Structural cortical connectivity changes in a cohort of people at high risk at schizophrenia (PhD)

The precise causes of schizophrenia are unknown, but a complex interaction of genetic and environmental risk factors are involved, causing abnormalities in both structure and function in the brain. It has been demonstrated that cortical structure puts profound constraints on cortical activity patterns: e.g. a synapse cannot form if two neuronal structures are too far apart (e.g. Stepanyants & Chklovskii, 2005). The brain can be considered as a complex network that shows small-world properties and might be scale free (Sporns & Honey, 2006). I propose to study changes of cortical structure and in structural magnetic resonance imaging (MRI) scans from the Edinburgh High Risk Study of Schizophrenia (EHRS), while keeping in mind that structural connectivity can be related to functional connectivity (Honey et al., 2007). During the EHRS a large cohort of people at high genetic risk were scanned at several points in time. To gain more understanding about the development of schizophrenia it is proposed to analyse cortical thickness and folding patterns (gyrification) in the EHRS cohort and compare the results with healthy controls. It is hypothesised that over time people at high risk of schizophrenia will show different changes in cortical structure and its small-world properties than healthy controls. The second part of this project aims to simulate how differences in cortical structure might predict differences in functional connectivity patterns. It is proposed to construct a Hopfield network with morphologically realistic neurons that need to balance activation in their network according to a homeostatic mechanism. Additionally a simulation model will be constructed to study whether changes at microcircuit level might result in changes at the macroscopic level of cortical thickness and gyrification.

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Related Publications and Presentations

  • Betty M. Tijms, Peggy Series, David Willshaw, and Stephen Lawrie, “Similarity based extraction of individual networks from grey matter MRI scans.”, Cerebral Cortex, 2011.

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