# Fusion Learning from Dynamic Functional Connectivity: Combining the Amplitude and Phase of fMRI Signals to Identify Brain Disorders

arXiv: https://arxiv.org/abs/2603.24603 · HTML: https://mlsanity.com/paper/2603.24603/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 14 Mar 2026.

## Builds on (most cited first, 10 of 10)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Activating More Pixels in Image Super-Resolution Transformer](https://mlsanity.com/paper/2205.04437/) (2022)
- [Brain Network Transformer](https://mlsanity.com/paper/2210.06681/) (2022)
- [Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention](https://mlsanity.com/paper/2105.13495/) (2021)
- [Lightweight Attentional Feature Fusion: A New Baseline for Text-to-Video Retrieval](https://mlsanity.com/paper/2112.01832/) (2021)
- [BrainNPT: Pre-training of Transformer networks for brain network classification](https://mlsanity.com/paper/2305.01666/) (2023)
- [Dynamic Brain Transformer with Multi-level Attention for Functional Brain Network Analysis](https://mlsanity.com/paper/2309.01941/) (2023)
- [Evaluating phase synchronization methods in fMRI: a comparison study and new approaches](https://mlsanity.com/paper/2009.10126/) (2020)
- [MMViT: Multiscale Multiview Vision Transformers](https://mlsanity.com/paper/2305.00104/) (2023)
- [Deep Chronnectome Learning via Full Bidirectional Long Short-Term Memory Networks for MCI Diagnosis](https://mlsanity.com/paper/1808.10383/) (2018)

## Abstract (arXiv)

Dynamic functional connectivity (dFC) derived from resting-state functional magnetic resonance imaging (fMRI) has been extensively utilized in brain science research. The sliding window correlation (SWC) method is a widely used approach for constructing dFC by computing correlation coefficients between amplitude time series of signals from pairs of brain regions. In this study, we propose an integrated approach that incorporates both amplitude and phase information of fMRI signals to improve the detection of brain disorders. Specifically, we introduce a multi-scale fusion learning framework, namely MSFL, which leverages two complementary dFC features derived from SWC and phase synchronization (PS). Here, SWC captures amplitude correlations, while PS measures phase coherence within dFC. We evaluated the efficacy of MSFL in classifying autism spectrum disorder and major depressive disorder using two publicly available datasets: ABIDE I and REST-meta-MDD, respectively. The results indicate that MSFL significantly outperforms existing comparative models. Moreover, we performed model explanation analysis using the SHAP framework, which showed that both types of dFC features from SWC and PS contribute to detecting brain disorders.