This study presents an integrated approach for advancing functional Near-Infrared Spectroscopy (fNIRS) neuroimaging through the synthesis of data and application of machine learning models. By addressing the scarcity of high-quality neuroimaging datasets, this work harnesses Monte Carlo simulations and parametric head models to generate a comprehensive synthetic dataset, reflecting a wide spectrum of conditions. We developed a containerized environment employing Docker and Xarray for standardized and reproducible data analysis, facilitating meaningful comparisons across different signal processing modalities. Additionally, a cloud-based infrastructure is established for scalable data generation and processing, enhancing the accessibility and quality of neuroimaging data. The combination of synthetic data generation with machine learning techniques holds promise for improving the accuracy, efficiency, and applicability of fNIRS tomography, potentially revolutionizing diagnostics and treatment strategies for neurological conditions. The methodologies and infrastructure developed herein set new standards in data simulation and analysis, paving the way for future research in neuroimaging and the broader biomedical engineering field.

翻译：本研究提出了一种集成方法，通过数据合成与机器学习模型应用来推进功能性近红外光谱（fNIRS）神经影像学的发展。针对高质量神经影像数据集稀缺的问题，本文利用蒙特卡洛模拟和参数化头部模型生成了涵盖广泛条件的综合合成数据集。我们开发了基于Docker和Xarray的容器化环境，用于标准化、可复现的数据分析，从而促进不同信号处理模态间的有意义的比较。此外，建立了云端基础设施以实现可扩展的数据生成与处理，提升了神经影像数据的可及性和质量。合成数据生成与机器学习技术的结合有望提高fNIRS断层成像的准确性、效率和适用性，可能彻底改变神经系统疾病的诊断与治疗策略。本文所开发的方法与基础设施为数据仿真与分析树立了新标准，为未来神经影像学及更广泛的生物医学工程领域研究铺平了道路。