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Study Objectives The differentiation of isolated rapid eye movement (REM) sleep behavior disorder (iRBD) or its prodromal phase (prodromal RBD) from other disorders with motor activity during sleep is critical for identifying α-synucleinopathy in an early stage. Currently, definite RBD diagnosis requires video polysomnography (vPSG). The aim of this study was to evaluate automated 3D video analysis of leg movements during REM sleep as objective diagnostic tool for iRBD. Methods A total of 122 participants (40 iRBD, 18 prodromal RBD, 64 participants with other disorders with motor activity during sleep) were recruited among patients undergoing vPSG at the Sleep Disorders Unit, Department of Neurology, Medical University of Innsbruck. 3D videos synchronous to vPSG were recorded. Lower limb movements rate, duration, extent, and intensity were computed using a newly developed software. Results The analyzed 3D movement features were significantly increased in subjects with iRBD compared to prodromal RBD and other disorders with motor activity during sleep. Minor leg jerks with a duration < 2 seconds discriminated with the highest accuracy (90.4%) iRBD from other motor activity during sleep. Automatic 3D analysis did not differentiate between prodromal RBD and other disorders with motor activity during sleep. Conclusions Automated 3D video analysis of leg movements during REM sleep is a promising diagnostic tool for identifying subjects with iRBD in a sleep laboratory population and is able to distinguish iRBD from subjects with other motor activities during sleep. For future application as a screening, further studies should investigate usefulness of this tool when no information about sleep stages from vPSG is available and in the home environment.

This study investigates the impact of generalized tonic-clonic seizures (GTCS) and antiepileptic drugs (AED) during pregnancy on gestational age (GA) and anthropometric data of newborns. One hundred twenty-nine singleton pregnancies resulting in live births from September 1999 to October 2010 in 106 women with epilepsy on AED therapy, recorded within the framework of the EURAP (International Registry of Antiepileptic Drugs and Pregnancy) program at the Department of Neurology, Medical University Innsbruck, Austria, were studied. Occurrence of ≥ 1 GTCS during pregnancy was associated with a shorter GA [median (range) 37.5 [35.1-41.6] vs. 39.7 [29.1-46.3] weeks; p ≤ 0.001], an overall five times higher preterm risk (p = 0.042) and a reduced birth weight in boys (2,900 [2,050-3,870] vs. 3,205 [1,575-4,355] g; p = 0.040). In primipara, when compared to multipara, GTCS ≥ 1 significantly reduced the GA (37.9 [35.1-41.6] vs. 39.7 [29.4-44.9] weeks; p = 0.020) and raised the incidence of low birth weight (LBW) (p = 0.022) in neonates. Antiepileptic drug polytherapy significantly increased the risk for small-for-gestational-age regarding weight (SGA(W); p = 0.035) and regarding weight and/or length (SGA(W/L); p = 0.046) when compared to monotherapy. GTCS during pregnancy was associated with diverse negative effects comprising shorter GA, an increased incidence of prematurity and LBW in primiparous women. Furthermore, AED polytherapy was correlated with an enhanced risk for SGA delivery. Re-evaluating the need for drug therapy (in particular polytherapy), maintaining seizure control for a given period before pregnancy and counseling about the importance of preventing GTCS might improve pregnancy outcome in women with epilepsy.

Acyclovir resistance is rarely seen in herpes simplex virus (HSV) type I encephalitis. Prevalence rates vary between 0.5 % in immunocompetent patients (Christophers et al. 1998; Fife et al. 1994) and 3.5–10 % in immunocompromised patients (Stranska et al. 2005). We report a 45-year-old, immunocompetent (negative HIV antigen/antibody testing), female patient, without previous illness who developed—after a febrile prodromal stage—aphasia and psychomotor slowing. Cerebral magnetic resonance imaging (cMRI) showed right temporal and insular T2-hyperintense lesions with spreading to the contralateral temporal lobe. Cerebrospinal fluid (CSF) analysis yielded lymphocytic pleocytosis and elevated protein level. Polymerase chain reaction testing for HSV type I showed a positive result in repeat lumbar puncture. HSV type I encephalitis was diagnosed and intravenous acyclovir treatment was initiated (750 mg t.i.d.). Acyclovir treatment was intensified to 1000 mg t.i.d., due to clinical deterioration, ongoing pleocytosis and progression on cMRI 5 days after initiation of antiviral therapy. In parallel, acyclovir resistance testing showed mutation of thymidine kinase gene at position A156V prompting foscarnet therapy (60 mg t.i.d.). Patient’s condition improved dramatically over 2 weeks. Acyclovir resistance is rare but should be considered in case of clinical worsening of patient’s condition. To our knowledge, this is the first report of acyclovir resistance in HSV type I encephalitis of an immunocompetent and previously healthy patient in Austria.

Differentiation of central disorders of hypersomnolence (DOH) is challenging but important for patient care. This study aimed to investigate whether biomarkers derived from sleep structure evaluated both by manual scoring as well as with artificial intelligence (AI) algorithms allow distinction of patients with different DOH. We included video-polysomnography data of 40 narcolepsy type 1 (NT1), 26 narcolepsy type 2 (NT2), 23 idiopathic hypersomnia (IH) patients and 54 subjects with subjective excessive daytime sleepiness (sEDS). Sleep experts manually scored sleep stages. A previously validated AI algorithm was employed to obtain automatic hypnograms and hypnodensity graphs (where each epoch is represented as a mixture of sleep stage probabilities). One-thousand-three features describing sleep architecture and instability were extracted from manual/automatic hypnogram and hypnodensity graphs. After feature selection, random forest classifiers were trained and tested in a 5-fold-cross-validation scheme to distinguish groups pairwise (NT1-vs-NT2, NT1-vs-IH, …) and single groups from the pooled remaining ones (NT1-vs-rest, NT2-vs-rest,…). The accuracy/F1-score values obtained in the test sets were: 0.74±0.04/0.79±0.05 (NT1-vs-NT2), 0.89±0.09/0.91±0.08 (NT1-vs-IH), 0.93±0.06/0.91±0.07 (NT1-vs-sEDS), 0.88±0.04/0.80±0.07 (NT1-vs-rest), 0.65±0.10/0.70±0.09 (NT2-vs-IH), 0.72±0.12/0.60±0.10 (NT2-vs-sEDS), 0.54±0.19/0.38±0.13 (NT2-vs-rest), 0.57±0.11/0.35±0.18 (IH-vs-sEDS), 0.71±0.08/0.35±0.10 (IH-vs-rest) and 0.76±0.08/0.71±0.13 (sEDS-vs-rest). The results confirm previous findings on sleep instability in NT1 patients and show that combining manual and automatic AI-based sleep analysis could be useful for better distinction of NT2 from IH, but no precise sleep biomarker of NT2 or IH could be identified. Validation in a larger and multi-centric cohort is needed to confirm these findings.