title: Improving the precision of fMRI BOLD signal deconvolution with implications for connectivity analysis.
abstract: An important, open problem in neuroimaging analyses is developing analytical methods that ensure precise inferences about neural activity underlying fMRI BOLD signal despite the known presence of confounds. Here, we develop and test a new meta-algorithm for conducting semi-blind (i.e., no knowledge of stimulus timings) deconvolution of the BOLD signal that estimates, via bootstrapping, both the underlying neural events driving BOLD as well as the confidence of these estimates. Our approach includes two improvements over the current best performing deconvolution approach; 1) we optimize the parametric form of the deconvolution feature space; and, 2) we pre-classify neural event estimates into two subgroups, either known or unknown, based on the confidence of the estimates prior to conducting neural event classification. This knows-what-it-knows approach significantly improves neural event classification over the current best performing algorithm, as tested in a detailed computer simulation of highly-confounded fMRI BOLD signal. We then implemented a massively parallelized version of the bootstrapping-based deconvolution algorithm and executed it on a high-performance computer to conduct large scale (i.e., voxelwise) estimation of the neural events for a group of 17 human subjects. We show that by restricting the computation of inter-regional correlation to include only those neural events estimated with high-confidence the method appeared to have higher sensitivity for identifying the default mode network compared to a standard BOLD signal correlation analysis when compared across subjects.
authors.affiliation: Department of Computer Science, University of Arkansas at Little Rock (UALR), 2801 S. University Ave., Little Rock, AR, USA 72204. Electronic address: kabush@ualr.edu.
authors.forename: Keith
authors.initials: K
authors.lastname: Bush
authors.affiliation: Brain Imaging Research Center, University of Arkansas for Medical Sciences (UAMS), 4301 W. Markham St., Little Rock, AR, USA 72205. Electronic address: jcisler@uams.edu.
authors.forename: Josh
authors.initials: J
authors.lastname: Cisler
authors.affiliation: Division of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), 4301 W. Markham St., Little Rock, AR, USA 72205. Electronic address: jbian@uams.edu.
authors.forename: Jiang
authors.initials: J
authors.lastname: Bian
authors.affiliation: Department of Computer Science, University of Arkansas at Little Rock (UALR), 2801 S. University Ave., Little Rock, AR, USA 72204. Electronic address: gxhazaroglu@ualr.edu.
authors.forename: Gokce
authors.initials: G
authors.lastname: Hazaroglu
authors.affiliation: Department of Computer Science, University of Arkansas at Little Rock (UALR), 2801 S. University Ave., Little Rock, AR, USA 72204. Electronic address: oxhazaroglu@ualr.edu.
authors.forename: Onder
authors.initials: O
authors.lastname: Hazaroglu
authors.affiliation: Brain Imaging Research Center, University of Arkansas for Medical Sciences (UAMS), 4301 W. Markham St., Little Rock, AR, USA 72205. Electronic address: cdkilts@uams.edu.
authors.forename: Clint
authors.initials: C
authors.lastname: Kilts
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D015203:Reproducibility of Results
keywords: BOLD; Bootstrapping; Deconvolution; Functional connectivity; Imaging analysis; fMRI