PROJECT SUMMARY/ABSTRACT We propose to broadly disseminate and extend intuitive, powerful cloud-based resources for optical brain mapping that facilitate efficient, accurate, and standardized processing that will harmonize the emerging set of optical measurement strategies within the growing ecosystem of network level analyses used throughout the greater brain mapping community. The neuroimaging community faces numerous challenges in data collection, preprocessing, estimation of brain connectivity, and analyses of relationships between brain connectivity and behavior. An ever-expanding community of researchers are employing optical methods based on functional near infrared spectroscopy (fNIRS) in order to infer pathophysiological state of tissue, such as inflammation and metabolism for detection/characterization of disease or cerebral hemodynamics for understanding human brain health, development, and aging. Recent developments of high-density diffuse optical tomography (HD-DOT), a silent, flexible, and scalable technology have demonstrated dramatically improved anatomical specificity and image quality over traditional fNIRS. Further, recent developments in wearable HD-DOT, even using frequency domain and time resolved strategies, open the door to unconstrained mapping of naturalistic human brain function with superior image quality than previously possible. Given the growing worldwide adoption of fNIRS and HD-DOT methods and further developments of next-generation optical brain mapping methods via the BRAIN Initiative, there is an urgent and present need for standardized, accessible and flexible tools that directly support workflows from optical tissue parameter recovery to functional brain mapping to relating variance in brain function to behavior and outcome. To address these needs, our teams have developed and validated computational tools including NIRFAST, NeuroDOT, and Network Level Analyses (NLA), for tissue parameter recovery, optical brain mapping, and model-based connectome-wide association studies of brain function and behavior, respectively. While these tools each support growing user communities, the tools are based in Matlab, which significantly limits accessibility and adoption. Additionally, much of these analyses are computationally intensive and expensive, limiting full use to institution-based, server-level resources. Further, extant widely available software packages for fNIRS are limited in scope and do not support the full set of pipelines for end- to-end analyses that together NIRFAST, NeuroDOT, and NLA provide. We therefore propose herein to utilize funding from RFA-NS-23-026 to address this unmet need for data resources with (1) greater dissemination and training for our tools, (2) cloud deployment of our software to increase scale and accessibility, while easing the computational burden for the user, and (3) expanded utility of these powerful, flexible tools to meet the evolving needs of users at the forefront of optica...