Biomimetic IGA neuron growth modeling with neurite morphometric features and CNN-based prediction

TL;DR

This paper presents a biomimetic neuron growth model integrating neurite morphometric features with an IGA-C approach and employs a CNN to efficiently predict neurite development, aiding neurodegenerative disease research.

Contribution

It introduces a novel neuron growth simulation method that combines morphometric data with IGA-C and a CNN-based prediction model for realistic and computationally efficient results.

Findings

Achieved biomimetic neuron growth patterns with stage transitions based on neurite length.

CNN model predicts neurite morphology with 97.77% accuracy.

CNN reduces computational time by seven orders of magnitude.

Abstract

Neuron growth is a complex, multi-stage process that develops sophisticated morphologies and interwoven neurite networks. Recent advances have enabled us to examine the effects of neuron growth factors and seek causes for neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. A computational tool that studies neuron growth could shed crucial insights into the effects of various factors and help find a neurodegeneration cure. However, there lacks a computational tool to accurately and realistically simulate neuron growth within reasonable time frames. Bio-phenomenon models ignore potential factors and cannot generate realistic results, and bio-physics models require computationally expensive high-order governing equations. This paper incorporates experimental neurite features into a phase field method-based neuron growth model using an isogeometric analysis collocation (IGA-C) approach. Based on a semi-automated quantitative analysis of neurite morphology, we obtain relative turning angle, average tortuosity, neurite endpoints, average segment length, and the total length of neurites. We use the total neurite length to determine the evolving days in vitro (DIV) and select corresponding neurite features to drive and constrain neuron growth. This approach archives biomimetic neuron growth patterns with automatic growth stage transitions by incorporating corresponding DIV neurite morphometric data based on the total neurite length of the evolving neurite morphology. Furthermore, we built a convolutional neural network (CNN) to significantly reduce computational costs for predicting neurite growth. With a customized convolutional autoencoder as the backbone, our CNN model can predict neurite patterns with a high prediction accuracy, 97.77%, while taking 7 orders of magnitude less computational times than our IGA-C solver.