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Using diffusion MRI to discriminate areas of cortical grey matter


Ganepola, Tharindu; Nagy, Zoltán; Ghosh, Aurobrata; Papadopoulo, Theodore; Alexander, Daniel C; Sereno, Martin I (2017). Using diffusion MRI to discriminate areas of cortical grey matter. NeuroImage:Epub ahead of print.

Abstract

Cortical area parcellation is a challenging problem that is often approached by combining structural imaging (e.g., quantitative T1, diffusion-based connectivity) with functional imaging (e.g., task activations, topological mapping, resting state correlations). Diffusion MRI (dMRI) has been widely adopted to analyse white matter microstructure, but scarcely used to distinguish grey matter regions because of the reduced anisotropy there. Nevertheless, differences in the texture of the cortical 'fabric' have long been mapped by histologists to distinguish cortical areas. Reliable area-specific contrast in the dMRI signal has previously been demonstrated in selected occipital and sensorimotor areas. We expand upon these findings by testing several diffusion-based feature sets in a series of classification tasks. Using Human Connectome Project (HCP) 3T datasets and a supervised learning approach, we demonstrate that diffusion MRI is sensitive to architectonic differences between a large number of different cortical areas defined in the HCP parcellation. By employing a surface-based cortical imaging pipeline, which defines diffusion features relative to local cortical surface orientation, we show that we can differentiate areas from their neighbours with higher accuracy than when using only fractional anisotropy or mean diffusivity. The results suggest that grey matter diffusion may provide a new, independent source of information for dividing up the cortex.

Abstract

Cortical area parcellation is a challenging problem that is often approached by combining structural imaging (e.g., quantitative T1, diffusion-based connectivity) with functional imaging (e.g., task activations, topological mapping, resting state correlations). Diffusion MRI (dMRI) has been widely adopted to analyse white matter microstructure, but scarcely used to distinguish grey matter regions because of the reduced anisotropy there. Nevertheless, differences in the texture of the cortical 'fabric' have long been mapped by histologists to distinguish cortical areas. Reliable area-specific contrast in the dMRI signal has previously been demonstrated in selected occipital and sensorimotor areas. We expand upon these findings by testing several diffusion-based feature sets in a series of classification tasks. Using Human Connectome Project (HCP) 3T datasets and a supervised learning approach, we demonstrate that diffusion MRI is sensitive to architectonic differences between a large number of different cortical areas defined in the HCP parcellation. By employing a surface-based cortical imaging pipeline, which defines diffusion features relative to local cortical surface orientation, we show that we can differentiate areas from their neighbours with higher accuracy than when using only fractional anisotropy or mean diffusivity. The results suggest that grey matter diffusion may provide a new, independent source of information for dividing up the cortex.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Economics
Dewey Decimal Classification:330 Economics
Uncontrolled Keywords:Cortex, cortical surface, architectonics, grey matter, parcellation, HARDI, dMRI, supervised leaning
Language:English
Date:2017
Deposited On:23 Jan 2018 20:17
Last Modified:19 Aug 2018 13:33
Publisher:Elsevier
ISSN:1053-8119
Additional Information:Open Access gemäss https://www.sciencedirect.com
OA Status:Green
Publisher DOI:https://doi.org/10.1016/j.neuroimage.2017.12.046
PubMed ID:29274501
Project Information:
  • : FunderSNSF
  • : Grant ID31003A_166118
  • : Project TitleAdvanced non-invasive human in-vivo cortical parcellation using multimodal MRI
  • : FunderH2020
  • : Grant ID694665
  • : Project TitleCoBCoM - Computational Brain Connectivity Mapping

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