Dataset In-vivo estimation of axonal morphology from MRI and EEG data

This dataset includes the data underlying the conclusions made in the scientific article:<br> "In-vivo estimation of axonal morphology from MRI and EEG data"<br> Rita Oliveira, Andria Pelentritou, Giulia Di Domenicantonio, Marzia De Lucia, Antoine Lutti https://www.frontiersin.org/articles/10.3389/fnins.2022.874023 The main objective is to use data collected in-vivo in humans to estimate microscopic morphologic features of the white matter tracts. The in-vivo data estimated along a white matter tract of interest includes:<br> • the MRI g-ratio sampled along the visual transcallosal white matter tract<br> • a measure of conduction velocity estimated from an EEG measure of interhemispheric transfer time (IHTT) The microscopic morphologic features of white matter we estimate are:<br> • the axonal radius distribution, P(r)<br> • the g-ratio dependence on the radius, g(r) -------------------------------------------------------------------------<br> CONTENT: This package includes data for all the 14 subjects used in the corresponding scientific article:<br> • G-ratio values sampled along the transcallosal visual tract <br> double vector (# MRI_gratio samples x 1): G_ratio_samples.mat<br> • Length of the transcallosal visual tract <br> double: Tract_length.mat<br> • Current source densities (pA.m) of each trial, brain vertice and time <br> point for the left brain visual cortex <br> double 3 matrix (#trials x #vertices x #timepoints): Source_reconstruction_left_brain_V1V2.mat <br> • Current source densities (pA.m) of each trial, brain vertice and time <br> point for the right brain visual cortex <br> double 3 matrix (#trials x #vertices x #timepoints): Source_reconstruction_right_brain_V1V2.mat <br> • Vector of the time sample of the EEG epochs<br> double vector (1 x #time points): time_vec.mat The codes used in the analysis of this data are available on our online repository: https://github.com/LREN-physics/AxonalMorphology. -------------------------------------------------------------------------<br> AUTHORS: Author: Rita Oliveira<br> PIs: Marzia De Lucia, Antoine Lutti Laboratory for Neuroimaging Research Lausanne University Hospital &amp; University of Lausanne, Lausanne, Switzerland Copyright (C) 2022 Laboratory for Neuroimaging Research