AUTHOR=Areces-Gonzalez Ariosky , Paz-Linares Deirel , Riaz Usama , Wang Ying , Li Min , Razzaq Fuleah A. , Bosch-Bayard Jorge F. , Gonzalez-Moreira Eduardo , Lifespan Brain Chart Consortium (LBCC) , Global Brain Consortium (GBC) , Cuban Human Brain Mapping Project (CHBMP) , Ontivero-Ortega Marlis , Galan-Garcia Lidice , Martínez-Montes Eduardo , Minati Ludovico , Valdes-Sosa Mitchell J. , Bringas-Vega Maria L. , Valdes-Sosa Pedro A. TITLE=CiftiStorm pipeline: facilitating reproducible EEG/MEG source connectomics JOURNAL=Frontiers in Neuroscience VOLUME=18 YEAR=2024 URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2024.1237245 DOI=10.3389/fnins.2024.1237245 ISSN=1662-453X ABSTRACT=

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.