Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification

Dynamic functional connectivity (dFC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) help us understand fundamental dynamic characteristics of human brains, thereby providing an efficient solution for automated identification of brain diseases, such as Alzheimer's disease (AD) and its prodromal stage. Existing studies have applied deep learning methods to dFC network analysis and achieved good performance compared with traditional machine learning methods. However, they seldom take advantage of sequential information conveyed in dFC networks that could be informative to improve the diagnosis performance. In this paper, we propose a convolutional recurrent neural network (CRNN) for automated brain disease classification with rs-fMRI data. Specifically, we first construct dFC networks from rs-fMRI data using a sliding window strategy. Then, we employ three convolutional layers and long short-term memory (LSTM) layer to extract high-level features of dFC networks and also preserve the sequential information of extracted features, followed by three fully connected layers for brain disease classification. Experimental results on 174 subjects with 563 rs-fMRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate the effectiveness of our proposed method in binary and multi-category classification tasks

Investigating transcranial direct current stimulation and its therapeutic potential

Transcranial direct current stimulation (tDCS) is a popular non-invasive brain stimulation technique, which has the potential to modulate cortical excitability. The effects of tDCS are known to outlast the stimulation period, and in some cases, repeated applications have been found to produce long lasting clinically relevant effects. The primary aim of this thesis was to explore the reliability and therapeutic potential of this technique. In Chapters 3 and 4 transcranial magnetic stimulation (TMS) was used to measure tDCS effects. These experiments revealed substantial variability regarding the way in which healthy adults responded to stimulation. Notably, there were differences between participants regarding the direction and magnitude of change in cortical excitability. Furthermore, even when group level effects were found reliably, there was substantial intra-subject variability across repeated testing sessions. Subsequent experiments in Chapters 5 and 6, explored the biological and behavioural effects of tDCS in individuals with Gille de la Tourette’s syndrome (GTS). GTS is a neurodevelopmental disorder characterised by motor and phonic tics which have been linked to hyper excitability within motor-cortical regions. Therefore, these experiments aimed to reduce cortical excitability of targeted regions in the hope that this would impact on tics. Disappointingly, no such effects were found immediately after a single session of tDCS (Chapter 5). Consequently, it was hypothesised that repeated applications may be necessary for significant reductions in tics to occur. This was investigated in Chapter 6 using an in-depth case study. The results were encouraging, in particular there was a substantial drop in tics following 10 days of tDCS at 1.5mA intensity. The stimulation was well tolerated and the treatment regimens were closely adhered to, despite tDCS being delivered in the participants own home with remote supervision. A weaker stimulation intensity was not as effective. The findings of Chapters 3-6 highlight that the optimal stimulation parameters may vary from person to person, and that exploration of individual data is critical in therapeutic contexts. The results also suggest that tDCS may be helpful as a treatment for GTS and furthermore highlight the feasibility of home use stimulation

Multi-atlas segmentation using clustering, local non-linear manifold embeddings and target-specific templates

Multi-atlas segmentation (MAS) has become an established technique for the automated delineation of anatomical structures. The often manually annotated labels from each of multiple pre-segmented images (atlases) are typically transferred to a target through the spatial mapping of corresponding structures of interest. The mapping can be estimated by pairwise registration between each atlas and the target or by creating an intermediate population template for spatial normalisation of atlases and targets. The former is done at runtime which is computationally expensive but provides high accuracy. In the latter approach the template can be constructed from the atlases offline requiring only one registration to the target at runtime. Although this is computationally more efficient, the composition of deformation fields can lead to decreased accuracy. Our goal was to develop a MAS method which was both efficient and accurate. In our approach we create a target-specific template (TST) which has a high similarity to the target and serves as intermediate step to increase registration accuracy. The TST is constructed from the atlas images that are most similar to the target. These images are determined in low-dimensional manifold spaces on the basis of deformation fields in local regions of interest. We also introduce a clustering approach to divide atlas labels into meaningful sub-regions of interest and increase local specificity for TST construction and label fusion. Our approach was tested on a variety of MR brain datasets and applied to an in-house dataset. We achieve state-of-the-art accuracy while being computationally much more efficient than competing methods. This efficiency opens the door to the use of larger sets of atlases which could lead to further improvement in segmentation accuracy

Investigating transcranial direct current stimulation and its therapeutic potential

Transcranial direct current stimulation (tDCS) is a popular non-invasive brain stimulation technique, which has the potential to modulate cortical excitability. The effects of tDCS are known to outlast the stimulation period, and in some cases, repeated applications have been found to produce long lasting clinically relevant effects. The primary aim of this thesis was to explore the reliability and therapeutic potential of this technique. In Chapters 3 and 4 transcranial magnetic stimulation (TMS) was used to measure tDCS effects. These experiments revealed substantial variability regarding the way in which healthy adults responded to stimulation. Notably, there were differences between participants regarding the direction and magnitude of change in cortical excitability. Furthermore, even when group level effects were found reliably, there was substantial intra-subject variability across repeated testing sessions. Subsequent experiments in Chapters 5 and 6, explored the biological and behavioural effects of tDCS in individuals with Gille de la Tourette’s syndrome (GTS). GTS is a neurodevelopmental disorder characterised by motor and phonic tics which have been linked to hyper excitability within motor-cortical regions. Therefore, these experiments aimed to reduce cortical excitability of targeted regions in the hope that this would impact on tics. Disappointingly, no such effects were found immediately after a single session of tDCS (Chapter 5). Consequently, it was hypothesised that repeated applications may be necessary for significant reductions in tics to occur. This was investigated in Chapter 6 using an in-depth case study. The results were encouraging, in particular there was a substantial drop in tics following 10 days of tDCS at 1.5mA intensity. The stimulation was well tolerated and the treatment regimens were closely adhered to, despite tDCS being delivered in the participants own home with remote supervision. A weaker stimulation intensity was not as effective. The findings of Chapters 3-6 highlight that the optimal stimulation parameters may vary from person to person, and that exploration of individual data is critical in therapeutic contexts. The results also suggest that tDCS may be helpful as a treatment for GTS and furthermore highlight the feasibility of home use stimulation

Multi-atlas segmentation using clustering, local non-linear manifold embeddings and target-specific templates

Multi-atlas segmentation (MAS) has become an established technique for the automated delineation of anatomical structures. The often manually annotated labels from each of multiple pre-segmented images (atlases) are typically transferred to a target through the spatial mapping of corresponding structures of interest. The mapping can be estimated by pairwise registration between each atlas and the target or by creating an intermediate population template for spatial normalisation of atlases and targets. The former is done at runtime which is computationally expensive but provides high accuracy. In the latter approach the template can be constructed from the atlases offline requiring only one registration to the target at runtime. Although this is computationally more efficient, the composition of deformation fields can lead to decreased accuracy. Our goal was to develop a MAS method which was both efficient and accurate. In our approach we create a target-specific template (TST) which has a high similarity to the target and serves as intermediate step to increase registration accuracy. The TST is constructed from the atlas images that are most similar to the target. These images are determined in low-dimensional manifold spaces on the basis of deformation fields in local regions of interest. We also introduce a clustering approach to divide atlas labels into meaningful sub-regions of interest and increase local specificity for TST construction and label fusion. Our approach was tested on a variety of MR brain datasets and applied to an in-house dataset. We achieve state-of-the-art accuracy while being computationally much more efficient than competing methods. This efficiency opens the door to the use of larger sets of atlases which could lead to further improvement in segmentation accuracy

Visual Exploration And Information Analytics Of High-Dimensional Medical Images

Data visualization has transformed how we analyze increasingly large and complex data sets. Advanced visual tools logically represent data in a way that communicates the most important information inherent within it and culminate the analysis with an insightful conclusion. Automated analysis disciplines - such as data mining, machine learning, and statistics - have traditionally been the most dominant fields for data analysis. It has been complemented with a near-ubiquitous adoption of specialized hardware and software environments that handle the storage, retrieval, and pre- and postprocessing of digital data. The addition of interactive visualization tools allows an active human participant in the model creation process. The advantage is a data-driven approach where the constraints and assumptions of the model can be explored and chosen based on human insight and confirmed on demand by the analytic system. This translates to a better understanding of data and a more effective knowledge discovery. This trend has become very popular across various domains, not limited to machine learning, simulation, computer vision, genetics, stock market, data mining, and geography. In this dissertation, we highlight the role of visualization within the context of medical image analysis in the field of neuroimaging. The analysis of brain images has uncovered amazing traits about its underlying dynamics. Multiple image modalities capture qualitatively different internal brain mechanisms and abstract it within the information space of that modality. Computational studies based on these modalities help correlate the high-level brain function measurements with abnormal human behavior. These functional maps are easily projected in the physical space through accurate 3-D brain reconstructions and visualized in excellent detail from different anatomical vantage points. Statistical models built for comparative analysis across subject groups test for significant variance within the features and localize abnormal behaviors contextualizing the high-level brain activity. Currently, the task of identifying the features is based on empirical evidence, and preparing data for testing is time-consuming. Correlations among features are usually ignored due to lack of insight. With a multitude of features available and with new emerging modalities appearing, the process of identifying the salient features and their interdependencies becomes more difficult to perceive. This limits the analysis only to certain discernible features, thus limiting human judgments regarding the most important process that governs the symptom and hinders prediction. These shortcomings can be addressed using an analytical system that leverages data-driven techniques for guiding the user toward discovering relevant hypotheses. The research contributions within this dissertation encompass multidisciplinary fields of study not limited to geometry processing, computer vision, and 3-D visualization. However, the principal achievement of this research is the design and development of an interactive system for multimodality integration of medical images. The research proceeds in various stages, which are important to reach the desired goal. The different stages are briefly described as follows: First, we develop a rigorous geometry computation framework for brain surface matching. The brain is a highly convoluted structure of closed topology. Surface parameterization explicitly captures the non-Euclidean geometry of the cortical surface and helps derive a more accurate registration of brain surfaces. We describe a technique based on conformal parameterization that creates a bijective mapping to the canonical domain, where surface operations can be performed with improved efficiency and feasibility. Subdividing the brain into a finite set of anatomical elements provides the structural basis for a categorical division of anatomical view points and a spatial context for statistical analysis. We present statistically significant results of our analysis into functional and morphological features for a variety of brain disorders. Second, we design and develop an intelligent and interactive system for visual analysis of brain disorders by utilizing the complete feature space across all modalities. Each subdivided anatomical unit is specialized by a vector of features that overlap within that element. The analytical framework provides the necessary interactivity for exploration of salient features and discovering relevant hypotheses. It provides visualization tools for confirming model results and an easy-to-use interface for manipulating parameters for feature selection and filtering. It provides coordinated display views for visualizing multiple features across multiple subject groups, visual representations for highlighting interdependencies and correlations between features, and an efficient data-management solution for maintaining provenance and issuing formal data queries to the back end