Investigating Neurological Disease

Author(s):  
Albert Hofman ◽  
Richard Mayeux
Keyword(s):  
PLoS ONE ◽  
2019 ◽  
Vol 14 (3) ◽  
pp. e0214395 ◽  
Author(s):  
Radka Bužgová ◽  
Radka Kozáková ◽  
Lubica Juríčková

SLEEP ◽  
2021 ◽  
Author(s):  
Brice V McConnell ◽  
Eugene Kronberg ◽  
Peter D Teale ◽  
Stefan H Sillau ◽  
Grace M Fishback ◽  
...  

Abstract Study Objectives Slow wave and spindle coupling supports memory consolidation, and loss of coupling is linked with cognitive decline and neurodegeneration. Coupling is proposed to be a possible biomarker of neurological disease, yet little is known about the different subtypes of coupling that normally occur throughout human development and aging. Here we identify distinct subtypes of spindles within slow wave upstates and describe their relationships with sleep stage across the human lifespan. Methods Coupling within a cross-sectional cohort of 582 subjects was quantified from stages N2 and N3 sleep across ages 6-88 years old. Results were analyzed across the study population via mixed model regression. Within a subset of subjects, we further utilized coupling to identify discrete subtypes of slow waves by their coupled spindles. Results Two different subtypes of spindles were identified during the upstates of (distinct) slow waves: an “early-fast” spindle, more common in stage N2 sleep, and a “late-fast” spindle, more common in stage N3. We further found stages N2 and N3 sleep contain a mixture of discrete subtypes of slow waves, each identified by their unique coupled-spindle timing and frequency. The relative contribution of coupling subtypes shifts across the human lifespan, and a deeper sleep phenotype prevails with increasing age. Conclusions Distinct subtypes of slow waves and coupled spindles form the composite of slow wave sleep. Our findings support a model of sleep-dependent synaptic regulation via discrete slow wave/spindle coupling subtypes and advance a conceptual framework for the development of coupling-based biomarkers in age-associated neurological disease.


Methods ◽  
2021 ◽  
Author(s):  
Shaochong Zhang ◽  
Lu Chen ◽  
Yining Zhang ◽  
Dong Fang

2021 ◽  
Vol 31 (5) ◽  
pp. 522-530
Author(s):  
Stephanie Cung ◽  
Matthew L. Ritz ◽  
Melissa M. Masaracchia

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