BrainMass：利用大规模自监督学习推进用于诊断的脑网络分析
BrainMass: Advancing Brain Network Analysis for Diagnosis with Large-scale Self-Supervised Learning
AI总结：
本文提出BrainMass脑网络自监督框架，基于30个数据集的70,781个样本与pFC增强，结合MRM和LRA提升脑疾病诊断的泛化、少样本及零样本能力。
摘要：
通过自监督学习在大规模数据集上预训练的基础模型在各类任务中展现出卓越的通用性。由于医学数据具有异质性且难以收集，这种方法对医学图像分析和神经科学研究尤其有益，因为它无需大量昂贵标注即可简化广泛的下游任务。然而，目前针对脑网络基础模型的研究仍然有限，这限制了其在广泛神经科学研究中的适应性和泛化能力。本研究旨在弥补这一空白。具体而言：（1）我们通过整理来自30个数据集的图像，构建了一个综合数据集，其中包含来自46,686名参与者的70,781个样本。此外，我们提出伪功能连接（pFC），通过随机丢弃BOLD信号的某些时间点，进一步生成数百万个增强脑网络。（2）我们提出用于脑网络自监督学习的BrainMass框架，该框架通过掩码建模和特征对齐实现。BrainMass采用Mask-ROI Modeling（MRM，掩码感兴趣区建模）来增强网络内依赖关系和区域特异性。此外，Latent Representation Alignment（LRA，潜在表征对齐）模块用于约束同一参与者、具有相似拓扑属性的增强脑网络，通过对齐其潜在嵌入，使其产生相似的潜在表征。在8项内部任务和7项外部脑疾病诊断任务上的大量实验表明，BrainMass具有优越性能，凸显了其显著的泛化能力和适应性。尽管如此，BrainMass仍展现出强大的少样本/零样本学习能力，并对多种疾病呈现出有意义的解释，展示了其在临床应用中的潜在用途。
英文摘要：
Foundation models pretrained on large-scale datasets via self-supervised learning demonstrate exceptional versatility across various tasks. Due to the heterogeneity and hard-to-collect medical data, this approach is especially beneficial for medical image analysis and neuroscience research, as it streamlines broad downstream tasks without the need for numerous costly annotations. However, there has been limited investigation into brain network foundation models, limiting their adaptability and generalizability for broad neuroscience studies. In this study, we aim to bridge this gap. In particular, (1) we curated a comprehensive dataset by collating images from 30 datasets, which comprises 70,781 samples of 46,686 participants. Moreover, we introduce pseudo-functional connectivity (pFC) to further generates millions of augmented brain networks by randomly dropping certain timepoints of the BOLD signal. (2) We propose the BrainMass framework for brain network self-supervised learning via mask modeling and feature alignment. BrainMass employs Mask-ROI Modeling (MRM) to bolster intra-network dependencies and regional specificity. Furthermore, Latent Representation Alignment (LRA) module is utilized to regularize augmented brain networks of the same participant with similar topological properties to yield similar latent representations by aligning their latent embeddings. Extensive experiments on eight internal tasks and seven external brain disorder diagnosis tasks show BrainMass's superior performance, highlighting its significant generalizability and adaptability. Nonetheless, BrainMass demonstrates powerful few/zero-shot learning abilities and exhibits meaningful interpretation to various diseases, showcasing its potential use for clinical applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
