Reconstructing Brain Causal Dynamics for Subject and Task Fingerprints using fMRI Time-series Data

TL;DR

This paper introduces a novel causal dynamic modeling approach using fMRI data to improve subject and task fingerprinting, revealing biologically relevant brain interaction signatures and visualizations.

Contribution

It develops a two-timescale linear state-space model capturing causal brain signatures and integrates them with modal decomposition and GNNs for enhanced identification and classification.

Findings

Causal signatures outperform non-causality methods in subject and task identification.

Visualizations of brain reachability landscapes provide new insights into brain activation.

Model-based signatures show clear biological relevance with established brain functions.

Abstract

Purpose: Recently, there has been a revived interest in system neuroscience causation models, driven by their unique capability to unravel complex relationships in multi-scale brain networks. In this paper, we present a novel method that leverages causal dynamics to achieve effective fMRI-based subject and task fingerprinting. Methods: By applying an implicit-explicit discretization scheme, we develop a two-timescale linear state-space model. Through data-driven identification of its parameters, the model captures causal signatures, including directed interactions among brain regions from a spatial perspective, and disentangled fast and slow dynamic modes of brain activity from a temporal perspective. These causal signatures are then integrated with: (i) a modal decomposition and projection method for model-based subject identification, and (ii) a Graph Neural Network (GNN) framework for learning-based task classification. Furthermore, we introduce the concept of the brain reachability landscape as a novel visualization tool, which quantitatively characterizes the maximum possible activation levels of brain regions under various fMRI tasks. Results: We evaluate the proposed approach using the Human Connectome Project dataset and demonstrate its advantage over non-causality-based methods. The obtained causal signatures are visualized and demonstrate clear biological relevance with established understandings of brain function. Conclusion: We verified the feasibility and effectiveness of utilizing brain causal signatures for subject and task fingerprinting. Additionally, our work paves the way for further studies on causal fingerprints with potential applications in both healthy controls and neurodegenerative diseases.