Full Paper

Complex data analysis in high-resolution SSFP fMRI

  1. Jongho Lee1,*,
  2. Morteza Shahram2,
  3. Armin Schwartzman3,4,
  4. John M. Pauly1

Article first published online: 24 APR 2007

DOI: 10.1002/mrm.21195

Issue

Magnetic Resonance in Medicine

Magnetic Resonance in Medicine

Volume 57, Issue 5, pages 905–917, May 2007

Additional Information

How to Cite

Lee, J., Shahram, M., Schwartzman, A. and Pauly, J. M. (2007), Complex data analysis in high-resolution SSFP fMRI. Magn. Reson. Med., 57: 905–917. doi: 10.1002/mrm.21195

Author Information

  1. 1

    Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, California, USA

  2. 2

    Department of Statistics, Stanford University, Stanford, California, USA

  3. 3

    Department of Biostatistics, Harvard School of Public Health and Dana-Farber Cancer Institute, Boston, Massachusetts, USA

  4. 4

    Department of Biostatistics, and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

*Room 208, Packard Electrical Engineering Bldg., Stanford University, Stanford, CA 94305-9510

Publication History

  1. Issue published online: 24 APR 2007
  2. Article first published online: 24 APR 2007
  3. Manuscript Accepted: 1 JAN 2007
  4. Manuscript Revised: 4 DEC 2006
  5. Manuscript Received: 13 JUL 2006

Funded by

  • National Institutes of Health. Grant Number: R21EB002969
  • GE Medical Systems

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Keywords:

  • complex;
  • multivariate;
  • SSFP;
  • phase activation;
  • BOSS

Abstract

In transition-band steady-state free precession (SSFP) functional MRI (fMRI), functional contrast originates from a bulk frequency shift induced by a deoxygenated hemoglobin concentration change in the activated brain regions. This frequency shift causes a magnitude and/or phase-signal change depending on the off-resonance distribution of a voxel in the balanced-SSFP (bSSFP) profile. However, in early low-resolution studies, only the magnitude signal activations were shown. In this paper the task-correlated phase-signal change is presented in a high-resolution (1 × 1 × 1 mm3) study. To include this phase activation in a functional analysis, a new complex domain data analysis method is proposed. The results show statistically significant phase-signal changes in a large number of voxels comparable to that of the magnitude-activated voxels. The complex-data analysis method successfully includes these phase activations in the activation map and thus provides wider coverage compared to magnitude-data analysis results. Magn Reson Med 57:905–917, 2007. © 2007 Wiley-Liss, Inc.

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