Alzheimer's Disease: A Survey

Alzheimer's Diseases (AD) is one of the type of dementia. This is one of the harmful disease which can lead to death and yet there is no treatment. There is no current technique which is 100% accurate for the treatment of this disease. In recent years, Neuroimaging combined with machine learning techniques have been used for detection of Alzheimer's disease. Based on our survey we came across many methods like Convolution Neural Network (CNN) where in each brain area is been split into small three dimensional patches which acts as input samples for CNN. The other method used was Deep Neural Networks (DNN) where the brain MRI images are segmented to extract the brain chambers and then features are extracted from the segmented area. There are many such methods which can be used for detection of Alzheimer’s Disease

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

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

Automated detection of Alzheimer disease using MRI images and deep neural networks- A review

Early detection of Alzheimer disease is crucial for deploying interventions and slowing the disease progression. A lot of machine learning and deep learning algorithms have been explored in the past decade with the aim of building an automated detection for Alzheimer. Advancements in data augmentation techniques and advanced deep learning architectures have opened up new frontiers in this field, and research is moving at a rapid speed. Hence, the purpose of this survey is to provide an overview of recent research on deep learning models for Alzheimer disease diagnosis. In addition to categorizing the numerous data sources, neural network architectures, and commonly used assessment measures, we also classify implementation and reproducibility. Our objective is to assist interested researchers in keeping up with the newest developments and in reproducing earlier investigations as benchmarks. In addition, we also indicate future research directions for this topic.Comment: 22 Pages, 5 Figures, 7 Table

Predicting Alzheimer’s disease progression using multi-modal deep learning approach

Alzheimer’s disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer’s Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials

BRAIN AGE AS A MEASURE OF BRAIN RESERVE IN NEUROPSYCHIATRIC DISORDERS

Aging represents a highly heterogeneous process with highly variable clinical outcomes. Differential expression of risk and resilience factors may provide explanations for this variability. Gaining a better understanding of resilience in aging is critical as it will allow for improved individualized outcome prediction, as well as providing insight for targeted interventions that may improve the process of aging. Currently, the prevailing models of neurocognitive resilience are cognitive reserve and brain reserve. The theory of cognitive reserve suggests that those with greater cognitive reserve may better cope with loss of brain integrity through presence of more adaptable and efficient neural systems. Most studies utilize education level to assess cognitive reserve; however, many proxy measures are subjective and susceptible to hindsight bias. The concept of brain reserve overlaps with that of cognitive reserve but focuses instead on the biological characteristics that allow the brain to be resilient to the effects of aging and pathological insults. It is generally thought that with sufficient brain substrate (e.g., larger grey matter volumes, greater synaptic density, more elaborate network complexity), the brain is more capable of preserving normal functioning and maintaining homeostasis despite the presence of factors of neurodegeneration or trauma. Overall, the main goals of this dissertation are to demonstrate the impact of cognitive and brain reserve on neuropsychological outcomes and brain activation patterns (Aim 1, Chapters 2 and 3), to utilize machine learning brain age prediction as a novel proxy of brain reserve (Aim 2, Chapter 4), and to utilize brain age prediction in several iv neuropsychiatric disorders to predict outcome or gain a better understanding on the disease process (Aim 3, Chapters 5, 6, 7)

Depression & cognition in the elderly : neuroimaging perspective

This thesis examines the relationship between depression and brain structure in the elderly with (Study I, III) and without (Study II, IV) cognitive impairment (Alzheimer’s disease and mild cognitive impairment). Individuals from four independent cohorts were included. Participants had either a depressive episode (Study II, III) or depressive symptoms, as measured with different depression scales (Study I, IV). Studies I and II have cross-sectional design, and studies III and IV are longitudinal. Main outcomes were cortical thickness of the brain and volumes of different structures (hippocampus, ventral diencephalon, including hypothalamus and corpus callosum), or atrophy rate of the thickness and volumes (Study IV). We found in all the cohorts that depressive symptoms were associated with cortical thinning in the same region – the left temporoparietal junction. Depression-related thinning was observed in three cohorts (Studies I, IV) in superior temporal cortex and temporal pole. In two non-demented cohorts (Studies II, IV) angular cortex was also involved in depression. Longitudinal analysis revealed that thinning in these regions is secondary to depressive symptoms (study IV). In two cohorts (Study I, II) fusiform cortex was involved in depression. In study IV, we also were able to assess thinning which developed in parallel with depressive symptoms. It covered medial superior frontal cortex and lingual cortex. The number of depressive episodes was associated with cortical thinning in the left temporal pole in women (Study II) and reduced volume of the right ventral diencephalon in both – men and women (Study III). We have found moderating effect of gender on the relationship between cortical thickness and depression onset. Women with late-onset depression (>65 years) but not men had the widespread thinning in the prefrontal cortex compared to early-onset depressed. The volume of the right hippocampus and thickness of the superior frontal cortex were positively associated with a level of global cognition measured with the mini-mental state examination (MMSE) This effect was more pronounced in the subgroup of late-onset depressed (Study II). The volume of the right ventral diencephalon was associated with cognitive decline (MCI or dementia diagnosis) one year later in the elderly with a depressive episode (study III). Adding baseline MMSE to the classifier increased its accuracy. Total and phosphorylated tau were associated with cortical thinning in the cluster covering right posterior cingulate cortex and precuneus and cluster covering right parahippocampal and fusiform gyri in the AD patients with depressive symptoms from the KI cohort (Study I). No association has been found in non-depressed AD patients. Higher baseline saliva cortisol levels in non-demented individuals (Study IV) were associated with widespread cortical atrophy in temporal, prefrontal and parietal cortex bilaterally and the right hippocampus, independently of age and MMSE. To sum-up, depression was associated with thinning (Studies I, II) and subsequent atrophy (Study IV) in the superior temporal, supramarginal, temporal pole, lingual, fusiform and parahippocampal cortex. Cortical thinning in the superior frontal and lingual regions developed in parallel or prior to the depressive symptoms. The afore-mentioned regions are involved in social perception (processing of the information about others, experience positive emotions related to other people and building an integrative picture of another person), and are among the first to be impaired in Alzheimer’s disease. Elevated cortisol explained atrophy in these and a number of other regions, including the hippocampus, suggesting that depression and Alzheimer’s disease may be connected via cortisol-related brain damage. Depression-related atrophy in the ventral diencephalon leads to impaired cognitive performance. Assessment of cognitive function during the depressive episode, combined with brain structural measurements may have a prognostic value. Future studies should evaluate if a detailed neurocognitive assessment of elderly patients during the depressive episode would help to identify those at high risk of dementia. It is also important to test if stress-reduction interventions in individuals at-risk of Alzheimer’s disease would be effective in its prevention

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