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Adra N, Dümmer LW, Paixao L, Tesh RA, Sun H, Ganglberger W, Westmeijer M, Da Silva Cardoso M, Kumar A, Ye E, Henry J, Cash SS, Kitchener E, Leveroni CL, Au R, Rosand J, Salinas J, Lam AD, Thomas RJ, Westover MB. Decoding information about cognitive health from the brainwaves of sleep. Sci Rep 2023; 13:11448. [PMID: 37454163 PMCID: PMC10349883 DOI: 10.1038/s41598-023-37128-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 06/16/2023] [Indexed: 07/18/2023] Open
Abstract
Sleep electroencephalogram (EEG) signals likely encode brain health information that may identify individuals at high risk for age-related brain diseases. Here, we evaluate the correlation of a previously proposed brain age biomarker, the "brain age index" (BAI), with cognitive test scores and use machine learning to develop and validate a series of new sleep EEG-based indices, termed "sleep cognitive indices" (SCIs), that are directly optimized to correlate with specific cognitive scores. Three overarching cognitive processes were examined: total, fluid (a measure of cognitive processes involved in reasoning-based problem solving and susceptible to aging and neuropathology), and crystallized cognition (a measure of cognitive processes involved in applying acquired knowledge toward problem-solving). We show that SCI decoded information about total cognition (Pearson's r = 0.37) and fluid cognition (Pearson's r = 0.56), while BAI correlated only with crystallized cognition (Pearson's r = - 0.25). Overall, these sleep EEG-derived biomarkers may provide accessible and clinically meaningful indicators of neurocognitive health.
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Affiliation(s)
- Noor Adra
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
| | - Lisa W Dümmer
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- University of Groningen, Groningen, The Netherlands
| | - Luis Paixao
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Ryan A Tesh
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
| | - Haoqi Sun
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
| | - Wolfgang Ganglberger
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Sleep and Health Zurich, University of Zurich, Zurich, Switzerland
| | - Mike Westmeijer
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Utrecht University, Utrecht, The Netherlands
| | - Madalena Da Silva Cardoso
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Anagha Kumar
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Elissa Ye
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Jonathan Henry
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Erin Kitchener
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
| | | | - Rhoda Au
- Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Jonathan Rosand
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
| | - Joel Salinas
- New York University Grossman School of Medicine, New York, NY, USA
| | - Alice D Lam
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
| | - Robert J Thomas
- Division of Pulmonary, Critical Care, and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center (BIDMC), Boston, MA, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA.
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA.
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA.
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Adra N, Sun H, Ganglberger W, Ye EM, Dümmer LW, Tesh RA, Westmeijer M, Cardoso MDS, Kitchener E, Ouyang A, Salinas J, Rosand J, Cash SS, Thomas RJ, Westover MB. Optimal spindle detection parameters for predicting cognitive performance. Sleep 2022; 45:zsac001. [PMID: 34984446 PMCID: PMC8996023 DOI: 10.1093/sleep/zsac001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 12/07/2021] [Indexed: 01/07/2023] Open
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.
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Affiliation(s)
- Noor Adra
- 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
| | - Haoqi Sun
- 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
| | - Wolfgang Ganglberger
- 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
| | - Elissa M Ye
- 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
| | - Lisa W Dümmer
- 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
| | - Ryan A Tesh
- 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
| | - Mike Westmeijer
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
| | - Madalena Da Silva Cardoso
- 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
| | - Erin Kitchener
- 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
| | - An Ouyang
- 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
| | - Joel Salinas
- Harvard Medical School, Boston, MA, USA
- Department of Neurology, Center for Cognitive Neurology, New York University Grossman School of Medicine, New York, NY, USA
| | - Jonathan Rosand
- 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
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Robert J Thomas
- 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
| | - M Brandon Westover
- 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
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Dijkema LM, Dümmer LW, Generaal JD, Klunder MB, Bouwknegt A, Keus F, van der Horst ICC. Simple example of a practical solution to make patient feedback more useful. BMJ Qual Saf 2017; 27:155. [PMID: 28747325 DOI: 10.1136/bmjqs-2017-007070] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Accepted: 06/23/2017] [Indexed: 11/03/2022]
Affiliation(s)
- L Marjon Dijkema
- Department of Critical Care, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Lisa W Dümmer
- Department of Critical Care, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Jasmijn D Generaal
- Department of Critical Care, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Merel B Klunder
- Department of Critical Care, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Anna Bouwknegt
- Department of Critical Care, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Frederik Keus
- Department of Critical Care, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Iwan C C van der Horst
- Department of Critical Care, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
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