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dc.contributor.authorShahid, Syed Salman
dc.contributor.authorKerskens, Christian M.
dc.contributor.authorBurrows, Malcolm
dc.contributor.authorWitney, Alice G.
dc.date.accessioned2021-02-09T16:33:57Z
dc.date.available2021-02-09T16:33:57Z
dc.date.issued2021-02-09
dc.date.submitted2020-05-12
dc.identifier.others41598-021-82187-3
dc.identifier.other82187
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/317403
dc.description.abstractAbstract: To understand brain function it is necessary to characterize both the underlying structural connectivity between neurons and the physiological integrity of these connections. Previous research exploring insect brain connectivity has typically used electron microscopy techniques, but this methodology cannot be applied to living animals and so cannot be used to understand dynamic physiological processes. The relatively large brain of the desert locust, Schistercera gregaria (Forksȧl) is ideal for exploring a novel methodology; micro diffusion magnetic resonance imaging (micro-dMRI) for the characterization of neuronal connectivity in an insect brain. The diffusion-weighted imaging (DWI) data were acquired on a preclinical system using a customised multi-shell diffusion MRI scheme optimized to image the locust brain. Endogenous imaging contrasts from the averaged DWIs and Diffusion Kurtosis Imaging (DKI) scheme were applied to classify various anatomical features and diffusion patterns in neuropils, respectively. The application of micro-dMRI modelling to the locust brain provides a novel means of identifying anatomical regions and inferring connectivity of large tracts in an insect brain. Furthermore, quantitative imaging indices derived from the kurtosis model that include fractional anisotropy (FA), mean diffusivity (MD) and kurtosis anisotropy (KA) can be extracted. These metrics could, in future, be used to quantify longitudinal structural changes in the nervous system of the locust brain that occur due to environmental stressors or ageing.
dc.languageen
dc.publisherNature Publishing Group UK
dc.rightsAttribution 4.0 International (CC BY 4.0)en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectArticle
dc.subject/631/1647
dc.subject/631/57
dc.subject/631/378
dc.subject/631/443
dc.subject/631/601
dc.subject/639/766
dc.subjectarticle
dc.titleElucidating the complex organization of neural micro-domains in the locust Schistocerca gregaria using dMRI
dc.typeArticle
dc.date.updated2021-02-09T16:33:57Z
prism.issueIdentifier1
prism.publicationNameScientific Reports
prism.volume11
dc.identifier.doi10.17863/CAM.64516
dcterms.dateAccepted2021-01-13
rioxxterms.versionofrecord10.1038/s41598-021-82187-3
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidShahid, Syed Salman [0000-0002-0914-6933]
dc.contributor.orcidKerskens, Christian M. [0000-0003-0823-4648]
dc.contributor.orcidBurrows, Malcolm [0000-0003-1386-5065]
dc.contributor.orcidWitney, Alice G. [0000-0002-3726-8479]
dc.identifier.eissn2045-2322


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's licence is described as Attribution 4.0 International (CC BY 4.0)