Predicting age, cognitive scores, and sleep stages from sleep EEG with a multi-task deep neural network using the Framingham Heart Study

Introduction: Impaired sleep quality, quantity and timing are associated with neurological and mental health disorders, potentially through disruption of functional and anatomical neuronal pathways. Sleep state oscillations likely encode brain health, therefore, sleep may provide accessible biomarkers for estimating brain health. One potential biomarker is sleep electroencephalogram (EEG)-based brain age, which if elevated above chronological age, could indicate abnormal or accelerated aging. Another potential method to build a biomarker is to directly estimate cognitive function from sleep data. Here, we investigate how well age and neuropsychological test scores can be predicted from EEG using an artificial deep neural network. Materials and Methods: We used data from 735 participants of the Framingham Heart Study (FHS). Each participant had at a minimum one polysomnographic recording (PSG) and one neuropsychiatric evaluation for fluid intelligence (Wechsler Adult Intelligence Scale, NPS). Additionally, age at evaluation was available for each participant, resulting in 1244 PSG-age-NPS triplets. We solely used EEG data from C3 electrodes for this analysis. The EEG signal, recorded in 125 Hz, was bandpass-filtered between 0-20Hz and transformed into the time-frequency domain with the multitaper method (2 second window length, 1 second step size). The resulting spectrograms were all zero-padded to 11 hours, harmonizing the numerical dimensions of the sleep representations across subjects. We developed an artificial deep neural network with a “U-Net”-like architecture, i.e., consisting of an encoder and decoder part. The network’s input was a subject’s EEG spectrogram, and its tasks were to predict sleep stages for 30-second epochs, the subject’s age, and the subject’s neuropsychological test score (NPS). The model mainly consisted of convolution-batch normalization-max pooling/upsampling layers, with a total of 11 million parameters. Fully connected layers for the age and NPS prediction tasks consisted of 15,600 parameters. The loss functions used were cross entropy for the sleep staging task and mean squared error for the age and NPS tasks. We evaluated the model’s performance with 10-fold cross-validation. Results: Sleep staging: The accuracies (means and standard deviations across folds) per sleep stage were Wake 0.64 (0.02), N2 0.61 (0.02), N3 0.59 (0.04), NREM combined 0.79 (0.01), and REM 0.53 (0.06). Cohen’s Kappa was k=0.54 (0.02). Predicting age: Pearson correlation between chronological age and predicted age was r= 0.60 (0.06). Predicting neuropsychological test score: Pearson correlation between the NPS and the predicted cognitive score was r= 0.25 (0.07). Conclusions: A multi-task deep learning network was able to predict sleep stages, age, and cognitive scores from sleep EEG with varying performance precision. While there was a strong correlation between predicted age and chronological age, predicted cognition scores correlated weakly to moderately with the actual scores. It is likely that prediction performance for all tasks can be increased with larger and more variable datasets. The sleep-based, subject-specific estimates for age and cognition may serve as biomarkers for brain health, a hypothesis we will test in future studies. Acknowledgements: Research was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health, 1R01AG062989

Optimal spindle detection parameters for predicting cognitive performance

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