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

## TL;DR

This paper introduces a new method using fMRI data to identify individuals and tasks based on brain causal dynamics, showing biological relevance and potential for neuroscience applications.

## Contribution

A novel two-timescale state-space model and brain reachability landscape visualization for capturing causal brain dynamics in subject and task fingerprinting.

## Key findings

- The model captures causal brain region interactions and disentangled dynamic modes from fMRI data.
- Causal signatures improve subject identification and task classification compared to non-causal methods.
- The brain reachability landscape provides a new way to visualize and quantify brain activation under different tasks.

## Abstract

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.

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.

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.

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.