Inferring functional relations from synthetic fMRI data using large-scale Nonlinear Granger Causality (lsNGC)

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

doi:10.1117/12.2613389

4 April 2022 Inferring functional relations from synthetic fMRI data using large-scale Nonlinear Granger Causality (lsNGC)

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

Author Affiliations +

Proceedings Volume 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging; 120360A (2022) https://doi.org/10.1117/12.2613389
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Inferring relationships among the elements in multivariate observational time-series data is challenging. Representing the interactions as graphs with edges and nodes can describe such relations. While the number of nodal observations in resting-state functional Magnetic Resonance Imaging (rs-fMRI) can rise up to millions of points, such as representing each voxel in a neuroimaging study, the number of temporal observations may remain scarce, leading to ill-posed problems in large-scale data. Here, we recently proposed a novel method for network connectivity analysis, large-scale Nonlinear Granger Causality (lsNGC), which combines the principle of Granger causality and nonlinear dimensionality reduction using Gaussian kernels leading to radial basis function neural networks for time-series prediction. In this study, we apply lsNGC on synthetic rs-fMRI data with known ground truth and compare its performance to competing state-of-the-art methods. We find that the proposed lsNGC method significantly outperforms the existing methods in accuracy, as measured by the Area Under the Receiver Operating Characteristic (AUROC, 0.867 ± 0.028), with p <10^-9 as compared to competing methods, thus quantitatively alarming the merits of lsNGC for the analysis of large-scale brain networks in neuroimaging studies.

Conference Presentation

Citation Download Citation

Axel Wismüller, M. Ali Vosoughi, Adora M. DSouza, and Anas Z. Abidin "Inferring functional relations from synthetic fMRI data using large-scale Nonlinear Granger Causality (lsNGC)", Proc. SPIE 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging, 120360A (4 April 2022); https://doi.org/10.1117/12.2613389

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