Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis

Dementia is a growing problem that affects elderly people worldwide. More accurate evaluation of dementia diagnosis can help during the medical examination. Several methods for computer-aided dementia diagnosis have been proposed using resonance imaging scans to discriminate between patients with Alzheimer’s disease (AD) or mild cognitive impairment (MCI) and healthy controls (NC). Nonetheless, the computer-aided diagnosis is especially challenging because of the heterogeneous and intermediate nature of MCI. We address the automated dementia diagnosis by introducing a novel supervised pretraining approach that takes advantage of the artificial neural network (ANN) for complex classification tasks. The proposal initializes an ANN based on linear projections to achieve more discriminating spaces. Such projections are estimated by maximizing the centered kernel alignment criterion that assesses the affinity between the resonance imaging data kernel matrix and the label target matrix. As a result, the performed linear embedding allows accounting for features that contribute the most to the MCI class discrimination. We compare the supervised pretraining approach to two unsupervised initialization methods (autoencoders and Principal Component Analysis) and against the best four performing classification methods of the 2014 CADDementia challenge. As a result, our proposal outperforms all the baselines (7% of classification accuracy and area under the receiver-operating-characteristic curve) at the time it reduces the class biasing

Brain connectivity-patterns representation based on electroencephalography network analysis

Brain connectivity has emerged as a neuronal analysis tool widely used to explore brain functions and supply relevant information in the study of the cognitive processes. However, current methodologies used to assess brain connectivity are not always exact and as a result, possible spurious connections may appear. Moreover, measuring the connection between all possible pairs of EEG-channels leads to high dimensional matrices with either redundant or irrelevant information. To avoid problems in connectivity analysis and issues of high computational cost, a selection stage of the most significant connections can be implemented. Nevertheless, there is not a standard method yet to extract connections and the definition of significant connections may vary accordingly with the object of study. Therefore, to develop an accurate methodology, information inherent to each specific problem should be included. In this work, three different tools are presented, that execute the extraction of significant connections considering the experimental scenario. The first tool, tested on a BCI dataset, finds the set of connections that best discriminate two MI classes. Consequently, a kernel-based methodology of feature selection is used to rank each connection by its contribution in the classes discrimination. Finally, the significant connections will be the smaller set that achieves the best classification accuracy. The second methodology is used in a study of the significant connectivity patterns in attention networks. To this end, the connectivity of two classes (target and non-target) in an oddball paradigm experiment is extracted. Here, the significant connections are selected as the ones that differ the most, statistically speaking, between target and non-target. Finally, in a study of the recovery of a subject with aphasia, differences in connectivity, related to improvements produced by therapy were found. In this study, connections that change through the sessions of treatment at the level of amplitude and structure were extracted. Also, a set of significant connections that changed increasingly between the sessions was selected. For all the proposed methodologies, the brain connectivity is computed over EEG signals and the extraction of the significant connections is based on information inherent to the data or the experiment. In general, the selection of connections allows the considerable reduction of connectivity characteristics, this facilitates the physiological interpretation of the experiments and can improve the performance and computational cost of the systems that use these featuresResumen: La conectividad cerebral se ha convertido en una herramienta de análisis neuronal ampliamente utilizada para explorar funciones cerebrales y proporcionar información relevante en el estudio de los procesos cognitivos. Sin embargo, las metodologías actuales utilizadas para evaluar la conectividad cerebral no siempre son exactas y, como resultado, pueden aparecer posibles conexiones falsas. Además, cuando se mide la conexión entre todos los posibles pares de canales de EEG, se obtienen matrices de alta dimensión con información redundante o irrelevante. Para evitar problemas en el análisis de conectividad y alto costo computacional, se puede implementar una etapa de selección de las conexiones más importantes. Sin embargo, todavía no existe un método estándar para extraer conexiones y la definición de conexiones significativas puede variar de acuerdo con el objeto de estudio. Por lo tanto, para desarrollar una metodología precisa, se debe incluir información inherente a cada problema. En este trabajo, se presentan tres herramientas diferentes que ejecutan una extracción de conexiones significativas considerando el escenario del experimento. La primera herramienta, probada en una base de datos de MI, encuentra el conjunto de conexiones que mejor discrimina dos clases. Para esto se utiliza una metodología de selección de características basada en kernels para asignar un peso de contribución a cada conexión. Finalmente, las conexiones significativas serán en conjunto más pequeño que logre el mejor acierto de clasificación. La segunda metodología, se utiliza en un estudio de los patrones de conectividad significativos en redes de atención. Para esto, se extrae la conectividad de dos clases: target y no target en un experimento de paradigma Oddball. Aquí, las conexiones significativas se seleccionan como las que más se diferencian, estadísticamente, entre target y no target. Finalmente, en un estudio de recuperación de un sujeto con afasia, se encontraron diferencias en la conectividad relacionadas con las mejoras producidas terapia. Conexiones que cambian a través de las sesiones de terapia a nivel de amplitud y de estructura fueron extraídas. Además, se definieron y se seleccionaron como conexiones significativas las cuales tienen un cambio creciente entre las sesiones. Para todas las metodologías propuestas, la conectividad cerebral se calcula sobre señales de EEG y la extracción de las conexiones significativas se basa en información inherente a los datos o el experimento. En general, la selección de conexiones permite una reducción considerable de características de conectividad, esto facilita la interpretación fisiológica de los experimentos y puede mejorar el rendimiento y el costo computacional de los sistemas que utilizan estas característicasMaestrí

Computer-Aided Diagnosis for Early Identification of Multi-Type Dementia using Deep Neural Networks

With millions of people suffering from dementia worldwide, the global prevalence of this condition has a significant impact on the global economy. As well, its prevalence has a negative impact on patients’ lives and their caregivers’ physical and emotional states. Dementia can be developed as a result of some risk factors as well as it has many forms whose signs are sometimes similar. While there is currently no cure for dementia, effective early diagnosis is essential in managing it. Early diagnosis helps people in finding suitable therapies that reduce or even prevent further deterioration of cognitive abilities, and in taking control of their conditions and planning for the future. Furthermore, it also facilitates the research efforts to understand causes and signs of dementia. Early diagnosis is based on the classification of features extracted from three-dimensional brain images. The features have to accurately capture main dementia-related anatomical variations of brain structures, such as hippocampus size, gray and white matter tissues’ volumes, and brain volume. In recent years, numerous researchers have been seeking the development of new or improved Computer-Aided Diagnosis (CAD) technologies to accurately detect dementia. The CAD approaches aim to assist radiologists in increasing the accuracy of the diagnosis and reducing false positives. However, there is a number of limitations and open issues in the state-of-the-art, that need to be addressed. These limitations include that literature to date has focused on differentiating multi-stage of Alzheimer’s disease severity ignoring other dementia types that can be as devastating or even more. Furthermore, the high dimensionality of neuroimages, as well as the complexity of dementia biomarkers, can hinder classification performance. Moreover, the augmentation of neuroimaging analysis with contextual information has received limited attention to-date due to the discrepancies and irregularities of the various forms of data. This work focuses on addressing the need for differentiating between multiple types of dementia in early stages. The objective of this thesis is to automatically discriminate normal controls from patients with various types of dementia in early phases of the disease. This thesis proposes a novel CAD approach, integrating a stacked sparse auto-encoder (SSAE) with a two- dimensional convolutional neural network (CNN) for early identification of multiple types of dementia based on using the discriminant features, extracted from neuroimages, incorporated with the context information. By applying SSAE to intensities extracted from magnetic resonance (MR) neuroimages, SSAE can reduce the high dimensionality of neuroimages and learn changes, exploiting important discrimination features for classification. This research work also proposes to integrate features extracted from MR neuroimages with patients’ contextual information through fusing multi-classifier to enhance the early prediction of various types of dementia. The effectiveness of the proposed method is evaluated on OASIS dataset using five different relevant performance metrics, including accuracy, f1-score, sensitivity, specificity, and precision-recall curve. Across a cohort of 4000 MR neuroimages (176 × 176) as well as the contextual information, and clinical diagnosis of patients serving as the ground truth, the proposed CAD approach was shown to have an improved F-measure of 93% and an average area under Precision-Recall curve of 94%. The proposed method provides a significant improvement in classification output, resulted in high and reproducible accuracy rates of 95% with a sensitivity of 93%, and a specificity of 88%