is motivated by the many intriguing relationships between spindles and various diseases (e.g., schizophrenia, Parkinson, Alzheimer, autism, mental retardation), recovery processes (e.g., post brain stroke), and cognitive faculties (e.g., memory consolidation, intelligence, sleep preservation). Nonetheless, a methodological wall has impeded the study of sleep spindles. Their investigation rests heavily on our ability to reliably and consistently identify spindle patterns from background EEG activity, a task involving many obstacles, including: a fuzzy definition of spindles, low inter-expert agreement on their scoring, lack of consensus on standard techniques for their automated detection, low reproducibility of observed characteristics and correlates, unavailability of large, standardized, high-quality databases, and inconsistencies in the methods used to evaluate the performance of automated detectors.

The primary aims of this research topic were to bring together world-class researchers on a project designed to facilitate exchanges on methodological difficulties encountered in assessing sleep spindles and to promote standardized spindle-related resources. In preparing their contributions, authors were encouraged to use existing – or to propose new – publically available resources for assessing sleep spindles. To allow fair and accurate comparison of reported results, the authors were also encouraged to validate their tools on a common benchmark. A database containing expert spindle scoring (i.e., the Montreal Archive of Sleep Studies) was made publically available for that purpose.

In the last decade, sleep spindles have attracted steadily increasing attention. This interest is motivated by the many intriguing relationships between spindles and various diseases (e.g., schizophrenia, Parkinson, Alzheimer, autism, mental retardation), recovery processes (e.g., post brain stroke), and cognitive faculties (e.g., memory consolidation, intelligence, sleep preservation). Nonetheless, a methodological wall has impeded the study of sleep spindles. Their investigation rests heavily on our ability to reliably and consistently identify spindle patterns from background EEG activity, a task involving many obstacles, including: a fuzzy definition of spindles, low inter-expert agreement on their scoring, lack of consensus on standard techniques for their automated detection, low reproducibility of observed characteristics and correlates, unavailability of large, standardized, high-quality databases, and inconsistencies in the methods used to evaluate the performance of automated detectors.

The primary aims of this research topic were to bring together world-class researchers on a project designed to facilitate exchanges on methodological difficulties encountered in assessing sleep spindles and to promote standardized spindle-related resources. In preparing their contributions, authors were encouraged to use existing – or to propose new – publically available resources for assessing sleep spindles. To allow fair and accurate comparison of reported results, the authors were also encouraged to validate their tools on a common benchmark. A database containing expert spindle scoring (i.e., the Montreal Archive of Sleep Studies) was made publically available for that purpose.

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Editors

Christian O'Reilly

University of South Carolina

Montreal University

Stanford University

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Frontiers in Human Neuroscience

Human Neuroscience Archive, Brain Imaging and Stimulation, Cognitive Neuroscience, Brain Health and Clinical Neuroscience

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