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Quantification of the statistical effects of spatiotemporal processing of nontask FMRI data. Brain Connect 2014 Nov;4(9):649-61



Pubmed ID


Pubmed Central ID




Scopus ID

2-s2.0-84927911115 (requires institutional sign-in at Scopus site)   6 Citations


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.

Author List

Karaman M, Nencka AS, Bruce IP, Rowe DB


Andrew S. Nencka PhD Director, Associate Professor in the Radiology department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Brain Mapping
Computer Simulation
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Models, Neurological
Phantoms, Imaging
Time Factors