Optimal spindle detection parameters for predicting cognitive performance.

Autor:	Adra N; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.; Clinical Data Animation Center (CDAC), Boston, MA, USA.; Henry and Allison McCance Center for Brain Health at Mass General, Boston, MA, USA., Sun H; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.; Clinical Data Animation Center (CDAC), Boston, MA, USA.; Henry and Allison McCance Center for Brain Health at Mass General, Boston, MA, USA.; Harvard Medical School, Boston, MA, USA., Ganglberger W; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.; Clinical Data Animation Center (CDAC), Boston, MA, USA.; Henry and Allison McCance Center for Brain Health at Mass General, Boston, MA, USA., Ye EM; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.; Clinical Data Animation Center (CDAC), Boston, MA, USA.; Henry and Allison McCance Center for Brain Health at Mass General, Boston, MA, USA., Dümmer LW; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.; Clinical Data Animation Center (CDAC), Boston, MA, USA.; Henry and Allison McCance Center for Brain Health at Mass General, Boston, MA, USA.; University of Groningen, Groningen, The Netherlands., Tesh RA; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.; Clinical Data Animation Center (CDAC), Boston, MA, USA.; Henry and Allison McCance Center for Brain Health at Mass General, Boston, MA, USA., Westmeijer M; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.; Clinical Data Animation Center (CDAC), Boston, MA, USA., Cardoso MDS; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.; Clinical Data Animation Center (CDAC), Boston, MA, USA.; Henry and Allison McCance Center for Brain Health at Mass General, Boston, MA, USA., Kitchener E; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.; Clinical Data Animation Center (CDAC), Boston, MA, USA.; Henry and Allison McCance Center for Brain Health at Mass General, Boston, MA, USA.; Harvard Medical School, Boston, MA, USA., Ouyang A; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.; Clinical Data Animation Center (CDAC), Boston, MA, USA.; Henry and Allison McCance Center for Brain Health at Mass General, Boston, MA, USA.; Harvard Medical School, Boston, MA, USA., Salinas J; Harvard Medical School, Boston, MA, USA.; Department of Neurology, Center for Cognitive Neurology, New York University Grossman School of Medicine, New York, NY, USA., Rosand J; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.; Clinical Data Animation Center (CDAC), Boston, MA, USA.; Henry and Allison McCance Center for Brain Health at Mass General, Boston, MA, USA.; Harvard Medical School, Boston, MA, USA., Cash SS; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.; Harvard Medical School, Boston, MA, USA., Thomas RJ; Harvard Medical School, Boston, MA, USA.; Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA., Westover MB; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.; Clinical Data Animation Center (CDAC), Boston, MA, USA.; Henry and Allison McCance Center for Brain Health at Mass General, Boston, MA, USA.; Harvard Medical School, Boston, MA, USA.
Jazyk:	angličtina
Zdroj:	Sleep [Sleep] 2022 Apr 11; Vol. 45 (4).
DOI:	10.1093/sleep/zsac001
Abstrakt:	Study Objectives: Alterations in sleep spindles have been linked to cognitive impairment. This finding has contributed to a growing interest in identifying sleep-based biomarkers of cognition and neurodegeneration, including sleep spindles. However, flexibility surrounding spindle definitions and algorithm parameter settings present a methodological challenge. The aim of this study was to characterize how spindle detection parameter settings influence the association between spindle features and cognition and to identify parameters with the strongest association with cognition. Methods: Adult patients (n = 167, 49 ± 18 years) completed the NIH Toolbox Cognition Battery after undergoing overnight diagnostic polysomnography recordings for suspected sleep disorders. We explored 1000 combinations across seven parameters in Luna, an open-source spindle detector, and used four features of detected spindles (amplitude, density, duration, and peak frequency) to fit linear multiple regression models to predict cognitive scores. Results: Spindle features (amplitude, density, duration, and mean frequency) were associated with the ability to predict raw fluid cognition scores (r = 0.503) and age-adjusted fluid cognition scores (r = 0.315) with the best spindle parameters. Fast spindle features generally showed better performance relative to slow spindle features. Spindle features weakly predicted total cognition and poorly predicted crystallized cognition regardless of parameter settings. Conclusions: Our exploration of spindle detection parameters identified optimal parameters for studies of fluid cognition and revealed the role of parameter interactions for both slow and fast spindles. Our findings support sleep spindles as a sleep-based biomarker of fluid cognition. (© The Author(s) 2022. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)
Databáze:	MEDLINE
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