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1. Speech Biomarkers in Rapid Eye Movement Sleep Behavior Disorder and Parkinson Disease

Author

Jacques Montplaisir, Paul C. Timm, Erik K. St. Louis, Evi Holzknecht, Petr Dusek, Klaus Seppi, Evžen Růžička, Luke N. Teigen, Tereza Tykalová, Sara Marelli, Ronald B. Postuma, Andrea Galbiati, Karel Sonka, Michal Novotný, Amélie Pelletier, Mahboubeh Habibi, Yves Dauvilliers, Annette Janzen, Wolfgang H. Oertel, Jan Rusz, Luigi Ferini-Strambi, Jan Hlavnička, Birgit Högl, Ambra Stefani, Jean Gagnon, Elisa Evangelista, Anna Laura Rassu, Rusz, Jan, Hlavnička, Jan, Novotný, Michal, Tykalová, Tereza, Pelletier, Amelie, Montplaisir, Jacque, Gagnon, Jean-Francoi, Dušek, Petr, Galbiati, Andrea, Marelli, Sara, Timm, Paul C, Teigen, Luke N, Janzen, Annette, Habibi, Mahboubeh, Stefani, Ambra, Holzknecht, Evi, Seppi, Klau, Evangelista, Elisa, Rassu, Anna Laura, Dauvilliers, Yve, Högl, Birgit, Oertel, Wolfgang, St Louis, Erik K, Ferini-Strambi, Luigi, Růžička, Evžen, Postuma, Ronald B, Šonka, Karel, Czech Technical University in Prague (CTU), First Faculty of Medicine Charles University [Prague], Hôpital du Sacré-Coeur de Montréal, McGill University Health Center [Montreal] (MUHC), Universita Vita Salute San Raffaele = Vita-Salute San Raffaele University [Milan, Italie] (UniSR), Mayo Clinic [Rochester], Philipps Universität Marburg = Philipps University of Marburg, Innsbruck Medical University = Medizinische Universität Innsbruck (IMU), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Institut des Neurosciences de Montpellier (INM), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), and Retiveau, Nolwenn

Subjects
0301 basic medicine, Male, Disease, REM Sleep Behavior Disorder, Audiology, Behavior disorder, Dysarthria, MESH: Aged, 80 and over, 0302 clinical medicine, Research Articles, MESH: Aged, Aged, 80 and over, MESH: Middle Aged, MESH: Speech, Parkinsonism, Parkinson Disease, Middle Aged, Europe, Neurology, Disease Progression, Biomarker (medicine), MESH: Disease Progression, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Female, medicine.symptom, Research Article, medicine.medical_specialty, Rapid eye movement sleep, Prodromal Symptoms, REM sleep behavior disorder, 03 medical and health sciences, medicine, Humans, Speech, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], MESH: Prodromal Symptoms, Aged, MESH: Humans, business.industry, Disease progression, medicine.disease, MESH: Male, 030104 developmental biology, MESH: REM Sleep Behavior Disorder, MESH: Biomarkers, MESH: Europe, Neurology (clinical), business, MESH: Female, MESH: Parkinson Disease, 030217 neurology & neurosurgery, Biomarkers
Abstract

International audience; Objective: 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. ANN NEUROL 2021;90:62-75.

Published
2021
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2. Disorders of Arousal in adults: new diagnostic tools for clinical practice

Author

Régis Lopez, Rosalia Cilea, Giuseppe Loddo, Federica Provini, Yves Dauvilliers, Loddo, Giuseppe, Lopez, Régi, Cilea, Rosalia, Dauvilliers, Yve, and Provini, Federica

Subjects
medicine.medical_specialty, Neurology, Confusional arousals, lcsh:Medicine, Electroencephalography, Confusional arousal, Non-rapid eye movement sleep, REM sleep behavior disorder, 03 medical and health sciences, 0302 clinical medicine, Disorder of arousal, Sleepwalking, Sleep terrors, Confusional arousals, EEG, Video-polysomnography, Home video, Diagnostic criteria, Motor patterns, Slow-wave sleep, Fragmentation index, Sleep terrors, Medicine, EEG, 030304 developmental biology, Slow-wave sleep, 0303 health sciences, medicine.diagnostic_test, business.industry, Sleep terror, lcsh:R, medicine.disease, Video-polysomnography, Disorder of arousal, Sleepwalking, business, 030217 neurology & neurosurgery, Clinical psychology
Abstract

Disorders of Arousal (DOA) are mental and motor behaviors arising from NREM sleep. They comprise a spectrum of manifestations of increasing intensity from confusional arousals to sleep terrors to sleepwalking. Although DOA in childhood are usually harmless, in adulthood they are often associated with injurious or violent behaviors to the patient or others. Driving motor vehicles, suspected suicide, and even homicide or attempted homicide have been described during sleepwalking in adults. Furthermore, adult DOA need to be differentiated from other sleep disorders such as Sleep-related Hypermotor Epilepsy or REM Sleep Behavior Disorder. Although many aspects of DOA have been clarified in the last two decades there is still a lack of objective and quantitative diagnostic criteria for DOA. Recent advances in EEG analysis and in the semiological characterization of DOA motor patterns have provided a better definition of DOA diagnosis. Our article focuses on the DOA diagnostic process describing accurately the newest DOA clinical, EEG and video-polysomnographic tools in order to aid clinicians in DOA assessment.

Published
2019
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3. New 2013 incidence peak in childhood narcolepsy: more than vaccination?

Author

Eva Feketeova, Giuseppe Plazzi, Karel Sonka, Fabio Pizza, Rolf Fronczek, Mauro Manconi, Zhongxing Zhang, Geert Mayer, Teresa Paiva, Birgit Högl, Johannes Mathis, Yves Dauvilliers, Joan Santamaria, Jari K. Gool, Gert Jan Lammers, Aleksandra Wierzbicka, Markku Partinen, Christian R. Baumann, Francesca Canellas, Michel Lecendreux, Rafael del Rio-Villegas, Claudio L. Bassetti, António Martins da Silva, Rosa Peraita-Adrados, Ramin Khatami, Peter Young, Raphael Heinzer, Sebastiaan Overeem, Zhang, Zhongxing, Gool, Jari, Fronczek, Rolf, Dauvilliers, Yve, Bassetti, Claudio L A, Mayer, Geert, Plazzi, Giuseppe, Pizza, Fabio, Santamaria, Joan, Partinen, Markku, Overeem, Sebastiaan, Peraita-Adrados, Rosa, da Silva, Antonio Martin, Sonka, Karel, Del Rio-Villegas, Rafael, Heinzer, Raphael, Wierzbicka, Aleksandra, Young, Peter, Högl, Birgit, Manconi, Mauro, Feketeova, Eva, Mathis, Johanne, Paiva, Teresa, Canellas, Francesca, Lecendreux, Michel, Baumann, Christian R, Lammers, Gert Jan, Khatami, Ramin, University of Zurich, and Signal Processing Systems

Subjects
Adult, medicine.medical_specialty, Pediatrics, Asia, Adolescent, 610 Medicine & health, narcolepsy, SDG 3 – Goede gezondheid en welzijn, childhood narcolepsy, 03 medical and health sciences, 0302 clinical medicine, Influenza A Virus, H1N1 Subtype, SDG 3 - Good Health and Well-being, Physiology (medical), Pandemic, Epidemiology, Influenza, Human, medicine, Influenza A Virus, Pandemrix, Humans, H1N1 Subtype, 030212 general & internal medicine, H1N1 influenza, Child, Europe, Incidence, Vaccination, Influenza Vaccines, Narcolepsy, HLA-DQB1, business.industry, Incidence (epidemiology), medicine.disease, Confidence interval, Influenza, 10040 Clinic for Neurology, Neurology (clinical), business, 030217 neurology & neurosurgery, Human
Abstract

Increased incidence rates of narcolepsy type-1 (NT1) have been reported worldwide after the 2009–2010 H1N1 influenza pandemic (pH1N1). While some European countries found an association between the NT1 incidence increase and the H1N1 vaccination Pandemrix, reports from Asian countries suggested the H1N1 virus itself to be linked to the increased NT1 incidence. Using robust data-driven modeling approaches, that is, locally estimated scatterplot smoothing methods, we analyzed the number of de novo NT1 cases (n = 508) in the last two decades using the European Narcolepsy Network database. We confirmed the peak of NT1 incidence in 2010, that is, 2.54-fold (95% confidence interval [CI]: [2.11, 3.19]) increase in NT1 onset following 2009–2010 pH1N1. This peak in 2010 was found in both childhood NT1 (2.75-fold increase, 95% CI: [1.95, 4.69]) and adulthood NT1 (2.43-fold increase, 95% CI: [2.05, 2.97]). In addition, we identified a new peak in 2013 that is age-specific for children/adolescents (i.e. 2.09-fold increase, 95% CI: [1.52, 3.32]). Most of these children/adolescents were HLA DQB1*06:02 positive and showed a subacute disease onset consistent with an immune-mediated type of narcolepsy. The new 2013 incidence peak is likely not related to Pandemrix as it was not used after 2010. Our results suggest that the increased NT1 incidence after 2009–2010 pH1N1 is not unique and our study provides an opportunity to develop new hypotheses, for example, considering other (influenza) viruses or epidemiological events to further investigate the pathophysiology of immune-mediated narcolepsy.

Published
2020

4. LRRK2 protective haplotype and full sequencing study in REM sleep behavior disorder

Author

Peter Young, Birgit Högl, Wolfgang H. Oertel, Michele T.M. Hu, Giuseppe Plazzi, Valérie Cochen De Cock, Dan Spiegelman, Yves Dauvilliers, Elena Antelmi, Jacques Montplaisir, Ambra Stefani, Friederike Sixel-Döring, Jennifer A. Ruskey, Christelle Charley Monaca, Brit Mollenhauer, Ziv Gan-Or, Isabelle Arnulf, Guy A. Rouleau, Ronald B. Postuma, Bouchra Ouled Amar Bencheikh, Claudia Trenkwalder, Luigi Ferini-Strambi, Jean-François Gagnon, Anna Heidbreder, Ouled Amar Bencheikh, Bouchra, Ruskey, Jennifer A., Arnulf, Isabelle, Dauvilliers, Yve, Monaca, Christelle Charley, De Cock, Valérie Cochen, Gagnon, Jean-Françoi, Spiegelman, Dan, Hu, Michele T.M., Högl, Birgit, Stefani, Ambra, Ferini-Strambi, Luigi, Plazzi, Giuseppe, Antelmi, Elena, Young, Peter, Heidbreder, Anna, Mollenhauer, Brit, Sixel-Döring, Friederike, Trenkwalder, Claudia, Oertel, Wolfgang, Montplaisir, Jacques Y., Postuma, Ronald B., Rouleau, Guy A., Gan-Or, Ziv, Ouled Amar Bencheikh, B., Ruskey, J. A., Arnulf, I., Dauvilliers, Y., Monaca, C. C., De Cock, V. C., Gagnon, J. -F., Spiegelman, D., M. T. M., Hu, Hogl, B., Stefani, A., Ferini-Strambi, L., Plazzi, G., Antelmi, E., Young, P., Heidbreder, A., Mollenhauer, B., Sixel-Doring, F., Trenkwalder, C., Oertel, W., Montplaisir, J. Y., Postuma, R. B., Rouleau, G. A., Gan-Or, Z., Service des Pathologies du sommeil [CHU Pitié-Salpêtrière], CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Département de neurologie [Montpellier], Université Montpellier 1 (UM1)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Hôpital Gui de Chauliac [Montpellier]-Université de Montpellier (UM), Neuropsychiatrie : recherche épidémiologique et clinique (PSNREC), Université Montpellier 1 (UM1)-Université de Montpellier (UM)-Institut National de la Santé et de la Recherche Médicale (INSERM), Service de neurophysiologie clinique (CHRU Lille), Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Euromov (EuroMov), Université de Montpellier (UM), and Clinique Beau Soleil [Montpellier]

Subjects
Male, 0301 basic medicine, REM sleep behavior disorder, Disease, Biology, Leucine-Rich Repeat Serine-Threonine Protein Kinase-2, 03 medical and health sciences, 0302 clinical medicine, Atrophy, Genetic, Genetics, medicine, LRRK2, Parkinson disease, Neurology, Geriatrics and Gerontology, Neurology (clinical), Humans, Coding region, Dementia, Aged, Synucleinopathies, Haplotype, High-Throughput Nucleotide Sequencing, Sequence Analysis, DNA, Middle Aged, Protective Factors, medicine.disease, 3. Good health, 030104 developmental biology, Haplotypes, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Female, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, 030217 neurology & neurosurgery
Abstract

Background: Individuals with rapid eye movement (REM)-sleep behavior disorder (RBD) are likely to progress to synucleinopathies, mainly Parkinson's disease (PD), dementia with Lewy-bodies (DLB) and multiple system atrophy (MSA). The genetics of RBD only partially overlaps with PD and DLB, and the role of LRRK2 variants in risk for RBD is still not clear.Methods: The full coding sequence, exon-intron boundaries and 5′ and 3′ untranslated regions of LRRK2 were sequenced using targeted next-generation sequencing. A total of 350 RBD patients and 869 controls were sequenced, and regression and burden models were used to examine the association between LRRK2 variants and RBD.Results: No pathogenic mutations that are known to cause PD were identified in RBD patients. The p.N551K-p.R1398H-p.K1423K haplotype was associated with a reduced risk for RBD (OR = 0.66, 95% CI 0.44–0.98, p = 0.0055 for the tagging p.N551K substitution). A common variant, p.S1647T, was nominally associated with risk for RBD (OR = 1.28, 95% CI 1.05–1.56, p = 0.029). Burden analysis identified associations with domains and exons that were derived by the variants of the protective haplotype, and no burden of other rare variants was identified.Conclusions: Carriers of the LRRK2 p.N551K-p.R1398H-p.K1423K haplotype have a reduced risk for developing RBD, yet PD-causing mutations probably have minor or no role in RBD. Additional work is needed to confirm these results and to identify the mechanism associated with reduced risk for RBD.

Published
2018
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5. The clinical spectrum of childhood narcolepsy

Author

Yves Dauvilliers, Elena Antelmi, Fabio Pizza, Emanuela Postiglione, Michel Lecendreux, Giuseppe Plazzi, Hôpital Robert Debré, Département de neurologie [Montpellier], Université Montpellier 1 (UM1)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Hôpital Gui de Chauliac [Montpellier]-Université de Montpellier (UM), Neuropsychiatrie : recherche épidémiologique et clinique (PSNREC), Université Montpellier 1 (UM1)-Université de Montpellier (UM)-Institut National de la Santé et de la Recherche Médicale (INSERM), Postiglione, Emanuela, Antelmi, Elena, Pizza, Fabio, Lecendreux, Michel, Dauvilliers, Yve, Plazzi, Giuseppe, Hôpital Gui de Chauliac [Montpellier]-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Université Montpellier 1 (UM1)-Université de Montpellier (UM), and Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM)

Subjects
Pulmonary and Respiratory Medicine, medicine.medical_specialty, Neurology, Cataplexy, Polysomnography, Excessive daytime sleepiness, Weight Gain, Movement disorder, Narcolepsy, Obesity, Precocious puberty, Treatment, Neurology (clinical), Physiology (medical), Irritability, 03 medical and health sciences, 0302 clinical medicine, medicine, Humans, 030212 general & internal medicine, Child, Psychiatry, medicine.disease, Hypotonia, 3. Good health, Mood, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], medicine.symptom, Psychology, Sleep paralysis, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, 030217 neurology & neurosurgery
Abstract

International audience; Narcolepsy type 1 is a life-long, severe, multifaceted disease often arising in childhood or adolescence. Beyond the classical symptoms (excessive daytime sleepiness, cataplexy, hallucinations, sleep paralysis and nocturnal fragmented sleep), metabolic, endocrinological, psychiatric and psychosocial aspects must be considered. Despite the increased awareness after H1N1 pandemic influenza and vaccination, narcolepsy is still misdiagnosed and unrecognized. The peculiar presentation of symptoms in narcoleptic children could in part explain the misdiagnoses. Excessive daytime sleepiness presenting as chronic drowsiness or irritability could be stigmatized as laziness or misinterpreted as behavior or inattention disorder. The persistent hypotonia and the complex hyperkinetic movements that characterize cataplexy close to the onset, could be misdiagnosed as a movement disorder or as other neurologic conditions. The consequent therapeutic delay could turn into dramatic consequences. The narcolepsy onset, indeed, is associated with abrupt weight gain and sometimes with precocious puberty that require a prompt recognition and treatment to avoid auxological and metabolic complications. Moreover, narcoleptic children could have behavioral and psychiatric disorders ranging from mood to psychotic ones that need ad hoc management. Accordingly, spreading the awareness outside the sleep specialist community is necessary in order to reduce the diagnostic delay and to obtain prompt and multidisciplinary management.

Published
2018
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6. Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy

Author

Oscar Carrillo, Fang Han, Samuel T. Kuna, Aditya Ambati, Poul Jennum, Ambra Stefani, Sabine Scholz, Birgit Högl, Fabio Pizza, Helge Bjarup Dissing Sørensen, Jens B. Stephansen, Eileen B. Leary, Lucie Barateau, Paul E. Peppard, Ling Lin, Dimitri Perrin, Mads Olsen, Tae-Won Kim, Emmanuel Mignot, Paula K. Schweitzer, Stefano Vandi, Seung Chul Hong, Giuseppe Plazzi, Yves Dauvilliers, Elena Antelmi, Alexander Neergaard Olesen, Clete A. Kushida, Han Yan, Yun L. Sun, Hyatt Moore, Département de neurologie [Montpellier], Hôpital Gui de Chauliac [Montpellier]-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Université Montpellier 1 (UM1)-Université de Montpellier (UM), Neuropsychiatrie : recherche épidémiologique et clinique (PSNREC), Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Université Montpellier 1 (UM1)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Hôpital Gui de Chauliac [Montpellier]-Université de Montpellier (UM), Université Montpellier 1 (UM1)-Université de Montpellier (UM)-Institut National de la Santé et de la Recherche Médicale (INSERM), Stephansen, Jens B., Olesen, Alexander N., Olsen, Mad, Ambati, Aditya, Leary, Eileen B., Moore, Hyatt E., Carrillo, Oscar, Lin, Ling, Han, Fang, Yan, Han, Sun, Yun L., Dauvilliers, Yve, Scholz, Sabine, Barateau, Lucie, Hogl, Birgit, Stefani, Ambra, Hong, Seung Chul, Kim, Tae Won, Pizza, Fabio, Plazzi, Giuseppe, Vandi, Stefano, Antelmi, Elena, Perrin, Dimitri, Kuna, Samuel T., Schweitzer, Paula K., Kushida, Clete, Peppard, Paul E., Sorensen, Helge B. D., Jennum, Poul, and Mignot, Emmanuel

Subjects
Genetics and Molecular Biology (all), Male, Computer science, General Physics and Astronomy, Polysomnography, Audiology, Biochemistry, Cohort Studies, 0302 clinical medicine, HLA-DQ beta-Chains, lcsh:Science, HLA-DQ beta-Chains/analysis, Sleep Stages, Narcolepsy/diagnosis, Multidisciplinary, Artificial neural network, medicine.diagnostic_test, Chemistry (all), Middle Aged, 3. Good health, Algorithm, Female, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Sleep (system call), Algorithms, Human, Adult, medicine.medical_specialty, Adolescent, Neural Networks, Science, Sleep Stage, Sensitivity and Specificity, Article, General Biochemistry, Genetics and Molecular Biology, Physics and Astronomy (all), Young Adult, Computer, 03 medical and health sciences, medicine, Humans, Aged, Narcolepsy, HLA-DQ beta-Chain, Biochemistry, Genetics and Molecular Biology (all), General Chemistry, Neural Networks (Computer), medicine.disease, Sleep Stages/immunology, 030228 respiratory system, lcsh:Q, Neural Networks, Computer, Cohort Studie, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, 030217 neurology & neurosurgery
Abstract

Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used neural networks in approximately 3,000 normal and abnormal sleep recordings to automate sleep stage scoring, producing a hypnodensity graph—a probability distribution conveying more information than classical hypnograms. Accuracy of sleep stage scoring was validated in 70 subjects assessed by six scorers. The best model performed better than any individual scorer (87% versus consensus). It also reliably scores sleep down to 5 s instead of 30 s scoring epochs. A T1N marker based on unusual sleep stage overlaps achieved a specificity of 96% and a sensitivity of 91%, validated in independent datasets. Addition of HLA-DQB1*06:02 typing increased specificity to 99%. Our method can reduce time spent in sleep clinics and automates T1N diagnosis. It also opens the possibility of diagnosing T1N using home sleep studies., The diagnosis of sleep disorders such as narcolepsy and insomnia currently requires experts to interpret sleep recordings (polysomnography). Here, the authors introduce a neural network analysis method for polysomnography that could reduce time spent in sleep clinics and automate narcolepsy diagnosis.

Published
2018
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7. Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning

Author

Aleksandra Wierzbicka, Fabio Pizza, Francesca Canellas, António Martins da Silva, Rosa Peraita-Adrados, Ramin Khatami, Teresa Paiva, Laura Lillo-Triguero, Peter Young, Mauro Manconi, Joan Santamaria, Carles Gaig, Birgit Högl, Yves Dauvilliers, Elena Antelmi, Lucie Barateau, Alex Iranzo, Gianina Luca, Markku Partinen, Michel Lecendreux, Johannes Mathis, Geert Mayer, Carole Pesenti, Raphael Heinzer, Zhongxing Zhang, Pablo Medrano-Martínez, Claudio L. Bassetti, Giuseppe Plazzi, Christian R. Baumann, Gert Jan Lammers, Eva Feketeova, Karel Sonka, José Haba-Rubio, Rafael del Rio-Villegas, Corina Gorban, Sebastiaan Overeem, Rolf Fronczek, Signal Processing Systems, Biomedical Diagnostics Lab, Neuropsychiatrie : recherche épidémiologique et clinique (PSNREC), Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), University of Bologna, Centre de Recherche Saint-Antoine (UMRS893), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Zhang, Zhongxing, Mayer, Geert, Dauvilliers, Yve, Plazzi, Giuseppe, Pizza, Fabio, Fronczek, Rolf, Santamaria, Joan, Partinen, Markku, Overeem, Sebastiaan, Peraita-Adrados, Rosa, Da Silva, Antonio Martin, Sonka, Karel, Rio-Villegas, Rafael Del, Heinzer, Raphael, Wierzbicka, Aleksandra, Young, Peter, Högl, Birgit, Bassetti, Claudio L., Manconi, Mauro, Feketeova, Eva, Mathis, Johanne, Paiva, Teresa, Canellas, Francesca, Lecendreux, Michel, Baumann, Christian R., Barateau, Lucie, Pesenti, Carole, Antelmi, Elena, Gaig, Carle, Iranzo, Alex, Lillo-Triguero, Laura, Medrano-Martínez, Pablo, Haba-Rubio, José, Gorban, Corina, Luca, Gianina, Lammers, Gert Jan, and Khatami, Ramin

Subjects
Multiple Sleep Latency Test, Male, sueño, Data Interpretation, Cataplexy, Databases, Factual, Computer science, [SDV]Life Sciences [q-bio], humanos, lcsh:Medicine, Excessive daytime sleepiness, Datasets as Topic, narcolepsia, Polysomnography, computer.software_genre, polisomnografía, 0302 clinical medicine, Feature (machine learning), lcsh:Science, narcolepsy, machine learning, Multidisciplinary, medicine.diagnostic_test, Sleep disorders, adulto, Statistical, Sleep Latency, 3. Good health, adulto joven, Data Interpretation, Statistical, Female, Supervised Machine Learning, medicine.symptom, enfermedades raras, Adult, Sleep, REM, 610 Medicine & health, Machine learning, Models, Biological, Article, 03 medical and health sciences, Databases, Young Adult, Rare Diseases, curva ROC, procesos estocásticos, medicine, Humans, Factual, Narcolepsy, Stochastic Processes, business.industry, lcsh:R, Supervised learning, medicine.disease, narcolepsy, machine learning, 030228 respiratory system, ROC Curve, lcsh:Q, Artificial intelligence, Sleep, business, computer, 030217 neurology & neurosurgery
Abstract

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 rapideye-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., The EU-NN database is financed by the EU-NN. The EU-NN has received financial support from UCB Pharma Brussels for developing the EU-NN database.

Published
2018
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8. The distinguishing motor features of cataplexy: a study from video recorded attacks

Author

Christian R. Baumann, Yves Dauvilliers, Elena Antelmi, Mark J. Edwards, Michele Tinazzi, Stefano Vandi, Sebastiaan Overeem, Roberto Erro, Kailash P. Bhatia, Stefano Meletti, Rocco Liguori, Fabio Pizza, Alex Iranzo, Giuseppe Plazzi, University of Zurich, Plazzi, Giuseppe, Signal Processing Systems, Biomedical Diagnostics Lab, Pizza, Fabio, Antelmi, Elena, Vandi, Stefano, Meletti, Stefano, Erro, Roberto, Baumann, Christian R, Bhatia, Kailash P, Dauvilliers, Yve, Edwards, Mark J, Iranzo, Alex, Overeem, Sebastiaan, Tinazzi, Michele, Liguori, Rocco, Alma Mater Studiorum Università di Bologna [Bologna] (UNIBO), University of Bologna, Neuropsychiatrie : recherche épidémiologique et clinique (PSNREC), Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), and Université Montpellier 1 (UM1)-Université de Montpellier (UM)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Adult, Male, medicine.medical_specialty, Cataplexy, Facial hypotonia, [SDV]Life Sciences [q-bio], media_common.quotation_subject, Posture, Video Recording, pseudocataplexy, 610 Medicine & health, narcolepsy, Audiology, cataplectic facies, cataplexy, functional neurological symptom disorder, Laughter, 03 medical and health sciences, 0302 clinical medicine, 2737 Physiology (medical), Ptosis, Physiology (medical), Functional neurological symptom disorder, Medicine, Humans, 030212 general & internal medicine, Conversion disorder, media_common, Facial expression, business.industry, cataplectic facie, medicine.disease, 3. Good health, 10040 Clinic for Neurology, Facial Expression, 2728 Neurology (clinical), Muscle Hypotonia, Female, Neurology (clinical), medicine.symptom, business, 030217 neurology & neurosurgery, Narcolepsy
Abstract

International audience; Study Objectives: To describe the motor pattern of cataplexy and to determine its phenomenological differences from pseudocataplexy in the differential diagnosis of episodic falls. Methods: We selected 30 video-recorded cataplexy and 21 pseudocataplexy attacks in 17 and 10 patients evaluated for suspected narcolepsy and with final diagnosis of narcolepsy type 1 and conversion disorder, respectively, together with self-reported attacks features, and asked expert neurologists to blindly evaluate the motor features of the attacks. Video documented and self-reported attack features of cataplexy and pseudocataplexy were contrasted. Results: Video-recorded cataplexy can be positively differentiated from pseudocataplexy by the occurrence of facial hypotonia (ptosis, mouth opening, tongue protrusion) intermingled by jerks and grimaces abruptly interrupting laughter behavior (i.e. smile, facial expression) and postural control (head drops, trunk fall) under clear emotional trigger. Facial involvement is present in both partial and generalized cataplexy. Conversely, generalized pseudocataplexy is associated with persistence of deep tendon reflexes during the attack. Self-reported features confirmed the important role of positive emotions (laughter, telling a joke) in triggering the attacks, as well as the more frequent occurrence of partial body involvement in cataplexy compared with pseudocataplexy. Conclusions: Cataplexy is characterized by abrupt facial involvement during laughter behavior. Video recording of suspected cataplexy attacks allows the identification of positive clinical signs useful for diagnosis and, possibly in the future, for severity assessment.

Published
2018
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9. Full sequencing and haplotype analysis of MAPT in Parkinson's disease and rapid eye movement sleep behavior disorder

Author

Li, J, Ruskey, J, Arnulf, I, Dauvilliers, Y, Hu, M, Högl, B, Leblond, C, Zhou, S, Ambalavanan, A, Ross, J, Bourassa, C, Spiegelman, D, Laurent, S, Stefani, A, Charley Monaca, C, Cochen De Cock, V, Boivin, M, Ferini-Strambi, L, Plazzi, G, Antelmi, E, Young, P, Heidbreder, A, Labbe, C, Ferman, T, Dion, P, Fan, D, Desautels, A, Gagnon, J, Dupré, N, Fon, E, Montplaisir, J, Boeve, B, Postuma, R, Rouleau, G, Ross, O, Gan-Or, Z, Li, J., Ruskey, J. A., Arnulf, I., Dauvilliers, Y., M. T. M., Hu, Hogl, B., Leblond, C. S., Zhou, S., Ambalavanan, A., Ross, J. P., Bourassa, C. V., Spiegelman, D., Laurent, S. B., Stefani, A., Charley Monaca, C., Cochen De Cock, V., Boivin, M., Ferini-Strambi, L., Plazzi, G., Antelmi, E., Young, P., Heidbreder, A., Labbe, C., Ferman, T. J., Dion, P. A., Fan, D., Desautels, A., Gagnon, J. -F., Dupre, N., Fon, E. A., Montplaisir, J. Y., Boeve, B. F., Postuma, R. B., Rouleau, G. A., Ross, O. A., Gan-Or, Z., Service des Pathologies du sommeil [CHU Pitié-Salpêtrière], CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Département de neurologie [Montpellier], Université Montpellier 1 (UM1)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Hôpital Gui de Chauliac [Montpellier]-Université de Montpellier (UM), Neuropsychiatrie : recherche épidémiologique et clinique (PSNREC), Université Montpellier 1 (UM1)-Université de Montpellier (UM)-Institut National de la Santé et de la Recherche Médicale (INSERM), Service de neurophysiologie clinique (CHRU Lille), Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Euromov (EuroMov), Université de Montpellier (UM), Clinique Beau Soleil [Montpellier], Li, Jiao, Ruskey, Jennifer A., Arnulf, Isabelle, Dauvilliers, Yve, Hu, Michele T.M., Högl, Birgit, Leblond, Claire S., Zhou, Sirui, Ambalavanan, Amirthagowri, Ross, Jay P., Bourassa, Cynthia V., Spiegelman, Dan, Laurent, Sandra B, Stefani, Ambra, Charley Monaca, Christelle, Cochen De Cock, Valérie, Boivin, Michel, Ferini-Strambi, Luigi, Plazzi, Giuseppe, Antelmi, Elena, Young, Peter, Heidbreder, Anna, Labbe, Catherine, Ferman, Tanis J., Dion, Patrick A., Fan, Dongsheng, Desautels, Alex, Gagnon, Jean-Françoi, Dupré, Nicola, Fon, Edward A., Montplaisir, Jacques Y., Boeve, Bradley F., Postuma, Ronald B., Rouleau, Guy A., Ross, Owen A., and Gan-Or, Ziv

Subjects
Lewy Body Disease, Male, Principal Component Analysis, Genotype, Parkinson's disease, REM sleep behavior disorder, Parkinson Disease, tau Proteins, Middle Aged, Polymorphism, Single Nucleotide, Cohort Studies, Neurology, Gene Frequency, genetics, MAPT, Neurology (clinical), Humans, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Female, Genetic Predisposition to Disease, genetic, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, Aged
Abstract

Background: MAPT haplotypes are associated with PD, but their association with rapid eye movement sleep behavior disorder is unclear. Objective: To study the role of MAPT variants in rapid eye movement sleep behavior disorder. Methods: Two cohorts were included: (A) PD (n = 600), rapid eye movement sleep behavior disorder (n = 613) patients, and controls (n = 981); (B) dementia with Lewy bodies patients with rapid eye movement sleep behavior disorder (n = 271) and controls (n = 950). MAPT‐associated variants and the entire coding sequence of MAPT were analyzed. Age‐, sex‐, and ethnicity‐adjusted analyses were performed to examine the association between MAPT, PD, and rapid eye movement sleep behavior disorder. Results: MAPT‐H2 variants were associated with PD (odds ratios: 0.62‐0.65; P = 0.010‐0.019), but not with rapid eye movement sleep behavior disorder. In PD, the H1 haplotype odds ratio was 1.60 (95% confidence interval: 1.12‐2.28; P = 0.009), and the H2 odds ratio was 0.68 (95% confidence interval: 0.48‐0.96; P = 0.03). The H2/H1 haplotypes were not associated with rapid eye movement sleep behavior disorder. Conclusions: Our results confirm the protective effect of the MAPT‐H2 haplotype in PD, and define its components. Furthermore, our results suggest that MAPT does not play a major role in rapid eye movement sleep behavior disorder, emphasizing different genetic background than in PD in this locus.

Published
2018
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10. POLLAR: Impact of air POLLution on Asthma and Rhinitis; a European Institute of Innovation and Technology Health (EIT Health) project

Author

Oliver Pfaar, Jean-Louis Pépin, Jean-Marc Bourez, Chitra Dinesh, Daniel Laune, Josep M. Antó, Michael Bewick, Jean Bousquet, E. Dupas, Giovanni Pau, Yves Dauvilliers, Landry Stéphane Zeng Eyindanga, Nils E. Billo, Julia Ayache, João Fonseca, Renaud Tamisier, Xavier Rodó, Sylvie Arnavielhe, Mehdi Cheraitia, Judith Garcia-Aymerich, Samuel Benveniste, Wienczyslawa Czarlewski, Ludger Klimek, Nuria Calves Venturos, Ismael Nujurally, Xavier Basagaña, Ingrid Jullian-Desayes, Jade Lu Dac, R. Picard, Isabella Annesi-Maesano, Hing Kin Chan, T. Dedeu, Contre les MAladies Chroniques pour un VIeillissement Actif en Languedoc-Roussillon (MACVIA-LR), Université Montpellier 1 (UM1)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Universitaire de Nîmes (CHU Nîmes)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-European Innovation Partnership on Active and Healthy Ageing Reference Site (EIP on AHA), Commission Européenne-Commission Européenne-Organisation Mondiale de la Santé / World Health Organization Office (OMS / WHO), Vieillissement et Maladies chroniques : approches épidémiologique et de santé publique (VIMA), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), CIBER de Epidemiología y Salud Pública (CIBERESP), Universitat Pompeu Fabra [Barcelona] (UPF), Centre de Recherche en Informatique (CRI), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Neuropsychiatrie : recherche épidémiologique et clinique (PSNREC), Université Montpellier 1 (UM1)-Université de Montpellier (UM)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), HP2 : Hypoxie et physiopathologies cardiovasculaires et respiratoires., CHU de Grenoble , Centre Hospitalier Universitaire de Grenoble, France-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Networks and Performance Analysis (NPA), LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Computer Science Department [Los Angeles] (UCLA), University of California [Los Angeles] (UCLA), University of California-University of California, Institució Catalana de Recerca i Estudis Avançats (ICREA), Hypoxie : Physiopathologie Respiratoire et Cardiovasculaire (HP2 ), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Bousquet, Jean, Anto, Josep M., Annesi-Maesano, Isabella, Dedeu, Toni, Dupas, Eve, Pépin, Jean-Loui, Eyindanga, Landry Stephane Zeng, Arnavielhe, Sylvie, Ayache, Julia, Basagana, Xavier, Benveniste, Samuel, Venturos, Nuria Calve, Chan, Hing Kin, Cheraitia, Mehdi, Dauvilliers, Yve, Garcia-Aymerich, Judith, Jullian-Desayes, Ingrid, Dinesh, Chitra, Laune, Daniel, Dac, Jade Lu, Nujurally, Ismael, Pau, Giovanni, Picard, Robert, Rodo, Xavier, Tamisier, Renaud, Bewick, Michael, Billo, Nils E., Czarlewski, Wienczyslawa, Fonseca, Joao, Klimek, Ludger, Pfaar, Oliver, and Bourez, Jean-Marc

Subjects
Pulmonary and Respiratory Medicine, Pollution, Machine learning, Pollution, Rhinitis, Connected Sensors, media_common.quotation_subject, Immunology, Population, Air pollution, medicine.disease_cause, 03 medical and health sciences, 0302 clinical medicine, Environmental health, medicine, Immunology and Allergy, Climate change, 030212 general & internal medicine, [SDV.IMM.ALL]Life Sciences [q-bio]/Immunology/Allergology, education, mHealth, Asma, media_common, Asthma, Rhinitis, [SDV.EE.SANT]Life Sciences [q-bio]/Ecology, environment/Health, Work productivity, Sleep disorder, education.field_of_study, business.industry, Sleep apnea, RC581-607, medicine.disease, 3. Good health, 030228 respiratory system, 13. Climate action, Pollen, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Immunologic diseases. Allergy, business
Abstract

International audience; Allergic rhinitis (AR) is impacted by allergens and air pollution but interactions between air pollution, sleep and allergic diseases are insufficiently understood. POLLAR (Impact of air POLLution on sleep, Asthma and Rhinitis) is a project of the European Institute of Innovation and Technology (EIT Health). It will use a freely-existing application for AR monitoring that has been tested in 23 countries (the Allergy Diary, iOS and Android, 17,000 users, TLR8). The Allergy Diary will be combined with a new tool allowing queries on allergen, pollen (TLR2), sleep quality and disorders (TRL2) as well as existing longitudinal and geolocalized pollution data. Machine learning will be used to assess the relationship between air pollution, sleep and AR comparing polluted and non-polluted areas in 6 EU countries. Data generated in 2018 will be confirmed in 2019 and extended by the individual prospective assessment of pollution (portable sensor, TLR7) in AR. Sleep apnea patients will be used as a demonstrator of sleep disorder that can be modulated in terms of symptoms and severity by air pollution and AR. The geographic information system GIS will map the results. Consequences on quality of life (EQ-5D), asthma, school, work and sleep will be monitored and disseminated towards the population. The impacts of POLLAR will be (1) to propose novel care pathways integrating pollution, sleep and patients' literacy, (2) to study sleep consequences of pollution and its impact on frequent chronic diseases, (3) to improve work productivity, (4) to propose the basis for a sentinel network at the EU level for pollution and allergy, (5) to assess the societal implications of the interaction. MASK paper N°32.

Published
2018
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11. National Sleep Foundation's sleep quality recommendations: first report

Author

Giuseppe Plazzi, David Gozal, Frank J. Daly, Monica P. Mallampalli, Alon Y. Avidan, Andrew D. Krystal, Constance H. Fung, Kenneth L. Lichstein, Frank A.J.L. Scheer, Michael V. Vitiello, Max Hirshkowitz, Raffaele Ferri, Maurice M. Ohayon, Steven M. Albert, Robert Rawding, Virend K. Somers, Yves Dauvilliers, Nancy Hazen, Emerson M. Wickwire, Département de neurologie [Montpellier], Hôpital Gui de Chauliac [Montpellier]-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Université Montpellier 1 (UM1)-Université de Montpellier (UM), Ohayon, Maurice, Wickwire, Emerson M., Hirshkowitz, Max, Albert, Steven M., Avidan, Alon, Daly, Frank J., Dauvilliers, Yve, Ferri, Raffaele, Fung, Constance, Gozal, David, Hazen, Nancy, Krystal, Andrew, Lichstein, Kenneth, Mallampalli, Monica, Plazzi, Giuseppe, Rawding, Robert, Scheer, Frank A., Somers, Virend, and Vitiello, Michael V.

Subjects
Gerontology, medicine.medical_specialty, Aging, media_common.quotation_subject, Sleep efficiency, Alternative medicine, Guidelines as Topic, 03 medical and health sciences, Behavioral Neuroscience, 0302 clinical medicine, Voting, Humans, Psychology, Medicine, 030212 general & internal medicine, 10. No inequality, ComputingMilieux_MISCELLANEOUS, Sleep Medicine Specialty, media_common, business.industry, Public health, Sleep architecture, Neurosciences, Stakeholder, Sleep quality, United States, 3. Good health, Systematic review, Evidence-Based Practice, Public Health and Health Services, Sleep (system call), Sleep onset, Sleep, Sleep Research, business, Inclusion (education), 030217 neurology & neurosurgery, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, Foundations
Abstract

Objectives To provide evidence-based recommendations and guidance to the public regarding indicators of good sleep quality across the life-span. Methods The National Sleep Foundation assembled a panel of experts from the sleep community and representatives appointed by stakeholder organizations (Sleep Quality Consensus Panel). A systematic literature review identified 277 studies meeting inclusion criteria. Abstracts and full-text articles were provided to the panelists for review and discussion. A modified Delphi RAND/UCLA Appropriateness Method with 3 rounds of voting was used to determine agreement. Results For most of the sleep continuity variables (sleep latency, number of awakenings >5 minutes, wake after sleep onset, and sleep efficiency), the panel members agreed that these measures were appropriate indicators of good sleep quality across the life-span. However, overall, there was less or no consensus regarding sleep architecture or nap-related variables as elements of good sleep quality. Conclusions There is consensus among experts regarding some indicators of sleep quality among otherwise healthy individuals. Education and public health initiatives regarding good sleep quality will require sustained and collaborative efforts from multiple stakeholders. Future research should explore how sleep architecture and naps relate to sleep quality. Implications and limitations of the consensus recommendations are discussed.

Published
2017
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12. The dementia-associated APOE ε4 allele is not associated with rapid eye movement sleep behavior disorder

Author

Christelle Charley Monaca, Isabelle Arnulf, Guy A. Rouleau, Yves Dauvilliers, Elena Antelmi, Stephanie Strong, Ambra Stefani, Michel Boivin, Patrick A. Dion, Birgit Högl, Alex Desautels, Michele T.M. Hu, Luigi Ferini-Strambi, Valérie Cochen De Cock, Peter Young, Giuseppe Plazzi, Claire S. Leblond, Nicolas Dupré, Michal Rolinski, Anna Heidbreder, Thomas R Barber, Ronald B. Postuma, Ziv Gan-Or, Jay P. Ross, Jacques Montplaisir, Samuel Evetts, Judes Poirier, Simon C. Warby, Jean-François Gagnon, Gan-Or, Ziv, Montplaisir, Jacques Y., Ross, Jay P., Poirier, Jude, Warby, Simon C., Arnulf, Isabelle, Strong, Stephanie, Dauvilliers, Yve, Leblond, Claire S., Michele T. M., Hu, Högl, Birgit, Stefani, Ambra, Monaca, Christelle Charley, De Cock, Valérie Cochen, Boivin, Michel, Ferini-Strambi, Luigi, Plazzi, Giuseppe, Antelmi, Elena, Young, Peter, Heidbreder, Anna, Barber, Thomas R., Evetts, Samuel G., Rolinski, Michal, Dion, Patrick A., Desautels, Alex, Gagnon, Jean-Françoi, Dupré, Nicola, Postuma, Ronald B., Rouleau, Guy A., McGill University = Université McGill [Montréal, Canada], Hôpital du Sacré-Coeur de Montréal, Service des Pathologies du sommeil [CHU Pitié-Salpêtrière], CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Neuropsychiatrie : recherche épidémiologique et clinique (PSNREC), Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), University of Oxford [Oxford], Department of Neurology, Innsbruck Medical University [Austria] (IMU), Centre Hospitalier Universitaire de Lille (CHU de Lille), Euromov (EuroMov), Université de Montpellier (UM), Faculté de médecine de l'Université Laval [Québec] (ULaval), Universita Vita Salute San Raffaele = Vita-Salute San Raffaele University [Milan, Italie] (UniSR), University of Bologna, University of Münster, Centre hospitalier universitaire de Québec (CHUQ), Centre Hospitalier Universitaire de Québec, Montplaisir, Jacques Y, Ross, Jay P, Warby, Simon C, Leblond, Claire S, Hu, Michele T M, Barber, Thomas R, Evetts, Samuel G, Dion, Patrick A, Postuma, Ronald B, and Rouleau, Guy A

Subjects
0301 basic medicine, Apolipoprotein E, Male, Aging, Parkinson's disease, Dementia with Lewy bodie, [SDV]Life Sciences [q-bio], Dementia with Lewy bodies, REM sleep behavior disorder, Gastroenterology, 0302 clinical medicine, Gene Frequency, Risk Factors, Allele, General Neuroscience, Parkinson Disease, 3. Good health, Female, Supranuclear Palsy, Progressive, APOE, Human, Adult, Lewy Body Disease, medicine.medical_specialty, Adolescent, Single-nucleotide polymorphism, Genetic Association Studie, Polymorphism, Single Nucleotide, 03 medical and health sciences, Young Adult, Apolipoproteins E, Genetic, Internal medicine, Genetics, medicine, Humans, Dementia, Allele frequency, Alleles, Genetic Association Studies, Synucleinopathies, Neuroscience (all), business.industry, Risk Factor, Multiple System Atrophy, medicine.disease, 030104 developmental biology, Neurology (clinical), Geriatrics and Gerontology, business, Neuroscience, 030217 neurology & neurosurgery, Developmental Biology
Abstract

International audience; The present study aimed to examine whether the APOE ε4 allele, associated with dementia with Lewy bodies (DLB), and possibly with dementia in Parkinson's disease (PD), is also associated with idiopathic rapid eye movement sleep behavior disorder (RBD). Two single nucleotide polymorphisms, rs429358 and rs7412, were genotyped in RBD patients (n = 480) and in controls (n = 823). APOE ε4 allele frequency was 0.14 among RBD patients and 0.13 among controls (OR = 1.11, 95% CI: 0.88-1.40, p = 0.41). APOE ε4 allele frequencies were similar in those who converted to DLB (0.14) and those who converted to Parkinson's disease (0.12) or multiple system atrophy (0.14, p = 1.0). The APOE ε4 allele is neither a risk factor for RBD nor it is associated with conversion from RBD to DLB or other synucleinopathies.

Published
2017
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13. From state dissociation to status dissociatus

Author

Giuseppe Plazzi, Yves Dauvilliers, Elena Antelmi, Alex Iranzo, Kailash P. Bhatia, Carlos H. Schenck, Isabelle Arnulf, Rocco Liguori, Raffaele Ferri, University of Bologna, CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Neuropsychiatrie : recherche épidémiologique et clinique (PSNREC), Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Sobell Department of Motor Neuroscience and Movement Disorders, The National Hospital for Neurology and Neurosurgery-UCL Institute of Neurology, Department of Neurological Sciences, Alma Mater Studiorum Università di Bologna [Bologna] (UNIBO), Antelmi, Elena, Ferri, Raffaele, Iranzo, Alex, Arnulf, Isabelle, Dauvilliers, Yve, Bhatia, Kailash P, Liguori, Rocco, Schenck, Carlos H, and Plazzi, Giuseppe

Subjects
0301 basic medicine, Pulmonary and Respiratory Medicine, Sleep Wake Disorders, Parasomnias, Cataplexy, REM sleep behavior disorder, Sleep, REM, Non-rapid eye movement sleep, Status dissociatu, 03 medical and health sciences, 0302 clinical medicine, Physiology (medical), medicine, Animals, Humans, State dissociation, Agrypnia Excitata, Narcolepsy, Parasomnia, Status dissociatus, Neurology (clinical), Neurology, medicine.disease, 030104 developmental biology, Hypnopompic, Wakefulness, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], medicine.symptom, Psychology, Sleep paralysis, Neuroscience, 030217 neurology & neurosurgery, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Abstract

International audience; The states of being are conventionally defined by the simultaneous occurrence of behavioral, neurophysiological and autonomic descriptors. State dissociation disorders are due to the intrusion of features typical of a different state into an ongoing state. Disorders related to these conditions are classified according to the ongoing main state and comprise: 1) Dissociation from prevailing wakefulness as seen in hypnagogic or hypnopompic hallucinations, automatic behaviors, sleep drunkenness, cataplexy and sleep paralysis 2) Dissociation from rapid eye movement (REM) sleep as seen in REM sleep behavior disorder and lucid dreaming and 3) Dissociation from NREM sleep as seen in the disorders of arousal. The extreme expression of states dissociation is characterized by the asynchronous occurrence of the various components of the different states that prevents the recognition of any state of being. This condition has been named status dissociatus. According to the underlying disorders/diseases and to their severity, among status dissociatus we may recognize disorders in which such an extreme dissociation occurs only at night time or intermittently (i.e., autoimmune encephalopathies, narcolepsy type 1 and IgLON5 parasomnia), and others in which it occurs nearly continuously with complete loss of any conventionally defined state of being, and of the circadian pattern (agrypnia excitata). Here, we render a comprehensive review of all diseases/disorders associated with state dissociation and status dissociatus and propose a critical classification of this complex scenario.

Published
2016
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