As with the functional magnetic imaging community, multiple challenges exist in standardization of data format, analysis considerations, and processing pipelines. To address these challenges for the optical functional neuroimaging community, we are developing NeuroDOT, a fully self-contained and end-user-friendly tool that provides the flexibility for multiple array-based imaging modalities, and offers format compatibility, spatial registration, and analytical breadth and sophistication for post-processing. NeuroDOT is available on GitHub (https://github.com/WUSTL-ORL/NeuroDOT_Beta). To aid in end-user support at multiple levels of familiarity and expertise, beyond the basic functionality, NeuroDOT contains data samples, support files, help sections, appendices, and tutorials. 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.
Network Level Analysis
Determining the mechanisms by which the human brain generates cognition, perception, and emotion hinges upon quantifying the relationships between coordinated brain activity and behavior. NIH-funded brain mapping initiatives such as the Human Connectome Project (HCP) and the Adolescent Cognitive and Behavioral Development (ABCD) study, have accelerated the production of large brain connectivity (i.e. connectome) and behavioral datasets. Contemporary connectome research views the brain as a large-scale, complex network composed of nonadjacent, yet functionally and/or anatomically connected brain regions. We leverage the inherent network architecture of this brain connectome in order to investigate fundamental biological mechanisms underlying behavior, exposure, and outcome. To facilitate these investigations, we have developed Network Level Analysis (NLA) toolbox as a comprehensive, versatile tool for use in connectome-wide association studies. NLA applies statistics commonly used in the genomics literature, known as ‘enrichment’ or ‘over expression analysis’ to assess connectome-wide associations with behavior.
NLA toolbox is available on github(https://github.com/WUSTL-ORL/NetworkLevelAnalysis_v1.0).
Additional details on these NLA methods and applications in human brain development can be found in the following publications: Eggebrecht et al., (2017) Cerebral Cortex; Marrus et al., (2017) Cerebral Cortex; Wheelock et al., (2018) NeuroImage; Wheelock et al., (2019) Developmental Cognitive Neuroscience; Thomason et al. (2019) NeuroImage; McKinnon et al. (2019) Biological Psychiatry CNNI; Marek et al. (2019) Developmental Cognitive Neuroscience; Wheelock et al. (2020) Cerebral Cortex.