Processing functional near infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) data poses several challenges including costly software licenses (e.g. MATLAB) and compatibility between a wide variety of data formats (NIRS, SNIRF, BIDS, NIFTI, 4dfp, GIFTI, etc.). NeuroDOT is a toolbox available in both MATLAB and Python that has functions and pipelines for data preprocessing, anatomical light modeling, image reconstruction, analysis, and visualization. NeuroDOT’s standard pipelines provide a self-contained, straightforward method of processing and analysis for fNIRS and DOT data. Additionally, NeuroDOT utilizes cloud-based computing with XNAT to lessen computational burden, promote reproducibility of results, and simplify processing.
High density diffuse optical tomography (HD-DOT) combines logistical advantages of fNIRS with enhanced image quality, validated extensively against fMRI in adults and neonates. However, HD-DOT is yet to be evaluated in preschool-age children. Here we present an HD-DOT system optimized for preschoolers, including a 128-source by 125-detector console, light-weight fiber optics, and an expanded field-of-view. We validated the system by mapping cortical activations during visual, auditory, and motor tasks in adults. We then imaged children while they watched movies, finding reproducible patterns of brain activity and showing that feature regressors can map functionally specific regions from movie-viewing data in preschoolers.
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