Connectivity-Driven Parcellation Methods for the Human Cerebral Cortex

In this thesis, we present robust and fully-automated methods for the subdivision of the entire human cerebral cortex based on connectivity information. Our contributions are four-fold: First, we propose a clustering approach to delineate a cortical parcellation that provides a reliable abstraction of the brain's functional organisation. Second, we cast the parcellation problem as a feature reduction problem and make use of manifold learning and image segmentation techniques to identify cortical regions with distinct structural connectivity patterns. Third, we present a multi-layer graphical model that combines within- and between-subject connectivity, which is then decomposed into a cortical parcellation that can represent the whole population, while accounting for the variability across subjects. Finally, we conduct a large-scale, systematic comparison of existing parcellation methods, with a focus on providing some insight into the reliability of brain parcellations in terms of reflecting the underlying connectivity, as well as, revealing their impact on network analysis. We evaluate the proposed parcellation methods on publicly available data from the Human Connectome Project and a plethora of quantitative and qualitative evaluation techniques investigated in the literature. Experiments across multiple resolutions demonstrate the accuracy of the presented methods at both subject and group levels with regards to reproducibility and fidelity to the data. The neuro-biological interpretation of the proposed parcellations is also investigated by comparing parcel boundaries with well-structured properties of the cerebral cortex. Results show the advantage of connectivity-driven parcellations over traditional approaches in terms of better fitting the underlying connectivity.Comment: Abstract is summarised to satisfy the character limit imposed by Arxiv. Please refer to the pdf for the full text. Forked from https://spiral.imperial.ac.uk/handle/10044/1/5476

Nonlinear dimension reduction and activation detection for fmri dataset

Functional magnetic resonance imaging (fMRI) has been established as a powerful method for brain mapping. Different physical phenomena contribute to the dynamical changes in the fMRI signal, the task-related hemodynamic responses, non-task-related physiological rhythms, machine and motion artifacts, etc. In this paper, we propose a new approach for fMRI data analysis. Each fMRI time series is viewed as a point in R T. We are interested in learning the organization of the points in high dimensions and extracting useful information for data classification. A nonlinear manifold learning technique is applied to obtain a low dimensional embedding of a dataset. The embedding differentiates time series with different temporal patterns. By assuming that the subset of activated time series forms a low dimensional structure, we partition the dataset and separate subsets of points with low dimensionality. The correspondence between low dimensional subsets and time series that contain task-related responses is verified and the activation maps are generated accordingly. The proposed approach is data-driven. It does not require a model for the hemodynamic response. We have conducted several experiments with synthetic and in-vivo datasets that demonstrate the performance of our approach. 1