Cognitive functioning, behavior, and quality of life after stroke in childhood

Rationale: To provide a better understanding of cognitive functioning, motor outcome, behavior and quality of life after childhood stroke and to study the relationship between variables expected to influence rehabilitation and outcome (age at stroke, time elapsed since stroke, lateralization, location and size of lesion). Methods: Children who suffered from stroke between birth and their eighteenth year of life underwent an assessment consisting of cognitive tests (WISC-III, WAIS-R, K-ABC, TAP, Rey-Figure, German Version of the CVLT) and questionnaires (Conner's Scales, KIDSCREEN). Results: Twenty-one patients after stroke in childhood (15 males, mean 11;11 years, SD 4;3, range 6;10-21;2) participated in the study. Mean Intelligence Quotients (IQ) were situated within the normal range (mean Full Scale IQ 96.5, range IQ 79-129). However, significantly more patients showed deficits in various cognitive domains than expected from a healthy population (Performance IQ p = .000; Digit Span p = .000, Arithmetic's p = .007, Divided Attention p = .028, Alertness p = .002). Verbal IQ was significantly better than Performance IQ in 13 of 17 patients, independent of the hemispheric side of lesion. Symptoms of ADHD occurred more often in the patients' sample than in a healthy population (learning difficulties/inattention p = .000; impulsivity/hyperactivity p = .006; psychosomatics p = .006). Certain aspects of quality of life were reduced (autonomy p = .003; parents' relation p = .003; social acceptance p = .037). Three patients had a right-sided hemiparesis, mean values of motor functions of the other patients were slightly impaired (sequential finger movements p = .000, hand alternation p = .001, foot tapping p = .043). In patients without hemiparesis, there was no relation between the lateralization of lesion and motor outcome. Lesion that occurred in the midst of childhood (5-10 years) led to better cognitive outcome than lesion in the very early (0-5 years) or late childhood (10-18 years). Other variables such as presence of seizure, elapsed time since stroke and size of lesion had a small to no impact on prognosis. Conclusion: Moderate cognitive and motor deficits, behavioral problems, and impairment in some aspects of quality of life frequently remain after stroke in childhood. Visuospatial functions are more often reduced than verbal functions, independent of the hemispheric side of lesion. This indicates a functional superiority of verbal skills compared to visuospatial skills in the process of recovery after brain injury. Compared to the cognitive outcome following stroke in adults, cognitive sequelae after childhood stroke do indicate neither the lateralization nor the location of the lesion focus. Age at stroke seems to be the only determining factor influencing cognitive outcome.

Changes in blood flow velocity in the middle and anterior cerebral arteries evoked by walking

PURPOSE: Transcranial Doppler sonography (TCD) is an established method for assessing changes in blood flow velocity (BFV) coupled to brain activity. Our objective was to investigate whether walking induces measurable changes in BFV in healthy subjects. METHODS: Changes in BFV in both middle cerebral arteries (MCAs) of 40 healthy adult subjects during walking on a treadmill were measured using bilateral TCD. In 8 of the 40 subjects, 1 anterior cerebral artery (ACA) was monitored simultaneously with the contralateral MCA. The percentage increase in BFV (BFVI%) compared with the baseline velocity (V(0)), the percentage decrease in BFV (BFVD%) compared with the V(0), and the normalized ACA-MCA ratio were analyzed. RESULTS: The overall mean (+/- standard deviation [SD]) V(0) was 59.9 +/- 11.6 cm/second in the left MCA and 60.1 +/- 12.9 cm/second in the right MCA. Women had higher V(0) values than men had. Walking evoked an initial mean overall BFVI% in both left (8.4 +/- 5.1%) and right MCAs (9.1 +/- 5.1%), followed by a decrease to below baseline values in 38 of 40 subjects. A statistically significant increase of the normalized ACA-MCA ratio was measured, indicating that changes in BFV in the ACA territory were coupled to brain activation during walking. CONCLUSIONS: The use of functional TCD showed different changes in BFV in the ACAs and MCAs during walking. This method may be an interesting tool for monitoring progress in patients with motor deficits of the legs, such as paresis.

Necessary or unnecessary? a critical glance on spine surgery

Patients with complaints and symptoms caused by spinal degenerative diseases demonstrate a high rate of spontaneous improvement. Except of severe neurological symptoms such as high grade motor deficits, medically intractable pain and vegetative symptoms (cauda syndrome) operations require 1) symptoms, 2) a mechanical cause visible on imaging that sufficiently explains the symptoms, 3) a completed conservative treatment protocol performed over a 4) 6-12 week period. According to the evidence found in the literature, patients with lumbar disk herniation significantly benefit from surgery by a faster relieve of pain and return to social and professional activity, however, the results are converging after a period of 1-2 years. Surgery of lumbar spinal stenosis is considered a gold standard and superior to conservative care when symptoms are severe and leg pain is present. Bilateral microsurgical decompression using a bilateral or a unilateral approach with over-the-top decompression of the contralateral nerve root are superior to laminectomy as the decompression procedure. Lumbar fusion is only indicated in patients with spinal stenosis when a major or mobile spondylolisthesis is diagnosed. There is no indication of prophylactic surgery to avoid a "dangerous" deficit that might develop in the future.

Distributed System for Cognitive Stimulation Over Interactive TV.

Descripción y evaluación de sistema de estimulación cognitiva a través de la TDT orientada a personas con enfermedad de Parkinson, con supervisión por parte de sus terapeutas de forma remota. Abstract: This paper details the full design, implementation, and validation of an e-health service in order to improve the community health care services for patients with cognitive disorders. Specifically, the new service allows Parkinson’s disease patients benefit from the possibility of doing cognitive stimulation therapy (CST) at home by using a familiar device such as a TV set. Its use instead of a PC could be a major advantage for some patients whose lack of familiarity with the use of a PC means that they can do therapy only in the presence of a therapist. For these patients this solution could bring about a great improvement in their autonomy. At the same time, this service provides therapists with the ability to conduct follow-up of therapy sessions via the web,benefiting from greater and easier control of the therapy exercises performed by patients and allowing them to customize new exercises in accordance with the particular needs of each patient. As a result, this kind of CST is considered to be a complement of other therapies oriented to the Parkinson patients. Furthermore, with small changes, the system could be useful for patients with a different cognitive disease such as Alzheimer’s or mild cognitive impairment.

Optimisation of algorithms to compute information theoretic indexes

Analysis of big amount of data is a field with many years of research. It is centred in getting significant values, to make it easier to understand and interpret data. Being the analysis of interdependence between time series an important field of research, mainly as a result of advances in the characterization of dynamical systems from the signals they produce. In the medicine sphere, it is easy to find many researches that try to understand the brain behaviour, its operation mode and its internal connections. The human brain comprises approximately 1011 neurons, each of which makes about 103 synaptic connections. This huge number of connections between individual processing elements provides the fundamental substrate for neuronal ensembles to become transiently synchronized or functionally connected. A similar complex network configuration and dynamics can also be found at the macroscopic scales of systems neuroscience and brain imaging. The emergence of dynamically coupled cell assemblies represents the neurophysiological substrate for cognitive function such as perception, learning, thinking. Understanding the complex network organization of the brain on the basis of neuroimaging data represents one of the most impervious challenges for systems neuroscience. Brain connectivity is an elusive concept that refers to diferent interrelated aspects of brain organization: structural, functional connectivity (FC) and efective connectivity (EC). Structural connectivity refers to a network of physical connections linking sets of neurons, it is the anatomical structur of brain networks. However, FC refers to the statistical dependence between the signals stemming from two distinct units within a nervous system, while EC refers to the causal interactions between them. This research opens the door to try to resolve diseases related with the brain, like Parkinson’s disease, senile dementia, mild cognitive impairment, etc. One of the most important project associated with Alzheimer’s research and other diseases are enclosed in the European project called Blue Brain. The center for Biomedical Technology (CTB) of Universidad Politecnica de Madrid (UPM) forms part of the project. The CTB researches have developed a magnetoencephalography (MEG) data processing tool that allow to visualise and analyse data in an intuitive way. This tool receives the name of HERMES, and it is presented in this document. Analysis of big amount of data is a field with many years of research. It is centred in getting significant values, to make it easier to understand and interpret data. Being the analysis of interdependence between time series an important field of research, mainly as a result of advances in the characterization of dynamical systems from the signals they produce. In the medicine sphere, it is easy to find many researches that try to understand the brain behaviour, its operation mode and its internal connections. The human brain comprises approximately 1011 neurons, each of which makes about 103 synaptic connections. This huge number of connections between individual processing elements provides the fundamental substrate for neuronal ensembles to become transiently synchronized or functionally connected. A similar complex network configuration and dynamics can also be found at the macroscopic scales of systems neuroscience and brain imaging. The emergence of dynamically coupled cell assemblies represents the neurophysiological substrate for cognitive function such as perception, learning, thinking. Understanding the complex network organization of the brain on the basis of neuroimaging data represents one of the most impervious challenges for systems neuroscience. Brain connectivity is an elusive concept that refers to diferent interrelated aspects of brain organization: structural, functional connectivity (FC) and efective connectivity (EC). Structural connectivity refers to a network of physical connections linking sets of neurons, it is the anatomical structur of brain networks. However, FC refers to the statistical dependence between the signals stemming from two distinct units within a nervous system, while EC refers to the causal interactions between them. This research opens the door to try to resolve diseases related with the brain, like Parkinson’s disease, senile dementia, mild cognitive impairment, etc. One of the most important project associated with Alzheimer’s research and other diseases are enclosed in the European project called Blue Brain. The center for Biomedical Technology (CTB) of Universidad Politecnica de Madrid (UPM) forms part of the project. The CTB researches have developed a magnetoencephalography (MEG) data processing tool that allow to visualise and analyse data in an intuitive way. This tool receives the name of HERMES, and it is presented in this document.

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

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.

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.

Clinical progression and outcome of dysphagia following paediatric traumatic brain injury: a prospective study

Objectives : To provide a preliminary clinical profile of the resolution and outcomes of oral-motor impairment and swallowing function in a group of paediatric dysphagia patients post-traumatic brain injury (TBI). To document the level of cognitive impairment parallel to the return to oral intake, and to investigate the correlation between the resolution of impaired swallow function versus the resolution of oral-motor impairment and cognitive impairment. Participants : Thirteen children admitted to an acute care setting for TBI. Main outcome measures : A series of oral-motor (Verbal Motor Production Assessment for Children, Frenchay Dysarthria Assessment, Schedule for Oral Motor Assessment) and swallowing (Paramatta Hospital's Assessment for Dysphagia) assessments, an outcome measure for swallowing (Royal Brisbane Hospital's Outcome Measure for Swallowing), and a cognitive rating scale (Rancho Level of Cognitive Functioning Scale). Results : Across the patient group, oral-motor deficits resolved to normal status between 3 and 11 weeks post-referral (and at an average of 12 weeks post-injury) and swallowing function and resolution to normal diet status were achieved by 3-11 weeks post-referral (and at an average of 12 weeks post-injury). The resolution of dysphagia and the resolution of oral-motor impairment and cognitive impairment were all highly correlated. Conclusion : The provision of a preliminary profile of oral-motor functioning and dysphagia resolution, and data on the linear relationship between swallowing impairment and cognition, will provide baseline information on the course of rehabilitation of dysphagia in the paediatric population post-TBI. Such data will contribute to more informed service provision and rehabilitation planning for paediatric patients post-TBI.

Beyond verbal fluency: Investigating the long-term effects of bilateral subthalamic (STN) deep brain stimulation (DBS) on language in two cases

Cognitive functioning has been described as largely impervious to chronic STN-DBS administered over 12-month periods. In relation to the domain of language, however, the effects of STN-DBS are yet to be thoroughly delineated. Verbal fluency tasks represent an almost exclusively applied index of linguistic proficiency relative to neuropsychological research within this population. Comprehensive investigations of the impact of STN-DBS on language function, however, have never been undertaken. The more precise elucidation of the role of the STN in the mediation of language processes, by way of assessments which probe language comprehension and production mechanisms, served as the primary focus of this research. Longitudinal analysis also afforded consideration of the way in which cognitive-linguistic circuits respond to STN-DBS over time. Bilateral STN-DBS primarily effected clinically reliable fluctuations (i.e., both improvements and declines) in performance in both subjects on tasks demanding cognitive-linguistic flexibility in the formulation and comprehension of complex language. Of particular note, both subjects demonstrated a cumulative increase in the proportion of reliable post-operative improvements achieved over time. The findings of this research lend support to models of subcortical participation in language which endorse a role for the STN, and suggest that bilateral STN-DBS may serve to enhance the proficiency of basal ganglia-thalamocortical linguistic circuits over time.