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Granger causality has been increasingly used to identify causal influence between time series based on multivariate autoregressive models.
Causal coherence[40 42] or partial directed coherence[43, 44], based on multivariate autoregressive modeling may be used for this purpose.
This approach is based on multivariate autoregressive (MVAR) modeling that integrates temporal and spatial information.
In this work, we investigate the feasibility to estimating causal interactions between brain regions based on multivariate autoregressive models (MAR models) fitted to magnetoencephalographic (MEG) sensor measurements.
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Here, we surpassed PLA by an advanced measure, partial-directed-coherence (PDC) based on multivariate-autoregressive modeling.
In particular, we intended to unravel the causal connectivity between the brain sources of iGBRs by using the measure of partial-directed coherence (PDC), which is based on multivariate-autoregressive modeling.
To evaluate causal flows between ROIs, we computed pGC using Seth's Granger Causal Connectivity Analysis toolbox, based on multivariate vector autoregressive (MVAR) models including lags of multiple time-series [41], as described [42].
The candidates of the outliers were selected automatically by a jackknife outlier analysis based on multivariate statistics.
The CFR techniques used in both of the two reconstructions are based on multivariate linear regression approaches, which are based on calibrating proxy records against instrumental data12,13,14,15,16,17.
In this study an innovative statistical procedure based on multivariate techniques is applied.
Furthermore, a deciding weight method based on multivariate statistical estimation is applied for weight optimization.
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