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Abstract #1912

Statistical Analysis of Apparent Fibre Density: Supra-Threshold Clustering Over Space & Orientation

David Raffelt1,2, J-Donald Tournier3,4, Gerard Ridgway5, Stephen Rose6, Robert Henderson7, Stuart Crozier2, Alan Connelly3,4, Olivier Salvado1

1The Australian E-Health Research Centre, CSIRO, Brisbane, QLD, Australia; 2Biomedical Engineering, School of ITEE, University of Queensland, Brisbane, QLD, Australia; 3Brain Research Institute, Florey Neuroscience Institutes (Austin), Melbourne, VIC, Australia; 4Department of Medicine, University of Melbourne, Melbourne, VIC, Australia; 5Institute of Neurology, University College London, London, United Kingdom; 6Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia; 7Department of Neurology, Royal Brisbane & Women's Hospital, Brisbane, QLD, Australia

Apparent Fibre Density (AFD) is a new measure based on information provided by Fibre Orientation Distributions. AFD enables voxel-based analysis to be performed over space and orientation, and therefore population differences may be attributed to a single fibre within a voxel containing multiple fibres. Performing comparisons over many orientations within each voxel increases the number of multiple comparisons. We present a method for cluster-based inference of spatially extended differences in AFD by identifying clusters of contiguous supra-threshold directions using neighbours defined in space and orientation. The proposed method is demonstrated using a cohort of Motor Neurone Disease and healthy subjects.