Imaging biomarkers extraction and classification for Prion disease

Prion diseases are a group of rare neurodegenerative conditions characterised by a high rate of progression and highly heterogeneous phenotypes. Whilst the most common form of prion disease occurs sporadically (sporadic Creutzfeldt-Jakob disease, sCJD), other forms are caused by inheritance of prion protein gene mutations or exposure to prions. To date, there are no accurate imaging biomarkers that can be used to predict the future diagnosis of a subject or to quantify the progression of symptoms over time. Besides, CJD is commonly mistaken for other forms of dementia. Due to the large heterogeneity of phenotypes of prion disease and the lack of a consistent spatial pattern of disease progression, the approaches used to study other types of neurodegenerative diseases are not satisfactory to capture the progression of the human form of prion disease. Using a tailored framework, I extracted quantitative imaging biomarkers for characterisation of patients with Prion diseases. Following the extraction of patient-specific imaging biomarkers from multiple images, I implemented a Gaussian Process approach to correlated symptoms with disease types and stages. The model was used on three different tasks: diagnosis, differential diagnosis and stratification, addressing an unmet need to automatically identify patients with or at risk of developing Prion disease. The work presented in this thesis has been extensively validated in a unique Prion disease cohort, comprising both the inherited and sporadic forms of the disease. The model has shown to be effective in the prediction of this illness. Furthermore, this approach may have used in other disorders with heterogeneous imaging features, being an added value for the understanding of neurodegenerative diseases. Lastly, given the rarity of this disease, I also addressed the issue of missing data and the limitations raised by it. Overall, this work presents progress towards modelling of Prion diseases and which computational methodologies are potentially suitable for its characterisation

Learning to synthesise the ageing brain without longitudinal data

How will my face look when I get older? Or, for a more challenging question: How will my brain look when I get older? To answer this question one must devise (and learn from data) a multivariate auto-regressive function which given an image and a desired target age generates an output image. While collecting data for faces may be easier, collecting longitudinal brain data is not trivial. We propose a deep learning-based method that learns to simulate subject-specific brain ageing trajectories without relying on longitudinal data. Our method synthesises images conditioned on two factors: age (a continuous variable), and status of Alzheimer's Disease (AD, an ordinal variable). With an adversarial formulation we learn the joint distribution of brain appearance, age and AD status, and define reconstruction losses to address the challenging problem of preserving subject identity. We compare with several benchmarks using two widely used datasets. We evaluate the quality and realism of synthesised images using ground-truth longitudinal data and a pre-trained age predictor. We show that, despite the use of cross-sectional data, our model learns patterns of gray matter atrophy in the middle temporal gyrus in patients with AD. To demonstrate generalisation ability, we train on one dataset and evaluate predictions on the other. In conclusion, our model shows an ability to separate age, disease influence and anatomy using only 2D cross-sectional data that should be useful in large studies into neurodegenerative disease, that aim to combine several data sources. To facilitate such future studies by the community at large our code is made available at https://github.com/xiat0616/BrainAgeing

Méthodes numériques et statistiques pour l'analyse de trajectoire dans un cadre de geométrie Riemannienne.

This PhD proposes new Riemannian geometry tools for the analysis of longitudinal observations of neuro-degenerative subjects. First, we propose a numerical scheme to compute the parallel transport along geodesics. This scheme is efficient as long as the co-metric can be computed efficiently. Then, we tackle the issue of Riemannian manifold learning. We provide some minimal theoretical sanity checks to illustrate that the procedure of Riemannian metric estimation can be relevant. Then, we propose to learn a Riemannian manifold so as to model subject's progressions as geodesics on this manifold. This allows fast inference, extrapolation and classification of the subjects.Cette thèse porte sur l'élaboration d'outils de géométrie riemannienne et de leur application en vue de la modélisation longitudinale de sujets atteints de maladies neuro-dégénératives. Dans une première partie, nous prouvons la convergence d'un schéma numérique pour le transport parallèle. Ce schéma reste efficace tant que l'inverse de la métrique peut être calculé rapidement. Dans une deuxième partie, nous proposons l'apprentissage une variété et une métrique riemannienne. Après quelques résultats théoriques encourageants, nous proposons d'optimiser la modélisation de progression de sujets comme des géodésiques sur cette variété

The rising role of cognitive reserve and associated compensatory brain networks in spinocerebellar ataxia type 2

Pre-existing or enhanced cognitive abilities influence symptom onset and severity in neurodegenerative diseases, which improve an individual's ability to deal with neurodegeneration. This process is named cognitive reserve (CR), and it has acquired high visibility in the field of neurodegeneration. However, the investigation of CR has been neglected in the context of cerebellar neurodegenerative disorders. The present study assessed CR and its impact on cognitive abilities in spinocerebellar ataxia type 2 (SCA2), which is a rare cerebellar neurodegenerative disease. We investigated the existence of CR networks in terms of compensatory mechanisms and neural reserve driven by increased cerebello-cerebral functional connectivity. The CR of 12 SCA2 patients was assessed using the Cognitive Reserve Index Questionnaire (CRIq), which was developed for appraising life-span CR. Patients underwent several neuropsychological tests to evaluate cognitive functioning and a functional MRI examination. Network based statistics analysis was used to assess functional brain networks. The results revealed significant correlations of CRIq measures with cognitive domains and patterns of increased connectivity in specific cerebellar and cerebral regions, which likely indicated CR networks. This study showed that CR may influence disease-related cognitive deficits, and it was related to the effective use of specific cerebello-cerebral networks that reflect a CR biomarker

Pattern recognition and machine learning for magnetic resonance images with kernel methods

The aim of this thesis is to apply a particular category of machine learning and pattern recognition algorithms, namely the kernel methods, to both functional and anatomical magnetic resonance images (MRI). This work specifically focused on supervised learning methods. Both methodological and practical aspects are described in this thesis. Kernel methods have the computational advantage for high dimensional data, therefore they are idea for imaging data. The procedures can be broadly divided into two components: the construction of the kernels and the actual kernel algorithms themselves. Pre-processed functional or anatomical images can be computed into a linear kernel or a non-linear kernel. We introduce both kernel regression and kernel classification algorithms in two main categories: probabilistic methods and non-probabilistic methods. For practical applications, kernel classification methods were applied to decode the cognitive or sensory states of the subject from the fMRI signal and were also applied to discriminate patients with neurological diseases from normal people using anatomical MRI. Kernel regression methods were used to predict the regressors in the design of fMRI experiments, and clinical ratings from the anatomical scans

Artificial Intelligence, Mathematical Modeling and Magnetic Resonance Imaging for Precision Medicine in Neurology and Neuroradiology

La tesi affronta la possibilità di utilizzare metodi matematici, tecniche di simulazione, teorie fisiche riadattate e algoritmi di intelligenza artificiale per soddisfare le esigenze cliniche in neuroradiologia e neurologia al fine di descrivere e prevedere i patterns e l’evoluzione temporale di una malattia, nonché di supportare il processo decisionale clinico. La tesi è suddivisa in tre parti. La prima parte riguarda lo sviluppo di un workflow radiomico combinato con algoritmi di Machine Learning al fine di prevedere parametri che favoriscono la descrizione quantitativa dei cambiamenti anatomici e del coinvolgimento muscolare nei disordini neuromuscolari, con particolare attenzione alla distrofia facioscapolo-omerale. Il workflow proposto si basa su sequenze di risonanza magnetica convenzionali disponibili nella maggior parte dei centri neuromuscolari e, dunque, può essere utilizzato come strumento non invasivo per monitorare anche i più piccoli cambiamenti nei disturbi neuromuscolari oltre che per la valutazione della progressione della malattia nel tempo. La seconda parte riguarda l’utilizzo di un modello cinetico per descrivere la crescita tumorale basato sugli strumenti della meccanica statistica per sistemi multi-agente e che tiene in considerazione gli effetti delle incertezze cliniche legate alla variabilità della progressione tumorale nei diversi pazienti. L'azione dei protocolli terapeutici è modellata come controllo che agisce a livello microscopico modificando la natura della distribuzione risultante. Viene mostrato come lo scenario controllato permetta di smorzare le incertezze associate alla variabilità della dinamica tumorale. Inoltre, sono stati introdotti metodi di simulazione numerica basati sulla formulazione stochastic Galerkin del modello cinetico sviluppato. La terza parte si riferisce ad un progetto ancora in corso che tenta di descrivere una porzione di cervello attraverso la teoria quantistica dei campi e di simularne il comportamento attraverso l'implementazione di una rete neurale con una funzione di attivazione costruita ad hoc e che simula la funzione di risposta del modello biologico neuronale. E’ stato ottenuto che, nelle condizioni studiate, l'attività della porzione di cervello può essere descritta fino a O(6), i.e, considerando l’interazione fino a sei campi, come un processo gaussiano. Il framework quantistico definito può essere esteso anche al caso di un processo non gaussiano, ovvero al caso di una teoria di campo quantistico interagente utilizzando l’approccio della teoria wilsoniana di campo efficace.The thesis addresses the possibility of using mathematical methods, simulation techniques, repurposed physical theories and artificial intelligence algorithms to fulfill clinical needs in neuroradiology and neurology. The aim is to describe and to predict disease patterns and its evolution over time as well as to support clinical decision-making processes. The thesis is divided into three parts. Part 1 is related to the development of a Radiomic workflow combined with Machine Learning algorithms in order to predict parameters that quantify muscular anatomical involvement in neuromuscular diseases, with special focus on Facioscapulohumeral dystrophy. The proposed workflow relies on conventional Magnetic Resonance Imaging sequences available in most neuromuscular centers and it can be used as a non-invasive tool to monitor even fine change in neuromuscular disorders and to evaluate longitudinal diseases’ progression over time. Part 2 is about the description of a kinetic model for tumor growth by means of classical tools of statistical mechanics for many-agent systems also taking into account the effects of clinical uncertainties related to patients’ variability in tumor progression. The action of therapeutic protocols is modeled as feedback control at the microscopic level. The controlled scenario allows the dumping of uncertainties associated with the variability in tumors’ dynamics. Suitable numerical methods, based on Stochastic Galerkin formulation of the derived kinetic model, are introduced. Part 3 refers to a still-on going project that attempts to describe a brain portion through a quantum field theory and to simulate its behavior through the implementation of a neural network with an ad-hoc activation function mimicking the biological neuron model response function. Under considered conditions, the brain portion activity can be expressed up to O(6), i.e., up to six fields interaction, as a Gaussian Process. The defined quantum field framework may also be extended to the case of a Non-Gaussian Process behavior, or rather to an interacting quantum field theory in a Wilsonian Effective Field theory approach

Classification of Alzheimer's Disease and Mild Cognitive Impairment Using Longitudinal FDG-PET Images

RÉSUMÉ La maladie d’Alzheimer (MA) est la principale cause de maladies dégénératives et se caractérise par un début insidieux, une perte de mémoire précoce, des déficits verbaux et visuo-spatiaux (associés à la destruction des lobes temporal et pariétal), un développement progressif et une absence de signes neurologiques tôt dans l’apparition de la maladie. Aucun traitement n’est disponible en ce moment pour guérir la MA. Les traitements actuels peuvent souvent ralentir de façon significative la progression de la maladie. La capacité de diagnostiquer la MA à son stade initial a un impact majeur sur l’intervention clinique et la planification thérapeutique, réduisant ainsi les coûts associés aux soins de longue durée. La distinction entre les différents stades de la démence est essentielle afin de ralentir la progression de la MA. La différenciation entre les patients ayant la MA, une déficience cognitive légère précoce (DCLP), une déficience cognitive légère tardive (DCLT) ou un état cognitif normal (CN) est un domaine de recherche qui a suscité beaucoup d’intérêt durant la dernière décennie. Les images obtenues par tomographie par émission de positrons (TEP) font partie des meilleures méthodes accessibles pour faciliter la distinction entre ces différentes classes. Du point de vue de la neuro-imagerie, les images TEP par fluorodésoxyglucose (FDG) pour le métabolisme cérébral du glucose et pour les plaques amyloïdes (AV45) sont considérées comme des biomarqueurs ayant une puissance diagnostique élevée. Cependant, seules quelques approches ont étudié l’efficacité de considérer uniquement les zones actives localisées par la TEP à des fins de classification. La question de recherche principale de ce travail est de démontrer la capacité des images TEP à classer les résultats de façon précise et de comparer les résultats de deux méthodes d’imagerie TEP (FDG et AV45). Afin de déterminer la meilleure façon de classer les sujets dans les catégories MA, DCLP, DCLT ou CN en utilisant exclusivement les images TEP, nous proposons une procédure qui utilise les caractéristiques apprises à partir d’images TEP identifiées sémantiquement. Les machines à vecteurs de support (MVS) sont déjà utilisées pour faire de nombreuses classifications et font partie des techniques les plus utilisées pour la classification basée sur la neuro-imagerie, comme pour la MA. Les MVS linéaires et la fonction de base radiale (FBR)-MVS sont deux noyaux populaires utilisés dans notre classification. L’analyse en composante principale (ACP) est utilisée pour diminuer la taille des données suivie par les MVS linéaires qui sont une autre méthode de classification. Les forêts d’arbres décisionnels (FAD) sont aussi exécutées pour rendre les résultats obtenus par MVS comparables. L’objectif général de ce travail est de concevoir un ensemble d’outils déjà existants pour classer la MA et les différents stades de DCL. Suivant les étapes de normalisation et de prétraitement, une méthode d’enregistrement TEP-IRM ultimodale et déformable est proposée afin de fusionner l’atlas du MNI au scan TEP de chaque patient et de développer une méthode simple de segmentation basée sur l’atlas du cerveau dans le but de générer un volume étiqueté avec 10 régions d’intérêt communes. La procédure a deux approches : la première utilise l’intensité des voxels des régions d’intérêt, et la seconde, l’intensité des voxels du cerveau en entier. La méthode a été testée sur 660 sujets provenant de la base de données de l’(Alzheimer’s Disease Neuroimaging Initiative) et a été comparée à une approche qui incluait le cerveau en entier. La précision de la classification entre la MA et les CN a été mesurée à 91,7% et à 91,2% en utilisant la FBR et les FAD, respectivement, sur des données combinant les caractéristiques multirégionales des FDG-TEP des examens transversal et de suivi. Une amélioration considérable a été notée pour la précision de classification entre les DCLP et DCLT avec un taux de 72,5%. La précision de classification entre la MA et les CN en utilisant AV45-TEP avec les données combinées a été mesurée à 90,8% et à 87,9% pour la FBR et les FAD, respectivement. Cette procédure démontre le potentiel des caractéristiques multirégionales de la TEP pour améliorer l’évaluation cognitive. Les résultats observés confirment qu’il est possible de se fier uniquement aux images TEP sans ajout d’autres bio-marqueurs pour obtenir une précision de classification élevée.----------ABSTRACT Alzheimer’s disease (AD) is the most general cause of degenerative dementia, characterized by insidious onset early memory loss, language and visuospatial deficits (associated with the destruction of the temporal and parietal lobes), a progressive course, and lack of early neurological signs early in the course of disease. There is currently no absolute cure for AD but some treatments can slow down the progression of the disease in early stages of AD. The ability to diagnose AD at an early stage has a great impact on the clinical intervention and treatment planning, and hence reduces costs associated with long-term care. In addition, discrimination of different stages of dementia is crucial to slow down the progression of AD. Distinguishing patients with AD, early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and normal controls (NC) is an extremely active research area, which has garnered significant attention in the past decade. Positron emission tomography (PET) images are one of the best accessible ways to discriminate between different classes. From a neuroimaging point of view, PET images of fluorodeoxyglucose (FDG) for cerebral glucose metabolism and amyloid plaque images (AV45) are considered a highly powerful diagnostic biomarker, but few approaches have investigated the efficacy of focusing on localized PETactive areas for classification purposes. The main research question of this work is to show the ability of using PET images to achieve accurate classification results and to compare the results of two imaging methods of PET (FDG and AV45). To find the best scenario to classify our subjects into AD, EMCI, LMCI, and NC using PET images exclusively, we proposed a pipeline using learned features from semantically labelled PET images to perform group classification using four classifiers. Support vector machines (SVMs) are already applied in a wide variety of classifications, and it is one of the most popular techniques in classification based on neuroimaging like AD. Linear SVMs and radial basis function (RBF) SVMs are two common kernels used in our classification. Principal component analysis (PCA) is used to reduce the dimension of our data followed by linear SVMs, which is another method of classification. Random forest (RF) is also applied to make our SVM results comparable. The general objective of this work is to design a set of existing tools for classifying AD and different stages of MCI. Following normalization and pre-processing steps, a multi-modal PET-MRI registration method is proposed to fuse the Montreal Neurological Institute (MNI) atlas to PET images of each patient which is registered to its corresponding MRI scan, developing a simple method of segmentation based on a brain atlas generated from a fully labelled volume with 10 common regions of interest (ROIs). This pipeline can be used in two ways: (1) using voxel intensities from specific regions of interest (multi-region approach), and (2) using voxel intensities from the entire brain (whole brain approach). The method was tested on 660 subjects from the Alzheimer’s Disease Neuroimaging Initiative database and compared to a whole-brain approach. The classification accuracy of AD vs NC was measured at 91.7 % and 91.2 % when using RBF-SVM and RF, respectively, on combining both multi-region features from FDG-PET on cross-sectional and follow-up exams. A considerable improvement compare to the similar works in the EMCI vs LMCI classification accuracy was achieved at 72.5 %. The classification accuracy of AD versus NC using AV45-PET on the combined data was measured at 90.8 % and 87.9 % using RBF-SVM and RF, respectively. The pipeline demonstrates the potential of exploiting longitudinal multi-region PET features to improve cognitive assessment. We can achieve high accuracy using only PET images. This suggests that PET images are a rich source of discriminative information for this task. We note that other methods rely on the combination of multiple sources

Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

INTRODUCTION: The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS: We used standard searches to find publications using ADNI data. RESULTS: (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION: Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial desig

Landmark Localization, Feature Matching and Biomarker Discovery from Magnetic Resonance Images

The work presented in this thesis proposes several methods that can be roughly divided into three different categories: I) landmark localization in medical images, II) feature matching for image registration, and III) biomarker discovery in neuroimaging. The first part deals with the identification of anatomical landmarks. The motivation stems from the fact that the manual identification and labeling of these landmarks is very time consuming and prone to observer errors, especially when large datasets must be analyzed. In this thesis we present three methods to tackle this challenge: A landmark descriptor based on local self-similarities (SS), a subspace building framework based on manifold learning and a sparse coding landmark descriptor based on data-specific learned dictionary basis. The second part of this thesis deals with finding matching features between a pair of images. These matches can be used to perform a registration between them. Registration is a powerful tool that allows mapping images in a common space in order to aid in their analysis. Accurate registration can be challenging to achieve using intensity based registration algorithms. Here, a framework is proposed for learning correspondences in pairs of images by matching SS features and random sample and consensus (RANSAC) is employed as a robust model estimator to learn a deformation model based on feature matches. Finally, the third part of the thesis deals with biomarker discovery using machine learning. In this section a framework for feature extraction from learned low-dimensional subspaces that represent inter-subject variability is proposed. The manifold subspace is built using data-driven regions of interest (ROI). These regions are learned via sparse regression, with stability selection. Also, probabilistic distribution models for different stages in the disease trajectory are estimated for different class populations in the low-dimensional manifold and used to construct a probabilistic scoring function.Open Acces

Cortical Dynamics of Language

The human capability for fluent speech profoundly directs inter-personal communication and, by extension, self-expression. Language is lost in millions of people each year due to trauma, stroke, neurodegeneration, and neoplasms with devastating impact to social interaction and quality of life. The following investigations were designed to elucidate the neurobiological foundation of speech production, building towards a universal cognitive model of language in the brain. Understanding the dynamical mechanisms supporting cortical network behavior will significantly advance the understanding of how both focal and disconnection injuries yield neurological deficits, informing the development of therapeutic approaches