Neurodevelopmental disorders (NDDs) have arisen as one of the most prevailing chronic diseases within the US. Often associated with severe adverse impacts on the formation of vital central and peripheral nervous systems during the neurodevelopmental process, NDDs are comprised of a broad spectrum of disorders, such as autism spectrum disorder, attention deficit hyperactivity disorder, and epilepsy, characterized by progressive and pervasive detriments to cognitive, speech, memory, motor, and other neurological functions in patients. However, the heterogeneous nature of NDDs poses a significant roadblock to identifying the exact pathogenesis, impeding accurate diagnosis and the development of targeted treatment planning. A computational NDDs model holds immense potential in enhancing our understanding of the multifaceted factors involved and could assist in identifying the root causes to expedite treatment development. To tackle this challenge, we introduce optimal neurotrophin concentration to the driving force and degradation of neurotrophin to the synaptogenesis process of a 2D phase field neuron growth model using isogeometric analysis to simulate neurite retraction and atrophy. The optimal neurotrophin concentration effectively captures the inverse relationship between neurotrophin levels and neurite survival, while its degradation regulates concentration levels. Leveraging dynamic domain expansion, the model efficiently expands the domain based on outgrowth patterns to minimize degrees of freedom. Based on truncated T-splines, our model simulates the evolving process of complex neurite structures by applying local refinement adaptively to the cell/neurite boundary. Furthermore, a thorough parameter investigation is conducted with detailed comparisons against neuron cell cultures in experiments, enhancing our fundamental understanding of the mechanisms underlying NDDs.

翻译：神经发育障碍（NDDs）已成为美国最普遍的慢性疾病之一。这类障碍通常与神经发育过程中中枢及外周神经系统关键结构形成受到严重不良影响相关，涵盖自闭症谱系障碍、注意力缺陷多动障碍、癫痫等多种疾病谱系，其共同特征表现为患者认知、语言、记忆、运动及其他神经功能出现进行性、弥漫性损害。然而，NDDs的异质性特质为明确其发病机制设置了重大障碍，阻碍了精准诊断与靶向治疗方案的制定。计算性NDDs模型在增强我们对多维度致病因素的理解方面具有巨大潜力，并能协助溯源病因以加速治疗研发。为应对这一挑战，我们通过等几何分析构建二维相场神经元生长模型，在驱动力中引入最佳神经营养因子浓度，并在突触发生过程中纳入神经营养因子降解机制，以模拟神经突退缩与萎缩过程。最佳神经营养因子浓度有效捕捉了神经营养因子水平与神经突存活之间的负相关关系，而其降解过程则调控着浓度水平。通过动态域扩展技术，该模型能根据神经突生长模式高效扩展计算域以降低自由度。基于截断T样条方法，模型通过对细胞/神经突边界自适应实施局部细化，实现了复杂神经突结构演化过程的模拟。此外，研究通过详尽的参数分析并与实验中的神经元细胞培养结果进行系统对比，深化了我们对NDDs潜在机制的基础认知。