IBMA (Image-based meta-analysis)

With statistical maps of different datasets tested using same analysis pipeline, and the demography of each sample, users can perform meta-analysis to merge the multisite statistics using image-based or matrix-based meta-analysis. We have implemented Stouffer’s z-score method, Fisher’s method, fixed/mixed effects model, Worsley and Friston’s method and Nichols’ method.

_images/ibma.png
  • data: matrix
    • filetype: files in the filetype will be searched in input directories.
    • data dir: directory where all ttest2*.mat results are stored.
  • data: volume
    • center info: number of subjects for different centers. A csv format table is required. N1 and N2 is the number of subjects in each group.
      center N1 N2
      ttest2_center1_a_vs_b 40 39
      ttest2_center2_a_vs_b 38 37

    • mask: could be whole brain mask or gray matter mask.

    • id index: identifier to find unique string for each subject

    • filetype: files in the filetype will be searched in input directories.

    • data dir: directories can be input either using a *.txt file or spm select window.

  • Multiple comparison correction methods (voxel-wise)
    • threshold: the level of MULCC
    • fdrID: false discovery rate (independent input)
    • fdrN: false discovery rate (inputs not independent)
    • bonf: Bonferroni correction for family wise error rate
  • IBMA Methods:
  • out dir: output directory for saving results.
  • Buttons:
    • S: Save parameters of the current panel to a *.mat file. The *.mat can be further loaded for the panel or be used in a script processing.
    • L: Load parameters from *.mat for the current panel.
    • ?: Help information.
  • References:
  1. Stouffer’s z-score

    Stouffer, S.A., Suchman, E.A., DeVinney, L.C., Star, S.A. and Williams Jr, R.M., 1949. The American soldier: Adjustment during army life.(Studies in social psychology in World War II), Vol. 1. Princeton University Press, Princeton,.

  2. Fisher

    Fisher, R.A. (1925). Statistical Methods for Research Workers. Oliver and Boyd (Edinburgh). ISBN 0-05-002170-2.

  3. Fixed/mixed Effects Model

    Hedges, L.V. (1992). Meta-Analysis. Journal of Educational and Behavioral Statistics. 17(4), 279-296. doi: 10.3102/10769986017004279.

    Konstantopoulos, S. (2006). Fixed and mixed effects models in meta-analysis. Iza Discussion Papers.

  4. Worsley and Friston’s method

    Worsley, K.J., and Friston, K.J. (2000). A test for a conjunction. Statistics & Probability Letters. 47(2), 135-140. doi: 10.1016/S0167-7152(99)00149-2.

  5. Nichols’s method

    Nichols, T., Brett, M., Andersson, J., Wager, T., and Poline, J.B. (2005). Valid conjunction inference with the minimum statistic. Neuroimage. 25(3), 653-660. doi: 10.1016/j.neuroimage.2004.12.005.

  6. Salimi-Khorshidi G, Smith SM, Keltner JR, Wager TD, Nichols TE. Meta-analysis of neuroimaging data: a comparison of image-based and coordinate-based pooling of studies. Neuroimage 2009; 45(3): 810-23.

  7. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate - a Practical and Powerful Approach to Multiple Testing. J Roy Stat Soc B Met 1995; 57(1): 289-300.

  8. Benjamini Y, Yekutieli D. The control of the false discovery rate in multiple testing under dependency. Ann Stat 2001; 29(4): 1165-88.

  9. Lazar NA, Luna B, Sweeney JA, Eddy WF. Combining brains: a survey of methods for statistical pooling of information. Neuroimage 2002; 16(2): 538-50.