Year: 2017
Type: article
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.
Source: NeuroImage
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,081
Funders: National Science Foundation, European Commission, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, National Institutes of Health, McDonnell Center for Systems Neuroscience, Medical Research Council, Engineering and Physical Sciences Research Council, National Institute of Mental Health, H2020 European Research Council
Awards: G1433481316, Anatomy-Driven Brain Connectivity Mapping, Advanced non-invasive human in-vivo cortical parcellation using multimodal MRI, G2810537366, A biophysical simulation framework for magnetic resonance microstructure imaging, Direct Measurements of Microstructure from MRI, Medical image computing for next-generation healthcare technology, Mapping the Human Connectome: Structure, Function, and Heritability, Cardiff University-Equipment Account, Computational Brain Connectivity Mapping, G3848525072, G4014141827, G4510347995, G4657827649, G4714631627, G5346136505, G5651206546, National Facility for In Vivo MR Imaging of Human Tissue Microstructure, G68706309, G6935074504, Cognitive functions of multisensory cortical maps, G7628549173, G8057189669, G818010940, G8299918676, G8390508323, G844244329, G9000089182
OpenAlex ID: W2781259897