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Med Eng Phys. 2014 Sep;36(9):1212-7. doi: 10.1016/j.medengphy.2014.06.012. Epub 2014 Jul 8.

Fast computation of voxel-level brain connectivity maps from resting-state functional MRI using l₁-norm as approximation of Pearson's temporal correlation: proof-of-concept and example vector hardware implementation.

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  • 1Scientific Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy; MR-Lab, Center for Mind/Brain Science, University of Trento, Italy. Electronic address:
  • 2MR-Lab, Center for Mind/Brain Science, University of Trento, Italy.
  • 3Neuroradiology Unit, Fondazione IRCCS IstitutoNeurologico Carlo Besta, Milan, Italy.


An outstanding issue in graph-based analysis of resting-state functional MRI is choice of network nodes. Individual consideration of entire brain voxels may represent a less biased approach than parcellating the cortex according to pre-determined atlases, but entails establishing connectedness for 1(9)-1(11) links, with often prohibitive computational cost. Using a representative Human Connectome Project dataset, we show that, following appropriate time-series normalization, it may be possible to accelerate connectivity determination replacing Pearson correlation with l1-norm. Even though the adjacency matrices derived from correlation coefficients and l1-norms are not identical, their similarity is high. Further, we describe and provide in full an example vector hardware implementation of l1-norm on an array of 4096 zero instruction-set processors. Calculation times <1000 s are attainable, removing the major deterrent to voxel-based resting-sate network mapping and revealing fine-grained node degree heterogeneity. L1-norm should be given consideration as a substitute for correlation in very high-density resting-state functional connectivity analyses.

Copyright © 2014 IPEM. Published by Elsevier Ltd. All rights reserved.


Brain networks; Connectome; Correlation coefficient; Parallel processing; Resting-state functional MRI (rs-fMRI); Vector hardware co-processor; l(1)-Norm

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