Machine Learning Algorithms for Pattern Discovery in Spatio-temporal Data With Application to Brain Imaging Analysis

Abstract

Temporal networks have become increasingly pervasive in many real-world applications. Due to the existence of diverse and evolving entities in such networks, understanding the structure and characterizing patterns in them is a complex task. A prime real-world example of such networks is the functional connectivity of the brain. These networks are commonly generated by measuring the statistical relationship between the oxygenation level-dependent signal of spatially separate regions of the brain over the time of an experiment involving a task being performed or at rest in an MRI scanner. Due to certain characteristics of fMRI data, such as high dimensionality and high noise level, extracting spatio-temporal patterns in such networks is a complicated task. Therefore, it is necessary for state-of-the-art data-driven analytical methods to be developed and employed for this domain. In this thesis, we suggest methodological tools within the area of spatio-temporal pattern discovery to explore and address several questions in the domain of computational neuroscience. One of the important objectives in neuroimaging research is the detection of informative brain regions for characterizing the distinction between the activation patterns of the brains among groups with different cognitive conditions. Popular approaches for achieving this goal include the multivariate pattern analysis(MVPA), regularization-based methods, and other machine learning based approaches. However, these approaches suffer from a number of limitations, such as requirement of manual tuning of parameter as well as incorrect identification of truly informative regions in certain cases. We therefore propose a maximum relevance minimum redundancy search algorithm to alleviate these limitations while increasing the precision of detection of infor- mative activation clusters. The second question that this thesis work addresses is how to detect the temporal ties in a dynamic connectivity network that are not formed at random or due to local properties of the nodes. To explore the solution to this problem, a null model is proposed that estimates the latent characteristics of the distributions of the temporal links through optimization, followed by a statistical test to filter the links whose formation can be reduced to the local properties of their interacting nodes. We demonstrate the benefits of this approach by applying it to a real resting state fMRI dataset, and provide further discussion on various aspects and advantages of it. Lastly, this dissertation delves into the task of learning a spatio-temporal representation to discover contextual patterns in evolutionary structured data. For this purpose, a representation learning approach is proposed based on the transformer model to extract the spatio-temporal contextual information from the fMRI data. Representation learning is a core component in data-driven modeling of various complex phenomena. Learning a contextually informative set of features can specially benefit the analysis of fMRI data due to the complexities and dynamic dependencies present in such datasets. The proposed framework takes the multivariate BOLD time series of the regions of the brain as well as their functional connectivity network simultaneously as the input to create a set of meaningful features which can in turn be used in var- ious downstream tasks such as classification, feature extraction, and statistical analysis. This architecture uses the attention mechanism as well as the graph convolution neural network to jointly inject the contextual information regarding the dynamics in time series data and their connectivity into the representation. The benefits of this framework are demonstrated by applying it to two resting state fMRI datasets, and further discussion is provided on various aspects and advantages of it over a number of commonly adopted architectures.