The Virtual Brain (TVB) is a widely-used software library for connectome-based modelling of large-scale neural dynamics and neuroimaging data. To date, TVB models have mainly focused on brain dynamics as measured by fMRI, EEG, and LFPs. Functional Near Infrared Spectroscopy (FNIRS) is another noninvasive neuroimaging modality, which like fMRI measures haemodynamic signals reflecting neural activity, that has major potential in cognitive and clinical neuroscience.
The aim of this GSoC project will be to build out the modelling and analysis capacity of TVB for simulations of whole-head, high-resolution FNIRS signals, as well as concurrently recorded EEG. This shall include writing code implementing a temporal forward model for FNIRS-specific haemodynamic signals, a spatial forward model for optical sensor projection, and running simulations exploring dynamics of concurrent FNIRS-EEG activity. In terms of hardware, these development activities will be primarily focused on the Kernel Flow FNIRS+EEG system, that we will have access to and be using as part of the project.
Candidates should have experience with Python for scientific computing, and a strong interest in computational neuroscience and neuroimaging. Experience with one or more of the following is desirable: FNIRS/fMRI/EEG data analyses, neural mass modelling, numerical simulations and numerical optimization / model fitting problems in neuroscience or other domains. The project will provide excellent experience and training for students interested in pursuing research in human neuroimaging, theoretical/computational/cognitive/clinical neuroscience, and brain-computer interfaces.
Tags: Python, TVB, Computational Neuroscience, Neural mass modelling, Connectome, Biophysics, FNIRS, EEG, electrophysiology