TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models
Year: 2021
Type: preprint
Abstract: Reference anatomies of the brain and corresponding atlases play a central role in experimental neuroimaging workflows and are the foundation for reporting standardized results. The choice of such references —i.e., templates— and atlases is one relevant source of methodological variability across studies, which has recently been brought to attention as an important challenge to reproducibility in neuroscience. TemplateFlow is a publicly available framework for human and nonhuman brain models. The framework combines an open database with software for access, management, and vetting, allowing scientists to distribute their resources under FAIR —findable, accessible, interoperable, reusable— principles. TemplateFlow supports a multifaceted insight into brains across species, and enables multiverse analyses testing whether results generalize across standard references, scales, and in the long term, species, thereby contributing to increasing the reliability of neuroimaging results.
Source: Research Square
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Funders: National Science Foundation, Laura and John Arnold Foundation, Wellcome Trust, King's College London, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Medical Research Council, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Institute of Biomedical Imaging and Bioengineering
Awards: Uncovering the interplay of structure, function, and dynamics of brain connectivity using MRI, Collaborative Proposal: CRCNS US-German Data Sharing Proposal: DataLad - a decentralized system for integrated discovery, management, and publication of digital objects of science, NIPreps: integrating neuroimaging preprocessing workflows across modalities, populations, and species, G339059223, Causal mapping of emotion networks with concurrent electrical stimulation and fMRI, DTP Bid Led by King's College London, Fractionating the human frontoparietal cortex: combining meta-analytic and real-time optimization approaches, BIDS-Derivatives: A data standard for derived data and models in the BRAIN Initiative, Fractionating the human frontoparietal cortex: combining meta-analytic and real-time optimization approaches, Nipype: Dataflows for Reproducible Biomedical Research, G8085324931, ReproNim: A Center for Reproducible Neuroimaging Computation, OpenNeuro: An open archive for analysis and sharing of BRAIN Initiative data
OpenAlex ID: W4250504104