912 resultados para ENFERMEDAD DE PARKINSON -DIAGNOSTICO - INVESTIGACIONES


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The present research sought to investigate the role of the basal ganglia in timing of sub- and supra-second intervals via an examination of the ability of people with Parkinson's disease (PD) to make temporal judgments in two ranges, 100-500 ms, and 1-5 s. Eighteen nondemented medicated patients with PD were compared with 14 matched controls on a duration-bisection task in which participants were required to discriminate auditory and visual signal durations within each time range. Results showed that patients with PD exhibited more variable duration judgments across both signal modality and duration range than controls, although closer analyses confirmed a timing deficit in the longer duration range only. The findings presented here suggest the bisection procedure may be a useful tool in identifying timing impairments in PD and, more generally, reaffirm the hypothesised role of the basal ganglia in temporal perception at the level of the attentionally mediated internal clock as well as memory retrieval and/or decision-making processes. (c) 2007 Elsevier Inc. All rights reserved.

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Parkinson's disease patients may have difficulty decoding prosodic emotion cues. These data suggest that the basal ganglia are involved, but may reflect dorsolateral prefrontal cortex dysfunction. An auditory emotional n-back task and cognitive n-back task were administered to 33 patients and 33 older adult controls, as were an auditory emotional Stroop task and cognitive Stroop task. No deficit was observed on the emotion decoding tasks; this did not alter with increased frontal lobe load. However, on the cognitive tasks, patients performed worse than older adult controls, suggesting that cognitive deficits may be more prominent. The impact of frontal lobe dysfunction on prosodic emotion cue decoding may only become apparent once frontal lobe pathology rises above a threshold.

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While the beneficial effect of levodopa on traditional motor control tasks have been well documented over the decades. its effect on speech motor control has rarely been objectively examined and the existing literature remains inconclusive. This paper aims to examine the effect of levodopa on speech in patients with Parkinson's disease. It was hypothesized that levodopa would improve preparatory motor set related activity and alleviate hypophonia. Patients fasted and abstained from levodopa overnight. Motor examination and speech testing was performed the following day, pre-levodopa during their "off' state, then at hourly intervals post-medication to obtain the best "on" state. All speech stimuli showed a consistent tendency for increased loudness and faster rate during the "on" state, but this was accompanied by a greater extent of intensity decay. Pitch and articulation remained unchanged. Levodopa effectively upscaled the overall gain setting of vocal amplitude and tempo, similar to its well-known effect on limb movement. However, unlike limb movement, this effect on the final acoustic product of speech may or may not be advantageous, depending on the existing speech profile of individual patients. (C) 2007 Movement Disorder Society.

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The visual perception of size in different regions of external space was studied in Parkinson's disease (PD). A group of patients with worse left-sided symptoms (LPD) was compared with a group with worse right-sided symptoms (RPD) and with a group of age-matched controls on judgements of the relative height or width of two rectangles presented in different regions of external space. The relevant dimension of one rectangle (the 'standard') was held constant, while that of the other (the 'variable') was varied in a method of constant stimuli. The point of subjective equality (PSE) of rectangle width or height was obtained by probit analysis as the mean of the resulting psychometric function. When the standard was in left space, the PSE of the LPD group occurred when the variable was smaller, and when the standard was in right space, when the variable was larger. Similarly, when the standard rectangle was presented in upper space, and the variable in lower space, the PSE occurred when the variable was smaller, an effect which was similar in both left and right spaces. In all these experiments, the PSEs for both the controls and the RPD group did not differ significantly, and were close to a physical match, and the slopes of the psychometric functions were steeper in the controls than the patients, though not significantly so. The data suggest that objects appear smaller in the left and upper visual spaces in LPD, probably because of right hemisphere impairment. (C) 2002 Elsevier Science Ltd. All rights reserved.

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Between 8 and 40% of Parkinson disease (PD) patients will have visual hallucinations (VHs) during the course of their illness. Although cognitive impairment has been identified as a risk factor for hallucinations, more specific neuropsychological deficits underlying such phenomena have not been established. Research in psychopathology has converged to suggest that hallucinations are associated with confusion between internal representations of events and real events (i.e. impaired-source monitoring). We evaluated three groups: 17 Parkinson's patients with visual hallucinations, 20 Parkinson's patients without hallucinations and 20 age-matched controls, using tests of visual imagery, visual perception and memory, including tests of source monitoring and recollective experience. The study revealed that Parkinson's patients with hallucinations appear to have intact visual imagery processes and spatial perception. However, there were impairments in object perception and recognition memory, and poor recollection of the encoding episode in comparison to both non-hallucinating Parkinson's patients and healthy controls. Errors were especially likely to occur when encoding and retrieval cues were in different modalities. The findings raise the possibility that visual hallucinations in Parkinson's patients could stem from a combination of faulty perceptual processing of environmental stimuli, and less detailed recollection of experience combined with intact image generation. (C) 2002 Elsevier Science Ltd. All fights reserved.

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In this paper we present the initial results using an artificial neural network to predict the onset of Parkinson's Disease tremors in a human subject. Data for the network was obtained from implanted deep brain electrodes. A tuned artificial neural network was shown to be able to identify the pattern of the onset tremor from these real time recordings.

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Deep Brain Stimulation (DBS) is a treatment routinely used to alleviate the symptoms of Parkinson's disease (PD). In this type of treatment, electrical pulses are applied through electrodes implanted into the basal ganglia of the patient. As the symptoms are not permanent in most patients, it is desirable to develop an on-demand stimulator, applying pulses only when onset of the symptoms is detected. This study evaluates a feature set created for the detection of tremor - a cardinal symptom of PD. The designed feature set was based on standard signal features and researched properties of the electrical signals recorded from subthalamic nucleus (STN) within the basal ganglia, which together included temporal, spectral, statistical, autocorrelation and fractal properties. The most characterized tremor related features were selected using statistical testing and backward algorithms then used for classification on unseen patient signals. The spectral features were among the most efficient at detecting tremor, notably spectral bands 3.5-5.5 Hz and 0-1 Hz proved to be highly significant. The classification results for determination of tremor achieved 94% sensitivity with specificity equaling one.

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Mutations in leucine-rich repeat kinase 2 (LRRK2) are the most common cause of Parkinson's disease (PD). LRRK2 contains a Ras of complex proteins (ROC) domain that may act as a GTPase to regulate its protein kinase activity. The structure of ROC and the mechanism(s) by which it regulates kinase activity are not known. Here, we report the crystal structure of the LRRK2 ROC domain in complex with GDP-Mg2+ at 2.0-Å resolution. The structure displays a dimeric fold generated by extensive domain-swapping, resulting in a pair of active sites constructed with essential functional groups contributed from both monomers. Two PD-associated pathogenic residues, R1441 and I1371, are located at the interface of two monomers and provide exquisite interactions to stabilize the ROC dimer. The structure demonstrates that loss of stabilizing forces in the ROC dimer is likely related to decreased GTPase activity resulting from mutations at these sites. Our data suggest that the ROC domain may regulate LRRK2 kinase activity as a dimer, possibly via the C-terminal of ROC (COR) domain as a molecular hinge. The structure of the LRRK2 ROC domain also represents a signature from a previously undescribed class of GTPases from complex proteins and results may provide a unique molecular target for therapeutics in PD.

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Approximately 20 % of individuals with Parkinson's disease (PD) report a positive family history. Yet, a large portion of causal and disease-modifying variants is still unknown. We used exome sequencing in two affected individuals from a family with late-onset PD to identify 15 potentially causal variants. Segregation analysis and frequency assessment in 862 PD cases and 1,014 ethnically matched controls highlighted variants in EEF1D and LRRK1 as the best candidates. Mutation screening of the coding regions of these genes in 862 cases and 1,014 controls revealed several novel non-synonymous variants in both genes in cases and controls. An in silico multi-model bioinformatics analysis was used to prioritize identified variants in LRRK1 for functional follow- up. However, protein expression, subcellular localization, and cell viability were not affected by the identified variants. Although it has yet to be proven conclusively that variants in LRRK1 are indeed causative of PD, our data strengthen a possible role for LRRK1 in addition to LRRK2 in the genetic underpinnings of PD but, at the same time, highlight the difficulties encountered in the study of rare variants identified by next-generation sequencing in diseases with autosomal dominant or complex patterns of inheritance.

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Parkinson is a neurodegenerative disease, in which tremor is the main symptom. This paper investigates the use of different classification methods to identify tremors experienced by Parkinsonian patients.Some previous research has focussed tremor analysis on external body signals (e.g., electromyography, accelerometer signals, etc.). Our advantage is that we have access to sub-cortical data, which facilitates the applicability of the obtained results into real medical devices since we are dealing with brain signals directly. Local field potentials (LFP) were recorded in the subthalamic nucleus of 7 Parkinsonian patients through the implanted electrodes of a deep brain stimulation (DBS) device prior to its internalization. Measured LFP signals were preprocessed by means of splinting, down sampling, filtering, normalization and rec-tification. Then, feature extraction was conducted through a multi-level decomposition via a wavelettrans form. Finally, artificial intelligence techniques were applied to feature selection, clustering of tremor types, and tremor detection.The key contribution of this paper is to present initial results which indicate, to a high degree of certainty, that there appear to be two distinct subgroups of patients within the group-1 of patients according to the Consensus Statement of the Movement Disorder Society on Tremor. Such results may well lead to different resultant treatments for the patients involved, depending on how their tremor has been classified. Moreover, we propose a new approach for demand driven stimulation, in which tremor detection is also based on the subtype of tremor the patient has. Applying this knowledge to the tremor detection problem, it can be concluded that the results improve when patient clustering is applied prior to detection.

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Background Recent studies indicate an increased frequency of mutations in the gene encoding glucocerebrosidase (GBA), a deficiency of which causes Gaucher`s disease, among patients with Parkinson`s disease. We aimed to ascertain the frequency of GBA mutations in an ethnically diverse group of patients with Parkinson`s disease. Methods Sixteen centers participated in our international, collaborative study: five from the Americas, six from Europe, two from Israel, and three from Asia. Each center genotyped a standard DNA panel to permit comparison of the genotyping results across centers. Genotypes and phenotypic data from a total of 5691 patients with Parkinson`s disease (780 Ashkenazi Jews) and 4898 controls (387 Ashkenazi Jews) were analyzed, with multivariate logistic-regression models and the Mantel-Haenszel procedure used to estimate odds ratios across centers. Results All 16 centers could detect two GBA mutations, L444P and N370S. Among Ashkenazi Jewish subjects, either mutation was found in 15% of patients and 3% of controls, and among non-Ashkenazi Jewish subjects, either mutation was found in 3% of patients and less than 1% of controls. GBA was fully sequenced for 1883 non-Ashkenazi Jewish patients, and mutations were identified in 7%, showing that limited mutation screening can miss half the mutant alleles. The odds ratio for any GBA mutation in patients versus controls was 5.43 across centers. As compared with patients who did not carry a GBA mutation, those with a GBA mutation presented earlier with the disease, were more likely to have affected relatives, and were more likely to have atypical clinical manifestations. Conclusions Data collected from 16 centers demonstrate that there is a strong association between GBA mutations and Parkinson`s disease.