Improving the precision of fMRI BOLD signal deconvolution with implications for connectivity analysis

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...

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Veröffentlicht in:Magnetic resonance imaging Jg. 33; H. 10; S. 1314 - 1323
Hauptverfasser: Bush, Keith, Cisler, Josh, Bian, Jiang, Hazaroglu, Gokce, Hazaroglu, Onder, Kilts, Clint
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands Elsevier Inc 01.12.2015
Schlagworte:
Adult
Algorithms
BOLD
Bootstrapping
Brain - physiology
Brain Mapping - methods
Deconvolution
Female
fMRI
Functional connectivity
Humans
Image Processing, Computer-Assisted - methods
Imaging analysis
Magnetic Resonance Imaging - methods
Male
Radiology
Reproducibility of Results
Bootstrapping
Functional connectivity
fMRI
Deconvolution
BOLD
Imaging analysis
ISSN:0730-725X, 1873-5894, 1873-5894
Online-Zugang:Volltext
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Zusammenfassung: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.
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ISSN:0730-725X
1873-5894
1873-5894
DOI:10.1016/j.mri.2015.07.007