Large-scale Augmented Granger Causality (lsAGC) for Connectivity
Analysis in Complex Systems: From Computer Simulations to Functional MRI
(fMRI)
- URL: http://arxiv.org/abs/2101.09354v1
- Date: Sun, 10 Jan 2021 01:44:48 GMT
- Title: Large-scale Augmented Granger Causality (lsAGC) for Connectivity
Analysis in Complex Systems: From Computer Simulations to Functional MRI
(fMRI)
- Authors: Axel Wismuller and M. Ali Vosoughi
- Abstract summary: We introduce large-scale Augmented Granger Causality (lsAGC) as a method for connectivity analysis in complex systems.
lsAGC algorithm combines dimension reduction with source time-series augmentation.
We quantitatively evaluate the performance of lsAGC on synthetic directional time-series networks with known ground truth.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We introduce large-scale Augmented Granger Causality (lsAGC) as a method for
connectivity analysis in complex systems. The lsAGC algorithm combines
dimension reduction with source time-series augmentation and uses predictive
time-series modeling for estimating directed causal relationships among
time-series. This method is a multivariate approach, since it is capable of
identifying the influence of each time-series on any other time-series in the
presence of all other time-series of the underlying dynamic system. We
quantitatively evaluate the performance of lsAGC on synthetic directional
time-series networks with known ground truth. As a reference method, we compare
our results with cross-correlation, which is typically used as a standard
measure of connectivity in the functional MRI (fMRI) literature. Using
extensive simulations for a wide range of time-series lengths and two different
signal-to-noise ratios of 5 and 15 dB, lsAGC consistently outperforms
cross-correlation at accurately detecting network connections, using Receiver
Operator Characteristic Curve (ROC) analysis, across all tested time-series
lengths and noise levels. In addition, as an outlook to possible clinical
application, we perform a preliminary qualitative analysis of connectivity
matrices for fMRI data of Autism Spectrum Disorder (ASD) patients and typical
controls, using a subset of 59 subjects of the Autism Brain Imaging Data
Exchange II (ABIDE II) data repository. Our results suggest that lsAGC, by
extracting sparse connectivity matrices, may be useful for network analysis in
complex systems, and may be applicable to clinical fMRI analysis in future
research, such as targeting disease-related classification or regression tasks
on clinical data.
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