Causality-based Subject and Task Fingerprints using fMRI Time-series Data (2409.18298v1)

Abstract: Recently, there has been a revived interest in system neuroscience causation models due to their unique capability to unravel complex relationships in multi-scale brain networks. In this paper, our goal is to verify the feasibility and effectiveness of using a causality-based approach for fMRI fingerprinting. Specifically, we propose an innovative method that utilizes the causal dynamics activities of the brain to identify the unique cognitive patterns of individuals (e.g., subject fingerprint) and fMRI tasks (e.g., task fingerprint). The key novelty of our approach stems from the development of a two-timescale linear state-space model to extract 'spatio-temporal' (aka causal) signatures from an individual's fMRI time series data. To the best of our knowledge, we pioneer and subsequently quantify, in this paper, the concept of 'causal fingerprint.' Our method is well-separated from other fingerprint studies as we quantify fingerprints from a cause-and-effect perspective, which are then incorporated with a modal decomposition and projection method to perform subject identification and a GNN-based (Graph Neural Network) model to perform task identification. Finally, we show that the experimental results and comparisons with non-causality-based methods demonstrate the effectiveness of the proposed methods. We visualize the obtained causal signatures and discuss their biological relevance in light of the existing understanding of brain functionalities. Collectively, our work paves the way for further studies on causal fingerprints with potential applications in both healthy controls and neurodegenerative diseases.

• The paper presents a novel causality-based approach using a two-timescale linear state-space model to extract spatio-temporal fMRI signatures.
• It employs modal decomposition for subject fingerprinting, achieving around 80% identification accuracy compared to traditional correlation methods.
• The method leverages a graph neural network for task identification, demonstrating near-perfect accuracy in certain fMRI tasks with significant neuroscience implications.

Causality-based Subject and Task Fingerprints using fMRI Time-series Data

The paper, "Causality-based Subject and Task Fingerprints using fMRI Time-series Data," addresses a pertinent challenge in the domain of system neuroscience: the identification of individual subjects and fMRI tasks based on causal dynamics extracted from fMRI time-series data. The research leverages the unique capability of causation models to unravel complex interactive relationships within multi-scale brain networks. This is achieved by proposing a novel methodology that uses a two-timescale linear state-space model to extract spatio-temporal (causal) signatures from fMRI data, allowing the quantification and utilization of these signatures for subject and task fingerprinting.

Key Contributions

The central contribution of this paper is the development and validation of a method to utilize causal signatures derived from fMRI time-series data for subject and task identification. The key components of the proposed approach include:

1. Two-Timescale Linear State-Space Model: This model extracts causal dynamics (spatio-temporal signatures) from the fMRI data. The model represents brain interactions on two temporal scales, capturing both immediate and delayed interactions among brain regions.
2. Modal Decomposition and Projection (MD{content}P): For subject fingerprinting, these causal signatures are analyzed using modal decomposition, which projects the time-series data into a space that emphasizes the dynamic modes of brain activity. This allows for robust identification of individuals based on unique cognitive patterns.
3. Graph Neural Network (GNN) Model: For task fingerprinting, the extracted causal signatures are used to construct graph representations of brain activities. A GNN is then employed to differentiate between various fMRI tasks based on these signatures.

Numerical Validation and Results

The proposed methods were validated using fMRI data from the Human Connectome Project (HCP), which includes time-series data for multiple subjects and tasks. Several key findings and numerical results are highlighted:

• Subject Identification: The proposed CM+MD{content}P approach achieves an identification accuracy of approximately 80% in distinguishing between individuals performing resting-state fMRI tasks. This is significantly higher than the accuracy obtained using traditional Functional Connectivity (FC) with Correlation (CoR) methods, which hover around 45%.
• Task Identification: The CM+GNN method demonstrated high accuracies across various fMRI tasks, with some tasks like resting state achieving a perfect 100% accuracy. The method also outperformed alternative approaches, including those based on raw data and functional connectivity.

Implications and Future Directions

The implications of this research are two-fold. Practically, the methodology developed could significantly enhance the ability to distinguish between different cognitive states and individual identities based on fMRI data. This has potential applications in personalized medicine, neuroimaging-based diagnostics, brain-computer interfaces, and more.

Theoretically, the introduction of causal fingerprinting opens new avenues for understanding brain functionality through the lens of causation. Unlike traditional approaches, which often rely on correlation-based methods, causality-based approaches provide richer insights into the directional and temporal dynamics of brain interactions.

Future research could further extend this work by exploring different hypotheses for system input selections, analyzing higher-resolution brain parcellations, and applying the methodology to datasets involving subjects with neurodegenerative diseases. This could pave the way for more granular and comprehensive models of brain activity, enhancing both the precision and scope of fMRI fingerprinting techniques.

Conclusion

This paper substantiates the feasibility and effectiveness of using causality-derived signatures from fMRI time-series data for the purposes of subject and task fingerprinting. The integration of a two-timescale linear state-space model with advanced classification methods like MD{content}P and GNN showcases an innovative approach to capturing the nuanced interactions of brain networks. The methods proposed not only outperform existing ones in accuracy but also offer a more detailed understanding of the underlying causal structures, highlighting the potential of causality-based models in advancing neuroscience research and applications.