State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; School of Systems Science, Beijing Normal University, Beijing 100875, China.
Neuroimage. 2022 Aug 15;257:119297. doi: 10.1016/j.neuroimage.2022.119297. Epub 2022 May 12.
The accumulation of multisite large-sample MRI datasets collected during large brain research projects in the last decade has provided critical resources for understanding the neurobiological mechanisms underlying cognitive functions and brain disorders. However, the significant site effects observed in imaging data and their derived structural and functional features have prevented the derivation of consistent findings across multiple studies. The development of harmonization methods that can effectively eliminate complex site effects while maintaining biological characteristics in neuroimaging data has become a vital and urgent requirement for multisite imaging studies. Here, we propose a deep learning-based framework to harmonize imaging data obtained from pairs of sites, in which site factors and brain features can be disentangled and encoded. We trained the proposed framework with a publicly available traveling subject dataset from the Strategic Research Program for Brain Sciences (SRPBS) and harmonized the gray matter volume maps derived from eight source sites to a target site. The proposed framework significantly eliminated intersite differences in gray matter volumes. The embedded encoders successfully captured both the abstract textures of site factors and the concrete brain features. Moreover, the proposed framework exhibited outstanding performance relative to conventional statistical harmonization methods in terms of site effect removal, data distribution homogenization, and intrasubject similarity improvement. Finally, the proposed harmonization network provided fixable expandability, through which new sites could be linked to the target site via indirect schema without retraining the whole model. Together, the proposed method offers a powerful and interpretable deep learning-based harmonization framework for multisite neuroimaging data that can enhance reliability and reproducibility in multisite studies regarding brain development and brain disorders.
在过去十年的大型大脑研究项目中，积累了大量多站点大样本 MRI 数据集，为理解认知功能和大脑疾病的神经生物学机制提供了重要资源。然而，在成像数据及其衍生的结构和功能特征中观察到的显著站点效应，阻止了在多个研究中得出一致的发现。开发能够有效消除复杂站点效应同时保持神经影像学数据中生物学特征的协调方法，已成为多站点成像研究的一个至关重要和紧迫的需求。在这里，我们提出了一种基于深度学习的框架，用于协调来自两个站点的成像数据，其中可以分离和编码站点因素和大脑特征。我们使用来自战略脑科学研究计划 (SRPBS) 的公开旅行受试者数据集来训练所提出的框架，并协调来自八个源站点的灰质体积图到目标站点。所提出的框架显著消除了灰质体积的站点间差异。嵌入的编码器成功地捕获了站点因素的抽象纹理和具体的大脑特征。此外，与传统的统计协调方法相比，所提出的框架在去除站点效应、数据分布均匀化和提高个体内相似性方面表现出卓越的性能。最后，所提出的协调网络提供了可固定的扩展性，通过该扩展性，新站点可以通过间接模式与目标站点相连，而无需重新训练整个模型。总之，该方法提供了一个强大且可解释的基于深度学习的多站点神经影像学数据协调框架，可以提高关于大脑发育和大脑疾病的多站点研究的可靠性和可重复性。
