Extracting morphological networks from individual grey matter MRI scans in healthy subjects and people at high risk for schizophrenia

Abstract

Recently graph theory has been successfully applied to magnetic resonance imaging data. However, it remains unclear as to what the nodes and edges in a network should represent. This problem is particularly difficult when extracting morphological networks (i.e., from grey matter segmentations). Existing morphological network studies have used anatomical regions as nodes that are connected by edges when these regions covary in thickness or volume across a sample of subjects. Covariance in cortical thickness or volume has been hypothesised to be caused by anatomical connectivity, experience driven plasticity and/or mutual trophic influences. A limitation of this approach is that it requires magnetic resonance imaging (MRI) scans to be warped into a standard template.

These warping processes could filter out subtle structural differences that are of most interest in, for example, clinical studies.

The focus of the work in this thesis was to address these limitations by contributing a new method to extract morphological networks from individual cortices. Briefly, this method divides the cortex into small regions of interest that keep the three-dimensional structure intact, and edges are placed between any two regions that have a statistically similar grey matter structure. The method was developed in a sample of 14 healthy individuals, who were scanned at two different time points. For the first time individual grey matter networks based on intracortical similarity were studied. The topological organisation of intracortical similarities was significantly different from random topology. Additionally, the graph theoretical properties were reproducible over time supporting the robustness of the method. All network properties closely resembled those reported in other imaging studies.

The second study in this thesis focussed on the question whether extracting networks from individual scans would be more sensitive than traditional methods (that use warping procedures) to subtle grey matter differences in MRI data. In order to investigate this question, the method was applied to the first round of scans from the Edinburgh High Risk study of Schizophrenia (EHRS), before any of the subjects was diagnosed with (symptoms of) the disease. Where traditional methods failed to find differences at the whole brain level between the high risk group and healthy controls, the new method did find subtle disruptions of global network topology between the groups.

Finally, the diagnostic value of the networks was studied with exploratory analyses that found that, in comparison to healthy controls, people at high risk of schizophrenia showed more intracortical similarities in the left angular gyrus.

Furthermore within the high risk group an increase of intracortical similarities could predict disease outcome up to 74% accuracy.

The main conclusion of this thesis was that the new method provides a robust and concise statistical description of the grey matter structure in individual cortices, that is of particular importance for the study of clinical populations when structural disruptions are subtle.