Funktionelle Analyse der Plasmamembran-Ca 2+-ATPase 2 -PMCA2- in Modellen der Neurodegeneration

Calcium (Ca2+) ist ein ubiquitär vorkommendes Signalmolekül, das an der Regulation zahlreicher zellulärer Prozesse, von der Proliferation bis zum programmierten Zelltod, beteiligt ist. Daher müssen die intrazellulären Ca2+-Spiegel streng kontrolliert werden. Veränderungen der Ca2+-Homöostase während der altersassoziierten Neurodegeneration können dazu beitragen, dass Neuronen vulnerabler sind. So wurden erhöhte Ca2+-Konzentrationen in gealterten Neuronen, begleitet von einer erhöhten Vulnerabilität, beobachtet (Hajieva et al., 2009a). Weiterhin wird angenommen, dass der selektive Untergang von dopaminergen Neuronen bei der Parkinson Erkrankung auf eine erhöhte Ca2+-Last zurückzuführen sein könnte, da diese Neuronen einem ständigen Ca2+-Influx,rnaufgrund einer besonderen Isoform (CaV 1.3) spannungsgesteuerter Ca2+-Kanäle des L-Typs, ausgesetzt sind (Chan et al., 2007). Bislang wurden die molekularen Mechanismen, die einem Ca2+-Anstieg zu Grunde liegen und dessen Auswirkung jedoch nicht vollständig aufgeklärt und daher in der vorliegenden Arbeit untersucht. Um Veränderungen der Ca2+-Homöostase während der altersassoziiertenrnNeurodegeneration zu analysieren wurden primäre Mittelhirnzellen aus Rattenembryonen und SH-SY5Y-Neuroblastomazellen mit dem Neurotoxin 1-Methyl-4-Phenyl-Pyridin (MPP+), das bei der Etablierung von Modellen der Parkinson-Erkrankung breite Anwendung findet, behandelt. Veränderungen der intrazellulären Ca2+-Konzentration wurden mit einem auf dem grün fluoreszierenden Protein (GFP)-basierten Ca2+-Indikator,rn„Cameleon cpYC 3.6“ (Nagai et al., 2004), ermittelt. Dabei wurde in dieser Arbeit gezeigt, dass MPP+ die Abregulation der neuronenspezifischen ATP-abhängigen Ca2+-Pumpe der Plasmamembran (PMCA2) induziert, die mit der Ca2+-ATPase des endoplasmatischen Retikulums (SERCA) und dem Na+/Ca2+-Austauscher (NCX) das zelluläre Ca2+-Effluxsystem bildet, was zu einer erhöhten zytosolischen Ca2+-Konzentration führt. Die PMCA2-Abnahme wurde sowohl auf Transkriptionsebene als auch auf Proteinebene demonstriert, während keine signifikanten Veränderungen der SERCA- und NCX-Proteinmengen festgestellt wurden. Als Ursache der Reduktion der PMCA2-Expression wurde eine Abnahme des Transkriptionsfaktors Phospho-CREB ermittelt, dessen Phosphorylierungsstatus abhängig von der Proteinkinase A (PKA) war. Dieser Mechanismus wurde einerseits unter MPP+-Einfluss und andererseits vermittelt durch endogene molekulare Modulatoren gezeigt. Interessanterweise konnten die durch MPP+ induzierte PMCA2-Abregulation und der zytosolische Ca2+-Anstieg durch die Aktivierung der PKA verhindert werden. Parallel dazu wurde eine MPP+-abhängige verringerte mitochondriale Ca2+-Konzentration nachgewiesen, welche mit einer Abnahme des mitochondrialen Membranpotentials korrelierte. Darüber hinaus kam es als Folge der PMCA2-Abnahme zu einem verminderten neuronalen Überleben.rnVeränderungen der Ca2+-Homöostase wurden auch während der normalen Alterung inrnprimären Fibroblasten und bei Mäusen nachgewiesen. Dabei wurden verringerte PMCA und SERCA-Proteinmengen in gealterten Fibroblasten, einhergehend mit einem Anstieg der zytosolischen Ca2+-Konzentration demonstriert. Weiterhin wurden verringerte PMCA2-Proteinmengen im Mittelhirn von gealterten Mäusen (C57B/6) detektiert.rnDer zelluläre Ca2+-Efflux ist somit sowohl im Zuge der physiologischen Alterung als auch in einem altersbezogenen Krankheitsmodell beeinträchtigt, was das neuronale Überleben beeinflussen kann. In zukünftige Studien soll aufgeklärt werden, welche Auswirkungen einer PMCA2-Reduktion genau zu dem Verlust von Neuronen führen bzw. ob durch eine PMCA2-Überexpression neurodegenerative Prozesse verhindert werden können.

Cluster of Leptospirosis Acquired Through River Surfing in Switzerland

Background.  In Switzerland, leptospirosis is still considered as a travel-associated disease. After the surprising diagnosis of leptospirosis in a patient who was initially suspected as having primary human immunodeficiency virus infection, we recognized that acquisition of leptospirosis occurred through recreational activities and we identified additional affected individuals. Methods.  Detailed anamnesis, excluding occupational exposure, acquisition abroad, and pet contacts, enabled us to detect the source of infection and identify a cluster of leptospirosis. Convalescent sera testing was performed to confirm Leptospira infection. Microscopic agglutination tests were used to determine the infecting serovar. Results.  We identified a cluster of leptospirosis in young, previously healthy persons. Acquisition of leptospirosis was traced back to a surfing spot on a river in Switzerland (Reuss, Aargau). Clinical presentation was indistinct. Two of the 3 reported cases required hospitalization, and 1 case even suffered from meningitis. Serologic tests indicated infection with the serovar Grippotyphosa in all cases. With the exception of the case with meningitis, no antibiotics were administered, because leptospirosis was diagnosed after spontaneous resolution of most symptoms. Despite a prolonged period of convalescence in 2 cases, full recovery was achieved. Recent reports on beavers suffering from leptospirosis in this region underline the possible water-borne infection of the 3 cases and raise the question of potential wildlife reservoirs. Conclusions.  Insufficient awareness of caregivers, which may be promoted by the missing obligation to report human leptospirosis, combined with the multifaceted presentation of the disease result in significant underdiagnosis. More frequent consideration of leptospirosis as differential diagnosis is inevitable, particularly as veterinary data suggest re-emergence of the disease.

Multi-dimensional classification using Bayesian networks for stationary and evolving streaming data

Hoy en día, con la evolución continua y rápida de las tecnologías de la información y los dispositivos de computación, se recogen y almacenan continuamente grandes volúmenes de datos en distintos dominios y a través de diversas aplicaciones del mundo real. La extracción de conocimiento útil de una cantidad tan enorme de datos no se puede realizar habitualmente de forma manual, y requiere el uso de técnicas adecuadas de aprendizaje automático y de minería de datos. La clasificación es una de las técnicas más importantes que ha sido aplicada con éxito a varias áreas. En general, la clasificación se compone de dos pasos principales: en primer lugar, aprender un modelo de clasificación o clasificador a partir de un conjunto de datos de entrenamiento, y en segundo lugar, clasificar las nuevas instancias de datos utilizando el clasificador aprendido. La clasificación es supervisada cuando todas las etiquetas están presentes en los datos de entrenamiento (es decir, datos completamente etiquetados), semi-supervisada cuando sólo algunas etiquetas son conocidas (es decir, datos parcialmente etiquetados), y no supervisada cuando todas las etiquetas están ausentes en los datos de entrenamiento (es decir, datos no etiquetados). Además, aparte de esta taxonomía, el problema de clasificación se puede categorizar en unidimensional o multidimensional en función del número de variables clase, una o más, respectivamente; o también puede ser categorizado en estacionario o cambiante con el tiempo en función de las características de los datos y de la tasa de cambio subyacente. A lo largo de esta tesis, tratamos el problema de clasificación desde tres perspectivas diferentes, a saber, clasificación supervisada multidimensional estacionaria, clasificación semisupervisada unidimensional cambiante con el tiempo, y clasificación supervisada multidimensional cambiante con el tiempo. Para llevar a cabo esta tarea, hemos usado básicamente los clasificadores Bayesianos como modelos. La primera contribución, dirigiéndose al problema de clasificación supervisada multidimensional estacionaria, se compone de dos nuevos métodos de aprendizaje de clasificadores Bayesianos multidimensionales a partir de datos estacionarios. Los métodos se proponen desde dos puntos de vista diferentes. El primer método, denominado CB-MBC, se basa en una estrategia de envoltura de selección de variables que es voraz y hacia delante, mientras que el segundo, denominado MB-MBC, es una estrategia de filtrado de variables con una aproximación basada en restricciones y en el manto de Markov. Ambos métodos han sido aplicados a dos problemas reales importantes, a saber, la predicción de los inhibidores de la transcriptasa inversa y de la proteasa para el problema de infección por el virus de la inmunodeficiencia humana tipo 1 (HIV-1), y la predicción del European Quality of Life-5 Dimensions (EQ-5D) a partir de los cuestionarios de la enfermedad de Parkinson con 39 ítems (PDQ-39). El estudio experimental incluye comparaciones de CB-MBC y MB-MBC con los métodos del estado del arte de la clasificación multidimensional, así como con métodos comúnmente utilizados para resolver el problema de predicción de la enfermedad de Parkinson, a saber, la regresión logística multinomial, mínimos cuadrados ordinarios, y mínimas desviaciones absolutas censuradas. En ambas aplicaciones, los resultados han sido prometedores con respecto a la precisión de la clasificación, así como en relación al análisis de las estructuras gráficas que identifican interacciones conocidas y novedosas entre las variables. La segunda contribución, referida al problema de clasificación semi-supervisada unidimensional cambiante con el tiempo, consiste en un método nuevo (CPL-DS) para clasificar flujos de datos parcialmente etiquetados. Los flujos de datos difieren de los conjuntos de datos estacionarios en su proceso de generación muy rápido y en su aspecto de cambio de concepto. Es decir, los conceptos aprendidos y/o la distribución subyacente están probablemente cambiando y evolucionando en el tiempo, lo que hace que el modelo de clasificación actual sea obsoleto y deba ser actualizado. CPL-DS utiliza la divergencia de Kullback-Leibler y el método de bootstrapping para cuantificar y detectar tres tipos posibles de cambio: en las predictoras, en la a posteriori de la clase o en ambas. Después, si se detecta cualquier cambio, un nuevo modelo de clasificación se aprende usando el algoritmo EM; si no, el modelo de clasificación actual se mantiene sin modificaciones. CPL-DS es general, ya que puede ser aplicado a varios modelos de clasificación. Usando dos modelos diferentes, el clasificador naive Bayes y la regresión logística, CPL-DS se ha probado con flujos de datos sintéticos y también se ha aplicado al problema real de la detección de código malware, en el cual los nuevos ficheros recibidos deben ser continuamente clasificados en malware o goodware. Los resultados experimentales muestran que nuestro método es efectivo para la detección de diferentes tipos de cambio a partir de los flujos de datos parcialmente etiquetados y también tiene una buena precisión de la clasificación. Finalmente, la tercera contribución, sobre el problema de clasificación supervisada multidimensional cambiante con el tiempo, consiste en dos métodos adaptativos, a saber, Locally Adpative-MB-MBC (LA-MB-MBC) y Globally Adpative-MB-MBC (GA-MB-MBC). Ambos métodos monitorizan el cambio de concepto a lo largo del tiempo utilizando la log-verosimilitud media como métrica y el test de Page-Hinkley. Luego, si se detecta un cambio de concepto, LA-MB-MBC adapta el actual clasificador Bayesiano multidimensional localmente alrededor de cada nodo cambiado, mientras que GA-MB-MBC aprende un nuevo clasificador Bayesiano multidimensional. El estudio experimental realizado usando flujos de datos sintéticos multidimensionales indica los méritos de los métodos adaptativos propuestos. ABSTRACT Nowadays, with the ongoing and rapid evolution of information technology and computing devices, large volumes of data are continuously collected and stored in different domains and through various real-world applications. Extracting useful knowledge from such a huge amount of data usually cannot be performed manually, and requires the use of adequate machine learning and data mining techniques. Classification is one of the most important techniques that has been successfully applied to several areas. Roughly speaking, classification consists of two main steps: first, learn a classification model or classifier from an available training data, and secondly, classify the new incoming unseen data instances using the learned classifier. Classification is supervised when the whole class values are present in the training data (i.e., fully labeled data), semi-supervised when only some class values are known (i.e., partially labeled data), and unsupervised when the whole class values are missing in the training data (i.e., unlabeled data). In addition, besides this taxonomy, the classification problem can be categorized into uni-dimensional or multi-dimensional depending on the number of class variables, one or more, respectively; or can be also categorized into stationary or streaming depending on the characteristics of the data and the rate of change underlying it. Through this thesis, we deal with the classification problem under three different settings, namely, supervised multi-dimensional stationary classification, semi-supervised unidimensional streaming classification, and supervised multi-dimensional streaming classification. To accomplish this task, we basically used Bayesian network classifiers as models. The first contribution, addressing the supervised multi-dimensional stationary classification problem, consists of two new methods for learning multi-dimensional Bayesian network classifiers from stationary data. They are proposed from two different points of view. The first method, named CB-MBC, is based on a wrapper greedy forward selection approach, while the second one, named MB-MBC, is a filter constraint-based approach based on Markov blankets. Both methods are applied to two important real-world problems, namely, the prediction of the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors, and the prediction of the European Quality of Life-5 Dimensions (EQ-5D) from 39-item Parkinson’s Disease Questionnaire (PDQ-39). The experimental study includes comparisons of CB-MBC and MB-MBC against state-of-the-art multi-dimensional classification methods, as well as against commonly used methods for solving the Parkinson’s disease prediction problem, namely, multinomial logistic regression, ordinary least squares, and censored least absolute deviations. For both considered case studies, results are promising in terms of classification accuracy as well as regarding the analysis of the learned MBC graphical structures identifying known and novel interactions among variables. The second contribution, addressing the semi-supervised uni-dimensional streaming classification problem, consists of a novel method (CPL-DS) for classifying partially labeled data streams. Data streams differ from the stationary data sets by their highly rapid generation process and their concept-drifting aspect. That is, the learned concepts and/or the underlying distribution are likely changing and evolving over time, which makes the current classification model out-of-date requiring to be updated. CPL-DS uses the Kullback-Leibler divergence and bootstrapping method to quantify and detect three possible kinds of drift: feature, conditional or dual. Then, if any occurs, a new classification model is learned using the expectation-maximization algorithm; otherwise, the current classification model is kept unchanged. CPL-DS is general as it can be applied to several classification models. Using two different models, namely, naive Bayes classifier and logistic regression, CPL-DS is tested with synthetic data streams and applied to the real-world problem of malware detection, where the new received files should be continuously classified into malware or goodware. Experimental results show that our approach is effective for detecting different kinds of drift from partially labeled data streams, as well as having a good classification performance. Finally, the third contribution, addressing the supervised multi-dimensional streaming classification problem, consists of two adaptive methods, namely, Locally Adaptive-MB-MBC (LA-MB-MBC) and Globally Adaptive-MB-MBC (GA-MB-MBC). Both methods monitor the concept drift over time using the average log-likelihood score and the Page-Hinkley test. Then, if a drift is detected, LA-MB-MBC adapts the current multi-dimensional Bayesian network classifier locally around each changed node, whereas GA-MB-MBC learns a new multi-dimensional Bayesian network classifier from scratch. Experimental study carried out using synthetic multi-dimensional data streams shows the merits of both proposed adaptive methods.

Herpes simplex virus vector-mediated expression of Bcl-2 prevents 6-hydroxydopamine-induced degeneration of neurons in the substantia nigra in vivo

6-Hydroxydopamine (6-OHDA) is widely used to selectively lesion dopaminergic neurons of the substantia nigra (SN) in the creation of animal models of Parkinson’s disease. In vitro, the death of PC-12 cells caused by exposure to 6-OHDA occurs with characteristics consistent with an apoptotic mechanism of cell death. To test the hypothesis that apoptotic pathways are involved in the death of dopaminergic neurons of the SN caused by 6-OHDA, we created a replication-defective genomic herpes simplex virus-based vector containing the coding sequence for the antiapoptotic peptide Bcl-2 under the transcriptional control of the simian cytomegalovirus immediate early promoter. Transfection of primary cortical neurons in culture with the Bcl-2-producing vector protected those cells from naturally occurring cell death over 3 weeks. Injection of the Bcl-2-expressing vector into SN of rats 1 week before injection of 6-OHDA into the ipsilateral striatum increased the survival of neurons in the SN, detected either by retrograde labeling of those cells with fluorogold or by tyrosine hydroxylase immunocytochemistry, by 50%. These results, demonstrating that death of nigral neurons induced by 6-OHDA lesioning may be blocked by the expression of Bcl-2, are consistent with the notion that cell death in this model system is at least in part apoptotic in nature and suggest that a Bcl-2-expressing vector may have therapeutic potential in the treatment of Parkinson’s disease.

Inactivation of tyrosine hydroxylase by nitration following exposure to peroxynitrite and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)

The decrement in dopamine levels exceeds the loss of dopaminergic neurons in Parkinson’s disease (PD) patients and experimental models of PD. This discrepancy is poorly understood and may represent an important event in the pathogenesis of PD. Herein, we report that the rate-limiting enzyme in dopamine synthesis, tyrosine hydroxylase (TH), is a selective target for nitration following exposure of PC12 cells to either peroxynitrite or 1-methyl-4-phenylpyridiniun ion (MPP+). Nitration of TH also occurs in mouse striatum after MPTP administration. Nitration of tyrosine residues in TH results in loss of enzymatic activity. In the mouse striatum, tyrosine nitration-mediated loss in TH activity parallels the decline in dopamine levels whereas the levels of TH protein remain unchanged for the first 6 hr post MPTP injection. Striatal TH was not nitrated in mice overexpressing copper/zinc superoxide dismutase after MPTP administration, supporting a critical role for superoxide in TH tyrosine nitration. These results indicate that tyrosine nitration-induced TH inactivation and consequently dopamine synthesis failure, represents an early and thus far unidentified biochemical event in MPTP neurotoxic process. The resemblance of the MPTP model with PD suggests that a similar phenomenon may occur in PD, influencing the severity of parkisonian symptoms.

Mortality in people taking selegiline: observational study

Objective: To evaluate mortality among patients with Parkinson’s disease receiving different treatment.

Avaliação quantitativa dos efeitos da levodopa e da estimulação do núcleo subtalâmico sobre o equilíbrio em pacientes com doença de Parkinson

INTRODUÇÃO: Os efeitos da levodopa (LD) e da estimulação cerebral profunda (ECP) de núcleo subtalâmico (STN) sobre o equilíbrio e sintomas axiais são até o momento controversos. OBJETIVOS: Avaliar quantitativamente os efeitos da ECP de STN e da LD sobre o equilíbrio estático em pacientes com DP operados, em comparação com a LD em pacientes não operados. MÉTODOS: Trinta e um pacientes submetidos a ECP de STN entre 3 meses e 1 ano e meio antes da avaliação e 26 controles portadores de DP não operados, estágios Hoehn e Yahr 2 a 4 foram avaliados usando UPDRS para avaliação clínica e plataforma de força para avaliar oscilações posturais. O primeiro grupo foi avaliado com ECP e sem medicação, com ECP e com medicação e sem ECP e sem medicação. O segundo grupo foi avaliado com e sem medicação. Cada paciente foi avaliado com os olhos abertos e fechados. O deslocamento do centro de pressão anteroposterior, laterolateral, a área, velocidade e deslocamento total linear foram medidos pela plataforma de força. Os dados paramétricos foram comparados usando o teste t de Student e os dados não-paramétricos foram comparados pelo teste de Kruskal-Wallis. A avaliação clínica consistiu na parte 3 da escala UPDRS e na escala Hoehn e Yahr. Nível de significância estatística considerada foi p=0,05. RESULTADOS: Os pacientes não operados oscilaram mais quando sob efeito da levodopa do que sem medicação. No grupo operado, a maior oscilação é no grupo com ECP desligada e sem medicação. Tende a reduzir sob efeito da ECP apresenta redução significativa sob efeito simultâneo de ECP e levodopa. CONCLUSÃO: A associação da ECP de NST com medicação tem impacto positivo sobre o controle postural. O efeito da ECP de NST reverte o efeito negativo da levodopa sobre as oscilações observadas em pacientes não operados

Towards Closed-loop Deep Brain Stimulation: Behavior Recognition from Human STN

Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinson’s disease (PD). Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and side effects by adjusting stimulation parameters based on patient’s behavior. Thus behavior detection is a major step in designing such systems. Various physiological signals can be used to recognize the behaviors. Subthalamic Nucleus (STN) Local field Potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. This thesis proposes novel detection and classification techniques for behavior recognition based on deep brain LFP. Behavior detection from such signals is the vital step in developing the next generation of closed-loop DBS devices. LFP recordings from 13 subjects are utilized in this study to design and evaluate our method. Recordings were performed during the surgery and the subjects were asked to perform various behavioral tasks. Various techniques are used understand how the behaviors modulate the STN. One method studies the time-frequency patterns in the STN LFP during the tasks. Another method measures the temporal inter-hemispheric connectivity of the STN as well as the connectivity between STN and Pre-frontal Cortex (PFC). Experimental results demonstrate that different behaviors create different m odulation patterns in STN and it’s connectivity. We use these patterns as features to classify behaviors. A method for single trial recognition of the patient’s current task is proposed. This method uses wavelet coefficients as features and support vector machine (SVM) as the classifier for recognition of a selection of behaviors: speech, motor, and random. The proposed method is 82.4% accurate for the binary classification and 73.2% for classifying three tasks. As the next step, a practical behavior detection method which asynchronously detects behaviors is proposed. This method does not use any priori knowledge of behavior onsets and is capable of asynchronously detect the finger movements of PD patients. Our study indicates that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from STN. We utilize a non-linear regression method to measure this inter-hemispheric connectivity and to detect the finger movements. Our experimental results using STN LFP recorded from eight patients with PD demonstrate this is a promising approach for behavior detection and developing novel closed-loop DBS systems.

RNA-Seq expression profile of genes related to neurodegenerative disorders affecting the human retina

Human neurodegenerative diseases, such as Parkinson’s disease (PD) and the neuromuscular disorders called dystroglycanopathies (DGPs), cause retinal impairments. We have used RNA-Seq technology to catalog all known genes linked to PD and DGPs expressed in the human retina and quantitate their mRNA levels in terms of FPKM. We have also characterized their expression profiles in the retina by determining their exonic, intronic and exon-intron junction expression levels, as well as the alternative splicing pattern of particular genes. We believe these data could pave the way toward understanding the molecular bases of sight deficiencies associated with neurodegenerative disorders.

A doença de Parkinson no doente idoso

Trabalho Final do Curso de Mestrado Integrado em Medicina, Faculdade de Medicina, Universidade de Lisboa, 2014

Effets de la stimulation magnétique transcrânienne dans la maladie de Parkinson avec déficits cognitifs légers

Beaucoup de patients atteints de la maladie de Parkinson (MP) peuvent souffrir de troubles cognitifs dès les étapes initiales de la maladie et jusqu’à 80% d’entre eux vont développer une démence. Des altérations fonctionnelles au niveau du cortex préfrontal dorsolatéral (CPFDL), possiblement en relation avec le noyau caudé, seraient à l’origine de certains de ces déficits cognitifs. Des résultats antérieurs de notre groupe ont montré une augmentation de l’activité et de la connectivité dans la boucle cortico-striatale cognitive suite à la stimulation magnétique transcrânienne (SMT) utilisant des paramètres « theta burst » intermittent (iTBS) sur le CPFDL gauche. Pour cette étude, 24 patients atteints de la MP avec des troubles cognitifs ont été séparées en 2 groupes : le groupe iTBS active (N=15) et le groupe sham (stimulation simulée, N=9). Une batterie neuropsychologique détaillée évaluant cinq domaines cognitifs (attention, fonctions exécutives, langage, mémoire et habiletés visuo-spatiales) a été administrée lors des jours 1, 8, 17 et 37. Le protocole iTBS a été appliqué sur le CPFDL gauche durant les jours 2, 4 et 7. Les scores z ont été calculés pour chaque domaine cognitif et pour la cognition globale. Les résultats ont montré une augmentation significative de la cognition globale jusqu’à 10 jours suivant l’iTBS active, particulièrement au niveau de l’attention, des fonctions exécutives et des habiletés visuo-spatiales. Cet effet sur la cognition globale n’est pas répliqué dans le groupe sham. Ces résultats suggèrent donc que l’iTBS peut moduler la performance cognitive chez les patients atteints de MP avec des déficits cognitifs.

In-home Assessment of Turning and Transitions using Inertial Sensors in Older Adults with Dementia and Older Caregivers

Thesis (Master's)--University of Washington, 2016-06

Dopamine and semantic activation: An investigation of masked direct and indirect priming

To investigate the effects of dopamine on the dynamics of semantic activation, 39 healthy volunteers were randomly assigned to ingest either a placebo (n = 24) or a levodopa (it = 16) capsule. Participants then performed a lexical decision task that implemented a masked priming paradigm. Direct and indirect semantic priming was measured across stimulus onset asynchronies (SOAs) of 250, 500 and 1200 ms. The results revealed significant direct and indirect semantic priming effects for the placebo group at SOAs of 250 ms and 500 ms, but no significant direct or indirect priming effects at the 1200 ms SOA. In contrast, the levodopa group showed significant direct and indirect semantic priming effects at the 250 ms SOA, while no significant direct or indirect priming effects were evident at the SOAs of 500 ins or 1200 ms. These results suggest that dopamine has a role in modulating both automatic and attentional aspects of semantic activation according to a specific time course. The implications of these results for current theories of dopaminergic modulation of semantic activation are discussed.

Australian norms for a quantitative olfactory function test

Quantitative olfactory assessment is often neglected in clinical practice, although olfactory loss can assist diagnosis and leads to significant morbidity. The aim of this study was to develop normative data for the Australian population for the 'Sniffin' Sticks', an internationally established olfactory function test. As in other populations, Australian females performed better than males and both lost olfactory function with age. From the normative data, criterion test scores for males and females were established for clinical classifications ('normosmic', 'hyposmic', and 'anosmic'). These clinical classifications were assessed in Parkinson's patients: 81.1 % were anosmic or severely hyposmic and only 7.7% were normosmic. A new term ('prebyosmia') is introduced to describe age-related loss of olfactory capacity of unknown aetiology. With these norms, the Sniffin' Sticks can be used in the Australian population to compare an individual's olfactory function against the population of others of similar age and sex and to identify olfactory dysfunction. (C) 2003 Elsevier Ltd. All rights reserved.

Reappraising contemporary theories of subcortical participation in language: Proposing an interhemispheric regulatory function for the subthalamic nucleus (STN) in the mediation of high-level linguistic processes

Apropos the basal ganglia, the dominant striatum and globus pallidus internus (GPi) have been hypothesised to represent integral components of subcortical language circuitry. Working subcortical language theories, however, have failed thus far to consider a role for the STN in the mediation of linguistic processes, a structure recently defined as the driving force of basal ganglia output. The aim of this research was to investigate the impact of surgically induced functional inhibition of the STN upon linguistic abilities, within the context of established models of basal ganglia participation in language. Two males with surgically induced 'lesions' of the dominant and non-dominant dorsolateral STN, aimed at relieving Parkinsonian motor symptoms, served as experimental subjects. General and high-level language profiles were compiled for each subject up to 1 month prior to and 3 months following neurosurgery, within the drug-on state (i.e., when optimally medicated). Comparable post-operative alterations in linguistic performance were observed subsequent to surgically induced functional inhibition of the left and right STN. More specifically, higher proportions of reliable decline as opposed to improvement in post-operative performance were demonstrated by both subjects on complex language tasks, hypothesised to entail the interplay of cognitive-linguistic processes. The outcomes of the current research challenge unilateralised models of functional basal ganglia organisation with the proposal of a potential interhemispheric regulatory function for the STN in the mediation of high-level linguistic processes.