Published February 10, 2022 | Version v1.0
Dataset Open

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

  • 1. Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Switzerland

Description

This dataset includes the data underlying the conclusions made in the scientific article:
"In-vivo estimation of axonal morphology from MRI and EEG data"
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:
    •  the MRI g-ratio sampled along the visual transcallosal white matter tract
    •  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:
    •  the axonal radius distribution, P(r)
    •  the g-ratio dependence on the radius, g(r)

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CONTENT:

This package includes data for all the 14 subjects used in the corresponding scientific article:
    •  G-ratio values sampled along the transcallosal visual tract 
            double vector (# MRI_gratio samples x 1): G_ratio_samples.mat
    •  Length of the transcallosal visual tract 
            double: Tract_length.mat
    •  Current source densities (pA.m) of each trial, brain vertice and time 
        point for the left brain visual cortex 
            double 3 matrix (#trials x #vertices x #timepoints): Source_reconstruction_left_brain_V1V2.mat 
    •  Current source densities (pA.m) of each trial, brain vertice and time 
        point for the right brain visual cortex 
            double 3 matrix (#trials x #vertices x #timepoints): Source_reconstruction_right_brain_V1V2.mat 
    •  Vector of the time sample of the EEG epochs
            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.

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AUTHORS:

Author: Rita Oliveira
PIs: Marzia De Lucia, Antoine Lutti

Laboratory for Neuroimaging Research

Lausanne University Hospital & University of Lausanne, Lausanne, Switzerland

Copyright (C) 2022 Laboratory for Neuroimaging Research

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AxonalMorphologyProject_Data.zip

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Additional details

Funding

Advanced quantitative MRI biomarkers of Parkinson's Disease - towards in-vivo histology 320030_184784
Swiss National Science Foundation
Detection of unexpected events as driven by heartbeat signals in human sleep CRSK-3_196194
Swiss National Science Foundation