Shannon entropy applied to the analysis of event-related fMRI time series

Araújo, Dráulio Barros de; Tedeschi, Walfred; Santos, Antônio Carlos dos; Elías, Jorge; Neves, Ubiraci P. C.; Baffa, Oswaldo

doi:10.1016/s1053-8119(03)00306-9

Cited by 75 publications

(55 citation statements)

References 22 publications

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“…Therefore, fMRI has motivated a number of researchers to perform studies on brain recovery after stroke, most of which are based on blood oxygenation level dependent (BOLD) mechanism that rely on hemoglobin oxidative state changes. To perform BOLD-fMRI one needs to assume that the signal is stable throughout data acquisition 1 . However, many confounders may disturb such assumption, as disrupted neurovascular coupling in patients with cerebrovascular diseases 2 .…”

mentioning

confidence: 99%

Rapid BOLD fMRI signal loss in the primary motor cortex of a stroke patient

Carvalho

Pontes-Neto

Fábio

et al. 2008

Arq. Neuro-Psiquiatr.

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mentioning

confidence: 99%

Rapid BOLD fMRI signal loss in the primary motor cortex of a stroke patient

Carvalho

Pontes-Neto

Fábio

et al. 2008

Arq. Neuro-Psiquiatr.

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“…During that epoch the time course s(t) of the (simulated) BOLD signal of an active voxel exhibits a peak and then returns to a baseline level (Fig.1). As for the calculation of Shannon entropy in a previous work [7], an entire epoch (window W ) of the experiment is divided into two time intervals: half windows W 1 (related to the signal increase) and W 2 (corresponding mainly to baseline values). The probability distribution of signal levels (p 1 ) corresponding to the BOLD response in the first time period is clearly expected to differ from that one (p 2 ) corresponding to the baseline signal values so that the relative entropy within this epoch is calculated as a measure of the "distance" between p 1 and p 2 .…”

Section: Relative Entropymentioning

confidence: 99%

“…On the other hand, statistical methods can infer how significative is the difference between the signals corresponding to periods of stimulation and non-stimulation but do not need a priori knowledge of the form of the HRF. In the late years, methods based on information measures, such as the Shannon and Tsallis entropies and the generalized mutual information, have been employed as alternatives for the conventional analysis of fMRI data [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A numerical study of the Kullback-Leibler distance in functional magnetic resonance imaging

Cabella¹,

Sturzbecher²,

Tedeschi³

et al. 2008

Braz. J. Phys.

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The Kullback-Leibler distance (or relative entropy) is applied in the analysis of functional magnetic resonance (fMRI) data series. Our study is designed for event-related (ER) experiments, where a brief stimulus is presented and a long period of rest is followed. In particular, this relative entropy is used as a measure of the "distance" between the probability distributions p 1 and p 2 of the signal levels related to stimulus and non-stimulus. In order to avoid undesirable divergences of the Kullback-Leibler distance, a small positive parameter δ is introduced in the definition of the probability functions in such a way that it does not bias the comparison between both distributions. Numerical simulations are performed so as to determine the probability densities of the mean Kullback-Leibler distance D (throughout the N epochs of the whole experiment). For small values of N (N < 30), such probability densities f (D) are found to be fitted very well by Gamma distributions (χ 2 < 0.0009). The sensitivity and specificity of the method are evaluated by construction of the receiver operating characteristic (ROC) curves for some values of signal-to-noise ratio (SNR). The functional maps corresponding to real data series from an asymptomatic volunteer submitted to an ER motor stimulus is obtained by using the proposed technique. The maps present activation in primary and secondary motor brain areas. Both simulated and real data analyses indicate that the relative entropy can be useful for fMRI analysis in the information measure scenario.

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“…Some examples include the study of single cell information coding in the fly [de Ruyter van Steveninck et al, 1997] and primate visual systems [Victor, 2000;Reich et al, 2001;Simoncelli and Olshausen, 2001;Kang et al, 2004], in the primary motor cortex [Paz et al, 2003], neural and population coding in general [Borst and Theunissen, 1999;Panzeri et al, 2003;Schneidman et al, 2003], as well as the study of cortical synaptic communication [Fuhrmann et al, 2002;Goldman et al, 2002]. However, to our knowledge, except for a few applications using ICA [McKeown et al, 1998;Arfanakis et al, 2000;Calhoun et al, 2000;Moritz et al, 2000] for cluster analysis, only one study has made an attempt to apply information theory for the analysis of fMRI time-series, by breaking the event-related responses into two epochs and computing the entropy at each half [de Araujo et al, 2003]. This analysis does not assume a constant shape of an HRF, but nevertheless assumes a general structure of signal with a maximal change at the first half of the eventrelated response.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal information analysis of event-related BOLD responses

et al. 2007

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A new approach for analysis of event related fMRI (BOLD) signals is proposed. The technique is based on measures from information theory and is used both for spatial localization of task related activity, as well as for extracting temporal information regarding the task dependent propagation of activation across different brain regions. This approach enables whole brain visualization of voxels (areas) most involved in coding of a specific task condition, the time at which they are most informative about the condition, as well as their average amplitude at that preferred time. The approach does not require prior assumptions about the shape of the hemodynamic response function (HRF), nor about linear relations between BOLD response and presented stimuli (or task conditions). We show that relative delays between different brain regions can also be computed without prior knowledge of the experimental design, suggesting a general method that could be applied for analysis of differential time delays that occur during natural, uncontrolled conditions. Here we analyze BOLD signals recorded during performance of a motor learning task. We show that during motor learning, the BOLD response of unimodal motor cortical areas precedes the response in higher-order multimodal association areas, including posterior parietal cortex. Brain areas found to be associated with reduced activity during motor learning, predominantly in prefrontal brain regions, are informative about the task typically at significantly later times.

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Shannon entropy applied to the analysis of event-related fMRI time series

Cited by 75 publications

References 22 publications

Rapid BOLD fMRI signal loss in the primary motor cortex of a stroke patient

Rapid BOLD fMRI signal loss in the primary motor cortex of a stroke patient

A numerical study of the Kullback-Leibler distance in functional magnetic resonance imaging

Spatio-temporal information analysis of event-related BOLD responses

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