Nocturnal paroxysmal dystonia – Case report

Nocturnal paroxysmal dystonia describes a syndrome consisting of recurrent motor episodes of dystonic–dyskinetic features arising from non-rapid eye movement (NREM) sleep. In the article, the authors present female case of nocturnal paroxysmal dystonia. The patient has had attacks since her childhood and was eventually diagnosed at the age of 48. Therapy with carbamazepine considerably reduced the frequency and entent of seizures. The present case evidences that nocturnal paroxysmal dystonia still is a diagnostic challenge for clinicians. Especially, we emphasize the importance of polysomnography in the verification of the diagnosis

Genetics of rapid eye movement sleep in humans.

The trait-like nature of electroencephalogram (EEG) is well established. Furthermore, EEG of wake and non-rapid eye movement (non-REM) sleep has been shown to be highly heritable. However, the genetic effects on REM sleep EEG microstructure are as yet unknown. REM sleep is of special interest since animal and human data suggest a connection between REM sleep abnormalities and the pathophysiology of psychiatric and neurological diseases. Here we report the results of a study in monozygotic (MZ) and dizygotic (DZ) twins examining the heritability of REM sleep EEG. We studied the architecture, spectral composition and phasic parameters of REM sleep and identified genetic effects on whole investigated EEG frequency spectrum as well as phasic REM parameters (REM density, REM activity and organization of REMs in bursts). In addition, cluster analysis based on the morphology of the EEG frequency spectrum revealed that the similarity among MZ twins is close to intra-individual stability. The observed strong genetic effects on REM sleep characteristics establish REM sleep as an important source of endophenotypes for psychiatric and neurological diseases

Guidelines for the use of second-generation long-acting antipsychotics

Long-acting injectable antipsychotics constitute a valuable alternative for the treatment of psychotic disorders, mainly schizophrenia. They assure a more stable drug level, improve treatment compliance, and increase the chances for favorable and long-lasting improvement. Additionally, the long-acting second-generation antipsychotics combine the values of longacting injectable drugs with the values of atypical antipsychotics. Four second generation long-acting antipsychotics have been described: risperidone, olanzapine, aripiprazole and paliperidone. The indications for their use, treatment strategy, tolerance, and potential interactions are discussed

The European Academy for Cognitive Behavioural Therapy for Insomnia : An initiative of the European Insomnia Network to promote implementation and dissemination of treatment

Insomnia, the most prevalent sleep disorder worldwide, confers marked risks for both physical and mental health. Furthermore, insomnia is associated with considerable direct and indirect healthcare costs. Recent guidelines in the US and Europe unequivocally conclude that cognitive behavioural therapy for insomnia (CBT‐I) should be the first‐line treatment for the disorder. Current treatment approaches are in stark contrast to these clear recommendations, not least across Europe, where, if any treatment at all is delivered, hypnotic medication still is the dominant therapeutic modality. To address this situation, a Task Force of the European Sleep Research Society and the European Insomnia Network met in May 2018. The Task Force proposed establishing a European CBT‐I Academy that would enable a Europe‐wide system of standardized CBT‐I training and training centre accreditation. This article summarizes the deliberations of the Task Force concerning definition and ingredients of CBT‐I, preconditions for health professionals to teach CBT‐I, the way in which CBT‐I should be taught, who should be taught CBT‐I and to whom CBT‐I should be administered. Furthermore, diverse aspects of CBT‐I care and delivery were discussed and incorporated into a stepped‐care model for insomnia.Peer reviewe

Biological markers for anxiety disorders, OCD and PTSD: A consensus statement. Part II: Neurochemistry, neurophysiology and neurocognition.

OBJECTIVE: Biomarkers are defined as anatomical, biochemical or physiological traits that are specific to certain disorders or syndromes. The objective of this paper is to summarise the current knowledge of biomarkers for anxiety disorders, obsessive-compulsive disorder (OCD) and posttraumatic stress disorder (PTSD). METHODS: Findings in biomarker research were reviewed by a task force of international experts in the field, consisting of members of the World Federation of Societies for Biological Psychiatry Task Force on Biological Markers and of the European College of Neuropsychopharmacology Anxiety Disorders Research Network. RESULTS: The present article (Part II) summarises findings on potential biomarkers in neurochemistry (neurotransmitters such as serotonin, norepinephrine, dopamine or GABA, neuropeptides such as cholecystokinin, neurokinins, atrial natriuretic peptide, or oxytocin, the HPA axis, neurotrophic factors such as NGF and BDNF, immunology and CO2 hypersensitivity), neurophysiology (EEG, heart rate variability) and neurocognition. The accompanying paper (Part I) focuses on neuroimaging and genetics. CONCLUSIONS: Although at present, none of the putative biomarkers is sufficient and specific as a diagnostic tool, an abundance of high quality research has accumulated that should improve our understanding of the neurobiological causes of anxiety disorders, OCD and PTSD.The present work was supported by the Anxiety Disorders Research Network (ADRN) within the European College of Neuropsychopharmacology Network Initiative (ECNP-NI). Katherina Domschke’s work was supported by the German Research Foundation (DFG), Collaborative Research Centre “Fear, Anxiety, Anxiety Disorders” SFB-TRR-58, project C02.This is the author accepted manuscript. The final version is available from Taylor & Francis via http://dx.doi.org/10.1080/15622975.2016.119086

EEG alterations during treatment with olanzapine

The aim of this naturalistic observational study was to investigate EEG alterations in patients under olanzapine treatment with a special regard to olanzapine dose and plasma concentration. Twenty-two in-patients of a psychiatric university ward with the monodiagnosis of paranoid schizophrenia (ICD-10: F20.0), who received a monotherapy of olanzapine were included in this study. All patients had a normal alpha-EEG before drug therapy, and did not suffer from brain-organic dysfunctions, as verified by clinical examination and cMRI scans. EEG and olanzapine plasma levels were determined under steady-state conditions (between 18 and 22 days after begin of treatment). In 9 patients (40.9%), pathological EEG changes (one with spike-waves) consecutive to olanzapine treatment were observed. The dose of olanzapine was significantly higher in patients with changes of the EEG than in patients without changes (24.4 mg/day (SD: 8.1) vs. 12.7 mg/day (SD: 4.8); T = −4.3, df = 21, P < 0.001). In patients with EEG changes, the blood plasma concentration of olanzapine (45.6 μg/l (SD: 30.9) vs. 26.3 μg/l (SD: 21.6) tended to be also higher. The sensitivity of olanzapine dosage to predict EEG changes was 66.7%, the specificity 100% (Youden-index: 0.67). EEG abnormalities during olanzapine treatment are common. These are significantly dose dependent. Thus, EEG control recordings should be mandatory during olanzapine treatment with special emphasis on dosages exceeding 20 mg per day, although keeping in mind that EEGs have only a limited predictive power regarding future epileptic seizures

Sick leave duration as a potential marker of functionality and disease severity in depression

Objective: To discuss the impact of depression on work and how depression-related sick leave duration could be a potential indicator and outcome for measuring functionality in depression. Methods: Our review was based on a literature search and expert opinion that emerged during a virtual meeting of European psychiatrists that was convened to discuss this topic. Results: Current evidence demonstrates that depression-related sick leave duration is influenced by multiple disease-, patient- and work-related factors, together with societal attitudes towards depression and socioeconomic conditions. A wide variety of pharmacological and non-pharmacological treatments and work-based interventions are effective in reducing depression-related sick leave duration and/or facilitating return to work. Recent real-world evidence showed that patients treated with antidepressant monotherapy appear to recover their working life faster than those receiving combination therapy. Although depression-related sick leave duration was found to correlate with severity of depressive symptoms, it cannot be used alone as a viable marker for disease severity. Conclusions: Given its multifactorial nature, depression-related sick leave duration is not on its own a viable outcome measure of depression severity but could be used as a secondary outcome alongside more formal severity measures and may also represent a useful measure of functionality in depression. Key points Depression in the working population and depression-related sick leave have a profound economic impact on society Depression-related sick leave duration is influenced by multiple disease-, patient- and work-related factors, together with societal attitudes towards depression and socioeconomic conditions A wide variety of pharmacological and non-pharmacological treatments and work-based interventions have been shown to be effective in reducing depression-related sick leave duration and/or facilitating return to work In terms of pharmacological intervention, recent real-world evidence has shown that patients treated with antidepressant monotherapy are able to recover their working life faster than those treated with combination therapy Although depression-related sick leave duration has been shown to correlate with severity of depressive symptoms, it is not a viable outcome measure of depression severity on its own, but could be used as secondary outcome alongside more formal clinician- and patient-rated severity measures Depression-related sick leave duration may, however, represent a viable outcome for measuring functionality in depression

Automatic Human Sleep Stage Scoring Using Deep Neural Networks

The classification of sleep stages is the first and an important step in the quantitative analysis of polysomnographic recordings. Sleep stage scoring relies heavily on visual pattern recognition by a human expert and is time consuming and subjective. Thus, there is a need for automatic classification. In this work we developed machine learning algorithms for sleep classification: random forest (RF) classification based on features and artificial neural networks (ANNs) working both with features and raw data. We tested our methods in healthy subjects and in patients. Most algorithms yielded good results comparable to human interrater agreement. Our study revealed that deep neural networks (DNNs) working with raw data performed better than feature-based methods. We also demonstrated that taking the local temporal structure of sleep into account a priori is important. Our results demonstrate the utility of neural network architectures for the classification of sleep