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September 2018 Complex-valued time series modeling for improved activation detection in fMRI studies
Daniel W. Adrian, Ranjan Maitra, Daniel B. Rowe
Ann. Appl. Stat. 12(3): 1451-1478 (September 2018). DOI: 10.1214/17-AOAS1117


A complex-valued data-based model with $p$th 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.


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Daniel W. Adrian. Ranjan Maitra. Daniel B. Rowe. "Complex-valued time series modeling for improved activation detection in fMRI studies." Ann. Appl. Stat. 12 (3) 1451 - 1478, September 2018.


Received: 1 December 2016; Revised: 1 September 2017; Published: September 2018
First available in Project Euclid: 11 September 2018

zbMATH: 06979638
MathSciNet: MR3852684
Digital Object Identifier: 10.1214/17-AOAS1117

Rights: Copyright © 2018 Institute of Mathematical Statistics


Vol.12 • No. 3 • September 2018
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