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iTT Toolbox for SPM


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The purpose of thresholding in functional MRI (fMRI) analyses is to classify image voxels as activated, deactivated, or not involved. To accomplish this goal most statistical methods determine a level of significance (i.e. define a threshold). Voxel-based approaches [1, 2] that apply only one threshold always rely on a compromise between specificity and sensitivity. Some more advanced approaches attempt to provide both high specificity and sensitivity e.g. using information about clusters of activated voxels [3-5]. However, current thresholding strategies for the analysis of functional MRI (fMRI) datasets may suffer from specific limitations (e.g. with respect to the required smoothness) or lead to reduced performance for a low signal-to-noise ratio (SNR). Although a previously proposed two-threshold (TT) method offers a promising solution to these problems [6], the use of preset settings limits its performance. This work offers an optimised TT approach that estimates the required parameters in an iterative manner.



The TT method assumes that the actual distribution (histogram) of statistical parameters comprises both truly activated voxels and underlying noise. Because the noise is expected to be Gaussian distributed [7-10], a Gaussian function is fitted to the central part of the histogram to identify activated voxels above a strict upper threshold (p = 0.0001). Subsequently, a second lower threshold (p = 0.05) is applied to direct neighbours of the voxels selected in the first step. These probabilistic thresholds are derived from the fitted Gaussian curve.  Altogether, the TT method therefore employs four parameters: the upper and lower cuts determining the central part of the histogram for Gaussian fitting (horizontal lines in Fig. 1) and the upper and lower thresholds determining statistically significant activated voxels (vertical lines in Fig. 1).
Instead of using preset parameters, the iTT method iteratively optimises three of these parameters (i.e. the upper and lower cuts as well as the upper threshold) in two steps taking the default settings as starting point. The lower threshold remains the only parameter which can be modified by the user to allow for some adjustments.



In general, the iTT method presents with remarkable sensitivity and good specificity that outperforms almost all conventional approaches in all cases. This also holds true for challenging conditions such as high spatial resolution (right column in Fig. 2), the absence of filtering (lower row in Fig. 2), high noise level, or a low number of task repetitions.


iTT Toolbox for SPM

The iterative Two Threshold (iTT) approach has been added to SPM in the form of a Toolbox thus enabling the user to choose “iTT” or “TT” (original TT – without iterative optimisation) for thresholding approach and select the lower threshold (default p = 0.05). The upper threshold is determined by the iterative optimization (iTT) or set to p = 0.0001 (TT), although this default value can be changed.


The iTT toolbox contains a separate graphic user interface for configuration, which may be selected from the Toolbox list.
It serves to modify the default values for the upper and lower threshold and further controls the display of the histogram.


When using this toolbox, please use the following reference:
Auer T , Schweizer R, Frahm J. An iterative two-threshold analysis for single-subject functional MRI of the human brain. Eur Radiol. 2011 Jun 28. [Epub ahead of print] PMID: 21710268 [PubMed - as supplied by publisher]




1.    Genovese, C.R., N.A. Lazar, and T. Nichols, Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage, 2002. 15(4): p. 870-8.
2.    Worsley, K.J., et al., A unified statistical approach for determining significant signals in images of cerebral activation. Hum Brain Mapp, 1996. 4(1): p. 58-73.
3.    Friston, K.J., et al., Assessing the significance of focal activations using their spatial extent. Human Brain Mapping, 1993. 1(3): p. 210-220.
4.    Worsley, K.J., et al., A three-dimensional statistical analysis for CBF activation studies in human brain. J Cereb Blood Flow Metab, 1992. 12(6): p. 900-18.
5.    Zhang, H., T.E. Nichols, and T.D. Johnson, Cluster mass inference via random field theory. Neuroimage, 2009. 44(1): p. 51-61.
6.    Auer, T. and J. Frahm, Functional MRI Using One- and Two-Threshold Approaches in SPM5. Neuroimage, 2009. 47(Supplement 1): p. S102.
7.    Auer, T., et al., A novel group analysis for functional MRI of the human brain based on a two-threshold correlation (TTC) method. J Neurosci Methods, 2008. 167(2): p. 335-9.
8.    Baudewig, J., et al., Thresholding in correlation analyses of magnetic resonance functional neuroimaging. Magn Reson Imaging, 2003. 21(10): p. 1121-30.
9.    Gudbjartsson, H. and S. Patz, The Rician distribution of noisy MRI data. Magn Reson Med, 1995. 34(6): p. 910-4.
10.    Kleinschmidt, A., et al., On the Use of Temporal Correlation-Coefficients for Magnetic-Resonance Mapping of Functional Brain Activation - Individualized Thresholds and Spatial Response Delineation. International Journal of Imaging Systems and Technology, 1995. 6(2-3): p. 238-44.



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