Divergent brain connectivity is thought to underlie the behavioral and cognitive symptoms observed in many neurodevelopmental disorders. Quantifying divergence from neurotypical connectivity patterns offers a promising pathway to inform diagnosis and therapeutic interventions. While advanced neuroimaging techniques, such as diffusion MRI (dMRI), have facilitated the mapping of brain's structural connectome, the challenge lies in accurately modeling developmental trajectories within these complex networked structures to create robust neurodivergence markers. In this work, we present the Brain Representation via Individualized Deep Generative Embedding (BRIDGE) framework, which integrates normative modeling with a bio-inspired deep generative model to create a reference trajectory of connectivity transformation as part of neurotypical development. This will enable the assessment of neurodivergence by comparing individuals to the established neurotypical trajectory. BRIDGE provides a global neurodivergence score based on the difference between connectivity-based brain age and chronological age, along with region-wise neurodivergence maps that highlight localized connectivity differences. Application of BRIDGE to a large cohort of children with autism spectrum disorder demonstrates that the global neurodivergence score correlates with clinical assessments in autism, and the regional map offers insights into the heterogeneity at the individual level in neurodevelopmental disorders. Together, the neurodivergence score and map form powerful tools for quantifying developmental divergence in connectivity patterns, advancing the development of imaging markers for personalized diagnosis and intervention in various clinical contexts.

翻译：脑连接异常被认为是许多神经发育障碍中观察到的行为与认知症状的基础。量化与神经典型连接模式的偏离，为诊断与治疗干预提供了有前景的路径。尽管先进的神经影像技术，如扩散磁共振成像（dMRI），已促进了大脑结构连接组的绘制，但挑战在于如何准确建模这些复杂网络结构内部的发育轨迹，以创建稳健的神经分化标志物。在本研究中，我们提出了基于个体化深度生成嵌入的脑表征（BRIDGE）框架，该框架将规范建模与受生物启发的深度生成模型相结合，以构建神经典型发育过程中连接性转变的参考轨迹。通过将个体与已建立的神经典型轨迹进行比较，这将实现对神经分化的评估。BRIDGE提供了一个基于连接性脑年龄与实际年龄差异的全局神经分化评分，以及突出局部连接差异的区域性神经分化图谱。将BRIDGE应用于一个大型自闭症谱系障碍儿童队列表明，全局神经分化评分与自闭症的临床评估相关，而区域性图谱则揭示了神经发育障碍在个体水平上的异质性。神经分化评分与图谱共同构成了量化连接模式发育偏离的强大工具，推动了在各种临床背景下用于个性化诊断与干预的影像标志物的开发。