Lange Michal A, Chen Yingying, Fu Haoying, Korada Amith, Guo Changyong, Ma Yao-Ying
Department of Pharmacology and Toxicology, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
bioRxiv. 2024 Nov 19:2024.09.30.615860. doi: 10.1101/2024.09.30.615860.
Advances in imaging using miniature microscopes have enabled researchers to study single-neuron activity in freely-moving animals. Tools such as MiniAN and CalmAn have been developed to convert isual signals umerical data, collectively referred to as CalV2N. However, substantial challenges remain in analyzing the large datasets generated by CalV2N, particularly in integrating data streams, evaluating CalV2N output quality, and reliably and efficiently identifying transients. In this study, we introduce CalTrig, an open-source graphical user interface (GUI) tool designed to address these challenges at the post-CalV2N stage of data processing. CalTrig integrates multiple data streams, including imaging, neuronal footprints, traces, and behavioral tracking, and offers capabilities for evaluating the quality of CalV2N outputs. It enables synchronized visualization and efficient transient identification. We evaluated four machine learning models (i.e., GRU, LSTM, Transformer, and Local Transformer) for transient detection. Our results indicate that the GRU model offers the highest predictability and computational efficiency, achieving stable performance across training sessions, different animals and even among different brain regions. The integration of manual, parameter-based, and machine learning-based detection methods in CalTrig provides flexibility and accuracy for various research applications. The user-friendly interface and low computing demands of CalTrig make it accessible to neuroscientists without programming expertise. We further conclude that CalTrig enables deeper exploration of brain function, supports hypothesis generation about neuronal mechanisms, and opens new avenues for understanding neurological disorders and developing treatments.
使用微型显微镜进行成像技术的进步，使研究人员能够在自由活动的动物身上研究单个神经元的活动。诸如MiniAN和CalmAn等工具已被开发出来，用于将视觉信号转换为数值数据，统称为CalV2N。然而，在分析由CalV2N生成的大型数据集时，仍然存在重大挑战，特别是在整合数据流、评估CalV2N输出质量以及可靠且高效地识别瞬变信号方面。在本研究中，我们引入了CalTrig，这是一个开源的图形用户界面（GUI）工具，旨在解决数据处理的CalV2N后阶段的这些挑战。CalTrig整合了多个数据流，包括成像、神经元足迹、轨迹和行为跟踪，并提供评估CalV2N输出质量的功能。它能够实现同步可视化和高效的瞬变信号识别。我们评估了四种机器学习模型（即门控循环单元（GRU）、长短期记忆网络（LSTM）、Transformer和局部Transformer）用于瞬变信号检测。我们的结果表明，GRU模型具有最高的可预测性和计算效率，在不同的训练阶段、不同的动物甚至不同的脑区都能实现稳定的性能。CalTrig中手动、基于参数和基于机器学习的检测方法的整合，为各种研究应用提供了灵活性和准确性。CalTrig用户友好的界面和低计算需求，使得没有编程专业知识的神经科学家也能够使用。我们进一步得出结论，CalTrig能够更深入地探索脑功能，支持关于神经元机制的假设生成，并为理解神经疾病和开发治疗方法开辟新途径。
