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

Minati, Ludovico, Cercignani, Mara and Chan, Dennis (2013) 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. Medical Engineering & Physics, 35 (10). pp. 1532-1539. ISSN 1873-4030

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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.

Item Type: Article
Keywords: Functional connectivity; Graph-based analysis; Network topology; Scale freeness; Functional magnetic resonance imaging (fMRI); Parallel processing
Schools and Departments: Brighton and Sussex Medical School > Clinical Medicine
Brighton and Sussex Medical School > Neuroscience
Subjects: R Medicine
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Depositing User: Patricia Butler
Date Deposited: 24 Jul 2013 08:53
Last Modified: 27 Jul 2017 17:44
URI: http://sro.sussex.ac.uk/id/eprint/45747
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