Research Article

Functional imaging analysis contest (FIAC) analysis according to AFNI and SUMA

  1. Ziad S. Saad1,*,
  2. Gang Chen1,
  3. Richard C. Reynolds1,
  4. Patricia P. Christidis1,
  5. Kenneth R. Hammett1,
  6. Patrick S.F. Bellgowan2,
  7. Robert W. Cox1

Article first published online: 27 MAR 2006

DOI: 10.1002/hbm.20247

Issue

Human Brain Mapping

Human Brain Mapping

Volume 27, Issue 5, pages 417–424, May 2006

Additional Information

How to Cite

Saad, Z. S., Chen, G., Reynolds, R. C., Christidis, P. P., Hammett, K. R., Bellgowan, P. S. and Cox, R. W. (2006), Functional imaging analysis contest (FIAC) analysis according to AFNI and SUMA. Human Brain Mapping, 27: 417–424. doi: 10.1002/hbm.20247

Author Information

  1. 1

    Scientific and Statistical Computing Core, National Institute of Mental Health, NIH, Department of Health and Human Services, Bethesda, Maryland

  2. 2

    Laboratory of Brain and Cognition, National Institute of Mental Health, NIH, Department of Health and Human Services, Bethesda, Maryland

*Scientific and Statistical Computing Core, National Institute of Mental Health, 10 Center Dr., Room 1D80, Bethesda, MD 20892-1148

Publication History

  1. Issue published online: 13 APR 2006
  2. Article first published online: 27 MAR 2006
  3. Manuscript Accepted: 29 DEC 2005
  4. Manuscript Received: 21 OCT 2005

Funded by

  • Intramural Program of the NIH, NIMH

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Keywords:

  • fMRI;
  • time series deconvolution;
  • surface-based analysis

Abstract

The Functional Imaging Analysis Contest (FIAC) datasets were analyzed with the AFNI software package. Two types of linear regression analyses were carried out: “fixed shape” hemodynamic response, where a preselected incomplete gamma function is used to model each brief activation episode, and “variable shape” analysis, where the temporal shape of the response model in each stimulus block class is allowed to vary separately in each voxel. These time series regressions were carried out both in the volume and on the original data projected to individual standardized cortical surface models. Intersubject analyses were carried out voxel-wise on the regression amplitudes obtained from these time series results, using a multi-way within-subject analysis of variance (ANOVA). Group analysis of the block design demonstrated a significant repetition suppression of the BOLD signal within blocks in the superior and middle temporal gyrus. This effect may represent differences in the response to the first stimulus following a period of silence compared to the remaining sentences in the block. Analyzing the event-related data, Brodmann area 31 showed significant sentence effect and consecutive-sentence repetition effect. However, no significant speaker effect was found; these results may be consistent with the instructions to the subjects that they would be tested on the sentence content. Sentence by speaker interaction effects were found in bilateral middle temporal gyrus, left inferior frontal, and left inferior temporal gyrus. Hum Brain Mapp, 2006. © 2006 Wiley-Liss, Inc.

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