Moving Beyond Functional Connectivity: Time-Series Modeling for fMRI-Based Brain Disorder Classification

TL;DR

This paper demonstrates that direct temporal modeling of raw fMRI BOLD signals using advanced time-series models outperforms traditional functional connectivity methods for brain disorder classification, leading to improved accuracy and robustness.

Contribution

It introduces DeCI, a novel framework that effectively decomposes and models fMRI signals, advancing beyond static connectivity approaches for brain disorder classification.

Findings

Temporal models outperform FC-based methods in classification accuracy.

DeCI achieves superior generalization across datasets.

Disentangling cycle and drift improves model robustness.

Abstract

Functional magnetic resonance imaging (fMRI) enables non-invasive brain disorder classification by capturing blood-oxygen-level-dependent (BOLD) signals. However, most existing methods rely on functional connectivity (FC) via Pearson correlation, which reduces 4D BOLD signals to static 2D matrices, discarding temporal dynamics and capturing only linear inter-regional relationships. In this work, we benchmark state-of-the-art temporal models (e.g., time-series models such as PatchTST, TimesNet, and TimeMixer) on raw BOLD signals across five public datasets. Results show these models consistently outperform traditional FC-based approaches, highlighting the value of directly modeling temporal information such as cycle-like oscillatory fluctuations and drift-like slow baseline trends. Building on this insight, we propose DeCI, a simple yet effective framework that integrates two key principles: (i) Cycle and Drift Decomposition to disentangle cycle and drift within each ROI (Region of Interest); and (ii) Channel-Independence to model each ROI separately, improving robustness and reducing overfitting. Extensive experiments demonstrate that DeCI achieves superior classification accuracy and generalization compared to both FC-based and temporal baselines. Our findings advocate for a shift toward end-to-end temporal modeling in fMRI analysis to better capture complex brain dynamics. The code is available at https://github.com/Levi-Ackman/DeCI.