Complex-valued time series modeling for improved activation detection in fMRI studies Adrian, Daniel Maitra, Ranjan Rowe, Daniel
dc.contributor.department Statistics 2019-06-27T09:42:44.000 2020-07-02T06:56:56Z 2020-07-02T06:56:56Z Mon Jan 01 00:00:00 UTC 2018 2018-01-01
dc.description.abstract <p>A complex-valued data-based model with pth order autoregressive errors and general real/imaginary error covariance structure is proposed as an alternative to the commonly used magnitude-only data-based autoregressive model for fMRI time series. Likelihood-ratio-test-based activation statistics are derived for both models and compared for experimental and simulated data. For a dataset from a right-hand finger-tapping experiment, the activation map obtained using complex-valued modeling more clearly identifies the primary activation region (left functional central sulcus) than the magnitude-only model. Such improved accuracy in mapping the left functional central sulcus has important implications in neurosurgical planning for tumor and epilepsy patients. Additionally, we develop magnitude and phase detrending procedures for complex-valued time series and examine the effect of spatial smoothing. These methods improve the power of complex-valued data-based activation statistics. Our results advocate for the use of the complex-valued data and the modeling of its dependence structures as a more efficient and reliable tool in fMRI experiments over the current practice of using only magnitude-valued datasets.</p>
dc.description.comments <p>This article is published as Adrian, Daniel W., Ranjan Maitra, and Daniel B. Rowe. "Complex-valued time series modeling for improved activation detection in fMRI studies." <em>The Annals of Applied Statistics</em> 12, no. 3 (2018): 1451-1478. DOI: <a href="" target="_blank">10.1214/17-AOAS1117</a>. Posted with permission.</p>
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dc.identifier archive/
dc.identifier.articleid 1166
dc.identifier.contextkey 14422620
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/169
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 21:07:47 UTC 2022
dc.source.uri 10.1214/17-AOAS1117
dc.subject.disciplines Applied Statistics
dc.subject.disciplines Other Analytical, Diagnostic and Therapeutic Techniques and Equipment
dc.title Complex-valued time series modeling for improved activation detection in fMRI studies
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
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