Department of Molecular Physiology and Biophysics, Carver College of Medicine, University of Iowa, Iowa City, IA, USA.
Department of Otolaryngology Head-Neck Surgery, Carver College of Medicine, University of Iowa, Iowa City, IA, USA.
BMC Bioinformatics. 2023 Aug 24;24(1):320. doi: 10.1186/s12859-023-05444-4.
Quantitative analysis of neurite growth and morphology is essential for understanding the determinants of neural development and regeneration, however, it is complicated by the labor-intensive process of measuring diverse parameters of neurite outgrowth. Consequently, automated approaches have been developed to study neurite morphology in a high-throughput and comprehensive manner. These approaches include computer-automated algorithms known as 'convolutional neural networks' (CNNs)-powerful models capable of learning complex tasks without the biases of hand-crafted models. Nevertheless, their complexity often relegates them to functioning as 'black boxes.' Therefore, research in the field of explainable AI is imperative to comprehend the relationship between CNN image analysis output and predefined morphological parameters of neurite growth in order to assess the applicability of these machine learning approaches. In this study, drawing inspiration from the field of automated feature selection, we investigate the correlation between quantified metrics of neurite morphology and the image analysis results from NeuriteNet-a CNN developed to analyze neurite growth. NeuriteNet accurately distinguishes images of neurite growth based on different treatment groups within two separate experimental systems. These systems differentiate between neurons cultured on different substrate conditions and neurons subjected to drug treatment inhibiting neurite outgrowth. By examining the model's function and patterns of activation underlying its classification decisions, we discover that NeuriteNet focuses on aspects of neuron morphology that represent quantifiable metrics distinguishing these groups. Additionally, it incorporates factors that are not encompassed by neuron morphology tracing analyses. NeuriteNet presents a novel tool ideally suited for screening morphological differences in heterogeneous neuron groups while also providing impetus for targeted follow-up studies.
定量分析神经突生长和形态对于理解神经发育和再生的决定因素至关重要，但由于需要测量神经突生长的各种参数，因此这个过程非常繁琐。因此，已经开发出自动化方法来高通量和全面地研究神经突形态。这些方法包括被称为“卷积神经网络”（CNN）的计算机自动算法——这些强大的模型能够学习复杂的任务，而不会受到手工模型的偏见影响。然而，它们的复杂性往往使它们成为“黑盒子”。因此，解释性人工智能领域的研究对于理解 CNN 图像分析输出与神经突生长的预定义形态参数之间的关系是必不可少的，以便评估这些机器学习方法的适用性。在这项研究中，我们从自动化特征选择领域中汲取灵感，研究了神经突形态的定量度量与 NeuriteNet（一种用于分析神经突生长的 CNN）的图像分析结果之间的相关性。NeuriteNet 能够准确地区分基于两个独立实验系统中不同处理组的神经突生长图像。这两个系统区分了在不同基质条件下培养的神经元和接受抑制神经突生长的药物处理的神经元。通过检查模型在分类决策背后的功能和激活模式，我们发现 NeuriteNet 专注于能够区分这些组的可量化度量的神经元形态方面。此外，它还包含了神经元形态追踪分析所不包含的因素。NeuriteNet 提供了一种新颖的工具，非常适合筛选异质神经元群体中的形态差异，同时也为有针对性的后续研究提供了动力。
