Liu Yuanzhe, Seguin Caio, Mansour L Sina, Tian Ye Ella, Di Biase Maria A, Zalesky Andrew
Systems Lab, Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia; Department of Biomedical Engineering, Faculty of Engineering & Information Technology, The University of Melbourne, Melbourne, VIC, Australia.
Systems Lab, Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia.
Med Image Anal. 2025 Aug 8;106:103761. doi: 10.1016/j.media.2025.103761.
An individual's connectome is unique. Interindividual variation in connectome architecture associates with disease status, cognition, lifestyle factors, and other personal attributes. While models to predict personal attributes from a person's connectome are abundant, the inverse task-inferring connectome architecture from an individual's personal profile-has not been widely studied. Here, we introduce a deep model to generate a person's entire connectome exclusively based on their age, sex, body phenotypes, cognition, and lifestyle factors. Using the richly phenotyped UK Biobank connectome cohort (N=8,086), we demonstrate that our model can generate network architectures that closely recapitulate connectomes mapped empirically using diffusion MRI and tractography. We find that age, sex, and body phenotypes exert the strongest influence on the connectome generation process, with an impact approximately four times greater than that of cognition and lifestyle factors. Regional differences in the importance of measures were observed, including an increased importance of cognition in the association cortex relative to the visual system. We further show that generated connectomes can improve the training of machine learning models and reduce their predictive errors. Our work demonstrates the feasibility of inferring brain connectivity from an individual's personal data and enables future applications of connectome generation such as data augmentation and anonymous data sharing.
个体的连接组是独一无二的。连接组结构的个体间差异与疾病状态、认知、生活方式因素及其他个人属性相关。虽然从人的连接组预测个人属性的模型众多，但从个体的个人资料推断连接组结构这一反向任务尚未得到广泛研究。在此，我们引入一种深度模型，仅基于个体的年龄、性别、身体表型、认知和生活方式因素来生成其完整的连接组。利用具有丰富表型的英国生物银行连接组队列（N = 8086），我们证明我们的模型能够生成与使用扩散磁共振成像和纤维束成像技术通过实验绘制的连接组紧密相似的网络结构。我们发现年龄、性别和身体表型对连接组生成过程的影响最强，其影响程度约为认知和生活方式因素的四倍。观察到测量指标重要性的区域差异，包括在联合皮层中认知相对于视觉系统的重要性增加。我们进一步表明，生成的连接组能够改善机器学习模型的训练并减少其预测误差。我们的工作证明了从个体的个人数据推断脑连接性的可行性，并为连接组生成在数据增强和匿名数据共享等方面的未来应用提供了可能。
