Cargando…
In vivo Estimation of Axonal Morphology From Magnetic Resonance Imaging and Electroencephalography Data
PURPOSE: We present a novel approach that allows the estimation of morphological features of axonal fibers from data acquired in vivo in humans. This approach allows the assessment of white matter microscopic properties non-invasively with improved specificity. THEORY: The proposed approach is based...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9070985/ https://www.ncbi.nlm.nih.gov/pubmed/35527816 http://dx.doi.org/10.3389/fnins.2022.874023 |
_version_ | 1784700751371567104 |
---|---|
author | Oliveira, Rita Pelentritou, Andria Di Domenicantonio, Giulia De Lucia, Marzia Lutti, Antoine |
author_facet | Oliveira, Rita Pelentritou, Andria Di Domenicantonio, Giulia De Lucia, Marzia Lutti, Antoine |
author_sort | Oliveira, Rita |
collection | PubMed |
description | PURPOSE: We present a novel approach that allows the estimation of morphological features of axonal fibers from data acquired in vivo in humans. This approach allows the assessment of white matter microscopic properties non-invasively with improved specificity. THEORY: The proposed approach is based on a biophysical model of Magnetic Resonance Imaging (MRI) data and of axonal conduction velocity estimates obtained with Electroencephalography (EEG). In a white matter tract of interest, these data depend on (1) the distribution of axonal radius [P(r)] and (2) the g-ratio of the individual axons that compose this tract [g(r)]. P(r) is assumed to follow a Gamma distribution with mode and scale parameters, M and θ, and g(r) is described by a power law with parameters α and β. METHODS: MRI and EEG data were recorded from 14 healthy volunteers. MRI data were collected with a 3T scanner. MRI-measured g-ratio maps were computed and sampled along the visual transcallosal tract. EEG data were recorded using a 128-lead system with a visual Poffenberg paradigm. The interhemispheric transfer time and axonal conduction velocity were computed from the EEG current density at the group level. Using the MRI and EEG measures and the proposed model, we estimated morphological properties of axons in the visual transcallosal tract. RESULTS: The estimated interhemispheric transfer time was 11.72 ± 2.87 ms, leading to an average conduction velocity across subjects of 13.22 ± 1.18 m/s. Out of the 4 free parameters of the proposed model, we estimated θ – the width of the right tail of the axonal radius distribution – and β – the scaling factor of the axonal g-ratio, a measure of fiber myelination. Across subjects, the parameter θ was 0.40 ± 0.07 μm and the parameter β was 0.67 ± 0.02 μm(−α). CONCLUSION: The estimates of axonal radius and myelination are consistent with histological findings, illustrating the feasibility of this approach. The proposed method allows the measurement of the distribution of axonal radius and myelination within a white matter tract, opening new avenues for the combined study of brain structure and function, and for in vivo histological studies of the human brain. |
format | Online Article Text |
id | pubmed-9070985 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90709852022-05-06 In vivo Estimation of Axonal Morphology From Magnetic Resonance Imaging and Electroencephalography Data Oliveira, Rita Pelentritou, Andria Di Domenicantonio, Giulia De Lucia, Marzia Lutti, Antoine Front Neurosci Neuroscience PURPOSE: We present a novel approach that allows the estimation of morphological features of axonal fibers from data acquired in vivo in humans. This approach allows the assessment of white matter microscopic properties non-invasively with improved specificity. THEORY: The proposed approach is based on a biophysical model of Magnetic Resonance Imaging (MRI) data and of axonal conduction velocity estimates obtained with Electroencephalography (EEG). In a white matter tract of interest, these data depend on (1) the distribution of axonal radius [P(r)] and (2) the g-ratio of the individual axons that compose this tract [g(r)]. P(r) is assumed to follow a Gamma distribution with mode and scale parameters, M and θ, and g(r) is described by a power law with parameters α and β. METHODS: MRI and EEG data were recorded from 14 healthy volunteers. MRI data were collected with a 3T scanner. MRI-measured g-ratio maps were computed and sampled along the visual transcallosal tract. EEG data were recorded using a 128-lead system with a visual Poffenberg paradigm. The interhemispheric transfer time and axonal conduction velocity were computed from the EEG current density at the group level. Using the MRI and EEG measures and the proposed model, we estimated morphological properties of axons in the visual transcallosal tract. RESULTS: The estimated interhemispheric transfer time was 11.72 ± 2.87 ms, leading to an average conduction velocity across subjects of 13.22 ± 1.18 m/s. Out of the 4 free parameters of the proposed model, we estimated θ – the width of the right tail of the axonal radius distribution – and β – the scaling factor of the axonal g-ratio, a measure of fiber myelination. Across subjects, the parameter θ was 0.40 ± 0.07 μm and the parameter β was 0.67 ± 0.02 μm(−α). CONCLUSION: The estimates of axonal radius and myelination are consistent with histological findings, illustrating the feasibility of this approach. The proposed method allows the measurement of the distribution of axonal radius and myelination within a white matter tract, opening new avenues for the combined study of brain structure and function, and for in vivo histological studies of the human brain. Frontiers Media S.A. 2022-04-21 /pmc/articles/PMC9070985/ /pubmed/35527816 http://dx.doi.org/10.3389/fnins.2022.874023 Text en Copyright © 2022 Oliveira, Pelentritou, Di Domenicantonio, De Lucia and Lutti. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Oliveira, Rita Pelentritou, Andria Di Domenicantonio, Giulia De Lucia, Marzia Lutti, Antoine In vivo Estimation of Axonal Morphology From Magnetic Resonance Imaging and Electroencephalography Data |
title | In vivo Estimation of Axonal Morphology From Magnetic Resonance Imaging and Electroencephalography Data |
title_full | In vivo Estimation of Axonal Morphology From Magnetic Resonance Imaging and Electroencephalography Data |
title_fullStr | In vivo Estimation of Axonal Morphology From Magnetic Resonance Imaging and Electroencephalography Data |
title_full_unstemmed | In vivo Estimation of Axonal Morphology From Magnetic Resonance Imaging and Electroencephalography Data |
title_short | In vivo Estimation of Axonal Morphology From Magnetic Resonance Imaging and Electroencephalography Data |
title_sort | in vivo estimation of axonal morphology from magnetic resonance imaging and electroencephalography data |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9070985/ https://www.ncbi.nlm.nih.gov/pubmed/35527816 http://dx.doi.org/10.3389/fnins.2022.874023 |
work_keys_str_mv | AT oliveirarita invivoestimationofaxonalmorphologyfrommagneticresonanceimagingandelectroencephalographydata AT pelentritouandria invivoestimationofaxonalmorphologyfrommagneticresonanceimagingandelectroencephalographydata AT didomenicantoniogiulia invivoestimationofaxonalmorphologyfrommagneticresonanceimagingandelectroencephalographydata AT deluciamarzia invivoestimationofaxonalmorphologyfrommagneticresonanceimagingandelectroencephalographydata AT luttiantoine invivoestimationofaxonalmorphologyfrommagneticresonanceimagingandelectroencephalographydata |