Inferring Dynamic Communication with Graph Diffusion Autoregression from rs-fMRI and Tractography
Presented During: Poster Session 3
Friday, June 27, 2025: 01:45 PM - 03:45 PM
Friday, June 27, 2025: 01:45 PM - 03:45 PM
Presented During: Poster Session 4
Saturday, June 28, 2025: 01:45 PM - 03:45 PM
Saturday, June 28, 2025: 01:45 PM - 03:45 PM
Poster No:
1447
Submission Type:
Abstract Submission
Authors:
Felix Schwock1, Daniel Nordgren1, Les Atlas1, Azadeh Yazdan-Shahmorad1, Hesamoddin Jahanian1
Institutions:
1University of Washington, Seattle, WA
First Author:
Co-Author(s):
Introduction:
Resting-state functional MRI (rs-fMRI) is increasingly recognized as a promising tool for measuring brain network connectivity and identifying biomarkers for neurological disorders. However, current processing methods often lack the sensitivity and specificity required for clinical applications. This limitation arises partly from traditional approaches that treat functional connectivity as a stationary variable, focusing primarily on temporal cross-correlation between time series, while overlooking the complex, dynamic spatiotemporal patterns underlying brain connectivity.
We propose a new paradigm for analyzing rs-fMRI data that departs from traditional approaches in two key ways: 1) Instead of relying on simple cross-correlation, we employ a graph diffusion autoregression model to capture the signal flow between brain regions; and 2) We integrate spatial information derived from tractography to enhance the analysis.
We propose a new paradigm for analyzing rs-fMRI data that departs from traditional approaches in two key ways: 1) Instead of relying on simple cross-correlation, we employ a graph diffusion autoregression model to capture the signal flow between brain regions; and 2) We integrate spatial information derived from tractography to enhance the analysis.
Methods:
To infer the dynamic information flow between brain regions we recently developed the graph diffusion autoregressive (GDAR) model (Schwock et al., 2024). In this framework, the spatiotemporal dynamics of neural activity are parametrically modeled on top of a structural brain graph via a network diffusion process (Fig. 1), which has recently been shown to accurately model the propagation of signals across brain networks (Seguin et al., 2023). In contrast to static functional connectivity measures, our model produces a time varying communication signal on the graph edges, referred to as GDAR flow, that can be further processed to extract biomarkers for neurological disorders. To validate the model, we have trained and tested it on 23 fMRI sessions form 9 young healthy subjects (ages 25-62) and assessed its performance on predicting future blood oxygen-level dependent (BOLD) signals, as well as measured the test-retest correlation of the extracted GDAR flow signal (Fig. 2). Furthermore, we have applied the GDAR model to fMRI data from 10 old-cognitively normal subjects (ages 63-93) to assess changes in neural flow due to aging. For all subjects, structural connectivity graphs were modeled using tractography data from (Yeh, 2022).
Results:
Critically, we found that the average GDAR flow is not correlated with classical functional connectivity estimates, measured using the Pearson correlation coefficient (R² = 0.0269). This demonstrates that the estimated communication signal is fundamentally different from temporal correlation signals, providing a novel framework for analyzing rs-fMRI data.
In the young healthy group, the GDAR model accurately predicted future BOLD activity with an average normalized root mean square error of 0.247, highlighting its strength in modeling BOLD signals across different brain regions. Furthermore, the estimated communication signal demonstrates high stability within a single scan session and across multiple sessions for the same subject, with average test-retest correlation scores of 0.632.
Finally, comparisons with fMRI data from young and old healthy subjects reveal significant differences in the estimated communication signal. Specifically, a strong increase in neural communication was observed in older subjects, reflecting the model's ability to capture physiological changes in the brain associated with aging.
In the young healthy group, the GDAR model accurately predicted future BOLD activity with an average normalized root mean square error of 0.247, highlighting its strength in modeling BOLD signals across different brain regions. Furthermore, the estimated communication signal demonstrates high stability within a single scan session and across multiple sessions for the same subject, with average test-retest correlation scores of 0.632.
Finally, comparisons with fMRI data from young and old healthy subjects reveal significant differences in the estimated communication signal. Specifically, a strong increase in neural communication was observed in older subjects, reflecting the model's ability to capture physiological changes in the brain associated with aging.
Conclusions:
We propose a novel method for studying dynamic brain network connectivity that, unlike most functional connectivity approaches naturally combines structural and functional brain imaging data. Our model captures the dynamics of rs-fMRI signals and, critically, demonstrates superior sensitivity and reliability in analyzing brain networks at the single-subject level, where traditional temporal-correlation-based methods often fall short. We believe this approach represents a significant advancement in rs-fMRI analysis, moving the field closer to developing a powerful tool for identifying new biomarkers for neurological disorders.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Methods Development 2
Multivariate Approaches
Task-Independent and Resting-State Analysis
Keywords:
Data analysis
FUNCTIONAL MRI
Modeling
Multivariate
Statistical Methods
Tractography
1|2Indicates the priority used for review
Abstract Information
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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Provide references using APA citation style.
Seguin, C., et al. (2023). Communication dynamics in the human connectome shape the cortex-wide propagation of direct electrical stimulation. Neuron, 111(9), 1391-1401.e5.
Yeh, F.-C. (2022). Population-based tract-to-region connectome of the human brain and its hierarchical topology. Nature Communications, 13(1), 4933.