Validation of the International REM Sleep Behavior Disorder Study Group Symptoms Severity Scale (IRBD-SSS): a new tool to assess RBD clinical severity.

BACKGROUND: Currently, no standard scale has been validated to assess overall severity of RBD symptoms in the home environment. We aimed to evaluate the psychometric properties of the International RBD Severity Scale (IRBD-SSS), a new tool designed by the International RBD Study Group. METHODS: Two versions of the IRBD-SSS were created, one for the patient (IRBD-SSS-PT) and another for the bedpartner (IRBD-SSS-BP), both consisting of 3 components, namely vocalizations, body movements and injury, with a fourth component (patient version only) evaluating dream content. To score each dimension, the frequency and severity/impact of behaviors during the previous month are multiplied. Psychometric properties of the IRBD-SSS were assessed, including reproducibility. RESULTS: A total of 188 subjects including n = 132 RBD patients (n = 94 isolated RBD and n = 38 symptomatic RBD) and n = 52 bedpartners were enrolled from eight Sleep centers across France and Italy. Participants completed the scale at baseline and after one week. Acceptability of the scale was excellent in patients (97%) and bedpartners (98%). Internal consistency was acceptable for IRBD-SSS-PT (Cronbach α = 0.75) while slightly low for IRBD-SSS-BP (Cronbach α = 0.49). Concurrent validity was good for both patient (r = 0.70;p < 0.001, see Figure) and bedpartner (r = 0.69;p < 0.001) IRBD-SSS. Reproducibility was high for IRBD-SSS-PT (Lin's coefficient of agreement = 0.85 [0.81;0.90]) and good for the IRBD-SSS-BP (0.79 [0.68;0.90] (p < 0.001). CONCLUSIONS: Both the patient and bedpartner versions of the IRBD-SSS showed excellent acceptability, acceptable internal consistency, good external validity and high reproducibility. IRBD-SSS is a useful tool to test the severity of RBD symptoms in clinical settings and clinical trials. TRIAL REGISTRATION: NCT04071899.

Phenotyping patients treated for obstructive sleep apnea with persistent objective impaired alertness or subjective sleepiness.

BACKGROUND AND OBJECTIVES: Sleepiness in patients with obstructive sleep apnea (OSA) is associated with accidental and economic burden, as well as cardiovascular risk. Despite OSA treatment, 10-28 % of patients report residual sleepiness. Its determinants, as well as those of objective impaired alertness remain poorly known. In this study, we investigated factors associated with residual subjective sleepiness and objective impaired alertness in patients treated for OSA. METHODS: Consecutive OSA treated patients referred for maintenance of wakefulness tests (MWT) at a tertiary university center were recruited between 2017 and 2020. Clinical data and polysomnography parameters were compared between patients with vs without subjective sleepiness (Epworth Sleepiness Scale, ESS≥11) and those with vs without impaired alertness (at least one trial with sleep onset on MWT). A multivariate logistic model was used to assess explanatory variables of MWT and ESS results. RESULTS: We included 141 patients, of whom 12.8 % had both subjective sleepiness and objective impaired alertness, 17.7 % objective impaired alertness only and 9.2 % subjective sleepiness only. Self-reported history of car accident/near miss, smoking history and ESS≥11 were significantly associated with objective impaired alertness whereas residual Apnea-hypopnea Index and CPAP use were not. The only significant variable associated with ESS at the time of MWT evaluation was initial ESS. Patients with objective impaired alertness only were more often smokers (52 % vs 19 %, p = 0.01), had a higher body mass index (BMI) (32 vs 29 kg/m2, p = 0.05), and showed lower initial ESS (11 vs 13, p < 0.01). CONCLUSIONS: More than one third of OSA treated patients referred for MWT have objective impaired alertness and/or subjective sleepiness. Our findings highlight the need for a comprehensive medical assessment including accident history, subjective sleepiness and comorbidities. Particular attention should be paid to smoking patients with high BMI, who are at risk of impaired alertness with no report of subjective sleepiness.

Interictal stereo-electroencephalography features below 45 Hz are sufficient for correct localization of the epileptogenic zone and postsurgical outcome prediction.

OBJECTIVE: Evidence suggests that the most promising results in interictal localization of the epileptogenic zone (EZ) are achieved by a combination of multiple stereo-electroencephalography (SEEG) biomarkers in machine learning models. These biomarkers usually include SEEG features calculated in standard frequency bands, but also high-frequency (HF) bands. Unfortunately, HF features require extra effort to record, store, and process. Here we investigate the added value of these HF features for EZ localization and postsurgical outcome prediction. METHODS: In 50 patients we analyzed 30 min of SEEG recorded during non-rapid eye movement sleep and tested a logistic regression model with three different sets of features. The first model used broadband features (1-500 Hz); the second model used low-frequency features up to 45 Hz; and the third model used HF features above 65 Hz. The EZ localization by each model was evaluated by various metrics including the area under the precision-recall curve (AUPRC) and the positive predictive value (PPV). The differences between the models were tested by the Wilcoxon signed-rank tests and Cliff's Delta effect size. The differences in outcome predictions based on PPV values were further tested by the McNemar test. RESULTS: The AUPRC score of the random chance classifier was .098. The models (broad-band, low-frequency, high-frequency) achieved median AUPRCs of .608, .582, and .522, respectively, and correctly predicted outcomes in 38, 38, and 33 patients. There were no statistically significant differences in AUPRC or any other metric between the three models. Adding HF features to the model did not have any additional contribution. SIGNIFICANCE: Low-frequency features are sufficient for correct localization of the EZ and outcome prediction with no additional value when considering HF features. This finding allows significant simplification of the feature calculation process and opens the possibility of using these models in SEEG recordings with lower sampling rates, as commonly performed in clinical routines.

Oscillatory and nonoscillatory sleep electroencephalographic biomarkers of the epileptic network.

OBJECTIVE: In addition to the oscillatory brain activity, the nonoscillatory (scale-free) components of the background electroencephalogram (EEG) may provide further information about the complexity of the underlying neuronal network. As epilepsy is considered a network disease, such scale-free metrics might help to delineate the epileptic network. Here, we performed an analysis of the sleep oscillatory (spindle, slow wave, and rhythmic spectral power) and nonoscillatory (H exponent) intracranial EEG using multiple interictal features to estimate whether and how they deviate from normalcy in 38 adults with drug-resistant epilepsy. METHODS: To quantify intracranial EEG abnormalities within and outside the seizure onset areas, patients' values were adjusted based on normative maps derived from the open-access Montreal Neurological Institute open iEEG Atlas. In a subset of 29 patients who underwent resective surgery, we estimated the predictive value of these features to identify the epileptogenic zone in those with a good postsurgical outcome. RESULTS: We found that distinct sleep oscillatory and nonoscillatory metrics behave differently across the epileptic network, with the strongest differences observed for (1) a reduction in spindle activity (spindle rates and rhythmic sigma power in the 10-16 Hz band), (2) a higher rhythmic gamma power (30-80 Hz), and (3) a higher H exponent (steeper 1/f slope). As expected, epileptic spikes were also highest in the seizure onset areas. Furthermore, in surgical patients, the H exponent achieved the highest performance (balanced accuracy of .76) for classifying resected versus nonresected channels in good outcome patients. SIGNIFICANCE: This work suggests that nonoscillatory components of the intracranial EEG signal could serve as promising interictal sleep candidates of epileptogenicity in patients with drug-resistant epilepsy. Our findings further advance the understanding of epilepsy as a disease, whereby absence or loss of sleep physiology may provide information complementary to pathological epileptic processes.

The timing of sleep spindles is modulated by the respiratory cycle in humans.

OBJECTIVE: Coupling of sleep spindles with cortical slow waves and hippocampus sharp-waves ripples is crucial for sleep-related memory consolidation. Recent literature evidenced that nasal respiration modulates neural activity in large-scale brain networks. In rodents, this respiratory drive strongly varies according to vigilance states. Whether sleep oscillations are also respiration-modulated in humans remains open. In this work, we investigated the influence of breathing on sleep spindles during non-rapid-eye-movement sleep in humans. METHODS: Full night polysomnography of twenty healthy participants were analysed. Spindles and slow waves were automatically detected during N2 and N3 stages. Spindle-related sigma power as well as spindle and slow wave events were analysed according to the respiratory phase. RESULTS: We found a significant coupling between both slow and fast spindles and the respiration cycle, with enhanced sigma activity and occurrence probability of spindles during the middle part of the expiration phase. A different coupling was observed for slow waves negative peaks which were rather distributed around the two respiration phase transitions. CONCLUSION: Our findings suggest that breathing cycle influences the dynamics of brain activity during non-rapid-eye-movement sleep. SIGNIFICANCE: This coupling may enable sleep spindles to synchronize with other sleep oscillations and facilitate information transfer between distributed brain networks.

Intracerebral Dynamics of Sleep Arousals: A Combined Scalp-Intracranial EEG Study.

As an intrinsic component of sleep architecture, sleep arousals represent an intermediate state between sleep and wakefulness and are important for sleep-wake regulation. They are defined in an all-or-none manner, whereas they actually present a wide range of scalp-electroencephalography (EEG) activity patterns. It is poorly understood how these arousals differ in their mechanisms. Stereo-EEG (SEEG) provides the unique opportunity to record intracranial activities in superficial and deep structures in humans. Using combined polysomnography and SEEG, we quantitatively categorized arousals during nonrapid eye movement sleep into slow wave (SW) and non-SW arousals based on whether they co-occurred with a scalp-EEG SW event. We then investigated their intracranial correlates in up to 26 brain regions from 26 patients (12 females). Across both arousal types, intracranial theta, alpha, sigma, and beta activities increased in up to 25 regions (p < 0.05; d = 0.06-0.63), while gamma and high-frequency (HF) activities decreased in up to 18 regions across the five brain lobes (p < 0.05; d = 0.06-0.44). Intracranial delta power widely increased across five lobes during SW arousals (p < 0.05 in 22 regions; d = 0.10-0.39), while it widely decreased during non-SW arousals (p < 0.05 in 19 regions; d = 0.10-0.30). Despite these main patterns, unique activities were observed locally in some regions such as the hippocampus and middle cingulate cortex, indicating spatial heterogeneity of arousal responses. Our results suggest that non-SW arousals correspond to a higher level of brain activation than SW arousals. The decrease in HF activities could potentially explain the absence of awareness and recollection during arousals.

Timing matters for accurate identification of the epileptogenic zone.

OBJECTIVE: Interictal biomarkers of the epileptogenic zone (EZ) and their use in machine learning models open promising avenues for improvement of epilepsy surgery evaluation. Currently, most studies restrict their analysis to short segments of intracranial EEG (iEEG). METHODS: We used 2381 hours of iEEG data from 25 patients to systematically select 5-minute segments across various interictal conditions. Then, we tested machine learning models for EZ localization using iEEG features calculated within these individual segments or across them and evaluated the performance by the area under the precision-recall curve (PRAUC). RESULTS: On average, models achieved a score of 0.421 (the result of the chance classifier was 0.062). However, the PRAUC varied significantly across the segments (0.323-0.493). Overall, NREM sleep achieved the highest scores, with the best results of 0.493 in N2. When using data from all segments, the model performed significantly better than single segments, except NREM sleep segments. CONCLUSIONS: The model based on a short segment of iEEG recording can achieve similar results as a model based on prolonged recordings. The analyzed segment should, however, be carefully and systematically selected, preferably from NREM sleep. SIGNIFICANCE: Random selection of short iEEG segments may give rise to inaccurate localization of the EZ.

Effectiveness of an intervention program on physical activity in children with narcolepsy type 1.

OBJECTIVES: Physical activity (PA) is recommended as part of the management of narcolepsy type 1 (NT1). This study aimed at 1) characterizing PA in children and adolescents treated for NT1 using objective and subjective measurements, 2) evaluating how PA is associated with NT1 symptoms and comorbidities, and 3) evaluating the effects of an Adapted Physical Activity (APA) program on PA and clinical characteristics. PATIENTS/METHODS: Patients with NT1 from the National Reference Center of Narcolepsy (Lyon, France) were consecutively included in an APA intervention protocol. Narcolepsy symptoms and comorbidities were collected using standardized questionnaires and sustained attention was evaluated using the Bron-Lyon Attention Stability Test before and after the four-week APA intervention. PA was measured objectively using actigraphy throughout the study. RESULTS: Twenty-seven NT1 patients were included (median age 14.7 years [8.3-18.4], cataplexy 88.9%, obesity 37.0%). At baseline, 52.4% of the patients had satisfactory PA levels according to international recommendations. Patients with leisure-time PA (LTPA) showed higher quality of life than patients without. 45% of the patients increased PA during the intervention compared to baseline. These responsive patients had more depressive feelings and tended to have lower objective PA than non-responsive patients at baseline. No significant correlation was found between PA levels before and during the intervention and other clinical data. CONCLUSIONS: Most children with NT1 showed satisfying PA levels despite their daytime sleepiness. LTPA engagement was associated with higher quality of life. An APA intervention could be effective in children with narcolepsy, especially for those with depressive feelings.

From physiological awakening to pathological sleep inertia: Neurophysiological and behavioural characteristics of the sleep-to-wake transition.

Sleep inertia refers to the transient physiological state of hypoarousal upon awakening, associated with various degrees of impaired neurobehavioral performance, confusion, a desire to return to sleep and often a negative emotional state. Scalp and intracranial electro-encephalography as well as functional imaging studies have provided evidence that the sleep inertia phenomenon is underpinned by an heterogenous cerebral state mixing local sleep and local wake patterns of activity, at the neuronal and network levels. Sleep inertia is modulated by homeostasis and circadian processes, sleep stage upon awakening, and individual factors; this translates into a huge variability in its intensity even under physiological conditions. In sleep disorders, especially in hypersomnolence disorders such as idiopathic hypersomnia, sleep inertia may be a daily, serious and long-lasting symptom leading to severe impairment. To date, few tools have been developed to assess sleep inertia in clinical practice. They include mainly questionnaires and behavioral tests such as the psychomotor vigilance task. Only one neurophysiological protocol has been evaluated in hypersomnia, the forced awakening test which is based on an event-related potentials paradigm upon awakening. This contrasts with the major functional consequences of sleep inertia and its potentially dangerous consequences in subjects required to perform safety-critical tasks soon after awakening. There is a great need to identify reproducible biomarkers correlated with sleep inertia-associated cognitive and behavioral impairment. These biomarkers will aim at better understanding and measuring sleep inertia in physiological and pathological conditions, as well as objectively evaluating wake-promoting treatments or non-pharmacological countermeasures to reduce this phenomenon.

Sleepiness should be reinvestigated through the lens of clinical neurophysiology: A mixed expertal and big-data Natural Language Processing approach.

Historically, the field of sleep medicine has revolved around electrophysiological tools. However, the use of these tools as a neurophysiological method of investigation seems to be underrepresented today, from both international recommendations and sleep centers, in contrast to behavioral and psychometric tools. The aim of this article is to combine a data-driven approach and neurophysiological and sleep medicine expertise to confirm or refute the hypothesis that neurophysiology has declined in favor of behavioral or self-reported dimensions in sleep medicine for the investigation of sleepiness, despite the use of electrophysiological tools. Using Natural Language Processing methods, we analyzed the abstracts of the 18,370 articles indexed by PubMed containing the terms 'sleepiness' or 'sleepy' in the title, abstract, or keywords. For this purpose, we examined these abstracts using two methods: a lexical network, enabling the identification of concepts (neurophysiological or clinical) related to sleepiness in these articles and their interconnections; furthermore, we analyzed the temporal evolution of these concepts to extract historical trends. These results confirm the hypothesis that neurophysiology has declined in favor of behavioral or self-reported dimensions in sleep medicine for the investigation of sleepiness. In order to bring sleepiness measurements closer to brain functioning and to reintroduce neurophysiology into sleep medicine, we discuss two strategies: the first is reanalyzing electrophysiological signals collected during the standard sleep electrophysiological test; the second takes advantage of the current trend towards dimensional models of sleepiness to situate clinical neurophysiology at the heart of the redefinition of sleepiness.

Objective evaluation of excessive daytime sleepiness.

Excessive daytime sleepiness (EDS) is multifactorial. It combines, among other things, an excessive propensity to fall asleep ("physiological sleepiness") and a continuous non-imperative sleepiness (or drowsiness/hypo-arousal) leading to difficulties remaining awake and maintaining sustained attention and vigilance over the long term ("manifest sleepiness"). There is no stand-alone biological measure of EDS. EDS measures can either capture the severity of physiological sleepiness, which corresponds to the propensity to fall asleep, or the severity of manifest sleepiness, which corresponds to behavioral consequences of sleepiness and reduced vigilance. Neuropsychological tests (The psychomotor vigilance task (PVT), Oxford Sleep Resistance Test (OSLeR), Sustained Attention to Response Task (SART)) explore manifest sleepiness through several sustained attention tests but the lack of normative values and standardized protocols make the results difficult to interpret and use in clinical practice. Neurophysiological tests explore the two main aspects of EDS, i.e. the propensity to fall asleep (Multiple sleep latency test, MSLT) and the capacity to remain awake (Maintenance of wakefulness test, MWT). The MSLT and the MWT are widely used in clinical practice. The MSLT is recognized as the "gold standard" test for measuring the severity of the propensity to fall asleep and it is a diagnostic criterion for narcolepsy. The MWT measures the ability to stay awake. The MWT is not a diagnostic test as it is recommended only to evaluate the evolution of EDS and efficacy of EDS treatment. Even if some efforts to standardize the protocols for administration of these tests have been ongoing, MSLT and MWT have numerous limitations: age effect, floor or ceiling effects, binding protocol, no normal or cutoff value (or determined in small samples), and no or low test-retest values in some pathologies. Moreover, the recommended electrophysiological set-up and the determination of sleep onset using the 30­sec epochs scoring rule show some limitations. New, more precise neurophysiological techniques should aim to detect very brief periods of physiological sleepiness and, in the future, the brain local phenomenon of sleepiness likely to underpin drowsiness, which could be called "physiological drowsiness".

Impaired post-sleep apnea autonomic arousals in patients with drug-resistant epilepsy.

OBJECTIVE: Sudden and unexpected deaths in epilepsy (SUDEP) pathophysiology may involve an interaction between respiratory dysfunction and sleep/wake state regulation. We investigated whether patients with epilepsy exhibit impaired sleep apnea-related arousals. METHODS: Patients with drug-resistant (N = 20) or drug-sensitive (N = 20) epilepsy and obstructive sleep apnea, as well as patients with sleep apnea but without epilepsy (controls, N = 20) were included. We explored (1) the respiratory arousal threshold based on nadir oxygen saturation, apnea-hypopnea index, and fraction of hypopnea among respiratory events; (2) the cardiac autonomic response to apnea/hypopnea quantified as percentages of changes from the baseline in RR intervals (RRI), high (HF) and low (LF) frequency powers, and LF/HF. RESULTS: The respiratory arousal threshold did not differ between groups. At arousal onset, RRI decreased (-9.42%) and LF power (179%) and LF/HF ratio (190%) increased. This was followed by an increase in HF power (118%), p < 0.05. The RRI decrease was lower in drug-resistant (-7.40%) than in drug-sensitive patients (-9.94%) and controls (-10.91%), p < 0.05. LF and HF power increases were higher in drug-resistant (188%/126%) than in drug-sensitive patients (172%/126%) and controls (177%/115%), p < 0.05. CONCLUSIONS: Cardiac reactivity following sleep apnea is impaired in drug-resistant epilepsy. SIGNIFICANCE: This autonomic dysfunction might contribute to SUDEP pathophysiology.

Psychobehavioural profile in narcolepsy type 1 with and without REM sleep behaviour disorder.

REM sleep behaviour disorder (RBD) is common in narcolepsy type 1 (NT1). Abnormalities in the reward system have been observed in NT1, possibly related to impaired orexin projections towards the mesolimbic reward system, but also in RBD when associated with Parkinson's disease. Our study aimed to explore the psychobehavioural profile of NT1 patients with and without RBD compared with healthy controls (HC). Forty patients with NT1 were compared with 20 sex- and age-matched HC. All patients with NT1 underwent a video-polysomnography including a measure of REM sleep without atonia (RSWA). The following neuropsychobehavioural variables were assessed: apathy, impulsivity, depression, cognition, subjective and objective attention, sensation-seeking, and behavioural addictions. The patient population included 22 patients with NT1-RBD and 18 patients with NT1-noRBD. Compared with the healthy controls, patients with NT1 had higher scores of apathy, impulsivity, and depression; a lower score on global cognition, and poorer self-perceived attention. No differences were found between patients with NT1 with and without RBD in all neuropsychological variables, except for impaired objective attention in patients with NT1-RBD. In patients with NT1, a positive correlation was observed between RSWA and both apathy and impulsivity subscale. Moreover, in patients with NT1-RBD, RSWA was positively correlated with depression. Patients with NT1 showed higher depression, apathy, and impulsivity compared with controls. These measures correlate with the severity of RSWA, suggesting a transdiagnostic association between RBD and abnormalities of the reward system at least for patients with NT1.

Spotlight on Sleep Stage Classification Based on EEG.

The recommendations for identifying sleep stages based on the interpretation of electrophysiological signals (electroencephalography [EEG], electro-oculography [EOG], and electromyography [EMG]), derived from the Rechtschaffen and Kales manual, were published in 2007 at the initiative of the American Academy of Sleep Medicine, and regularly updated over years. They offer an important tool to assess objective markers in different types of sleep/wake subjective complaints. With the aims and advantages of simplicity, reproducibility and standardization of practices in research and, most of all, in sleep medicine, they have overall changed little in the way they describe sleep. However, our knowledge on sleep/wake physiology and sleep disorders has evolved since then. High-density electroencephalography and intracranial electroencephalography studies have highlighted local regulation of sleep mechanisms, with spatio-temporal heterogeneity in vigilance states. Progress in the understanding of sleep disorders has allowed the identification of electrophysiological biomarkers better correlated with clinical symptoms and outcomes than standard sleep parameters. Finally, the huge development of sleep medicine, with a demand for explorations far exceeding the supply, has led to the development of alternative studies, which can be carried out at home, based on a smaller number of electrophysiological signals and on their automatic analysis. In this perspective article, we aim to examine how our description of sleep has been constructed, has evolved, and may still be reshaped in the light of advances in knowledge of sleep physiology and the development of technical recording and analysis tools. After presenting the strengths and limitations of the classification of sleep stages, we propose to challenge the "EEG-EOG-EMG" paradigm by discussing the physiological signals required for sleep stages identification, provide an overview of new tools and automatic analysis methods and propose avenues for the development of new approaches to describe and understand sleep/wake states.

Regional variability in intracerebral properties of NREM to REM sleep transitions in humans.

Transitions between wake and sleep states show a progressive pattern underpinned by local sleep regulation. In contrast, little evidence is available on non-rapid eye movement (NREM) to rapid eye movement (REM) sleep boundaries, considered as mainly reflecting subcortical regulation. Using polysomnography (PSG) combined with stereoelectroencephalography (SEEG) in humans undergoing epilepsy presurgical evaluation, we explored the dynamics of NREM-to-REM transitions. PSG was used to visually score transitions and identify REM sleep features. SEEG-based local transitions were determined automatically with a machine learning algorithm using features validated for automatic intra-cranial sleep scoring (10.5281/zenodo.7410501). We analyzed 2988 channel-transitions from 29 patients. The average transition time from all intracerebral channels to the first visually marked REM sleep epoch was 8 s ± 1 min 58 s, with a great heterogeneity between brain areas. Transitions were observed first in the lateral occipital cortex, preceding scalp transition by 1 min 57 s ± 2 min 14 s (d = -0.83), and close to the first sawtooth wave marker. Regions with late transitions were the inferior frontal and orbital gyri (1 min 1 s ± 2 min 1 s, d = 0.43, and 1 min 1 s ± 2 min 5 s, d = 0.43, after scalp transition). Intracranial transitions were earlier than scalp transitions as the night advanced (last sleep cycle, d = -0.81). We show a reproducible gradual pattern of REM sleep initiation, suggesting the involvement of cortical mechanisms of regulation. This provides clues for understanding oneiric experiences occurring at the NREM/REM boundary.

Revisiting the maintenance of wakefulness test: from intra-/inter-scorer agreement to normative values in patients treated for obstructive sleep apnea.

The Maintenance of Wakefulness Test is widely used to objectively assess sleepiness and make safety-related decisions, but its interpretation is subjective and normative values remain debated. Our work aimed to determine normative thresholds in non-subjectively sleepy patients with well-treated obstructive sleep apnea, and to assess intra- and inter-scorer variability. We included maintenance of wakefulness tests of 141 consecutive patients with treated obstructive sleep apnea (90% men, mean (SD) age 47.5 (9.2) years, mean (SD) pre-treatment apnea-hypopnea index of 43.8 (20.3) events/h). Sleep onset latencies were independently scored by two experts. Discordant scorings were reviewed to reach a consensus and half of the cohort was double-scored by each scorer. Intra- and inter-scorer variability was assessed using Cohen's kappa for 40, 33, and 19 min mean sleep latency thresholds. Consensual mean sleep latencies were compared between four groups according to subjective sleepiness (Epworth Sleepiness Scale score < versus ≥11) and residual apnea-hypopnea index (< versus ≥15 events/h). In well-treated non-sleepy patients (n = 76), the consensual mean (SD) sleep latency was 38.4 (4.2) min (lower normal limit [mean - 2SD] = 30 min), and 80% of them did not fall asleep. Intra-scorer agreement on mean sleep latency was high but inter-scorer was only fair (Cohen's kappa 0.54 for 33-min threshold, 0.27 for 19-min threshold), resulting in changes in latency category in 4%-12% of patients. A higher sleepiness score but not the residual apnea-hypopnea index was significantly associated with a lower mean sleep latency. Our findings suggest a higher than usually accepted normative threshold (30 min) in this context and emphasise the need for more reproducible scoring approaches.

Unveiled central hypoventilation after tracheotomy in anti-IgLON5 disease: a case report.

Anti-IgLON5 disease is a recently described entity that has been associated with neurological symptoms and sleep disturbances including sleep breathing disorders. Sleep stridor as well as obstructive and less often central sleep apnea have been reported but rarely needing ventilation on tracheotomy. We report the case of a patient in whom obstructive sleep apnea with secondary development of dysphagia and recurrent aspiration pneumonia led to the diagnosis of anti-IgLON 5 disease. Acute respiratory failure due to laryngospasm required intubation and eventually tracheotomy. Yet hypoventilation persisted, and polysomnography demonstrated central sleep apnea alternating with sleep-related tachypnea. Nocturnal ventilation was thus reintroduced. The association of obstructive sleep apnea with dysphagia is a potential red flag for anti-IgLON5 disease, which remains an overlooked diagnosis. Breathing disorders can be complex in this context, with a mixed obstructive and central pattern whose central component can be unveiled after tracheotomy. This highlights the importance of closely monitoring sleep and respiration even after tracheotomy. CITATION: Tankéré P, Le Cam P, Folliet L, et al. Unveiled central hypoventilation after tracheotomy in anti-IgLON5 disease: a case report. J Clin Sleep Med. 2023;19(9):1701-1704.

Progressive narcolepsy: how to deal with intermediate hypocretin-1 values?

According to the International Classification of Sleep Disorders, third edition guidelines, the diagnosis of narcolepsy type 1 is based on the association of excessive daytime sleepiness plus either cataplexy and electrophysiological criteria, or a cerebrospinal fluid hypocretin-1 concentration below 110 pg/mL. This threshold remains debated, and recent works have proposed alternative values in the intermediate (110 to 200 pg/mL) zone. We report the case of a patient who presented with typical clinical symptoms of narcolepsy type 1 developing over six years but in whom initial polysomnography and multiple sleep latency test were negative and cerebrospinal fluid hypocretin-1 was intermediate (132 pg/mL). Cerebrospinal fluid hypocretin-1 reassessment four years later found a dramatic decrease, < 50 pg/mL, and the multiple sleep latency test proved to be abnormal, eventually allowing to confirm the diagnosis. This case highlights the importance of reassessing patients with intermediate hypocretin-1 values and contributes to the debate on the determination of alternative cerebrospinal fluid hypocretin1 thresholds for narcolepsy type 1 diagnosis. CITATION: Ricordeau F, Bridoux A, Raverot V, Peter-Derex L. Progressive narcolepsy: how to deal with intermediate hypocretin-1 values? J Clin Sleep Med. 2023;19(7):1375-1378.

Association of Delayed Sleep/Wake Rhythm with Depression During the First COVID-19 Lockdown in France.

Purpose: The containment of the population during the COVID-19 pandemic led to the emergence or recurrence of psychiatric conditions and sleep disorders. The influence of sleep/wake rhythm on mental health is well known. The objective of our study was to evaluate the link between the shift in sleep/wake rhythm and the presence of depressive symptoms during the March to May 2020 lockdown in the French population. Participants and Methods: Participants (n = 2513) were recruited via newspapers and social networks in March 2020. We evaluated i) the chronotype before and during the lockdown, assessed by the change in mid-sleep time on work-free days corrected for sleep debt on workdays (delta MSFsc); ii) morningness-eveningness circadian preference (Horne & Ostberg questionnaire); iii) depressive symptoms (Patient Health Questionnaire-9, PHQ-9). The delta MSFsc and the PHQ-9 score were compared between circadian preference types. A multivariate model adjusted for age, sex, circadian preference, housing type, and marital status was used to assess the influence of delta MSFsc on the PHQ-9 score in the whole population. Results: The population consisted of 77% women, of median (IQR) age 39 (30-48) years. Compared with the pre-lockdown period, the median (IQR) MSFsc was shifted by 30 (0-66) min during the lockdown, with a significant difference between evening [60 (15-120) min], morning [15 (0-46) min] and neutral [30 (0-70) min] circadian type individuals, p < 0.001. One-third of all participants had moderate to severe depressive symptoms (PHQ-9 ≥ 10). A 1-hour shift in MSFsc was associated with a 0.50-point increase [95% CI (0.28; 0.72), p < 0.001] in the PHQ-9. Conclusion: A phase delay in the chronotype was observed in the general population during lockdown. Such disruption was associated with depressive symptoms but the direction of the relationship remains hypothetical. The impact on mental health of preventive measures targeting the sleep/wake rhythm in this context needs further evaluation.