Dissociative Symptoms are Highly Prevalent in Adults with Narcolepsy Type 1.

INTRODUCTION: The core symptoms of narcolepsy such as excessive daytime sleepiness and cataplexy are well known. However, there is mounting evidence for a much broader symptom spectrum, including psychiatric symptoms. Disordered sleep has previously been linked with dissociative symptoms, which may imply that patients with narcolepsy are more prone to develop such symptoms. OBJECTIVES: To investigate the frequency of dissociative symptoms in adult patients with narcolepsy type 1 compared to population controls. METHODS: In a retrospective case control study, sixty adult patients fulfilling the criteria for narcolepsy type 1 and 120 matched population control subjects received a structured interview using the Schedules for Clinical Assessment in Neuropsychiatry (SCAN) to assess dissociative symptoms and disorders. RESULTS: A majority of narcolepsy patients reported dissociative symptoms, and even fulfilled the DSM-IV-TR criteria of a dissociative disorder (62% vs 1% in controls, p < .001). Most frequently reported symptoms were "dissociative amnesia" (37% vs 1%, p < .001) and "dissociative disorder of voluntary movement" (32% vs 1%, p < .001). CONCLUSION: Dissociative symptoms are strikingly prevalent in adult patients with narcolepsy type 1. Although a formal diagnosis of dissociation disorder should not be made as the symptoms can be explained by narcolepsy as an underlying condition, the findings do illustrate the extent and severity of the dissociative symptoms. As for the pathophysiological mechanism, there may be symptom overlap between narcolepsy and dissociation disorder. However, there may also be a more direct link between disrupted sleep and dissociative symptoms. In either case, the high frequency of occurrence of dissociative symptoms should result in an active inquiry by doctors, to improve therapeutic management and guidance.

An emotion recognition subtyping approach to studying the heterogeneity and comorbidity of autism spectrum disorders and attention-deficit/hyperactivity disorder.

BACKGROUND: Emotion recognition dysfunction has been reported in both autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). This suggests that emotion recognition is a cross-disorder trait that may be utilised to understand the heterogeneous psychopathology of ASD and ADHD. We aimed to identify emotion recognition subtypes and to examine their relation with quantitative and diagnostic measures of ASD and ADHD to gain further insight into disorder comorbidity and heterogeneity. METHODS: Factor mixture modelling was used on speed and accuracy measures of auditory and visual emotion recognition tasks. These were administered to children and adolescents with ASD (N = 89), comorbid ASD + ADHD (N = 64), their unaffected siblings (N = 122), ADHD (N = 111), their unaffected siblings (N = 69), and controls (N = 220). Identified classes were compared on diagnostic and quantitative symptom measures. RESULTS: A four-class solution was revealed, with the following emotion recognition abilities: (1) average visual, impulsive auditory; (2) average-strong visual and auditory; (3) impulsive/imprecise visual, average auditory; (4) weak visual and auditory. The weakest performing class (4) contained the highest percentage of patients (66.07%) and the lowest percentage controls (10.09%), scoring the highest on ASD/ADHD measures. The best performing class (2) demonstrated the opposite: 48.98% patients, 15.26% controls with relatively low scores on ASD/ADHD measures. CONCLUSIONS: Subgroups of youths can be identified that differ both in quantitative and qualitative aspects of emotion recognition abilities. Weak emotion recognition abilities across sensory domains are linked to an increased risk for ASD as well as ADHD, although emotion recognition impairments alone are neither necessary nor sufficient parts of these disorders.

Visual and auditory emotion recognition problems as familial cross-disorder phenomenon in ASD and ADHD.

Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are frequently comorbid disorders. Emotion recognition problems are considered an important familial deficit in ASD, but this is unknown in ADHD. Very few studies have directly compared emotion recognition performance of youth with ASD and/or ADHD and of their unaffected siblings across age to quantify the contribution of emotion recognition problems to the ADHD phenotype. We therefore devised a study of 64 ASD+ADHD participants, 89 ASD-only participants, 111 ADHD-only participants, 122 unaffected ASD(+ADHD) siblings, 69 unaffected ADHD-only siblings and 220 controls aged 7-18 years, who had completed two tasks assessing auditory and visual emotion recognition. Factor analysis was used to detect underlying dimensions of emotion recognition capacity. Linear mixed models were used to compare performance across groups and to assess age effects. The factor-analysis revealed four factors separating speed and accuracy regarding visual and auditory emotion recognition. ASD+ADHD, ASD-only, and ADHD-only participants all performed worse than controls. ASD+ADHD, ASD-only, and ADHD-only participants did not differ in the severity of their emotion recognition problems. Both unaffected sibling groups performed intermediate between patients and controls. For ASD+ADHD and ADHD-only participants, group differences were more marked in adolescence than childhood, whereas in ASD participants this was not observed. We conclude that emotion recognition problems are a familial deficit in ADHD to a similar extent as in ASD. Emotion recognition problems specifically - and social cognition problems more generally - should be assessed in clinical practice for ADHD.

Do High and Low Extremes of ADHD and ASD Trait Continua Represent Maladaptive Behavioral and Cognitive Outcomes? A Population-Based Study.

OBJECTIVE: Attention deficit hyperactivity disorder (ADHD) and autism spectrum disorders (ASD) are thought to reflect the high, symptomatic extreme of quantitative trait continua. However, extreme deviations in either direction on disorder continua, high and low, may both represent maladaptive behavioral and cognitive outcomes. We aimed to test this hypothesis. METHOD: In a population sample of 378 children, ADHD and ASD traits were rated by parents on questionnaires that provide resolution at high and low extremes of the ADHD and ASD trait continua. ADHD and ASD traits were related to parent-ratings of internalizing and externalizing behavior problems and tests of cognitive functioning using polynomial regression. RESULTS: The low ends of the ADHD and ASD trait continua were related to fewer behavior problems and better cognitive functioning than symptomatic ends. CONCLUSION: Studying the correlates of the low continuum ends may deepen our understanding of the mechanisms underlying adaptive behavioral and cognitive outcomes.

Variation in the Early Trajectories of Autism Symptoms Is Related to the Development of Language, Cognition, and Behavior Problems.

OBJECTIVE: The objectives of this study were to model more homogeneous subgroups within autism spectrum disorder (ASD) based on early trajectories of core symptoms; and to further characterize these subgroups in terms of trajectories of language, cognition, co-occurring (attention-deficit/hyperactivity disorder [ADHD]-related) traits and clinical outcome diagnosis. METHOD: Children (N = 203) referred for possible ASD at ages 1 to 4 years were assessed at three time points at intervals ranging from 9 months to 3 years. Assessments included standardized measures for ASD (Autism Diagnostic Observation Schedule [ADOS]), language (ADOS-language item), nonverbal IQ (NV-IQ; different tests adequate to chronological/mental age), and parent-reported behavioral problems (Infant-Toddler Social and Emotional Assessment, Child Behavior Checklist). RESULTS: Latent-class growth curve analysis with ADOS total scores led to the identification of three main stable and two small improving groups: a severe-stable group (19.5% of sample)-the only group without considerable language improvement-showed persistent low NV-IQ and marked increase in attention problems over time; a moderate-stable group (21.7%) with below-average increasing NV-IQ; and a mild-stable group (48%) with stable-average NV-IQ and the highest scores on ADHD-related traits, whose ASD outcome diagnoses increased despite stable-low ASD scores. Two groups (each 5.4%) improved: one moved from severe to moderate ASD scores, and the other moved from moderate to mild/nonspectrum scores. Both of these groups improved on language, NV-IQ, and ADHD-related traits. CONCLUSION: Results support the high stability of ASD symptoms into various severity levels, but also highlight the significant contribution of non-ASD domains in defining and explaining the different ASD trajectories.

Homogeneous Combinations of ASD-ADHD Traits and Their Cognitive and Behavioral Correlates in a Population-Based Sample.

OBJECTIVE: Autism Spectrum Disorders (ASD) and ADHD are assumed to be the extreme manifestations of continuous heterogeneous traits that frequently co-occur. This study aims to identify subgroups of children with distinct ASD-ADHD trait profiles in the general population, using measures sensitive across both trait continua, and show how these subgroups differ in cognitive functioning. METHOD: We examined 378 children (6-13 years) from a population-based sample. RESULTS: Latent class analyses (LCA) detected three concordant classes with low (10.1%), medium (54.2%), or high (13.2%) scores on both traits, and two discordant classes with more ADHD than ASD characteristics (ADHD > ASD, 18.3%) and vice versa (ASD > ADHD, 4.2%). Findings suggest that ASD and ADHD traits usually are strongly related in the unaffected population, and that a minority of children displays atypical discordant trait profiles characterized by differential visual-spatial functioning. CONCLUSION: This dissociation suggests that heterogeneity in ASD and ADHD is rooted in heterogeneity in the lower unaffected end of the distribution.

Prognostic association of cardiac anxiety with new cardiac events and mortality following myocardial infarction.

BACKGROUND: General anxiety and depressive symptoms following a myocardial infarction are associated with a worse cardiac prognosis. However, the contribution of specific aspects of anxiety within this context remains unclear. AIMS: To evaluate the independent prognostic association of cardiac anxiety with cardiac outcome after myocardial infarction. METHOD: We administered the Cardiac Anxiety Questionnaire (CAQ) during hospital admission (baseline, n = 193) and 4 months (n = 147/193) after discharge. CAQ subscale scores reflect fear, attention, avoidance and safety-seeking behaviour. Study end-point was a major adverse cardiac event (MACE): readmission for ischemic cardiac disease or all-cause mortality. In Cox regression analysis, we adjusted for age, cardiac disease severity and depressive symptoms. RESULTS: The CAQ sum score at baseline and at 4 months significantly predicted a MACE (HRbaseline = 1.59, 95% CI 1.04-2.43; HR4-months = 1.77, 95% CI 1.04-3.02) with a mean follow-up of 4.2 (s.d. = 2.0) years and 4.3 (s.d. = 1.7) years respectively. Analyses of subscale scores revealed that this effect was particularly driven by avoidance (HRbaseline = 1.23, 95% CI 0.99-1.53; HR4-months = 1.77, 95% CI 1.04-1.83). CONCLUSIONS: Cardiac anxiety, particularly anxiety-related avoidance of exercise, is an important prognostic factor for a MACE in patients after myocardial infarction, independent of cardiac disease severity and depressive symptoms.

Understanding disease processes by partitioned dynamic Bayesian networks.

For many clinical problems in patients the underlying pathophysiological process changes in the course of time as a result of medical interventions. In model building for such problems, the typical scarcity of data in a clinical setting has been often compensated by utilizing time homogeneous models, such as dynamic Bayesian networks. As a consequence, the specificities of the underlying process are lost in the obtained models. In the current work, we propose the new concept of partitioned dynamic Bayesian networks to capture distribution regime changes, i.e. time non-homogeneity, benefiting from an intuitive and compact representation with the solid theoretical foundation of Bayesian network models. In order to balance specificity and simplicity in real-world scenarios, we propose a heuristic algorithm to search and learn these non-homogeneous models taking into account a preference for less complex models. An extensive set of experiments were ran, in which simulating experiments show that the heuristic algorithm was capable of constructing well-suited solutions, in terms of goodness of fit and statistical distance to the original distributions, in consonance with the underlying processes that generated data, whether it was homogeneous or non-homogeneous. Finally, a study case on psychotic depression was conducted using non-homogeneous models learned by the heuristic, leading to insightful answers for clinically relevant questions concerning the dynamics of this mental disorder.

Multilevel temporal Bayesian networks can model longitudinal change in multimorbidity.

OBJECTIVES: Although the course of single diseases can be studied using traditional epidemiologic techniques, these methods cannot capture the complex joint evolutionary course of multiple disorders. In this study, multilevel temporal Bayesian networks were adopted to study the course of multimorbidity in the expectation that this would yield new clinical insight. STUDY DESIGN AND SETTING: Clinical data of patients were extracted from 90 general practice registries in the Netherlands. One and half million patient-years were used for analysis. The simultaneous progression of six chronic cardiovascular conditions was investigated, correcting for both patient and practice-related variables. RESULTS: Cumulative incidence rates of one or more new morbidities rapidly increase with the number of morbidities present at baseline, ranging up to 47% and 76% for 3- and 5-year follow-ups, respectively. Hypertension and lipid disorders, as health risk factors, increase the cumulative incidence rates of both individual and multiple disorders. Moreover, in their presence, the observed cumulative incidence rates of combinations of cardiovascular disorders, that is, multimorbidity differs significantly from the expected rates. CONCLUSION: There are clear synergies between health risks and chronic diseases when multimorbidity within a patient progresses over time. The method used here supports a more comprehensive analysis of such synergies compared with what can be obtained by traditional statistics.

Multilevel Bayesian networks for the analysis of hierarchical health care data.

OBJECTIVE: Large health care datasets normally have a hierarchical structure, in terms of levels, as the data have been obtained from different practices, hospitals, or regions. Multilevel regression is the technique commonly used to deal with such multilevel data. However, for the statistical analysis of interactions between entities from a domain, multilevel regression yields little to no insight. While Bayesian networks have proved to be useful for analysis of interactions, they do not have the capability to deal with hierarchical data. In this paper, we describe a new formalism, which we call multilevel Bayesian networks; its effectiveness for the analysis of hierarchically structured health care data is studied from the perspective of multimorbidity. METHODS: Multilevel Bayesian networks are formally defined and applied to analyze clinical data from family practices in The Netherlands with the aim to predict interactions between heart failure and diabetes mellitus. We compare the results obtained with multilevel regression. RESULTS: The results obtained by multilevel Bayesian networks closely resembled those obtained by multilevel regression. For both diseases, the area under the curve of the prediction model improved, and the net reclassification improvements were significantly positive. In addition, the models offered considerable more insight, through its internal structure, into the interactions between the diseases. CONCLUSIONS: Multilevel Bayesian networks offer a suitable alternative to multilevel regression when analyzing hierarchical health care data. They provide more insight into the interactions between multiple diseases. Moreover, a multilevel Bayesian network model can be used for the prediction of the occurrence of multiple diseases, even when some of the predictors are unknown, which is typically the case in medicine.

The structure of autism spectrum disorder symptoms in the general population at 18 months.

It is unclear whether symptoms of autism spectrum disorder (ASD) in young children in the population fit the three-factor structure of ASD as described in the DSM-IV, and cluster together in individual subjects. This study analysed questionnaire data on ASD symptoms filled in by mothers of 11,332 18-month-old children that was collected in the context of the Norwegian Mother and Child Cohort Study conducted by the Norwegian Institute of Public Health. Confirmatory Factor Analyses showed that the three-factor model had a significantly better fit then the two- and one-factor model of ASD symptoms. Latent class analysis revealed four homogeneous groups of children (classes) with different scores for Social Interaction and Communication at one hand and Stereotypies/Rigidity at the other hand.

Are autism spectrum disorder and attention-deficit/hyperactivity disorder different manifestations of one overarching disorder? Cognitive and symptom evidence from a clinical and population-based sample.

OBJECTIVE: Autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD) frequently co-occur. Given the heterogeneity of both disorders, several more homogeneous ASD-ADHD comorbidity subgroups may exist. The current study examined whether such subgroups exist, and whether their overlap or distinctiveness in associated comorbid symptoms and cognitive profiles gives support for a gradient overarching disorder hypothesis or a separate disorders hypothesis. METHOD: Latent class analysis was performed on Social Communication Questionnaire (SCQ) and Conners' Parent Rating Scale (CPRS-R:L) data for 644 children and adolescents (5 through 17 years of age). Classes were compared for comorbid symptoms and cognitive profiles of motor speed and variability, executive functioning, attention, emotion recognition, and detail-focused processing style. RESULTS: Latent class analysis revealed five classes: two without behavioral problems, one with only ADHD behavior, and two with both clinical symptom levels of ASD and ADHD but with one domain more prominent than the other (ADHD[+ASD] and ASD[+ADHD]). In accordance with the gradient overarching disorder hypothesis were the presence of an ADHD class without ASD symptoms and the absence of an ASD class without ADHD symptoms, as well as cognitive functioning of the simple ADHD class being less impaired than that of both comorbid classes. In conflict with this hypothesis was that there was some specificity of cognitive deficits across classes. CONCLUSIONS: The overlapping cognitive deficits may be used to further unravel the shared etiological underpinnings of ASD and ADHD, and the nonoverlapping deficits may indicate why some children develop ADHD despite their enhanced risk for ASD. The two subtypes of children with both ASD and ADHD behavior will most likely benefit from different clinical approaches.

Symptomatic overlap between attention-deficit/hyperactivity disorder and borderline personality disorder in women: the role of temperament and character traits.

OBJECTIVE: There is substantial symptomatic overlap between attention-deficit/hyperactivity disorder (ADHD) and borderline personality disorder (BPD) in adults, but the nature of the relationship between these disorders needs further clarification. The role of temperament and character traits in the differentiation of classes of patients with similar ADHD and BPD symptom profiles was examined and possible pathways between early temperament and future ADHD and/or BPD were hypothesized. METHODS: Structured diagnostic interviews were conducted in 103 female patients to assess current Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition symptoms of ADHD and BPD, and parent interviews were used to assess ADHD symptoms in childhood. Classes of subjects with homogeneous symptom profiles were identified using latent class analysis. Temperament and character traits were assessed using the Temperament and Character Inventory of Cloninger et al; scores were then compared across the latent classes. RESULTS: Latent class analysis revealed 4 mutually exclusive classes of patients: 1 with only ADHD symptoms; 1 with BPD symptoms and ADHD symptoms of hyperactivity; 1 with BPD symptoms and ADHD symptoms of inattention, hyperactivity, and impulsivity; and 1 with BPD symptoms and ADHD symptoms of inattention and hyperactivity. High Novelty Seeking was found in all classes except for the class with symptoms of BPD and only the hyperactivity aspect of ADHD. The highest Novelty Seeking temperament scores were found in that class of patients with both symptoms of BPD and symptoms in all areas of ADHD. High Harm Avoidance, low Cooperativeness, and low Self-directedness were specifically related to classes containing BPD symptoms. CONCLUSIONS: Classes of ADHD and BPD symptoms are associated with specific temperament and character configurations. Novelty Seeking was associated with the inattention symptoms of ADHD. An outspoken Novelty Seeking temperament suggests vulnerability for the development of ADHD and co-occurring BPD. Contrary to patients with combined ADHD and BPD symptoms, patients with only symptoms of ADHD showed normal character development and thus an absence of a personality disorder. Assessment of temperament and character traits can improve our understanding of the complex relationship between ADHD and BPD.

Probabilistic causal models of multimorbidity concepts.

Multimorbidity, i.e., the presence of multiple diseases within one person, is a significant health-care problem for western societies: diagnosis, prognosis and treatment in the presence of of multiple diseases can be complex due to the various interactions between diseases. A literature review reveals that there is a variety of definitions that describe different concepts with respect to multimorbidity, both for the cause of multimorbidity as well as the implications of multimorbidity. To be able to aid computerized decision support systems within patient care, e.g. electronic clinical guidelines that can be personalized given the patient's problems, these multimorbidity aspects need to be defined rigorously in a formal language. In this paper, we employ causal Bayesian networks to define and analyze a novel framework that can be used to model a spectrum of aspects related to multimorbidity. We conclude that this framework provides a solid basis for modeling interactions between multiple diseases.

Severe fatigue in narcolepsy with cataplexy.

Excessive daytime sleepiness (EDS) is the core symptom of narcolepsy. However, there have been indications that fatigue - which should be separated from EDS--is also a frequent complaint. We determined the prevalence of severe fatigue in a group of narcolepsy patients and its relation with excessive daytime sleepiness, psychological distress, functional impairment and quality of life. We included 80 patients fulfilling the International Classification of Sleep Disorders (ICSD)-2 diagnostic criteria of narcolepsy with cataplexy. Fatigue was measured using the Checklist Individual Strength (CIS). In addition psychological distress, including symptoms of depression, functional impairment and quality of life, were assessed. Comparisons were made between patients with (CIS-fatigue score ≥ 35) and without severe experienced fatigue. Fifty patients (62.5%) reported severe fatigue. There were no sex or age differences between patients with and without severe fatigue. Both fatigued and non-fatigued patients had the same amount of daytime sleepiness (Epworth Sleepiness Score 14.3 ± 4.2 versus 13.1 ± 4.4, P = 0.22), confirming the separation between sleepiness and fatigue. Interestingly, fatigued patients more often used stimulant medication (64% versus 40%, P = 0.02). Severe fatigue was associated with a significantly increased functional impairment, increased depressive symptoms and a lowered general quality of life. In conclusion, a majority of patients with narcolepsy suffer from severe fatigue, which can be distinguished from daytime sleepiness, and results in severe functional impairment.

Lifespan attention deficit/hyperactivity disorder and borderline personality disorder symptoms in female patients: a latent class approach.

Attention-deficit/hyperactivity disorder (ADHD) and borderline personality disorder (BPD) are frequently comorbid. To contribute to a better understanding of the associations regularly found between ADHD and BPD, on the one hand, and the developmental pathways for these disorders, on the other hand, latent class analyses (LCA) were undertaken to identify classes differing in profiles of childhood symptoms of ADHD and adult symptoms of ADHD and BPD. Diagnostic interviews with 103 female outpatients meeting the criteria for ADHD and/or BPD were used to assess current DSM-IV symptoms; childhood symptoms of ADHD were assessed in parent interviews. The latent classes were examined in relation to the DSM-IV conceptualizations of ADHD and BPD. And relations between childhood and adult classes were examined to hypothesize about developmental trajectories. LCA revealed an optimal solution with four distinct symptom profiles: only ADHD symptoms; BPD symptoms and only ADHD symptoms of hyperactivity; BPD symptoms and ADHD symptoms of inattention and hyperactivity; BPD symptoms and ADHD symptoms of inattention, hyperactivity and impulsivity. All patients with BPD had some ADHD symptoms in both adulthood and childhood. Hyperactivity was least discriminative of adult classes. Adult hyperactivity was not always preceded by childhood hyperactivity; some cases of comorbid ADHD and BPD symptoms were not preceded by significant childhood ADHD symptoms; and some cases of predominantly BPD symptoms could be traced back to combined symptoms of ADHD in childhood. The results underline the importance of taking ADHD diagnoses into account with BPD. ADHD classification subtypes may not be permanent over time, and different developmental pathways to adult ADHD and BPD should therefore be investigated.

Improved diagnostic validity of the ADOS revised algorithms: a replication study in an independent sample.

Recently, Gotham et al. (2007) proposed revised algorithms for the Autism Diagnostic Observation Schedule (ADOS) with improved diagnostic validity. The aim of the current study was to replicate predictive validity, factor structure, and correlations with age and verbal and nonverbal IQ of the ADOS revised algorithms for Modules 1 and 2 in a large independent Dutch sample (N = 532). Results showed that the improvement of diagnostic validity was most apparent for autism, except in very young or low functioning children. Results for other autism spectrum disorders were less consistent. Overall, these findings support the use of the more homogeneous revised algorithms, with the use of similar items across developmental cells making it easier to compare ADOS scores within and between individuals.

Anxiety and mood disorders in narcolepsy: a case-control study.

INTRODUCTION: Narcolepsy is a primary sleeping disorder with excessive daytime sleepiness and cataplexy as core symptoms. There is increasing interest in the psychiatric phenotype of narcolepsy. Although many authors suggest an overrepresentation of mood disorders, few systematic studies have been performed and conflicting results have been reported. Anxiety disorders in narcolepsy have only received little attention. METHODS: We performed a case-control study in 60 narcolepsy patients and 120 age- and sex-matched controls from a previous population study. The Schedules for Clinical Assessment in Neuropsychiatry were used to assess symptoms and diagnostic classifications of mood and anxiety disorders. RESULTS: Symptoms of mood disorders were reported by about one third of patients. However, the prevalence of formal mood disorder diagnoses - including major depression - was not increased. In contrast, more than half of the narcolepsy patients had anxiety or panic attacks. Thirty-five percent of the patients could be diagnosed with anxiety disorder (odds ratio=15.6), with social phobia being the most important diagnosis. There was no influence of age, sex, duration of illness or medication use on the prevalence of mood or anxiety symptoms and disorders. DISCUSSION: Anxiety disorders, especially panic attacks and social phobias, often affect patients with narcolepsy. Although symptoms of mood disorders are present in many patients, the prevalence of major depression is not increased. Anxiety and mood symptoms could be secondary complications of the chronic symptoms of narcolepsy. Recent studies have shown that narcolepsy is caused by defective hypocretin signaling. As hypocretin neurotransmission is also involved in stress regulation and addiction, this raises the possibility that mood and anxiety symptoms are primary disease phenomena in narcolepsy.