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Abstract #0736

Consensus between pipelines in whole-brain structural connectivity networks

Christopher S Parker 1,2 , Fani Deligianni 2 , M. Jorge Cardoso 1 , Pankaj Daga 1 , Marc Modat 1 , Chris A Clark 2 , Sebastien Ourselin 1,3 , and Jonathan D Clayden 2

1 Centre for Medical Image Computing, UCL, London, United Kingdom, 2 Imaging and Biophysics Unit, UCL, London, United Kingdom, 3 Dementia Research Centre, UCL, London, United Kingdom

A variety of image processing pipelines have been used to reconstruct whole-brain structural connectivity networks from diffusion MRI data. The choice of reconstruction method can impact network topology measures. We assessed similarity in networks obtained using two alternative and independent state-of-the-art reconstruction pipelines in order to identify core connections emerging robustly in both. We found high convergence between group-averaged networks across a range of network densities and identified a consensus network, which had high convergence and anatomical plausibility. Future work will investigate convergence using finer node scale parcellations, allowing a more detailed analysis of the convergence structure.

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