JUST: A joint universal and specific brain templates generation framework

作者：Jichang Zhang, Tongtong Che, Zekun Yang, Haoying Bai, Xiuying Wang, Shuyu Li · 发表于：NeuroImage · 年份：2026 · DOI：10.1016/j.neuroimage.2026.122140 · 研究领域：EEG and Brain-Computer Interfaces、Functional Brain Connectivity Studies、Neural dynamics and brain function

Constructing universal and specific brain templates from a population or its constituent cohorts, respectively, is essential for neuroimaging research. Generation-based methods have excelled in learning either universal or specific templates, while showing the potential for handling large-scale data. However, a unified framework that jointly learns universal and specific templates is still lacking, resulting in limited computational efficiency. Moreover, in the construction process, existing methods mainly rely on similarity optimization between specific templates and matched-cohort images, without explicitly leveraging distinguishing information from mismatched cohorts, which may limit the learned template specificity. To address these challenges, we propose a novel brain template generation framework, dubbed JUST, that efficiently synergizes universal and specific template learning and explicitly leverages template-cohort divergence to enhance the specificity during the learning process. Firstly, JUST unifies the previously independent constructions of universal and specific templates via a versatile conditional generator that produces deformations between a universal template and specific templates. Secondly, JUST involves formulating inherent correlations among specific templates and all the cohorts, subsequently using a contrastive loss to enforce template specificity. Additionally, we present the Template Distinguishability Score (TDS), an innovative metric that quantif...