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=Volume 18 - 2024

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 a 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 the 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.