Frequency and evolution of sleep-wake disturbances after ischemic stroke: A 2-year prospective study of 437 patients.

OBJECTIVE In the absence of systematic and longitudinal data, this study prospectively assessed both frequency and evolution of sleep-wake disturbances (SWD) after stroke. METHODS In 437 consecutively recruited patients with ischemic stroke or transient ischemic attack (TIA), stroke characteristics and outcome were assessed within the 1st week and 3.2 ± 0.3 years (M±SD) after the acute event. SWD were assessed by interview and questionnaires at 1 and 3 months as well as 1 and 2 years after the acute event. Sleep disordered breathing (SDB) was assessed by respirography in the acute phase and repeated in one fifth of the participants 3 months and 1 year later. RESULTS Patients (63.8% male, 87% ischemic stroke and mean age 65.1 ± 13.0 years) presented with mean NIHSS-score of 3.5 ± 4.5 at admission. In the acute phase, respiratory event index was >15/h in 34% and >30/h in 15% of patients. Over the entire observation period, the frequencies of excessive daytime sleepiness (EDS), fatigue and insomnia varied between 10-14%, 22-28% and 20-28%, respectively. Mean insomnia and EDS scores decreased from acute to chronic stroke, whereas restless legs syndrome (RLS) percentages (6-9%) and mean fatigue scores remained similar. Mean self-reported sleep duration was enhanced at acute stroke (month 1: 07:54 ± 01:27h) and decreased at chronic stage (year 2: 07:43 ± 01:20h). CONCLUSIONS This study documents a high frequency of SDB, insomnia, fatigue and a prolonged sleep duration after stroke/TIA, which can persist for years. Considering the negative effects of SWD on physical, brain and mental health these data suggest the need for a systematic assessment and management of post-stroke SWD

Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning

Narcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum of poorly defined diseases with excessive daytime sleepiness as a core feature. Due to the considerable overlap of symptoms and the rarity of the diseases, it is difficult to identify distinct phenotypes of CH. Machine learning (ML) can help to identify phenotypes as it learns to recognize clinical features invisible for humans. Here we apply ML to data from the huge European Narcolepsy Network (EU-NN) that contains hundreds of mixed features of narcolepsy making it difficult to analyze with classical statistics. Stochastic gradient boosting, a supervised learning model with built-in feature selection, results in high performances in testing set. While cataplexy features are recognized as the most influential predictors, machine find additional features, e.g. mean rapid-eye-movement sleep latency of multiple sleep latency test contributes to classify NT1 and NT2 as confirmed by classical statistical analysis. Our results suggest ML can identify features of CH on machine scale from complex databases, thus providing 'ideas' and promising candidates for future diagnostic classifications.</p

The Comparison of Early Hemodynamic Response to Single-Pulse Transcranial Magnetic Stimulation following Inhibitory or Excitatory Theta Burst Stimulation on Motor Cortex

We present a new study design aiming to enhance the understanding of the mechanism by which continuous theta burst stimulation (cTBS) or intermittent theta burst stimulation (iTBS) paradigms elicit cortical modulation. Using near-infrared spectroscopy (NIRS), we compared the cortical hemodynamics of the previously inhibited (after cTBS) or excited (after iTBS) left primary motor cortex (M1) as elicited by single-pulse TMS (spTMS) in a cross-over design. Mean relative changes in hemodynamics within 6 s of the stimulus were compared using a two-sample t-test (p p = 0.04) in blood volume (BV) compared to baseline. There were no significant changes in other hemodynamic parameters (oxygenated/deoxygenated hemoglobin). spTMS after cTBS induced a larger increase in BV than spTMS after iTBS (p = 0.021) and sham stimulus after cTBS (p = 0.009). BV showed no significant difference between real and sham stimuli after iTBS (p = 0.37). The greater hemodynamic changes suggest increased vasomotor reactivity after cTBS compared to iTBS. In addition, cTBS could decrease lateral inhibition, allowing activation of surrounding areas after cTBS