Artificial intelligence applied to neuroimaging data in Parkinsonian syndromes: Actuality and expectations

Idiopathic Parkinson's Disease (iPD) is a common motor neurodegenerative disorder. It affects more frequently the elderly population, causing a significant emotional burden both for the patient and caregivers, due to the disease-related onset of motor and cognitive disabilities. iPD's clinical hallmark is the onset of cardinal motor symptoms such as bradykinesia, rest tremor, rigidity, and postural instability. However, these symptoms appear when the neurodegenerative process is already in an advanced stage. Furthermore, the greatest challenge is to distinguish iPD from other similar neurodegenerative disorders, "atypical parkinsonisms", such as Multisystem Atrophy, Progressive Supranuclear Palsy and Cortical Basal Degeneration, since they share many phenotypic manifestations, especially in the early stages. The diagnosis of these neurodegenerative motor disorders is essentially clinical. Consequently, the diagnostic accuracy mainly depends on the professional knowledge and experience of the physician. Recent advances in artificial intelligence have made it possible to analyze the large amount of clinical and instrumental information in the medical field. The application machine learning algorithms to the analysis of neuroimaging data appear to be a promising tool for identifying microstructural alterations related to the pathological process in order to explain the onset of symptoms and the spread of the neurodegenerative process. In this context, the search for quantitative biomarkers capable of identifying parkinsonian patients in the prodromal phases of the disease, of correctly distinguishing them from atypical parkinsonisms and of predicting clinical evolution and response to therapy represent the main goal of most current clinical research studies. Our aim was to review the recent literature and describe the current knowledge about the contribution given by machine learning applications to research and clinical management of parkinsonian syndromes

Pathophysiological mechanisms in Parkinson`s Disease and Dystonia – converging aetiologies

In this thesis I used a range of experimental approaches including genetics, enzyme activity measurements, histology and imaging to explore converging pathophysiological mechanisms of Parkinson`s disease and dystonia, two conditions with frequent clinical overlap. First, based on a combined retro- and prospective cohort of patients, using a combination of lysosomal enzyme activity measurements in peripheral blood and brain samples, as well as a target gene approach, I provide first evidence of reduced levels of enzyme activity in Glucocerebrosidase and the presence of GBA mutations, indicating lysosomal abnormality, in a relevant proportion of patients with dystonia of previously unknown origin. Second, based on a retrospective cohort of patients, I detail that a relevant proportion of genetically confirmed mitochondrial disease patients present with a movement disorder phenotype - predominantly dystonia and parkinsonism. Analysing volumetric MRI data, I describe a patterned cerebellar atrophy in these particular patients. This also includes the first cases of isolated dystonia due to mitochondrial disease, adding the latter as a potential aetiology for dystonia of unknown origin. Third, I used a combination of post-GWAS population genetic approaches and tissue-based experiments to explore in how far the strong association between advancing age and Parkinson ́s disease is mediated via telomere length. Although the initial finding of an association between genetically determined telomere length and PD risk did not replicate in independent cohorts, I provide evidence that telomere length in human putamen physiologically shortens with advancing age and 3 is regulated differently than in other brain regions. This is unique in the human brain, implying a particular age-related vulnerability of the striatum, part of the nigro-striatal network, crucially involved in PD pathophysiology. I conclude by discussing the above findings in light of the current literature, expand on their relevance and possible direction of future experiments

Neural Correlates of Parkinsonian Syndromes

The thesis investigated objective neuroimaging biomarkers in parkinsonian syndromes, which could be applied to increase diagnostic accuracy. To find convergence of the literature concerning disease-specific patterns in Parkinson’s disease and progressive supranuclear palsy, we conducted meta-analyses. In Parkinson’s disease glucose hypometabolism was re- vealed in bilateral inferior parietal cortex and left caudate nucleus and focal gray matter atrophy in the middle occipital gyrus. In progressive supranu- clear palsy we identified gray matter atrophy in the midbrain and white mat- ter atrophy in the cerebral/cerebellar pedunculi and midbrain. In sum, in Parkinson’s disease hypometabolism outperforms atrophy and in progres- sive supranuclear palsy we validated pathognomonic markers as disease- specific. Our studies create a novel framework to investigate disease- specific regional alterations for use in clinical routine. Further, we inves- tigated neural correlates by voxel-based morphometry and discriminated disease and clinical syndrome by multivariate pattern recognition in sin- gle patients with corticobasal syndrome and corticobasal syndrome with a unique syndrome - alien/ anarchic limb phenomenon. We found gray matter volume differences between patients and controls in asymmetric frontotem- poral/ occipital regions, motor areas, and insulae. The frontoparietal gyrus including the supplementary motor area contralateral to the side of the af- fected limb was specific for alien/ anarchic limb phenomenon. The predic- tion of the disease among controls was 79.0% accurate. The prediction of the specific syndrome within a disease reached an accuracy of 81.3%. In conclusion, we reliably classified patients and controls by objective pattern recognition. Moreover, we were able to predict a specific clinical syndrome within a disease, paving the way to individualized disease prediction.:SELBSTSTÄNDIGKEITSERKLÄRUNG I ACKNOWLEDGMENTS II SUMMARY III ZUSAMMENFASSUNG VIII BIBLIOGRAPHISCHE DARSTELLUNG XIV CONTENTS XVI 1 GENERAL INTRODUCTION 1 1.1 ParkinsonianSyndromes .................... 2 1.2 Parkinson’sDisease ....................... 2 1.2.1 DiagnosticCriteria .................... 3 1.3 ProgressiveSupranuclearPalsy ................ 4 1.3.1 DiagnosticCriteria .................... 5 1.4 CorticobasalDegeneration ................... 5 1.4.1 DiagnosticCriteria .................... 7 1.5 ImagingBiomarkers ....................... 7 1.6 CurrentThesis .......................... 9 1.6.1 MotivationandFramework ............... 9 1.6.2 ResearchQuestions................... 9 2 GENERAL MATERIALS AND METHODS 12 2.1 MagneticResonanceImaging.................. 12 2.2 AnalyticalMethods........................ 13 2.2.1 Meta-Analysis ...................... 13 2.2.2 Voxel-BasedMorphometry ............... 14 2.2.3 Support-Vector Machine Classification . . . . . . . . . 15 2.3 Multi-CentricData ........................ 16 2.4 ClinicalAssessment ....................... 17 3 Study 1 4 Study 2 5 Study 3 6 Study 4 7 Study 5 8 DISCUSSION 73 8.1 MainFindings........................... 73 8.2 Statistical Approaches to Find Imaging Biomarker . . . . . . 76 8.3 Brain Alterations and their Utility as Imaging Biomarker . . . . 77 8.4 Limitations ............................ 78 8.5 Contributions of the Current Thesis and Future Directions . . 79 9 REFERENCES APPENDIX XVIII LIST OF AUTHORSHIP XXVII CURRICULUM VITÆ XXXVII

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

Classification of patients with parkinsonian syndromes using medical imaging and artificial intelligence algorithms

The distinction of Parkinsonian Syndromes (PS) is challenging due to similarities of symptoms and signs at early stages of disease. Thus, the need of accurate methods for differential diagnosis at those early stages has emerged. To improve the evaluation of medical images, artificial intelligence turns out to be a useful tool. Parkinson’s Disease, the commonest PS, is characterized by the degeneration of dopamine neurons in the substantia nigra which is detected by the dopamine transporter scan (DaTscanTM), a single photon-emission tomography (SPECT) exam that uses of a radiotracer that binds dopamine receptors. In fact, by using such exam it was possible to identify a sub-group of PD patients known as “Scans without evidence of dopaminergic deficit” (SWEDD) that present a normal exam, unlike PD patients. In this study, an approach based on Convolutional Neural Networks (CNNs) was proposed for classifying PD patients, SWEDD patients and healthy subjects using SPECT and Magnetic Resonance Imaging (MRI) images. Then, these images were divided into subsets of slices in the axial view that contains particular regions of interest since 2D images are the norm in clinical practice. The classifier evaluation was performed with Cohen’s Kappa and Receiver Operating Characteristic (ROC) curve. The results obtained allow to conclude that the CNN using imaging information of the Basal Ganglia and the mesencephalon was able to distinguish PD patients from healthy subjects since achieved 97.4% accuracy using MRI and 92.4% accuracy using SPECT, and PD from SWEDD with 97.3% accuracy using MRI and 93.3% accuracy using SPECT. Nonetheless, using the same approach, it was not possible to discriminate SWEDD patients from healthy subjects (60% accuracy) using DaTscanTM and MRI. These results allow to conclude that this approach may be a useful tool to aid in PD diagnosis in the future

Alzheimer’s And Parkinson’s Disease Classification Using Deep Learning Based On MRI: A Review

Neurodegenerative disorders present a current challenge for accurate diagnosis and for providing precise prognostic information. Alzheimer’s disease (AD) and Parkinson's disease (PD), may take several years to obtain a definitive diagnosis. Due to the increased aging population in developed countries, neurodegenerative diseases such as AD and PD have become more prevalent and thus new technologies and more accurate tests are needed to improve and accelerate the diagnostic procedure in the early stages of these diseases. Deep learning has shown significant promise in computer-assisted AD and PD diagnosis based on MRI with the widespread use of artificial intelligence in the medical domain. This article analyses and evaluates the effectiveness of existing Deep learning (DL)-based approaches to identify neurological illnesses using MRI data obtained using various modalities, including functional and structural MRI. Several current research issues are identified toward the conclusion, along with several potential future study directions

SPECT imaging and Automatic Classification Methods in Movement Disorders

This work investigates neuroimaging as applied to movement disorders by the use of radionuclide imaging techniques. There are two focuses in this work: 1) The optimisation of the SPECT imaging process including acquisition and image reconstruction. 2) The development and optimisation of automated analysis techniques The first part has included practical measurements of camera performance using a range of phantoms. Filtered back projection and iterative methods of image reconstruction were compared and optimised. Compensation methods for attenuation and scatter are assessed. Iterative methods are shown to improve image quality over filtered back projection for a range of image quality indexes. Quantitative improvements are shown when attenuation and scatter compensation techniques are applied, but at the expense of increased noise. The clinical acquisition and processing procedures were adjusted accordingly. A large database of clinical studies was used to compare commercially available DaTSCAN quantification software programs. A novel automatic analysis technique was then developed by combining Principal Component Analysis (PCA) and machine learning techniques (including Support Vector Machines, and Naive Bayes). The accuracy of the various classification methods under different conditions is investigated and discussed. The thesis concludes that the described method can allow automatic classification of clinical images with equal or greater accuracy to that of commercially available systems

Theoretical and experimental considerations of selective vulnerability In Parkinson's disease

Les maladies neurodégénératives sont typiquement caractérisées en fonction de leurs symptômes et des observations pathologiques effectuées après le décès. Les symptômes spécifiques à la maladie sont également normalement associés aux dysfonctionnements et à la dégénérescence de certaines sous- populations spécifiques de neurones dans le système nerveux. La maladie de Parkinson (MP) est une maladie neurodégénérative principalement caractérisée par des symptômes moteurs dus à la dégénérescence spécifique des neurones dopaminergiques (DA) de la substantia nigra pars compacta (SNpc/SNc). Il semble cependant que les neurones DA de la SNc ne soient pas la seule population de neurones qui dégénère dans la MP. L'analyse post-mortem, l'imagerie in vivo et les symptômes cliniques démontrent que le dysfonctionnement et la dégénérescence se produisent dans plusieurs autres régions du système nerveux, incluant par exemple des neurones noradrénergiques (NA) du locus coeruleus (LC), des neurones sérotoninergiques des noyaux du raphé et des neurones cholinergiques du noyau moteur dorsal du nerf vague (DMV) et du noyau pédonculopontin. Comme d'autres maladies neurodégénératives, la MP est causée par plusieurs facteurs, tant génétiques qu'environnementaux. De nombreuses observations suggèrent que ces facteurs soient associés au dysfonctionnement de plusieurs systèmes ou fonctions cellulaires incluant la production d’énergie par la mitochondrie, l’élimination de protéines dysfonctionnelles par le protéasome et le lysosome, la régulation de l’équilibre entre la production d'espèces oxydatives réactives et les mécanismes antioxydants, la régulation des niveaux de calcium intracellulaire et l’inflammation. Il semble donc que le dysfonctionnement de ces facteurs converge pour provoquer la dégénérescence neuronale dans le contexte du vieillissement. Ce qui rend les neurones de certaines régions du système nerveux intrinsèquement plus vulnérables aux facteurs associés à la MP est une question fondamentale qui n’est pas résolue pour le moment. Les travaux de cette thèse sont basés sur l’hypothèse proposant que cette vulnérabilité sélective découle de propriétés communes retrouvées chez les neurones vulnérables. En particulier, les neurones vulnérables auraient en commun d’être des neurones de projections dotés d’un axone complexe qui projette sur de longues distances, formant un nombre très élevé de terminaisons axonales sur de vastes territoires du système nerveux. De plus, ces neurones présenteraient des propriétés physiologiques distinctives, incluant notamment une décharge autonome (pacemaker). Ensemble, ces caractéristiques pourraient contribuer à placer ces neurones dans des conditions de fonctionnement aux limites de leur capacités bioénergétiques et homéostatiques, rendant difficile toute adaptation aux dysfonctionnements cellulaires associés au vieillissement et causés par les facteurs de risques de la MP. Dans cette thèse, je présenterai une revue systématique de la littérature sur la perte de neurones dans le cerveau des personnes atteintes de la maladie de Parkinson, montrant que l'identité neurochimique précise des neurones qui dégénèrent dans la maladie de Parkinson, et l'ordre temporel dans lequel cela se produit, n’est pas clair. Cependant, en analysant les points de vue présentés dans les publications citant cette revue, nous avons remarqué que la majorité de ceux-ci ne reflètent pas le message central de notre publication. Puisque l’identification de l’identité des neurones vulnérables et non vulnérables à la MP est fondamentale pour le développement de théories et hypothèses sur les causes de la MP, nous suivons cette première publication avec une lettre réaffirmant l'importance de faire face aux problèmes identifiés dans notre revue. Nous présentons ensuite des données in vitro montrant que les neurones vulnérables à la MP, comparés à ceux qui sont moins vulnérables, ont une capacité intrinsèque à développer des champs axonaux plus importants et plus complexes, avec un nombre plus élevé de sites actifs de libération de neurotransmetteurs. De plus, nous constatons que ces observations sont corrélées à une vulnérabilité plus élevée face à un stress oxydatif pertinent pour la MP. Ces données sont en accord avec l'hypothèse selon laquelle le domaine axonal, et en particulier le nombre de sites de libération de neurotransmetteurs par neurone, est un facteur important qui contribue à rendre un neurone sélectivement vulnérable dans le contexte de la MP. Enfin, nous présentons une méthode d’analyse d’image open-source visant à aider les biologistes et les neuroscientifiques à automatiser la quantification du nombre de neurones dans des cultures primaires de neurones, telle qu’utilisée dans les travaux de cette thèse. Nous proposons que cet algorithme simple — mais robuste — permettra aux biologistes d'automatiser le comptage des neurones avec une grande précision, quelque chose de difficile à effectuer avec les autres approches open-source disponibles présentement. Nous espérons que les travaux présentés dans cette thèse permettront de contribuer à raffiner les théories visant à expliquer l’origine de la MP et à terme, de développer de nouvelles approches thérapeutiques.Neurodegenerative diseases are typically characterized based on their symptoms, and pathological factors identified after death. The disease-specific symptoms are due to the dysfunction and degeneration of specific subpopulations of neurons, which cause dysfunction in particular brain functions. Parkinson's disease (PD) is a neurodegenerative disease primarily characterized by motor symptoms due to the specific degeneration of dopamine (DA) neurons of the substantia nigra pars compacta (SNpc/SNc): a population of neurons essential for motor control. SNc DA neurons are, however, not the only population of neurons that degenerate in PD. Post-mortem analysis, in vivo imaging, and clinical symptoms demonstrate that dysfunction and degeneration occur in several other neuronal nuclei. These include, but are not limited to, noradrenergic (NA) locus coeruleus (LC) neurons, serotonin neurons of the raphe nuclei, and cholinergic neurons of the dorsal motor nucleus of the vagus (DMV) and pedunculopontine nucleus. Like other neurodegenerative diseases, PD is linked to several risk factors, both genetic and environmental. The evidence suggests that these risk factors are associated with the dysfunction in systems of cellular bioenergetics (including mitochondrial function); proteostatic homeostasis; endolysosomal function; an imbalance between the production of reactive oxidative species (ROS), and antioxidant mechanisms; calcium homeostasis; alpha-synuclein misfolding; and neuroinflammation. Consequently, together with aging, these risk factors converge on causing the selective degeneration of "PD-vulnerable" nuclei. What makes these neurons intrinsically vulnerable to PD-associated risk factors is a fundamental question — and understanding these neurons will reveal biological mechanisms that we can target to protect these cells from degeneration. Our best hypotheses to explain why these neurons are based on the observations that most PD- vulnerable neurons are long-range projection neuromodulatory neurons sharing common characteristics: projecting to voluminous territories, having very long and highly branched unmyelinated axonal domains with vast numbers of neurotransmitter release sites, and exhibiting a unique physiology such as pacemaker firing. Taken together, this suggests that these neurons function at the tail-end of their bioenergetic and homeostatic capacity, unable to tolerate any further demands, such as those incurred in the presence of risk factors associated with PD. In this thesis, I will present a systematic review on the literature on purported cell loss in PD brains, showing that — given the actual primary evidence — the precise neurochemical identity of neurons that degenerate in PD, and the temporal order of this degeneration, is far less clear than described by most publications. This review — at the time of writing — has gone on to be highly cited. However, analyzing the claims made in publications citing this review, we discover that the majority of claims do not reflect the core message of our publication. Since the identity of PD-vulnerable and non-PD-vulnerable neurons is fundamental to theory and hypotheses when trying to understand PD, we follow this first publication with a letter restating the importance to address our observations. We then present in vitro data showing that classically PD-vulnerable neurons, when compared to non-PD vulnerable neurons, have an intrinsic capacity to develop larger and more complex axonal domains, with higher numbers of active neurotransmitter release sites. Moreover, we find that these observations correlate to elevated vulnerability to PD-relevant stress assays. These data provide additional support for the hypothesis that the axonal domain — and in particular — the number of active neurotransmitter sites per neuron, is a cell-autonomous factor rendering a neuron selectively vulnerable in the context of PD. Finally, we present an open-source tool to support biologists and neuroscientists in automating the quantification of neuron numbers in medium-throughput primary cell cultures. Where the application of other open-source solutions is either too simplistic for the use-case or technically challenging to implement, this simple — yet robust algorithm — allows biologists with limited computational nous to automate neuron counting with high precision. We hope that the work presented in this thesis will contribute to the refinement of theories aimed at explaining the origin of PD and, ultimately, to the development of new therapeutic approaches

Disease progression and genetic risk factors in the primary tauopathies

The primary tauopathies are a group of progressive neurodegenerative diseases within the frontotemporal lobar degeneration spectrum (FTLD) characterised by the accumulation of misfolded, hyperphosphorylated microtubule-associated tau protein (MAPT) within neurons and glial cells. They can be classified according to the underlying ratio of three-repeat (3R) to four-repeat (4R) tau and include Pick’s disease (PiD), which is the only 3R tauopathy, and the 4R tauopathies the most common of which are progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). There are no disease modifying therapies currently available, with research complicated by the wide variability in clinical presentations for each underlying pathology, with presentations often overlapping, as well as the frequent occurrence of atypical presentations that may mimic other non-FTLD pathologies. Although progress has been made in understanding the genetic contribution to disease risk in the more common 4R tauopathies (PSP and CBD), very little is known about the genetics of the 3R tauopathy PiD. There are two broad aims to this thesis; firstly, to use data-driven generative models of disease progression to try and more accurately stage and subtype patients presenting with PSP and corticobasal syndrome (CBS, the most common presentation of CBD), and secondly to identify genetic drivers of disease risk and progression in PiD. Given the rarity of these disorders, as part of this PhD I had to assemble two large cohorts through international collaboration, the 4R tau imaging cohort and the Pick’s disease International Consortium (PIC), to build large enough sample sizes to enable the required analyses. In Chapter 3 I use a probabilistic event-based modelling (EBM) approach applied to structural MRI data to determine the sequence of brain atrophy changes in clinically diagnosed PSP - Richardson syndrome (PSP-RS). The sequence of atrophy predicted by the model broadly mirrors the sequential spread of tau pathology in PSP post-mortem staging studies, and has potential utility to stratify PSP patients on entry into clinical trials based on disease stage, as well as track disease progression. To better characterise the spatiotemporal heterogeneity of the 4R tauopathies, I go on to use Subtype and Stage Inference (SuStaIn), an unsupervised machine algorithm, to identify population subgroups with distinct patterns of atrophy in PSP (Chapter 4) and CBS (Chapter 5). The SuStaIn model provides data-driven evidence for the existence of two spatiotemporal subtypes of atrophy in clinically diagnosed PSP, giving insights into the relationship between pathology and clinical syndrome. In CBS I identify two distinct imaging subtypes that are differentially associated with underlying pathology, and potentially a third subtype that if confirmed in a larger dataset may allow the differentiation of CBD from both PSP and AD pathology using a baseline MRI scan. In Chapter 6 I investigate the association between the MAPT H1/H2 haplotype and PiD, showing for the first time that the H2 haplotype, known to be strongly protective against developing PSP or CBD, is associated with an increased risk of PiD. This is an important finding and has implications for the future development of MAPT isoform-specific therapeutic strategies for the primary tauopathies. In Chapter 7 I perform the first genome wide association study (GWAS) in PiD, identifying five genomic loci that are nominally associated with risk of disease. The top two loci implicate perturbed GABAergic signalling (KCTD8) and dysregulation of the ubiquitin proteosome system (TRIM22) in the pathogenesis of PiD. In the final chapter (Chapter 8) I investigate the genetic determinants of survival in PiD, by carrying out a Cox proportional hazards genome wide survival study (GWSS). I identify a genome-wide significant association with survival on chromosome 3, within the NLGN1 gene. which encodes a synaptic scaffolding protein located at the neuronal pre-synaptic membrane. Loss of synaptic integrity with resulting dysregulation of synaptic transmission leading to increased pathological tau accumulation is a plausible mechanism though which NLGN1 dysfunction could impact on survival in PiD