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 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier Inc
01.12.2015
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| ISSN: | 0730-725X, 1873-5894, 1873-5894 |
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| 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. |
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| AbstractList | 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. 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. 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.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. |
| Author | Cisler, Josh Bush, Keith Hazaroglu, Gokce Kilts, Clint Bian, Jiang Hazaroglu, Onder |
| AuthorAffiliation | a Department of Computer Science, University of Arkansas at Little Rock (UALR), 2801 S. University Ave., Little Rock, Arkansas, USA 72204 c Division of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), 4301 W. Markham St., Little Rock, Arkansas, USA 72205 b Brain Imaging Research Center, University of Arkansas for Medical Sciences (UAMS), 4301 W. Markham St., Little Rock, Arkansas, USA 72205 |
| AuthorAffiliation_xml | – name: b Brain Imaging Research Center, University of Arkansas for Medical Sciences (UAMS), 4301 W. Markham St., Little Rock, Arkansas, USA 72205 – name: a Department of Computer Science, University of Arkansas at Little Rock (UALR), 2801 S. University Ave., Little Rock, Arkansas, USA 72204 – name: c Division of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), 4301 W. Markham St., Little Rock, Arkansas, USA 72205 |
| Author_xml | – sequence: 1 givenname: Keith surname: Bush fullname: Bush, Keith email: kabush@ualr.edu organization: Department of Computer Science, University of Arkansas at Little Rock (UALR), 2801 S. University Ave., Little Rock, AR, USA 72204 – sequence: 2 givenname: Josh surname: Cisler fullname: Cisler, Josh email: jcisler@uams.edu organization: Brain Imaging Research Center, University of Arkansas for Medical Sciences (UAMS), 4301 W. Markham St., Little Rock, AR, USA 72205 – sequence: 3 givenname: Jiang orcidid: 0000-0002-2238-5429 surname: Bian fullname: Bian, Jiang email: jbian@uams.edu organization: Division of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), 4301 W. Markham St., Little Rock, AR, USA 72205 – sequence: 4 givenname: Gokce surname: Hazaroglu fullname: Hazaroglu, Gokce email: gxhazaroglu@ualr.edu organization: Department of Computer Science, University of Arkansas at Little Rock (UALR), 2801 S. University Ave., Little Rock, AR, USA 72204 – sequence: 5 givenname: Onder surname: Hazaroglu fullname: Hazaroglu, Onder email: oxhazaroglu@ualr.edu organization: Department of Computer Science, University of Arkansas at Little Rock (UALR), 2801 S. University Ave., Little Rock, AR, USA 72204 – sequence: 6 givenname: Clint surname: Kilts fullname: Kilts, Clint email: cdkilts@uams.edu organization: Brain Imaging Research Center, University of Arkansas for Medical Sciences (UAMS), 4301 W. Markham St., Little Rock, AR, USA 72205 |
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| SubjectTerms | 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 |
| Title | Improving the precision of fMRI BOLD signal deconvolution with implications for connectivity analysis |
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