2024/06/30 by Kuanren Qian, Genesis Omana Suarez, Qian, Kuanren +13 · 1 voice · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Mathematics · #Advanced Numerical Analysis Techniques #Applied mathematics #Artificial Intelligence in Education #Computer science #Domain (mathematical analysis) #Engineering #FOS: Biological sciences #FOS: Mathematics #Finite element method #Isogeometric analysis #Mathematical analysis #Mathematics #Neurons and Cognition (q-bio.NC) #Numerical Analysis (math.NA) #Structural engineering #math.NA #q-bio.NC
paper · pdf · doi:10.48550/arxiv.2407.00810
openalex publication_date 2024/06/30 · arxiv published 2024/06/30 · arxiv updated 2024/07/03 · openalex created_date 2024/07/05 · openalex updated_date 2026/07/28
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.