Dynamic structure for the Virtual Connectome
The Virtual Brain (TVB) is a "framework for the simulation of the dynamics of large-scale brain networks with biologically realistic connectivity". Tractography data can be combined with a variety of neural mass models in order to predict experimental and clinical observables such as local field potential, EEG and fMRI measures. TVB is composed of a suite of tools primarily written in Python for connectivity analysis, generation and visualization for individual machines or clusters.
To investigate computationally intensive dynamic models of structural and functional connectivity, we are extending TVB’s parameter sweeping capabilities and performance of specific models to supercomputing scales. This work is part of our “VirtualConnectome” collaboration with the Brain Modes research group led by Petra Ritter at Charité in Berlin and the Computational Cognitive Neuroscience Laboratory led by Olaf Sporns at Indiana University.
Our Contributions
Our work at the SimLab Neuroscience involves both the methodological improvement of software at scale, and some of the mathematical underpinnings for theoretical modeling as well as integration of hybrid multiscale models. At the software engineering level, we contribute to performance optimization, numerical analysis, parallelization and the theory of high-dimensional parameter search, in addition to guidance on HPC usage and integration with other available software tools.
Acknowledgments
This work is supported by the Hemholtz Portfolio Theme “Supercomputing and Modeling for the Human Brain” and a Germany/USA Collaborative Research in Computational Neuroscience grant from the BMBF and NIH.