  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.
