biostudies-literatureUnknown18Areces-Gonzalez AWe 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.Frontiers in neuroscience1237245https://www.ebi.ac.uk/biostudies/studies/S-EPMC11047451biostudies-literatureCiftiStorm pipeline: facilitating reproducible EEG/MEG source connectomics.PMC11047451Lifespan Brain Chart Consortium (LBCC)Valdes-Sosa PAMartinez-Montes ERiaz UGalan-Garcia LCuban Human Brain Mapping Project (CHBMP)Ontivero-Ortega MPaz-Linares DLi MGlobal Brain Consortium (GBC)Areces-Gonzalez ARazzaq FAGonzalez-Moreira EBringas-Vega MLMinati LWang YValdes-Sosa MJBosch-Bayard JFfalseCiftiStorm pipeline: facilitating reproducible EEG/MEG source connectomics.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.2024-01-01T00:00:00Z20242026-06-27T03:21:19.483Z2026-06-27T03:17:17.545ZS-EPMC110474513868045210.3389/fnins.2024.1237245