Elucidating risk for mental disorders
Biological systems are complex and noisy. Understanding such systems requires expertise in interdisciplinary fields such as mathematics, engineering, neuroscience and psychiatry. Under the aegis of the IAFBG (Integration of analyses among fMRI, biophysical models and genetic data) project, scientists worked on integrating genetic and fMRI data with mathematical models with significant success. A major issue with mental disorders is accurate diagnosis, particularly when symptoms are similar (schizophrenia and bipolar disorder, for instance). Researchers came up with a fresh statistical approach to identify common characteristics. In addition, they came up with two potential biomarkers to detect epilepsy. Such methods could contribute to a biochemical index database for the trans-diagnosis of mental disorders. Scientists developed a toolbox for spatiotemporal Granger causality analysis, which is available online.This tool could prove useful in detecting abnormal information flow in mental disorder patients by monitoring information transfer using brain imaging data. Using novel models for emergent neural bursting behaviours, IAFBG identified the mechanism involved in regulating synchronisation propagation via oxytocin and inhibition neurotransmitters. These findings could be used to analyse response to hormone and glutamate application during treatment of mental disorders. They also developed methods to analyse collected dynamical behaviours in networks of coupled dynamical systems such as neuronal networks and learning systems. Researchers exploited fMRI and genetic data to establish an innovative approach. This method associated DISC-1 gene single nucleotide polymorphisms and schizophrenia in the precuneus (a key brain region). This framework is already being employed by other collaborating research groups in China and the United Kingdom. Using fMRI data and computational modelling, IAFBG characterised brain ageing with functional entropy as a measure to quantify excitatory neurons and excitatory conductance. The computational tool itself could be used to detect pathological variations in neurons and neuronal transmitters. Besides knowledge transfer through paper publications and conferences, project activities have facilitated further collaborations between China and Europe. Clinical application of tools such as fMRI analysis for diagnosis and inherited risk identification for mental disorders could enhance diagnostic and therapeutic efficacy. This in turn should improve patient prognosis and quality of life.
