Decreased aperiodic neural activity in Parkinson's disease and dementia with Lewy bodies

Divergent Associations of Slow-Wave Sleep versus Rapid Eye Movement Sleep with Plasma Amyloid-Beta

OBJECTIVE

Recent evidence shows that during slow-wave sleep (SWS), the brain is cleared from potentially toxic metabolites, such as the amyloid-beta protein. Poor sleep or elevated cortisol levels can worsen amyloid-beta clearance, potentially leading to the formation of amyloid plaques, a neuropathological hallmark of Alzheimer disease. Here, we explored how nocturnal neural and endocrine activity affects amyloid-beta fluctuations in the peripheral blood.

METHODS

We acquired simultaneous polysomnography and all-night blood sampling in 60 healthy volunteers aged 20-68 years. Nocturnal plasma concentrations of amyloid-beta-40, amyloid-beta-42, cortisol, and growth hormone were assessed every 20 minutes. Amyloid-beta fluctuations were modeled with sleep stages, (non)oscillatory power, and hormones as predictors while controlling for age and participant-specific random effects.

RESULTS

Amyloid-beta-40 and amyloid-beta-42 levels correlated positively with growth hormone concentrations, SWS proportion, and slow-wave (0.3-4Hz) oscillatory and high-band (30-48Hz) nonoscillatory power, but negatively with cortisol concentrations and rapid eye movement sleep (REM) proportion measured 40-100 minutes previously (all t values > |3|, p values < 0.003). Older participants showed higher amyloid-beta-40 levels.

INTERPRETATION

Slow-wave oscillations are associated with higher plasma amyloid-beta levels, whereas REM sleep is related to decreased amyloid-beta plasma levels, possibly representing changes in central amyloid-beta production or clearance. Strong associations between cortisol, growth hormone, and amyloid-beta presumably reflect the sleep-regulating role of the corresponding releasing hormones. A positive association between age and amyloid-beta-40 may indicate that peripheral clearance becomes less efficient with age. ANN NEUROL 2024;96:46-60.

Oscillatory and non-oscillatory features of the magnetoencephalic sensorimotor rhythm in Parkinson's disease

Parkinson's disease (PD) is associated with changes in neural activity in the sensorimotor alpha and beta bands. Using magnetoencephalography (MEG), we investigated the role of spontaneous neuronal activity within the somatosensory cortex in a large cohort of early- to mid-stage PD patients (N = 78) on Parkinsonian medication and age- and sex-matched healthy controls (N = 60) using source reconstructed resting-state MEG. We quantified features of the time series data in terms of oscillatory alpha power and central alpha frequency, beta power and central beta frequency, and 1/f broadband characteristics using power spectral density. Furthermore, we characterised transient oscillatory burst events in the mu-beta band time-domain signals. We examined the relationship between these signal features and the patients' disease state, symptom severity, age, sex, and cortical thickness. PD patients and healthy controls differed on PSD broadband characteristics, with PD patients showing a steeper 1/f exponential slope and higher 1/f offset. PD patients further showed a steeper age-related decrease in the burst rate. Out of all the signal features of the sensorimotor activity, the burst rate was associated with increased severity of bradykinesia, whereas the burst duration was associated with axial symptoms. Our study shows that general non-oscillatory features (broadband 1/f exponent and offset) of the sensorimotor signals are related to disease state and oscillatory burst rate scales with symptom severity in PD.

Construction of machine learning models for recognizing comorbid anxiety in epilepsy patients based on their clinical and quantitative EEG features

BACKGROUND

This study aimed to construct prediction models for the recognizing of anxiety disorders (AD) in patients with epilepsy (PWEs) by combining clinical features with quantitative electroencephalogram (qEEG) features and using machine learning (ML).

METHODS

Nineteen clinical features and 20-min resting-state EEG were collected from 71 PWEs comorbid with AD and another 60 PWEs without AD who met the inclusion-exclusion criteria of this study. The EEG were preprocessed and 684 Phase Locking Value (PLV) and 76 Lempel-Ziv Complexity (LZC) features on four bands were extracted. The Fisher score method was used to rank all the derived features. We constructed four models for recognizing AD in PWEs, whether PWEs based on different combinations of features using eXtreme gradient boosting (XGboost) and evaluated these models using the five-fold cross-validation method.

RESULTS

The prediction model constructed by combining the clinical, PLV, and LZC features showed the best performance, with an accuracy of 96.18%, precision of 94.29%, sensitivity of 98.33%, F1-score of 96.06%, and Area Under the Curve (AUC) of 0.96. The Fisher score ranking results displayed that the top ten features were depression, educational attainment, α_P3LZC, α_T6-PzPLV, α_F7LZC, β_Fp2-O1PLV, θ_T4-CzPLV, θ_F7-PzPLV, α_Fp2LZC, and θ_T4-PzPLV.

CONCLUSIONS

The model, constructed by combining the clinical and qEEG features PLV and LZC, efficiently identified the presence of AD comorbidity in PWEs and might have the potential to complement the clinical diagnosis. Our findings suggest that LZC features in the α band and PLV features in Fp2-O1 may be potential biomarkers for diagnosing AD in PWEs.

Sustained polyphasic sleep restriction abolishes human growth hormone release

STUDY OBJECTIVES

Voluntary sleep restriction is a common phenomenon in industrialized societies aiming to increase time spent awake and thus productivity. We explored how restricting sleep to a radically polyphasic schedule affects neural, cognitive, and endocrine characteristics.

METHODS

Ten young healthy participants were restricted to one 20-minute nap opportunity at the end of every 4 hours (i.e. six sleep episodes per 24 hours) without any extended core sleep window, which resulted in a cumulative sleep amount of just 2 hours per day (i.e. ~20 minutes per bout).

RESULTS

All but one participant terminated this schedule during the first month. The remaining participant (a 25-year-old male) succeeded in adhering to a polyphasic schedule for five out of the eight planned weeks. Cognitive and psychiatric measures showed modest changes during polyphasic as compared to monophasic sleep, while in-blood cortisol or melatonin release patterns and amounts were apparently unaltered. In contrast, growth hormone release was almost entirely abolished (>95% decrease), with the residual release showing a considerably changed polyphasic secretional pattern.

CONCLUSIONS

Even though the study was initiated by volunteers with exceptional intrinsic motivation and commitment, none of them could tolerate the intended 8 weeks of the polyphasic schedule. Considering the decreased vigilance, abolished growth hormone release, and neurophysiological sleep changes observed, it is doubtful that radically polyphasic sleep schedules can subserve the different functions of sleep to a sufficient degree.

Test-retest reliability of spectral parameterization by 1/f characterization using SpecParam

SpecParam (formally known as FOOOF) allows for the refined measurements of electroencephalography periodic and aperiodic activity, and potentially provides a non-invasive measurement of excitation: inhibition balance. However, little is known about the psychometric properties of this technique. This is integral for understanding the usefulness of SpecParam as a tool to determine differences in measurements of cognitive function, and electroencephalography activity. We used intraclass correlation coefficients to examine the test-retest reliability of parameterized activity across three sessions (90 minutes apart and 30 days later) in 49 healthy young adults at rest with eyes open, eyes closed, and during three eyes closed cognitive tasks including subtraction (Math), music recall (Music), and episodic memory (Memory). Intraclass correlation coefficients were good for the aperiodic exponent and offset (intraclass correlation coefficients > 0.70) and parameterized periodic activity (intraclass correlation coefficients > 0.66 for alpha and beta power, central frequency, and bandwidth) across conditions. Across all three sessions, SpecParam performed poorly in eyes open (40% of participants had poor fits over non-central sites) and had poor test-retest reliability for parameterized periodic activity. SpecParam mostly provides reliable metrics of individual differences in parameterized neural activity. More work is needed to understand the suitability of eyes open resting data for parameterization using SpecParam.