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An Unsupervised Group Average Cortical Parcellation Using Diffusion MRI to Probe Cytoarchitecture

Tara Ganepola, Zoltan Nagy, Daniel C. Alexander, and Martin I. Sereno

Abstract Cortical parcellations provide valuable localisation resources for other neuroimaging modalities such as fMRI as well as insight into the structure-function relationship of the brain. The venerable but now dated ex vivo Brodmann map is currently being superseded by in vivo techniques that can better take into account intersubject variability. One popular in vivo method focusses on myeloarchitecture by measuring T1. This, however, probes only one aspect of cortical microstructure and is less useful in regions of low myelination. In contrast, diffusion MRI (dMRI) is sensitive to several additional microstructural features and can potentially provide a richer set of information regarding the architecture of grey matter microcircuitry. The following study used 3T HARDI data of multiple subjects to produce an entirely unsupervised, hemisphere-wide, group-average, parcellation. A qualitative assessment of the resulting cortical parcellation demonstrates several spatially coherent clusters in areas corresponding to well known functional anatomical areas. In addition, it exhibits some cluster boundaries that correlate with independently derived myelin mapping data for the same set of subjects, whilst also providing distinct clusters in areas (e.g., within MT+) where myelination is a less informative measurement.

T. Ganepola (H)

Department of Cognitive, Perceptual and Brain Sciences, University College London, London, UK

Department of Computer Science, Centre for Medical Image Computing, University College London, London, UK e-mail: This email address is being protected from spam bots, you need Javascript enabled to view it

Z. Nagy

Laboratory for Social and Neural Systems ResearchUniversity of Zurich, Zurich, Switzerland Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK D.C. Alexander

Department of Computer Science, Centre for Medical Image Computing, University College London, London, UK

M.I. Sereno

Department of Cognitive, Perceptual and Brain Sciences, University College London, London, UK

© Springer International Publishing AG 2017

A. Fuster et al. (eds.), Computational Diffusion MRI, Mathematics

and Visualization, DOI 10.1007/978-3-319-54130-3_12

 
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