Quantitative electroencephalography from freely moving rats is commonly used as a translational tool for predicting drug‐effects in humans. We hypothesized that drug‐effects may be expressed differently depending on whether the rat is in active locomotion or sitting still during recording sessions, and proposed automatic state‐detection as a viable tool for estimating drug‐effects free of hypo‐/hyperlocomotion‐induced effects. We aimed at developing a fully automatic and validated method for detecting two behavioural states: active and inactive, in one‐second intervals and to use the method for evaluating ketamine, DOI, d‐cycloserine, d‐amphetamine, and diazepam effects specifically within each state. The developed state‐detector attained high precision with more than 90% of the detected time correctly classified, and multiple differences between the two detected states were discovered. Ketamine‐induced delta activity was found specifically related to locomotion. Ketamine and DOI suppressed theta and beta oscillations exclusively during inactivity. Characteristic gamma and high‐frequency oscillations (HFO) enhancements of the NMDAR and 5HT 2A modulators, speculated associated with locomotion, were profound and often largest during the inactive state. State‐specific analyses, theoretically eliminating biases from altered occurrence of locomotion, revealed only few effects of d‐amphetamine and diazepam. Overall, drug‐effects were most abundant in the inactive state. In conclusion, this new validated and automatic locomotion state‐detection method enables fast and reliable state‐specific analysis facilitating discovery of state‐dependent drug‐effects and control for altered occurrence of locomotion. This may ultimately lead to better cross‐species translation of electrophysiological effects of pharmacological modulations.

Idiopathic rapid eye-movement (REM) sleep behavior disorder (iRBD) has been found to be a strong early predictor for later development into Parkinson's disease (PD). iRBD is diagnosed by polysomnography but the manual evaluation is laborious, why the aims of this study are to develop supportive methods for detecting iRBD from electroencephalo-graphic (EEG) signals recorded during REM sleep. This method classified subjects from their EEG similarity with the two classes iRBD patients and control subjects. The feature sets used for classifying subjects were based on the relative powers of the EEG signals in different frequency bands. The classification was based on the fast and classical K-means and Bayesian classifiers. With a subject-specific re-scaling of the feature set and the use of a Bayesian classifier the performance reached 90% in both sensitivity and specificity. For the purpose of reducing the feature count, the features were evaluated with the statistical Smith-Satterthwaite test and by using sequential forward selection a well-performing feature subset was found which contained only five features, while attaining a sensitivity and a specificity of both 80 %.

Background Multiple system atrophy (MSA) is a rare and aggressive neurodegenerative disease that typically leads to death 6 to 10 years after symptom onset. The rapid evolution renders it crucial to understand the general disease progression and factors affecting the disease course. Objectives The aims of this study were to develop a novel disease‐progression model to estimate a population‐level MSA progression trajectory and predict patient‐specific continuous disease stages describing the degree of progress into the disease. Methods The disease‐progression model estimated a population‐level progression trajectory of subscales of the Unified MSA Rating Scale and the Unified Parkinson's Disease Rating Scale using patients in the European MSA natural history study. The predicted disease continuum was validated via multiple analyses based on reported anchor points, and the effect of MSA subtype on the rate of disease progression was evaluated. Results The predicted disease continuum spanned approximately 6 years, with an estimated average duration of 51 months for a patient with global disability score 0 to reach the highest level of 4. The predicted continuous disease stages were shown to be correlated with time of symptom onset and predictive of survival time. MSA motor subtype was found to significantly affect disease progression, with MSA‐parkinsonian (MSA‐P) type patients having an accelerated rate of progression. Conclusions The proposed modeling framework introduces a new method of analyzing and interpreting the progression of MSA. It can provide new insights and opportunities for investigating covariate effects on the rate of progression and provide well‐founded predictions of patient‐level future progressions. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Electrocardiographic (ECG) recording using adhesive patch-type ECG monitors (PEMs) has several advantages over conventional ECG recorders. However, due to the unconventional electrode locations used in PEM systems, the morphology of the acquired ECG signals may differ from conventional ECG leads used in the clinic impeding clinical interpretation. In this study, recordings from an ePatch® lead system involving three torso sites are compared with concurrently recorded standard 12-lead ECG. Pearson's correlation coefficients (CC) of -0.90 and 0.91 is found between two of the PEM signals and the standard 12-lead ECG signals aVR and V2, respectively. Deriving the 12-lead ECG from the PEM leads through linear transforms on a subject-specific basis yield CC values ranging from 0.78 to 0.96 between measured and derived leads. The corresponding CC values for the PEM ECG leads range from 0.88 to 0.95. It is found that the PEM lead system captures "residual" information not contained in the standard 12-lead ECG and i.a. a negative deflection after the T-wave is discovered in the PEM signals.