BrainSymphony: A parameter-efficient multimodal foundation model for brain dynamics with limited data

TL;DR

BrainSymphony is a lightweight, parameter-efficient multimodal model that integrates fMRI and diffusion MRI data, outperforming larger models in neuroscience tasks with less data and enhanced interpretability.

Contribution

Introduces BrainSymphony, a novel compact multimodal foundation model that efficiently combines fMRI and structural connectivity data without architectural modifications.

Findings

Outperforms larger models on prediction and classification benchmarks

Requires substantially less data than state-of-the-art models

Reveals drug-induced brain reorganization through interpretability analyses

Abstract

Foundation models are transforming neuroscience but are often prohibitively large, data-hungry, and difficult to deploy. Here, we introduce BrainSymphony, a lightweight and parameter-efficient foundation model with plug-and-play integration of fMRI time series and diffusion-derived structural connectivity, allowing unimodal or multimodal training and deployment without architectural changes while requiring substantially less data compared to the state-of-the-art. The model processes fMRI time series through parallel spatial and temporal transformer streams, distilled into compact embeddings by a Perceiver module, while a novel signed graph transformer encodes anatomical connectivity from diffusion MRI. These complementary representations are then combined through an adaptive fusion mechanism. Despite its compact design, BrainSymphony consistently outperforms larger models on benchmarks spanning prediction, classification, and unsupervised network discovery. Highlighting the model's generalizability and interpretability, attention maps reveal drug-induced context-dependent reorganization of cortical hierarchies in an independent psilocybin neuroimaging dataset. BrainSymphony delivers accessible, interpretable, and clinically meaningful results and demonstrates that architecturally informed, multimodal models can surpass much larger counterparts and advance applications of AI in neuroscience.