-
Notifications
You must be signed in to change notification settings - Fork 2
Home
Tool for MEEG data processing based on Brain Connectivity Variable Resolution Tomographic Analysis (BC-VARETA) Model.
Electrophysiological Source Imaging (ESI) is the technical term encompassing the type of reconstruction or inverse methods for the functional images of the brain or body defined as a current source profile causing their remote observations as a peripheral electromagnetic field profile acquired by sensors. ESI resolution in time and frequency is ideal and promises a mean to elucidate in these domains the intricate mechanisms that govern brain function during a resting state or task. inverse solutions for the corresponding inverse problem of electromagnetism ESI achieving a group of imaging methods with the aim of uncovering the mechanisms underpinning brain function with appropriate temporal and spectral resolution. and time: Brain Connectivity Variable Resolution Electromagnetic Tomographic Analysis (BC-VARETA). BC-VARETA is meant to distribute our recently advances developed methods on the third generation of nonlinear methods for MEEG Time Series analysis. In the state of the art of MEEG analysis, the methodology underlying our tool (BC-VARETA) brings out several assets. First: Constitutes a truly Bayesian Identification approach of Linear Dynamical Systems in the Frequency Domain, grounded in more consistent models (third generation) for the joint nonlinear estimation of MEEG Sources Activity and Connectivity. Second: Achieves Super-Resolution, through the iterative solution of a Sparse Hermitian Sources Graphical Model that underlies the Connectivity Target Function. Third: Tackles efficiently in High Dimensional and Complex sets the estimation of connectivity, those constituting technical issues that challenge current MEEG source analysis methods. Fourth: Incorporates priors at the connectivity level by penalizing the groups of variables, corresponding to the Gray Matter anatomical segmentation, and including a probability mask of the anatomically plausible connections, given by synaptic transmission in the short-range (spatially invariant empirical Kernel of the connections strength decay with distance) and long-range (White Matter tracks connectivity strength from Diffusion Tensor Imaging). Along with the implementation of our method, we include in this toolbox a benchmark for the validation of MEEG source analysis methods, that would serve for the evaluation of sophisticated methodologies (third generation). It incorporates two elements. First: A realistic simulation framework, for the generation of MEEG synthetic data, given an underlying source connectivity structure. Second: Sensitive quality measures that allow for a reliable evaluation of the source activity and connectivity reconstruction performance, based on the Spatial Dispersion and Earth Movers’ Distance, in both source and connectivity space.
- the README file for the current release
- the [FAQ] and
- the [Installation and Usage Instructions] guide