Early Identification of Alzheimer’s Disease Using Medical Imaging: A Review From a Machine Learning Approach Perspective

Alzheimer’s disease (AD) is the leading cause of dementia in aged adults, affecting up to 70% of the dementia patients, and posing a serious public health hazard in the twenty-first century. AD is a progressive, irreversible and neuro-degenerative disease with a long pre-clinical period, affecting brain cells leading to memory loss, misperception, learning problems, and improper decisions. Given its significance, presently no treatment options are available, although disease advancement can be retarded through medication. Unfortunately, AD is diagnosed at a very later stage, after irreversible damages to the brain cells have occurred, when there is no scope to prevent further cognitive decline. The use of non-invasive neuroimaging procedures capable of detecting AD at preliminary stages is crucial for providing treatment retarding disease progression, and has stood as a promising area of research. We conducted a comprehensive assessment of papers employing machine learning to predict AD using neuroimaging data. Most of the studies employed brain images from Alzheimer’s disease neuroimaging initiative (ADNI) dataset, consisting of magnetic resonance image (MRI) and positron emission tomography (PET) images. The most widely used method, the support vector machine (SVM), has a mean accuracy of 75.4 percent, whereas convolutional neural networks(CNN) have a mean accuracy of 78.5 percent. Better classification accuracy has been achieved by combining MRI and PET, rather using single neuroimaging technique. Overall, more complicated models, like deep learning, paired with multimodal and multidimensional data (neuroimaging, cognitive, clinical, behavioral and genetic) produced superlative results. However, promising results have been achieved, still there is a room for performance improvement of the proposed methods, providing assistance to healthcare professionals and clinician

EVALUATING THE MICROBIOME TO BOOST RECOVERY FROM STROKE: THE EMBRS STUDY

Accumulating evidence suggests that gut microbes modulate brain plasticity via the bidirectional gut-brain axis and may play a role in stroke rehabilitation. A severely imbalanced microbial community has been shown to occur following stroke, causing a systemic flood of neuro- and immunomodulatory substances due to increased gut permeability and decreased gut motility. Here we measure post-stroke increased gut dysbiosis and how it correlates with gut permeability and subsequent cognitive impairment. We recruited 12 participants with acute stroke, 12 healthy control participants, and 18 participants who had risk factors for stroke, but had not had a stroke. We measured the gut microbiome with whole shotgun sequencing on stool samples. We measured cognitive and emotional health with MRI imaging and the NIH toolbox. We normalized all variables and used linear regression methods to identify gut microbial levels associations with cognitive and emotional assessments. Beta diversity analysis revealed that the bacteria populations of the stroke group were statistically dissimilar from the risk factors and healthy control groups. Relative abundance analysis revealed notable decreases in butyrate-producing microbial taxa. The stroke group had higher levels of the leaky gut marker alpha-1-antitrypsin than the control groups, and roseburia species were negatively correlated with alpha-1-antitrypsin. Several Actinobacteria species were associated with cerebral blood flow and white matter integrity in areas of the brain responsible for language, learning, and memory. Stroke participants scored lower on the picture vocabulary and list sorting tests than those in the control groups. Stroke participants who had higher levels of roseburia performed better on the picture vocabulary task. We found that microbial communities are disrupted in a stroke population. Many of the disrupted bacteria have previously been reported to have correlates to health and disease. This preparatory study will lay the foundation for the development of therapeutics targeting the gut following stroke

DEEP-AD: The deep learning model for diagnostic classification and prognostic prediction of alzheimer's disease

In terms of context, the aim of this dissertation is to aid neuroradiologists in their clinical judgment regarding the early detection of AD by using DL. To that aim, the system design research methodology is suggested in this dissertation for achieving three goals. The first goal is to investigate the DL models that have performed well at identifying patterns associated with AD, as well as the accuracy so far attained, limitations, and gaps. A systematic review of the literature (SLR) revealed a shortage of empirical studies on the early identification of AD through DL. In this regard, thirteen empirical studies were identified and examined. We concluded that three-dimensional (3D) DL models have been generated far less often and that their performance is also inadequate to qualify them for clinical trials. The second goal is to provide the neuroradiologist with the computer-interpretable information they need to analyze neuroimaging biomarkers. Given this context, the next step in this dissertation is to find the optimum DL model to analyze neuroimaging biomarkers. It has been achieved in two steps. In the first step, eight state-of-the-art DL models have been implemented by training from scratch using end-to-end learning (E2EL) for two binary classification tasks (AD vs. CN and AD vs. stable MCI) and compared by utilizing MRI scans from the publicly accessible datasets of neuroimaging biomarkers. Comparative analysis is carried out by utilizing efficiency-effects graphs, comprehensive indicators, and ranking mechanisms. For the training of the AD vs. sMCI task, the EfficientNet-B0 model gets the highest value for the comprehensive indicator and has the fewest parameters. DenseNet264 performed better than the others in terms of evaluation matrices, but since it has the most parameters, it costs more to train. For the AD vs. CN task by DenseNet264, we achieved 100% accuracy for training and 99.56% accuracy for testing. However, the classification accuracy was still only 82.5% for the AD vs. sMCI task. In the second step, fusion of transfer learning (TL) with E2EL is applied to train the EfficientNet-B0 for the AD vs. sMCI task, which achieved 95.29% accuracy for training and 93.10% accuracy for testing. Additionally, we have also implemented EfficientNet-B0 for the multiclass AD vs. CN vs. sMCI classification task with E2EL to be used in ensemble of models and achieved 85.66% training accuracy and 87.38% testing accuracy. To evaluate the model’s robustness, neuroradiologists must validate the implemented model. As a result, the third goal of this dissertation is to create a tool that neuroradiologists may use at their convenience. To achieve this objective, this dissertation proposes a web-based application (DEEP-AD) that has been created by making an ensemble of Efficient-Net B0 and DenseNet 264 (based on the contribution of goal 2). The accuracy of a DEEP-AD prototype has undergone repeated evaluation and improvement. First, we validated 41 subjects of Spanish MRI datasets (acquired from HT Medica, Madrid, Spain), achieving an accuracy of 82.90%, which was later verified by neuroradiologists. The results of these evaluation studies showed the accomplishment of such goals and relevant directions for future research in applied DL for the early detection of AD in clinical settings.En términos de contexto, el objetivo de esta tesis es ayudar a los neurorradiólogos en su juicio clínico sobre la detección precoz de la AD mediante el uso de DL. Para ello, en esta tesis se propone la metodología de investigación de diseño de sistemas para lograr tres objetivos. El segundo objetivo es proporcionar al neurorradiólogo la información interpretable por ordenador que necesita para analizar los biomarcadores de neuroimagen. Dado este contexto, el siguiente paso en esta tesis es encontrar el modelo DL óptimo para analizar biomarcadores de neuroimagen. Esto se ha logrado en dos pasos. En el primer paso, se han implementado ocho modelos DL de última generación mediante entrenamiento desde cero utilizando aprendizaje de extremo a extremo (E2EL) para dos tareas de clasificación binarias (AD vs. CN y AD vs. MCI estable) y se han comparado utilizando escaneos MRI de los conjuntos de datos de biomarcadores de neuroimagen de acceso público. El análisis comparativo se lleva a cabo utilizando gráficos de efecto-eficacia, indicadores exhaustivos y mecanismos de clasificación. Para el entrenamiento de la tarea AD vs. sMCI, el modelo EfficientNet-B0 obtiene el valor más alto para el indicador exhaustivo y tiene el menor número de parámetros. DenseNet264 obtuvo mejores resultados que los demás en términos de matrices de evaluación, pero al ser el que tiene más parámetros, su entrenamiento es más costoso. Para la tarea AD vs. CN de DenseNet264, conseguimos una accuracy del 100% en el entrenamiento y del 99,56% en las pruebas. Sin embargo, la accuracy de la clasificación fue sólo del 82,5% para la tarea AD vs. sMCI. En el segundo paso, se aplica la fusión del aprendizaje por transferencia (TL) con E2EL para entrenar la EfficientNet-B0 para la tarea AD vs. sMCI, que alcanzó una accuracy del 95,29% en el entrenamiento y del 93,10% en las pruebas. Además, también hemos implementado EfficientNet-B0 para la tarea de clasificación multiclase AD vs. CN vs. sMCI con E2EL para su uso en conjuntos de modelos y hemos obtenido una accuracy de entrenamiento del 85,66% y una precisión de prueba del 87,38%. Para evaluar la solidez del modelo, los neurorradiólogos deben validar el modelo implementado. Como resultado, el tercer objetivo de esta disertación es crear una herramienta que los neurorradiólogos puedan utilizar a su conveniencia. Para lograr este objetivo, esta disertación propone una aplicación basada en web (DEEP-AD) que ha sido creada haciendo un ensemble de Efficient-Net B0 y DenseNet 264 (basado en la contribución del objetivo 2). La accuracy del prototipo DEEP-AD ha sido sometida a repetidas evaluaciones y mejoras. En primer lugar, validamos 41 sujetos de conjuntos de datos de MRI españoles (adquiridos de HT Medica, Madrid, España), logrando una accuracy del 82,90%, que posteriormente fue verificada por neurorradiólogos. Los resultados de estos estudios de evaluación mostraron el cumplimiento de dichos objetivos y las direcciones relevantes para futuras investigaciones en DL, aplicada en la detección precoz de la AD en entornos clínicos.Escuela de DoctoradoDoctorado en Tecnologías de la Información y las Telecomunicacione

A Knowledge-based Integrative Modeling Approach for <em>In-Silico</em> Identification of Mechanistic Targets in Neurodegeneration with Focus on Alzheimer’s Disease

Dementia is the progressive decline in cognitive function due to damage or disease in the body beyond what might be expected from normal aging. Based on neuropathological and clinical criteria, dementia includes a spectrum of diseases, namely Alzheimer's dementia, Parkinson's dementia, Lewy Body disease, Alzheimer's dementia with Parkinson's, Pick's disease, Semantic dementia, and large and small vessel disease. It is thought that these disorders result from a combination of genetic and environmental risk factors. Despite accumulating knowledge that has been gained about pathophysiological and clinical characteristics of the disease, no coherent and integrative picture of molecular mechanisms underlying neurodegeneration in Alzheimer’s disease is available. Existing drugs only offer symptomatic relief to the patients and lack any efficient disease-modifying effects. The present research proposes a knowledge-based rationale towards integrative modeling of disease mechanism for identifying potential candidate targets and biomarkers in Alzheimer’s disease. Integrative disease modeling is an emerging knowledge-based paradigm in translational research that exploits the power of computational methods to collect, store, integrate, model and interpret accumulated disease information across different biological scales from molecules to phenotypes. It prepares the ground for transitioning from ‘descriptive’ to “mechanistic” representation of disease processes. The proposed approach was used to introduce an integrative framework, which integrates, on one hand, extracted knowledge from the literature using semantically supported text-mining technologies and, on the other hand, primary experimental data such as gene/protein expression or imaging readouts. The aim of such a hybrid integrative modeling approach was not only to provide a consolidated systems view on the disease mechanism as a whole but also to increase specificity and sensitivity of the mechanistic model by providing disease-specific context. This approach was successfully used for correlating clinical manifestations of the disease to their corresponding molecular events and led to the identification and modeling of three important mechanistic components underlying Alzheimer’s dementia, namely the CNS, the immune system and the endocrine components. These models were validated using a novel in-silico validation method, namely biomarker-guided pathway analysis and a pathway-based target identification approach was introduced, which resulted in the identification of the MAPK signaling pathway as a potential candidate target at the crossroad of the triad components underlying disease mechanism in Alzheimer’s dementia

Identification of Novel Fluid Biomarkers for Alzheimer\u27s Disease

Clinicopathological studies suggest that Alzheimer\u27s disease: AD) pathology begins to appear ~10-20 years before the resulting cognitive impairment draws medical attention. Biomarkers that can detect AD pathology in its early stages and predict dementia onset and progression would, therefore, be invaluable for patient care and efficient clinical trial design. To discover such biomarkers, we measured AD-associated changes in the cerebrospinal fluid: CSF) using an unbiased proteomics approach: two-dimensional difference gel electrophoresis with liquid chromatography tandem mass spectrometry). From this, we identified 47 proteins that differed in abundance between cognitively normal: Clinical Dementia Rating [CDR] 0) and mildly demented: CDR 1) subjects. To validate these findings, we measured a subset of the identified candidate biomarkers by enzyme linked immunosorbent assay: ELISA); promising candidates in this discovery cohort: N=47) were further evaluated by ELISA in a larger validation CSF cohort: N=292) that contained an additional very mildly demented: CDR 0.5) group. Levels of four novel biomarkers were significantly altered in AD, and Receiver-operating characteristic: ROC) analyses using a stepwise logistic regression model identified optimal panels containing these markers that distinguished CDR 0 from CDR\u3e0: tau, YKL-40, NCAM) and CDR 1 from CDR\u3c1: tau, chromogranin-A, carnosinase-I). Plasma levels of the most promising marker, YKL-40, were also found to be increased in CDR 0.5 and 1 groups and to correlate with CSF levels. Importantly, the CSF YKL-40/Aâ42 ratio predicted risk of developing cognitive impairment: CDR 0 to CDR\u3e0 conversion) as well as the best CSF biomarkers identified to date, tau/Aâ42 and p-tau181/Aâ42. Additionally, YKL-40 immunoreactivity was observed within astrocytes near a subset of amyloid plaques, implicating YKL-40 in the neuroinflammatory response to Aâ deposition. Utilizing an alternative, targeted proteomics approach to identify novel biomarkers, 333 CSF samples were evaluated for levels of 190 analytes using a multiplexed Luminex platform. The mean concentrations of 37 analytes were found to differ between CDR 0 and CDR\u3e0 participants. ROC and statistical machine learning algorithms identified novel biomarker panels that improved upon the ability of the current best biomarkers to discriminate very mildly demented from cognitively normal participants, and identified a novel biomarker, Calbindin, with significant prognostic potential

Modelling prognostic trajectories in Alzheimer’s disease

Progression to dementia due to Alzheimer’s Disease (AD) is a long and protracted process that involves multiple pathways of disease pathophysiology. Predicting these dynamic changes has major implications for timely and effective clinical management in AD. There are two reasons why at present we lack appropriate tools to make such predictions. First, a key feature of AD is the interactive nature of the relationships between biomarkers, such as accumulation of β-amyloid -a peptide that builds plaques between nerve cells-, tau -a protein found in the axons of nerve cells- and widespread neurodegeneration. Current models fail to capture these relationships because they are unable to successfully reduce the high dimensionality of biomarkers while exploiting informative multivariate relationships. Second, current models focus on simply predicting in a binary manner whether an individual will develop dementia due to AD or not, without informing clinicians about their predicted disease trajectory. This can result in administering inefficient treatment plans and hindering appropriate stratification for clinical trials. In this thesis, we overcome these challenges by using applied machine learning to build predictive models of patient disease trajectories in the earliest stages of AD. Specifically, to exploit the multi-dimensionality of biomarker data, we used a novel feature generation methodology Partial Least Squares regression with recursive feature elimination (PLSr-RFE). This method applies a hybrid-feature selection and feature construction method that captures co-morbidities in cognition and pathophysiology, resulting in an index of Alzheimer’s disease atrophy from structural MRI. We validated our choice of biomarker and the efficacy of our methodology by showing that the learnt pattern of grey matter atrophy is highly predictive of tau accumulation in an independent sample. Next, to go beyond predicting binary outcomes to deriving individualised prognostic scores of cognitive decline due to AD, we used a novel trajectory modelling approach (Generalised Metric Learning Vector Quantization – Scalar projection) that mines multimodal data from large AD research cohorts. Using this approach, we derive individualised prognostic scores of cognitive decline due to AD, revealing interactive cognitive, and biological factors that improve prediction accuracy. Next, we extended our machine learning framework to classify and stage early AD individuals based on future pathological tau accumulation. Our results show that the characteristic spreading pattern of tau in early AD can be predicted by baseline biomarkers, particularly when stratifying groups using multimodal data. Further, we showed that our prognostic index predicts individualised rates of future tau accumulation with high accuracy and regional specificity in an independent sample of cognitively unimpaired individuals. Overall, our work used machine learning to combine continuous information from AD biomarkers predicting pathophysiological changes at different stages in the AD cascade. The approaches presented in this thesis provide an excellent framework to support personalised clinical interventions and guide effective drug discovery trials

Identifying Multimodal Intermediate Phenotypes between Genetic Risk Factors and Disease Status in Alzheimer’s Disease

Neuroimaging genetics has attracted growing attention and interest, which is thought to be a powerful strategy to examine the influence of genetic variants (i.e., single nucleotide polymorphisms (SNPs)) on structures or functions of human brain. In recent studies, univariate or multivariate regression analysis methods are typically used to capture the effective associations between genetic variants and quantitative traits (QTs) such as brain imaging phenotypes. The identified imaging QTs, although associated with certain genetic markers, may not be all disease specific. A useful, but underexplored, scenario could be to discover only those QTs associated with both genetic markers and disease status for revealing the chain from genotype to phenotype to symptom. In addition, multimodal brain imaging phenotypes are extracted from different perspectives and imaging markers consistently showing up in multimodalities may provide more insights for mechanistic understanding of diseases (i.e., Alzheimer’s disease (AD)). In this work, we propose a general framework to exploit multi-modal brain imaging phenotypes as intermediate traits that bridge genetic risk factors and multi-class disease status. We applied our proposed method to explore the relation between the well-known AD risk SNP APOE rs429358 and three baseline brain imaging modalities (i.e., structural magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG-PET) and F-18 florbetapir PET scans amyloid imaging (AV45)) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The empirical results demonstrate that our proposed method not only helps improve the performances of imaging genetic associations, but also discovers robust and consistent regions of interests (ROIs) across multi-modalities to guide the disease-induced interpretation

Large-scale neuroimaging in Alzheimer’s disease and normal aging

Large-scale neuroimaging data is becoming increasingly available, providing a rich data source with which to study neurological conditions. In this thesis, I demonstrate the utility of large-scale neuroimaging as it applies to Alzheimer’s disease (AD) and normal aging, using univariate parametric mapping, regional analysis, and advanced machine learning. Specifically, this thesis covers: 1) validation and extension of prior studies using large-scale datasets; 2) AD diagnosis and normal aging evaluation empowered by large-scale datasets and advanced deep learning algorithms; 3) enhancement of cerebral blood volume (CBV) fMRI utility with retrospective CBV-fMRI technique. First, I demonstrated the utility of large-scale datasets for validating and extending prior studies using univariate analytics. I presented a study localizing AD-vulnerable regions more reliably and with better anatomical resolution using data from more than 350 subjects. Following a similar approach, I investigated the structural characteristics of healthy APOE ε4 homozygous subjects screened from a large-scale community-based study. To study the neuroimaging signatures of normal aging, we performed a large-scale joint CBV-fMRI and structural MRI study covering age 20-70s, and a structural MRI study of normal aging covering the full age-span, with the elder group screened from a large-scale clinic-based study ensuring no evidence of AD using both longitudinal follow-up and cerebrospinal fluid (CSF) biomarkers evidences. Second, I performed deep learning neuroimaging studies for AD diagnosis and normal aging evaluation, and investigated the regionality associated with each task. I developed an AD diagnosis method using a 3D convolutional neural network model trained and evaluated on ~4,600 structural MRI scans and further investigated a series of novel regionality analyses. I further extensively studied the utility of the structural MRI summary measure derived from the deep learning model in prodromal AD detection. This study constitutes a general analytic framework, which was followed to evaluate normal aging by performing deep learning-based age estimation in cognitively normal population using more than 6,000 scans. The deep learning neuroimaging models classified AD and estimated age with high accuracy, and also revealed regional patterns conforming to neuropathophysiology. The deep learning derived MRI measure demonstrated potential clinical utility, outperforming other AD pathology measures and biomarkers. In addition, I explored the utility of deep learning on positron emission tomography (PET) data for AD diagnosis and regionality analyses, further demonstrating the broad utility and generalizability of the method. Finally, I introduced a technique enabling CBV generation retrospectively from clinical contrast-enhanced scans. The derivation of meaningful functional measures from such clinical scans is only possible through calibration to a reference, which was built from the largest collection of research CBV-fMRI scans from our lab. This method was validated in an epilepsy study and demonstrated the potential to enhance the utility of CBV-fMRI by enriching the CBV-fMRI dataset. This technique is also applicable to AD and normal aging studies, and potentially enables deep learning based analytic approaches applied on CBV-fMRI with similar pipelines used in structural MRI. Collectively, this thesis demonstrates how mechanistic and diagnostic information on brain disorders can be extracted from large-scale neuroimaging data, using both classical statistical methods and advanced machine learning