Exploring directed network connectivity in complex systems using large-scale augmented Granger causality (lsAGC)

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

doi:10.1117/12.2611301

4 April 2022 Exploring directed network connectivity in complex systems using large-scale augmented Granger causality (lsAGC)

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

Author Affiliations +

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 120330O (2022) https://doi.org/10.1117/12.2611301
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

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, 4.8×10^3] sec for competing methods) benchmarks, demonstrating the potential of lsAGC for large-scale observations in neuroimaging studies of the human brain.

Conference Presentation

Citation Download Citation

Axel Wismüller, M. Ali Vosoughi, Adora DSouza, and Anas Z. Abidin "Exploring directed network connectivity in complex systems using large-scale augmented Granger causality (lsAGC)", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120330O (4 April 2022); https://doi.org/10.1117/12.2611301

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available