High-throughput digital twin framework for predicting neurite deterioration using MetaFormer attention

TL;DR

This paper presents a high-throughput digital twin framework that combines synthetic data, experimental images, and machine learning to accurately predict neurite deterioration in neurodevelopmental disorders, aiding research and treatment development.

Contribution

The novel integration of IGA-based synthetic data generation with MetaFormer-based ML models enables fast, accurate predictions of neurite deterioration, addressing data scarcity and complex morphological changes.

Findings

Average prediction errors of 1.9641% for synthetic data

Average prediction errors of 6.0339% for experimental data

Framework reduces experimental costs and time

Abstract

Neurodevelopmental disorders (NDDs) cover a variety of conditions, including autism spectrum disorder, attention-deficit/hyperactivity disorder, and epilepsy, which impair the central and peripheral nervous systems. Their high comorbidity and complex etiologies present significant challenges for accurate diagnosis and effective treatments. Conventional clinical and experimental studies are time-intensive, burdening research progress considerably. This paper introduces a high-throughput digital twin framework for modeling neurite deteriorations associated with NDDs, integrating synthetic data generation, experimental images, and machine learning (ML) models. The synthetic data generator utilizes an isogeometric analysis (IGA)-based phase field model to capture diverse neurite deterioration patterns such as neurite retraction, atrophy, and fragmentation while mitigating the limitations of scarce experimental data. The ML model utilizes MetaFormer-based gated spatiotemporal attention architecture with deep temporal layers and provides fast predictions. The framework effectively captures long-range temporal dependencies and intricate morphological transformations with average errors of 1.9641% and 6.0339% for synthetic and experimental neurite deterioration, respectively. Seamlessly integrating simulations, experiments, and ML, the digital twin framework can guide researchers to make informed experimental decisions by predicting potential experimental outcomes, significantly reducing costs and saving valuable time. It can also advance our understanding of neurite deterioration and provide a scalable solution for exploring complex neurological mechanisms, contributing to the development of targeted treatments.