Inertial sensor based full body 3D kinematics in the differential diagnosis between Parkinson’s Disease and mimics

The differential diagnosis of Parkinson’s Disease (PD) remains challenging with frequent mis and underdiagnosis. DAT-Scan has been a useful technique for assessing the lost integrity of the nigrostriatal pathway in PD and differentiating true parkinsonism from mimics. However, DAT-Scan remains unavailable in most non-specialized clinical centres, making imperative the search for other easy and low-cost solutions. This dissertation aimed to investigate the role of inertial sensors in distinguishing between the denervated and the non-denervated individuals. In this dissertation, we've used Inertial Sensor Based 3D Full Body Kinematics (FBK) and tested if this technique was able to distinguish between patients with changes in the DAT-Scan from those without. This was divided into two parts, being that firstly, a group of individuals was referred by the attending physician for DAT-Scan (123I-FP-CIT SPECT) to be able to compare FBK in those with and without evidence of dopaminergic depletion. Second, it was tested whether FBK could be used as a metric for the severity of dopaminergic depletion. Twenty-one patients participated in this study, being recruited from the Nuclear Medicine Unit in the Champalimaud Clinical Centre (CCC), Lisbon. Within these 21 patients, 10 of them had denervation (mean age, 68.4 ± 7.8 years) and the remaining 11 (mean age, 66.6 ± 7.4 years) did not present denervation. The analysis between the worst uptake ratio features and dimensional features, as well as the asymmetry indexes in the striatum revealed significant differences between denervated and non-denervated individuals. On the contrary, the kinematics did not do it. Overall, based on the collected kinematics data, it was identified that there was not any significant correlation between the kinematics and the DAT-Scan. What means that these kinematics variables were not able to explain the DAT-Scan. On the other hand, it was also checked that the kinematics data were strongly correlated to the motor symptoms (MDS-UPDRS III). This way, it was concluded that the classical biomechanics did not distinguish denervated from non-denervated individuals. Therefore, the kinematics could not give the same answer as the DAT-Scan. In spite of these results it would be relevant to keep researching other methods in order to find out the distinction between the denervation and no denervation in a low-cost way

Mining imaging and clinical data with machine learning approaches for the diagnosis and early detection of Parkinson\u27s disease

Parkinson\u27s disease (PD) is a common, progressive, and currently incurable neurodegenerative movement disorder. The diagnosis of PD is challenging, especially in the differential diagnosis of parkinsonism and in early PD detection. Due to the advantages of machine learning such as learning complex data patterns and making inferences for individuals, machine-learning techniques have been increasingly applied to the diagnosis of PD, and have shown some promising results. Machine-learning-based imaging applications have made it possible to help differentiate parkinsonism and detect PD at early stages automatically in a number of neuroimaging studies. Comparative studies have shown that machine-learning-based SPECT image analysis applications in PD have outperformed conventional semi-quantitative analysis in detecting PD-associated dopaminergic degeneration, performed comparably well as experts\u27 visual inspection, and helped improve PD diagnostic accuracy of radiologists. Using combined multi-modal (imaging and clinical) data in these applications may further enhance PD diagnosis and early detection. To integrate machine-learning-based diagnostic applications into clinical systems, further validation and optimization of these applications are needed to make them accurate and reliable. It is anticipated that machine-learning techniques will further help improve differential diagnosis of parkinsonism and early detection of PD, which may reduce the error rate of PD diagnosis and help detect PD at pre-motor stage to make it possible for early treatments (e.g., neuroprotective treatment) to slow down PD progression, prevent severe motor symptoms from emerging, and relieve patients from suffering

Clinical correlates and advanced processing of the dopamine transporter spect - applications in parkinsonism.

La visualización del transportador de dopamina (DAT) a través del SPECT con [123I]FP-CIT es una prueba de imagen ampliamente usada en el diagnóstico de la enfermedad de Parkinson (EP) y otros trastornos del movimiento que cursan con síntomas parkinsonianos. Dicha imagen permite visualizar y cuantificar los niveles de DAT en el estriado y sus regiones putamen y caudado, y es por tanto una herramienta útil para evaluar in-vivo el estado de las terminales presinápticos dopaminérgicos de la vía nigroestriada. En la práctica clínica es comúnmente utilizado para la diferenciación de parkinsonismos neurodegenerativos con afectación presináptica y otros trastornos del movimiento con síntomas similares pero sin afectación presináptica como el temblor esencial. En la imagen se suele observar un patrón de degeneración postero-anterior que se corresponde con la progresión de síntomas en la EP debido a la afectación progresiva de los circuitos de los ganglios basales. De hecho, numerosos estudios han mostrado que la falta de DAT en el putamen y caudado se correlacionan con síntomas motores y cognitivos, respectivamente. Sin embargo, a pesar de su uso extendido, su uso clínico dado los métodos de evaluación actuales se limita a determinar la presencia o no de degeneración nigroestriada. En esta tesis se plantea como hipótesis que el uso de métodos de procesamiento y evaluación más sofisticados, utilizando técnicas de procesamiento de imágenes y de reconocimiento de patrones a nivel de vóxel, podría potenciar el desarrollo de nuevas aplicaciones clínicas; incluyendo la evaluación de síntomas y el diagnóstico diferencial entre parkinsonismos. Para ello, hemos caracterizado clínicamente y recogido imágenes de SPECT de cientos de pacientes con EP y otros parkinsonismos, persiguiendo dos objetivos globales: i) investigar ciertos conceptos actuales sobre los síntomas motores y cognitivos en la EP; y ii) desarrollar nuevos métodos de procesamiento y evaluación que permitan extender el rango actual de aplicaciones clínicas de dicha prueba. Se presentan un total de 5 publicaciones agrupadas en dos temáticas, una para cada objetivo global. En la primera temática, se engloban dos trabajos con títulos: 1) Lower levels of uric acid and striatal dopamine in non-tremor dominant Parkinson's disease subtype, Plos One 2017 Mar 30;12(3):e0174644; y 2) Genetic factors influencing frontostriatal dysfunction and the development of dementia in Parkinson's disease, Plos One 2017 Apr 11;12(4):e0175560. En el trabajo 1 se investigaron las diferencias entre los niveles de ácido úrico y dopamina estriatal en los subtipos motores de EP: tremorígeno, intermedio, y con trastorno de la marcha e inestabilidad postural. Estudiamos 75 pacientes con EP de larga evolución y encontramos que aquellos que presentaron un predominio de temblor al inicio y mantuvieron este fenotípo clinico durante el curso de la enfermedad, tuvieron niveles de ácido úrico y dopamina estriatal mayores que aquellos que desarrollaron trastorno de la marcha e inestabilidad postural. Además, los niveles de ácido úrico y de dopamina estriatal se correlacionaron. Como conclusión, especulamos que niveles bajos de este antioxidante natural (el ácido úrico) puede reducer los niveles de neuroprotección y por tanto influenciar el perfil y curso de fenotipo motor en la EP. En el trabajo 2 se investigó la contribución de los principales factores genéticos descritos en la literatura en los síndromes duales de deterioro cognitivo en la EP (fronto-estriatal que conlleva un alto riesgo de síndrome disejecutivo – causado por falta de dopamina – y posterior-cortical que conlleva un alto riesgo de demencia). Evaluamos la imagen, el estado cognitivo y el genotipo de 298 pacientes con EP. Como resultado, observamos que el alelo APOE2, los polimorfismos SNCA rs356219 y COMT Val158Met, y las variantes patogénicas en GBA se asociaron con los niveles de denervación dopaminérgica estriatal, mientras que el alelo APOE4 y de nuevo las variaciones patogénicas en GBA se asociaron con el desarrollo de demencia (sugiriendo un doble rol del gen GBA). No encontramos ninguna relación del haplotipo MAPT H1 en ninguno de los síndromes. Concluimos que la dicotomía de los síndromes duales puede estar conducida por una dicotomía en estos factores genéticos. En la segunda temática, se presentan otros 3 trabajos más centrados en el desarrollo de metodología, titulados: 3) Machine learning models for the differential diagnosis of vascular parkinsonism and Parkinson's disease using [123I]FP-CIT SPECT, European Journal of Nuclear Medicine and Molecular Imaging, 2015 Jan;42(1):112-9; 4) A Bayesian spatial model for neuroimaging using multiscale functional parcellations, En revisión en la revista euroimage; y un último trabajo que está en elaboración y cuyos resultados preliminares fueron presentados recientemente: 5) Probabilistic intensity normalization of PET/SPECT images via Variational mixture of Gamma distributions, 30th Neural Information and Processing Systems Conference, November 2016, Barcelona, Spain. En el trabajo 3 se desarrollaron algoritmos usando imágenes de SPECT para distinguir un parkinsonismo secundario – el parkinsonismo vascular (PV) – de la EP. Observamos que una simple regresión logística – incluyendo los valores medios de captación estriatales, junto con el sexo, la edad, y los años de evolución – diferenció ambas entidades con un 90% de exactitud. De manera similar, encontramos que el uso de algoritmos objetivos y automáticos usando técnicas de machine learning basadas en vóxeles también discriminaron ambas entidades con un 90% de exactitud. Concluimos que el diagnóstico diferencial de ambas enfermedades puede ser asistido por algoritmos automáticos basados en imagen. En el trabajo 4 se desarrolló una nueva metodología, más allá del método estándar basado en vóxeles, para realizar inferencias en neuroimagen funcional. Se desarrolló un modelo multivariado espacial que permitió modelar imágenes de SPECT de sujetos sanos de manera muy eficiente con un número de parámetros muy inferior al número de vóxeles. Dicho modelo consiste en una superposición lineal de funciones base utilizando subparcelaciones multi-escala del estriado, éstas obtenidas tras procesar imágenes de resonancia magnética funcional. También demostramos la utilidad de nuestro modelo para desarrollar aplicaciones clínicas mediante la construcción de clasificadores para diferenciar la EP de controles sanos y un parkinsonismo atípico: la parálisis supranuclear progresiva. Esta nueva metodología ofrece ventajas sin precedentes para el análisis de neuroimagen con respecto al clásico modelo lineal general univariado basado en vóxel, incluyendo: i) mayor interpretabilidad de las señales cerebrales; ii) modelos parsimoniosos y por tanto incremento del poder estadístico; y iii) modelado de la correlación espacial entre regiones y a distintos niveles de granuralidad en neuroimagen funcional. Además, desarrollamos metodología bayesiana para detectar de manera automática (y cuantificar la incertidumbre) las regiones cerebrales que estén relacionadas con ciertas variables fenotípicas. En el trabajo 5 se desarrolló un método para armonizar la intensidad de las imágenes de SPECT producidas por distintos fabricantes (y calibración) de cámaras Gamma. El método se basa en modelar el histograma de la imagen con un modelo mixto de distribuciones Gamma. Se utilizó la función de densidad acumulada de la distribución Gamma que modela la región específica de captación para reparametrizar la imagen con valores de vóxel entre 0 y 1. Observamos que dicha normalización mejoró sustancialmente (hasta un 10%) el diagnóstico de EP cuando los algoritmos se desarrollaron usando imágenes de distintas cámaras y/o calibraciones. Dicha normalización puede suponer un paso clave en pre-procesado de estas imágenes de cara a la realización de estudios multicéntricos y el desarrollo de aplicaciones clínicas generalizables. Como conclusión es importante resaltar la relevancia de los trabajos. En los trabajos 1 y 2 hemos aportado resultados con biomarcadores de valor pronóstico en la progresión de la EP. En los trabajos 3, 4 y 5, hemos aportado una nueva metodología, muy superior a la existente, de procesamiento y evaluación de esta prueba de imagen. La metodología desarrollada en el trabajo 4 permite explorar regiones cerebrales a un de nivel de complejidad espacial y granularidad sin precedentes. Por ello, nuestro modelo podría captar las diferencias entre las imágenes de pacientes con distintas patologías y/o entre síntomas específicos residir en patrones espaciales sutiles y complejos. De hecho, en los trabajos 3 y 4 aportamos resultados excelentes en la diferenciación de la EP con otros síndromes parkinsonianos. Además, el trabajo 5 tiene el potencial de constituirse en el campo como un paso fundamental de pre-procesado, especialmente en estudios ulticéntricos y estudios que pretendan desarrollar aplicaciones clínicas generalizables, independientemente de la cámara Gamma y el centro donde se realice la prueba. Es importante señalar además que los métodos desarrollados se podrían igualmente aplicar para procesar y evaluar otro tipo de imágenes de medicina nuclear y/u otras regiones cerebrales. Es por ello que esperamos que este trabajo tenga un gran impacto en general en la evaluación de este tipo de imágenes y en el desarrollo de algoritmos que den soporte a la decisiones clínicas en trastornos del movimiento y potencialmente en otras enfermedades.The imaging of the dopamine transporter (DAT) with [123I]FP-CIT SPECT is a routinely used assessment in the diagnostic pipeline of Parkinson’s disease (PD) and other movement disorders that present with parkinsonian symptoms. In this scan, the levels of striatal DAT can be visualized and quantified, also at the region-of-interest (ROI) level in putamen and caudate, and therefore it constitutes an useful tool to assess in-vivo the state of the dopaminergic presynaptic terminals in the nigrostriatal pathway. In routine clinical practice it is especially utilized for the differential diagnlosis of presynaptic neurodegenerative disorders like PD and other non-presynaptic movement disorders like essential tremor. Also, numerous research studies have shown that striatal DAT deficits quantitatively correlate with motor and cognitive impairment in PD. Indeed, it can be seen in the image a posterior-to-anterior pattern of degeneration that well corresponds with disease progression due to the progressive lost of dopaminergic input into the motor and associative loops between the basal ganglia and the cortex. However, despite its known utility and widespread availability, its use with current assessment methods in real clinical practice is limited to determining the presence of nigrostriatal degeneration at a single-subject level in a binary fashion. We hypothesized in this thesis that an enhanced processing and assessment of this scan with modern image processing and pattern recognition techniques may help to boost its use in the clinic with new and more accurate applications, including symptom risk assessment and differential diagnosis with other parkinsonisms. We collected DAT scans of several hundreds of well-clinicallyphenotyped patients with PD and other parkinsonims, envisaging two main global objectives: i) to investigate some trending hypotheses and concepts about the motor and cognitive impairment in PD; and ii) to develop new processing and evaluation strategies with computational techniques to shed light into new clinical applications. A total of 5 publications are herein presented and grouped in two themes, one for each global objective. In the first theme, two works are presented, entitled: 1) Lower levels of uric acid and striatal dopamine in non-tremor dominant Parkinson's disease subtype, Plos One 2017 Mar 30;12(3):e0174644; and 2) Genetic factors influencing frontostriatal dysfunction and the development of dementia in Parkinson's disease, Plos One 2017 Apr 11;12(4):e0175560. In work 1 we investigated the differences in uric acid and striatal DAT in PD motor subtypes: tremor-dominant, intermediate, or postural instability and gait disorder (PIGD). We studied 75 PD patients of long-term evolution and found that those who presented with a tremor onset and maintained predominance of tremor, or, to a lesser extent, evolved to an intermediate phenotype, had higher levels of uric acid and striatal DAT binding than those who developed a IGD phenotype. We also found that uric acid and striatal DAT levels were highly correlated. We speculate that low levels of this natural antioxidant may lead to a lesser degree of neuroprotection and could therefore influence the motor phenotype and course. In work 2 we investigated the contribution to the dual syndromes of cognitive impairment in PD (frontostriatal dopamine-mediated and posterior cortical leading to dementia) of the main genetic risk factors decribed in the literature. We evaluated the scans, the cognitive status, and the genotypes of 298 PD patients and found that APOE2 allele, SNCA rs356219 and COMT Val158Met polymorphisms, and deleterious variants in GBA influenced striatal dopaminergic depletion, and that APOE4 allele and deleterious variants in GBA influenced dementia, thus suggesting a doubleedged role for GBA. We did not found any role of MAPT H1 haplotype. We conclude that the dichotomy of the dual syndromes may be driven by a broad dichotomy in these genetic factors. In the second theme, we present three other works with more focus on methodology, entitled: 3) Machine learning models for the differential diagnosis of vascular parkinsonism and Parkinson's disease using [123I]FP-CIT SPECT, European Journal of Nuclear Medicine and Molecular Imaging, 2015 Jan;42(1):112-9; 4) A Bayesian spatial model for neuroimaging using multiscale functional parcellations, Under Review in Neuroimage; and a last piece of work that it is in preparation for submission and that I have adapted for this thesis from 5) Probabilistic intensity normalization of PET/SPECT images via Variational mixture of Gamma distributions, 30th Neural Information and Processing Systems Conference, November 2016, Barcelona, Spain. In work 3 we developed analytical models using DAT SPECT data to discriminate vascular parkinsonism (VP) from PD. We collected scans from 80 VP and 164 PD and found that a simple logistic regression using the quantification of the striatal subregions putamen and caudate together with age, sex and disease duration discriminated both entities with over 90% accuracy. Also, we found that the use of more automated and rater-independent machine learning algorithms such as support vector machines with the voxel-wise data of the striatum also gives discrimination accuracy over 90%. We conclude that the differential diagnosis of both diseases can be aided by automated image-based algorithms. In work 4 we developed a new anaylsis framework to perform inferences with functional neuroimaging data. We developed a multivariate spatial model by which an imaged brain region can be efficiently represented in low dimensions with a linear superposition of basis functions. To demonstrate, we accurately modeled DATSCAN images from healthy subjects with a linear combination of multi-resolutional striatum parcellations derived from functional MRI experiments. We also demonstrate the utility of our model to develop clinical application by constructing accurate classifiers to differentiate PD from normal controls and patients with an atypical parkinsonism: the progressive supranuclear palsy. This approach offers unprecedent benefits with respect to classical univariate voxel methods, including: i) greater biological interpretability of the detected brain signals ii) parsimonity in the models and hence gain in statistical power; and iii) multi-range modeling of the spatial dependencies in brain images. Furthermore, we provide a bayesian analysis framework to automatically identifying brain subregions/subnetworks that are meaningful for particular phenotypic variables. In work 5 we developed a voxel-based intensity normalization method for DAT SPECT images aiming at overcoming the liminations of the current ROI-based normalization standard, namely ROI delineation dependence and intensity values dependence on Gamma camera. We found that the intensity histogram of a DAT SPECT image can be modeled as a mixture model of Gamma distributions. The cumulative distribution function (CDF) of the fitted Gamma distributions can be used to re-cast the voxel intensity values into a new normalized feature space between 0 and 1. We found that this re-parametrization equalized intensity across cameras and drastically improved the accuracy of PD diagnosis (up to 10%) when images from different cameras were pooled. Importantly, our method may constitute a key pre-processing step for group-level and multi-center studies. As a final remark, it is important to stress the relevance of the work. In the works 1 and 2, we have provided new insights on biomarkers that have prognostic value in the progression of PD. In the works 3, 4 and 5, which set the grounds of a new powerful approach to process and evaluate these images. The machine learning framework developed in work 4) allows to exploring brain regions at a unprecedent level of spatial complexity and granurality. Thus, challenging tasks such as the differential diagnosis between different parkinsonian disorders or the identification of fine-grained regions/networks responsible for specific parkinsonian symptoms can be tackled with the proposed approach. In fact, we obtained excellent results in works 3 and 4 in the differentiation of PD from other parkinsonian syndromes. Also, the work 5 may constitute a fundamental pre-processing step, especially in multi-center studies and studies aiming at developing generalizable clinical applications, regardless of the Gamma camera manufacturer and site where the scan is made. It is important to note that, besides DATSCAN, these methods could be also applied to other nuclearmedicine images and/or brain regions. We hope that this work will have an impact in the assessment of this type of images and in the development of algorithms supporting clinical decisions in movement disorders and potentially in other diseases as well.Premio Extraordinario de Doctorado U

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/

Neuropsychiatric, neuropsychological, and neuroimaging features in isolated REM sleep behavior disorder: The importance of MCI

Mild cognitive impairment (MCI) is frequently diagnosed in patients with isolated rapid eye movement (REM) sleep behavior disorder (iRBD), although the extent of MCI-associated neuropathology has not yet been quantified. The present study compared the differences in neuropsychiatric, neuropsychological, and neuroimaging markers of neurodegeneration in MCI-iRBD and iRBD patients with normal cognition

Optimized semi-quantitative analysis of dopamine transporter SPECT to support visual image interpretation in the diagnosis of parkinsonian syndromes

Zur Differenzierung von neurodegenerativen und nicht-neurodegenerativen Ursachen eines klinisch unklaren Parkinsonsyndroms wird die Dopamintransporter-SPECT (DAT-SPECT) eingesetzt. Neben der visuellen Bildinterpretation unterstützt die semi-quantitative Analyse der striatalen Dopamintransporter-Verfügbarkeit die Befundung. Die vorliegende Dissertationsschrift fasst drei Studien zusammen, die klinisch relevante Parameter der Bildentstehung und Bildverarbeitung in der semi-quantitativen Analyse der DAT-SPECT identifizierten, optimierten und hinsichtlich ihrer diagnostischen Genauigkeit untersuchten. In der ersten Studie wurde eine vollautomatische Methode zur Abgrenzung der äußeren Kopfkontur als Teil der Schwächungskorrektur nach Chang implementiert und gegenüber einer klinisch etablierten halbautomatischen Methode validiert. Die Auswertung eines multizentrischen Datensatzes ergab, dass beide Methoden zur Kopfabgrenzung sowohl vergleichbare semi-quantitative Werte als auch eine vergleichbare diagnostische Genauigkeit lieferten. Damit kann die vollautomatische Methode für den Einsatz in der klinischen Versorgung empfohlen werden, da keine Interaktion durch den Nutzer erforderlich ist. Die zweite Studie untersuchte zwei Methoden zur semi-quantitativen Abschätzung der Tracer Bindung hinsichtlich ihrer diagnostischen Genauigkeit. Der auflösungsunabhängige specific uptake size index (SUSI) zeigte bei Datenerhebung an unterschiedlichen Kamerasystemen eine höhere diagnostische Genauigkeit als der Standardparameter, das sogenannte specific binding ratio (SBR). Dies ist besonders relevant für multizentrische Studien. Sobald jedoch nur ein Kamerasystem eingesetzt wurde, ist der Standardparameter SBR dem SUSI vorzuziehen, da dieser bei vergleichbarer diagnostischer Performance weniger anfällig gegenüber einer fehlerhaften Abschätzung der nicht-spezifischen Tracer-Bindung in der Referenzregion ist. Ziel der dritten Studie war die Untersuchung des Einflusses der Größe der Normaldatenbank (NDB) auf die diagnostische Genauigkeit einer semi-quantitativen Auswertung der DAT-SPECT. Dabei erfolgte eine Simulation von unterschiedlichen Größen der NDB (n=5, 10, 15, …, 50) durch zufälliges Ziehen aus dem Pool an Kontrollen und Validierung der jeweiligen NDB in der Gesamtkohorte anhand von Klassifizierungsgenauigkeit, Sensitivität und Spezifität. Die Analyse ergab, dass ein Mindestumfang von 25 bis 30 DAT-SPECT-Datensätzen zur Bildung einer NDB notwendig ist. Eine Vergrößerung der NDB über 40 Fälle hinaus führt hingegen zu keiner weiteren relevanten Steigerung der diagnostischen Genauigkeit.Dopamine transporter SPECT (DAT-SPECT) is an established method to differentiate neurodegenerative and non-neurodegenerative causes in clinically uncertain parkinsonian syndromes. Besides visual image interpretation, semi-quantitative analysis of the striatal dopamine transporter availability is used to support medical diagnosis. The present doctoral thesis summarizes three studies that identified, optimized and validated clinically relevant, semi-quantitative parameters of DAT-SPECT image acquisition and processing with reference to their diagnostic accuracy. The first study proposed a fully automatic segmentation method of the outer head contour as a part of attenuation correction according to Chang and validated this method to a well-established semi-automatic method. Both methods for head delineation showed comparable semi-quantitative properties as well as comparable diagnostic accuracy based on multi-center patient data. For this reason, we suggest to use the fully automatic method in clinical patient care since no user interaction is required. A direct comparison of two semi-quantitative methods for estimation of tracer binding in reference to diagnostic accuracy was the aim of the second study. The spatial resolution independent specific uptake size index (SUSI) provided a higher diagnostic accuracy compared to the commonly used parameter, the specific binding ratio (SBR), when image acquisition is performed at various camera systems. This is highly relevant for multi-center image acquisition. However, in single-camera/mono-center settings SBR should be favored over SUSI, since SBR seemed to be less sensitive towards errors of the estimate of non-specific tracer uptake in the reference region with comparable diagnostic performance to SUSI. Rationale of the third study was to evaluate the impact of the size of the normal database (NDB) on the diagnostic performance of semi-quantitative analysis in DAT-SPECT. For it, simulation of NDB with different sizes (n=5, 10, 15, …, 50) by randomly selecting subjects from the subcohort of normal controls was implemented and validation of each particular NDB based on the overall cohort was done concerning diagnostic accuracy, sensitivity and specificity as performance measures. The study results suggested that 25 to 30 DAT-SPECT data sets should be the minimum size of NDB. Increasing the size of NDB beyond 40 data sets provided only very small further improvement in diagnostic accuracy

Can we improve the early diagnosis of Lewy body disease with more accurate quantification of nuclear medicine scans.

Ph.D ThesisThis thesis investigates the quantification of two scintigraphic biomarkers used for the diagnosis of dementia with Lewy bodies (DLB): 123I-FP-CIT (123I-N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl) nortropane), commonly known as DaTSCAN™,and cardiac 123I-MIBG(123I-metaiodobenzylguanidine). Accurate quantification is critical as we increasingly move towards diagnosis at the earlier mild cognitive impairment (MCI) stage, where more subtle changes from normality are expected. A range of novel approaches have been examined to overcome technical limitations that have previously been barriers to accurate quantification. Uniquely, this has been studied in cohorts of highly characterised dementia and MCI subjects as well as older adults with normal cognition recruited as age matched controls. The subject studies have been complemented by work using advanced anthropomorphic phantoms. Throughout, the innovative methods have been compared with the established ones. Results are presented in detail and clinical and research relevance is discussed together with proposals for optimal usage. Briefly, the key findings are:FP-CIT key findings•Specific binding ratio values (SBR) for FP-CIT images calculated by different software packages are systematically different, although give similar diagnostic accuracy. •Striatal uptake does not decrease with age in healthy older adults, as previously reported, indicating potential misdiagnosis if age correction is applied. •Absolute quantification separates normal and abnormal subjects less well than relative-quantification with SBR.•Advanced FP-CIT reconstruction methods using SPECT-CT and collimator modelling improve the accuracy of activity concentration measurements in a phantom.•Advanced FP-CIT reconstruction methods affect relative quantification with SBR, but not clinical interpretation. Cardiac MIBG key findings•Different methods of planar MIBG analysis are operator dependent and give systematically different results – recommendations are provided for an optimal method.•Establishing a normal threshold is critical. This thesis shows that previously published values may not be valid in a UK population and proposes a suitable alternative. •Images obtained soon after injection give similar accuracy as those obtained at 3.5 hours (the standard delayed method), and the latter scans could be omitted in the majority of cases. •Planar cardiac MIBG semi-quantification is significantly dependent on subject size. Using SPECT-CT gives greater separation between normal and abnormal scans than planar. II In summary, an in-depth and comprehensive study of technical aspects of Nuclear Medicine biomarker quantification using 123I labelled radiopharmaceuticals for the diagnosis of Lewy body disease is presented in this thesis. This provides a solid foundation for clinical and research application of these techniques in both early and established diseaseAlzheimer’s Societ

Neuroimaging correlates of progressive cognitive decline and clinical symptoms in prodromal Lewy body disease. A multimodal imaging study

Ph. D. Thesis.Introduction There has been an interest in earlier diagnosis of cognitive impairment at the prodromal stage. Mild cognitive impairment (MCI) is a prodromal cognitive phenotype of dementia. Differentiating MCI with Lewy bodies (MCI-LB) and MCI due to AD (MCI-AD) using clinical features alone is challenging and biomarkers are likely to aid diagnosis. This thesis investigated whether cross-sectional structural magnetic resonance imaging (MRI) or repeat 123I-FP-CIT single photon emission tomography (SPECT) could be utilised to differentiate between MCI-LB and MCI-AD. Methods Prospective repeat 133I-FP-CIT SPECT study: 85 subjects were included in this analysis, consisting of; healthy controls (HC) (n=29), MCI-AD (n=19), possible MCI-LB (n=10), probable MCI-LB (n=27). All subjects underwent comprehensive clinical and neuropsychological assessment as well as repeat 123I-FP-CIT SPECT and baseline cardiac 123I-MIBG scintigraphy. Cross- sectional MRI study: 97 subjects were included in this analysis, consisting of; HC (n=31), MCIAD (n=32), probable MCI-LB (n=34). All subjects underwent comprehensive clinical and neuropsychological assessment as well as baseline 123I-FP-CIT SPECT, cardiac 123I-MIBG scintigraphy and structural magnetic resonance imaging. Results Progressive dopaminergic loss was detected in MCI-LB in excess of HC, with mean annual striatal decline of 6% in the MCI-LB cohorts. MCI-AD had no difference in longitudinal striatal uptake when compared to HC. Structural MRI data found: (1) grey matter volume loss in the frontal and temporal lobes in MCI-LB compared to HC, (2) bilateral cerebellar volume reduction in MCI-LB compared to iii MCI-AD, (3) no relative preservation of the medical temporal lobe in MCI-LB compared to MCI-AD, (4) no cortical thickness difference between MCI-LB and MCI-AD (5) thalamic volume loss and relative preservation of the amygdala in MCI-LB compared to MCI-AD. Conclusion Sequential 123I-FP-CIT SPECT imaging is a promising biomarker for identifying MCI-LB. Structural MRI showed no difference in cortical indexes but some differences in subcortical and cerebellar measures between MCI-LB and MCI-AD