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Updated: Jan 12, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Reconstructing brain causal dynamics for subject and task fingerprints using fMRI time-series data.

Dachuan Song¹, Li Shen², Duy Duong-Tran²

¹Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA USA.

Health Information Science and Systems

|October 31, 2025

Summary

This study introduces a new method using causal dynamics for brain fingerprinting via functional magnetic resonance imaging (fMRI). The approach effectively identifies individuals and tasks by analyzing brain causal signatures, offering potential for clinical applications.

Keywords:

Brain causal dynamics FMRI fingerprinting Reachability analysis Two-timescale state-space model

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Last Updated: Jan 12, 2026

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Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Area of Science:

Neuroscience
Systems Neuroscience
Computational Neuroscience

Background:

Revived interest in systems neuroscience causation models for understanding complex brain networks.
Need for effective methods to analyze multi-scale brain network dynamics.

Purpose of the Study:

To present a novel method leveraging causal dynamics for fMRI-based subject and task fingerprinting.
To develop a model capturing causal signatures in brain activity.
To enable subject identification and task classification using these signatures.

Main Methods:

Developed a two-timescale linear state-space model using an implicit-explicit discretization scheme.
Identified model parameters to capture spatial directed interactions and temporal dynamic modes.
Integrated causal signatures with modal decomposition for subject identification and Graph Neural Network (GNN) for task classification.
Introduced the brain reachability landscape for visualizing brain region activation.

Main Results:

Evaluated the approach on the Human Connectome Project dataset, showing advantages over non-causality-based methods.
Visualized causal signatures, demonstrating clear biological relevance.
Successfully utilized brain causal signatures for subject and task fingerprinting.

Conclusions:

Verified the feasibility and effectiveness of brain causal signatures for fingerprinting.
Paved the way for further studies on causal fingerprints.
Highlighted potential applications in healthy controls and neurodegenerative diseases.