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

Bastiani, Matteo ; Oros-Peusquens, Ana-Maria ; Seehaus, Arne ; Brenner, Daniel ; Möllenhoff, Klaus ; Celik, Avdo ; Felder, Jörg ; Bratzke, Hansjürgen ; Shah, Nadim J. ; Galuske, Ralf ; Goebel, Rainer ; Roebroeck, Alard (2023)
Automatic Segmentation of Human Cortical Layer-Complexes and Architectural Areas Using Ex vivo Diffusion MRI and Its Validation.
In: Frontiers in Neuroscience, 2016, 10
doi: 10.26083/tuprints-00017053
Artikel, Zweitveröffentlichung, Verlagsversion

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Kurzbeschreibung (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.

Typ des Eintrags: Artikel
Erschienen: 2023
Autor(en): Bastiani, Matteo ; Oros-Peusquens, Ana-Maria ; Seehaus, Arne ; Brenner, Daniel ; Möllenhoff, Klaus ; Celik, Avdo ; Felder, Jörg ; Bratzke, Hansjürgen ; Shah, Nadim J. ; Galuske, Ralf ; Goebel, Rainer ; Roebroeck, Alard
Art des Eintrags: Zweitveröffentlichung
Titel: Automatic Segmentation of Human Cortical Layer-Complexes and Architectural Areas Using Ex vivo Diffusion MRI and Its Validation
Sprache: Englisch
Publikationsjahr: 5 Dezember 2023
Ort: Darmstadt
Publikationsdatum der Erstveröffentlichung: 2016
Ort der Erstveröffentlichung: Lausanne
Verlag: Frontiers Media S.A.
Titel der Zeitschrift, Zeitung oder Schriftenreihe: Frontiers in Neuroscience
Jahrgang/Volume einer Zeitschrift: 10
Kollation: 11 Seiten
DOI: 10.26083/tuprints-00017053
URL / URN: https://tuprints.ulb.tu-darmstadt.de/17053
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Herkunft: Zweitveröffentlichung DeepGreen
Kurzbeschreibung (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.

Freie Schlagworte: diffusion MRI, cortical layers and areas, ultra-high field MRI, MR-based histology, histological validation
Status: Verlagsversion
URN: urn:nbn:de:tuda-tuprints-170536
Sachgruppe der Dewey Dezimalklassifikatin (DDC): 500 Naturwissenschaften und Mathematik > 570 Biowissenschaften, Biologie
600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin, Gesundheit
Fachbereich(e)/-gebiet(e): 10 Fachbereich Biologie
10 Fachbereich Biologie > Systemische Neurophysiologie
Hinterlegungsdatum: 05 Dez 2023 13:42
Letzte Änderung: 06 Dez 2023 09:06
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