| Popis: |
Introduction To understand brain function it is necessary to characterize the underlying structural connectivity between neurons. Insects are ideal for analysing how neuronal connectivity shapes complex behaviour. Whilst the nervous system of insects is much simpler than vertebrates, they are animals that demonstrate a varied and complex behavioural repertoire. Previous research on insect brain connectivity has used microscopy techniques to examine the organization of neuronal networks. Methods The brain of the desert locust, Schistercera gregaria (Forksȧl) is an ideal insect to explore neuronal connectivity as it has a relatively large brain for an insect. Here we present a novel methodology to understand neuronal connectivity in the insect brain by using diffusion magnetic resonance imaging (dMRI). The diffusion-weighted imaging (DWI) data were acquired on a horizontal bore 7T Biospec micro-MRI system (Bruker, Etlingen Germany) using a customised multi-shell diffusion MRI scheme. Diffusion sensitizing gradients were applied in 26 diffusion encoding directions. Contrasts from the averaged DWIs and Diffusion Kurtosis Imaging (DKI) scheme were applied to classify various anatomical features and diffusion patterns in neuropils, respectively. Results This study explores the application of MRI to an insect model system, the locust Schistercera gregaria . It is demonstrated that micro-MRI and dMRI modelling enables anatomical regions and connectivity in the locust brain to be identified. Further the possibility of quantifying structural changes in samples of the locust brain is shown by the use of indices derived from the kurtosis model that include fractional anisotropy (FA), mean diffusivity (MD) and kurtosis anisotropy (KA). Discussion/Conclusion The application of dMRI to the locust brain provides a novel approach to characterizing neuronal connectivity in insect brains. As dMRI can be applied non-invasively in live animals, future work offers the possibility of monitoring neuronal connectivity longitudinally, as well as providing metrics to quantify the effect of environmental stressors. |
