Diagnosis and Treatment of Parkinson's Disease

Parkinson's disease is diagnosed by history and physical examination and there are no laboratory investigations available to aid the diagnosis of Parkinson's disease. Confirmation of diagnosis of Parkinson's disease thus remains a difficulty. This book brings forth an update of most recent developments made in terms of biomarkers and various imaging techniques with potential use for diagnosing Parkinson's disease. A detailed discussion about the differential diagnosis of Parkinson's disease also follows as Parkinson's disease may be difficult to differentiate from other mimicking conditions at times. As Parkinson's disease affects many systems of human body, a multimodality treatment of this condition is necessary to improve the quality of life of patients. This book provides detailed information on the currently available variety of treatments for Parkinson's disease including pharmacotherapy, physical therapy and surgical treatments of Parkinson's disease. Postoperative care of patients of Parkinson's disease has also been discussed in an organized manner in this text. Clinicians dealing with day to day problems caused by Parkinson's disease as well as other healthcare workers can use beneficial treatment outlines provided in this book

Chaudhuri’s Dashboard of Vitals in Parkinson’s syndrome: an unmet need underpinned by real life clinical tests

We have recently published the notion of the “vitals” of Parkinson’s, a conglomeration of signs and symptoms, largely nonmotor, that must not be missed and yet often not considered in neurological consultations, with considerable societal and personal detrimental consequences. This “dashboard,” termed the Chaudhuri’s vitals of Parkinson’s, are summarized as 5 key vital symptoms or signs and comprise of (a) motor, (b) nonmotor, (c) visual, gut, and oral health, (d) bone health and falls, and finally (e) comorbidities, comedication, and dopamine agonist side effects, such as impulse control disorders. Additionally, not addressing the vitals also may reflect inadequate management strategies, leading to worsening quality of life and diminished wellness, a new concept for people with Parkinson’s. In this paper, we discuss possible, simple to use, and clinically relevant tests that can be used to monitor the status of these vitals, so that these can be incorporated into clinical practice. We also use the term Parkinson’s syndrome to describe Parkinson’s disease, as the term “disease” is now abandoned in many countries, such as the U.K., reflecting the heterogeneity of Parkinson’s, which is now considered by many as a syndrome

Avaliação do potencial de técnicas de machine learning no diagnóstico diferencial da doença de Parkinson com base em imagem molecular

Trabalho final de Mestrado para obtenção do grau de Mestre em Engenharia Biomédica.A doença de Parkinson (DP) é uma doença neurodegenerativa que resulta da perda de neurónios dopaminérgicos na substância negra. É um grave problema de saúde pública que afeta 1-1,5% da população idosa a nível mundial. A perda dos neurónios dopaminérgicos devido à DP é um processo lento e que, de uma forma geral, pode demorar mais de uma década até que os primeiros sintomas sejam detetáveis, realçando a importância de um diagnóstico precoce para iniciar a terapêutica mais adequada o mais rapidamente possível [1]. O diagnóstico da DP é baseado na observação de sinais clínicos, nomeadamente a caracterização de uma variedade de sintomas motores, a resposta aos fármacos dopaminérgicos e a avaliação do padrão de captação (imagens) de radiofármacos específicos para avaliar a integridade do sistema dopaminérgico, usando equipamentos de SPECT (do inglês single-photon emission computed tomography) ou PET (do inglês positron emission tomography) [2]. Em grande parte dos casos, a avaliação visual destas imagens é suficiente para a caracterização do sistema dopaminérgico. No entanto, noutros casos, esta avaliação tem de ser complementada com uma análise quantitativa. Mesmo assim, por vezes ainda surgem dúvidas, que podem ser clarificadas com a utilização de técnicas de classificação baseadas em machinelearning [3]. As redes neuronais convolucionais (CNN, do inglês convolutional neural network) têm vindo a mostrar potencial na classificação de diversos tipos de imagens médicas, especialmente na área da oncologia [4],[5],[6] mas também existem exemplos de aplicação na área da neuroimagem [7],[8],[9]. Deste modo, pretendeu-se com este estudo avaliar o potencial das CNN, em comparação com outras técnicas muito populares, no diagnóstico diferencial da DP com base em imagens moleculares do cérebro obtidas com [123I] FP-CIT SPECT. Este trabalho incluiu um conjunto de 806 imagens cerebrais volumétricas obtidas com [123I]FP-CIT SPECT (208 controlos saudáveis e 598 doentes com DP). Os dados foram obtidos a partir da base de dados da Parkinson's Progression Markers Initiative (PPMI) (www.ppmi-info.org/data). Para cada sujeito, apenas foi considerado o primeiro exame [123I]FP-CIT SPECT (baseline ou screening). O protocolo de aquisição e pré-processamento de imagens encontra-se disponível em http://www.ppmi- info.org/study-design/research-documents-and-sops/. A técnica de classificação baseada em CNN foi comparada com os classificadores: k-vizinhos mais próximos (kNN, do inglês k-nearest neighbor), regressão logística (RL), árvores de decisão (AD), support vector machine (SVM) e redes neuronais artificiais (ANN, do inglês artificial neural networks). O classificador baseado em CNN foi treinado com imagens bidimensionais (dimensões: 88 mm × 82 mm) contendo a região do estriado, nomeadamente a projeção de intensidade máxima superior-inferior da cabeça. Os restantes classificadores foram treinados com cinco características extraídas da região do estriado tridimensional: potencial de ligação do caudato, potencial de ligação do putamen, rácio putamen para caudato, volume da região do estriado com "captação normal" e comprimento do eixo maior dessa região. Foram utilizados apenas os valores mínimos inter-hemisférios cerebral. Os dados foram divididos na razão 75:25 (75% para treino e 25% para teste). Cada uma das cinco características foi também estudada individualmente para avaliar o seu potencial de classificação em termos de desempenho (precisão, sensibilidade e especificidade). No conjunto de dados do teste, a precisão, sensibilidade, e especificidade da CNN para diferenciar imagens de doentes com DP das imagens de controlos saudáveis foi 96%, 98%, e 91%, respetivamente. Estes resultados foram muito semelhantes aos obtidos com os outros classificadores (kNN: 95%, 99%, 85%; RL: 94%, 97%, 86%; AD: 94%, 97%, 84%; SVM: 94%, 98%, 88%; e ANN: 94%, 97%, 86%). II. As diferenças de precisão não são estatisticamente significativas (teste Q de Cochran, p = 0,592). Individualmente, a característica que melhor diferenciou as imagens de doentes com DP das imagens dos controlos saudáveis foi o potencial de ligação do putamen com 93% de precisão, 93% de sensibilidade e 94% de especificidade no conjunto de dados do teste, usando o valor de corte que maximizou o coeficiente de Younden obtido do conjunto de dados de treino (valor de corte de 1,716). O classificador baseado em CNN provou ser tão robusto e preciso como os outros classificadores utilizados neste trabalho, com a vantagem de utilizar imagens como entrada direta, minimizando os passos iniciais de pré-processamento. Todos os classificadores aqui utilizados atingiram valores de precisão de classificação superiores aos frequentemente reportados na literatura para avaliação visual qualitativa. Assim, sugere-se a sua utilização como complemento à avaliação visual qualitativa e como ferramenta de treino para médicos especialista com reduzida experiência.Parkinson's disease (PD) is a neurodegenerative disease that results from the loss of dopaminergic neurons in the substantia nigra. It is a serious public health problem that affects 1 to 1.5% of the elderly population worldwide. The loss of dopaminergic neurons is a slow process that takes decades to happen, highlighting the importance of an early diagnosis to start the most adequate therapeutic regimen as soon as possible [1]. The diagnosis of PD is based on the observation of clinical signs, namely the characterization of a variety of motor symptoms, the response to dopaminergic drugs and evaluation of the uptake pattern (images) of specific radiopharmaceuticals to assess the integrity of the dopaminergic system [2]. In most cases, a visual assessment of these images is sufficient to characterize the dopaminergic system. However, in other cases this assessment must be complemented with a quantitative analysis. Even so, sometimes doubts still arise, which can be clarified with the use of classification techniques based on artificial intelligence, being machine learning the most frequently used [3]. In the context of artificial intelligence, convolutional neural networks (CNN) have been showing potential in various types of medical images, especially in the field of oncology [4],[5],[6], but there are also examples of application in the field of neuroimaging [7],[8],[9]. Thus, the aim of this study is to evaluatethe potential of CNN, in comparison to other popular techniques, in the differential diagnosis of PD based on [123I]FP-CIT SPECT images of the central nervous system, in particular the basal ganglia. This work included 806 [123I]FP-CIT SPECT brain images (208 health controls and 598 with PD). Data were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi- info.org/data). For each subject, only the first scan [123I]FP-CIT SPECT was considered (baseline or screening). The protocol of image acquisition and pre-processing is available at http://www.ppmi- info.org/study-design/research-documents-and-sops/. CNN was compared against k-nearest neighbour (kNN), logistic regression (LR), decision trees (DT), support vector machines (SVM) and artificial neural networks (ANN) classifiers. The CNN classifier was trained with 2-dimensional image patches (dimensions: 88 mm × 82 mm) containing the striatal region, extracted from the head superior-inferior maximum intensity projection. The remaining classifiers were trained with five features extracted from 3-dimensional striatal region: caudate binding potential, putamen binding potential, putamen to caudate ratio, volume of the striatal region with “normal uptake”, and the length of major axis of that region. Only the inter-hemisphere minimum was used. The split ratio of the dataset was 75:25 (75% for training and 25% for testing). Each of the five features was also considered individually to assess its potential for classification in terms of performance (accuracy, sensitivity, and specificity). In the test dataset, accuracy, sensitivity, and specificity of the CNN were 96%, 98%, and 91%, respectively. This finding was very similar to what we obtained with the other classifiers (kNN: 95%, 99%, 85%; LR: 94%, 97%, 86%, DT: 94%, 97%, 84%, SVM: 94%, 98%, 88% and ANN: 94%, 97%, 86%). The accuracy differences were not statistically significant (Cochran Q test, p = 0.592). Individually, the feature that best differentiated PD from normal scans was the putamen binding potential with 93% accuracy, 93% sensitivity and 94% specificity in the test dataset, based on the optimal cut-off (1.716) that maximizes Younden’s coefficient in the training dataset. IV CNN classifier proved to be as robust and accurate as the other classifiers frequently used in the type of problems, with the great advantage of using images as direct input. All machine learning-based classifiers tested are robust and very accurate in the classification of brain [123I]FP-CIT SPECT scans. Standard visual clinical evaluation should be complemented with quantification classification, and also used as a training tool.N/

Parkinson’s disease patients with heterozygous GBA-mutation: longitudinal phenotyping of motor and non-motor symptoms – more rapid progression compared to Parkinson’s disease patients without GBA-mutation

The following comprises a short summary of this clinical observation study including the objective, the applied methods and results as well as the discussion. A common disease such as Parkinson's disease, which is now understood as a systemic disease and goes far beyond pure motor disturbance, is clearly associated with the rare lysosomal disorder Gaucher’s disease. At first glance, GD has little in common with the second most frequent neurodegenerative disease worldwide. Nevertheless, the genetic origin of this compound is based on mutations in the GBA gene that lead to an increased risk of PD. Profound acknowledgement of prodromal and clinical symptoms of PDGBA as well as of the progression characteristics of this PD subgroup is of essential importance. Otherwise, one will not be able at all to detect subjects with the most relevant risk factor for PD and – as the next step – these subjects at risk for PD might not be included in clinical and experimental trials. This, however, is the only way to hopefully expand and deepen the current understanding of the underlying mechanisms on how GBA mutations exactly contribute to PD pathology. Based on these required investigations, the development of promising therapeutic options, that go far beyond the present symptomatic level, are conceivable and are expected to slow down or even stop PD progression in the future. Therefore, a clinical phenotyping of GBA patients was performed in this study. It revealed that the PDGBA group presented not significantly different from the PDIdiopathic group at the beginning of the 3-year period regarding motor and non-motor performance. However, at time of the examination in 2013, the PDGBA group was affected more severely than the comparison group: motor and cognitive impairment had worsened more rapidly. Moreover, higher doses of dopaminergic drugs were required, and H&Y disease stages reflected a faster progression of PDGBA to one PD-milestone that can be life-changing for PD patients: the endpoint of postural instability. Further, higher mortality rates for PDGBA patients were demonstrated in this study. Epigenetic and environmental factors may seem to play a relevant role in this subgroup of PD, as well as complex gene-gene interactions. Theories, attempting to explain the underlying pathology, range from the causal linkage of common diseases with common genetic variants (CDCV hypothesis) to the currently more probable assumption that common diseases, such as Parkinson's disease, are caused by a variety of singular and separately rare variants (CDRV). At the cellular level, moreover, several approaches are pursued, including the pathological interaction of GCase and α-syn, the impairment of lysosomal clearance, dysfunctional lipid metabolism, disturbances in the area of the proteasome as well as deficits in mitochondrial function. The primary background of this prospective study was to contribute to a better understanding of this neurodegenerative disease by phenotypically characterizing the subtype PDGBA. This is of crucial importance for following steps as to be able to make a diagnosis at a preferably early disease stage and thus, to prevent disease-associated and irreversibly neuronal cell loss by means of future disease-modifying, targeted therapies. Currently, promising therapeutic studies are in progress with the aim of increasing GCase activity or alternatively, minimizing its pathogenic substrate glucosylceramide

OPTICAL COHERENCE TOMOGRAPHY FOR NEUROSURGEY AND CANCER RESEARCH

Optical Coherence Tomography (OCT) provides non-labeling, real-time and high resolution images, which has the potential to transform the paradigm of surgical guidance and preclinical animal studies. The design and development of OCT devices for neurosurgery guidance and novel imaging algorithms for monitoring anti-cancer therapy have been pursued in this work. A forward-imaging needle-type OCT probe was developed which can fit into minimally invasive tools (I.D. ~ 1mm), detect the at-risk blood vessels, and identify tissue micro-landmarks. This promising guidance tool improves the safety and the accuracy of needle-based procedures, which are currently performed without imaging feedback. Despite the great imaging capability, OCT is limited by the shallow imaging depth (1-2 mm). In order to address this issue, the first MRI compatible OCT system has been developed. The multi-scale and multi-contrast MRI/OCT imaging combination significantly improves the accuracy of intra-operative MRI by two orders (from 1mm to 0.01 mm). In contrast to imaging systems, a thin (0.125 mm), low-cost (1/10 cost of OCT system) and simple fiber sensor technology called coherence gated Doppler (CGD) was developed which can be integrated with many surgical tools and aid in the avoidance of intracranial hemorrhage. Furthermore, intra-vital OCT is a powerful tool to study the mechanism of anti-cancer therapy. Photo-immunotherapy (PIT) is a low-side-effect cancer therapy based on an armed antibody conjugate that induces highly selective cancer cell necrosis after exposure to near infrared light both in vitro and in vivo. With novel algorithms that remove the bulk motion and track the vessel lumen automatically, OCT reveals dramatic hemodynamic changes during PIT and helps to elucidate the mechanisms behind the PIT treatment. The transformative guidance tools and the novel image processing algorithms pave a new avenue to better clinical outcomes and preclinical animal studies

VISUALIZATION OF ULTRASOUND INDUCED CAVITATION BUBBLES USING SYNCHROTRON ANALYZER BASED IMAGING

Ultrasound is recognized as the fastest growing medical modality for imaging and therapy. Being noninvasive, painless, portable, X-ray radiation-free and far less expensive than magnetic resonance imaging, ultrasound is widely used in medicine today. Despite these benefits, undesirable bioeffects of high-frequency sound waves have raised concerns; particularly, because ultrasound imaging has become an integral part of prenatal care today and is increasingly used for therapeutic applications. As such, ultrasound bioeffects must be carefully considered to ensure optimal benefits-to-risk ratio. In this context, few studies have been done to explore the physics (i.e. ‘cavitation’) behind the risk factors. One reason may be associated with the challenges in visualization of ultrasound-induced cavitation bubbles in situ. To address this issue, this research aims to develop a synchrotron-based assessment technique to enable visualization and characterization of ultrasound-induced microbubbles in a physiologically relevant medium under standard ultrasound operating conditions. The first objective is to identify a suitable synchrotron X-ray imaging technique for visualization of ultrasound-induced microbubbles in water. Two synchrotron X-ray phase-sensitive imaging techniques, in-line phase contrast imaging (PCI) and analyzer-based imaging (ABI), were evaluated. Results revealed the superiority of the ABI method compared to PCI for visualization of ultrasound-induced microbubbles. The second main objective is to employ the ABI method to assess the effects of ultrasound acoustic frequency and power on visualization and mapping of ultrasound-induced microbubble patterns in water. The time-averaged probability of ultrasound-induced microbubble occurrence along the ultrasound beam propagation in water was determined using the ABI method. Results showed the utility of synchrotron ABI for visualizing cavitation bubbles formed in water by clinical ultrasound systems working at high frequency and output powers as low as used for therapeutic systems. It was demonstrated that the X-ray ABI method has great potential for mapping ultrasound-induced microbubble patterns in a fluidic environment under different ultrasound operating conditions of clinical therapeutic devices. Taken together, this research represents an advance in detection techniques for visualization and mapping of ultrasound-induced microbubble patterns using the synchrotron X-ray ABI method without usage of contrast agents. Findings from this research will pave the road toward the development of a synchrotron-based detection technique for characterization of ultrasound-induced cavitation microbubbles in soft tissues in the future

Are patients with Parkinson’s disease who have either mild to moderate microsmia, severe microsmia or anosmia clinically different?

Introduction: Olfactory loss is a common non-motor symptom of Parkinson’s disease (PD), which has the potential to have a negative effect on quality of life. However, research examining PD patients with varying degrees of loss of sense of smell and whether they are clinically distinct and the implications of the loss of sense of smell when nursing a patient with PD appears to be poorly explained. Objective: To investigate whether patients with PD who have either mild/moderate microsmia, severe microsmia or anosmia (as measured by the University of Pennsylvania Smell Identification Test (UPSIT)) were clinically different when compared across a range of motor, non-motor and quality of life domains. Methodology: This is an open cross-sectional observational study, involving 112 patients (of both genders) who have a diagnosis of PD. Tools and scales used include the motor rating subscales in the Unified Parkinson’s Disease Rating Scale (UPDRS), the Non-motor Symptoms Questionnaire (NMSQ), the PDQ39 Quality of Life Questionnaire (PDQ39), the Hoehn and Yahr scale (H&Y), the Rapid Eye Movement Behaviour Disorder Screening Questionnaire, (RBD) and the Montreal Cognitive Assessment (MoCA). Results: Seventy-two males and forty females have been recruited for this study. Age ranged from 49 - 89 years (mean age 71 years). Eight-five (77%) of the PD patients were at stage 1 or 2 Hoehn and Yahr staging highlighting the study sample mainly consisted of PD patients with minimal or no functional impairment, without impairment of balance. Disease duration ranged from 6 months to 19 years (mean duration 5.5 years). All PD patients (except two) were considered to have either normal cognition or mild cognitive impairment, defined by the MoCA (mean MoCA 26.1). All the PD patients recruited for this study had loss of sense of smell and 91% had -in fact- severe microsmia or anosmia. Seventy-nine (70.5%) PD patients correctly detected a reduced sense of smell. Twenty-nine out of the 33 PD patients (97%) (self-reporting a normal sense of smell) had, in fact, a severe degree of loss of sense of smell (Mean UPSIT 16) without realising it. Overall loss of sense of smell was not correlated with severity or stage of PD, duration of disease, medication, smoking, the environment in which the PD patient was tested, whether they had phantosmia (persistent pleasant or disgusting smell) or taste problems. There was also no correlation between the motor, non-motor, rapid eye movement disorder and quality of life themes during whole group analysis. However, on sub-group analysis, a positive correlation was noted between sense of smell score and PD patients with normal cognition compared to those with mild cognitive impairment using MoCA ( =0.213, p=0.024) and non-motor symptom dribbling of saliva during the day (p=0.003), There was also a negative correlation in PDQ39 cognition theme (score =-0.012 p=0.036), minutes since last PD medication taken ( =-0.2634, p=0.008), timing of levodopa dose ( =-0.1875, p=0.015), and individual domains of the UPDRS motor scores, including posture ( = -.231 p=0.014) facial expression ( =-0.207 p=0.029) and arising from a chair ( =-0.190 p=0.045)

Glucocerebrosidase mutations and the pathogenesis of Parkinson disease

To date, a mutation of the glucocerebrosidase gene (GBA) is the strongest genetic risk factor associated to Parkinson’s disease (PD). This leads to my prospective cohort study of a GBA mutation positive cohort for early features of PD. This study indicates that as a group, GBA mutation positive individuals show deterioration in clinical markers consistent with the prodrome of PD. I have generated cell culture models from individuals within the clinical cohort studied, in order to delineate the molecular mechanism of mutant GBA to the pathogenesis of PD. My results on skin fibroblast cultures reproduce the glucocerebrosidase enzyme (GCase) enhancement seen from previous studies following treatment with pharmacological chaperone (PC) molecules. These data further provide support for a link between GBA mutations and changes in the autophagic/lysosomal system, which could predispose to neurodegeneration. Due to the limitation of fibroblasts as a model for interrogating the complete pathway in PD, I studied human adipose neural crest stem cell (NCSC) derived dopaminergic (DA) neurons. This model recapitulated the defects identified in the fibroblast model including: reductions in GCase activity and protein level, and lysosomal abnormalities including impairments of autophagy. In addition, reduced GCase was associated with increased α-synuclein (SNCA). PC treatment restored GCase function, upregulated macroautophagy and lead to a reduction in SNCA levels. PC therapy could represent a novel therapeutic approach for PD