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Automatic Segmentation of Human Cortical Layer-Complexes and Architectural Areas Using Ex vivo Diffusion MRI and Its Validation.

Bastiani, Matteo and Oros-Peusquens, Ana-Maria and Seehaus, Arne and Brenner, Daniel and Möllenhoff, Klaus and Celik, Avdo and Felder, Jörg and Bratzke, Hansjürgen and Shah, Nadim J. and Galuske, Ralf A. W. and Goebel, Rainer and Roebroeck, Alard (2016):
Automatic Segmentation of Human Cortical Layer-Complexes and Architectural Areas Using Ex vivo Diffusion MRI and Its Validation.
In: Frontiers in neuroscience, p. 487, 10, ISSN 1662-4548, [Article]

Abstract

Recently, several magnetic resonance imaging contrast mechanisms have been shown to distinguish cortical substructure corresponding to selected cortical layers. Here, we investigate cortical layer and area differentiation by automatized unsupervised clustering of high-resolution diffusion MRI data. Several groups of adjacent layers could be distinguished in human primary motor and premotor cortex. We then used the signature of diffusion MRI signals along cortical depth as a criterion to detect area boundaries and find borders at which the signature changes abruptly. We validate our clustering results by histological analysis of the same tissue. These results confirm earlier studies which show that diffusion MRI can probe layer-specific intracortical fiber organization and, moreover, suggests that it contains enough information to automatically classify architecturally distinct cortical areas. We discuss the strengths and weaknesses of the automatic clustering approach and its appeal for MR-based cortical histology.

Item Type: Article
Erschienen: 2016
Creators: Bastiani, Matteo and Oros-Peusquens, Ana-Maria and Seehaus, Arne and Brenner, Daniel and Möllenhoff, Klaus and Celik, Avdo and Felder, Jörg and Bratzke, Hansjürgen and Shah, Nadim J. and Galuske, Ralf A. W. and Goebel, Rainer and Roebroeck, Alard
Title: Automatic Segmentation of Human Cortical Layer-Complexes and Architectural Areas Using Ex vivo Diffusion MRI and Its Validation.
Language: English
Abstract:

Recently, several magnetic resonance imaging contrast mechanisms have been shown to distinguish cortical substructure corresponding to selected cortical layers. Here, we investigate cortical layer and area differentiation by automatized unsupervised clustering of high-resolution diffusion MRI data. Several groups of adjacent layers could be distinguished in human primary motor and premotor cortex. We then used the signature of diffusion MRI signals along cortical depth as a criterion to detect area boundaries and find borders at which the signature changes abruptly. We validate our clustering results by histological analysis of the same tissue. These results confirm earlier studies which show that diffusion MRI can probe layer-specific intracortical fiber organization and, moreover, suggests that it contains enough information to automatically classify architecturally distinct cortical areas. We discuss the strengths and weaknesses of the automatic clustering approach and its appeal for MR-based cortical histology.

Journal or Publication Title: Frontiers in neuroscience
Volume: 10
Divisions: 10 Department of Biology
10 Department of Biology > Systems Neurophysiology
Date Deposited: 29 Dec 2016 09:58
Identification Number: pmid:27891069
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