Comput Math Methods Med - Statistical analysis of single-trial Granger causality spectra.

Tópicos

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Resumo

Granger causality analysis is becoming central for the analysis of interactions between neural populations and oscillatory networks. However, it is currently unclear whether single-trial estimates of Granger causality spectra can be used reliably to assess directional influence. We addressed this issue by combining single-trial Granger causality spectra with statistical inference based on general linear models. The approach was assessed on synthetic and neurophysiological data. Synthetic bivariate data was generated using two autoregressive processes with unidirectional coupling. We simulated two hypothetical experimental conditions: the first mimicked a constant and unidirectional coupling, whereas the second modelled a linear increase in coupling across trials. The statistical analysis of single-trial Granger causality spectra, based on t-tests and linear regression, successfully recovered the underlying pattern of directional influence. In addition, we characterised the minimum number of trials and coupling strengths required for significant detection of directionality. Finally, we demonstrated the relevance for neurophysiology by analysing two local field potentials (LFPs) simultaneously recorded from the prefrontal and premotor cortices of a macaque monkey performing a conditional visuomotor task. Our results suggest that the combination of single-trial Granger causality spectra and statistical inference provides a valuable tool for the analysis of large-scale cortical networks and brain connectivity.

Resumo Limpo

granger causal analysi becom central analysi interact neural popul oscillatori network howev current unclear whether singletri estim granger causal spectra can use reliabl assess direct influenc address issu combin singletri granger causal spectra statist infer base general linear model approach assess synthet neurophysiolog data synthet bivari data generat use two autoregress process unidirect coupl simul two hypothet experiment condit first mimick constant unidirect coupl wherea second model linear increas coupl across trial statist analysi singletri granger causal spectra base ttest linear regress success recov under pattern direct influenc addit characteris minimum number trial coupl strength requir signific detect direct final demonstr relev neurophysiolog analys two local field potenti lfps simultan record prefront premotor cortic macaqu monkey perform condit visuomotor task result suggest combin singletri granger causal spectra statist infer provid valuabl tool analysi largescal cortic network brain connect

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