用4万例脑影像数据训练动态模型，提升静息态fMRI重建质量
MnemoDyn: Learning Resting State Dynamics from 40K FMRI sequences
- 基于多尺度时间动态建模脑区活动变化
- 在4万例数据上训练，重建精度超越现有Transformer方法
- 适合小样本神经影像研究，通用性强
我们提出一种基于动力系统框架的静息态功能磁共振成像（rs-fMRI）模型MnemoDyn，基于约4万条来自多种公共及授权数据集的rs-fMRI序列进行训练。与多数采用Transformer架构的方法不同，本模型通过多分辨率时间建模，捕捉脑区分割后的时间动态特征。实验表明，MnemoDyn计算效率高，在不同人群和扫描协议间具有极强泛化能力。在与当前最优Transformer方法对比中，该模型始终表现出更优的重建质量。大规模预训练（非专有数据集）使模型在多个下游任务中表现优异。结果还表明，该模型在小样本研究中同样有效，对广泛使用静息态fMRI的神经影像研究具有重要意义。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。