Using diffusion MRI to discriminate areas of cortical grey matter

Tharindu Ganepola,Zoltan Nagy,Aurobrata Ghosh,Theodore Papadopoulo,Daniel C Alexander,Martin I Sereno

doi:10.1016/j.neuroimage.2017.12.046

Tharindu Ganepola, Zoltan Nagy + Show 4 more

Open Access

https://doi.org/10.1016/j.neuroimage.2017.12.046

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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.

Highlights

Studies of the microstructure of the human cerebral cortex revealed a laminar structure comprising of six layers of varying thickness, and cellular and axonal fibre composition (Berlin, 1858; Lewis and Clarke, 1878; Mountcastle, 1997)
The feature sets DT6, DT9, SH27 and 4T36 demonstrate a similar trend with steep improvement in accuracy from training group sizes (TS)=1 to TS=3 followed by more gradual, improvement up to TS=19
Our results provide support for including surface-based HARDI data analysis as an additional, independent measure of cortical microstructure to aid e.g. quantitative T1, which has been widely used as a proxy for myelin density

Summary

Introduction

Studies of the microstructure of the human cerebral cortex revealed a laminar structure comprising of six layers of varying thickness, and cellular and axonal fibre composition (Berlin, 1858; Lewis and Clarke, 1878; Mountcastle, 1997). The heterogeneous appearance of these layers, as well as differences in vertical and tangential fibre arrays in different parts of the cortical sheet, suggested that there might be a relationship between microstructural organisation and local functional specificity Pioneers in this field (Brodmann, 1909; Campbell, 1905; Vogt and Vogt, 1919; von Economo and Koskinas, 1925) published hemisphere-wide maps demarcating the boundaries of cyto- and myeloarchitectonic domains based on sectioning and histological staining of cadaver brains. They can be combined with additional multi-modal data from the same subject, to directly assess structure-function relationships, and lend themselves gracefully to observer-free algorithmic analyses Some of these improvements have been applied to ex vivo data using observer independent intensity analysis(Amunts et al, 2000, 1999; Bludau et al, 2014; Eickhoff et al, 2006; Geyer et al, 1996; Roland and Zilles, 1994; Schleicher et al, 2005; Zilles et al, 2002). Despite their resolution advantages, such works are still labour intensive and lack the flexibility offered by a potential in vivo pipeline

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