Wang Yixin, Peng Wei, Zhang Yu, Adeli Ehsan, Zhao Qingyu, Pohl Kilian M
Department of Bioengineering, Stanford University, Stanford, CA, USA.
Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
Mach Learn Clin Neuroimaging (2024). 2025;15266:24-34. doi: 10.1007/978-3-031-78761-4_3. Epub 2024 Dec 6.
Many longitudinal neuroimaging studies aim to improve the understanding of brain aging and diseases by studying the dynamic interactions between brain function and cognition. Doing so requires accurate encoding of their multidimensional relationship while accounting for individual variability over time. For this purpose, we propose an unsupervised learning model (called ntrastive Learning-based ph Generalized nonical Correlation Analysis (CoGraCa)) that encodes their relationship via Graph Attention Networks and generalized Canonical Correlational Analysis. To create brain-cognition fingerprints reflecting unique neural and cognitive phenotype of each person, the model also relies on individualized and multimodal contrastive learning. We apply CoGraCa to longitudinal dataset of healthy individuals consisting of resting-state functional MRI and cognitive measures acquired at multiple visits for each participant. The generated fingerprints effectively capture significant individual differences and outperform current single-modal and CCA-based multimodal models in identifying sex and age. More importantly, our encoding provides interpretable interactions between those two modalities.
许多纵向神经影像学研究旨在通过研究脑功能与认知之间的动态相互作用，增进对脑衰老和疾病的理解。要做到这一点，需要在考虑个体随时间变化的变异性的同时，准确编码它们的多维关系。为此，我们提出了一种无监督学习模型（称为基于对比学习的广义典型相关分析（CoGraCa）），该模型通过图注意力网络和广义典型相关分析对它们的关系进行编码。为了创建反映每个人独特神经和认知表型的脑认知指纹，该模型还依赖于个性化和多模态对比学习。我们将CoGraCa应用于健康个体的纵向数据集，该数据集包括静息态功能磁共振成像和每个参与者多次访视时获取的认知测量数据。生成的指纹有效地捕捉了显著的个体差异，并且在识别性别和年龄方面优于当前的单模态和基于典型相关分析的多模态模型。更重要的是，我们的编码提供了这两种模态之间可解释的相互作用。
