A Dual Cross-Attention Graph Learning Framework For Multimodal MRI-Based Major Depressive Disorder Detection

• URL: http://arxiv.org/abs/2604.10116v1
• Date: Sat, 11 Apr 2026 09:19:41 GMT
• Title: A Dual Cross-Attention Graph Learning Framework For Multimodal MRI-Based Major Depressive Disorder Detection
• Abstract summary: Major depressive disorder (MDD) is a prevalent mental disorder associated with complex neurobiological changes that cannot be fully captured using a single imaging modality.<n>We propose a dual cross-attention-based multimodal fusion framework that explicitly models bidirectional interactions between structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) representations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Major depressive disorder (MDD) is a prevalent mental disorder associated with complex neurobiological changes that cannot be fully captured using a single imaging modality. The use of multimodal magnetic resonance imaging (MRI) provides a more comprehensive understanding of brain changes by combining structural and functional data. Despite this, the effective integration of these modalities remains challenging. In this study, we propose a dual cross-attention-based multimodal fusion framework that explicitly models bidirectional interactions between structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) representations. The proposed approach is tested on the large-scale REST-meta-MDD dataset using both structural and functional brain atlas configurations. Numerous experiments conducted under a 10-fold stratified cross-validation demonstrated that the proposed fusion algorithm achieves robust and competitive performance across all atlas types. The proposed method consistently outperforms conventional feature-level concatenation for functional atlases, while maintaining comparable performance for structural atlases. The most effective dual cross-attention multimodal model obtained 84.71% accuracy, 86.42% sensitivity, 82.89% specificity, 84.34% precision, and 85.37% F1-score. These findings emphasize the importance of explicitly modeling cross-modal interactions for multimodal neuroimaging-based MDD classification.

Related papers

• SLaM-DiMM: Shared Latent Modeling for Diffusion Based Missing Modality Synthesis in MRI [0.0]
  Brain MRI scans are often found in four modalities, consisting of T1-weighted with and without contrast enhancement (T1ce and T1w), T2-weighted imaging (T2w) and Flair.<n>We propose SLaM-DiMM, a novel missing modality generation framework that harnesses the power of diffusion models to synthesize any of the four target MRI modalities from other available modalities.
  arXiv  Detail & Related papers  (2025-09-19T14:27:35Z)
• 4D Multimodal Co-attention Fusion Network with Latent Contrastive Alignment for Alzheimer's Diagnosis [24.771496672135395]
  We propose M2M-AlignNet: a geometry-aware co-attention network with latent alignment for early Alzheimer's diagnosis.<n>At the core of our approach is a multi-patch-to-multi-patch (M2M) contrastive loss function that quantifies and reduces representational discrepancies.<n>We conduct extensive experiments to confirm the effectiveness of our method and highlight the correspondance between fMRI and sMRI as AD biomarkers.
  arXiv  Detail & Related papers  (2025-04-23T15:18:55Z)
• Multi-modal Vision Pre-training for Medical Image Analysis [11.569448567735435]
  Self-supervised learning has greatly facilitated medical image analysis by suppressing the training data requirement for real-world applications.<n>We conduct a novel multi-modal image pre-training with three proxy tasks to facilitate the learning of cross-modality representations and correlations.<n>Our method is reported in comparison to state-of-the-art pre-training methods, with Dice Score improvement of 0.28%-14.47% across six segmentation benchmarks and a consistent accuracy boost of 0.65%-18.07% in four individual image classification tasks.
  arXiv  Detail & Related papers  (2024-10-14T15:12:16Z)
• 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)
• Transferring Ultrahigh-Field Representations for Intensity-Guided Brain Segmentation of Low-Field Magnetic Resonance Imaging [51.92395928517429]
  The use of 7T MRI is limited by its high cost and lower accessibility compared to low-field (LF) MRI. This study proposes a deep-learning framework that fuses the input LF magnetic resonance feature representations with the inferred 7T-like feature representations for brain image segmentation tasks.
  arXiv  Detail & Related papers  (2024-02-13T12:21:06Z)
• Cross-modality Guidance-aided Multi-modal Learning with Dual Attention for MRI Brain Tumor Grading [47.50733518140625]
  Brain tumor represents one of the most fatal cancers around the world, and is very common in children and the elderly. We propose a novel cross-modality guidance-aided multi-modal learning with dual attention for addressing the task of MRI brain tumor grading.
  arXiv  Detail & Related papers  (2024-01-17T07:54:49Z)
• Fusing Structural and Functional Connectivities using Disentangled VAE for Detecting MCI [9.916963496386089]
  A novel hierarchical structural-functional connectivity fusing (HSCF) model is proposed to construct brain structural-functional connectivity matrices. Results from a wide range of tests performed on the public Alzheimer's Disease Neuroimaging Initiative database show that the proposed model performs better than competing approaches.
  arXiv  Detail & Related papers  (2023-06-16T05:22:25Z)
• Cross-Attention is Not Enough: Incongruity-Aware Dynamic Hierarchical Fusion for Multimodal Affect Recognition [69.32305810128994]
  Incongruity between modalities poses a challenge for multimodal fusion, especially in affect recognition. We propose the Hierarchical Crossmodal Transformer with Dynamic Modality Gating (HCT-DMG), a lightweight incongruity-aware model. HCT-DMG: 1) outperforms previous multimodal models with a reduced size of approximately 0.8M parameters; 2) recognizes hard samples where incongruity makes affect recognition difficult; 3) mitigates the incongruity at the latent level in crossmodal attention.
  arXiv  Detail & Related papers  (2023-05-23T01:24:15Z)
• Hierarchical Graph Convolutional Network Built by Multiscale Atlases for Brain Disorder Diagnosis Using Functional Connectivity [48.75665245214903]
  We propose a novel framework to perform multiscale FCN analysis for brain disorder diagnosis. We first use a set of well-defined multiscale atlases to compute multiscale FCNs. Then, we utilize biologically meaningful brain hierarchical relationships among the regions in multiscale atlases to perform nodal pooling.
  arXiv  Detail & Related papers  (2022-09-22T04:17:57Z)
• Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction [68.80715727288514]
  We show how to unfold an iterative MGDUN algorithm into a novel model-guided deep unfolding network by taking the MRI observation matrix. In this paper, we propose a novel Model-Guided interpretable Deep Unfolding Network (MGDUN) for medical image SR reconstruction.
  arXiv  Detail & Related papers  (2022-09-15T03:58:30Z)
• Mapping individual differences in cortical architecture using multi-view representation learning [0.0]
  We introduce a novel machine learning method which allows combining the activation-and connectivity-based information respectively measured through task-fMRI and resting-state fMRI. It combines a multi-view deep autoencoder which is designed to fuse the two fMRI modalities into a joint representation space within which a predictive model is trained to guess a scalar score that characterizes the patient.
  arXiv  Detail & Related papers  (2020-04-01T09:01:25Z)

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.