  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.
