Unveiling causal relationships among time-series in multivariate observational data is a challenging research topic. Such data may be represented by graphs, where nodes represent time-series, and edges directed causal influence scores between them. If the number of nodes exceeds the number of temporal observations, conventional methods, such as standard Granger causality, are of limited value, because estimating the free parameters of time-series predictors lead to under-determined problems. A typical example for this situation is functional Magnetic Resonance Imaging (fMRI), where the number of nodal observations is large, usually ranging from 102 to 105 time-series, while the number of temporal observations is low, usually less than 103. Hence, innovative approaches are required to address the challenges arising from such data sets. Recently, we have proposed the large-scale Augmented Granger Causality (lsAGC) algorithm, which is based on augmenting a dimensionalityreduced representation of the system’s state-space by supplementing data from the conditional source timeseries taken from the original input space. Here, we apply lsAGC on synthetic fMRI data with known ground truth and compare its performance to state-of-the-art methods leveraging the benefits of information-theoretic metrics. Our results suggest that the proposed lsAGC method significantly outperforms existing methods, both in diagnostic accuracy with Area Under the Receiver Operating Characteristic (AUROC = 0.894 vs. [0.727, 0.762] for competing methods, p < 10−9), and computation time (0.7 sec vs. [9.7, 4.8×103] sec for competing methods) benchmarks, demonstrating the potential of lsAGC for large-scale observations in neuroimaging studies of the human brain.
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