Noor Adra1,2,3, Haoqi Sun1,2,3,4, Wolfgang Ganglberger1,2,3, Elissa M Ye1,2,3, Lisa W Dümmer1,2,3,5, Ryan A Tesh1,2,3, Mike Westmeijer1,2, Madalena Da Silva Cardoso1,2,3, Erin Kitchener1,2,3,4, An Ouyang1,2,3,4, Joel Salinas4,6, Jonathan Rosand1,2,3,4, Sydney S Cash1,4, Robert J Thomas4,7, M Brandon Westover1,2,3,4. 1. Department of Neurology, Massachusetts General Hospital, Boston, MA, USA. 2. Clinical Data Animation Center (CDAC), Boston, MA, USA. 3. Henry and Allison McCance Center for Brain Health at Mass General, Boston, MA, USA. 4. Harvard Medical School, Boston, MA, USA. 5. University of Groningen, Groningen, The Netherlands. 6. Department of Neurology, Center for Cognitive Neurology, New York University Grossman School of Medicine, New York, NY, USA. 7. Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Abstract
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.
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.
Authors: Johanna F A Schwarz; Torbjörn Åkerstedt; Eva Lindberg; Georg Gruber; Håkan Fischer; Jenny Theorell-Haglöw Journal: J Sleep Res Date: 2017-01-17 Impact factor: 3.981
Authors: Sandra Weintraub; Sureyya S Dikmen; Robert K Heaton; David S Tulsky; Philip D Zelazo; Patricia J Bauer; Noelle E Carlozzi; Jerry Slotkin; David Blitz; Kathleen Wallner-Allen; Nathan A Fox; Jennifer L Beaumont; Dan Mungas; Cindy J Nowinski; Jennifer Richler; Joanne A Deocampo; Jacob E Anderson; Jennifer J Manly; Beth Borosh; Richard Havlik; Kevin Conway; Emmeline Edwards; Lisa Freund; Jonathan W King; Claudia Moy; Ellen Witt; Richard C Gershon Journal: Neurology Date: 2013-03-12 Impact factor: 9.910