[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121094-en":3,"doc-seo-121094-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121094,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Advancing fNIRS Neuroimaging through Synthetic Data Generation and Machine Learning Applications","An integrated workflow advances functional Near-Infrared Spectroscopy (fNIRS) neuroimaging by combining synthetic data generation with machine learning model application. To mitigate limited high-quality neuroimaging datasets, the approach uses Monte Carlo simulations and parametric head models to create a broad synthetic dataset spanning diverse conditions. A containerized analysis environment built with Docker and Xarray supports standardized, reproducible signal-processing comparisons across modalities. A cloud infrastructure further enables scalable generation and processing, improving fNIRS tomography’s accuracy, efficiency, and diagnostic relevance for neurological conditions.","Advancing fNIRS Neuroimaging through Synthetic Data Generation and Machine Learning  \nApplications  \nEitan Waks, Johns Hopkins University,  \narXiv :2405 . 11242v1 [ ee ss . SP] 18 May 2024  \nAbstract—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 highquality 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.  \nIndex Terms—Functional Near-Infrared Spectroscopy (fNIRS), Synthetic Data Generation, Monte Carlo Simulations, Machine Learning, Neuroimaging, Parametric Head Models, Data Analysis Environment, Cloud-Based Infrastructure.  \nI. BACKGROUND: HISTORY OF FUNCTIONAL  \nNEAR-INFRARED SPECTROSCOPY  \nTHE development of optical methods for assessing  \nchanges in the optical properties of brain tissue began with Glenn Millikan’s invention of the muscle oximeter in the forties [1] . Frans Jbsis founded in vivo near-infrared spectroscopy (NIRS), utilizing the transparency of brain tissue in the NIR range to detect hemoglobin oxygenation noninvasively [2] . Marco Ferrari used prototype NIRS instruments to measure changes in brain oxygenation in experimental animal models and human adults. From 1980 to 1995, several companies collaborated with universities to develop NIRS prototypes [3] .  \nFunctional imaging is the assessment of physiological changes associated with brain activity. Functional MRI (fMRI) based on blood oxygenation level dependent (BOLD) imaging was first suggested in 1990 [4] and was followed by the discovery of human functional near-infrared spectroscopy (fNIRS) in 1992 by by Chance, Kato, Hoshi, and Villringer [3] . fNIRS detects changes in the optical properties of the cortex and provides maps or images of specific areas. The increase in oxygenated hemoglobin and the decrease in deoxygenated  \nhemoglobin reflect an increase in local blood flow and volume due to neurovascular coupling.  \nfNIRS is based on human tissues being relatively transparent to light in the NIR spectral window (650-1000 nm) and that NIR light can penetrate tissues due to scattering being more probable than absorption. Hemoglobin is the main chromophore that attenuates NIR light in tissue, and its absorption spectrum depends on its level of oxygenation. In the NIR spectral window light absorption increases as a function of frequency for oxygenated hemoglobin and decreases fordeoxygenated hemoglobin. The absorption characteristics are equal at approximately 810 nm. This is the isosbestic point. The Beer-Lambert Law relates the attenuation of light to the concentration of absorbing species in a medium, e.g. oxygenated hemoglobin and deoxygenated hemoglobin.  \nIn turbid media such as biological tissues, light scattering is a significant factor that must be considered. The Modified Beer-Lambert Law is a general form of the Beer-Lambert Law that takes into account the effects of light scattering in addition to light absorption by a med","cbCaiqEcIZXNs9EU","https://ap.wps.com/l/cbCaiqEcIZXNs9EU","pdf",4876456,1,21,"English","en",105,"# Abstract\n## Background: History of Functional Near-Infrared Spectroscopy","[{\"question\":\"Why does the study generate synthetic fNIRS neuroimaging data?\",\"answer\":\"To address the scarcity of high-quality neuroimaging datasets by producing a comprehensive synthetic dataset spanning many conditions.\"},{\"question\":\"What methods are used to create the synthetic dataset?\",\"answer\":\"Monte Carlo simulations together with parametric head models generate the synthetic data reflecting a wide range of conditions.\"},{\"question\":\"How does the study support reproducible analysis and comparisons?\",\"answer\":\"It develops a containerized environment using Docker and Xarray so different signal-processing modalities can be analyzed in a standardized, reproducible way.\"}]","Advancing fNIRS Neuroimaging through Synthetic Data Generation and Machine Learning Applications | 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