%0 Journal Article
%T CiftiStorm pipeline: facilitating reproducible EEG/MEG source connectomics.
%A Areces-Gonzalez A
%A Paz-Linares D
%A Riaz U
%A Wang Y
%A Li M
%A Razzaq FA
%A Bosch-Bayard JF
%A Gonzalez-Moreira E
%A  
%A  
%A  
%A Ontivero-Ortega M
%A Galan-Garcia L
%A Martínez-Montes E
%A Minati L
%A Valdes-Sosa MJ
%A Bringas-Vega ML
%A Valdes-Sosa PA
%J Front Neurosci
%V 18
%N 0
%D 2024
%M 38680452
%F 5.152
%R 10.3389/fnins.2024.1237245
%X We present CiftiStorm, an electrophysiological source imaging (ESI) pipeline incorporating recently developed methods to improve forward and inverse solutions. The CiftiStorm pipeline produces Human Connectome Project (HCP) and megconnectome-compliant outputs from dataset inputs with varying degrees of spatial resolution. The input data can range from low-sensor-density electroencephalogram (EEG) or magnetoencephalogram (MEG) recordings without structural magnetic resonance imaging (sMRI) to high-density EEG/MEG recordings with an HCP multimodal sMRI compliant protocol. CiftiStorm introduces a numerical quality control of the lead field and geometrical corrections to the head and source models for forward modeling. For the inverse modeling, we present a Bayesian estimation of the cross-spectrum of sources based on multiple priors. We facilitate ESI in the T1w/FSAverage32k high-resolution space obtained from individual sMRI. We validate this feature by comparing CiftiStorm outputs for EEG and MRI data from the Cuban Human Brain Mapping Project (CHBMP) acquired with technologies a decade before the HCP MEG and MRI standardized dataset.