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Details of Grant 

EPSRC Reference: EP/K026992/1
Title: Modelling Human Brain Development
Principal Investigator: Kaiser, Professor M
Other Investigators:
Researcher Co-Investigators:
Project Partners:
SCTIMST University of Bonn University of Manchester, The
University of Nottingham
Department: Computing Sciences
Organisation: Newcastle University
Scheme: Standard Research
Starts: 23 September 2013 Ends: 22 September 2016 Value (£): 465,494
EPSRC Research Topic Classifications:
EPSRC Industrial Sector Classifications:
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Panel History:
Panel DatePanel NameOutcome
27 Feb 2013 EPSRC ICT Responsive Mode - Feb 2013 Announced
Summary on Grant Application Form
The neural network of the human brain is arguably the most complex biological pattern; however, the mechanisms forming such neural systems are unclear. Neural systems show structurally emergent properties in terms of their topology and functionally emergent properties concerning information processing. Structural properties are the rise of modular and hierarchical connectivity, of long-distance connections, and of highly connected nodes. Functional properties are the distribution and integration of information, the formation of specialized modules dealing with different tasks, and a rapid reaction time due to parallel processing.

Recent advances in neuroimaging, using diffusion tensor imaging, allow us to observe how the human brain network differs over ages ranging from the embryonic to the adult stage (age of 20 years). This project will analyse how the human brain network arises during development by combining data analysis with simulations of brain development. Objectives are to develop a simulation of human brain development, to analyse network features of human brains at different developmental stages, and to compare simulations with real data to discover the underlying mechanisms for brain network development. Simulations are crucial to study the role of different developmental parameters on the final brain network as well as on intermediate networks during development. Understanding how parameters lead to (adult) network features will help to evaluate the contribution of these parameters to healthy and pathological development. Once understanding the time course of development, we should also be able to predict the probabilities of future stages of development. This will be crucial for giving a prognosis for the progression of developmental diseases. In addition to understanding the formation of human cognitive systems, these results will inform the design and update of artificial information processing systems.

Identifying these key developmental mechanisms will greatly improve our understanding of emergence in biological systems. In addition, it might lead to several predictions about the rise of brain disorders such as schizophrenia, epilepsy, and autism that often originate during development and are linked to changes in hub organization. Beyond biological pattern formation, such 'algorithms' for human brain development could inform us how to build artificial intelligent systems. Rather than constructing artificial brains in a top-down manner, applying identified mechanisms for neural network development will allow the emergence of intelligent information processing systems leading to systems that are more adaptable. In summary, the formation of human brains is a fundamental question that touches on the emergence of natural and man-made information processing systems.

Key Findings
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