Computing and Information Systems - Theses

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Start date: 2020 × End date: 2021 × Subject: Machine learning × Author: Sarwar, Tabinda ×

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    Mapping the structural connectome and predicting functional connectivity with deep learning methods
    Sarwar, Tabinda ( 2020)
    Mapping the human connectome is a major goal in neuroscience, where connectome refers to a comprehensive network description of the brain. This network is often represented as a graph, where nodes denote brain regions and edges represent white matter pathways. Tractography is a computational reconstruction method based on diffusion-weighted magnetic resonance imaging (dMRI) that estimates millions of streamlines that trace out the trajectories of white matter fiber bundles. The number of streamlines interconnecting each pair of regions comprising a predefined cortical parcellation is computed to yield a structural connectivity matrix. Network analyses of these connectivity matrices have yielded new insights into brain disorders (such as Schizophrenia, Alzheimer’s disease), cognition and neurodevelopmental processes. Moreover, the temporal dependence of neuronal activity patterns of different brain regions (functional connectivity) is also associated with underlying neuronal pathways (structural connectivity). In this thesis, we analyse the capabilities of state-of-the-art tractography algorithms (deterministic and probabilistic) for mapping connectomes and develop algorithms that overcome the limitations of conventional tractography algorithms for connectome mapping. Also, we utilize the structure-functional coupling for training Deep Neural Nets to predict the functional connectivity from structural connectivity. In the first part of the thesis, we develop numerical connectome phantoms that feature realistic network topologies and match to the fiber complexity of in vivo dMRI. The connectivity between pairs of regions was predefined for these phantoms. The phantoms are utilized to evaluate the performance of tensor-based and multi-fiber implementations of deterministic and probabilistic tractography. We found that multi-fiber deterministic tractography yields the most accurate connectome reconstructions, whereas probabilistic algorithms are hampered by an abundance of spurious connections. It is essential to omit connections with the fewest number of streamlines (thresholding) when using probabilistic algorithms for mapping connectomes. The study suggests that multi-fiber deterministic tractography is well suited for connectome mapping, regardless of the streamline threshold. In the second part, we propose a novel framework to map structural connectomes using deep learning. This framework not only enables connectome mapping with a convolutional neural network (CNN) but can also be straightforwardly incorporated into conventional connectome mapping pipelines (using tractography) to enhance accuracy. This framework involves decomposing the entire brain volume into overlapping blocks. Blocks are sufficiently small to ensure that a CNN can be efficiently trained to predict each block’s internal connectivity architecture. Later, a block stitching algorithm is proposed to rebuild the full brain volume from these blocks and thereby map end-to-end connectivity matrices. Performance is evaluated using simulated dMRI data generated from numerical connectome phantoms with known ground truth connectivity. Due to the redundancy achieved by allowing blocks to overlap, block decomposition and stitching steps can enhance the accuracy of probabilistic and deterministic tractography algorithms by up to 20-30%. Various studies have reported that functional brain connectivity is associated with underlying structural characteristics. In the third part of the thesis, we utilize this structure-functional coupling to develop a novel framework using deep learning that predicts functional connectivity from structural connectivity. The framework predicts functional connectivity without explicitly modelling the biophysical characteristics of the brain. We have demonstrated that a neural network can predict functional connectivity with high accuracy while preserving the inter-subject functional differences. Furthermore, we also demonstrated that functional connectivity could be used to predict human behavior, namely cognition. Altogether, the analyses and frameworks presented in this thesis aid in extracting structural connectivity and understanding the complex relationships between functional and structural connectivity in the human brain.