They are called BigBrain, Julich-Brain or EBRAINS – laypeople may quickly lose track. All these projects, however, link together for one common goal: decoding the brain. HIBALL, a German-Canadian research collaboration, now adds another piece to the puzzle.
When, in 2003, together with her Canadian colleague Prof. Alan Evans, the Jülich neuroscientist Prof. Katrin Amunts decided to make thousands of histological sections of a human brain, to stain and digitise them, it was completely unclear whether it would ever be possible to reconstruct this brain on the computer. At that time, there were no technical possibilities to cope with the huge amount of data.
The researchers did not get discouraged, however, and tackled it – successfully: BigBrain was published in 2013. Today, scientists use the 20-micrometre model as a reference brain. It offers unique neuroanatomical insights and the opportunity to test hypotheses. The research collaboration HIBALL, Helmholtz International BigBrain Analytics & Learning Laboratory, goes one step further: it aims to develop a brain model with an accuracy of one micrometre, that is, one thousandth of a millimetre. This is one of several projects being jointly tackled in HIBALL.
The expert on the atlas of the brain is Katrin Amunts. In HIBALL, the Jülich neuroscientist and Scientific Research Director of the Human Brain Project wants to bring together neuroinspired AI and brain research.
In the collaboration, more than 40 scientists are working on a brain atlas at the level of cellular resolution. They combine artificial intelligence, supercomputing and neuroscience. The results from HIBALL will be incorporated into the Human Brain Project (HBP), the European research flagship on the human brain; Katrin Amunts has been Scientific Director of the HBP since 2016.
In the USA and Canada, “highball” was also a term for a signal for trains rushing through a station at full speed. Is it a metaphor for the German-Canadian brain research cooperation, which is now officially gathering momentum?
Yes, we did indeed like this picture, and it played a role in the naming of the project. In a way, it stands for “let’s go ahead – at full speed”.
You’ve been working successfully with your Canadian colleagues – with Prof. Alan Evans in particular – since the 1990s. What makes the International Lab HIBALL so special for you?
The research cooperation takes collaboration to a new level, one that goes beyond the Jülich-Montreal bilateral axis. We now also have partners from AI and supercomputing on board to further develop the BigBrain brain model. Eventually, a reference brain is to be created which will form the basis for many other questions – for example in medicine.
The resolution in the BigBrain model is 20 micrometres. At HIBALL, you now want to create a brain model with an accuracy of one thousandth of a millimetre. Why?
At 20 micrometres, you might see most of the cells. However, there are also cells that are only ten micrometres in size, and these have previously remained “blurred”. Therefore, within each tissue section, we want to go to the level of one micrometre. The goal will be a spatial resolution at 1 x 1 x 1 micrometre. Only then can the cells with their different forms and extensions be seen and it can be understood how they are arranged in the brain. On this basis, brain functions and cognitive performance can be associated.
This extremely high resolution goes hand in hand with gigantic amounts of data. How do you intend to cope with these masses?
It’s a great challenge. At HIBALL, we talk about several petabytes. This can only be achieved by using completely new analytical methods – for example those that work with deep neural networks or machine learning – modern memory and communication technologies, and powerful computers. In addition, we want to analyse networks of nerve cells in the human brain and develop network models in order to deduce how the function of artificial neural networks can be improved.
So it’s a win-win situation for neuroscientists and AI experts?
Yes. On the one hand, AI and computing researchers benefit from BigBrain as well as the related knowledge and models of neural networks. On the other hand, we neuroscientists benefit from AI and computing. One thing is clear: we can no longer calculate and analyse the huge amount of data without the modern methods.
Is AI the key to understanding the entire brain one day, then?
AI is certainly one of the keys. At HIBALL, we use machine and deep learning methods, which both count among AI. Without them, we could not tackle such an ambitious project. That’s why we are pleased to have CIFAR as a partner, the research organization that is leading the Pan-Canadian Artificial Intelligence strategy. With MILA in Montreal, we got one of the world’s leading centres in the field of deep learning on board. Its scientific director, Yoshua Bengio, is one of the pioneers in this field.
Do you need deep learning methods in your everyday life as a neuroscientist?
Today, the amount of data in one single image and the complexity of the information are often so enormous that no scientist can analyse them “manually”. Automated and robust methods are needed to process and analyse these large image quantities. In many areas, deep-learning methods are increasingly supplementing classical image analysis methods. We neuroscientists see that artificial neural networks are extremely helpful in recognising certain patterns or in calculating three-dimensional brain models. Due to the artificial neural networks, the tools to solve such methodologically difficult questions have changed considerably in recent years.
What role does the supercomputing expertise from Jülich play in this context?
The combination of supercomputing and the deep learning methods at both Jülich and Montreal is particularly promising. With the volume of our brain data, we quickly reach capacity limits – so you need supercomputers, a lot of storage space and experts on site. This is what Jülich offers.
Plus: at HIBALL, we want to exchange large amounts of data between Canada and Germany – but there are a few thousand kilometres between them. For this reason, we want to create a common platform that is suitable for this exchange. There’s a lot of technological know-how in it, which the colleagues from the JSC bring with them. Together with their Canadian colleagues, they are laying the groundwork for cooperation and creating a platform that will connect the world: in the future, scientists from all countries are to use the HIBALL platform to calculate and research on the same data together, to exchange, download or upload it.
Today, the BigBrain model and the 3D maps are a prime example of shared big data. They can now be clicked on, rotated, zoomed in and marvelled at by anyone on the Internet. Is something similar to emerge from HIBALL in the end?
In principle, yes. When it comes to the 20-micrometre BigBrain model, we’re talking about one terabyte of data – even that’s not something that can “just quickly be downloaded”. That’s why we have developed web-based tools in which only the data you are looking at has to be transferred. This is similar to what has already proven successful in other areas. If we go to the 1-micrometre level, it’s going to be even more challenging. So we need to develop methods that allow data to be processed without having to transport it.
Neuroscientists, computer scientists, mathematicians – how do you even manage to speak the same “language” with your colleagues from AI and supercomputing?
This has never been easy, really, but interdisciplinary work is the essential basis for success. I think that this successful work across disciplines is indeed one of the greatest achievements of recent years. We also profit from this at HIBALL. We have common visions, there is a lively exchange – not only on paper. We live this cooperation. I myself might grab my mobile phone and just call!
At HIBALL, however, we also want to ensure that the next two generations of researchers will be qualified at the border between neuroscience and computing. We therefore support the transatlantic exchange of researchers at all career levels at HIBALL.
What is the significance that HIBALL has for Forschungszentrum Jülich?
The lab shows that innovative research is being conducted at Jülich across national borders in a particularly rapidly developing field of research. The Helmholtz Association funds only a few international labs – only three in 2018. The cooperation is also an important contribution to Forschungszentrum Jülich’s strategy, in which information and data sciences play a central role. With HIBALL and artificial intelligence, neuroscience has now set another strong priority.
What role does HIBALL play with regard to the European flagship initiative, the Human Brain Project, of which you are the scientific director?
HIBALL complements the development in the Human Brain Project, but also the Canadian initiative “Healthy Brains for Healthy Lives”. With these partnerships, HIBALL can have a far greater impact than if it acted alone, and the partner institutions also benefit from the strengthening at the interface between spatial high-resolution brain models, computing and AI. It’s of great advantage if one can bring about that many small and large cogs interact and develop a great dynamic. HIBALL will benefit the two major initiatives. We’re currently seeing how neuroscience is benefiting from the revolution in information technologies. HIBALL is to provide important impulses which will have an impact on neuroscience, medicine, and also on technology development and society.
A personal final question: you were born in 1962. Back then, during your studies, would you have thought that one day you would be able to “zoom in” on the world of the brain?
There has indeed been an incredible change since my student days, when I was already interested in image analysis to better understand the architecture of nerve tissue and its functions. At that time, we had an image analysis system from Leitz at the institute that made it possible to quantify and statistically describe the architecture of nerve cells. Back then, this seemed to me the right way to understand the organising principles of the brain, and this research shaped my further path.
When we started making histological sections for BigBrain in 2003, we didn’t have a concrete plan as to how exactly we could digitise the 7,404 images and reconstruct them in three dimensions. There was an infinite number of single problems that had not yet been solved. So we simply trusted that we would be able to create the technical conditions one day. And that was right! The cooperation with our partners in Montreal, with Alan Evans and his team, has led to a breakthrough in this.
The interview was held by Katja Lüers
In the HIBALL research project, experts from Germany and Canada want to develop a three-dimensional brain atlas with an accuracy of one micrometre. 40 scientists dovetail artificial intelligence, supercomputing and neuroscience. HIBALL links the European Human Brain Project with the Canadian “Healthy Brains for Healthy Lives” initiative.
The Human Brain Project (HBP) is one of the EU’s research flagships. In the project, researchers from 131 institutions in Europe are working to understand the structure and function of the brain. To this end, they combine expertise from neuroscience with digital techniques, develop new methods and set up a freely available digital infrastructure (EBRAINS). The HBP also incorporates data from the Julich-Brain brain atlas and the BigBrain reference brain.
Julich-Brain is the name of the first 3D atlas of the human brain that maps the variability of brain structure with microscopic resolution. Researchers digitised over 24,000 brain slices, assembled them in 3D and mapped them. As part of the EBRAINS infrastructure, the atlas serves to link data on function with the structure of the brain.
BigBrain is a 3D reconstruction of a single human brain from over 7,404 individual tissue slices with a resolution of 20 micrometres. With the model, it is possible to see and understand the complicated structure of the brain on the microscopic level in all three planes of space. BigBrain can also be used via EBRAINS.
The HBP infrastructure EBRAINS provides researchers from all over the world with access to data and tools for analysing and simulating the brain. For example, they can use the maps of the Julich-Brain atlas or employ AI-based methods to better understand the division of responsibilities between brain areas. EBRAINS will also be available after the end of the ten-year term of the HBP.
Photos: Mareen Fischinger, Amunts, Schiffer, Kiwitz, Dickscheid et al. (links: Wagstyl et al.), Orawan Pattarawimonchai/shutterstock.com