NeuroDot

By Adam Eggebrecht

The Washington University Optical Radiology Labs of Joe Culver and Adam Eggebrecht will soon release a new Matlab-based toolbox called NeuroDOT.

The purpose of NeuroDOT is to provide an extendable and user-friendly environment for analysis of optical data from raw light levels through multiple processing pipelines onto voxelated movies of brain function co-registered to the anatomy of a specific participant or an atlas. Currently supported analysis pipelines include general preprocessing, extensive data quality metrics and visualizations, anatomical head modeling, diffusion-based light modeling, regularized reconstruction, and multiple post-processing strategies.

As described during a presentation this spring at the meeting of the Optical Society of America in Hollywood, FL (USA), two major features of the toolbox are flexibility and usability. To ensure flexibility for the end user, NeuroDOT works seamlessly with data in formats used by multiple popular toolboxes for light-level data (e.g., Homer2), voxelated data (e.g., 4dfp, NIfTI), and mesh style data (e.g., NIRFAST, GIFTI, Freesurfer). To aid in end-user support at multiple levels of familiarity and expertise beyond basic functionality, NeuroDOT contains data samples, support files, help sections, appendices, and tutorials. Specifically, a set of anonymized and published data samples have been chosen to reflect common experimental paradigms in neuroimaging (e.g., retinotopy and language based tasks), and are provided in both raw and pre-processed versions to aid in troubleshooting and training for the new user. The extensive support files – all stored as .mat files – contain geometric information for specific examples of diffuse optical tomography arrays, along with associated sensitivity matrices, spectroscopy matrices, and standard atlases. Together with the documentation, these files provide a blueprint for users to create counterparts for their own systems. Additionally, “help” sections exist for each function and are searchable from the MATLAB command line, with a Help Viewer version as well. These are written and formatted in the style of their native MATLAB counterparts for familiarity and ease of use. Several appendices detail data structures, pipelines and their construction, and select visualizations of pipeline results from multiple data samples. Several tutorials are also included, each of which runs a data sample through a given pipeline to help the user harness the power and flexibility of NeuroDOT. Stay tuned to GitHub and NITRC for the upcoming initial release of the toolbox to the general community.

NeuroDOT toolbox page:
http://orl.wustl.edu/oicwiki/index.php/Configure_the_NeuroDOT_toolbox