Mapping the structural and functional organisation of the human brain via in vivo neuroimaging and complex network analysis
Lead Research Organisation:
University of Manchester
Department Name: School of Biological Sciences
Abstract
Project Description: (maximum of 4,000 characters)
Please make sure this description clearly indicates how the project sits within the BBSRC remit, how it enables new ways of working and how it aligns with the DTP themes (World Class Underpinning Biosciences, Industrial Biotechnology and Bioenergy or Agriculture and Food Security). If you have been awarded an in vivo skills supplement, please outline the in vivo skills the student will learn during the project.
The performance of the wide range of cognitive tasks including remembering, speaking, deciding and planning depends upon the interaction of complex networks of widely distributed brain regions. In order to increase our understanding of how the brain performs these tasks, it is important to understand not only what brain regions are involved but how these regions communicate with each other, that is, their connectivity. Network connectivity has recently been explored by two advanced techniques, one which examines the anatomical white matter pathways along which information flows (structural connectivity), and another which examines the brain regions which function together to perform components of a task (functional connectivity). However, how the different types of connectivity relate to each other is complex, and to fully understand how cognitive processes occur, an understanding of this relationship, and how the brain's anatomical structure influences functioning is crucial. Importantly, this knowledge is not only essential in understanding normal performance, but also its change across the lifespan, and observed impairments in different neurological diseases.
In order to improve our understanding of the organization and function of normal and impaired brain networks, researchers have begun to model the brain using complex network analysis (CNA). CNA is a statistical approach which can be used to provide measures to characterise different properties of particular brain networks, such as the efficiency with which information can be communicated throughout the network, and how resilient the network is to damage.
The current project aims to use CNA to map and describe the organisation of the brain's cognitive networks, and understand the relationship between behavioural performance and network architecture and functioning. In order to achieve this, CNA will be used to explore the relationship between structural and functional connectivity data, and their impact on behavioural performance. The project will utilise the wide range of behavioural and neuroimaging data available within the Division of Neuroscience and Experimental Psychology. This includes a range of sophisticated imaging methodologies, including diffusion imaging (capable of identifying the brain's structural connectivity), and functional MRI (capable of identifying functional connectivity), across a range of populations including both healthy individuals and those with a range of neurological impairment.
The current project exploits new ways of combining of innovative imaging and quantitative computational/analysis technologies for analysing in vivo measures of structural and functional brain connectivity. In doing so, it will develop and provide training in the core bioscience skills of mathematics and data analysis, aligning with the theme of World Class Underpinning Bioscience. Higher cortical brain function requires the coordinated action of a network of brain regions. A step change in our understanding of the relationship between anatomical structure and brain function requires the development and application of techniques that probe brain-wide network organisation and interactions. The proposed research will align and develop complex network analysis of structural and functional connectivity information to reveal the neural networks that underpin specialised, higher cortical functions. This novel underpinning technology can then be applied to a range of populations, enabling innovatio
Please make sure this description clearly indicates how the project sits within the BBSRC remit, how it enables new ways of working and how it aligns with the DTP themes (World Class Underpinning Biosciences, Industrial Biotechnology and Bioenergy or Agriculture and Food Security). If you have been awarded an in vivo skills supplement, please outline the in vivo skills the student will learn during the project.
The performance of the wide range of cognitive tasks including remembering, speaking, deciding and planning depends upon the interaction of complex networks of widely distributed brain regions. In order to increase our understanding of how the brain performs these tasks, it is important to understand not only what brain regions are involved but how these regions communicate with each other, that is, their connectivity. Network connectivity has recently been explored by two advanced techniques, one which examines the anatomical white matter pathways along which information flows (structural connectivity), and another which examines the brain regions which function together to perform components of a task (functional connectivity). However, how the different types of connectivity relate to each other is complex, and to fully understand how cognitive processes occur, an understanding of this relationship, and how the brain's anatomical structure influences functioning is crucial. Importantly, this knowledge is not only essential in understanding normal performance, but also its change across the lifespan, and observed impairments in different neurological diseases.
In order to improve our understanding of the organization and function of normal and impaired brain networks, researchers have begun to model the brain using complex network analysis (CNA). CNA is a statistical approach which can be used to provide measures to characterise different properties of particular brain networks, such as the efficiency with which information can be communicated throughout the network, and how resilient the network is to damage.
The current project aims to use CNA to map and describe the organisation of the brain's cognitive networks, and understand the relationship between behavioural performance and network architecture and functioning. In order to achieve this, CNA will be used to explore the relationship between structural and functional connectivity data, and their impact on behavioural performance. The project will utilise the wide range of behavioural and neuroimaging data available within the Division of Neuroscience and Experimental Psychology. This includes a range of sophisticated imaging methodologies, including diffusion imaging (capable of identifying the brain's structural connectivity), and functional MRI (capable of identifying functional connectivity), across a range of populations including both healthy individuals and those with a range of neurological impairment.
The current project exploits new ways of combining of innovative imaging and quantitative computational/analysis technologies for analysing in vivo measures of structural and functional brain connectivity. In doing so, it will develop and provide training in the core bioscience skills of mathematics and data analysis, aligning with the theme of World Class Underpinning Bioscience. Higher cortical brain function requires the coordinated action of a network of brain regions. A step change in our understanding of the relationship between anatomical structure and brain function requires the development and application of techniques that probe brain-wide network organisation and interactions. The proposed research will align and develop complex network analysis of structural and functional connectivity information to reveal the neural networks that underpin specialised, higher cortical functions. This novel underpinning technology can then be applied to a range of populations, enabling innovatio
Organisations
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| BB/M011208/1 | 01/10/2015 | 31/03/2024 | |||
| 2267801 | Studentship | BB/M011208/1 | 01/10/2019 | 30/09/2023 |