One of the defining features of INS is our dedication to conducting true translational research. It is our intention to traverse the ‘bench to bedside’ model, from start to finish. In order to achieve this, we often encounter instances where we need to actually develop the necessary tools to answer the questions at hand. Our capacity to build the technological steppingstones to ensure our foundational research translates to actual applications, primes us to lead scientific discovery to innovative and impactful new heights.
THE VIRTUAL BRAIN
The Virtual Brain (TVB) is an open-source neuroinformatics platform, which combines a large-scale brain network model and neuroimaging modality (s/EEG/MEG & fMRI) simulator, optimized for realistic brain connectivity & geometry and neural mass models, with a framework for constructing, visualizing and analyzing brain network models in a collaborative, project oriented user interface. Although TVB is lightweight enough to download and run on your local machine, TVB is also backed by dedicated high-performance computing hardware, complemented by clustered nodes for virtual machines running public-facing web services, located in the medical faculty of the La Timone campus. Our HPC enables users to exponentially grow their simulations both in quantity and processing speed.
AnyWave is a multi-platform software for visualizing and processing EEG/SEEG/MEG/XMG data from any EEG or MEG acquisition systems. AnyWave is modular and can load additionnal plug-ins to enhance its features and capabilities.
The development of AnyWave is supported by INSERM and Aix-Marseille University. The main developer is Bruno Colombet , member of the DynaMap research group at INS.
MULtiple connectivity ANalysis (MULAN) is a MATLAB toolbox to help researchers evaluate connectivity analysis methods in a systematic way. Prior to applying specific methods to a given dataset, MULAN can be used to generate relevant simulated signals, identify valid parameter ranges for the methods, and evaluate their performance and robustness against underlying features. Lastly, new methods can easily be added and tested. Available from Github.
Contact Huifang Wang for further information.