Study Objectives Parkinson’s disease (PD) commonly involves degeneration of sleep-wake regulating brainstem nuclei; likewise, sleep-wake disturbances are highly prevalent in PD patients. As polysomnography macroparameters typically show only minor changes in PD, we investigated sleep microstructure, particularly cyclic alternating pattern (CAP), and its relation to alterations of the noradrenergic system in these patients. Methods We analysed 27 PD patients and 13 healthy control (HC) subjects who underwent over-night polysomnography and 11C-MeNER positron emission tomography for evaluation of noradrenaline transporter density. Sleep macroparameters as well as CAP metrics were evaluated according to the consensus statement from 2001. Statistical analysis comprised group comparisons and correlation analysis of CAP metrics with clinical characteristics of PD patients as well as noradrenaline transporter density. Results PD patients and HC subjects were comparable in demographic characteristics (age, sex, body mass index) and polysomnography macroparameters. CAP rate as well as A index differed significantly between groups, with PD patients having a lower CAP rate (46.7 ± 6.6% versus 38.0 ± 11.6%, p = 0.015) and lower A index (49.0 ± 8.7/hour versus 40.1 ± 15.4/hour, p = 0.042). In PD patients, both CAP metrics correlated significantly with diminished noradrenaline transporter density in arousal prompting brainstem nuclei (locus coeruleus, raphe nuclei) as well as arousal propagating brain structures like thalamus and bitemporal cortex. Conclusions Sleep microstructure is more severely altered than sleep macrostructure in PD patients and is associated with widespread dysfunction of the noradrenergic arousal system.

Tracking how individual human brains change over extended timescales is crucial to clinical scenarios ranging from stroke recovery to healthy aging. The use of resting state (RS) activity for tracking is a promising possibility. However, it is unresolved how a person's RS activity over time can be decoded to distinguish neurophysiological changes from confounding cognitive variability. Here, we develop a method to screen RS activity changes for these confounding effects by formulating it as a problem of change classification.We demonstrate a novel solution to change classification by linking individual-specific change to inter-individual differences. Individual RS-electroencephalography (EEG) was acquired over 5 consecutive days including task states devised to simulate the effects of inter-day cognitive variation. As inter-individual differences are shaped by neurophysiological differences, the inter-individual differences in RS activity on 1 day were analysed (using machine learning) to identify distinctive configurations in each individual's RS activity. Using this configuration as a decision rule, an individual could be re-identified from 2-s samples of the instantaneous oscillatory power spectrum acquired on a different day both from RS and confounded RS with a limited loss in accuracy. Importantly, the low loss in accuracy in cross-day versus same-day classification was achieved with classifiers that combined information from multiple frequency bands at channels across the scalp (with a concentration at characteristic fronto-central and occipital zones). Taken together, these findings support the technical feasibility of screening RS activity for confounding effects and the suitability of Abbreviations: A I p ! B I q , train decision rule on state A from day p ( A I p ); test on state B from day q ( B I q ); A I p ∘ A I q ! B I r , train decision rule on state A from days p and q (aggregation); test on state B from day r; B δ , B θ, B α , B β1 , B β2 , mono-band feature sets for the delta (δ); theta (θ); alpha (α); low-beta (β 1 ); and high-beta (β 2 ) frequency bands; EEG, electroencephalography; L F , L FC , L CP , L PO , mono-location feature sets for the frontal; frontocentral, centro-parietal and parieto-occipital zones; NPÀ, absence (negative instance) of inter-day neurophysiological change; NP+, presence (positive instance) of inter-day neurophysiological change; RS, resting state; RS1, RS2, resting state Session 1, Session 2; X I d , combined sample distribution of individuals in state X from day d.

Background: Although sleep disturbances are highly prevalent in patients with Parkinson’s disease, sleep macroarchitecture metrics show only minor changes. Objective: To assess alterations of the cyclic alternating pattern (CAP) as a critical feature of sleep microarchitecture in patients with prodromal, recent, and established Parkinson’s disease. Methods: We evaluated overnight polysomnography for classic sleep macroarchitecture and CAP metrics in 68 patients at various disease stages and compared results to 22 age- and sex-matched controls. Results: Already at the prodromal stage, patients showed a significantly reduced CAP rate as a central characteristic of sleep microarchitecture. Temporal characteristics of CAP showed a gradual change over disease stages and correlated with motor performance. In contrast, the sleep macroarchitecture metrics did not differ between groups. Conclusion: Data suggest that alterations of sleep microarchitecture are an early and more sensitive characteristic of Parkinson’s disease than changes in sleep macroarchitecture.