Neural Comput - Reliability of information-based integration of EEG and fMRI data: a simulation study.

Tópicos

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Resumo

Most studies involving simultaneous electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data rely on the first-order, affine-linear correlation of EEG and fMRI features within the framework of the general linear model. An alternative is the use of information-based measures such as mutual information and entropy, which can also detect higher-order correlations present in the data. The estimate of information-theoretic quantities might be influenced by several parameters, such as the numerosity of the sample, the amount of correlation between variables, and the discretization (or binning) strategy of choice. While these issues have been investigated for invasive neurophysiological data and a number of bias-correction estimates have been developed, there has been no attempt to systematically examine the accuracy of information estimates for the multivariate distributions arising in the context of EEG-fMRI recordings. This is especially important given the differences between electrophysiological and EEG-fMRI recordings. In this study, we drew random samples from simulated bivariate and trivariate distributions, mimicking the statistical properties of EEG-fMRI data. We compared the estimated information shared by simulated random variables with its numerical value and found that the interaction between the binning strategy and the estimation method influences the accuracy of the estimate. Conditional on the simulation assumptions, we found that the equipopulated binning strategy yields the best and most consistent results across distributions and bias correction methods. We also found that within bias correction techniques, the asymptotically debiased (TPMC), the jackknife debiased (JD), and the best upper bound (BUB) approach give similar results, and those are consistent across distributions.

Resumo Limpo

studi involv simultan electroencephalograph eeg function magnet reson imag fmri data reli firstord affinelinear correl eeg fmri featur within framework general linear model altern use informationbas measur mutual inform entropi can also detect higherord correl present data estim informationtheoret quantiti might influenc sever paramet numeros sampl amount correl variabl discret bin strategi choic issu investig invas neurophysiolog data number biascorrect estim develop attempt systemat examin accuraci inform estim multivari distribut aris context eegfmri record especi import given differ electrophysiolog eegfmri record studi drew random sampl simul bivari trivari distribut mimick statist properti eegfmri data compar estim inform share simul random variabl numer valu found interact bin strategi estim method influenc accuraci estim condit simul assumpt found equipopul bin strategi yield best consist result across distribut bias correct method also found within bias correct techniqu asymptot debias tpmc jackknif debias jd best upper bound bub approach give similar result consist across distribut

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