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

• URL: http://arxiv.org/abs/2603.24603v1
• Date: Sat, 14 Mar 2026 04:57:17 GMT
• Title: Fusion Learning from Dynamic Functional Connectivity: Combining the Amplitude and Phase of fMRI Signals to Identify Brain Disorders
• Abstract summary: Dynamic functional connectivity (dFC) derived from resting-state functional magnetic resonance imaging (fMRI) has been extensively utilized in brain science research.<n>We propose an integrated approach that incorporates both amplitude and phase information of fMRI signals to improve the detection of brain disorders.
• Score: 1.0086894761792204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• DeMTS: Denoising Trajectories as Multivariate Time Series for Hallucination Detection in Diffusion Language Models [59.414664850109155]
  Diffusion large language models (D-LLMs) have emerged as a promising paradigm for text generation.<n>D-LLMs remain vulnerable to hallucinations, where fluent outputs may contain factually incorrect or unsupported content.
  arXiv  Detail & Related papers  (2026-07-24T06:45:43Z)
• Moving Beyond Functional Connectivity: Time-Series Modeling for fMRI-Based Brain Disorder Classification [8.837732238971187]
  Functional magnetic resonance imaging (fMRI) enables non-invasive brain disorder classification by capturing blood-oxygen-level-dependent (BOLD) signals.<n>Most existing methods rely on functional connectivity (FC) via Pearson correlation.<n>We benchmark state-of-the-art temporal models on raw BOLD signals across five public datasets.
  arXiv  Detail & Related papers  (2026-02-09T04:42:42Z)
• PAD: Phase-Amplitude Decoupling Fusion for Multi-Modal Land Cover Classification [49.37555541088792]
  Phase-Amplitude Decoupling (PAD) is a frequency-aware framework that separates phase (modality-shared) and amplitude (modality-complementary) components.<n>This work establishes a new paradigm for physics-aware multi-modal fusion in remote sensing.
  arXiv  Detail & Related papers  (2025-04-27T07:21:42Z)
• Classification of Mild Cognitive Impairment Based on Dynamic Functional Connectivity Using Spatio-Temporal Transformer [30.044545011553172]
  We propose a novel framework that jointly learns the embedding of both spatial and temporal information within dFC.<n> Experimental results on 345 subjects with 570 scans from the Alzheimers Disease Neuroimaging Initiative (ADNI) demonstrate the superiority of our proposed method.
  arXiv  Detail & Related papers  (2025-01-27T18:20:33Z)
• Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation [56.34634121544929]
  In this study, we first construct the brain-effective network via the dynamic causal model. We then introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE) This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks.
  arXiv  Detail & Related papers  (2024-05-21T20:37:07Z)
• An Interpretable Cross-Attentive Multi-modal MRI Fusion Framework for Schizophrenia Diagnosis [46.58592655409785]
  We propose a novel Cross-Attentive Multi-modal Fusion framework (CAMF) to capture both intra-modal and inter-modal relationships between fMRI and sMRI. Our approach significantly improves classification accuracy, as demonstrated by our evaluations on two extensive multi-modal brain imaging datasets. The gradient-guided Score-CAM is applied to interpret critical functional networks and brain regions involved in schizophrenia.
  arXiv  Detail & Related papers  (2024-03-29T20:32:30Z)
• DiffGAN-F2S: Symmetric and Efficient Denoising Diffusion GANs for Structural Connectivity Prediction from Brain fMRI [15.40111168345568]
  It is challenging to bridge the reliable non-linear mapping relations between structural connectivity (SC) and functional magnetic resonance imaging (fMRI) A novel diffusision generative adversarial network-based fMRI-to-SC model is proposed to predict SC from brain fMRI in an end-to-end manner.
  arXiv  Detail & Related papers  (2023-09-28T06:55:50Z)
• Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis [44.45598796591008]
  Brain imaging-to-graph generation (BIGG) framework is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment analysis. The hierarchical transformers in the generator are designed to estimate the noise at multiple scales. Evaluations of the ADNI dataset demonstrate the feasibility and efficacy of the proposed model.
  arXiv  Detail & Related papers  (2023-05-18T06:54:56Z)
• Joint fMRI Decoding and Encoding with Latent Embedding Alignment [77.66508125297754]
  We introduce a unified framework that addresses both fMRI decoding and encoding. Our model concurrently recovers visual stimuli from fMRI signals and predicts brain activity from images within a unified framework.
  arXiv  Detail & Related papers  (2023-03-26T14:14:58Z)
• MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning [92.91544082745196]
  Federated learning (FL) has been widely employed for medical image analysis. FL's performance is limited for multiple sclerosis (MS) lesion segmentation tasks. We propose the first FL MS lesion segmentation framework via two effective re-weighting mechanisms.
  arXiv  Detail & Related papers  (2022-05-03T14:06:03Z)
• Separating Stimulus-Induced and Background Components of Dynamic Functional Connectivity in Naturalistic fMRI [13.112514419777593]
  We propose a novel, data-driven approach to isolate stimulus-driven dynamic changes in brain functional connectivity (FC) from the background noise. To recover the shared low-rank subspace, we introduce a fusion-type penalty on the differences between the rows of the low-rank matrix. We develop an efficient algorithm via a linearized alternating direction method of multipliers to solve the fused-PCP.
  arXiv  Detail & Related papers  (2021-01-24T11:35:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.