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

2026-02-06

IEEE Transactions on Medical Imaging

AI Summary

This work proposes DeCI, a novel framework for fMRI-based brain disorder classification that overcomes the limitations of traditional approaches relying on static functional connectivity (FC). Conventional methods reduce 4D BOLD signals to 2D matrices, discarding temporal dynamics and capturing only linear relationships. In contrast, DeCI directly models raw BOLD time series by decomposing them into periodic and drift components and employing a channel-independent mechanism to effectively disentangle dynamic features of individual brain regions. Integrated with state-of-the-art end-to-end time series models—such as PatchTST, TimesNet, and TimeMixer—DeCI achieves significantly higher classification accuracy and generalization performance across five public datasets compared to both static FC-based methods and existing time series baselines.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Multi-modal Vision

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Web Mining and Content Analysis: Machine learning and data science for the Web

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: PatchTST, TimesNet, 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.

Problem

• fMRI
• brain disorder classification
• functional connectivity
• temporal dynamics
• BOLD signals

Innovation

• temporal modeling
• fMRI
• cycle-drift decomposition
• channel independence
• brain disorder classification