dpazlinares / bc-vareta-simpack

Hierarchical Hermitian Gaussian Graphical Model (HHGGM): Methodological approach to "caulk" the "Leakage Effect" in MEEG source activity and connectivity. HHGGM leverages two theoretical aspects. First: Joint estimation of source activity and connectivity as a frequency domain linear dynamical system identification approach. Second: Incorporating priors into the sources graphical model of the connectivity estimator. The claims of this theory are supported by a simulation framework, that uses realistic head models, diverse sources setup, biological/instrumentation noisy signals, and Inverse Crime evaluation. Also, by providing comparisons to state of the art methodologies.

BC-VARETA-simpack

Includes the routines of the Brain Connectivity Variable Resolution Tomographic Analysis (BC-VARETA), a Simulation Package for MEEG and other well established Methods for comparison purpose. BC-VARETA and the other Methods within the package extract the Source Activity and Connectivity given a single frequency component in the Fourier Transform Domain of an Individual MEEG Data. See the pdf file "Brief of Theory and Results" for an insight to this methodology.

References:

Paz-Linares, D., Gonzalez-Moreira, E., Martinez-Montes, E. and Valdes-Sosa, P.A., 2018. Note on the Estimation of Embedded Hermitian Gaussian Graphical Models for MEEG Source Activity and Connectivity Analysis in the Frequency Domain. Part I: Single Frequency Component and Subject. arXiv preprint arXiv:1810.01174. https://arxiv.org/abs/1810.01174

Paz-Linares, D., Gonzalez-Moreira, E., Martinez-Montes, E., Valdes-Hernandez, P.A., Bosch-Bayard, J., Bringas-Vega, M.L. and Valdes-Sosa, P.A., 2018. Caulking the Leakage Effect in MEEG Source Connectivity Analysis. arXiv preprint arXiv:1810.00786. https://arxiv.org/abs/1810.00786

First download the complementary file 'data.rar' from the link below, extract the contained subfolder and copy it along with the routines into a common folder

https://lstneuro-my.sharepoint.com/:u:/g/personal/deirel_paz_neuroinformatics-collaboratory_org/EVCqmmZ9d9dLrOTb37bpfrwB8yU3xAIk6AXJB7vzzkiz2g?e=Oa2Eqc

BC-VARETA-master:

• Main (execute this routine for demosntration): generates simulation of 4 cortical connected points and reproduces the results of BC-VARETA, sLORETA and LCMV.
• surfpatch_v1: creates parches around the cortical points for visualization of the connectivity in a reduced space
• bcvareta: executes BC-VARETA method
• bcvareta_initial_values: computes 'bcvareta' initialization
• screening_ssbl: extracts the posibly active generators as part of 'bcvareta_initial_values', using the Elastic Net Structured Sparse Bayesian Learning
• trascendent_term: nonlinear function for regularization parameters estimation within the function 'screening_ssbl'
• screening: applies a smoothing to the outputs of 'screening_ssbl'
• mkfilt_eloreta: computes eLORETA method
• mkfilt_lcmv: computes LCMV method
• data: folder containing leadfield, surfaces, colormaps, etc