RICE-DISTRIBUTED AUTOREGRESSIVE TIME SERIES MODELING OF MAGNITUDE FUNCTIONAL MRI DATA

• 成果类型: Article
• 署名作者: Adrian, Daniel W.; Maitra, Ranjan; Rowe, Daniel B.
• 署名单位: Grand Valley State University; Iowa State University; Marquette University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/24-AOAS1981
• 发表日期: 2025
• 页码: 1494-1513
• 关键词: maximum-likelihood-estimation; activation detection; statistical approach; rician distribution; brain activation; em algorithm; fmri; complex; noise; image

摘要

Functional magnetic resonance imaging (fMRI) data generally consist of time series image volumes of the magnitude of complex-valued observations at each voxel. However, incorporating Gaussian-based time series models and the Rice distribution-a more accurate model for the data-in the time series have been separated by a distributional mismatch. We bridge this gap by including pth-order autoregressive (AR) errors into the Gaussian model for the latent real and imaginary components underlying the Rice-distributed magnitude data. Parameter estimation is then done by augmenting the observed magnitude data with the missing phase data in an expectation-maximization (EM) algorithm framework and followed by AR order determination and computation of test statistics for activation detection. Using simulated and experimental low-SNR fMRI data, we compare the performance of this Ricean time series model with a Gaussian AR(p) model for the magnitude data and also with a complex Gaussian time series model for the entire complex-valued data. Our results show improved parameter estimation and activation detection under the Ricean AR(p) model for the magnitude data than its Gaussian counterpart. The model using the complex-valued data (which is rarely collected in practice) detects activation better than both magnitude-only models but only because it has more data. Thus, while our results here provide for the improved analysis of commonly-collected and archived magnitude-only fMRI datasets, they also argue strongly against the currently routine practice of discarding the phase of the complex-valued fMRI time series, advocating instead for their inclusion in the analysis.