MnemoDyn: Learning Resting State Dynamics from 40K FMRI sequences

Researchers introduced MnemoDyn, a dynamical-systems model for resting-state fMRI that was trained on roughly 40,000 rs-fMRI sequences from diverse public and available-by-permission datasets. The model outperforms state-of-the-art transformer-based approaches in reconstruction quality while being compute efficient and generalizing across populations and scanning protocols. Its effectiveness on small sample sizes has implications for neuroimaging studies.

arXiv:2608.23936v1 Announce Type: new Abstract: We present a dynamical-systems based model for resting-state functional magnetic resonance imaging rs-fMRI , trained on a dataset of roughly 40K rs-fMRI sequences covering a wide variety of public and available-by-permission datasets. While most existing proposals use transformer backbones, we utilize multi-resolution temporal modeling of the dynamics across parcellated brain regions. We show that MnemoDyn is compute efficient and generalizes very well across diverse populations and scanning protocols. When benchmarked against current state-of-the-art transformer-based approaches, MnemoDyn consistently delivers superior reconstruction quality. Overall, we find that with such large-scale pre-training on non-proprietary rs-fMRI datasets, we get a highly performant model for various downstream tasks. Our results also provide evidence of the efficacy of the model on small sample size studies which has implications for neuroimaging studies at large where resting state fMRI is a commonly acquired imaging modality.