The brain activation patterns related to sleep resistance remain to be discovered in health and disease. The maintenance of wakefulness test (MWT) is an objective neuropsychological assessment... Show moreThe brain activation patterns related to sleep resistance remain to be discovered in health and disease. The maintenance of wakefulness test (MWT) is an objective neuropsychological assessment often used to assess an individual's ability to resist sleep. It is frequently used in narcolepsy type 1, a disorder characterized by impaired sleep-wake control and the inability to resist daytime sleep. We investigated the neural correlates of active sleep resistance in 12 drug-free people with narcolepsy type 1 and 12 healthy controls. Simultaneous fMRI-EEG measurements were recorded during five cycles of two alternating conditions of active sleep resistance and waking rest. Cleaned EEG signals were used to verify wakefulness and task adherence. Pooling both subject groups, significantly higher fMRI activation when actively resisting sleep was seen in the brainstem, superior cerebellum, bilateral thalamus and visual cortices. In controls the activation clusters were generally smaller compared to patients and no significant activation was seen in the brainstem. Formal comparison between groups only found a significantly higher left primary visual cortex activation in patients during active sleep resistance. The active sleep resistance paradigm is a feasible fMRI task to study sleep resistance and induces evident arousal- and visual-related activity. Significantly higher left primary visual cortical activation in patients could be caused by an enhanced need of visual focus to resist sleep, or reflecting a more rapid descent in their level of alertness when resting. Show less
Background and Objectives: Recent studies fueled doubts as to whether all currently defined central disorders of hypersomnolence are stable entities, especially narcolepsy type 2 and idiopathic... Show moreBackground and Objectives: Recent studies fueled doubts as to whether all currently defined central disorders of hypersomnolence are stable entities, especially narcolepsy type 2 and idiopathic hypersomnia. New reliable biomarkers are needed, and the question arises of whether current diagnostic criteria of hypersomnolence disorders should be reassessed. The main aim of this data-driven observational study was to see whether data-driven algorithms would segregate narcolepsy type 1 and identify more reliable subgrouping of individuals without cataplexy with new clinical biomarkers. Methods: We used agglomerative hierarchical clustering, an unsupervised machine learning algorithm, to identify distinct hypersomnolence clusters in the large-scale European Narcolepsy Network database. We included 97 variables, covering all aspects of central hypersomnolence disorders such as symptoms, demographics, objective and subjective sleep measures, and laboratory biomarkers. We specifically focused on subgrouping of patients without cataplexy. The number of clusters was chosen to be the minimal number for which patients without cataplexy were put in distinct groups. Results: We included 1,078 unmedicated adolescents and adults. Seven clusters were identified, of which 4 clusters included predominantly individuals with cataplexy. The 2 most distinct clusters consisted of 158 and 157 patients, were dominated by those without cataplexy, and among other variables, significantly differed in presence of sleep drunkenness, subjective difficulty awakening, and weekend-week sleep length difference. Patients formally diagnosed as having narcolepsy type 2 and idiopathic hypersomnia were evenly mixed in these 2 clusters. Discussion Using a data-driven approach in the largest study on central disorders of hypersomnolence to date, our study identified distinct patient subgroups within the central disorders of hypersomnolence population. Our results contest inclusion of sleep-onset REM periods in diagnostic criteria for people without cataplexy and provide promising new variables for reliable diagnostic categories that better resemble different patient phenotypes. Cluster-guided classification will result in a more solid hypersomnolence classification system that is less vulnerable to instability of single features. Show less
Purpose: Narcolepsy type-1 (NT1) is a rare chronic neurological sleep disorder with excessive daytime sleepiness (EDS) as usual first and cataplexy as pathognomonic symptom. Shortening the NT1... Show morePurpose: Narcolepsy type-1 (NT1) is a rare chronic neurological sleep disorder with excessive daytime sleepiness (EDS) as usual first and cataplexy as pathognomonic symptom. Shortening the NT1 diagnostic delay is the key to reduce disease burden and related low quality of life. Here we investigated the changes of diagnostic delay over the diagnostic years (1990-2018) and the factors associated with the delay in Europe. Patients and Methods: We analyzed 580 NT1 patients (male: 325, female: 255) from 12 European countries using the European Narcolepsy Network database. We combined machine learning and linear mixed-effect regression to identify factors associated with the delay. Results: The mean age at EDS onset and diagnosis of our patients was 20.9 +/- 11.8 (mean +/- standard deviation) and 30.5 +/- 14.9 years old, respectively. Their mean and median diagnostic delay was 9.7 +/- 11.5 and 5.3 (interquartile range: 1.7-13.2 years) years, respectively. We did not find significant differences in the diagnostic delay over years in either the whole dataset or in individual countries, although the delay showed significant differences in various countries. The number of patients with short (<= 2-year) and long (>= 13-year) diagnostic delay equally increased over decades, suggesting that subgroups of NT1 patients with variable disease progression may co-exist. Younger age at cataplexy onset, longer interval between EDS and cataplexy onsets, lower cataplexy frequency, shorter duration of irresistible daytime sleep, lower daytime REM sleep propensity, and being female are associated with longer diagnostic delay. Conclusion: Our findings contrast the results of previous studies reporting shorter delay over time which is confounded by calendar year, because they characterized the changes in diagnostic delay over the symptom onset year. Our study indicates that new strategies such as increasing media attention/awareness and developing new biomarkers are needed to better detect EDS, cataplexy, and changes of nocturnal sleep in narcolepsy, in order to shorten the diagnostic interval. Show less
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... Show moreIncreased 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. Show less
Purpose of ReviewClinical presentation of central hypersomnolence disorders, including narcolepsy type 1 and 2 and idiopathic hypersomnia, is often similar, and determining the correct diagnosis... Show morePurpose of ReviewClinical presentation of central hypersomnolence disorders, including narcolepsy type 1 and 2 and idiopathic hypersomnia, is often similar, and determining the correct diagnosis remains challenging. Neuroimaging techniques have provided valuable insights into the pathophysiology of narcolepsy and idiopathic hypersomnia. Here, we review current structural and functional brain imaging findings in central hypersomnolence disorders and discuss the future perspectives of neuroimaging in these sleep disorders.Recent FindingsMost studies have focused on narcolepsy type 1 (or narcolepsy with cataplexy), showing inconsistent but extensive structural differences in the hypothalamus and its normally widespread projections. Functional studies have mainly focused on resting-state or emotion regulation in narcolepsy type 1 and have revealed disturbed activity in limbic and mesolimbic structures in relation to cataplexy. Finally, recent studies suggest a disruption of the default-mode network in patients with idiopathic hypersomnia.SummaryMost neuroimaging studies to date have been conducted in small samples, while narcolepsy type 2 (or narcolepsy without cataplexy) and idiopathic hypersomnia remain relatively understudied. Larger studies with consistent clinical phenotyping should be the focus of future investigations. In addition, multi-modal imaging methods will be crucial to resolve previous inconsistencies and identify reliable objective biomarkers that could aid in understanding the pathophysiology and potentially support the diagnostic process. Show less
Gool, J.K.; Werf, Y.D. van der; Lammers, G.J.; Fronczek, R. 2020
Vigilance complaints often occur in people with narcolepsy type 1 and severely impair effective daytime functioning. We tested the feasibility of a three-level sustained attention to response task ... Show moreVigilance complaints often occur in people with narcolepsy type 1 and severely impair effective daytime functioning. We tested the feasibility of a three-level sustained attention to response task (SART) paradigm within a magnetic resonance imaging (MRI) environment to understand brain architecture underlying vigilance regulation in individuals with narcolepsy type 1. Twelve medication-free people with narcolepsy type 1 and 11 matched controls were included. The SART included four repetitions of a baseline block and two difficulty levels requiring moderate and high vigilance. Outcome measures were between and within-group performance indices on error rates and reaction times, and functional MRI (fMRI) parameters: mean activity during the task and between-group activity differences across the three conditions and related to changes in activation over time (time-on-task) and error-related activity. Patients-but not controls-made significantly more mistakes with increasing difficulty. The modified SART is a feasible MRI vigilance task showing similar task-positive brain activity in both groups within the cingulo-opercular, frontoparietal, arousal, motor, and visual networks. During blocks of higher vigilance demand, patients had significantly lower activation in these regions than controls. Patients had lower error-related activity in the left pre- and postcentral gyrus. The time-on-task activity differences between groups suggest that those with narcolepsy are insufficiently capable of activating attention- and arousal-related regions when transitioning from attention initiation to stable attention, specifically when vigilance demand is high. They also show lower inhibitory motor activity in relation to errors, suggesting impaired executive functioning. Show less
Gool, J.K.; Fronczek, R.; Leemans, A.; Kies, D.A.; Lammers, G.J.; Werf, Y.D. van der 2019
Narcolepsy type 1 is caused by a selective loss of hypothalamic hypocretin-producing neurons, resulting in severely disturbed sleep-wake control and cataplexy. Hypocretin-producing neurons project... Show moreNarcolepsy type 1 is caused by a selective loss of hypothalamic hypocretin-producing neurons, resulting in severely disturbed sleep-wake control and cataplexy. Hypocretin-producing neurons project widely throughout the brain, influencing different neural networks. We assessed the extent of microstructural white matter organization and brain-wide structural connectivity abnormalities in a homogeneous group of twelve drug-free patients with narcolepsy type 1 and eleven matched healthy controls using diffusion tensor imaging with multimodal analysis techniques. First, tract-based spatial statistics (TBSS) was carried out using fractional anisotropy (FA) and mean, axial and radial diffusivity (MD, AD, RD). Second, quantitative analyses of mean FA, MD, AD and RD were conducted in predefined regions-of-interest, including sleep-wake regulation-related, limbic and reward system areas. Third, we performed hypothalamus-seeded tractography towards the thalamus, amygdala and midbrain. TBSS analyses yielded brain-wide significantly lower FA and higher RD in patients. Localized significantly lower FA and higher RD in the left ventral diencephalon and lower AD in the midbrain, were seen in patients. Lower FA was also found in patients in left hypothalamic fibers connecting with the midbrain. No significant MD and AD differences nor a correlation with disease duration were found. The brain-wide, localized ventral diencephalon (comprising the hypothalamus and different sleep- and motor-related nuclei) and hypothalamic connectivity differences clearly show a heretofore underestimated direct and/or indirect effect of hypocretin deficiency on microstructural white matter composition, presumably resulting from a combination of lower axonal density, lower myelination and/or greater axon diameter. Show less
Staal, S.L.; Hogendoorn, S.K.L.; Voets, S.A.; Tepper, R.C.; Veenstra, M.; Vos, I.I. de; ... ; Sartono, E. 2018
