Selective cellular vulnerability and pathology progression patterns in two mouse models of Parkinson’s disease

Parkinson's disease is a highly debilitating disorder classically characterized by the degeneration of dopaminergic midbrain neurons of the substantia nigra. The resulting nigrostriatal dopamine deficiency is thought to be responsible for the onset of the cardinal Parkinson's motor symptomtomatology; bradykinesia, rigidity, and resting tremor. However, recent studies show that Parkinson's disease is a multisystem disorder. Thus, it comes not only to degeneration in the nigrostriatal system, but also to pronounced cell loss in many other brain regions. Histopathologically, Parkinson's disease is characterized by the presence of so-called Lewy bodies or neurites. These are intracytoplasmic proteinaceous inclusions consisting mainly of aggregated α-synuclein. Two neuronal structures that both have pronounced Lewy pathology in Parkinson's disease and prominent neurodegeneration are the noradrenergic locus coeruleus and the neurochemically heterogeneous pedunculopontine nucleus. Remarkably, in the pedunculopontine nucleus Lewy pathology and neurodegeneration are predominantly restricted to the cholinergic cell population, while the GABAergic and glutamatergic cell groups exhibit only minor Lewy pathology and are largely spared of neurodegeneration. The present dissertation pursued two main goals. On the one hand, we investigated whether the selective vulnerability pattern of the cholinergic subpopulation of the pedunculopontine nucleus could be reproduced in a mouse model based on the intracerebral injection of preformed α-synuclein fibrils. Second, the brain-spreading pattern of two focal-induced α-synucleinopathy mouse models were compared with respect to the methodology used to initiate the aggregation process (vector-mediated overexpression vs. α-synuclein fibril model). In the first part of the study, we used a targeted intracerebral injection of preformed α-synuclein fibrils to induce a focal α-synucleinopathy in the pedunculopontine nucleus. Our data show that the injection of α-synuclein fibrils resulted in the recruitment and misfolding of endogenous α-synuclein leading to formation of Lewy body-like aggregates in neuronal perikarya and axons. Interestingly, the observed inclusion bodies were immunoreactive for S129-phosphorylated α-synuclein, p62 positive and resistant to proteinase K digestion. We thereby showed that the experimentally induced α-synuclein pathology possessed several key features of human Lewy pathology. Remarkably, the major burden of Lewy-like pathology and quantified cell loss was limited to the cholinergic subpopulation of the pedunculopontine nucleus, while the non-cholinergic neurons were largely spared of Lewy pathology and degeneration at any investigated time-point. Interestingly, in both fibril and monomer-α-synuclein (control) injected animals, induction of reactive microgliosis occurred, although no α-synuclein pathology was observed in the control group. Our analysis also showed that the formation of α-synuclein pathology was not limited to the immediate vicinity of the site of injection, but propageted over considerable distances to other interconnected brain regions. Since α-synuclein positive aggregates were found in neuronal cell bodies of distant brain regions, which lay all within the neuronal network of the pedunculopontine nucleus, it can be concluded that the α-synucleinopathy spread only within the neural network of the pedunculopontine nucleus. In the second part of the thesis, focal α-synucleinopathy was induced in the locus coeruleus by intracerebral injection of adeno-associated viral vectors containing the gene for human mutant A53T-α-synuclein or luciferase (control protein). The obtained data showed that local overexpression of human α-synuclein led to widespread propagation of the protein consistent with anterograde axonal transport. Analysis of the α-synuclein propagation pattern demonstrated that the brain-wide α-synucleinopathy was confined to the output regions of the noradrenergic locus coeruleus. Furthermore, there was no evidence of cell-to-cell transmission of human α-synuclein. Based on these findings we concluded that the induced Lewy-like pathology did not leave the noradrenergic locus coeruleus system in the studied time frame of 9 weeks. In addition, unbiased stereological quantification of the dopaminergic substantia nigra revealed no significant cell loss at the relatively short time-frame of 9 weeks. In conclusion, the studies presented in this dissertation show that cholinergic pedunculopontine neurons are significantly more vulnerable to α-synuclein fibril-induced α-synucleinopathy than non-cholinergic neurons. In addition, we were able to show that the brain-wide progression pattern of Lewy-like pathology is significantly different between the two studied α-synucleinopathy models. While in the fibril model the α-synucleinopathy pattern was consistent with cell-to-cell transmission of pathological α-synuclein species, we only observed axonal transport of α-synuclein but not cell-to-cell transmission in the overexpression-based model. The studies carried out within this dissertation therefore provide a valuable starting point for the further investigation of cellular vulnerability factors and mechanisms of disease progression

Predicting aging-related decline in physical performance with sparse electrophysiological source imaging

Objective: We introduce a methodology for selecting biomarkers from activation and connectivity derived from Electrophysiological Source Imaging (ESI). Specifically, we pursue the selection of stable biomarkers associated with cognitive decline based on source activation and connectivity patterns of resting-state EEG theta rhythm, used as predictors of physical performance decline in aging individuals measured by a Gait Speed (GS) slowing. Methods: Our two-step methodology involves estimating ESI using flexible sparse-smooth-nonnegative models, from which activation ESI (aESI) and connectivity ESI (cESI) features are derived. The Stable Sparse Classifier method then selects potential biomarkers related to GS changes. Results and Conclusions: Our predictive models using aESI outperform traditional methods such as the LORETA family. The models combining aESI and cESI features provide the best prediction of GS changes. Potential biomarkers from activation/connectivity patterns involve orbitofrontal and temporal cortical regions. Significance: The proposed methodology contributes to the understanding of activation and connectivity of GS-related ESI and provides features that are potential biomarkers of GS slowing. Given the known relationship between GS decline and cognitive impairment, this preliminary work opens novel paths to predict the progression of healthy and pathological aging and might allow an ESI-based evaluation of rehabilitation programs

Detection of emotions in Parkinson's disease using higher order spectral features from brain's electrical activity

Non-motor symptoms in Parkinson's disease (PD) involving cognition and emotion have been progressively receiving more attention in recent times. Electroencephalogram (EEG) signals, being an activity of central nervous system, can reflect the underlying true emotional state of a person. This paper presents a computational framework for classifying PD patients compared to healthy controls (HC) using emotional information from the brain's electrical activity

Unravelling the tangled web of atypical parkinsonism

This thesis focuses on sporadic parkinsonian syndromes that are associated with neurofibrillary degeneration and the accumulation of abnormal tau protein in the brain. The classic clinical presentation of corticobasal degeneration is a specific constellation of cortical and extrapyramidal signs, collectively termed corticobasal syndrome. The evaluation of all the archival cases with corticobasal degeneration in the Queen Square Brain Bank for Neurological Disorders reveals the high frequency of other phenotypic presentations. The result indicates that corticobasal degeneration commonly presents with a clinical picture, closely resembling progressive supranuclear palsy (PSP) or Richardson’s syndrome. On the other hand, cases with typical PSP pathology may occasionally present with a corticobasal syndrome. A quantitative assessment of the severity of tau pathology in different brain regions of the two phenotypic presentations of PSP reveals topographical differences that are closely linked with their respective clinical features. The features of repetitive finger tapping and handwriting in patients with PSP and Parkinson’s disease are compared and a distinct abnormality is identified in PSP which may be useful in differentiating PSP-parkinsonism from Parkinson’s disease. Twelve cases clinically presenting with a levodopa-responsive parkinsonian syndrome and post-mortem findings of nigral degeneration and predominant tau inclusions, which could not be readily classified into any recognised clinicopathological entity are also studied

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

RT-QuIC analysis of peripheral tissues and body fluids collected from patients with primary and secondary tauopathies

Neurodegenerative diseases(NDs)are fatal and incurable conditionscharacterized bythe progressive accumulation in specific brain regions of abnormally folded(misfolded)proteins, which are considered disease-specific biomarkers (DSB).These misfoldedproteins are able to spread through neuroanatomical connected regions and to accelerate the conformational conversion of native monomers (seeding), thus progressively amplifying the pathological process. Primary tauopathies are NDs associated withthe accumulation of misfolded tau and include Corticobasal degeneration (CBD), Progressive supranuclear palsy (PSP), Frontotemporal dementia and parkinsonism linked to chromosome 17 (FTDP-17) and other cases of Frontotemporal dementia (FTD). Alzheimer\u2019s disease (AD) can be considered a secondary tauopathy as it is characterized by tau misfolding in addition to amyloid-\u3b2 (A\u3b2) protein deposition. Synucleinopathies comprise a group of NDsassociated with the accumulation of misfolded \u3b1-synuclein (\u3b1S), including Parkinson\u2019s disease (PD) and other atypical parkinsonisms known as Multiple system atrophy (MSA) and Dementia with Lewy bodies (DLB).Given the overlap between clinical symptoms amongNDsand the lackofsensitive and specificdiagnostic tests, the definite diagnosis of NDs lay on neuropathological detectionof these misfolded proteinsin post-mortembrain tissues. However, recent findings have raised the possibility that trace-amount of DSBmight circulate in peripheral tissues and body fluids of affected individuals, thus constituting easily accessiblebiomarkers. For this reason, in my PhD projectweevaluated the ability of an extremely sensitivetechnique, named Real-Time Quaking Induced Conversion (RT-QuIC), to detect seeding activity of misfolded tau eventually present in peripheral tissues, such as olfactory mucosa (OM), and body fluids(urine and cerebrospinal fluid)collected from patients with clinical diagnosis of primary (FTDP-17, FTD, PSP, CBD) and secondary (AD) tauopathies.RT-QuIC assaywas optimized using a recombinant tau protein fragment named tauK18 (4R-tau)as substrate, whose aggregation was efficiently triggered(seeded) by the addition of minute amount (attograms) of tauK18 pre-formed fibrils (PFFs)previouslygenerated in vitro.We demonstrated that tauK18 RT-QuIC assay wasable to detect seeding activity of misfolded tau contained in brain samples of neuropathologically confirmed cases of FTDP-17, PSP,and AD. Thus, we performed RT-QuIC analysis of (i) OM, (ii) CSF and (iii) exosomes extracted from urine samples collected from patients with different primary and secondary tauopathies. As a comparison,we included in theanalysis samples belonging topatients with different synucleinopathies (PD,MSA,and DLB), Multiple sclerosis (MS), Non-demented patients (NDP) and healthy controls (HC). Results showed that tauK18 RT-QuIC assay was able to detect tau seeding activity in CBD and PSP OM samples, but also in some PD, MSA, DLB and MS cases. Similarly, RT-QuIC analysis of CSF samples displayed smalldifferences in tauseeding activity between AD and NDP cases. On the other hand, RT-QuIC analysis of urinary exosomes revealed that AD, FTD and CBD samples triggered tauK18 aggregation with higher efficiency if compared to HC, thus potentially discriminating between tauopathies and healthy subjects. We investigated the ability of PFFs generated in vitrofrom other NDs-associated proteins (3R-tau fragment named tauK19, \u3b1S, A\u3b21-40,and A\u3b21-42) to influence tauK18 aggregation (cross-seeding) and we found that some conformational variants of \u3b1S PFFs were able to cross-seed tauK18 aggregation, thus representing a potential issue for our assayand possibly explaining results obtained with theanalysis of OM samples. Moreover, preliminary structural analysis showed that final reaction productswere characterized by different morphologies when seeded by different (i) OM samples or by (ii) PFFs generated in vitrofrom tauK19, \u3b1S, A\u3b21-40,and A\u3b21-42, suggesting that biophysical assessments might help in discriminating between different seeding-competent samples. Although further retrospective analysis isrequired to confirm results obtained with ourtauK18 RT-QuIC assay, this preliminary study might lay the basis for the development of a new diagnostic approachwhich combines RT-QuIC and biophysical techniques to detect tau seeding activity in peripheral tissues and body fluids of patients with tauopathiesand to discriminatebetween different pathological conditions

FUNCTIONAL CONSEQUENCES OF AGGREGATED ALPHA-SYNUCLEIN HIPPOCAMPAL ACCUMULATION IN RATS

Neurodegenerative diseases (NDs) include a broad range of age-related neuropathological conditions characterized by a slow but unstoppable highly selective degenerative process. It involves distinct subsets of neurons in specific anatomic systems leading to variable disease phenotypes such as cognitive impairment, movement disorders or a combination of both. However, the gradual accumulation of misfolded protein aggregates in well-ordered structures, habitually called amyloid represent a common feature of NDs and is thought to be at the root of these diseases. some of these proteins are prion protein, beta amyloid, tau, TDP43 and alpha synuclein on which we focused our attention. Alpha-synuclein(α-syn) is a 140 amino acid protein widely expressed in the brain occurring as a soluble or membrane-associated protein at presynaptic nerve terminals. It is mainly involved in synaptic vesicle release and trafficking. In its membrane-bound protein form, α-syn has a predominantly α-helical structure which under certain conditions can alternatively fold into a β-sheet-rich structure that easily polymerizes into amyloid fibrils and aggregates. These aggregates acquire neurotoxicity affecting mitochondrial function, endoplasmic reticulum–Golgi trafficking, protein degradation and/or synaptic transmission, leading to neurodegeneration. Interestingly, aggregated forms of α-syn can recruit and seed the endogenous protein and initiate the spreading throughout cells, thus suggesting a prion-like mechanism. The occurrence of Lewy bodies/Lewy neurites containing misfolded fibrillar α-syn constitutes one of the pathological hallmarks of synucleinopathies such as Parkinson’s disease, dementia with Lewy Bodies and multiple system atrophy, all linked to memory impairments. While it has been shown that brainstem Lewy bodies may contribute to motor symptoms, the anatomical and neuropathological substrates for cognitive symptoms are still elusive. Therefore, in this PhD thesis I sought to investigate the progressive pathologic alterations and spreading of synthetic α-syn fibrils bilaterally injected into the hippocampus of adult female Sprague-Dawley rats, up to the onset of memory impairments. Animals underwent behavioral testing for sensory-motor and spatial learning and memory abilities at different time-points post-injection. At no time-point was any sensory-motor deficits observed that could affect performance in the Morris Water Maze task, nor was any reference memory disturbances detected in any of the injected animals. By contrast, significant impairments in working memory performance became evident at 12 months post-injection. These deficits were associated to a time-dependent increase in the levels of phosphorylated α-syn at serine 129 and in the stereologically-estimated numbers of proteinase K-resistant α-syn aggregates within the hippocampus. Interestingly, pathological α-syn aggregates were found in the entorhinal cortex and, by 12 months post-injection, also in the vertical limb of the diagonal band and the piriform cortices, all anatomically related to the injected sites. No pathological α-syn deposits were found within the Substantia Nigra, the Ventral Tegmental Area or the Striatum, nor was any obvious loss of dopaminergic, noradrenergic or cholinergic neurons detected in α-syn injected animals, compared to controls. This would suggest that the behavioral impairments seen in the α-syn injected animals might be determined by the long-term persisting α-syn neuropathology in the affected neurons rather than by neurodegeneration per se. This study confirms and extends previous observations showing that hippocampal α-syn pathology contribute to specific memory impairment. In addition, the α-syn preformed fibrils infusion procedure in the rat may represent a feasible tool to model synucleinopathies with which to test possible therapeutic interventions

A Review of the Assessment Methods of Voice Disorders in the Context of Parkinson's Disease

In recent years, a significant progress in the field of research dedicated to the treatment of disabilities has been witnessed. This is particularly true for neurological diseases, which generally influence the system that controls the execution of learned motor patterns. In addition to its importance for communication with the outside world and interaction with others, the voice is a reflection of our personality, moods and emotions. It is a way to provide information on health status, shape, intentions, age and even the social environment. It is also a working tool for many, but an important element of life for all. Patients with Parkinson’s disease (PD) are numerous and they suffer from hypokinetic dysarthria, which is manifested in all aspects of speech production: respiration, phonation, articulation, nasalization and prosody. This paper provides a review of the methods of the assessment of speech disorders in the context of PD and also discusses the limitations

Motor patterns evaluation of people with neuromuscular disorders for biomechanical risk management and job integration/reintegration

Neurological diseases are now the most common pathological condition and the leading cause of disability, progressively worsening the quality of life of those affected. Because of their high prevalence, they are also a social issue, burdening both the national health service and the working environment. It is therefore crucial to be able to characterize altered motor patterns in order to develop appropriate rehabilitation treatments with the primary goal of restoring patients' daily lives and optimizing their working abilities. In this thesis, I present a collection of published scientific articles I co-authored as well as two in progress in which we looked for appropriate indices for characterizing motor patterns of people with neuromuscular disorders that could be used to plan rehabilitation and job accommodation programs. We used instrumentation for motion analysis and wearable inertial sensors to compute kinematic, kinetic and electromyographic indices. These indices proved to be a useful tool for not only developing and validating a clinical and ergonomic rehabilitation pathway, but also for designing more ergonomic prosthetic and orthotic devices and controlling collaborative robots