Multivariate Hurst Exponent Estimation in FMRI. Application to Brain Decoding of Perceptual Learning

Abstract : So far considered as noise in neuroscience, irregular arrhyth-mic field potential activity accounts for the majority of the signal power recorded in EEG or MEG [1, 2]. This brain activity follows a power law spectrum P (f) ∼ 1/f β in the limit of low frequencies, which is a hallmark of scale invariance. Recently, several studies [1, 3–6] have shown that the slope β (or equivalently Hurst exponent H) tends to be modulated by task performance or cognitive state (eg, sleep vs awake). These observations were confirmed in fMRI [7–9] although the short length of fMRI time series makes these findings less reliable. In this paper, to compensate for the slower sampling rate in fMRI, we extend univariate wavelet-based Hurst exponent estimator to a multivariate setting using spatial regular-ization. Next, we demonstrate the relevance of the proposed tools on resting-state fMRI data recorded in three groups of individuals once they were specifically trained to a visual discrimination task during a MEG experiment [10]. In a supervised classification framework, our multivariate approach permits to better predict the type of training the participants received as compared to their univariate counterpart.
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Communication dans un congrès
13th IEEE International Symposium on Biomedical Imaging, Apr 2016, Prague, Czech Republic. 2016
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  • HAL Id : hal-01261976, version 1

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Hubert Pellé, Philippe Ciuciu, Mehdi Rahim, Elvis Dohmatob, Patrice Abry, et al.. Multivariate Hurst Exponent Estimation in FMRI. Application to Brain Decoding of Perceptual Learning. 13th IEEE International Symposium on Biomedical Imaging, Apr 2016, Prague, Czech Republic. 2016. 〈hal-01261976〉

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