The sensitivity of the honey bee, Apis mellifera L. (Hymeonoptera: Apidae), brain volume and density to behavior (plasticity) makes it a great model for exploring the interactions between experience, behavior, and brain structure. Plasticity in the adult bee brain has been demonstrated in previous experiments. This experiment was conducted to identify the potentials and limitations of MicroCT (micro computed tomograpy) scanning “live” bees as a more comprehensive, non-invasive method for brain morphology and physiology. Bench-top and synchrotron MicroCT were used to scan live bees. For improved tissue differentiation, bees were fed and injected with radiographic contrast. Images of optic lobes, ocelli, antennal lobes, and mushroom bodies were visualized in 2D and 3D rendering modes. Scanning of live bees (for the first time) enabled minimally-invasive imaging of physiological processes such as passage of contrast from gut to haemolymph, and preliminary brain perfusion studies. The use of microCT scanning for studying insects (collectively termed ‘diagnostic radioentomology’, or DR) is increasing. Our results indicate that it is feasible to observe plasticity of the honey bee brain in vivo using diagnostic radioentomology, and that progressive, real-time observations of these changes can be followed in individual live bees. Limitations of live bee scanning, such as movement errors and poor tissue differentiation, were identified; however, there is great potential for in-vivo, non-invasive diagnostic radioentomology imaging of the honey bee for brain morphology and physiology.

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Single-photon emission computerized tomography (SPECT) is a tool which can be used to image perfusion in the brain. Clinicians can use such images to help diagnose dementias such as Alzheimer's disease. Due to the intrinsic stochasticity in the photon imaging system, some form of statistical comparison of an individual image with a 'normal' patient database gives a clinician additional confidence in interpreting the image. Due to the variations between SPECT camera systems, ideally a normal patient database is required for each individual system. However, cost or ethical considerations often prohibit the collection of such a database for each new camera system. Some method of adapting existing normal patient databases to new camera systems would be beneficial. This paper introduces a method which may be regarded as a 'first-pass' attempt based on 2-norm regularization and a codebook of discrete spatially stationary convolutional kernels. Some preliminary illustrative results are presented, together with discussion on limitations and possible improvements.