Faculty Collaboration Database

A complex way to compute fMRI activation. Neuroimage 2004 Nov;23(3):1078-92

Date

11/06/2004

Pubmed ID

15528108

DOI

10.1016/j.neuroimage.2004.06.042

Scopus ID

2-s2.0-7444224673 (requires institutional sign-in at Scopus site)   89 Citations

Abstract

In functional magnetic resonance imaging, voxel time courses after Fourier or non-Fourier "image reconstruction" are complex valued as a result of phase imperfections due to magnetic field inhomogeneities. Nearly all fMRI studies derive functional "activation" based on magnitude voxel time courses [Bandettini, P., Jesmanowicz, A., Wong, E., Hyde, J.S., 1993. Processing strategies for time-course data sets in functional MRI of the human brain. Magn. Reson. Med. 30 (2): 161-173 and Cox, R.W., Jesmanowicz, A., Hyde, J.S., 1995. Real-time functional magnetic resonance imaging. Magn. Reson. Med. 33 (2): 230-236]. Here, we propose to directly model the entire complex or bivariate data rather than just the magnitude-only data. A nonlinear multiple regression model is used to model activation of the complex signal, and a likelihood ratio test is derived to determine activation in each voxel. We investigate the performance of the model on a real dataset, then compare the magnitude-only and complex models under varying signal-to-noise ratios in a simulation study with varying activation contrast effects.

Author List

Rowe DB, Logan BR

Author

Brent R. Logan PhD Director, Professor in the Data Science Institute department at Medical College of Wisconsin

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

Algorithms
Brain Mapping
Efferent Pathways
Fingers
Fourier Analysis
Humans
Image Processing, Computer-Assisted
Likelihood Functions
Magnetic Resonance Imaging
Models, Statistical
Motor Cortex
Nonlinear Dynamics