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Wavelet-Based Statistical Analysis of fMRI Data with High Spatial Resolution

D. Van De Ville, M. Seghier, F. Lazeyras, T. Blu, M. Unser

Proceedings of the CHUV Research Day (CHUV'07), Lausanne VD, Swiss Confederation, February 1, 2007, pp. 185.



Wavelet-based statistical parametric mapping (WSPM) analyzes fMRI data using a combination of powerful denoising in the wavelet domain with statistical testing in the spatial domain. It also guarantees strong type I error (false positives) control and thus high confidence in the detections. In this poster, we show the various stages of this framework and we propose a comparison of WSPM and SPM2, which is the de-facto standard for statistical analysis of fMRI data. WSPM is available to the neuro-imaging community as a toolbox for SPM.

One of the major advantages of WSPM is that is does not require to pre-smooth the data before statistical analysis, which is a prerequisite of the SPM approach. Therefore, potential high spatial resolution information available in the data is not lost and can be used to retrieve small and highly detailed activation patterns. As a typical result, we show the activation maps for SPM (6mm) and WSPM. The experimental paradigm was single-frequency acoustic stimulation (1.5T scanner; TR=1.2s; 1.8×1.8×3mm). For the same statistical significance (5% corrected), the activation patterns retrieved by WSPM are clearly more detailed than those by SPM2.

In the poster, we also include the results of the empirically measured sensivity and specificity using a reproducibility analysis for multi-session data using both WSPM and SPM2. From this evaluation, we see that with WSPM we are able to obtain high spatial resolution without loss of sensitivity.

SPM (6mm)

SPM (6mm)

WSPM

WSPM


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