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

Ludovico Minati, Domenico Zacà, Ludovico D'Incerti, Jorge Jovicich

Research output: Contribution to journalArticle

Abstract

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 19-111 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 1-norm should be given consideration as a substitute for correlation in very high-density resting-state functional connectivity analyses.

Original languageEnglish
Pages (from-to)1212-1217
Number of pages6
JournalMedical Engineering and Physics
Volume36
Issue number9
DOIs
Publication statusPublished - 2014

Keywords

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

ASJC Scopus subject areas

  • Biomedical Engineering
  • Biophysics
  • Medicine(all)

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