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Research topics

Neuroscience - Visual Computing, Data Science and Big Data

The brain is similar in complexity to the universe. Its exploration is part of neuroscience. The most up-to-date imaging methods and progress in the development of genetic tools allow ever deeper insights into the functioning of nerve cells, neuronal networks and the brain as a whole. New technologies enable rapid generation of very large volumes of data, therefore causing a shift in neuroscience methods to ever-increasing data-driven research. One of the greatest challenges in everyday research is now often the consolidation and straightforward access to this data in their overall context – because every piece of data only gives an insight into a small partial aspect of the brain.

For more than a decade now, in cooperation with renowned international researchers and institutes, VRVis has been developing both data science methods to carry out computer-aided analysis of neuroscientific data and large data platforms to manage, integrate, visualise and particularly to mine large collections of neuroscientific image and network data and their molecular context.

Through enabling targeted data analysis in this way, expensive experiments can now be better planned, helping to accelerate research.

Reference partner

  • Science: Austrian Bioimaging, Institute of Molecular Pathology Vienna (IMP), University Leipzig, University Freiburg, RWTH Aachen, HHMI, CNRS, Stanford University, TU Wien
  • Industry and infrastructures: Boehringer-Ingelheim, TEFOR

CONTACT

Dipl.-Math.in Dr.in Katja Bühler

science(at)vrvis.at+43 1 908 98 92

Our portfolio

Big data management and mining

A researcher points with a pen to a screen where a brain data platform can be seen.
BrainTrawler is a web-based visual analysis tool that allows real-time access to very large amounts of data. BrainTrawler is designed for the usual neuroscientific workflows and makes it possible to examine the data of certain genes and the connection system within the brain in certain brain regions, for example to understand the underlying dynamics of behavioral patterns.

Since 2008, VRVis has been developing complete platforms for web-based management of the visualisation, integration and mining of brain data. The USP of the system is the very high-performing spatial indexing methods, which permit intuitive and almost real-time browsing through the content of tens of thousands of 3D data sets for similar or overlapping substructures. Our developments are based on our modular Brain* framework.

Our systems are now installed in numerous institutions and offer (partially public) access to large collections of data. The BrainBase system is currently hosting collections of data on the larval and adult brain of Drosophila melanogaster and the zebrafish. (See for example larvalbrain.org, fruitfly.tefor.net, zebrafish.tefor.net).

Together with neuroscientists at the Haubensak Lab and Boehringer-Ingelheim, we are developing BrainTrawler for collections of data on the brain of mice and humans. Using spatial indexing, we are able to analyse gene expression data and complex brain networks in real time. Classifying the data to hierarchically organised anatomical structures make it possible to search at different anatomical levels. Together with intuitive network visualisation, iterative visual queries and quantitative information, BrainTrawler enables multimodal networks to be genetically dissected at local/global level within the spatial context.

Selection of publications

Data visualisation

Visualization pipeline showing how 3D brain network data is analyzed.
The VRVis paper "Spatial-data-driven layouting for brain network visualization" presents the innovative spatial-data-driven approach possible with Brain* for better analysis of 3D brain networks in 2D node-link diagrams.

The visualisation of neuroscientific data for publications and also for interactive data exploration within the context of the Brain* system is an important research area of VRVis. For example, several publications focussed particularly on the visualisation and interactive analysis of brain network data, as well as the implementation of very high-performance web-based 2D and 3D visualisation of brain data (see for example larvalbrain.org).

Selection of publications

Analysis of complex and heterogeneous data from behavioural studies and neuron recordings

The sketch of a brain is shown in light grey and the neuronal loops that make up the gut feeling are visualized in colourful cross-connections.
The neuronal basis of the gut feeling: Neuronal loop between the amygdala (black), the basal forebrain (yellow) and the insular cortex (cyan).

We develop and explore tailor-made data science and visual analytics solutions to analyse the often very demanding, heterogeneous data from behavioural and pharmacological experiments. There is a wide variety of data types: In addition to recordings of neuronal activities, we also apply our methods to data which has been extracted from video recordings of behavioural studies. 

In addition to “conventional” methods of machine and deep learning, such as classifications and cluster analyses, we also use methods from information theory or psychophysical models.

Selected publication

Computational Neuroscience

Three-stage visualization from the VRVis publication on Molecular Archaeology.
The paper on the evolutionary role of the human brain in language and strategic thinking was published in the journal Cell Reports in 2022

Computational Neuroscience gains new insights based on existing data and mathematical methods. In cooperation with various partners we developed new approaches to e.g. calculate new functional brain regions fusing tens of thousands of image data sets of neurons, or to reconstruct functional partial networks which correlate with certain genes. Currently we attempt to map the origin of human cognitive features by combining evolutionary genomics with functional neuroanatomy to recreate the neuro-genetical development of human brain functions more comprehensively and in a more direct way.

It is our goal to enhance the performance of complex calculations on extremely big data sets. For this purpose, we research and develop highly parallelized solutions and use selected available high-performance computing infrastructure.

    Selection of publications

    References

    Virtual Reality for Life Sciences

    The tools of Extended Reality – Augmented Reality (AR) and Virtual Reality (VR) – are being used more and more often in the Life Sciences. Digital Realities help enhance scientific experiments, trainings and procedures by creating cheap and controllable immersive environments true to reality.

    For the department of Neurobiology at the University of Vienna VRVis created a setup for the research into the reactions of hunting spiders. The spider moves on a ball of about one meter in diameter inside a projected virtual environment. A camera picks up on the movements and the ball is rotated accordingly, so that the spider always remains in the center of the setup. In this manner, various visual stimulations can be produced in correlation with spider movements, even ones, that would not be possible in a purely physical setup.

    References

    Our projects

    Awards