El cambio sintagmático-paradigmático en las asociaciones verbales en monolingües y bilingües

Fac. de PsicologíaTRUEProQuestpu

Restless legs syndrome in patients with high serum ferritin and normal iron levels

Trabajo presentado como póster en el 18.º Congreso de la European Sleep Research Society, celebrado en Innsbruck (Austria) en 2006Objetivo. Documentar la asociación entre síndrome de piernas inquietas (SPI) y concentraciones de ferritina elevadas en cinco pacientes. Pacientes y métodos. Estudiamos a cinco varones con una edad media de 59 años (rango: 36-73 años). Todos los pacientes fueron remitidos por SPI (dos de ellos donantes de sangre), en dos casos asociado a síndrome de apnea obstructiva del sueño. Se efectuaron registros videopolisomnográficos y se realizó una analítica para determinar los niveles de ferritina y hierro en plasma. Resultados. Los cinco pacientes presentaban criterios clínicos de SPI: parestesias en las pantorrillas asociadas a una necesidad imperiosa de mover las piernas, inquietud motora, agravamiento de los síntomas por la tarde y por la noche, mejoría con el movimiento, dificultad de conciliación del sueño y despertares nocturnos frecuentes. La exploración neurológica, el electroencefalograma, el electromiograma y la resonancia magnética cerebral fueron normales. Los registros videopolisomnográficos mostraron sueño nocturno fragmentado, reducción del tiempo total de sueño, escasa eficiencia, índice de apnea-hipopnea > 10/h en dos casos, y en los cinco casos, índice de movimientos periódicos de las piernas por hora de sueño > 5/h. En todos los casos los niveles de hierro sérico estaban dentro de los límites normales y la concentración de ferritina era elevada. Conclusiones. La asociación entre SPI con movimientos periódicos de las piernas durante el sueño, hierro sérico normal y ferritina elevada no se ha descrito previamente. El hallazgo de la disminución de concentración de ferritina en uno de los pacientes –meses más tarde del tratamiento con fármacos dopaminérgicos– apoya la implicación de un mecanismo dopaminérgico en la fisiopatología del SPIAim. To document the association between restless legs syndrome (RLS) and high ferritin levels in five patients. Patients and methods. The five patients were male, mean age: 59 years (range: 36-73 years). The patients were referred for RLS (two of them blood donors), in two cases associated with obstructive sleep apnea. Patients underwent a video-PSG recording. Serum iron and serum ferritin were determined. Results. All patients fulfilled the clinical criteria for RLS: leg paresthesias associated with an urge to move, motor restlessness, worsening of symptoms during the evening and night, and partial relief with activity, difficulty falling asleep, and presence of nocturnal awakenings due to RLS. Neurological examination, EEGs, EMGs and MRIs were normal. Video-PSGs recordings showed a disturbed and fragmented sleep with a reduction in total sleep time, low sleep efficiency, respiratory abnormalities with an apnea-hipopnea index > 10/h in two cases, and in all of them a periodic leg movements index > 5/h. The serum iron levels were within the normal range in all cases, whereas those in serum ferritin levels were high. Conclusions. To our knowledge the association of normal serum iron with high serum ferritin levels in patients diagnosed clinically and polygraphically as having RLS with periodic leg movements has not been described before. The notion of an involvement of a dopaminergic mechanism in the pathophysiology of RLS is supported by the decrease in the values of serum ferritin concentration observed in one patient during follow-up while being treated with dopaminergic agent

Evaluación neuropsicológica y evolución cognitiva de un enfermo de Alzheimer bilingüe

We present the case of a university-educated bilingual (Italian-Spanish) Alzheimer patient aged 61. After a neuropsychological assessment carried out at the Applied Psychology Service of the Faculty of Psychology at the Universidad Nacional de Educación a Distancia (UNED), an individualized integral cognitive stimulation program was started that lasted three years. The interest of this case lies in the fact that the progress of this patient in the two languages was followed through a series of cognitive tasks (mainly linguistic and categorization). The results show that, as the illness develops, the patient cannot separate both languages and interferences appear. His first language remains intact for a longer period of time, as studies of this type have shown.Presentamos el caso de un paciente de Alzheimer bilingüe (italiano-español) con estudios universitarios de 61 años. Tras la evaluación neuropsicológica, realizada en el Servicio de Psicología Aplicada (SPA) de la Universidad Nacional de Educación a Distancia (UNED), se inició un programa de estimulación cognitiva individualizada que duró tres años. El interés de este caso radica en el hecho de que la evolución del paciente en las dos lenguas fue seguida a través de una serie de tareas cognitivas (principalmente lingüísticas y de categorización). Los resultados muestran que, según avanza la enfermedad, el paciente no puede separar ambas lenguas y ocurren las interferencias. Su primera lengua se conserva durante un mayor periodo de tiempo, como los estudios de este tipo han mostrado

Data-Driven Phenotyping of Central Disorders of Hypersomnolence With Unsupervised Clustering

Background and ObjectivesRecent 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.MethodsWe 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.ResultsWe 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.DiscussionUsing 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

Data-Driven Phenotyping of Central Disorders of Hypersomnolence With Unsupervised Clustering.

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 hypersomnia. New reliable biomarkers are needed and the question arises whether current diagnostic criteria of hypersomnolence disorders should be reassessed. The main aim of this data-driven observational study was to see if 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 1078 unmedicated adolescents and adults. Seven clusters were identified, of which four clusters included predominantly individuals with cataplexy. The two most distinct clusters consisted of 158 and 157 patients respectively, were dominated by those without cataplexy and, amongst other variables, significantly differed in presence of sleep drunkenness, subjective difficulty awakening and weekend-week sleep length difference. Patients formally diagnosed as narcolepsy type 2 and idiopathic hypersomnia were evenly mixed in these two 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 rapid eye moment periods (SOREMPs) 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

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

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 rapid-eye-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.</p

Complex movement disorders at disease onset in childhood narcolepsy with cataplexy

Narcolepsy with cataplexy is characterized by daytime sleepiness, cataplexy (sudden loss of bilateral muscle tone triggered by emotions), sleep paralysis, hypnagogic hallucinations and disturbed nocturnal sleep. Narcolepsy with cataplexy is most often associated with human leucocyte antigen-DQB1*0602 and is caused by the loss of hypocretin-producing neurons in the hypothalamus of likely autoimmune aetiology. Noting that children with narcolepsy often display complex abnormal motor behaviours close to disease onset that do not meet the classical definition of cataplexy, we systematically analysed motor features in 39 children with narcolepsy with cataplexy in comparison with 25 age- and sex-matched healthy controls. We found that patients with narcolepsy with cataplexy displayed a complex array of ‘negative’ (hypotonia) and ‘active’ (ranging from perioral movements to dyskinetic–dystonic movements or stereotypies) motor disturbances. ‘Active’ and ‘negative’ motor scores correlated positively with the presence of hypotonic features at neurological examination and negatively with disease duration, whereas ‘negative’ motor scores also correlated negatively with age at disease onset. These observations suggest that paediatric narcolepsy with cataplexy often co-occurs with a complex movement disorder at disease onset, a phenomenon that may vanish later in the course of the disease. Further studies are warranted to assess clinical course and whether the associated movement disorder is also caused by hypocretin deficiency or by additional neurochemical abnormalities