  Network inference algorithms are valuable tools for the study of large-scale
neuroimaging datasets. Multivariate transfer entropy is well suited for this
task, being a model-free measure that captures nonlinear and lagged
dependencies between time series to infer a minimal directed network model.
Greedy algorithms have been proposed to efficiently deal with high-dimensional
datasets while avoiding redundant inferences and capturing synergistic effects.
However, multiple statistical comparisons may inflate the false positive rate
and are computationally demanding, which limited the size of previous
validation studies. The algorithm we present---as implemented in the IDTxl
open-source software---addresses these challenges by employing hierarchical
statistical tests to control the family-wise error rate and to allow for
efficient parallelisation. The method was validated on synthetic datasets
involving random networks of increasing size (up to 100 nodes), for both linear
and nonlinear dynamics. The performance increased with the length of the time
series, reaching consistently high precision, recall, and specificity (>98% on
average) for 10000 time samples. Varying the statistical significance threshold
showed a more favourable precision-recall trade-off for longer time series.
Both the network size and the sample size are one order of magnitude larger
than previously demonstrated, showing feasibility for typical EEG and MEG
experiments.
