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Extracting microanatomical information beyond the image resolution of MRI would provide valuable tools for diagnostics and neuroscientific research. A number of mathematical models already suggest microstructural interpretations of diffusion MRI (dMRI) data. Examples of such microstructural features could be cell bodies and neurites, e.g. the axon's diameter or their orientational distribution for global connectivity analysis using tractography, and have previously only been possible to access through conventional histology of post mortem tissue or invasive biopsies. The prospect of gaining the same knowledge non-invasively from the whole living human brain could push the frontiers for the diagnosis of neurological and psychiatric diseases. It could also provide a general understanding of the development and natural variability in the healthy brain across a population. However, due to a limited image resolution, most of the dMRI measures are indirect estimations and may depend on the whole chain from experimental parameter settings to model assumptions and implementation. Here, we review current literature in this field and highlight the integrative work across anatomical length scales that is needed to validate and trust a new dMRI method. We encourage interdisciplinary collaborations and data sharing in regards to applying and developing new validation techniques to improve the specificity of future dMRI methods.
Pubmed ID: 29920374
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View all literature mentionsConsortium to comprehensively map long-distance brain connections and their variability. It is acquiring data and developing analysis pipelines for several modalities of neuroimaging data plus behavioral and genetic data from healthy adults.
View all literature mentionsMap of neural connections in mouse brain, built on an array of transgenic mice genetically engineered to target specific cell types. In addition to the connectivity data, information about the transgenic mouse lines and genetic tracers is available. Consists of high resolution 2-D projectivity image data that can be viewed side-by-side with the associated reference atlas and other reference datasets. Enables 3-D visualization and spatial/ontological search of connectivity models through a combination of manual and informatics analyses.
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