A heterogeneous database for movement knowledge extraction in Parkinson's disease

This paper presents the design and methodology used to create a heterogeneous database for knowledge movement extraction in Parkinson's Disease. This database is being constructed as part of REM- PARK project and is composed of movement measurements acquired from inertial sensors, standard medical scales as Uni ed Parkinson's Disease Rating Scale, and other information obtained from 90 Parkinson's Disease patients. The signals obtained will be used to create movement disorder detection algorithms using supervised learning techniques. The different sources of information and the need of labelled data pose many challenges which the methodology described in this paper addresses. Some preliminary data obtained are presented.Postprint (published version

A double closed loop to enhance the quality of life of Parkinson's disease patients: REMPARK system

This paper presents REMPARK system, a novel approach to deal with Parkinson's Disease (PD). REMPARK system comprises two closed loops of actuation onto PD. The first loop consists in a wearable system that, based on a belt-worn movement sensor, detects movement alterations that activate an auditory cueing system controlled by a smartphone in order to improve patient's gait. The belt-worn sensor analyzes patient's movement through real-time learning algorithms that were developed on the basis of a database previously collected from 93 PD patients. The second loop consists in disease management based on the data collected during long periods and that enables neurologists to tailor medication of their PD patients and follow the disease evolution. REMPARK system is going to be tested in 40 PD patients in Spain, Ireland, Italy and Israel. This paper describes the approach followed to obtain this system, its components, functionalities and trials in which the system will be validated.Postprint (published version

Impulsivity Relates to Relative Preservation of Mesolimbic Connectivity in Patients with Parkinson Disease.

IntroductionThe relationship between Parkinson Disease (PD) pathology, dopamine replacement therapy (DRT), and impulse control disorder (ICD) development is still incompletely understood. Given the sensorimotor-lateral substantia nigra (SN) selective degeneration associated with PD, we posit that a relative sparing of the limbic-medial SN in the context of DRT drives impulsive, reward-seeking behavior in PD patients with recent history of severe impulsivity.MethodsImpulsive and control participants were selected from a consecutive list of PD patients receiving pre-operative deep brain stimulation (DBS) planning scans including 3T structural MRI and 64 direction diffusion tensor imaging (DTI). Using previously identified substantia nigra (SN) subsegment network connectivity profiles to develop classification targets, split-hemisphere target-based SN segmentation with probabilistic tractography was performed. The relative subsegment volumes and strength of connectivity between the SN and the limbic, associative, and motor network targets were compared.ResultsOur results show that there is greater probability of connectivity between the SN and limbic network targets relative to motor and associative network targets in PD patients with recent history of severe impulsivity as compared to PD patients without impulsivity (P = 0.0075). We did not observe relative volumetric subsegment differences across groups.ConclusionFirstly, our results suggest that fine-grained, atlas-derived classification targets may be used in PD to parcellate and classify functionally distinct subsegments of the SN, with the apparent preservation of previously reported topographical limbic-medial SN, associative-ventral SN, and sensorimotor-lateral SN orientation. We suggest that relative, as opposed to absolute, degeneration amongst SN-associated dopaminergic networks relates to the impulsivity phenotype in PD

Clinical drug development in Parkinson’s disease : descriptive and exploratory analysis of success and failure pathways

Tese de mestrado, Neurociências, Universidade de Lisboa, Faculdade de Medicina, 2021INTRODUÇÃO: A marca patológica da Doença de Parkinson (DP) é a perda de neurónios dopaminérgicos, predominantemente nos gânglios de base, e a agregação de proteínas dos corpos de Lewys (LBs) nas células nervosas restantes. O início do desenvolvimento clínico de fármacos na DP começou com uma formulação oral de levodopa em 1967. Desde então, os ensaios clínicos foram essenciais para entender quão seguros são os tratamentos e para trazer novos compostos ao mercado com efeito terapêutico comprovado. Biofarmacêuticas em todo o mundo desenvolvem tratamentos inovadores para cobrir necessidades médicas ainda não exploradas. Contudo, e embora os numerosos avanços no desenvolvimento de fármacos nos últimos 20 anos, ensaios clínicos ainda não produziram um número significativo de medicamentos novos e seguros na DP. Efetivamente, menos de 10% dos medicamentos testados foram aprovados pelas agências reguladoras. É então importante identificar fatores que contribuam para uma trajetória de licenciamento de sucesso. Nas últimas décadas tem sido feito um esforço rigoroso em várias áreas como a genética, bioquímica, epigenética, ómica, clínica e imagem para definir marcadores fiáveis para melhorar a qualidade dos ensaios clínicos. Será que marcadores farmacológicos podem melhorar as abordagens para restaurar a dopamina no núcleo estriado de forma direcionada ou para prevenir a neurodegeneração e progressão da DP? OBJETIVOS: O principal objetivo desta tese é a descrição dos programas de desenvolvimento terapêutico na DP e a avaliação crítica das causas de atrito dos compostos nas fases distintas do desenvolvimento do fármaco, i.e., desde a fase 1 até à fase 4 dos estudos da DP. Quais as razões para o insucesso no desenvolvimento de fármacos na DP ser elevado? Quais as razões do decréscimo do número de compostos licenciados nas últimas duas décadas? O insucesso no desenvolvimento de drogas é observado em todas as fases de desenvolvimento, incluindo nas fases tardias? Estará o insucesso relacionado com ensaios iniciais não informativos? Para responder a estas questões, o propósito desta tese centra-se em identificar as razões de sucesso no desenvolvimento de fármacos na DP. MÉTODOS: Esta tese investigou 1304 ensaios clínicos na DP. A metodologia compreendeu uma revisão de literatura, o desenho de parâmetros de seleção de ensaios, uma extração de dados, uma análise de dados, a criação do PDCard Database e a análise estatística. Para esse efeito, foram utilizadas informações de ensaios clínicos relativos à doença de Parkinson que estavam registadas em 3 plataformas: clinicaltrials.gov, World Health Organization (WHO) International Clinical Trials Search Portal (ICTRP) e Australian New Zealand Clinical Trials Registry (ANZCTR). Uma base de dados incorporada, chamada PDCard Database, foi obtida usando os critérios de extração e análise de dados, totalizando uma soma de 613 estudos de intervenção em G20 – Doença de Parkinson. Em seguida, foi realizada uma análise exploratória das vias de sucesso e insucesso, e foram calculadas as respetivas taxas para cada fase experimental. RESULTADOS: Os programas de desenvolvimento de medicamentos para a DP entre o ano 2000 e agosto de 2019 foram analisados e os dados publicados no banco de dados PDCard. Ao analisar o banco de dados, verifica-se que há uma alta frequência de insucesso no desenvolvimento de fármacos, bem como uma diminuição no número de compostos licenciados para DP nas últimas duas décadas. Para estudar essa tendência, as vias de sucesso e insucesso dos 613 ensaios clínicos e dos 187 compostos testados, totalizando 77496 participantes, são exploradas. As 50 variáveis foram (i) categorizadas, (ii) sistematizadas e (iii) organizadas, com base nas diretrizes do SPIRIT. Cada uma das variáveis foi adicionada manualmente e extraída da publicação original do ensaio clínico ou dos bancos de dados clinictrials.gov, WHO ICTRP e ANZCTR. Objetivamente, o banco de dados inclui 115 ensaios clínicos na fase 1, 23 ensaios clínicos na fase 1|2, 194 ensaios clínicos na fase 2, 19 ensaios clínicos na fase 2|3, 172 ensaios clínicos na fase 3 e 90 ensaios clínicos na fase 4. 42,90% eram estudos multicêntricos, enquanto apenas 13,38% são monocentrados. 74,55% dos estudos foram randomizados e 9,79% são estudos não randomizados. 67,86% são estudos concluídos, 11,91% são estudos em fase de recrutamento, 9,62% são estudos que não estão a recrutar, 8,16% são estudos terminados e 2,45% são estudos desqualificados. São apresentadas falhas no desenvolvimento de fármacos em todas as fases do programa de desenvolvimento, incluindo estágios tardios. Efetivamente, de todas as causas para terminar um ensaio clínico, na fase 1, o mais importante foi o primeiro milestone não ser atingido. Na fase 2, a falta de eficiência foi a principal causa. Na fase 2|3, o principal motivo foi que era improvável fornecer evidências de efeito significativo. Na Fase 3, a falta de eficácia foi o principal motivo para o término do estudo. Portanto, as falhas estão principalmente relacionadas aos estudos de fases precoces serem pouco informativos. A maioria dos estudos (79,93%) recrutou indivíduos adultos ou idosos. Apenas 10,77% dos estudos incluíram indivíduos saudáveis. A maioria das condições estudadas nos ensaios clínicos são doenças do sistema nervoso (IV), relacionadas com a condição DP, totalizando 265 estudos. Os outros 264 ensaios clínicos estudaram a eficácia e a segurança do próprio medicamento, sem especificar uma condição relacionada com a DP. A maioria dos ensaios é projetado para fins de tratamento. A eficácia é o primary gold predominante em todas as fases do desenvolvimento na população estudada. Como o principal objetivo desta tese foi identificar as razões para o sucesso ou insucesso no programa de desenvolvimento do fármaco, dois tipos de caminhos foram comparados: a taxa de sucesso vs. a taxa de insucesso. A taxa de sucesso é mais alta na vigilância e monitorização pós-mercado, i.e. em compostos licenciados em fase 4, e é mínima na transição do active pharmaceutical ingredient (API) da fase 2 e 1|2 para a fase 3, i.e. antes das provas regulatórias. Assim sendo, 613 ensaios clínicos em DP realizados de 1998 a 2019 e 187 compostos foram amplamente estudados. Destes 187 compostos, apenas 29 passaram os ensaios de confirmação e foram finalmente considerados caminhos de sucesso, que permitiram o licenciamento do fármaco. Boehringer Ingelheim, Sandoz e GlaxoSmithKline foram as grandes empresas farmacêuticas que conduziram mais estudos completando a totalidade do programa de desenvolvimento do fármaco, incluindo a vigilância e monitorização pós-mercado de moléculas em fase 4. De todos os 29 compostos testados, 12 foram considerados novas entidades moleculares. As novas entidades moleculares mais estudadas foram a rasagilina (30,77%), seguida pela rotigotina, ropinirol e pramipexol. De todos os 29 compostos aprovados, 75,44% foram testados numa amostra de indivíduos em todas as fases da DP. Os ensaios clínicos de quadrupla ocultação (47,22%) são os que produziram mais compostos licenciados por ensaios confirmatórios, seguidos pelos de ocultação dupla (38,89%). Os estudos paralelos (59,65%) foram o tipo de desenho que produziu mais compostos aprovados, seguidos pelos estudos de grupo único (26,32%) e pelos estudos cruzados (12,28%). Os estudos (76,79%) que utilizaram a via oral como via preferencial de administração e comprimidos como formulação preferida foram os que tiveram mais sucesso nas fases confirmatórias do programa de desenvolvimento do fármaco. Os moduladores dos recetores de dopamina, assim como as formulações puras de levodopa e as combinações de levodopa, representam 52,63% das vias de sucesso nos últimos 20 anos. Seguidamente, os moduladores da monoamina oxidase (19,30%), como a selegilina e a rasagilina, foram os compostos testados com mais sucesso. Em terceiro lugar, os moduladores de recetores adrenérgicos (5,26%), como a droxidopa e o mirabegron, e os moduladores da catecol o-metiltransferase (5,26%), como o entacapone e o opicapone, foram os compostos aprovados pelas autoridades reguladoras com mais sucesso. Dos 29 compostos licenciados e comercializados que passaram nos ensaios confirmatórios, 26,32% dos estudos que foram realizados foram para medicamentos não parkinsonianos, enquanto 73,68% dos estudos que foram realizados foram para agentes antiparkinsonianos. Nos últimos 20 anos, e resumindo as vias de sucesso no programa de desenvolvimento, 15 agentes antiparkinsonianos passaram primariamente na fase 3 e, portanto, completaram as provas regulatórias. Após o processo e aprovação do New Drug Application, apenas 14 APIs foram registados para a DP e, subsequentemente, concluíram com êxito os estudos de vigilância e monitorização pós mercado. Os agentes antiparkinsonianos bem-sucedidos são a amantadina, a apomorfina, a carbidopa/levodopa, a levodopa, a levodopa/carbidopa/entacapona, a entacapona, a opicapona, o pramipexol, a rasagilina, a rivastigmina, o ropinirol, a rotigotina, a safinamida e a selegilina. Esses 14 agentes antiparkinsonianos diferentes foram comercializados por 16 promotores diferentes e em 20 formulações distintas. A Novartis foi a farmacêutica que licenciou mais compostos. Contrastando com os caminhos de sucesso, os caminhos de insucesso que forçaram a suspensão do ensaio antes da fase final do programa de desenvolvimento também são descritos. Nos últimos 20 anos, os motivos foram principalmente devido à falta de eficácia, segurança e financiamento. Trinta e cinco compostos falharam na fase 3 e seis compostos falharam na fase 4. A maioria dos estudos que falharam a completa aprovação após os estudos de vigilância e monitorização pós-mercado (34%), não publicam os motivos da interrupção ou retirada do ensaio clínico. CONCLUSÃO: A tese revisou o estado de arte do desenvolvimento clínico de fármacos na doença de Parkinson entre 1998 e 2019. A problemática de explorar as causas do sucesso e insucesso é um tópico de tendência na neurociência. Fundamentalmente, o trabalho desta dissertação respondeu aos objetivos inicialmente definidos. Espera-se, então, que este trabalho leve a novas e interessantes questões, que possam justificar uma análise multivariada das 50 variáveis obtidas. O panorama dos programas de desenvolvimento terapêutico da DP foi investigado e as causas de atrito dos compostos nos distintos estágios do desenvolvimento farmacológico, da fase 1 à fase 4 nos estudos da doença de Parkinson, foram avaliadas criticamente. Com efeito, há uma diminuição do sucesso no desenvolvimento de fármacos e do número de compostos licenciados nos últimos 20 anos. Além disso, falhas no desenvolvimento de medicamentos são observadas em todas as fases do desenvolvimento, incluindo em fases mais tardias, que estão maioritariamente relacionadas a ensaios clínicos não informativos principalmente em estágios iniciais. Duas vias foram comparadas: (i) uma via de sucesso, completando as provas regulatórias e ensaios confirmatórios, com uma taxa de sucesso de 16%, que permitiu o licenciamento e a vigilância e monitorização pós-mercado de 29 medicamentos; e (ii) uma via de insucesso com uma taxa de insucesso de 84%, que levou à suspensão de 158 compostos antes da fase final do programa de desenvolvimento. Finalmente, dos 613 ensaios clínicos em DP realizados entre 1998 e agosto de 2019 e dos 187 compostos, apenas 14 APIs foram finalmente aprovados e comercializados como agentes antiparkinsonianos. A taxa de sucesso específica para agentes antiparkinsonianos é igual a 7% e a taxa de insucesso específica de agentes antiparkinsonianos é igual a 93%.INTRODUCTION: The pathological hallmark of Parkinson’s Disease (PD) is the loss of dopaminergic neurons, most prominently in certain parts of the basal ganglia, and the aggregation of Lewy bodies proteins in remaining nerve cells. The beginning of clinical drug development in PD started with an orally formulation of levodopa in 1967. Since then, clinical trials were essential to understand how safe the treatments are and to bring new compounds to the market with a proven therapeutic effect. Biopharmaceutical companies around the world attempted to develop innovative therapies to achieve unmet medical necessities. Although the numerous advances on drug development in PD, a significant number of clinical trials still fail to produce new and safe medicines. Indeed, less than 10% of the drugs have been approved by regulatory agencies. It is then important to identify factors that contribute to a successful licensing path in regard to PD. Rigorous efforts have been made in the last decades in various areas such as genetic, biochemistry, epigenetic, omics, clinic and imaging to define reliable markers to improve the quality of clinical trials. Can drug related markers improve the pharmacologic approaches for restoring striatal dopamine in a targeted and physiological manner or to prevent ongoing neurodegeneration and progression of disease? OBJECTIVES: The principal aim of this thesis is the description of the landscape of PD therapeutic development programs and critically appraise the causes of compound attrition in the distinct stages of drug development, from phase 1 to phase 4 in Parkinson Disease’s studies. Why is it elevated the frequency of drug development failures? Why have it been a decrease in the number of approved compounds in the last two decades? Are failures in drug development observed in all phases of drug development including late stages? Are failures related with non-informative early stage trials? To answer these questions, the outcome of this thesis is centered in identifying the reasons for development drug success. METHODS: This thesis researched 1304 PD clinical trials. The methodology comprehended a literature research, the design of trial selection parameters, the data extraction, the data analysis, the creation of the PDCard Database and the statistical analysis. For that matter, this study used information from clinical trials relating to Parkinson's disease that were recorded on the 3 platforms: clinicaltrials.gov, World Health Organization (WHO) International Clinical Trials Search Portal (ICTRP) and Australian New Zealand Clinical Trials Registry (ANZCTR). An embedded type database, named PDCard Database, was finally obtained using criteria the data extraction and analysis, totaling a sum of 613 interventional studies of G20 – Parkinson’s disease. Then an exploratory analysis of success and failure paths was conducted, and success and failure rates for each trial phase were calculated. RESULTS: PD drug development registry between 2000 and August 2019 was analyzed, and data published in the PDCard Database. By analyzing the database, it is shown that there is a high frequency of drug development failures and there is a decrease in the number of licensed compounds in the last two decades. For that matter, success and failure paths of the 613 clinical trials and 187 tested compounds, in 77496 total participants, are explored. Fifty variables were (i) categorized (ii) systemized and (iii) organized, based on the SPIRIT guidelines. Each of the variables was added manually and extracted from the original publication of the clinical trial or the clinicaltrials.gov, WHO ICTRP and ANZCTZ. Objectively, the database includes 115 clinical trials in phase 1, 23 clinical trials in phase 1|2, 194 clinical trials in phase 2, 19 clinical trials in phase 2|3, 172 clinical trials in phase 3 and 90 clinical trials in phase 4. 42,90% were multicentered studies, while only 13,38% are monocentered. 74.55% of the studies were randomized and 9,79% are non-randomized studies. 67,86% are completed studies, 11,91% are studies in recruiting phase, 9,62% are studies that are not recruiting, 8,16% are terminated studies and 2,45% are withdrawn studies. It is presented that failures in drug development are observed in all phases of drug development including late stages. Effectively, from all the terminated study causes of all studies, in Phase 1 the most important was first milestone was not met. In Phase 2, lack of efficiency was the primary cause. In Phase 2|3, the main reason was that it was unlikely to provide evidence of significant effect. In Phase 3, lack of efficacy was the main reason for terminating the study. Therefore, failures are mostly related with non informative early stage trials. The majority of the studies (79,93%) have recruited adult or older adult subjects. Only 10.77% of clinical trials studies included healthy subjects. Most of the diseases studied in clinical trials are diseases of the nervous system (IV), totaling 265 studies. Otherwise, 264 of the clinical trials were studying the efficacy and safety of the drug itself, without specifying PD as a condition to the study. The majority of the trials are designed for treatment purpose. Efficacy is the predominating gold at all phases of development in the study population. As the main outcome of this thesis was identifying the reasons for development drug success or failure, two types of paths were compared: the success rate vs the failure rate. The success rate is higher in PostMarketing Surveillance (PMS), i.e. in phase 4 licensed compounds, and is minimum in the transition of the API from phases 2 and 1|2 to phase 3, i.e. before regulatory proofing. Henceforth, 613 clinical trials in PD conducted from 1998 to 2019, and 187 compounds were vastly studied. From these 187 compounds, only 29 passed confirmatory trials and were finally considered successful paths, that allowed drug licensing. Boehringer Ingelheim, Sandoz and GlaxoSmithKline were the big pharma that conducted more studies completing the full drug development program, including postmarketing surveillance of molecules in phase 4. From all the 29 compounds that were tested, 12 were considered new molecular entities. The new molecular entities that were more studied were rasagiline (30,77%), followed by rotigotine, ropinirole and pramipexole. From all the successful 29 approved compounds, 75,44% were tested in a sample of subjects with all stages of PD. Clinical trials that used quadruple blinding (47,22%) are the ones that produced more licensed compounds that passed confirmatory trials, followed by double blinding (38,89%). The parallel assignment (59,65%) was the type of design that produced more approved compounds, followed by single group assignment (26,32%) and crossover assignment (12,28%). The studies (76,79%) that used oral as a preferred route of administration, and tablets as a preferred drug formulation are the ones that approved more compounds passing confirmatory trials. Dopamine receptor modulators, like pure levodopa and levodopa combinations, represent 52,63% of the success path in the last 20 years. In second, monoamine oxidase modulators (19,30%), like selegiline and rasagiline, were tested in PD drug development. Thirdly, adrenergic receptor modulators (5,26%), like droxidopa and mirabegron, and catechol o-methyltransferase modulators (5,26%), like entacapone and opicapone, were approved compounds that passed confirmatory trials by the regulatory authorities. From the 29 licensed compounds that passed confirmatory trials, 26,32% of the studies conducted were for non-parkinsonians drugs, while 73,68% of the studies conducted were for antiparkinsonian agents. In the past 20 years, and reviewing the successful path, 15 antiparkinsonian agents primarily passed phase 3 and thus showed regulatory proof. After NDA process and approval, only 14 APIs were registered for PD and, subsequently, completed successfully the postmarketing surveillance studies. The successful antiparkinsonian agents are amantadine, apomorphine, carbidopa/levodopa, levodopa, levodopa/carbidopa/entacapone, entacapone, opicapone, pramipexole, rasagiline, rivastigmine, ropinirole, rotigotine, safinamide and selegiline. Those 14 different antiparkinsonian agents were marketed by 16 different sponsors, in 20 different formulations. Novartis was the big pharma licensing more compounds. Contrasting the success paths, the failure paths that forced the suspension of trial before the final phase of the development program are also described. In the last 20 years, the reasons were mainly due to efficacy, safety and financing. Thirty-five compounds failed to succeed phase 3, and six compounds failed to succeed phase 4. The majority of studies that failed to be approved (34%) do not publish the reasons for terminating or withdrawing the clinical trial. CONCLUSION: The thesis reviewed the state of the art of clinical drug development in Parkinson’s Disease from 1998 to 2019. The problematic of exploring the causes for success/unsuccess is a trending topic in neuroscience. Fundamentally, the work of this dissertation responded to the initially defined objectives. It is then expected that this work will lead to new and interesting questions, which might justify a future multivariate analysis of the 50 obtained variables. The landscape of PD therapeutic development programs was probed and the causes of compound attrition in the distinct stages of drug development, from phase 1 to phase 4 in Parkinson Disease’s studies were critically appraised. Indeed, there is a decrease on the success of drug development and in the number of approved compounds that passed confirmatory trials in the last 20 years. Moreover, failures in drug development are observed in all phases of development including late stages, but are mostly related with non-informative early stage trials. Two paths were compared: (i) a successful pathway, passing regulatory proof and confirmatory trials, with a success rate of 16%, that

CLASSIFICATION OF PARKINSON'S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK

In our aging culture, neurodegenerative disorders like Parkinson's disease (PD) are among the most serious health issues. It is a neurological condition that has social and economic effects on individuals. It happens because the brain's dopamine-producing cells are unable to produce enough of the chemical to support the body's motor functions. The main symptoms of this illness are eyesight, excretion activity, speech, and mobility issues, followed by depression, anxiety, sleep issues, and panic attacks. The main aim of this research is to develop a workable clinical decision-making framework that aids the physician in diagnosing patients with PD influence. In this research, we proposed a technique to classify Parkinson’s disease by MRI brain images. Initially, normalize the input data using the min-max normalization method and then remove noise from input images using a median filter. Then utilizing the Binary Dragonfly Algorithm to select the features. Furthermore, to segment the diseased part from MRI brain images using the technique Dense-UNet. Then, classify the disease as if it’s Parkinson’s disease or health control using the Deep Residual Convolutional Neural Network (DRCNN) technique along with Enhanced Whale Optimization Algorithm (EWOA) to get better classification accuracy. Here, we use the public Parkinson’s Progression Marker Initiative (PPMI) dataset for Parkinson’s MRI images. The accuracy, sensitivity, specificity, and precision metrics will be utilized with manually gathered data to assess the efficacy of the proposed methodology

Dance movement therapy and falls prevention

Falls are a leading cause of morbidity, healthcare use and mortality. Dance is a popular form of physical activity among older people and previous research has suggested that it may improve various health outcomes in this population, including balance, gait and muscle performance. A systematic review of the potential benefits of dance on falls and fear of falling is lacking. Thus, we conducted a systematic review considering all randomized controls trials (RCTs) investigating if dance can reduce falls and improve fear of falling in older adults. Major databases were searched from inception until 1 March 2017 and a total of 10 RCTs were identified, which included a total of 680 people (n = 356 dance, n = 324 control). Overall, the mean age of the samples was 69.4 years, and 75.2% were female. Across four RCTs, dance therapy reduced falls versus usual care in only one study. Dance therapy improved fear of falling in two out of three included RCTs. There were no serious adverse events reported in the RCTs. In summary, we found a paucity of studies investigating the effect of dance on falls and fear of falling and the evidence base is preliminary and equivocal. Given the heterogeneity of the included samples and interventions, in addition to the short-term follow-up, no firm conclusions can be drawn. However, dance appears to be safe and, given its popularity and demonstrated benefits on other health/wellbeing outcomes in older adults, it is important that future research considers its potential benefits on falls/fear of falling in older age

Impact of motor fluctuations on real-life gait in Parkinson’s patients

Background people with PD (PWP) have an increased risk of becoming inactive. Wearable sensors can provide insights into daily physical activity and walking patterns. Research questions (1) is the severity of motor fluctuations associated with sensor-derived average daily walking quantity? (2) is the severity of motor fluctuations associated with the amount of change in sensor-derived walking quantity after levodopa intake? Methods 304 Dutch PWP from the Parkinson@Home study were included. At baseline, all participants received a clinical examination. During the follow-up period (median: 97 days; 25-Interquartile range-IQR: 91 days, 75-IQR: 188 days), participants used the Fox Wearable Companion app and streamed smartwatch accelerometer data to a cloud platform. The first research question was assessed by linear regression on the sensor-derived mean time spent walking/day with the severity of fluctuations (MDS-UPDRS item 4.4) as independent variable, controlled for age and MDS-UPDRS part-III score. The second research question was assessed by linear regression on the sensor-derived mean post-levodopa walking quantity, with the sensor-derived mean pre-levodopa walking quantity and severity of fluctuations as independent variables, controlled for mean time spent walking per day, age and MDS-UPDRS part-III score. Results PWP spent most time walking between 8am and 1pm, summing up to 72 ± 39 (mean ± standard deviation) minutes of walking/day. The severity of motor fluctuations did not influence the mean time spent walking (B = 2.4 ± 1.9, p = 0.20), but higher age (B = −1.3 ± 0.3, p = < 0.001) and greater severity of motor symptoms (B = −0.6 ± 0.2, p < 0.001) was associated with less time spent walking (F(3,216) = 14.6, p<.001, R2 =.17). The severity of fluctuations was not associated with the amount of change in time spent walking in relation to levodopa intake in any part of the day. Significance Analysis of sensor-derived gait quantity suggests that the severity of motor fluctuations is not associated with changes in real-life walking patterns in mildly to moderate affected PWP

Longitudinal clustering analysis and prediction of Parkinson\u27s disease progression using radiomics and hybrid machine learning

Background: We employed machine learning approaches to (I) determine distinct progression trajectories in Parkinson\u27s disease (PD) (unsupervised clustering task), and (II) predict progression trajectories (supervised prediction task), from early (years 0 and 1) data, making use of clinical and imaging features. Methods: We studied PD-subjects derived from longitudinal datasets (years 0, 1, 2 & 4; Parkinson\u27s Progressive Marker Initiative). We extracted and analyzed 981 features, including motor, non-motor, and radiomics features extracted for each region-of-interest (ROIs: left/right caudate and putamen) using our standardized standardized environment for radiomics analysis (SERA) radiomics software. Segmentation of ROIs on dopamine transposer - single photon emission computed tomography (DAT SPECT) images were performed via magnetic resonance images (MRI). After performing cross-sectional clustering on 885 subjects (original dataset) to identify disease subtypes, we identified optimal longitudinal trajectories using hybrid machine learning systems (HMLS), including principal component analysis (PCA) + K-Means algorithms (KMA) followed by Bayesian information criterion (BIC), Calinski-Harabatz criterion (CHC), and elbow criterion (EC). Subsequently, prediction of the identified trajectories from early year data was performed using multiple HMLSs including 16 Dimension Reduction Algorithms (DRA) and 10 classification algorithms. Results: We identified 3 distinct progression trajectories. Hotelling\u27s t squared test (HTST) showed that the identified trajectories were distinct. The trajectories included those with (I, II) disease escalation (2 trajectories, 27% and 38% of patients) and (III) stable disease (1 trajectory, 35% of patients). For trajectory prediction from early year data, HMLSs including the stochastic neighbor embedding algorithm (SNEA, as a DRA) as well as locally linear embedding algorithm (LLEA, as a DRA), linked with the new probabilistic neural network classifier (NPNNC, as a classifier), resulted in accuracies of 78.4% and 79.2% respectively, while other HMLSs such as SNEA + Lib_SVM (library for support vector machines) and t_SNE (t-distributed stochastic neighbor embedding) + NPNNC resulted in 76.5% and 76.1% respectively. Conclusions: This study moves beyond cross-sectional PD subtyping to clustering of longitudinal disease trajectories. We conclude that combining medical information with SPECT-based radiomics features, and optimal utilization of HMLSs, can identify distinct disease trajectories in PD patients, and enable effective prediction of disease trajectories from early year data

Deep Learning Based Abnormal Gait Classification System Study with Heterogeneous Sensor Network

Gait is one of the important biological characteristics of the human body. Abnormal gait is mostly related to the lesion site and has been demonstrated to play a guiding role in clinical research such as medical diagnosis and disease prevention. In order to promote the research of automatic gait pattern recognition, this paper introduces the research status of abnormal gait recognition and systems analysis of the common gait recognition technologies. Based on this, two gait information extraction methods, sensor-based and vision-based, are studied, including wearable system design and deep neural network-based algorithm design. In the sensor-based study, we proposed a lower limb data acquisition system. The experiment was designed to collect acceleration signals and sEMG signals under normal and pathological gaits. Specifically, wearable hardware-based on MSP430 and upper computer software based on Labview is designed. The hardware system consists of EMG foot ring, high-precision IMU and pressure-sensitive intelligent insole. Data of 15 healthy persons and 15 hemiplegic patients during walking were collected. The classification of gait was carried out based on sEMG and the average accuracy rate can reach 92.8% for CNN. For IMU signals five kinds of abnormal gait are trained based on three models: BPNN, LSTM, and CNN. The experimental results show that the system combined with the neural network can classify different pathological gaits well, and the average accuracy rate of the six-classifications task can reach 93%. In vision-based research, by using human keypoint detection technology, we obtain the precise location of the key points through the fusion of thermal mapping and offset, thus extracts the space-time information of the key points. However, the results show that even the state-of-the-art is not good enough for replacing IMU in gait analysis and classification. The good news is the rhythm wave can be observed within 2 m, which proves that the temporal and spatial information of the key points extracted is highly correlated with the acceleration information collected by IMU, which paved the way for the visual-based abnormal gait classification algorithm.步态指人走路时表现出来的姿态，是人体重要生物特征之一。异常步态多与病变部位有关，作为反映人体健康状况和行为能力的重要特征，其被论证在医疗诊断、疾病预防等临床研究中具有指导作用。为了促进步态模式自动识别的研究，本文介绍了异常步态识别的研究现状，系统地分析了常见步态识别技术以及算法，以此为基础研究了基于传感器与基于视觉两种步态信息提取方法，内容包括可穿戴系统设计与基于深度神经网络的算法设计。 在基于传感器的研究中，本工作开发了下肢步态信息采集系统，并利用该信息采集系统设计实验，采集正常与不同病理步态下的加速度信号与肌电信号，搭建深度神经网络完成分类任务。具体的，在系统搭建部分设计了基于MSP430的可穿戴硬件设备以及基于Labview的上位机软件，该硬件系统由肌电脚环，高精度IMU以及压感智能鞋垫组成，该上位机软件接收、解包蓝牙数据并计算出步频步长等常用步态参数。 在基于运动信号与基于表面肌电的研究中，采集了15名健康人与15名偏瘫病人的步态数据，并针对表面肌电信号训练卷积神经网络进行帕金森步态的识别与分类，平均准确率可达92.8%。针对运动信号训练了反向传播神经网络，LSTM以及卷积神经网络三种模型进行五种异常步态的分类任务。实验结果表明，本工作中步态信息采集系统结合神经网络模型，可以很好地对不同病理步态进行分类，六分类平均正确率可达93%。 在基于视觉的研究中，本文利用人体关键点检测技术，首先检测出图片中的一个或多个人，接着对边界框做图像分割，接着采用全卷积resnet对每一个边界框中的人物的主要关节点做热力图并分析偏移量，最后通过热力图与偏移的融合得到关键点的精确定位。通过该算法提取了不同步态下姿态关键点时空信息，为基于视觉的步态分析系统提供了基础条件。但实验结果表明目前最高准确率的人体关键点检测算法不足以替代IMU实现步态分析与分类。但在2m之内可以观察到节律信息，证明了所提取的关键点时空信息与IMU采集的加速度信息呈现较高相关度，为基于视觉的异常步态分类算法铺平了道路