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

arXiv: https://arxiv.org/abs/2409.18298 · HTML: https://mlsanity.com/paper/2409.18298/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 56 references that are in the map.
- First posted on arXiv 26 Sep 2024.

## Builds on (most cited first, 11 of 11)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [Dynamic mode decomposition with control](https://mlsanity.com/paper/1409.6358/) (2014)
- [Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth](https://mlsanity.com/paper/2105.04550/) (2021)
- [Decoding and mapping task states of the human brain via deep learning](https://mlsanity.com/paper/1801.09858/) (2018)
- [Controllability of Brain Networks](https://mlsanity.com/paper/1406.5197/) (2014)
- [Connections Between Nuclear Norm and Frobenius Norm Based Representations](https://mlsanity.com/paper/1502.07423/) (2015)
- [GEFF: Graph Embedding for Functional Fingerprinting](https://mlsanity.com/paper/2001.06605/) (2020)
- [Improving Functional Connectome Fingerprinting with Degree-Normalization](https://mlsanity.com/paper/2011.10079/) (2020)
- [Tangent functional connectomes uncover more unique phenotypic traits](https://mlsanity.com/paper/2212.06394/) (2022)
- [Efficient Reconstruction of Neural Mass Dynamics Modeled by Linear-Threshold Networks](https://mlsanity.com/paper/2308.14231/) (2023)

## Built on by (most cited first, 1 of 1)

- [Elastic Spectral State Space Models for Train-Once Budgeted Inference](https://mlsanity.com/paper/2601.22488/) (2026)

## Abstract (arXiv)

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.