Final Report Summary - IAFBG (Integration of Analyses among fMRI, Biophysical Models and Genetic Data)
This project combines the three background topics: functional magnetic resonance imaging (fMRI), bioinformatics and biophysical mathematical models, and is related to the interdisciplinary fields among mathematics, engineering, neuroscience and psychiatry. It is to establish a framework composed of modelling, mathematical analysis and statistical inference and involve modelling and mathematical analysis to understand the underlying mechanisms due to the intrinsic complexity and noise of biological systems.
Let us list a few main results and their impacts in the following.
1. Causality in functional brain images. A new framework of Granger causality was proposed based on temporal-spatial optimisation, which provided a powerful and robust tool to probe the information transfer between brain regions from brain imaging data, and identify the abnormal information flow in mental disorder patients. A toolbox for spatio-temporal Granger causality analysis is developed and available on (“http://www.dcs.warwick.ac.uk/~feng/causality.html”).
2. Neuronal synchronization and rhythms. Novel models were proposed to analyse the emergent neural bursting behaviours, for instance synchronization, which are thought of importance in information coding and transfer in brain. By this way, we found the mechanisation how the oxytocin (a sort of hormone) and inhibition neurotransmitters regulate synchronization propagation. Our findings will provide the theoretic foreground of the application of hormone and glutamate in treatment of mental disorders.
3. Key brain region in the association of DISC-1 gene SNPs and schizophrenia. A novel framework of association study of genetic data and brain imaging data was proposed and employed to identify the key brain region, precuneus, in the association of the DISC-1 gene and schizophrenia. This approach is being used in a number of our collaborated research groups, including King College of London (UK), Nottingham (UK), Huaxi Hospital and Xiangya Hospital (China).
4. Network and rhythmic characteristics of mental disorders. Two biomarkers were proposed to distinguish the epilepsy patients and heathy control. A novel statistic approach was proposed to identify the common characteristic between two symptom-similar mental disorders, for instance, schizophrenia and bi-polar. Combined with the 2nd item, these methods and findings will help the build-up of biochemical index database for the trans-diagnosis of mental disorders, which is our current on-going work.
5. Aging phenomenon reflected in human brain images. An integration framework of a large-scale neuronal network and functional brain imaging data was developed. This approach provides a computational tool to discovery and infer the underlying variants of neuron and neuronal transmitters in mental disorders, according to the abnormality probed by brain imaging. Our findings help understand the aging of brain in terms of the number of excitatory neurons and excitatory conductance decreasing.
6. Network modelling and collective dynamics analysis. Several novel methods were developed to analyse collected dynamical behaviours in networks of coupled dynamical systems. These results and approach provides fundamental methodology for dynamic analyses of neuronal networks and learning systems.
The incoming researcher conducted knowledge transfer activities by publishing the research papers, visiting nine research groups in ERA and China, and attending seven conferences/workshop by giving talks. A net of collaboration is being built up by the activities of the incoming researcher, including Nottingham, Brunel, Bristol, Warwick and King College of London in UK, Siena University, Centre for Complex System at Florence and University of Naples Federico II in Italy, Max Planck Institute for maths in the sciences and PIK in Germany, Fudan University, Jinglin Hospital, Xiangya Hospital and Huaxi Hospital in China.
In addition, the outreach activities included a homepage: http://homepage.fudan.edu.cn/luwenlian/mciif/ of the project, a presentation entitled: “Computational Medicine” on the opening day (2013-6-22) of Warwick, a public speech entitled “Integration and Analyses among Data and Models: towards Understanding Mental Disorders” at PIK in Potsdam, November 11, 2014.
To sum up, the impact of this project is threefold. First, new theories and methods developed in this project will enhance the research in Europe. Second, the joint working and knowledge transfer carried out in this project between research groups in Europe and China will bond the collaboration relationship between ERA and China. Third, new findings and techniques on genetic-imaging help build up a measurable biochemical index system towards quantitative diagnosis of mental disorders, which, if being realised, may essentially change understanding and treatment of mental disorders. Besides the research institutes, the hospitals will benefit from the results of this project, including the software development of fMRI analysis for diagnosis and inherited risk identification, which are being undertaken by the hospital mentioned above. In the future, the on-launch Brain Project by the Chinese government will take the scopes of this project and our in-going and future research plans into its framework.
Let us list a few main results and their impacts in the following.
1. Causality in functional brain images. A new framework of Granger causality was proposed based on temporal-spatial optimisation, which provided a powerful and robust tool to probe the information transfer between brain regions from brain imaging data, and identify the abnormal information flow in mental disorder patients. A toolbox for spatio-temporal Granger causality analysis is developed and available on (“http://www.dcs.warwick.ac.uk/~feng/causality.html”).
2. Neuronal synchronization and rhythms. Novel models were proposed to analyse the emergent neural bursting behaviours, for instance synchronization, which are thought of importance in information coding and transfer in brain. By this way, we found the mechanisation how the oxytocin (a sort of hormone) and inhibition neurotransmitters regulate synchronization propagation. Our findings will provide the theoretic foreground of the application of hormone and glutamate in treatment of mental disorders.
3. Key brain region in the association of DISC-1 gene SNPs and schizophrenia. A novel framework of association study of genetic data and brain imaging data was proposed and employed to identify the key brain region, precuneus, in the association of the DISC-1 gene and schizophrenia. This approach is being used in a number of our collaborated research groups, including King College of London (UK), Nottingham (UK), Huaxi Hospital and Xiangya Hospital (China).
4. Network and rhythmic characteristics of mental disorders. Two biomarkers were proposed to distinguish the epilepsy patients and heathy control. A novel statistic approach was proposed to identify the common characteristic between two symptom-similar mental disorders, for instance, schizophrenia and bi-polar. Combined with the 2nd item, these methods and findings will help the build-up of biochemical index database for the trans-diagnosis of mental disorders, which is our current on-going work.
5. Aging phenomenon reflected in human brain images. An integration framework of a large-scale neuronal network and functional brain imaging data was developed. This approach provides a computational tool to discovery and infer the underlying variants of neuron and neuronal transmitters in mental disorders, according to the abnormality probed by brain imaging. Our findings help understand the aging of brain in terms of the number of excitatory neurons and excitatory conductance decreasing.
6. Network modelling and collective dynamics analysis. Several novel methods were developed to analyse collected dynamical behaviours in networks of coupled dynamical systems. These results and approach provides fundamental methodology for dynamic analyses of neuronal networks and learning systems.
The incoming researcher conducted knowledge transfer activities by publishing the research papers, visiting nine research groups in ERA and China, and attending seven conferences/workshop by giving talks. A net of collaboration is being built up by the activities of the incoming researcher, including Nottingham, Brunel, Bristol, Warwick and King College of London in UK, Siena University, Centre for Complex System at Florence and University of Naples Federico II in Italy, Max Planck Institute for maths in the sciences and PIK in Germany, Fudan University, Jinglin Hospital, Xiangya Hospital and Huaxi Hospital in China.
In addition, the outreach activities included a homepage: http://homepage.fudan.edu.cn/luwenlian/mciif/ of the project, a presentation entitled: “Computational Medicine” on the opening day (2013-6-22) of Warwick, a public speech entitled “Integration and Analyses among Data and Models: towards Understanding Mental Disorders” at PIK in Potsdam, November 11, 2014.
To sum up, the impact of this project is threefold. First, new theories and methods developed in this project will enhance the research in Europe. Second, the joint working and knowledge transfer carried out in this project between research groups in Europe and China will bond the collaboration relationship between ERA and China. Third, new findings and techniques on genetic-imaging help build up a measurable biochemical index system towards quantitative diagnosis of mental disorders, which, if being realised, may essentially change understanding and treatment of mental disorders. Besides the research institutes, the hospitals will benefit from the results of this project, including the software development of fMRI analysis for diagnosis and inherited risk identification, which are being undertaken by the hospital mentioned above. In the future, the on-launch Brain Project by the Chinese government will take the scopes of this project and our in-going and future research plans into its framework.