Rapid geodesic mapping of brain functional connectivity: Implementation of a dedicated co-processor in a field-programmable gate array (FPGA) and application to resting state functional MRI

Ludovico Minati, Mara Cercignani, Dennis Chan

Research output: Contribution to journalArticlepeer-review

Abstract

Graph theory-based analyses of brain network topology can be used to model the spatiotemporal correlations in neural activity detected through fMRI, and such approaches have wide-ranging potential, from detection of alterations in preclinical Alzheimer's disease through to command identification in brain-machine interfaces. However, due to prohibitive computational costs, graph-based analyses to date have principally focused on measuring connection density rather than mapping the topological architecture in full by exhaustive shortest-path determination. This paper outlines a solution to this problem through parallel implementation of Dijkstra's algorithm in programmable logic. The processor design is optimized for large, sparse graphs and provided in full as synthesizable VHDL code. An acceleration factor between 15 and 18 is obtained on a representative resting-state fMRI dataset, and maps of Euclidean path length reveal the anticipated heterogeneous cortical involvement in long-range integrative processing. These results enable high-resolution geodesic connectivity mapping for resting-state fMRI in patient populations and real-time geodesic mapping to support identification of imagined actions for fMRI-based brain-machine interfaces.

Original languageEnglish
Pages (from-to)1532-1539
Number of pages8
JournalMedical Engineering and Physics
Volume35
Issue number10
DOIs
Publication statusPublished - Oct 2013

Keywords

  • Dijkstra's algorithm
  • Field-programmable gate array (FPGA)
  • Functional connectivity
  • Graph theory
  • Network topology
  • Resting-state functional MRI (rs-fMRI)

ASJC Scopus subject areas

  • Biomedical Engineering
  • Biophysics

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