title: Detection of scale-freeness in brain connectivity by functional MRI: signal processing aspects and implementation of an open hardware co-processor.
abstract: An outstanding issue in graph-theoretical studies of brain functional connectivity is the lack of formal criteria for choosing parcellation granularity and correlation threshold. Here, we propose detectability of scale-freeness as a benchmark to evaluate time-series extraction settings. Scale-freeness, i.e., power-law distribution of node connections, is a fundamental topological property that is highly conserved across biological networks, and as such needs to be manifest within plausible reconstructions of brain connectivity. We demonstrate that scale-free network topology only emerges when adequately fine cortical parcellations are adopted alongside an appropriate correlation threshold, and provide the full design of the first open-source hardware platform to accelerate the calculation of large linear regression arrays.
authors.affiliation: Clinical Imaging Sciences Centre (CISC), Brighton & Sussex Medical School, Falmer, UK. l.minati@bsms.ac.uk
authors.forename: Ludovico
authors.initials: L
authors.lastname: Minati
authors.forename: Anna
authors.initials: A
authors.lastname: Nigri
authors.forename: Mara
authors.initials: M
authors.lastname: Cercignani
authors.forename: Dennis
authors.initials: D
authors.lastname: Chan
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology*; D003201:Computers; D005260:Female; D064368:Healthy Volunteers; D006801:Humans; D008279:Magnetic Resonance Imaging; D009415:Nerve Net / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D013997:Time Factors
keywords: Functional connectivity; Functional magnetic resonance imaging (fMRI); Graph-based analysis; Network topology; Parallel processing; Scale freeness