PD_manager: an mHealth platform for Parkinson's disease Management

Parkinson’s disease (PD) current clinical management is mostly based on patient’s subjective report about the effects of treatments and on medical examinations that unfortunately represent only a snapshot of a highly fluctuating clinical condition. This traditional approach requires time, it is biased by patient’s judgment and is often not completely reliable, especially in moderate advanced stages. The main purpose of the EU funded project PD_manager (Horizon 2020, Grant Agreement n° 643706) is to build and evaluate an innovative, mHealth, patient-centric system for PD remote monitoring. After a first phase of research and development, a set of wearable devices has been selected and tested on 20 patients. The raw data recorded have been used to feed algorithms necessary to recognize motor symptoms. In parallel, other applications have been developed to test also the main non-motor symptoms. On a second phase, a case- control randomized multicentric study has been designed and performed to assess the acceptability and utility of the PD_manager system at patients’ home, compared to the current gold standard for home monitoring, represented by symptoms diaries. 136 couples of patients and caregivers have been recruited, and at the end of the trial the system was found to be very well tolerated and easy to use, compared to diaries. The developed System is able to recognize motor and non-motor symptoms, helping healthcare professionals in taking decisions on therapeutic strategies. Moreover, PD_manager could represent a useful tool for patient's self-monitoring and self-care promotion

Robust identification of Parkinson\u27s disease subtypes using radiomics and hybrid machine learning

OBJECTIVES: It is important to subdivide Parkinson\u27s disease (PD) into subtypes, enabling potentially earlier disease recognition and tailored treatment strategies. We aimed to identify reproducible PD subtypes robust to variations in the number of patients and features. METHODS: We applied multiple feature-reduction and cluster-analysis methods to cross-sectional and timeless data, extracted from longitudinal datasets (years 0, 1, 2 & 4; Parkinson\u27s Progressive Marker Initiative; 885 PD/163 healthy-control visits; 35 datasets with combinations of non-imaging, conventional-imaging, and radiomics features from DAT-SPECT images). Hybrid machine-learning systems were constructed invoking 16 feature-reduction algorithms, 8 clustering algorithms, and 16 classifiers (C-index clustering evaluation used on each trajectory). We subsequently performed: i) identification of optimal subtypes, ii) multiple independent tests to assess reproducibility, iii) further confirmation by a statistical approach, iv) test of reproducibility to the size of the samples. RESULTS: When using no radiomics features, the clusters were not robust to variations in features, whereas, utilizing radiomics information enabled consistent generation of clusters through ensemble analysis of trajectories. We arrived at 3 distinct subtypes, confirmed using the training and testing process of k-means, as well as Hotelling\u27s T2 test. The 3 identified PD subtypes were 1) mild; 2) intermediate; and 3) severe, especially in terms of dopaminergic deficit (imaging), with some escalating motor and non-motor manifestations. CONCLUSION: Appropriate hybrid systems and independent statistical tests enable robust identification of 3 distinct PD subtypes. This was assisted by utilizing radiomics features from SPECT images (segmented using MRI). The PD subtypes provided were robust to the number of the subjects, and features

Symptom analysis of Parkinson’s disease utilizing machine learning methods

While monitoring Parkinson’s disease progression or observing the everchanging severity stage of the disease, the patients are keeping symptom diaries and making regular visits to the neurologist clinic for evaluation. The diaries are based on patients own memories which tend to be unreliable in addition to the burdensome clinical appointments. Therefore, the research is focused on automatizing the burden with the help of machine learning classifiers. These classifiers are trained to either recognize the current severity stage of a patient or make a prediction about future outcomes, such as the progression rate of the disease or a freezing of gait event. The data on which the classifiers are trained with is gathered via wearable sensors that attain several gait parametrics from different walking tasks or daily activities conducted. This thesis presents several studies conducted during the years of 2020–2023 which aim to develop a machine learning algorithm to classify the correct state of the patient according to the disease stage, or predict medical outcomes before their occurring. Their performance metrics are evaluated, especially regarding their accuracy, sensitivity and specificity results. Additionally, this thesis introduces background of gait analysis and machine learning methods. The changes in gait that Parkinson’s disease inflicts are discussed alongside the clinical criteria used in evaluating the changes and patient’s condition. This thesis is a literature review, which aims to find the best possible machine learning algorithms for symptom analysis of Parkinson’s disease. It concludes that comprehensive conclusions are difficult to draw, since the algorithm performance can be analysed with several different metrics. Even though most of the algorithms gained adequate results, the research still includes several limitations to solve before the algorithm can be validated for clinical use as a symptom monitoring system.Parkinsonin taudin etenemisen seuranta perustuu potilaiden omiin oirepäiväkirjamerkintöihin. Lisäksi taudin jatkuvasti muuttuvaa vakavuusastetta seurataan säännöllisesti neurologin klinikalla. Päiväkirjat perustuvat potilaan omiin muistikuviin, jotka ovat yleensä epäluotettavia ja klinikalla käynti raskasta. Siksi tutkimus keskittyy taakan automatisointiin koneoppimismenetelmien avulla. Nämä algoritmit koulutetaan joko tunnistamaan taudin nykyinen vakavuusaste tai ennustamaan tulevia tuloksia, kuten taudin etenemisnopeutta tai kävelykyvyn jäätymistä. Tietoja, joilla koneoppimisalgoritmeja koulutetaan, kerätään puettavien sensoreiden avulla. Nämä keräävät dataa useista eri kävelyparametreista, jotka saadaan talteen erilaisia kävelytestejä hyödyntäen. Tässä työssä esitellään useita vuosina 2020–2023 tehtyjä tutkimuksia, joiden tarkoituksena on kehittää koneoppimisalgoritmeja, jotka luokittelevat potilaan oikeaan vakavuusastekategoriaan tai ennustavat lääketieteellisiä tuloksia ennen niiden ilmenemistä. Algoritmien suorituskykymittareita arvioidaan erityisesti tarkkuuden, herkkyyden ja spesifisyyden suhteen. Lisäksi työssä taustoitetaan kävelyanalyysin periaatteita, puettavia sensoreita sekä yleisimpiä koneoppimismenetelmiä, joita tutkimukset ovat käyttäneet. Parkinsonin taudin myötä kävelyyn kohdistuvia muutoksia käsitellään ja potilaan tilan arvioinnissa käytettyjä kliinisiä kriteerejä esitellään. Tämä työ on kirjallisuuskatsaus, jonka tavoitteena on löytää parhaat mahdolliset koneoppimisalgoritmit Parkinsonin taudin oireiden analysointiin. Tuloksista voidaan päätellä, että kattavaa johtopäätöstä on vaikea tehdä, koska algoritmien suorituskykyä voidaan analysoida useilla eri mittareilla. Vaikka suurin osa algoritmeista saivatkin onnistuneita tuloksia, tutkimukset sisälsivät silti useita rajoituksia, jotka ovat ratkaistava ennen kuin algoritmi voidaan validoida kliiniseen käyttöön oireiden seurantajärjestelmänä

Development of Markerless Systems for Automatic Analysis of Movements and Facial Expressions: Applications in Neurophysiology

This project is focused on the development of markerless methods for studying facial expressions and movements in neurology, focusing on Parkinson’s disease (PD) and disorders of consciousness (DOC). PD is a neurodegenerative illness that affects around 2% of the population over 65 years old. Impairments of voice/speech are among the main signs of PD. This set of impairments is called hypokinetic dysarthria, because of the reduced range of movements involved in speech. This reduction can be visible also in other facial muscles, leading to a hypomimia. Despite the high percentage of patients that suffer from dysarthria and hypomimia, only a few of them undergo speech therapy with the aim to improve the dynamic of articulatory/facial movements. The main reason is the lack of low cost methodologies that could be implemented at home. DOC after coma are Vegetative State (VS), characterized by the absence of self-awareness and awareness of the environment, and Minimally Conscious State (MCS), in which certain behaviors are sufficiently reproducible to be distinguished from reflex responses. The differential diagnosis between VS and MCS can be hard and prone to a high rate of misdiagnosis (~40%). This differential diagnosis is mainly based on neuro-behavioral scales. A key role to plan the rehabilitation in DOC patients is played by the first diagnosis after coma. In fact, MCS patients are more prone to a consciousness recovery than VS patients. Concerning PD the aim is the development of contactless systems that could be used to study symptoms related to speech and facial movements/expressions. The methods proposed here, based on acoustical analysis and video processing techniques could support patients during speech therapy also at home. Concerning DOC patients the project is focused on the assessment of reflex and cognitive responses to standardized stimuli. This would allow objectifying the perceptual analysis performed by clinicians

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

Quantitative Electroencephalography and genetics as biomarkers of dementia in Parkinson’s disease

The importance of cognitive decline in Parkinson’s disease (PD), which eventually progresses to dementia (PD-D) in the majority of surviving patients, has been widely recognised during the last decade. PD-D is associated with a twofold increase in mortality, increased caregiver strain and increased healthcare costs. Thus, early and correct identification of the PD patients with a risk of dementia is a challenging problem of neurology, which has led to the suggestion of various markers of cognitive decline in PD. If validated, these markers would offer the opportunity for disease modification and therapeutic intervention at a critical early stage of the illness, when the viable neuronal population is greater. The focus of this thesis was to assess how various factors - quantitative electroencephalography (qEEG) changes, genetics, deep brain stimulation (DBS), olfactory function, etc. – may be related with the risk of cognitive decline in PD patients. We performed four clinical studies with various design. These studies included PD patients who were dementia-free on inclusion, and control participants. Principal findings are the following: (1) increase of global median relative power theta (4–8 Hz), executive and working memory dysfunction are independent prognostic markers of severe cognitive decline in PD patients over a period of 3 years. (2) DBS of the subthalamic nuclei in a group of PD patients with mean age 63.2 years, in comparison with a group of younger patients (52.9 years), causes higher incidence of psychiatric events over 2 years of observation. However, these events were transient and did not outweigh the benefits of surgery. (3) Worsening of verbal fluency performance is an early cognitive outcome of DBS of the subthalamic nuclei in PD patients. (4) Among early appearing non-motor signs of Parkinson’s disease, alteration of olfaction but not EEG spectrum correlates with motor function. (5) A composite score approach seems to be a realistic goal in the search for biomarkers of severe cognitive decline