The Human Brain Project: an example of integrating knowledge

A new project aims at producing a model of the most complex organ known, the human brain.

The objective of the Human Brain Project (HBP) is to simulate the actual working of the brain, and one day having a compete simulation of the brain. It aims to collect, analyse and integrate vast quantities of information and knowledge from neuroscience and genetics. The project embodies many of the aims and challenges of the broader Digital Patient initiative. It requires integrating modelling methods on a hierarchical structure, simulating many interconnected physiological processes at once. It also faces the challenges of computational power to drive in-depth simulations, and the challenge of integrating knowledge from diverse sources and methodologies into a coherent knowledge base. 

The HBP is a European FET (Future and emerging technologies) flagship programme with 13 partners in 3 EU states. The project will move in stages, with the aim to model brain regions first, then systems, then eventually the whole brain. This will begin with much simpler animal brains before eventually paving the way for human brain simulation. It has the potential for furthering our knowledge and understanding of brain activity and disease.

Website: http://www.humanbrainproject.eu

Integrating Models: Linking medical imaging and physiological modelling

One of the pillars of the Digital Patient technologies will be that of ‘information blending, or fusion.’ We take a look at what this might mean in practise.

The ability to process, manipulate and combine health information already plays a big part in clinical practise, and this ability will increase enormously with Digital Patient technologies. Currently, medical imaging is routinely used to diagnose and understand pathologies. The raw imaging data provides useful information itself, but the use of image analysis technology allows us to extract far richer knowledge. Today, this includes image segmentation, allowing us to separate tissue types and visualise specific structures in 3D. Image registration can help us to map different datasets together, and extract useful combined information. 

Meanwhile, physiological models can provide predictive tools based on the underlying physiological and biological processes. These two technologies – imaging analysis and physiological modelling – can be combined in ways which can produce whole new types of information. For example, mechanical analysis such as Finite Element Analysis can be used to quantify mechanical stimuli in tissue using medical imaging as an input. This can then be mapped back to the medical image as a new ‘in silico’-generated image¹, showing patterns of stress in the tissue.

The integration of models and imaging extends to validation, for example when fluid flow models of cardiovascular fluid flow are compared to angiogram data. In future, this integration could go even further, with new technologies for information fusion. One interesting example is in multi-modal image registration. In this type of analysis, image information from two modalities (a chest MRI and mammography, for breast cancer imaging, for example) is combined in order to generate more useful information¹. A key problem is that the tissue shape may vary considerably for each modality – depending on the patient and tissue position. In this case, mechanical models of tissue deformation can help register the images by simulating the deformations the tissue undergoes under each modality. 

This use of models to enrich an existing information dataset has the potential to produce novel and valuable new types of information. 

¹http://www.biomedical-engineering-online.com/content/8/1/1

Clinical Working Groups

The clinical working groups will soon be assembled. These groups will provide vital end-user experience and views to the roadmap authors.

A major aim of this project from the beginning has been to bring end-users and other stakeholders into the roadmapping exercise. We see it as essential that the future Digital Patient is targeted at the needs of citizens, and is of assistance to healthcare professionals. To achieve this aim, we must get the opinions of clinicians on: 1) how Digital Patient technologies will/could be used by them in the clinic, and which areas are most promising; and 2) what are the challenges from an end-user point of view. 

The clinical working groups will be used to engage the clinical community. In these groups, practicing clinicians will work together on some core issues in the Digital Patient through online collaboration and conference calls. The groups will be built up initially from the clinicians already engaged with the project, i.e. those who have taken part through the first consultation meeting. As always, all other interested parties are welcome to join, by getting in contact with us (see our website www.digital-patient.net for details).

Our first objective for these groups is to present the key discussion points and issues raised at a major international research conference. The findings from the working groups will then fuel the roadmap writing process.