Campus Units

Statistics

Document Type

Article

Publication Version

Published Version

Publication Date

2018

Journal or Book Title

The Annals of Applied Statistics

Volume

12

Issue

3

First Page

1451

Last Page

1478

DOI

10.1214/17-AOAS1117

Abstract

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.

Comments

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." The Annals of Applied Statistics 12, no. 3 (2018): 1451-1478. DOI: 10.1214/17-AOAS1117. Posted with permission.

Copyright Owner

Institute of Mathematical Statistics

Language

en

File Format

application/pdf

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