Objective. Functional magnetic resonance imaging (fMRI) requires thresholds by which to identify brain regions with significant activation, particularly for experiments involving real-life paradigms. One conventional non-parametric approach to generating surrogate data involves decomposition of the original fMRI time series using the Fourier transform, after which the phase is randomized without altering the magnitude of individual frequency components. However, it has been reported that spontaneous brain signals could be non-stationary, which, if true, could lead to false-positive results.Approach. This paper introduces a randomization procedure based on the Hilbert-Huang transform by which to account for non-stationarity in fMRI time series derived from two fMRI datasets (stationary or non-stationary). The significance of individual voxels was determined by comparing the distribution of empirical data versus a surrogate distribution.Main results. In a comparison with conventional phase-randomization and wavelet-based permutation methods, the proposed method proved highly effective in generating activation maps indicating essential brain regions, while filtering out noise in the white matter.Significance. This work demonstrated the importance of considering the non-stationary nature of fMRI time series when selecting resampling methods by which to probe brain activity or identify functional networks in real-life fMRI experiments. We propose a statistical testing method to deal with the non-stationarity of continuous brain signals.
Keywords: EMD; Hilbert–Huang transform; fMRI; non-stationary; threshold.
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