Wavelet-Based fMRI Statistical Analysis and Spatial Interpretation: A Unifying Approach

Wavelet-based statistical analysis methods for fMRI are able to detect brain activity without smoothing the data. Typically, the statistical inference is performed in the wavelet domain by testing the t-values of each wavelet coefficient; subsequently, an activity map is reconstructed from the significant coefficients. The limitation of this approach is that there is no direct statistical interpretation of the reconstructed map. In this paper, we propose a new methodology that takes advantage of wavelet processing but keeps the statistical meaning in the spatial domain. We derive a spatial threshold with a proper non-stationary component and determine optimal threshold values by minimizing an approximation error. The sensitivity of our method is comparable to SPM's (Statistical Parametric Mapping).

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Proceedings of the Second IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI'04), Arlington VA, USA, 1167–1170

 Record created 2015-09-18, last modified 2018-03-17

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