BrainSymphony: A Transformer-Driven Fusion of fMRI Time Series and Structural Connectivity

• URL: http://arxiv.org/abs/2506.18314v1
• Date: Mon, 23 Jun 2025 06:00:21 GMT
• Title: BrainSymphony: A Transformer-Driven Fusion of fMRI Time Series and Structural Connectivity
• Authors: Moein Khajehnejad, Forough Habibollahi, Adeel Razi,
• Abstract summary: BrainSymphony is a lightweight, parameter-efficient foundation model for neuroimaging.<n>It achieves state-of-the-art performance while being pre-trained on significantly smaller public datasets.<n>BrainSymphony establishes that architecturally-aware, multimodal models can surpass their larger counterparts.
• Score: 2.3486335708866606
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing foundation models for neuroimaging are often prohibitively large and data-intensive. We introduce BrainSymphony, a lightweight, parameter-efficient foundation model that achieves state-of-the-art performance while being pre-trained on significantly smaller public datasets. BrainSymphony's strong multimodal architecture processes functional MRI data through parallel spatial and temporal transformer streams, which are then efficiently distilled into a unified representation by a Perceiver module. Concurrently, it models structural connectivity from diffusion MRI using a novel signed graph transformer to encode the brain's anatomical structure. These powerful, modality-specific representations are then integrated via an adaptive fusion gate. Despite its compact design, our model consistently outperforms larger models on a diverse range of downstream benchmarks, including classification, prediction, and unsupervised network identification tasks. Furthermore, our model revealed novel insights into brain dynamics using attention maps on a unique external psilocybin neuroimaging dataset (pre- and post-administration). BrainSymphony establishes that architecturally-aware, multimodal models can surpass their larger counterparts, paving the way for more accessible and powerful research in computational neuroscience.

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