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

TY - JOUR

T1 - Automatic Segmentation of Human Cortical Layer-Complexes and Architectural Areas Using Ex vivo Diffusion MRI and Its Validation

AU - Bastiani, Matteo

AU - Oros-Peusquens, Ana-Maria

AU - Seehaus, Arne

AU - Brenner, Daniel

AU - Möllenhoff, Klaus

AU - Celik, Avdo

AU - Felder, Jörg

AU - Bratzke, Hansjürgen

AU - Shah, Nadim J

AU - Galuske, Ralf

AU - Goebel, R.

AU - Roebroeck, Alard

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

U2 - 10.3389/fnins.2016.00487

DO - 10.3389/fnins.2016.00487

M3 - Article

C2 - 27891069

SN - 1662-4548

VL - 10

SP - 487

JO - Frontiers in Neuroscience

JF - Frontiers in Neuroscience

ER -