Exercício, funcionalidade e distúrbios do sono em pacientes com doença de Parkinson

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

First-principle data-driven models for assessment of motor disorders in Parkinson’s disease

Parkinson’s disease (PD) is an increasing neurological disorder in an aging society. The motor and non-motor symptoms of PD advance with the disease progression and occur in varying frequency and duration. In order to affirm the full extent of a patient’s condition, repeated assessments are necessary to adjust medical prescription. In clinical studies, symptoms are assessed using the unified Parkinson’s disease rating scale (UPDRS). On one hand, the subjective rating using UPDRS relies on clinical expertise. On the other hand, it requires the physical presence of patients in clinics which implies high logistical costs. Another limitation of clinical assessment is that the observation in hospital may not accurately represent a patient’s situation at home. For such reasons, the practical frequency of tracking PD symptoms may under-represent the true time scale of PD fluctuations and may result in an overall inaccurate assessment. Current technologies for at-home PD treatment are based on data-driven approaches for which the interpretation and reproduction of results are problematic.  The overall objective of this thesis is to develop and evaluate unobtrusive computer methods for enabling remote monitoring of patients with PD. It investigates first-principle data-driven model based novel signal and image processing techniques for extraction of clinically useful information from audio recordings of speech (in texts read aloud) and video recordings of gait and finger-tapping motor examinations. The aim is to map between PD symptoms severities estimated using novel computer methods and the clinical ratings based on UPDRS part-III (motor examination). A web-based test battery system consisting of self-assessment of symptoms and motor function tests was previously constructed for a touch screen mobile device. A comprehensive speech framework has been developed for this device to analyze text-dependent running speech by: (1) extracting novel signal features that are able to represent PD deficits in each individual component of the speech system, (2) mapping between clinical ratings and feature estimates of speech symptom severity, and (3) classifying between UPDRS part-III severity levels using speech features and statistical machine learning tools. A novel speech processing method called cepstral separation difference showed stronger ability to classify between speech symptom severities as compared to existing features of PD speech. In the case of finger tapping, the recorded videos of rapid finger tapping examination were processed using a novel computer-vision (CV) algorithm that extracts symptom information from video-based tapping signals using motion analysis of the index-finger which incorporates a face detection module for signal calibration. This algorithm was able to discriminate between UPDRS part III severity levels of finger tapping with high classification rates. Further analysis was performed on novel CV based gait features constructed using a standard human model to discriminate between a healthy gait and a Parkinsonian gait. The findings of this study suggest that the symptom severity levels in PD can be discriminated with high accuracies by involving a combination of first-principle (features) and data-driven (classification) approaches. The processing of audio and video recordings on one hand allows remote monitoring of speech, gait and finger-tapping examinations by the clinical staff. On the other hand, the first-principles approach eases the understanding of symptom estimates for clinicians. We have demonstrated that the selected features of speech, gait and finger tapping were able to discriminate between symptom severity levels, as well as, between healthy controls and PD patients with high classification rates. The findings support suitability of these methods to be used as decision support tools in the context of PD assessment.

A systematic review of characteristics and validity of monitoring technologies to assess Parkinson’s disease symptoms and signs

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

Quantification of upper limb motor symptoms of Parkinson’s disease using a smartphone

Panda

A review of Parkinson’s disease cardinal and dyskinetic motor symptoms assessment methods using sensor systems

This paper is reviewing objective assessments of Parkinson’s disease(PD) motor symptoms, cardinal, and dyskinesia, using sensor systems. It surveys the manifestation of PD symptoms, sensors that were used for their detection, types of signals (measures) as well as their signal processing (data analysis) methods. A summary of this review’s finding is represented in a table including devices (sensors), measures and methods that were used in each reviewed motor symptom assessment study. In the gathered studies among sensors, accelerometers and touch screen devices are the most widely used to detect PD symptoms and among symptoms, bradykinesia and tremor were found to be mostly evaluated. In general, machine learning methods are potentially promising for this. PD is a complex disease that requires continuous monitoring and multidimensional symptom analysis. Combining existing technologies to develop new sensor platforms may assist in assessing the overall symptom profile more accurately to develop useful tools towards supporting better treatment process.

Dissecting gastrointestinal dysfunction and inflammation in a “pre-motor” rodent model of Parkinson’s disease

Research on Parkinson’s disease (PD) has mainly focused on the degeneration of the dopaminergic neurons of nigro-striatal (NS) pathway; also, post-mortem studies have demonstrated that the noradrenergic and the serotonergic transmitter systems are also affected (Jellinger, 1999). Degeneration of these neuronal cell bodies is generally thought to start prior to the loss of dopaminergic neurons in the NS pathway and precedes the appearance of the motor symptoms that are the “hallmark” of PD. Gastrointestinal (GI) motility is often disturbed in PD, manifesting chiefly as impaired gastric emptying and constipation. These GI dysfunction symptoms may be the result of a loss in noradrenergic and serotonergic innervation. GI deficits were evaluated using an organ bath technique. Groups treated with different combinations of neurotoxins (6-OHDA alone, 6-OHDA + pCA or 6-OHDA + DSP-4) presented significant differences in gut contractility compared to control groups. Since a substantial body of literature suggests the presence of an inflammatory process in parkinsonian state (Whitton, 2007), changes in pro-inflammatory cytokines in the gut were assessed using a cytokine microarray. It has been found in this work that groups with a combined dopaminergic and noradrenergic lesion have a significant increase in both expressions of IL-13 and VEGF. IL-6 also shows a decrease in treatment groups; however this decrease did not reach statistical significance. The therapeutic value of Exendin-4 (EX-4) was evaluated. It has been previously demonstrated that EX-4, a glucagon-like peptide-1 receptor (GLP-1R) agonist, is neuroprotective in rodent models of PD (Harkavyi et al., 2008). In this thesis it has been found that EX-4 was able to reverse a decrease in gut contractility obtained through intracerebral bilateral 6-OHDA injection. Although more studies are required, EX-4 could be used as a possible therapy for the GI symptoms prominent in the early stages of PD.

PET and MR imaging in Parkinson’s disease patients with cognitive impairment. A study of dopaminergic dysfunction, amyloid deposition, cortical hypometabolism and brain atrophy

Parkinson’s disease (PD) is the second most common neurodegenerative disorder. It is characterized by a severe loss of substantia nigra dopaminergic neurons leading to dopamine depletion in the striatum. PD affects movement, producing motor symptoms such as rigidity, tremor and bradykinesia. Non-motor symptoms include autonomic dysfunction, neurobehavioral problems and cognitive impairment, which may lead to dementia. The pathophysiological basis of cognitive impairment and dementia in PD is unclear. The aim of this thesis was to study the pathophysiological basis of cognitive impairment and dementia in PD. We evaluated the relation between frontostriatal dopaminergic dysfunction and the cognitive symptoms in PD patients with [18F]Fdopa PET. We also combined [C]PIB and [¹⁸F]FDG PET and magnetic resonance imaging in PD patients with and without dementia. In addition, we analysed subregional striatal [¹⁸F]Fdopa PET data to find out whether a simple ratio approach would reliably separate PD patients from healthy controls. The impaired dopaminergic function of the frontostriatal regions was related to the impairment in cognitive functions, such as memory and cognitive processing in PD patients. PD patients with dementia showed an impaired glucose metabolism but not amyloid deposition in the cortical brain regions, and the hypometabolism was associated with the degree of cognitive impairment. PD patients had atrophy, both in the prefrontal cortex and in the hippocampus, and the hippocampal atrophy was related to impaired memory. A single 15-min scan 75 min after a tracer injection seemed to be sufficient for separating patients with PD from healthy controls in a clinical research environment. In conclusion, the occurrence of cognitive impairment and dementia in PD seems to be multifactorial and relates to changes, such as reduced dopaminergic activity, hypometabolism, brain atrophy and rarely to amyloid accumulation.

Prediction of Parkinson’s Disease tremor onset using a radial basis function neural network based on particle swarm optimization

Deep Brain Stimulation (DBS) has been successfully used throughout the world for the treatment of Parkinson's disease symptoms. To control abnormal spontaneous electrical activity in target brain areas DBS utilizes a continuous stimulation signal. This continuous power draw means that its implanted battery power source needs to be replaced every 18–24 months. To prolong the life span of the battery, a technique to accurately recognize and predict the onset of the Parkinson's disease tremors in human subjects and thus implement an on-demand stimulator is discussed here. The approach is to use a radial basis function neural network (RBFNN) based on particle swarm optimization (PSO) and principal component analysis (PCA) with Local Field Potential (LFP) data recorded via the stimulation electrodes to predict activity related to tremor onset. To test this approach, LFPs from the subthalamic nucleus (STN) obtained through deep brain electrodes implanted in a Parkinson patient are used to train the network. To validate the network's performance, electromyographic (EMG) signals from the patient's forearm are recorded in parallel with the LFPs to accurately determine occurrences of tremor, and these are compared to the performance of the network. It has been found that detection accuracies of up to 89% are possible. Performance comparisons have also been made between a conventional RBFNN and an RBFNN based on PSO which show a marginal decrease in performance but with notable reduction in computational overhead.

Parkinson’s Disease tremor classification – a comparison between Support Vector Machines and neural networks

Deep Brain Stimulation has been used in the study of and for treating Parkinson’s Disease (PD) tremor symptoms since the 1980s. In the research reported here we have carried out a comparative analysis to classify tremor onset based on intraoperative microelectrode recordings of a PD patient’s brain Local Field Potential (LFP) signals. In particular, we compared the performance of a Support Vector Machine (SVM) with two well known artificial neural network classifiers, namely a Multiple Layer Perceptron (MLP) and a Radial Basis Function Network (RBN). The results show that in this study, using specifically PD data, the SVM provided an overall better classification rate achieving an accuracy of 81% recognition.

Classification of Parkinson’s Disease using MultiPass Lvq,Logistic Model Tree,K-Star for Audio Data set : Classification of Parkinson Disease using Audio Dataset

Parkinson's disease (PD) is a degenerative illness whose cardinal symptoms include rigidity, tremor, and slowness of movement. In addition to its widely recognized effects PD can have a profound effect on speech and voice.The speech symptoms most commonly demonstrated by patients with PD are reduced vocal loudness, monopitch, disruptions of voice quality, and abnormally fast rate of speech. This cluster of speech symptoms is often termed Hypokinetic Dysarthria.The disease can be difficult to diagnose accurately, especially in its early stages, due to this reason, automatic techniques based on Artificial Intelligence should increase the diagnosing accuracy and to help the doctors make better decisions. The aim of the thesis work is to predict the PD based on the audio files collected from various patients.Audio files are preprocessed in order to attain the features.The preprocessed data contains 23 attributes and 195 instances. On an average there are six voice recordings per person, By using data compression technique such as Discrete Cosine Transform (DCT) number of instances can be minimized, after data compression, attribute selection is done using several WEKA build in methods such as ChiSquared, GainRatio, Infogain after identifying the important attributes, we evaluate attributes one by one by using stepwise regression.Based on the selected attributes we process in WEKA by using cost sensitive classifier with various algorithms like MultiPass LVQ, Logistic Model Tree(LMT), K-Star.The classified results shows on an average 80%.By using this features 95% approximate classification of PD is acheived.This shows that using the audio dataset, PD could be predicted with a higher level of accuracy.

A web application for follow-up of results from a mobile device test battery for parkinson’s disease patients

Background: A test battery consisting of self-assessments and motor tests (tapping and spiral drawing) was developed for a hand computer with touch screen in a telemedicine setting. Objectives: To develop and evaluate a web-based system that delivers decision support information to the treating clinical staff for assessing PD symptoms in their patients based on the test battery data. Methods: The test battery is currently being used in a clinical trial (DAPHNE, EudraCT No. 2005-002654-21) by sixty five patients with advanced Parkinson’s disease (PD) on 9991 test occasions (four tests per day during in all 362 week-long test periods) at nine clinics around Sweden. Test results are sent continuously from the hand unit over a mobile net to a central computer and processed with statistical methods. They are summarized into scores for different dimensions of the symptom state and an ‘overall test score’ reflecting the overall condition of the patient during a test period. The information in the web application is organized and presented graphically in a way that the general overview of the patient performance per test period is emphasized. Focus is on the overall test score, symptom dimensions and daily summaries. In a recent preliminary user evaluation, the web application was demonstrated to the fifteen study nurses who had used the test battery in the clinical trial. At least one patient per clinic was shown. Results: In general, the responses from nurses were positive. They claimed that the test results shown in the system were consistent with their own clinical observations. They could follow complications, changes and trends within their patients. Discussion: In conclusion, the system is able to summarise the various time series of motor test results and self-assessments during test periods and present them in a useful manner. Its main contribution is a novel and reliable way to capture and easily access symptom information from patients’ home environment. The convenient access to current symptom profile as well as symptom history provides a basis for individualized evaluation and adjustment of treatments.

A web-based system for visualizing upper limb motor performance of Parkinson’s disease patients

Objective To design, develop and set up a web-based system for enabling graphical visualization of upper limb motor performance (ULMP) of Parkinson’s disease (PD) patients to clinicians. Background Sixty-five patients diagnosed with advanced PD have used a test battery, implemented in a touch-screen handheld computer, in their home environment settings over the course of a 3-year clinical study. The test items consisted of objective measures of ULMP through a set of upper limb motor tests (finger to tapping and spiral drawings). For the tapping tests, patients were asked to perform alternate tapping of two buttons as fast and accurate as possible, first using the right hand and then the left hand. The test duration was 20 seconds. For the spiral drawing test, patients traced a pre-drawn Archimedes spiral using the dominant hand, and the test was repeated 3 times per test occasion. In total, the study database consisted of symptom assessments during 10079 test occasions. Methods Visualization of ULMP The web-based system is used by two neurologists for assessing the performance of PD patients during motor tests collected over the course of the said study. The system employs animations, scatter plots and time series graphs to visualize the ULMP of patients to the neurologists. The performance during spiral tests is depicted by animating the three spiral drawings, allowing the neurologists to observe real-time accelerations or hesitations and sharp changes during the actual drawing process. The tapping performance is visualized by displaying different types of graphs. Information presented included distribution of taps over the two buttons, horizontal tap distance vs. time, vertical tap distance vs. time, and tapping reaction time over the test length. Assessments Different scales are utilized by the neurologists to assess the observed impairments. For the spiral drawing performance, the neurologists rated firstly the ‘impairment’ using a 0 (no impairment) – 10 (extremely severe) scale, secondly three kinematic properties: ‘drawing speed’, ‘irregularity’ and ‘hesitation’ using a 0 (normal) – 4 (extremely severe) scale, and thirdly the probable ‘cause’ for the said impairment using 3 choices including Tremor, Bradykinesia/Rigidity and Dyskinesia. For the tapping performance, a 0 (normal) – 4 (extremely severe) scale is used for first rating four tapping properties: ‘tapping speed’, ‘accuracy’, ‘fatigue’, ‘arrhythmia’, and then the ‘global tapping severity’ (GTS). To achieve a common basis for assessment, initially one neurologist (DN) performed preliminary ratings by browsing through the database to collect and rate at least 20 samples of each GTS level and at least 33 samples of each ‘cause’ category. These preliminary ratings were then observed by the two neurologists (DN and PG) to be used as templates for rating of tests afterwards. In another track, the system randomly selected one test occasion per patient and visualized its items, that is tapping and spiral drawings, to the two neurologists. Statistical methods Inter-rater agreements were assessed using weighted Kappa coefficient. The internal consistency of properties of tapping and spiral drawing tests were assessed using Cronbach’s α test. One-way ANOVA test followed by Tukey multiple comparisons test was used to test if mean scores of properties of tapping and spiral drawing tests were different among GTS and ‘cause’ categories, respectively. Results When rating tapping graphs, inter-rater agreements (Kappa) were as follows: GTS (0.61), ‘tapping speed’ (0.89), ‘accuracy’ (0.66), ‘fatigue’ (0.57) and ‘arrhythmia’ (0.33). The poor inter-rater agreement when assessing “arrhythmia” may be as a result of observation of different things in the graphs, among the two raters. When rating animated spirals, both raters had very good agreement when assessing severity of spiral drawings, that is, ‘impairment’ (0.85) and irregularity (0.72). However, there were poor agreements between the two raters when assessing ‘cause’ (0.38) and time-information properties like ‘drawing speed’ (0.25) and ‘hesitation’ (0.21). Tapping properties, that is ‘tapping speed’, ‘accuracy’, ‘fatigue’ and ‘arrhythmia’ had satisfactory internal consistency with a Cronbach’s α coefficient of 0.77. In general, the trends of mean scores of tapping properties worsened with increasing levels of GTS. The mean scores of the four properties were significantly different to each other, only at different levels. In contrast from tapping properties, kinematic properties of spirals, that is ‘drawing speed’, ‘irregularity’ and ‘hesitation’ had a questionable consistency among them with a coefficient of 0.66. Bradykinetic spirals were associated with more impaired speed (mean = 83.7 % worse, P < 0.001) and hesitation (mean = 77.8% worse, P < 0.001), compared to dyskinetic spirals. Both these ‘cause’ categories had similar mean scores of ‘impairment’ and ‘irregularity’. Conclusions In contrast from current approaches used in clinical setting for the assessment of PD symptoms, this system enables clinicians to animate easily and realistically the ULMP of patients who at the same time are at their homes. Dynamic access of visualized motor tests may also be useful when observing and evaluating therapy-related complications such as under- and over-medications. In future, we foresee to utilize these manual ratings for developing and validating computer methods for automating the process of assessing ULMP of PD patients.

Long duration exercise program in individuals with parkinson´s disease: effects on functional capacity

The aim of this study was to investigate the effects of long duration exercise program on physical fitness components of functional capacity in individuals with Parkinson disease (PD) and to evaluate ongoing effects of exercise after 8 to 10-week follow-up without exercise. Twenty-four individuals with PD were randomly assigned to two groups: generalized exercise program and stretching exercise program (control group). The generalized exercise program provided training in physical fitness components of functional capacity. The stretching exercise program was characterized by low intensity and volume, mainly with static exercises. Both groups were evaluated before (BI) and after the 4-month (AI) exercise program. In addition, the individuals of generalized exercise program were also evaluated after 8-month exercise program and after 8 to 10- week follow-up without exercise. The generalized exercise program improved flexibility (BI - 38.50±12.42 cm; AI - 44.00±12.74 cm) and agility (BI - 30.59±7.54 s; AI - 28.56±8.20 s) while the stretching exercise program worsened coordination (BI - 23.27±6.58 s; AI - 28.06±7.37 s) and aerobic resistance (BI- 13.64±3.76 min; AI - 17.27±5.15 min) and improved balance (BI - 44.00±7.79 pts; AI - 46.57±6.53 pts). Lower-limb strength and UPDRS-motor scale scores were better at 8 months (14.75±2.92 rep and 26.25±13.97 pts, respectively) compared to baseline (13.13±2.59 rep and 31.63±12.82 pts, respectively) and 4 months (13.50±1.93 rep and 30.38±14.52 pts, respectively) for generalized exercise program. However, the benefits of 8 months of exercise were lost after 8 to 10-week follow-up without exercise (lower-limb strength - 12.43±3.15 rep and UPDRS-motor scale - 32.57±14.05 pts). In conclusion, generalized exercise program improved the functional capacity in individuals with PD, differently of stretching exercise program. In addition, a long duration exercise program promoted benefits for functional capacity and disease progression in individuals with PD. However, benefits were lost after a short period without exercise.

Effects of subthalamic nucleus stimulation on cognition and mood in Parkinson’s disease (PD)

Objective: To assess the neuropsychological outcome as a safety measure and quality control in patients with subthalamic nucleus (STN) stimulation for PD. Background: Deep brain stimulation (DBS) is considered a relatively safe treatment used in patients with movement disorders. However, neuropsychological alterations have been reported in patients with STN DBS for PD. Cognition and mood are important determinants of quality of life in PD patients and must be assessed for safety control. Methods: Seventeen consecutive patients (8 women) who underwent STN DBS for PD have been assessed before and 4 months after surgery. Besides motor symptoms (UPDRS-III), mood (Beck Depression Inventory, Hamilton Depression Rating Scale) and neuropsychological aspects, mainly executive functions, have been assessed (mini mental state examination, semantic and phonematic verbal fluency, go-no go test, stroop test, trail making test, tests of alertness and attention, digit span, wordlist learning, praxia, Boston naming test, figure drawing, visual perception). Paired t-tests were used for comparisons before and after surgery. Results: Patients were 61.6±7.8 years old at baseline assessment. All surgeries were performed without major adverse events. Motor symptoms ‘‘on’’ medication remained stable whereas they improved in the ‘‘off’’ condition (p<0.001). Mood was not depressed before surgery and remained unchanged at follow-up. All neuropsychological assessment outcome measures remained stable at follow-up with the exception of semantic verbal fluency and wordlist learning. Semantic verbal fluency decreased by 21±16% (p<0.001) and there was a trend to worse phonematic verbal fluency after surgery (p=0.06). Recall of a list of 10 words was worse after surgery only for the third attempt of recall (13%, p<0.005). Conclusions: Verbal fluency decreased in our patients after STN DBS, as previously reported. The procedure was otherwise safe and did not lead to deterioration of mood.

The Perceived Effects of Dance for Individuals with Parkinson’s Disease and Healthy Amateurs

The objective of the present study was to compare the effects of dance participation on physical and psychological functioning as perceived by two distinct groups of dancers: dancers with Parkinson's disease (PD) and healthy amateur (HA) dancers. Dancers in the Parkinson's sample group were gathered from participants in the Dance for PD® program, while healthy amateur dancers were recruited from university dance departments and through social media. Both groups were administered measures related to affect, self-efficacy, quality of life, and which aspects of dance classes were most helpful and/or challenging. Several open-ended questions for both groups were included, along with questions specific to each group. Results of the study indicated that there was no difference between the two groups on positive affect experienced while dancing, but that HA dancers experienced higher levels of negative affect than PD dancers. HA dancers exhibited higher levels of self-efficacy, but there was no difference between the groups on perceived quality of life. Additionally, both groups identified the same two components of dance classes as the most helpful: "moving and getting some exercise" and "doing something fun." Thematic analysis of responses to open-ended questions found that, in general, HA and PD dancers identified similar factors which made dance unique from other forms of exercise. The primary differences were that HA dancers more strongly emphasized artistic and spiritual components of dance, whereas PD dancers focused on the importance of the dance instructors and tailoring movements to individuals with PD. More differences were found between the two groups with respect to the negative aspects of dance classes. Notably, PD dancers identified almost no negative aspects, while HA dancers described internal and external pressure, criticism, and competition as problematic. Future research could benefit from ensuring that both groups are administered the same standardized measures to allow for additional comparisons between groups and with normative samples.