Brain imaging evidence of early involvement of subcortical regions in familial and sporadic Alzheimer's disease

Recent brain imaging studies have found changes in subcortical regions in presymptomatic autosomal dominant Alzheimer's disease (ADAD). These regions are also affected in sporadic Alzheimer's disease (sAD), but whether such changes are seen in early-stage disease is still uncertain. In this review, we discuss imaging studies published in the past 12 years that have found evidence of subcortical involvement in early-stage ADAD and/or sAD. Several papers have reported amyloid deposition in the striatum of presymptomatic ADAD mutation carriers, prior to amyloid deposition elsewhere. Altered caudate volume has also been implicated in early-stage ADAD, but findings have been variable. Less is known about subcortical involvement in sAD: the thalamus and striatum have been found to be atrophied in symptomatic patients, but their involvement in the preclinical phase remains unclear, in part due to the difficulties of studying this stage in sporadic disease. Longitudinal imaging studies comparing ADAD mutation carriers with individuals at high-risk for sAD may be needed to elucidate the significance of subcortical involvement in different AD clinical stages

Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects.

Structural imaging based on MRI is an integral component of the clinical assessment of patients with potential dementia. We here propose an individualized Gaussian process-based inference scheme for clinical decision support in healthy and pathological aging elderly subjects using MRI. The approach aims at quantitative and transparent support for clinicians who aim to detect structural abnormalities in patients at risk of Alzheimer's disease or other types of dementia. Firstly, we introduce a generative model incorporating our knowledge about normative decline of local and global gray matter volume across the brain in elderly. By supposing smooth structural trajectories the models account for the general course of age-related structural decline as well as late-life accelerated loss. Considering healthy subjects' demography and global brain parameters as informative about normal brain aging variability affords individualized predictions in single cases. Using Gaussian process models as a normative reference, we predict new subjects' brain scans and quantify the local gray matter abnormalities in terms of Normative Probability Maps (NPM) and global z-scores. By integrating the observed expectation error and the predictive uncertainty, the local maps and global scores exploit the advantages of Bayesian inference for clinical decisions and provide a valuable extension of diagnostic information about pathological aging. We validate the approach in simulated data and real MRI data. We train the GP framework using 1238 healthy subjects with ages 18-94years, and predict in 415 independent test subjects diagnosed as healthy controls, Mild Cognitive Impairment and Alzheimer's disease

Predicción del diagnóstico de la enfermedad de Alzheimer mediante deep-learning en imágenes 18F-FDG PET

La enfermedad de Alzheimer es una enfermedad neurodegenerativa que afectaa más de 50 millones de personas en todo el mundo. Es la forma más común dedemencia, con un 60-70% de los casos. Actualmente no existe una cura efectiva paraella, aunque sí existen algunos tratamientos que pueden ser eficaces si se aplican enlas fases tempranas de la enfermedad, permitiendo retrasar su evolución. Por ello, undiagnóstico preciso y con suficiente antelación es fundamental para poder tomarmedidas preventivas. El gran auge del deep-learning en los últimos años ha permitidoel desarrollo de diferentes sistemas de predicción que ayuden al diagnóstico de laenfermedad de Alzheimer a partir de imágenes cerebrales.El principal objetivo de este Trabajo de Fin de Grado es el desarrollo de unsistema de aprendizaje profundo basado en redes neuronales convolucionales que, apartir de imágenes 18F-FDG PET del cerebro sea capaz de predecir el diagnóstico finalentre pacientes enfermos (AD), con deterioro cognitivo leve (MCI) o cognitivamentenormales (CN). La obtención de las imágenes para el entrenamiento y test de la red sehan obtenido del repositorio de la Alzheimer's Disease Neuroimaging Initiative (ADNI).Se han desarrollado dos sistemas con dos arquitecturas diferentes: la originalpropuesta en (Ding et al., ,2019) y una mejora posterior de la misma propuesta en la literatura en uncontexto diferente. Las imágenes utilizadas son 3D mientras que las arquitecturasutilizadas se basan en convoluciones 2D. Por este motivo, las imágenes de 18F-FDG PEThan sido preprocesadas antes de ser cargadas en la red. Para el entrenamiento de lossistemas se ha hecho uso de las técnicas de transfer-learning y fine-tuning. Laimplementación del sistema y el preprocesado de las imágenes se ha realizado enPython 3.6.9, mediante el uso de las librerías de Keras (versión 2.2.4) y TensorFlow(versión 1.12.0). El entrenamiento y test de la red se ha realizado sobre una tarjetagráfica Titan RTX de 24 GBs de VRAM.Los experimentos realizados muestran que, ambos sistemas desarrolladospueden llegar a predecir AD hasta 66 meses (5 años y medio) antes del diagnósticofinal. El sistema basado en la arquitectura propuesta en (Ding et al., ,2019) es capaz de predecir eldiagnóstico final de Alzheimer con una precisión del 77.0% y un AUC de 0.84. Se haencontrado que el sistema entrenado con los pacientes de AD y CN es capaz dediagnosticar la enfermedad con una precisión del 87.5% y un AUC de 0.97 y se haanalizado cómo afecta en el rendimiento del sistema la introducción de datos depacientes con MCI. Con la arquitectura más moderna se ha conseguido mejorar losresultados con una precisión de 84.6% y un AUC de 0.89 en la predicción deldiagnóstico final de Alzheimer. Finalmente, se han realizado distintos análisis de lasredes neuronales convolucionales desarrolladas para comprender los puntos fuertes ydébiles de los modelos obtenidos.<br /

Generative FDG-PET and MRI model of aging and disease progression in Alzheimer's disease.

The failure of current strategies to provide an explanation for controversial findings on the pattern of pathophysiological changes in Alzheimer's Disease (AD) motivates the necessity to develop new integrative approaches based on multi-modal neuroimaging data that captures various aspects of disease pathology. Previous studies using [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) and structural magnetic resonance imaging (sMRI) report controversial results about time-line, spatial extent and magnitude of glucose hypometabolism and atrophy in AD that depend on clinical and demographic characteristics of the studied populations. Here, we provide and validate at a group level a generative anatomical model of glucose hypo-metabolism and atrophy progression in AD based on FDG-PET and sMRI data of 80 patients and 79 healthy controls to describe expected age and symptom severity related changes in AD relative to a baseline provided by healthy aging. We demonstrate a high level of anatomical accuracy for both modalities yielding strongly age- and symptom-severity- dependant glucose hypometabolism in temporal, parietal and precuneal regions and a more extensive network of atrophy in hippocampal, temporal, parietal, occipital and posterior caudate regions. The model suggests greater and more consistent changes in FDG-PET compared to sMRI at earlier and the inversion of this pattern at more advanced AD stages. Our model describes, integrates and predicts characteristic patterns of AD related pathology, uncontaminated by normal age effects, derived from multi-modal data. It further provides an integrative explanation for findings suggesting a dissociation between early- and late-onset AD. The generative model offers a basis for further development of individualized biomarkers allowing accurate early diagnosis and treatment evaluation

Generative FDG-PET and MRI model of aging and disease progression in Alzheimer's disease.

The failure of current strategies to provide an explanation for controversial findings on the pattern of pathophysiological changes in Alzheimer's Disease (AD) motivates the necessity to develop new integrative approaches based on multi-modal neuroimaging data that captures various aspects of disease pathology. Previous studies using [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) and structural magnetic resonance imaging (sMRI) report controversial results about time-line, spatial extent and magnitude of glucose hypometabolism and atrophy in AD that depend on clinical and demographic characteristics of the studied populations. Here, we provide and validate at a group level a generative anatomical model of glucose hypo-metabolism and atrophy progression in AD based on FDG-PET and sMRI data of 80 patients and 79 healthy controls to describe expected age and symptom severity related changes in AD relative to a baseline provided by healthy aging. We demonstrate a high level of anatomical accuracy for both modalities yielding strongly age- and symptom-severity- dependant glucose hypometabolism in temporal, parietal and precuneal regions and a more extensive network of atrophy in hippocampal, temporal, parietal, occipital and posterior caudate regions. The model suggests greater and more consistent changes in FDG-PET compared to sMRI at earlier and the inversion of this pattern at more advanced AD stages. Our model describes, integrates and predicts characteristic patterns of AD related pathology, uncontaminated by normal age effects, derived from multi-modal data. It further provides an integrative explanation for findings suggesting a dissociation between early- and late-onset AD. The generative model offers a basis for further development of individualized biomarkers allowing accurate early diagnosis and treatment evaluation

Development of Anatomical and Functional Magnetic Resonance Imaging Measures of Alzheimer Disease

Alzheimer disease is considered to be a progressive neurodegenerative condition, clinically characterized by cognitive dysfunction and memory impairments. Incorporating imaging biomarkers in the early diagnosis and monitoring of disease progression is increasingly important in the evaluation of novel treatments. The purpose of the work in this thesis was to develop and evaluate novel structural and functional biomarkers of disease to improve Alzheimer disease diagnosis and treatment monitoring. Our overarching hypothesis is that magnetic resonance imaging methods that sensitively measure brain structure and functional impairment have the potential to identify people with Alzheimer’s disease prior to the onset of cognitive decline. Since the hippocampus is considered to be one of the first brain structures affected by Alzheimer disease, in our first study a reliable and fully automated approach was developed to quantify medial temporal lobe atrophy using magnetic resonance imaging. This measurement of medial temporal lobe atrophy showed differences (pnovel biomarker of brain activity was developed based on a first-order textural feature of the resting state functional magnetic resonance imagining signal. The mean brain activity metric was shown to be significantly lower (pp18F labeled fluorodeoxyglucose positron emission tomography. In the final study, we examine whether combined measures of gait and cognition could predict medial temporal lobe atrophy over 18 months in a small cohort of people (N=22) with mild cognitive impairment. The results showed that measures of gait impairment can help to predict medial temporal lobe atrophy in people with mild cognitive impairment. The work in this thesis contributes to the growing evidence the specific magnetic resonance imaging measures of brain structure and function can be used to identify and monitor the progression of Alzheimer’s disease. Continued refinement of these methods, and larger longitudinal studies will be needed to establish whether the specific metrics of brain dysfunction developed in this thesis can be of clinical benefit and aid in drug development