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 neuron 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 possible mechanisms underlying NDDs.

中文翻译：

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使用等几何分析、动态域扩展和局部细化进行神经发育障碍建模


神经发育障碍 （NDD） 已成为美国最普遍的慢性疾病之一。NDD 通常由多种疾病组成，例如自闭症谱系障碍、注意力缺陷多动障碍和癫痫，其特征是对患者的认知、言语、记忆、运动和其他神经功能的进行性和普遍性损害。然而，NDD 的异质性对确定确切的发病机制构成了重大障碍，阻碍了准确诊断和靶向治疗计划的制定。计算 NDDs 模型在增强我们对所涉及的多方面因素的理解方面具有巨大潜力，并可以帮助确定根本原因以加快治疗开发。为了应对这一挑战，我们将最佳神经营养因子浓度引入 2D 相场神经元生长模型的突触发力和降解，使用等几何分析来模拟神经突回缩和萎缩。最佳神经营养因子浓度有效地捕获了神经营养因子水平与神经元存活之间的反比关系，而其降解调节了浓度水平。利用动态域扩展，该模型根据增长模式有效地扩展域，以最小化自由度。基于截断的 T 样条，我们的模型通过自适应地对细胞/神经突边界应用局部细化来模拟复杂神经突结构的演变过程。 此外，通过与实验中的神经元细胞培养物进行详细比较，进行了彻底的参数研究，增强了我们对 NDD 可能机制的基本理解。