Quantification of the statistical effects of spatiotemporal processing of nontask FMRI data.

Journal Article

Nontask functional magnetic resonance imaging (fMRI) has become one of the most popular noninvasive areas of brain mapping research for neuroscientists. In nontask fMRI, various sources of "noise" corrupt the measured blood oxygenation level-dependent signal. Many studies have aimed to attenuate the noise in reconstructed voxel measurements through spatial and temporal processing operations. While these solutions make the data more "appealing," many commonly used processing operations induce artificial correlations in the acquired data. As such, it becomes increasingly more difficult to derive the true underlying covariance structure once the data have been processed. As the goal of nontask fMRI studies is to determine, utilize, and analyze the true covariance structure of acquired data, such processing can lead to inaccurate and misleading conclusions drawn from the data if they are unaccounted for in the final connectivity analysis. In this article, we develop a framework that represents the spatiotemporal processing and reconstruction operations as linear operators, providing a means of precisely quantifying the correlations induced or modified by such processing rather than by performing lengthy Monte Carlo simulations. A framework of this kind allows one to appropriately model the statistical properties of the processed data, optimize the data processing pipeline, characterize excessive processing, and draw more accurate functional connectivity conclusions.

Full Text

Duke Authors

Cited Authors

  • Karaman, M; Nencka, AS; Bruce, IP; Rowe, DB

Published Date

  • November 2014

Published In

Volume / Issue

  • 4 / 9

Start / End Page

  • 649 - 661

PubMed ID

  • 25132113

Pubmed Central ID

  • 25132113

Electronic International Standard Serial Number (EISSN)

  • 2158-0022

Digital Object Identifier (DOI)

  • 10.1089/brain.2014.0278


  • eng

Conference Location

  • United States