fMRI-Diffusion: Generating fMRI Time Series Via a Temporal Transformer Diffusion Model for Major Depressive Disorder Diagnosis
Quick Take
fMRI-Diffusion synthesizes fMRI time series for improved Major Depressive Disorder diagnosis using a Temporal Transformer.
Key Points
- Generates ROI-level fMRI time series instead of FC matrices.
- Utilizes a Temporal Transformer for capturing temporal dependencies.
- Improves diagnostic accuracy across multiple classifiers and datasets.
Article Content
From source RSS / original summaryarXiv:2605. 24065v1 Announce Type: new Abstract: Diagnosing Major Depressive Disorder (MDD) from functional magnetic resonance imaging (fMRI) using functional connectivity (FC) analysis requires large amounts of labeled data that are scarce in clinical settings. Existing augmentation methods synthesize FC matrices, which compress fMRI recordings into static pairwise summaries and discard temporal information.
We propose fMRI-Diffusion, a framework that synthesizes region-of-interest (ROI)-level fMRI time series rather than FC matrices. A Temporal Transformer serves as the denoising network within a denoising diffusion probabilistic model, treating each time point as a token to capture temporal dependencies through self-attention.
A supervised pretraining strategy initializes the Transformer with task-relevant representations before diffusion training, and FC matrices are derived from the synthesized time series for classification. Experiments on the REST-meta-MDD dataset show that augmenting training data with synthetic time series consistently improves diagnostic accuracy across ten classifiers, six parcellation atlases, and three acquisition sites.
The method outperforms five recent FC-based synthesis approaches, with accuracy gains of up to 3. 7 percentage points over the strongest baseline. Ablation studies confirm the contributions of both the Transformer-based denoiser and the pretraining strategy. Distributional fidelity metrics remain below 0. 06 across all conditions, indicating close agreement between real and synthetic distributions.
These findings suggest that synthesizing fMRI time series before FC computation preserves temporal information lost in matrix-level augmentation and provides a practical strategy for MDD diagnosis under limited data.
Reader Mode unavailable (could not extract clean content).
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CV
See more →Deep Learning-Based Automated Quantification of TIMI Myocardial Perfusion Frame Count (DL-TMPFC) from Coronary Angiography: A Novel Framework for Rapid Assessment of Microvascular Dysfunction
DL-TMPFC automates TIMI Myocardial Perfusion Frame Count for rapid assessment of coronary microvascular dysfunction.