Longitudinal clustering analysis and prediction of Parkinson\u27s disease progression using radiomics and hybrid machine learning

Background: We employed machine learning approaches to (I) determine distinct progression trajectories in Parkinson\u27s disease (PD) (unsupervised clustering task), and (II) predict progression trajectories (supervised prediction task), from early (years 0 and 1) data, making use of clinical and imaging features. Methods: We studied PD-subjects derived from longitudinal datasets (years 0, 1, 2 & 4; Parkinson\u27s Progressive Marker Initiative). We extracted and analyzed 981 features, including motor, non-motor, and radiomics features extracted for each region-of-interest (ROIs: left/right caudate and putamen) using our standardized standardized environment for radiomics analysis (SERA) radiomics software. Segmentation of ROIs on dopamine transposer - single photon emission computed tomography (DAT SPECT) images were performed via magnetic resonance images (MRI). After performing cross-sectional clustering on 885 subjects (original dataset) to identify disease subtypes, we identified optimal longitudinal trajectories using hybrid machine learning systems (HMLS), including principal component analysis (PCA) + K-Means algorithms (KMA) followed by Bayesian information criterion (BIC), Calinski-Harabatz criterion (CHC), and elbow criterion (EC). Subsequently, prediction of the identified trajectories from early year data was performed using multiple HMLSs including 16 Dimension Reduction Algorithms (DRA) and 10 classification algorithms. Results: We identified 3 distinct progression trajectories. Hotelling\u27s t squared test (HTST) showed that the identified trajectories were distinct. The trajectories included those with (I, II) disease escalation (2 trajectories, 27% and 38% of patients) and (III) stable disease (1 trajectory, 35% of patients). For trajectory prediction from early year data, HMLSs including the stochastic neighbor embedding algorithm (SNEA, as a DRA) as well as locally linear embedding algorithm (LLEA, as a DRA), linked with the new probabilistic neural network classifier (NPNNC, as a classifier), resulted in accuracies of 78.4% and 79.2% respectively, while other HMLSs such as SNEA + Lib_SVM (library for support vector machines) and t_SNE (t-distributed stochastic neighbor embedding) + NPNNC resulted in 76.5% and 76.1% respectively. Conclusions: This study moves beyond cross-sectional PD subtyping to clustering of longitudinal disease trajectories. We conclude that combining medical information with SPECT-based radiomics features, and optimal utilization of HMLSs, can identify distinct disease trajectories in PD patients, and enable effective prediction of disease trajectories from early year data

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

A wearable biofeedback device to improve motor symptoms in Parkinson’s disease

Dissertação de mestrado em Engenharia BiomédicaThis dissertation presents the work done during the fifth year of the course Integrated Master’s in Biomedical Engineering, in Medical Electronics. This work was carried out in the Biomedical & Bioinspired Robotic Devices Lab (BiRD Lab) at the MicroElectroMechanics Center (CMEMS) established at the University of Minho. For validation purposes and data acquisition, it was developed a collaboration with the Clinical Academic Center (2CA), located at Braga Hospital. The knowledge acquired in the development of this master thesis is linked to the motor rehabilitation and assistance of abnormal gait caused by a neurological disease. Indeed, this dissertation has two main goals: (1) validate a wearable biofeedback system (WBS) used for Parkinson's disease patients (PD); and (2) develop a digital biomarker of PD based on kinematic-driven data acquired with the WBS. The first goal aims to study the effects of vibrotactile biofeedback to play an augmentative role to help PD patients mitigate gait-associated impairments, while the second goal seeks to bring a step advance in the use of front-end algorithms to develop a biomarker of PD based on inertial data acquired with wearable devices. Indeed, a WBS is intended to provide motor rehabilitation & assistance, but also to be used as a clinical decision support tool for the classification of the motor disability level. This system provides vibrotactile feedback to PD patients, so that they can integrate it into their normal physiological gait system, allowing them to overcome their gait difficulties related to the level/degree of the disease. The system is based on a user- centered design, considering the end-user driven, multitasking and less cognitive effort concepts. This manuscript presents all steps taken along this dissertation regarding: the literature review and respective critical analysis; implemented tech-based procedures; validation outcomes complemented with results discussion; and main conclusions and future challenges.Esta dissertação apresenta o trabalho realizado durante o quinto ano do curso Mestrado Integrado em Engenharia Biomédica, em Eletrónica Médica. Este trabalho foi realizado no Biomedical & Bioinspired Robotic Devices Lab (BiRD Lab) no MicroElectroMechanics Center (CMEMS) estabelecido na Universidade do Minho. Para efeitos de validação e aquisição de dados, foi desenvolvida uma colaboração com Clinical Academic Center (2CA), localizado no Hospital de Braga. Os conhecimentos adquiridos no desenvolvimento desta tese de mestrado estão ligados à reabilitação motora e assistência de marcha anormal causada por uma doença neurológica. De facto, esta dissertação tem dois objetivos principais: (1) validar um sistema de biofeedback vestível (WBS) utilizado por doentes com doença de Parkinson (DP); e (2) desenvolver um biomarcador digital de PD baseado em dados cinemáticos adquiridos com o WBS. O primeiro objetivo visa o estudo dos efeitos do biofeedback vibrotáctil para desempenhar um papel de reforço para ajudar os pacientes com PD a mitigar as deficiências associadas à marcha, enquanto o segundo objetivo procura trazer um avanço na utilização de algoritmos front-end para biomarcar PD baseado em dados inerciais adquiridos com o dispositivos vestível. De facto, a partir de um WBS pretende-se fornecer reabilitação motora e assistência, mas também utilizá-lo como ferramenta de apoio à decisão clínica para a classificação do nível de deficiência motora. Este sistema fornece feedback vibrotáctil aos pacientes com PD, para que possam integrá-lo no seu sistema de marcha fisiológica normal, permitindo-lhes ultrapassar as suas dificuldades de marcha relacionadas com o nível/grau da doença. O sistema baseia-se numa conceção centrada no utilizador, considerando o utilizador final, multitarefas e conceitos de esforço menos cognitivo. Portanto, este manuscrito apresenta todos os passos dados ao longo desta dissertação relativamente a: revisão da literatura e respetiva análise crítica; procedimentos de base tecnológica implementados; resultados de validação complementados com discussão de resultados; e principais conclusões e desafios futuros

FUNCTIONAL NETWORK CONNECTIVITY IN HUMAN BRAIN AND ITS APPLICATIONS IN AUTOMATIC DIAGNOSIS OF BRAIN DISORDERS

The human brain is one of the most complex systems known to the mankind. Over the past 3500 years, mankind has constantly investigated this remarkable system in order to understand its structure and function. Emerging of neuroimaging techniques such as functional magnetic resonance imaging (fMRI) have opened a non-invasive in-vivo window into brain function. Moreover, fMRI has made it possible to study brain disorders such as schizophrenia from a different angle unknown to researchers before. Human brain function can be divided into two categories: functional segregation and integration. It is well-understood that each region in the brain is specialized in certain cognitive or motor tasks. The information processed in these specialized regions in different temporal and spatial scales must be integrated in order to form a unified cognition or behavior. One way to assess functional integration is by measuring functional connectivity (FC) among specialized regions in the brain. Recently, there is growing interest in studying the FC among brain functional networks. This type of connectivity, which can be considered as a higher level of FC, is termed functional network connectivity (FNC) and measures the statistical dependencies among brain functional networks. Each functional network may consist of multiple remote brain regions. Four studies related to FNC are presented in this work. First FNC is compared during the resting-state and auditory oddball task (AOD). Most previous FNC studies have been focused on either resting-state or task-based data but have not directly compared these two. Secondly we propose an automatic diagnosis framework based on resting-state FNC features for mental disorders such as schizophrenia. Then, we investigate the proper preprocessing for fMRI time-series in order to conduct FNC studies. Specifically the impact of autocorrelated time-series on FNC will be comprehensively assessed in theory, simulation and real fMRI data. At the end, the notion of autoconnectivity as a new perspective on human brain functionality will be proposed. It will be shown that autoconnectivity is cognitive-state and mental-state dependent and we discuss how this source of information, previously believed to originate from physical and physiological noise, can be used to discriminate schizophrenia patients with high accuracy

Automatic Autism Spectrum Disorder Detection Using Artificial Intelligence Methods with MRI Neuroimaging: A Review

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, the process of diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist the specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We conclude by suggesting future approaches to detecting ASDs using AI techniques and MRI neuroimaging

Machine Learning for Multiclass Classification and Prediction of Alzheimer\u27s Disease

Alzheimer\u27s disease (AD) is an irreversible neurodegenerative disorder and a common form of dementia. This research aims to develop machine learning algorithms that diagnose and predict the progression of AD from multimodal heterogonous biomarkers with a focus placed on the early diagnosis. To meet this goal, several machine learning-based methods with their unique characteristics for feature extraction and automated classification, prediction, and visualization have been developed to discern subtle progression trends and predict the trajectory of disease progression. The methodology envisioned aims to enhance both the multiclass classification accuracy and prediction outcomes by effectively modeling the interplay between the multimodal biomarkers, handle the missing data challenge, and adequately extract all the relevant features that will be fed into the machine learning framework, all in order to understand the subtle changes that happen in the different stages of the disease. This research will also investigate the notion of multitasking to discover how the two processes of multiclass classification and prediction relate to one another in terms of the features they share and whether they could learn from one another for optimizing multiclass classification and prediction accuracy. This research work also delves into predicting cognitive scores of specific tests over time, using multimodal longitudinal data. The intent is to augment our prospects for analyzing the interplay between the different multimodal features used in the input space to the predicted cognitive scores. Moreover, the power of modality fusion, kernelization, and tensorization have also been investigated to efficiently extract important features hidden in the lower-dimensional feature space without being distracted by those deemed as irrelevant. With the adage that a picture is worth a thousand words, this dissertation introduces a unique color-coded visualization system with a fully integrated machine learning model for the enhanced diagnosis and prognosis of Alzheimer\u27s disease. The incentive here is to show that through visualization, the challenges imposed by both the variability and interrelatedness of the multimodal features could be overcome. Ultimately, this form of visualization via machine learning informs on the challenges faced with multiclass classification and adds insight into the decision-making process for a diagnosis and prognosis

Differentiation of Alzheimer's disease dementia, mild cognitive impairment and normal condition using PET-FDG and AV-45 imaging : a machine-learning approach

Nous avons utilisé l'imagerie TEP avec les traceurs F18-FDG et AV45 en conjonction avec les méthodes de classification du domaine du "Machine Learning". Les images ont été acquises en mode dynamique, une image toutes les 5 minutes. Les données ont été transformées par Analyse en Composantes Principales et Analyse en Composantes Indépendantes. Les images proviennent de trois sources différentes: la base de données ADNI (Alzheimer's Disease Neuroimaging Initiative) et deux protocoles réalisés au sein du centre TEP de l'hôpital Purpan. Pour évaluer la performance de la classification nous avons eu recours à la méthode de validation croisée LOOCV (Leave One Out Cross Validation). Nous donnons une comparaison entre les deux méthodes de classification les plus utilisées, SVM (Support Vector Machine) et les réseaux de neurones artificiels (ANN). La combinaison donnant le meilleur taux de classification semble être SVM et le traceur AV45. Cependant les confusions les plus importantes sont entre les patients MCI et les sujets normaux. Les patients Alzheimer se distinguent relativement mieux puisqu'ils sont retrouvés souvent à plus de 90%. Nous avons évalué la généralisation de telles méthodes de classification en réalisant l'apprentissage sur un ensemble de données et la classification sur un autre ensemble. Nous avons pu atteindre une spécificité de 100% et une sensibilité supérieure à 81%. La méthode SVM semble avoir une meilleure sensibilité que les réseaux de neurones. L'intérêt d'un tel travail est de pouvoir aider à terme au diagnostic de la maladie d'Alzheimer.We used PET imaging with tracers F18-FDG and AV45 in conjunction with the classification methods in the field of "Machine Learning". PET images were acquired in dynamic mode, an image every 5 minutes.The images used come from three different sources: the database ADNI (Alzheimer's Disease Neuro-Imaging Initiative, University of California Los Angeles) and two protocols performed in the PET center of the Purpan Hospital. The classification was applied after processing dynamic images by Principal Component Analysis and Independent Component Analysis. The data were separated into training set and test set. To evaluate the performance of the classification we used the method of cross-validation LOOCV (Leave One Out Cross Validation). We give a comparison between the two most widely used classification methods, SVM (Support Vector Machine) and artificial neural networks (ANN) for both tracers. The combination giving the best classification rate seems to be SVM and AV45 tracer. However the most important confusion is found between MCI patients and normal subjects. Alzheimer's patients differ somewhat better since they are often found in more than 90%. We evaluated the generalization of our methods by making learning from set of data and classification on another set . We reached the specifity score of 100% and sensitivity score of more than 81%. SVM method showed a bettrer sensitivity than Artificial Neural Network method. The value of such work is to help the clinicians in diagnosing Alzheimer's disease

Investigation of the Retinal Biomarkers of Alzheimer’s Disease and Atherosclerosis Using Hyperspectral Images

Le fait que l'oeil puisse être visualisé de manière non invasive ouvre des possibilités de mesure de biomarqueurs pour le diagnostic de conditions à long terme. Selon de nombreuses études, plusieurs maladies cardiovasculaires et neurodégénératives telles que la maladie d’Alzheimer (AD) et l’athérosclérose (ATH) se manifestent dans la rétine sous forme de modifications morphologiques pathologiques et / ou vasculaires. Des méthodes d'imagerie oculaire en deux dimensions et des techniques de tomographie par cohérence optique (OCT) en trois dimensions ont été développées pour fournir des descriptions des structures rétiniennes. Cependant, les images acquises par ces techniques permettent principalement de mesurer les caractéristiques spatiales et pas la variance relative de l’intensité des pixels sur différentes longueurs d’onde, de sorte que d’importantes caractéristiques liées aux tissus peuvent encore rester à découvrir. Dans cette étude, une caméra rétinienne métabolique hyperspectrale (MHRC) a été utilisée pour permettre l'acquisition d'une série d'images rétiniennes obtenues à des longueurs d'onde spécifiques couvrant le spectre du visible au proche infrarouge (NIR). Dans cette technique, le facteur de transmission, l'absorption et la diffusion de la lumière sont reflétés dans le spectre de la lumière émise par le tissu. Par conséquent, non seulement les caractéristiques spatiales communes mais également les « signatures spectrales » de biomolécules pourraient être révélées. Cela aide à trouver une plus grande variété de caractéristiques spatiales / spectrales pour une investigation plus précise des biomarqueurs rétiniens des maladies. En ce qui concerne les coûts et les limites associés aux diagnostics actuels de l’AD et de l’ATH, le but de cette thèse était d’analyser le contenu en informations d’images rétiniennes hyperspectrales riches en données dans le but de caractériser des informations discriminantes cachées liées aux tissus afin d’identifier des biomarqueurs possibles de ces deux maladies. À cette fin, une combinaison de caractéristiques vasculaires et de mesures de textures spatiales-spectrales ont été extraites de différentes régions anatomiques de la rétine. Dans le contexte de la maladie d'Alzheimer, des images rétiniennes de 20 cas présentant une altération cognitive et de 26 cas normaux cognitivement ont été acquises à l'aide de la caméra MHRC. Le statut amyloïde cérébral a été déterminé à partir de lectures binaires effectuées par un panel de 3 experts noteurs ayant participé à des études de TEP au 18F-Florbetaben. Des caractéristiques de l’image rétinienne ont été calculées, notamment la tortuosité et le diamètre des vaisseaux, ainsi que les mesures de textures spatiales-spectrales sur les artérioles, les veinules et le tissu environnant. Les veinules rétiniennes des sujets amyloïdes positifs (Aβ +) ont présenté une tortuosité moyenne plus élevée par rapport aux sujets amyloïdes négatifs (Aβ-). Le diamètre artériolaire des sujets Aβ + s'est avéré supérieur à celui des sujets Aβ- dans une zone adjacente à la tête du nerf optique. De plus, une différence significative entre les mesures de texture construites sur les artérioles rétiniennes et leurs régions adjacentes a été observée chez les sujets Aβ + par rapport aux Aβ-. Dans le contexte de l'ATH, 60 images rétiniennes de 30 ATH probables sur le plan clinique et 30 cas de contrôle ont été acquises. Les critères d'inclusion pour les sujets souffrant d'ATH comprenaient: l'infarctus du myocarde; angiographie coronaire montrant au moins une sténose coronaire (plus de 50%); et / ou une angioplastie coronaire; et /ou pontage coronaire. Les artérioles rétiniennes des sujets ATH ont montré un rétrécissement significatif par rapport aux sujets témoins. En outre, une différence significative entre les mesures de textures d'images prises sur les artérioles et les veinules rétiniennes et leurs régions adjacentes a été trouvée entre les sujets ATH et les sujets témoins. Nos études transversales ont montré que l’analyse hyperspectrale des images rétiniennes pouvait discerner avec une précision acceptable l’AD et l’ATH des sujets témoins correspondants.----------ABSTRACT The fact that eye can be visualized non-invasively, opens up possibilities to measure biomarkers for diagnosis of long-term conditions. A significant body of literature has demonstrated that many of the neurodegenerative and cardiovascular diseases such as Alzheimer’s disease (AD) and atherosclerosis (ATH) manifest themselves in retina as pathological and/or vasculature morphological changes. Methods for two-dimensional fundus imaging and techniques for three-dimensional optical coherence tomography (OCT) have been developed to provide descriptions of retinal structures. However, images acquired by these techniques mostly allow for measuring the spatial characteristics of the tissue and lack of the relative variances across differing wavelengths, thus important spectral features may remain uncovered. In this study, a Metabolic Hyperspectral Retinal Camera (MHRC) was used that permits the acquisition of a series of retinal images obtained at specific wavelengths covering the visible and near infrared (NIR) spectrum. In this technique, light transmittance, absorption, and scatter are reflected in the spectrum of light emitted from the tissue. Use of MHRC in this study was aimed to extract not only the common spatial features but also “spectral signatures” of biomolecules in retinal tissue. Regarding the costs and limitations of the current diagnostic methods for AD and ATH, the purpose of this thesis was to analyze the information content of data-rich hyperspectral retinal images to characterize tissue-related discriminatory information to identify possible biomarkers of Alzheimer’s disease and atherosclerosis. To this end, a combination of vascular features and spatial/spectral texture measures were extracted from different anatomical regions of the retina. In the context of AD, retinal images from 20 cognitively impaired and 26 cognitively unimpaired cases were acquired using MHRC. The cerebral amyloid status was determined from binary reads by a panel of three expert raters on 18F-Florbetaben PET studies. Our approach did not aim to visualize directly Aβ deposits in the retina but rather to determine a likely amyloid status based on sets of retinal image features highly correlated with the cerebral amyloid status. Retinal image features were calculated including vessels’ tortuosity and diameter. Spatial/spectral texture measures over arterioles, venules, and tissue around were also extracted. Retinal venules of amyloid positive subjects (Aβ+) showed a higher mean tortuosity compared to the amyloid negative (Aβ-) subjects. Arteriolar diameter of Aβ+ subjects was found to be higher than the Aβ- subjects in a zone adjacent to the optical nerve head. Furthermore, a significant difference between spatial/spectral texture measures built over retinal arterioles and surrounding tissues were observed in Aβ+ subjects when compared to the Aβ-. In the context of ATH, 60 retinal images from 30 clinically probable ATH and 30 control cases were acquired. Inclusion criteria for subjects suffering from ATH included: myocardial infarction; coronary angiography showing at least one coronary stenosis (more than 50%); and/or coronary angioplasty; and/or coronary bypass. Retinal arterioles of ATH subjects showed a significant narrowing when compared to control subjects. Moreover, a significant difference between image texture measures taken over retinal arterioles and retinal venules and their adjacent regions was observed between ATH subjects and control subjects. Our cross-sectional studies have shown that hyperspectral retinal image analysis could be used to discriminate AD and ATH from corresponding control subjects based on a non-invasive eye scan