Optimal spindle detection parameters for predicting cognitive performance

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. Conclusion 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.

Transient oscillation dynamics during sleep provide a robust basis for electroencephalographic phenotyping and biomarker identification

“…could indicate the most relevant, defining properties of physiologically important transient oscillations. Parameters and merge procedures can also be optimized for performance or for specific applications [ 101 ]. Furthermore, future modeling work can provide parametric models of the structure for the SO-power and phase histograms, providing a substantial dimensionality reduction of the current nonparametric forms.…”

Memory ability and retention performance relate differentially to sleep depth and spindle type

“…Our results emphasizing the relevance of spindles in N2 sleep are in line with previous reports showing a tendency of slow spindles in N2 to be positively related to other trait-like measures, such as the working memory subdomain of fluid intelligence (ADRA) and a strong tendency of N2 spindles to be increased in subjects performing good on the Wechlser Memory Scale-revised or a similar intelligence test. 36 , 46 , 53 Although MQ does not equate with these IQ scores its subtests rely partly on overlapping capacities and functional networks. 46 The relevance of NREM sleep depth is interesting in lieu of the differential network dynamics described by others.…”

“…Interestingly, despite indications for multiple evidence pointing toward qualitatively differential kinds of processing during light and deep NREM sleep, 12 , 24 , 25 , 26 , 27 , 28 , 29 , 30 only recently are the spindle types at different NREM sleep depths or in relation to SO occurrence 31 , 32 investigated together with functional correlates of sleep spindles. 20 , 31 , 32 , 33 , 34 , 35 , 36 , 37 …”

Artificial Intelligence in Sleep Medicine: The Dawn of a New Era

“… 35 , 36 Similarly, an ML algorithm analyzing sleep structure, frequency band powers, spindles, slow oscillations, and coherence in 10,784 sleep studies across 8044 participants effectively distinguished between those with dementia, mild cognitive impairment, and no cognitive impairments. 37 This ability underscores the significant potential of AI in detecting early markers of neurodegenerative disorders linked to sleep abnormalities, underscoring the need for more comprehensive international research to refine these predictive models.…”