Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: Evaluation in Alzheimer's disease

Background: Although convolutional neural networks (CNN) achieve high diagnostic accuracy for detecting Alzheimer's disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet applied in clinical routine. One important reason for this is a lack of model comprehensibility. Recently developed visualization methods for deriving CNN relevance maps may help to fill this gap. We investigated whether models with higher accuracy also rely more on discriminative brain regions predefined by prior knowledge. Methods: We trained a CNN for the detection of AD in N=663 T1-weighted MRI scans of patients with dementia and amnestic mild cognitive impairment (MCI) and verified the accuracy of the models via cross-validation and in three independent samples including N=1655 cases. We evaluated the association of relevance scores and hippocampus volume to validate the clinical utility of this approach. To improve model comprehensibility, we implemented an interactive visualization of 3D CNN relevance maps. Results: Across three independent datasets, group separation showed high accuracy for AD dementia vs. controls (AUC$\geq$0.92) and moderate accuracy for MCI vs. controls (AUC$\approx$0.75). Relevance maps indicated that hippocampal atrophy was considered as the most informative factor for AD detection, with additional contributions from atrophy in other cortical and subcortical regions. Relevance scores within the hippocampus were highly correlated with hippocampal volumes (Pearson's r$\approx$-0.86, p<0.001). Conclusion: The relevance maps highlighted atrophy in regions that we had hypothesized a priori. This strengthens the comprehensibility of the CNN models, which were trained in a purely data-driven manner based on the scans and diagnosis labels.Comment: 24 pages, 9 figures/tables, supplementary material, source code available on GitHu

Vision Transformers and Bi-LSTM for Alzheimer's Disease Diagnosis from 3D MRI

Alzheimer's is a brain disease that gets worse over time and affects memory, thinking, and behavior. Alzheimer's disease (AD) can be treated and managed if it is diagnosed early, which can slow the progression of symptoms and improve quality of life. In this study, we suggested using the Visual Transformer (ViT) and bi-LSTM to process MRI images for diagnosing Alzheimer's disease. We used ViT to extract features from the MRI and then map them to a feature sequence. Then, we used Bi-LSTM sequence modeling to keep the interdependencies between related features. In addition, we evaluated the performance of the proposed model for the binary classification of AD patients using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Finally, we evaluated our method against other deep learning models in the literature. The proposed method performs well in terms of accuracy, precision, F-score, and recall for the diagnosis of AD

Introducing Vision Transformer for Alzheimer's Disease classification task with 3D input

Many high-performance classification models utilize complex CNN-based architectures for Alzheimer's Disease classification. We aim to investigate two relevant questions regarding classification of Alzheimer's Disease using MRI: "Do Vision Transformer-based models perform better than CNN-based models?" and "Is it possible to use a shallow 3D CNN-based model to obtain satisfying results?" To achieve these goals, we propose two models that can take in and process 3D MRI scans: Convolutional Voxel Vision Transformer (CVVT) architecture, and ConvNet3D-4, a shallow 4-block 3D CNN-based model. Our results indicate that the shallow 3D CNN-based models are sufficient to achieve good classification results for Alzheimer's Disease using MRI scans

ADNet : diagnóstico assistido por computador para doença de Alzheimer usando rede neural convolucional 3D com cérebro inteiro

Orientadores: Anderson de Rezende Rocha, Marina WeilerDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Demência por doença de Alzheimer (DA) é uma síndrome clínica caracterizada por múltiplos problemas cognitivos, incluindo dificuldades na memória, funções executivas, linguagem e habilidades visuoespaciais. Sendo a forma mais comum de demência, essa doença mata mais do que câncer de mama e de próstata combinados, além de ser a sexta principal causa de morte nos Estados Unidos. A neuroimagem é uma das áreas de pesquisa mais promissoras para a detecção de biomarcadores estruturais da DA, onde uma técnica não invasiva é usada para capturar uma imagem digital do cérebro, a partir da qual especialistas extraem padrões e características da doença. Nesse contexto, os sistemas de diagnóstico assistido por computador (DAC) são abordagens que visam ajudar médicos e especialistas na interpretação de dados médicos, para fornecer diagnósticos aos pacientes. Em particular, redes neurais convolucionais (RNCs) são um tipo especial de rede neural artificial (RNA), que foram inspiradas em como o sistema visual funciona e, nesse sentido, têm sido cada vez mais utilizadas em tarefas de visão computacional, alcançando resultados impressionantes. Em nossa pesquisa, um dos principais objetivos foi utilizar o que há de mais avançado sobre aprendizagem profunda (por exemplo, RNC) para resolver o difícil problema de identificar biomarcadores estruturais da DA em imagem por ressonância magnética (IRM), considerando três grupos diferentes, ou seja, cognitivamente normal (CN), comprometimento cognitivo leve (CCL) e DA. Adaptamos redes convolucionais com dados fornecidos principalmente pela ADNI e avaliamos no desafio CADDementia, resultando em um cenário mais próximo das condições no mundo real, em que um sistema DAC é usado em um conjunto de dados diferente daquele usado no treinamento. Os principais desafios e contribuições da nossa pesquisa incluem a criação de um sistema de aprendizagem profunda que seja totalmente automático e comparativamente rápido, ao mesmo tempo em que apresenta resultados competitivos, sem usar qualquer conhecimento específico de domínio. Nomeamos nossa melhor arquitetura ADNet (Alzheimer's Disease Network) e nosso melhor método ADNet-DA (ADNet com adaptação de domínio), o qual superou a maioria das submissões no CADDementia, todas utilizando conhecimento prévio da doença, como regiões de interesse específicas do cérebro. A principal razão para não usar qualquer informação da doença em nosso sistema é fazer com que ele aprenda e extraia padrões relevantes de regiões importantes do cérebro automaticamente, que podem ser usados para apoiar os padrões atuais de diagnóstico e podem inclusive auxiliar em novas descobertas para diferentes ou novas doenças. Após explorar uma série de técnicas de visualização para interpretação de modelos, associada à inteligência artificial explicável (XAI), acreditamos que nosso método possa realmente ser empregado na prática médica. Ao diagnosticar pacientes, é possível que especialistas usem a ADNet para gerar uma diversidade de visualizações explicativas para uma determinada imagem, conforme ilustrado em nossa pesquisa, enquanto a ADNet-DA pode ajudar com o diagnóstico. Desta forma, os especialistas podem chegar a uma decisão mais informada e em menos tempoAbstract: Dementia by Alzheimer's disease (AD) is a clinical syndrome characterized by multiple cognitive problems, including difficulties in memory, executive functions, language and visuospatial skills. Being the most common form of dementia, this disease kills more than breast cancer and prostate cancer combined, and it is the sixth leading cause of death in the United States. Neuroimaging is one of the most promising areas of research for early detection of AD structural biomarkers, where a non-invasive technique is used to capture a digital image of the brain, from which specialists extract patterns and features of the disease. In this context, computer-aided diagnosis (CAD) systems are approaches that aim at assisting doctors and specialists in interpretation of medical data to provide diagnoses for patients. In particular, convolutional neural networks (CNNs) are a special kind of artificial neural network (ANN), which were inspired by how the visual system works, and, in this sense, have been increasingly used in computer vision tasks, achieving impressive results. In our research, one of the main goals was bringing to bear what is most advanced in deep learning research (e.g., CNN) to solve the difficult problem of identifying AD structural biomarkers in magnetic resonance imaging (MRI), considering three different groups, namely, cognitively normal (CN), mild cognitive impairment (MCI), and AD. We tailored convolutional networks with data primarily provided by ADNI, and evaluated them on the CADDementia challenge, thus resulting in a scenario very close to the real-world conditions, in which a CAD system is used on a dataset differently from the one used for training. The main challenges and contributions of our research include devising a deep learning system that is both completely automatic and comparatively fast, while also presenting competitive results, without using any domain specific knowledge. We named our best architecture ADNet (Alzheimer's Disease Network), and our best method ADNet-DA (ADNet with domain adaption), which outperformed most of the CADDementia submissions, all of them using prior knowledge from the disease, such as specific regions of interest of the brain. The main reason for not using any information from the disease in our system is to make it automatically learn and extract relevant patterns from important regions of the brain, which can be used to support current diagnosis standards, and may even assist in new discoveries for different or new diseases. After exploring a number of visualization techniques for model interpretability, associated with explainable artificial intelligence (XAI), we believe that our method can be actually employed in medical practice. While diagnosing patients, it is possible for specialists to use ADNet to generate a diversity of explanatory visualizations for a given image, as illustrated in our research, while ADNet-DA can assist with the diagnosis. This way, specialists can come up with a more informed decision and in less timeMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

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&#8217;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

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

Novel Deep Learning Models for Medical Imaging Analysis

abstract: Deep learning is a sub-field of machine learning in which models are developed to imitate the workings of the human brain in processing data and creating patterns for decision making. This dissertation is focused on developing deep learning models for medical imaging analysis of different modalities for different tasks including detection, segmentation and classification. Imaging modalities including digital mammography (DM), magnetic resonance imaging (MRI), positron emission tomography (PET) and computed tomography (CT) are studied in the dissertation for various medical applications. The first phase of the research is to develop a novel shallow-deep convolutional neural network (SD-CNN) model for improved breast cancer diagnosis. This model takes one type of medical image as input and synthesizes different modalities for additional feature sources; both original image and synthetic image are used for feature generation. This proposed architecture is validated in the application of breast cancer diagnosis and proved to be outperforming the competing models. Motivated by the success from the first phase, the second phase focuses on improving medical imaging synthesis performance with advanced deep learning architecture. A new architecture named deep residual inception encoder-decoder network (RIED-Net) is proposed. RIED-Net has the advantages of preserving pixel-level information and cross-modality feature transferring. The applicability of RIED-Net is validated in breast cancer diagnosis and Alzheimer’s disease (AD) staging. Recognizing medical imaging research often has multiples inter-related tasks, namely, detection, segmentation and classification, my third phase of the research is to develop a multi-task deep learning model. Specifically, a feature transfer enabled multi-task deep learning model (FT-MTL-Net) is proposed to transfer high-resolution features from segmentation task to low-resolution feature-based classification task. The application of FT-MTL-Net on breast cancer detection, segmentation and classification using DM images is studied. As a continuing effort on exploring the transfer learning in deep models for medical application, the last phase is to develop a deep learning model for both feature transfer and knowledge from pre-training age prediction task to new domain of Mild cognitive impairment (MCI) to AD conversion prediction task. It is validated in the application of predicting MCI patients’ conversion to AD with 3D MRI images.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review

Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor problems for people with a detrimental effect on the functioning of the nervous system. In order to diagnose MS, multiple screening methods have been proposed so far; among them, magnetic resonance imaging (MRI) has received considerable attention among physicians. MRI modalities provide physicians with fundamental information about the structure and function of the brain, which is crucial for the rapid diagnosis of MS lesions. Diagnosing MS using MRI is time-consuming, tedious, and prone to manual errors. Research on the implementation of computer aided diagnosis system (CADS) based on artificial intelligence (AI) to diagnose MS involves conventional machine learning and deep learning (DL) methods. In conventional machine learning, feature extraction, feature selection, and classification steps are carried out by using trial and error; on the contrary, these steps in DL are based on deep layers whose values are automatically learn. In this paper, a complete review of automated MS diagnosis methods performed using DL techniques with MRI neuroimaging modalities is provided. Initially, the steps involved in various CADS proposed using MRI modalities and DL techniques for MS diagnosis are investigated. The important preprocessing techniques employed in various works are analyzed. Most of the published papers on MS diagnosis using MRI modalities and DL are presented. The most significant challenges facing and future direction of automated diagnosis of MS using MRI modalities and DL techniques are also provided

Deep Interpretability Methods for Neuroimaging

Brain dynamics are highly complex and yet hold the key to understanding brain function and dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging data are noisy, high-dimensional, and not readily interpretable. The typical approach of reducing this data to low-dimensional features and focusing on the most predictive features comes with strong assumptions and can miss essential aspects of the underlying dynamics. In contrast, introspection of discriminatively trained deep learning models may uncover disorder-relevant elements of the signal at the level of individual time points and spatial locations. Nevertheless, the difficulty of reliable training on high-dimensional but small-sample datasets and the unclear relevance of the resulting predictive markers prevent the widespread use of deep learning in functional neuroimaging. In this dissertation, we address these challenges by proposing a deep learning framework to learn from high-dimensional dynamical data while maintaining stable, ecologically valid interpretations. The developed model is pre-trainable and alleviates the need to collect an enormous amount of neuroimaging samples to achieve optimal training. We also provide a quantitative validation module, Retain and Retrain (RAR), that can objectively verify the higher predictability of the dynamics learned by the model. Results successfully demonstrate that the proposed framework enables learning the fMRI dynamics directly from small data and capturing compact, stable interpretations of features predictive of function and dysfunction. We also comprehensively reviewed deep interpretability literature in the neuroimaging domain. Our analysis reveals the ongoing trend of interpretability practices in neuroimaging studies and identifies the gaps that should be addressed for effective human-machine collaboration in this domain. This dissertation also proposed a post hoc interpretability method, Geometrically Guided Integrated Gradients (GGIG), that leverages geometric properties of the functional space as learned by a deep learning model. With extensive experiments and quantitative validation on MNIST and ImageNet datasets, we demonstrate that GGIG outperforms integrated gradients (IG), which is considered to be a popular interpretability method in the literature. As GGIG is able to identify the contours of the discriminative regions in the input space, GGIG may be useful in various medical imaging tasks where fine-grained localization as an explanation is beneficial

Frameworks to Investigate Robustness and Disease Characterization/Prediction Utility of Time-Varying Functional Connectivity State Profiles of the Human Brain at Rest

Neuroimaging technologies aim at delineating the highly complex structural and functional organization of the human brain. In recent years, several unimodal as well as multimodal analyses of structural MRI (sMRI) and functional MRI (fMRI) neuroimaging modalities, leveraging advanced signal processing and machine learning based feature extraction algorithms, have opened new avenues in diagnosis of complex brain syndromes and neurocognitive disorders. Generically regarding these neuroimaging modalities as filtered, complimentary insights of brain’s anatomical and functional organization, multimodal data fusion efforts could enable more comprehensive mapping of brain structure and function. Large scale functional organization of the brain is often studied by viewing the brain as a complex, integrative network composed of spatially distributed, but functionally interacting, sub-networks that continually share and process information. Such whole-brain functional interactions, also referred to as patterns of functional connectivity (FC), are typically examined as levels of synchronous co-activation in the different functional networks of the brain. More recently, there has been a major paradigm shift from measuring the whole-brain FC in an oversimplified, time-averaged manner to additional exploration of time-varying mechanisms to identify the recurring, transient brain configurations or brain states, referred to as time-varying FC state profiles in this dissertation. Notably, prior studies based on time-varying FC approaches have made use of these relatively lower dimensional fMRI features to characterize pathophysiology and have also been reported to relate to demographic characterization, consciousness levels and cognition. In this dissertation, we corroborate the efficacy of time-varying FC state profiles of the human brain at rest by implementing statistical frameworks to evaluate their robustness and statistical significance through an in-depth, novel evaluation on multiple, independent partitions of a very large rest-fMRI dataset, as well as extensive validation testing on surrogate rest-fMRI datasets. In the following, we present a novel data-driven, blind source separation based multimodal (sMRI-fMRI) data fusion framework that uses the time-varying FC state profiles as features from the fMRI modality to characterize diseased brain conditions and substantiate brain structure-function relationships. Finally, we present a novel data-driven, deep learning based multimodal (sMRI-fMRI) data fusion framework that examines the degree of diagnostic and prognostic performance improvement based on time-varying FC state profiles as features from the fMRI modality. The approaches developed and tested in this dissertation evince high levels of robustness and highlight the utility of time-varying FC state profiles as potential biomarkers to characterize, diagnose and predict diseased brain conditions. As such, the findings in this work argue in favor of the view of FC investigations of the brain that are centered on time-varying FC approaches, and also highlight the benefits of combining multiple neuroimaging data modalities via data fusion