Investigation of large-scale extended Granger causality (lsXGC) on synthetic functional MRI data

Axel Wismüller; M. Ali Vosoughi; Adora DSouza; Anas Z. Abidin

doi:10.1117/12.2613380

4 April 2022 Investigation of large-scale extended Granger causality (lsXGC) on synthetic functional MRI data

Axel Wismüller, M. Ali Vosoughi, Adora DSouza, Anas Z. Abidin

Proceedings Volume 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging; 1203609 (2022) https://doi.org/10.1117/12.2613380
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

It is a challenging research endeavor to infer causal relationships in multivariate observational time-series. 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 free parameters of time-series predictors lead to underdetermined 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 Extended Granger Causality (lsXGC) algorithm, which is based on augmenting a dimensionality-reduced representation of the system’s state-space by supplementing data from the conditional source time-series taken from the original input space. Here, we apply lsXGC on synthetic fMRI data with known ground truth and compare its performance to state-of-the-art methods by leveraging the benefits of information-theoretic approaches. Our results suggest that the proposed lsXGC method significantly outperforms existing methods, both in diagnostic accuracy with Area Under the Receiver Operating Characteristic (AUROC = 0.849 vs. [0.727, 0.762] for competing methods, p < 10^-8), and computation time (3.4 sec vs. [9.7, 4.8 x 10³] sec for competing methods) benchmarks, demonstrating the potential of lsXGC for analyzing large-scale networks in neuroimaging studies of the human brain.

Conference Presentation

Citation Download Citation

Axel Wismüller, M. Ali Vosoughi, Adora DSouza, and Anas Z. Abidin "Investigation of large-scale extended Granger causality (lsXGC) on synthetic functional MRI data", Proc. SPIE 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1203609 (4 April 2022); https://doi.org/10.1117/12.2613380

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