Optimal spindle detection parameters for predicting cognitive performance.
EEG
cognition
sleep
sleep spindle
Journal
Sleep
ISSN: 1550-9109
Titre abrégé: Sleep
Pays: United States
ID NLM: 7809084
Informations de publication
Date de publication:
11 04 2022
11 04 2022
Historique:
received:
16
04
2021
revised:
07
12
2021
pubmed:
6
1
2022
medline:
13
4
2022
entrez:
5
1
2022
Statut:
ppublish
Résumé
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. 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. 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. 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.
Identifiants
pubmed: 34984446
pii: 6497517
doi: 10.1093/sleep/zsac001
pmc: PMC8996023
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NINDS NIH HHS
ID : L30 NS093525
Pays : United States
Organisme : NIA NIH HHS
ID : K23 AG057760
Pays : United States
Organisme : NIA NIH HHS
ID : RF1 AG064312
Pays : United States
Organisme : NIH HHS
ID : 1K23NS090900
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS102190
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS102574
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS107291
Pays : United States
Informations de copyright
© 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.
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