See Morris and Weil (doi: ) for a scientific commentary on this article. In a prospective multicentre study involving 1280 patients with idiopathic RBD, Postuma et al. show that approximately 6% of patients each year (>73.5% over 12 years) convert to full neurodegenerative disease. They test the predictive power of 21 prodromal markers of neurodegeneration, providing a template for planning neuroprotective trials.

IMPORTANCE Aging is associated with excessive daytime sleepiness (EDS), which has been linked to cognitive decline in the elderly. However, whether EDS is associated with the pathologic processes of Alzheimer disease remains unclear.OBJECTIVE To investigate whether EDS at baseline is associated with a longitudinal increase in regional β-amyloid (Aβ) accumulation in a cohort of elderly individuals without dementia. DESIGN, SETTING, AND PARTICIPANTSThis prospective analysis included participants enrolled in the Mayo Clinic Study of Aging, a longitudinal population-based study in Olmsted County, Minnesota. Of 2900 participants, 2172 (74.9%) agreed to undergo carbon 11-labeled Pittsburgh compound B positron emission tomography (PiB-PET). We included 283 participants 70 years or older without dementia who completed surveys assessing sleepiness at baseline and had at least 2 consecutive PiB-PET scans from January 1, 2009, through July 31, 2016, after excluding 45 (13.7%) who had a comorbid neurologic disorder. MAIN OUTCOMES AND MEASURESExcessive daytime sleepiness was defined as an Epworth Sleepiness Scale score of at least 10. The difference in Aβ levels between the 2 consecutive scans (ΔPiB) in Aβ-susceptible regions (prefrontal, anterior cingulate, posterior cingulate-precuneus, and parietal) was determined. Multiple linear regression models were fit to explore associations between baseline EDS and ΔPiB while adjusting for baseline age, sex, presence of the apolipoprotein E ε4 allele, educational level, baseline PiB uptake, global PiB positivity (standardized uptake value ratio Ն1.4), physical activity, cardiovascular comorbidities (obesity, hypertension, hyperlipidemia, and diabetes), reduced sleep duration, respiratory symptoms during sleep, depression, and interval between scans. RESULTSOf the initial 283 participants, mean (SD) age was 77.1 (4.8) years; 204 (72.1%) were men and 79 (27.9%) were women. Sixty-three participants (22.3%) had EDS. Baseline EDS was significantly associated with increased regional Aβ accumulation in the anterior cingulate

Background Sudden unexpected death in epilepsy ( SUDEP ) is the leading cause of epilepsy‐related death. SUDEP shares many features with sudden cardiac death and sudden unexplained death in the young and may have a similar genetic contribution. We aim to systematically review the literature on the genetics of SUDEP . Methods and Results PubMed, MEDLINE Epub Ahead of Print, Ovid Medline In‐Process & Other Non‐Indexed Citations, MEDLINE , EMBASE , Cochrane Database of Systematic Reviews, and Scopus were searched through April 4, 2017. English language human studies analyzing SUDEP for known sudden death, ion channel and arrhythmia‐related pathogenic variants, novel variant discovery, and copy number variant analyses were included. Aggregate descriptive statistics were generated; data were insufficient for meta‐analysis. A total of 8 studies with 161 unique individuals were included; mean was age 29.0 (± SD 14.2) years; 61% males; ECG data were reported in 7.5% of cases; 50.7% were found prone and 58% of deaths were nocturnal. Cause included all types of epilepsy. Antemortem diagnosis of Dravet syndrome and autism (with duplication of chromosome 15) was associated with 11% and 9% of cases. The most frequently detected known pathogenic variants at postmortem were in Na + and K + ion channel subunits, as were novel potentially pathogenic variants (11%). Overall, the majority of variants were of unknown significance. Analysis of copy number variant was insignificant. Conclusions SUDEP case adjudication and evaluation remains limited largely because of crucial missing data such as ECG s. The most frequent pathogenic/likely pathogenic variants identified by molecular autopsy are in ion channel or arrhythmia‐related genes, with an ≈11% discovery rate. Comprehensive postmortem examination should include examination of the heart and brain by specialized pathologists and blood storage.

Light therapy is increasingly applied in a variety of sleep medicine and psychiatric conditions including circadian rhythm sleep disorders, seasonal affective disorder, and dementia. This article reviews the neural underpinnings of circadian neurobiology crucial for understanding the influence of light therapy on brain function, common mood and sleep disorders in which light therapy may be effectively used, and applications of light therapy in clinical practice.

This multilanguage study used simple speech recording and high-end pattern analysis to provide sensitive and reliable noninvasive biomarkers of prodromal versus manifest α-synucleinopathy in patients with idiopathic rapid eye movement sleep behavior disorder (iRBD) and early-stage Parkinson disease (PD). Methods: We performed a multicenter study across the Czech, English, German, French, and Italian languages at 7 centers in Europe and North America. A total of 448 participants (337 males), including 150 with iRBD (mean duration of iRBD across language groups 0.5-3.4 years), 149 with PD (mean duration of disease across language groups 1.7-2.5 years), and 149 healthy controls were recorded; 350 of the participants completed the 12-month follow-up. We developed a fully automated acoustic quantitative assessment approach for the 7 distinctive patterns of hypokinetic dysarthria. Results: No differences in language that impacted clinical parkinsonian phenotypes were found. Compared with the controls, we found significant abnormalities of an overall acoustic speech severity measure via composite dysarthria index for both iRBD (p = 0.002) and PD (p < 0.001). However, only PD (p < 0.001) was perceptually distinct in a blinded subjective analysis. We found significant group differences between PD and controls for monopitch (p < 0.001), prolonged pauses (p < 0.001), and imprecise consonants (p = 0.03); only monopitch was able to differentiate iRBD patients from controls (p = 0.004). At the 12-month follow-up, a slight progression of overall acoustic speech impairment was noted for the iRBD (p = 0.04) and PD (p = 0.03) groups. Interpretation: Automated speech analysis might provide a useful additional biomarker of parkinsonism for the assessment of disease progression and therapeutic interventions.

ObjectiveTo determine whether REM sleep without atonia (RSWA) during polysomnography (PSG) predicts phenoconversion in patients with idiopathic REM sleep behavior disorder (iRBD), a prodromal feature of a neurodegenerative disease.MethodsWe analyzed RSWA in 60 patients with iRBD, including manual phasic, tonic, and any muscle activity in the submentalis and anterior tibialis muscles and the automated REM atonia index in the submentals. We identified patients who developed parkinsonism or mild cognitive impairment (MCI) during at least 3 years of follow-up after PSG. Kaplan-Meier analysis was performed and receiver operator curves were calculated to determine RSWA cutoffs predicting faster phenoconversion.ResultsTwenty-six (43%) patients developed parkinsonism (n = 17) or MCI (n = 9). Phenoconverters were older at iRBD diagnosis (p = 0.02). Median time to phenoconversion was 3.9 ± 2.5 years. iRBD phenoconverters had significantly more RSWA at diagnosis. Phenoconversion risk from iRBD diagnosis was 20% and 35% at 3 and 5 years, respectively, with greater risk in patients with iRBD with >46.4% any combined RSWA, which increased further to 30% and 55% at 3 and 5 years for patients >65 years of age at diagnosis.ConclusionsPatients with iRBD with higher amounts of polysomnographic RSWA had a greater risk of developing Parkinson disease or MCI. Patients with older age and higher RSWA amounts had more rapid phenoconversion than younger patients with RBD. Our study suggests that RSWA is a potential biomarker for risk stratification of iRBD phenoconversion that could facilitate prognostication for patients with iRBD.Classification of evidenceThis study provides Class II evidence that for patients with iRBD, increased RSWA correlates with increased risk for developing parkinsonism or MCI.

Objective: Electrical deep brain stimulation (DBS) is an established treatment for patients with drug-resistant epilepsy. Sleep disorders are common in people with epilepsy, and DBS may actually further disturb normal sleep patterns and sleep quality. Novel devices capable of DBS and continuous intracranial EEG (iEEG) telemetry enable detailed assessments of therapy efficacy and tracking sleep related comorbidities. Here, we investigate the feasibility of automated sleep classification using continuous iEEG data recorded from Papez’s circuit in four patients with drug resistant mesial temporal lobe epilepsy using an investigational implantable sensing and stimulation device with electrodes implanted in bilateral hippocampus (HPC) and anterior nucleus of thalamus (ANT). Approach: The iEEG recorded from HPC is used to classify sleep during concurent DBS targeting ANT. Simultaneous polysomnography and HPC sensing were used to train, validate and test an automated classifier for a range of ANT DBS frequencies: no stimulation, 2 Hz, 7 Hz, and high frequency (>100 Hz). Main results: We show that it is possible to build a patient specific automated sleep staging classifier using power in band features extracted from one HPC sensing channel. The patient specific classifiers performed well under all thalamic DBS frequencies with an average F1-score 0.894, and provided viable classification into awake and major sleep categories, rapid eye movement (REM) and non-REM. We retrospectively analyzed classification performance with gold-standard polysomnography annotations, and then prospectively deployed the classifier on chronic continuous iEEG data spanning multiple months to characterize sleep patterns in ambulatory patients living in their home environment. Significance: The ability to continuously track behavioral state and fully characterize sleep should prove useful for optimizing DBS for epilepsy and associated sleep, cognitive and mood comorbidities.

Objective. Automated behavioral state classification in intracranial EEG (iEEG) recordings may be beneficial for iEEG interpretation and quantifying sleep patterns to enable behavioral state dependent neuromodulation therapy in next generation implantable brain stimulation devices. Here, we introduce a fully automated unsupervised framework to differentiate between awake (AW), sleep (N2), and slow wave sleep (N3) using intracranial EEG (iEEG) only and validated with expert scored polysomnography. Approach. Data from eight patients undergoing evaluation for epilepsy surgery (age , three female) with intracranial depth electrodes for iEEG monitoring were included. Spectral power features (0.1–235 Hz) spanning several frequency bands from a single electrode were used to classify behavioral states of patients into AW, N2, and N3. Main results. Overall, classification accuracy of 94%, with 94% sensitivity and 93% specificity across eight subjects using multiple spectral power features from a single electrode was achieved. Classification performance of N3 sleep was significantly better (95%, sensitivity 95%, specificity 93%) than that of the N2 sleep phase (87%, sensitivity 78%, specificity 96%). Significance. Automated, unsupervised, and robust classification of behavioral states based on iEEG data is possible, and it is feasible to incorporate these algorithms into future implantable devices with limited computational power, memory, and number of electrodes for brain monitoring and stimulation.