Multivariate hurst exponent estimation in FMRI. Application to brain decoding of perceptual learning
Multivariate hurst exponent estimation in FMRI. Application to brain decoding of perceptual learning
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FMRI 中的多元赫斯特指数估计。
DOI：
10.1109/isbi.2016.7493433
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
V. V. Wassenhove
中科院分区：
文献类型：
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作者：
Hubert Pelle;P. Ciuciu;M. Rahim;Elvis Dohmatob;P. Abry;V. V. Wassenhove
到目前为止，不规则的脑电场电位活动被认为是神经科学中的噪声，占EEG或MEG中记录的信号功率的大部分[1，2]。这种脑活动在低频极限下遵循幂律谱P（f）~ 1/fβ，这是标度不变性的标志。最近，几项研究[1，3-6]表明斜率β（或等效的赫斯特指数H）往往受到任务表现或认知状态（例如，睡眠与清醒）的调节。这些观察结果在fMRI中得到了证实[7-9]，尽管fMRI时间序列的短长度使这些发现不太可靠。在本文中，为了补偿较慢的采样率在功能磁共振成像，我们扩展了单变量小波基赫斯特指数估计到多变量设置使用空间正则化。接下来，我们证明了所提出的工具在MEG实验期间专门训练视觉辨别任务后，在三组个体中记录的静息状态fMRI数据的相关性[10]。在监督分类框架中，我们的多变量方法允许更好地预测参与者接受的训练类型，而不是单变量。
So far considered as noise in neuroscience, irregular arrhythmic 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/fβ 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 regularization. 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.