Complement activation mainly mediates the association of heart rate variability and cognitive impairment in adults with obstructive sleep apnea without dementia

Predicting cognitive scores from wearable-based digital physiological features using machine learning: data from a clinical trial in mild cognitive impairment

BACKGROUND

Continuous assessment and remote monitoring of cognitive function in individuals with mild cognitive impairment (MCI) enables tracking therapeutic effects and modifying treatment to achieve better clinical outcomes. While standardized neuropsychological tests are inconvenient for this purpose, wearable sensor technology collecting physiological and behavioral data looks promising to provide proxy measures of cognitive function. The objective of this study was to evaluate the predictive ability of digital physiological features, based on sensor data from wrist-worn wearables, in determining neuropsychological test scores in individuals with MCI.

METHODS

We used the dataset collected from a 10-week single-arm clinical trial in older adults (50-70 years old) diagnosed with amnestic MCI (N = 30) who received a digitally delivered multidomain therapeutic intervention. Cognitive performance was assessed before and after the intervention using the Neuropsychological Test Battery (NTB) from which composite scores were calculated (executive function, processing speed, immediate memory, delayed memory and global cognition). The Empatica E4, a wrist-wearable medical-grade device, was used to collect physiological data including blood volume pulse, electrodermal activity, and skin temperature. We processed sensors' data and extracted a range of physiological features. We used interpolated NTB scores for 10-day intervals to test predictability of scores over short periods and to leverage the maximum of wearable data available. In addition, we used individually centered data which represents deviations from personal baselines. Supervised machine learning was used to train models predicting NTB scores from digital physiological features and demographics. Performance was evaluated using "leave-one-subject-out" and "leave-one-interval-out" cross-validation.

RESULTS

The final sample included 96 aggregated data intervals from 17 individuals. In total, 106 digital physiological features were extracted. We found that physiological features, especially measures of heart rate variability, correlated most strongly to the executive function compared to other cognitive composites. The model predicted the actual executive function scores with correlation r = 0.69 and intra-individual changes in executive function scores with r = 0.61.

CONCLUSIONS

Our findings demonstrated that wearable-based physiological measures, primarily HRV, have potential to be used for the continuous assessments of cognitive function in individuals with MCI.

Elevated complement component 8 gamma levels in astrocyte-derived exosomes are associated with cognitive impairment in obstructive sleep apnea patients without dementia

The complement system plays a crucial role in cognitive impairment in obstructive sleep apnea (OSA). The present study aimed to investigate the connections between complement component 8 gamma (C8G) levels in astrocyte-derived exosomes (ADEs) and cognitive impairment in OSA patients without dementia. This cross-sectional cohort study recruited 274 participants without dementia, including 124 OSA patients with mild cognitive impairment (MCI), 100 OSA patients without MCI, and 50 healthy control subjects. Enrolled participants underwent polysomnography (PSG) evaluation, neuropsychological scale assessment, magnetic resonance imaging scanning, and collection of peripheral blood samples for quantification of complement proteins in ADEs. The findings showed higher C8G concentrations in ADEs from OSA patients with MCI than in the controls and OSA without MCI group. Logistic regression analysis suggested that C8G levels in ADEs were independently associated with MCI in OSA patients. Multivariable linear regression analysis demonstrated that C8G levels in ADEs were significantly correlated with global cognitive scores and all cognitive subdomain scores after adjusting for demographic factors (age, sex, education), vascular risk factors (Body mass index, history of hypertension, diabetes, dyslipidemia), depressive symptoms measures, and apnea-hypopnea index (AHI) values. The levels of C8G were linearly positively related to the white matter hyperintensity (WMH) volumes in Pearson's correlation analysis. Our research confirmed that C8G levels are significantly associated with cognitive impairment in OSA patients, which paves the way for novel therapeutic targets for neurocognitive dysfunction progression in OSA patients in the future.

Synergy through integration of digital cognitive tests and wearable devices for mild cognitive impairment screening

Introduction

Advances in mobile computing platforms and the rapid development of wearable devices have made possible the continuous monitoring of patients with mild cognitive impairment (MCI) and their daily activities. Such rich data can reveal more subtle changes in patients' behavioral and physiological characteristics, providing new ways to detect MCI anytime, anywhere. Therefore, we aimed to investigate the feasibility and validity of digital cognitive tests and physiological sensors applied to MCI assessment.

Methods

We collected photoplethysmography (PPG), electrodermal activity (EDA) and electroencephalogram (EEG) signals from 120 participants (61 MCI patients, 59 healthy controls) during rest and cognitive testing. The features extracted from these physiological signals involved the time domain, frequency domain, time-frequency domain and statistics. Time and score features during the cognitive test are automatically recorded by the system. In addition, selected features of all modalities were classified by tenfold cross-validation using five different classifiers.

Results

The experimental results showed that the weighted soft voting strategy combining five classifiers achieved the highest classification accuracy (88.9%), precision (89.9%), recall (88.2%), and F1 score (89.0%). Compared to healthy controls, the MCI group typically took longer to recall, draw, and drag. Moreover, during cognitive testing, MCI patients showed lower heart rate variability, higher electrodermal activity values, and stronger brain activity in the alpha and beta bands.

Discussion

It was found that patients' classification performance improved when combining features from multiple modalities compared to using only tablet parameters or physiological features, indicating that our scheme could reveal MCI-related discriminative information. Furthermore, the best classification results on the digital span test across all tasks suggest that MCI patients may have deficits in attention and short-term memory that came to the fore earlier. Finally, integrating tablet cognitive tests and wearable sensors would provide a new direction for creating an easy-to-use and at-home self-check MCI screening tool.

Heart rate variability (HRV): Checklist for observational and experimental studies

Ford et al. provided (via review study) an overview of the psychiatric features associated with encephalitis mediated by autoantibodies targeting neuronal cellsurface antigens and describe indicators of potential immunopathology underlying psychiatric manifestations. In this scientific context of psychiatric manifestations, studies strongly suggest that scientists include heart rate variability assessment in these patients (because this is a way to evaluate the autonomic nervous system's sympathetic and parasympathetic components). Ford's paper has significant clinical relevance and will generate new studies (experimental and observational) on psychiatric manifestations; therefore, we have six suggestions for the authors and other scientists/researchers who will use this article to perform further research using the heart rate variability assessment. Our objective is to contribute to scientific research and help to strengthen the new studies that will emerge.