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.

通过合成数据和应用机器学习模型，本研究提出了一种整合方法，用于推进功能性近红外光谱（fNIRS）神经影像学。通过解决高质量神经影像数据的匮乏问题，该研究利用蒙特卡洛模拟和参数头部模型生成了一个全面的合成数据集，反映了各种条件。我们开发了一个采用 Docker 和 Xarray 的容器化环境，用于标准化和可重复的数据分析，便于在不同的信号处理模式之间进行有意义的比较。此外，建立了一个基于云的基础设施，用于可扩展的数据生成和处理，提高了神经影像数据的可访问性和质量。合成数据生成与机器学习技术的结合有望提高 fNIRS 断层扫描的准确性、效率和适用性，潜在地革新神经疾病的诊断和治疗策略。本文中开发的方法和基础设施为数据模拟和分析设立了新的标准，并为神经影像学和更广泛的生物医学工程领域的未来研究铺平了道路。