MnemoDyn: Learning Resting State Dynamics from 40K FMRI sequences

2026-08-24

AI Summary

本文提出一种基于动力系统的模型MnemoDyn，通过分析约40K个静息态功能磁共振成像序列，采用多分辨率时间建模方法，高效解决了跨人群和扫描协议的泛化问题。

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.

Problem

• resting-state fMRI
• dynamics learning
• large-scale dataset

Innovation

• multi-resolution temporal modeling
• dynamical-systems based model
• large-scale pre-training
• rs-fMRI