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Léa Dormegny,1,2 Reana Velizarova,1 Carmen M Schroder,1,3 Ulker Kilic-Huck,1,3 Henri Comtet,1,3 Hélène Dollfus,4 Patrice Bourgin,1,3 Elisabeth Ruppert1,3 1Centre des Troubles du Sommeil - CIRCSom, Hôpital Civil, University of Strasbourg, Strasbourg, 67091, France; 2Department of Ophthalmology, Hôpital Civil, University of Strasbourg, Strasbourg, 67091, France; 3Institute for Cellular and Integrative Neurosciences, CNRS - UPR 3212, Strasbourg, 67000, France; 4Centre des Affections Rares en Génétique Ophtalmologique (CARGO), University of Strasbourg, Strasbourg, 67091, FranceCorrespondence: Elisabeth RuppertCentre des Troubles du Sommeil - CIRCSom - Hôpital Civil, 1, place de l’Hôpital, BP 426, Cedex, Strasbourg, 67091, FranceTel +33 3 88 11 64 30Fax +33 3 88 11 51 53Email elisabeth.ruppert@chru-strasbourg.frObjective/Background: Bardet–Biedl syndrome (BBS) is a rare but well-recognized ciliopathy with high genetic and phenotypic heterogeneity. Cardinal features include obesity, diabetes and high blood pressure (HBP), which are often associated with sleep-disordered breathing. Also, the high prevalence of blindness due to retinal dystrophy could affect circadian sleep–wake rhythms. We characterized in this cohort of adult BBS patients sleep-disordered breathing, sleep quality, daytime sleepiness and chronotype.Patients and Methods: Thirty-two patients with genetically confirmed BBS were included in this observational single center study. Overnight respiratory polygraphy was performed for sleep apnea syndrome (SAS) in 30 patients. Quality of sleep, daytime sleepiness, fatigue and chronotype were assessed in 25 patients using Pittsburgh sleep quality index (PSQI), 14-day sleep diary (SD), Epworth sleepiness scale (ESS), Pichot fatigue scale (PFS) and Horne and Ostberg morningness-eveningness questionnaire (MEQ).Results: Patients’ mean age was 32± 11 years and mean BMI 32.6± 7.7 kg/m2. Eleven (35%) patients had HBP and 7 (22%) diabetes. Moderate to severe sleep apnea syndrome (SAS) was present in 5 (17%) and was not associated with altered sleep, daytime sleepiness or fatigue. Most of the patients (63%) evaluated their sleep as of good quality (PSQI ≤ 5). Median scores of sleep quality, daytime sleepiness and fatigue were normal (PSQI of 3.0 [2.0– 6.0], ESS of 9.0 [6.0– 13.0] and PFS of 8.0 [3.0– 13.0], respectively). Predominant chronotypes according to MEQ were either “intermediate” (57%) or “moderate morning” (29%). None had a free running sleep–wake cycle. 14-day SD revealed overall few awakenings at night and low daytime napping.Conclusions: Given the cardiovascular risk factors, systematic screening for SAS should be considered in BBS patients, regardless of sleep and daytime vigilance complaints. None of these highly visually impaired patients had a circadian sleep–wake rhythm disorder. Further objective assessments are needed to better characterize sleep and circadian rhythms in BBS patients.Keywords: Bardet–Biedl syndrome, ciliopathy, sleep disordered breathing, sleep apnea syndrome, chronotype, circadian rhythm sleep wake disorder

There is no doubt that the planet is warming quickly. What is uncertain, and hugely so, is the impact the warming will have on the climatologic and hydrologic processes that directly influence performance of existing and planned water-resources related projects, for example hydropower. Therefore, the prospect of climate change has become a key issue for the large dams/reservoirs community. This paper provides first an overview of the increasing awareness of climate change impacts on the performance and reliability of hydro projects within this community. Then, it presents a pragmatic approach to assess the climate resilience of hydro projects. This approach fully complies with the Hydropower Sector Climate Resilience Guide released by International Hydropower Association in 2019 [1]. The case study is a hydropower project on St Paul River in Liberia. The paper focuses on the methods and results of the Phase 3 climate stress test, namely the power generation performance under a wide range of different possible future climate scenarios. The modelling cascade is formed by the hydrological model GR4J and a hydropower model supported by Mike Hydro Basin software. It is used to simulate 35+ years of daily hydropower operation.

Abstract Background Classic galactosemia is a rare inherited metabolic disease with long-term complications, particularly in the psychosocial domain. Patients report a lower quality of social life, difficulties in interactions and social relationships, and a lower mental health. We hypothesised that social cognition deficits could partially explain this psychological symptomatology. Eleven adults with galactosemia and 31 control adults participated in the study. We measured social cognition skills in cognitive and affective theory of Mind, and in basic and complex emotion recognition. We explored psychosocial development and mental well-being. Results We found significant deficits on all 4 social cognition measures. Compared to controls, participants with galactosemia were impaired in the 2nd-order cognitive theory of mind, in affective theory of mind, and in basic and complex emotion recognition. Participants with galactosemia had a significant delay in their psychosexual development, but we found no delay in social development and no significant decrease in mental health. Conclusion Social cognition processes seem impaired among our participants with galactosemia. We discuss the future path research may follow. More research is needed to replicate and strengthen these results and establish the links between psychosocial complications and deficits in social cognition.

Abstract Depression is associated with dysregulated circadian rhythms, but the role of intrinsic clocks in mood-controlling brain regions remains poorly understood. We found increased circadian negative loop and decreased positive clock regulators expression in the medial prefrontal cortex (mPFC) of a mouse model of depression, and a subsequent clock countermodulation by the rapid antidepressant ketamine. Selective Bmal1KO in CaMK2a excitatory neurons revealed that the functional mPFC clock is an essential factor for the development of a depression-like phenotype and ketamine effects. Per2 silencing in mPFC produced antidepressant-like effects, while REV-ERB agonism enhanced the depression-like phenotype and suppressed ketamine action. Pharmacological potentiation of clock positive modulator ROR elicited antidepressant-like effects, upregulating plasticity protein Homer1a, synaptic AMPA receptors expression and plasticity-related slow wave activity specifically in the mPFC. Our data demonstrate a critical role for mPFC molecular clock in regulating depression-like behavior and the therapeutic potential of clock pharmacological manipulations influencing glutamatergic-dependent plasticity.

The evaluation of streamflow predictions forms an essential part of most hydrological modelling studies published in the literature. The evaluation process typically involves the computation of some evaluation metrics, but it can also involve the preliminary processing of the predictions as well as the subsequent processing of the computed metrics. In order for published hydrological studies to be reproducible, these steps need to be carefully documented by the authors. The availability of a single tool performing all of these tasks would simplify not only the documentation by the authors but also the reproducibility by the readers. However, this requires such a tool to be polyglot (i.e. usable in a variety of programming languages) and openly accessible so that it can be used by everyone in the hydrological community. To this end, we developed a new tool named evalhyd that offers metrics and functionalities for the evaluation of deterministic and probabilistic streamflow predictions. It is open source, and it can be used in Python, in R, in C++, or as a command line tool. This article describes the tool and illustrates its functionalities using Global Flood Awareness System (GloFAS) reforecasts over France as an example data set.

An hourly hydrological forecasting model (GRP), used for flood forecasting in France, has been enhanced by developing a semi-distributed version (GRPS). This new model addresses some limitations of the original lumped approach by integrating flow observations from upstream stations to improve downstream flood predictions. A comparison of GRP and GRPS was conducted on a large set of flood events in nested catchments in France. Results indicate that GRPS slightly outperforms GRP up to the lead time of the last upstream flow observations reaching the downstream station, though performance gains vary widely across events. Event classification identified detrimental interactions between data assimilation methods and river routing model errors. A sensitivity test suggested that enhancing both the propagation model and the assimilation scheme could reduce poor performance at short lead times for some events. The study also demonstrated that upstream forecast quality significantly impacts downstream forecast accuracy. Overall, the findings confirm the benefits of incorporating multiple flow observations into a semi-distributed model and suggest several avenues for further improvement.

BackgroundThe Collaborative Outcome study on Health and Functioning during Infection Times (COH-FIT; www.coh-fit.com) is an anonymous and global online survey measuring health and functioning during the COVID-19 pandemic. The aim of this study was to test concurrently the validity of COH-FIT items and the internal validity of the co-primary outcome, a composite psychopathology "P-score".MethodsThe COH-FIT survey has been translated into 30 languages (two blind forward-translations, consensus, one independent English back-translation, final harmonization). To measure mental health, 1-4 items ("COH-FIT items") were extracted from validated questionnaires (e.g. Patient Health Questionnaire 9). COH-FIT items measured anxiety, depressive, post-traumatic, obsessive-compulsive, bipolar and psychotic symptoms, as well as stress, sleep and concentration. COH-FIT Items which correlated r ≥ 0.5 with validated companion questionnaires, were initially retained. A P-score factor structure was then identified from these items using exploratory factor analysis (EFA) and confirmatory factor analyses (CFA) on data split into training and validation sets. Consistency of results across languages, gender and age was assessed.ResultsFrom >150,000 adult responses by May 6th, 2022, a subset of 22,456 completed both COH-FIT items and validated questionnaires. Concurrent validity was consistently demonstrated across different languages for COH-FIT items. CFA confirmed EFA results of five first-order factors (anxiety, depression, post-traumatic, psychotic, psychophysiologic symptoms) and revealed a single second-order factor P-score, with high internal reliability (ω = 0.95). Factor structure was consistent across age and sex.ConclusionsCOH-FIT is a valid instrument to globally measure mental health during infection times. The P-score is a valid measure of multidimensional mental health.

IntroductionThe current study aimed to investigate the rates of anxiety, clinical depression, and suicidality and their changes in health professionals during the COVID-19 outbreak.Materials and methodsThe data came from the larger COMET-G study. The study sample includes 12,792 health professionals from 40 countries (62.40% women aged 39.76 ± 11.70; 36.81% men aged 35.91 ± 11.00 and 0.78% non-binary gender aged 35.15 ± 13.03). Distress and clinical depression were identified with the use of a previously developed cut-off and algorithm, respectively.Statistical analysisDescriptive statistics were calculated. Chi-square tests, multiple forward stepwise linear regression analyses, and Factorial Analysis of Variance (ANOVA) tested relations among variables.ResultsClinical depression was detected in 13.16% with male doctors and 'non-binary genders' having the lowest rates (7.89 and 5.88% respectively) and 'non-binary gender' nurses and administrative staff had the highest (37.50%); distress was present in 15.19%. A significant percentage reported a deterioration in mental state, family dynamics, and everyday lifestyle. Persons with a history of mental disorders had higher rates of current depression (24.64% vs. 9.62%; p

Abstract Sleep and circadian rhythm disorders are very common in adolescents and have been linked to suicidal ideation. However, little is known about adolescent sleep before a suicide attempt (SA). The objectives of this study were to compare the sleep of adolescents aged 13 to 18 over a period of 4 weeks before a SA compared to a non-SA group, then to analyze the association between sleep, support social and well-being based on information from validated questionnaires. In 2015, 250 adolescents were included, 55 were recruited the day after a SA in French hospitals (before SA evaluations were retrospective). Logistic regression analyzes showed that during school days, bedtime was equivalent in both groups, but sleep onset latency was significantly longer in SA (86 min vs. 52 min, p = 0.016), and wake-up time was earlier (6 h 22 vs. 6 h 47, p = 0.002), resulting in a shorter total sleep time of 44 min (OR = 0.76, CI 95% [0.61–0.93]) the month preceding SA. Adolescents with longer sleep time performed better on perceived psychological well-being (p = 0.005), relationship with parents (p = 0.011) and school environment (p

Narcolepsy type 1 (NT1) is caused by a loss of hypocretin/orexin transmission. Risk factors include pandemic 2009 H1N1 influenza A infection and immunization with Pandemrix®. Here, we dissect disease mechanisms and interactions with environmental triggers in a multi-ethnic sample of 6,073 cases and 84,856 controls. We fine-mapped GWAS signals within HLA (DQ0602, DQB1*03:01 and DPB1*04:02) and discovered seven novel associations (CD207, NAB1, IKZF4-ERBB3, CTSC, DENND1B, SIRPG, PRF1). Significant signals at TRA and DQB1*06:02 loci were found in 245 vaccination-related cases, who also shared polygenic risk. T cell receptor associations in NT1 modulated TRAJ*24, TRAJ*28 and TRBV*4-2 chain-usage. Partitioned heritability and immune cell enrichment analyses found genetic signals to be driven by dendritic and helper T cells. Lastly comorbidity analysis using data from FinnGen, suggests shared effects between NT1 and other autoimmune diseases. NT1 genetic variants shape autoimmunity and response to environmental triggers, including influenza A infection and immunization with Pandemrix®.

IntroductionDuring the COVID-19 pandemic various degrees of lockdown were applied by countries around the world. It is considered that such measures have an adverse effect on mental health but the relationship of measure intensity with the mental health effect has not been thoroughly studied. Here we report data from the larger COMET-G study pertaining to this question.Material and methodsDuring the COVID-19 pandemic, data were gathered with an online questionnaire from 55,589 participants from 40 countries (64.85% females aged 35.80 ± 13.61; 34.05% males aged 34.90±13.29 and 1.10% other aged 31.64±13.15). Anxiety was measured with the STAI, depression with the CES-D and suicidality with the RASS. Distress and probable depression were identified with the use of a previously developed cut-off and algorithm respectively.Statistical analysisIt included the calculation of Relative Risk (RR), Factorial ANOVA and Multiple backwards stepwise linear regression analysis RESULTS: Approximately two-thirds were currently living under significant restrictions due to lockdown. For both males and females the risk to develop clinical depression correlated significantly with each and every level of increasing lockdown degree (RR 1.72 and 1.90 respectively). The combined lockdown and psychiatric history increased RR to 6.88 The overall relationship of lockdown with severity of depression, though significant was small.ConclusionsThe current study is the first which reports an almost linear relationship between lockdown degree and effect in mental health. Our findings, support previous suggestions concerning the need for a proactive targeted intervention to protect mental health more specifically in vulnerable groups.

Abstract Tumor immunosurveillance plays a major role in melanoma, prompting the development of immunotherapy strategies. The gut microbiota composition, influencing peripheral and tumoral immune tonus, earned its credentials among predictors of survival in melanoma. The MIND-DC phase III trial (NCT02993315) randomized (2:1 ratio) 148 patients with stage IIIB/C melanoma to adjuvant treatment with autologous natural dendritic cell (nDC) or placebo (PL). Overall, 144 patients collected serum and stool samples before and after 2 bimonthly injections to perform metabolomics (MB) and metagenomics (MG) as prespecified exploratory analysis. Clinical outcomes are reported separately. Here we show that different microbes were associated with prognosis, with the health-related Faecalibacterium prausnitzii standing out as the main beneficial taxon for no recurrence at 2 years (p = 0.008 at baseline, nDC arm). Therapy coincided with major MB perturbations (acylcarnitines, carboxylic and fatty acids). Despite randomization, nDC arm exhibited MG and MB bias at baseline: relative under-representation of F. prausnitzii, and perturbations of primary biliary acids (BA). F. prausnitzii anticorrelated with BA, medium- and long-chain acylcarnitines. Combined, these MG and MB biomarkers markedly determined prognosis. Altogether, the host-microbial interaction may play a role in localized melanoma. We value systematic MG and MB profiling in randomized trials to avoid baseline differences attributed to host-microbe interactions.

ABSTRACTWe compared the flood forecasts issued by a model used by operational services in France (GRP) and by a model developed to improve the simulation of floods resulting from intense rainfall (GR5H_RI). We selected 10,652 flood events from 19 years of hourly data available for 229 French catchments. The models were combined with a state-updating procedure to produce forecasts at 3, 6, 12 and 24 h lead times. Results indicate that the GR5H_RI model performs better on average than the GRP model at all lead times, particularly for forecasting flash floods (rise time

IntroductionThere are few published empirical data on the effects of COVID-19 on mental health, and until now, there is no large international study.Material and methodsDuring the COVID-19 pandemic, an online questionnaire gathered data from 55,589 participants from 40 countries (64.85% females aged 35.80 ± 13.61; 34.05% males aged 34.90±13.29 and 1.10% other aged 31.64±13.15). Distress and probable depression were identified with the use of a previously developed cut-off and algorithm respectively.Statistical analysisDescriptive statistics were calculated. Chi-square tests, multiple forward stepwise linear regression analyses and Factorial Analysis of Variance (ANOVA) tested relations among variables.ResultsProbable depression was detected in 17.80% and distress in 16.71%. A significant percentage reported a deterioration in mental state, family dynamics and everyday lifestyle. Persons with a history of mental disorders had higher rates of current depression (31.82% vs. 13.07%). At least half of participants were accepting (at least to a moderate degree) a non-bizarre conspiracy. The highest Relative Risk (RR) to develop depression was associated with history of Bipolar disorder and self-harm/attempts (RR = 5.88). Suicidality was not increased in persons without a history of any mental disorder. Based on these results a model was developed.ConclusionsThe final model revealed multiple vulnerabilities and an interplay leading from simple anxiety to probable depression and suicidality through distress. This could be of practical utility since many of these factors are modifiable. Future research and interventions should specifically focus on them.

IntroductionObstetrics research has predominantly focused on the management and identification of factors associated with labor dystocia. Despite these efforts, clinicians currently lack the necessary tools to effectively predict a woman's risk of experiencing labor dystocia. Therefore, the objective of this study was to create a predictive model for labor dystocia.Material and methodsThe study population included nulliparous women with a single baby in the cephalic presentation in spontaneous labor at term. With a cohort-based registry design utilizing data from the Copenhagen Pregnancy Cohort and the Danish Medical Birth Registry, we included women who had given birth from 2014 to 2020 at Copenhagen University Hospital-Rigshospitalet, Denmark. Logistic regression analysis, augmented by a super learner algorithm, was employed to construct the prediction model with candidate predictors pre-selected based on clinical reasoning and existing evidence. These predictors included maternal age, pre-pregnancy body mass index, height, gestational age, physical activity, self-reported medical condition, WHO-5 score, and fertility treatment. Model performance was evaluated using the area under the receiver operating characteristics curve (AUC) for discriminative capacity and Brier score for model calibration.ResultsA total of 12,445 women involving 5,525 events of labor dystocia (44%) were included. All candidate predictors were retained in the final model, which demonstrated discriminative ability with an AUC of 62.3% (95% CI:60.7-64.0) and Brier score of 0.24.ConclusionsOur model represents an initial advancement in the prediction of labor dystocia utilizing readily available information obtainable upon admission in active labor. As a next step further model development and external testing across other populations is warranted. With time a well-performing model may be a step towards facilitating risk stratification and the development of a user-friendly online tool for clinicians.

Ultradian light–dark cycles in rodents are a precious tool to study the direct effects of repeated light exposures on sleep, in order to better understand the underlying mechanisms. This study aims to precisely evaluate the effects of light and dark exposures, according to circadian time, on sleep and waking distribution and quality, and to determine if these effects depend on the duration of light and dark pulses. To do this, mice were exposed to 24 h-long ultradian light–dark cycles with different durations of pulses: T2 cycle (1 h of light/1 h of dark) and T7 cycle (3.5 h of light/3.5 h of dark). Exposure to light not only promotes NREM and REM sleep and inhibits wake, but also drastically alters alertness and modifies sleep depth. These effects are modulated by circadian time, appearing especially during early subjective night, and their kinetics is highly dependent on the duration of pulses, suggesting that in the case of pulses of longer duration, the homeostatic process could overtake light direct influence for shaping sleep and waking distribution.

The explosion of data generated during human interactions on-line presents an opportunity for cognitive scientists to evaluatetheir models on popular real-world tasks outside the confinesof the laboratory. We demonstrate this approach by evaluatingtwo cognitive models of generalization against two machinelearning approaches to recommendation on an online dataset ofover 100K human playlist selections. Across two experimentswe demonstrate that a model from cognitive science can bothbe efficiently implemented at scale and can capture generaliza-tion trends in human recommendation judgments which nei-ther machine learning model is capable of replicating. We usethese results to illustrate the opportunity internet-scale datasetsoffer to cognitive scientists, as well as to underscore the impor-tance of using insights from cognitive modeling to supplementthe standard predictive-analytic approach taken by many exist-ing machine learning approaches.

The importance of hierarchically structured representations fortractable planning has long been acknowledged. However, thequestions of how people discover such abstractions and how todefine a set of optimal abstractions remain open. This problemhas been explored in cognitive science in the problem solvingliterature and in computer science in hierarchical reinforce-ment learning. Here, we emphasize an algorithmic perspec-tive on learning hierarchical representations in which the ob-jective is to efficiently encode the structure of the problem, or,equivalently, to learn an algorithm with minimal length. Weintroduce a novel problem-solving paradigm that links prob-lem solving and program induction under the Markov Deci-sion Process (MDP) framework. Using this task, we target thequestion of whether humans discover hierarchical solutions bymaximizing efficiency in number of actions they generate or byminimizing the complexity of the resulting representation andfind evidence for the primacy of representational efficiency.

When people encode a representation of a scene, they do not necessarily represent the exact locations and orientationsof the constituent elements. Instead, people rely on preexisting inductive biases to simplify their encoding of new sceneconfigurations. We investigated people’s inductive biases in their memory for configurations of simple 2D shapes (such ascircles, triangles, etc.) using a serial reproduction paradigm (Bartlett, 1932). This paradigm establishes an iterative process inwhich information is transmitted through a chain of people (like the ”telephone” game). In our experiment, we asked peopleto memorize configurations of simple shapes (which were either generated at random or by other participants) and then askedthem to reproduce those configurations. In analyzing the final generation of reproductions, we found that people have strongpreferences for the scale of individual shapes, as well as the alignment, distance, overlap, and relative rotation between pairs ofshapes.

Growing evidence supports the hypothesis that narcolepsy with cataplexy is an autoimmune disease. We here report genome-wide association analyses for narcolepsy with replication and fine mapping across three ethnic groups (3,406 individuals of European ancestry, 2,414 Asians and 302 African Americans). We identify a SNP in the 3' untranslated region of P2RY11, the purinergic receptor subtype P2Y₁₁ gene, which is associated with narcolepsy (rs2305795, combined P = 6.1 × 10⁻¹⁰, odds ratio = 1.28, 95% CI 1.19-1.39, n = 5689). The disease-associated allele is correlated with reduced expression of P2RY11 in CD8(+) T lymphocytes (339% reduced, P = 0.003) and natural killer (NK) cells (P = 0.031), but not in other peripheral blood mononuclear cell types. The low expression variant is also associated with reduced P2RY11-mediated resistance to ATP-induced cell death in T lymphocytes (P = 0.0007) and natural killer cells (P = 0.001). These results identify P2RY11 as an important regulator of immune-cell survival, with possible implications in narcolepsy and other autoimmune diseases.

Actigraphy (ACT) is a non-invasive objective assessment tool for the study of sleep–wake rhythms. It is of particular interest in children with autism spectrum disorder (ASD), as sleep disorders are highly prevalent and have a significant impact on both cognitive and behavioral functions. As polysomnography (PSG), the gold standard for the assessment of sleep, is difficult to perform in children with ASD, ACT has become a tool of choice but has not yet been validated against PSG using state-of-the-art methodology. The main objective of this study was to assess, for the first time, the validity of ACT compared to PSG for the measurement of sleep in children with ASD. During the same night of hospitalization, PSG and ACT were conducted in 26 children (6 girls and 20 boys; mean age 5.4 years ± 1.6) diagnosed with ASD according to DSM-5 criteria and standardized diagnostic scales. Sleep parameters were total sleep time (TST), sleep latency (SL), wake after sleep onset (WASO), and sleep efficiency (SE). To compare PSG and ACT, we conducted sleep parameter agreement analyses including: intraclass correlation coefficient (ICC), Bland-Altman plots, and equivalence tests. The comparison also included an epoch-by-epoch (EBE) agreement analysis to determine sensitivity (ability to detect sleep) and specificity (ability to detect wake). According to equivalence tests, the difference between ACT and PSG measures was clinically acceptable for TST (

Narcolepsy type 1 is a rare disabling sleep disorder mainly characterized by excessive daytime sleepiness and cataplexy, an emotion-triggered sudden loss of muscle tone. Patients have a selective degeneration of hypocretin-producing neurons in the dorsolateral posterior hypothalamus with growing evidence supporting the hypothesis of an autoimmune mechanism. Few case studies that reported intravenous immunoglobulin therapy (IVIg) suggest the efficacy of IVIg when administered early after disease onset, but the results are controversial. In these retrospective case observations, IVIg cycles were initiated within one to four months after cataplexy onset in a twenty-seven-year-old man, a ten-year-old girl, and a seven-year-old boy, all three with early onset typical narcolepsy type 1. Efficacy of treatment (three IVIg cycles of 1 g/kg administered at four-week intervals) was evaluated based on clinical, polysomnographic, and multiple sleep latency test (mean latency and SOREM) follow-up. Two patients reported decreased cataplexy frequency and ameliorated daytime sleepiness, but no significant amelioration of polysomnographic parameters was observed. Given the possibility of spontaneous improvement of cataplexy frequency with self-behavioral adjustments, these observations would need to be confirmed by larger controlled studies. Based on the present study and current literature, proof of concept is still missing thus prohibiting the consideration of IVIg as an efficient treatment option.