It would be very helpful for the analysis of neuronal networks of the brain, if one could visualize these networks in 3 dimensions. Up to now this was only possible with limited resolution by sequential slicing and reconstruction of the brain. This time consuming attempt is easily hampered by artifacts as shrinkage and distortion induced by standard histological procedures.
To overcome these problems we developed a microscopy based on extreme darkfield illumination. This microscopy allows optical sectioning of whole mouse brains and was combined with an approach to clear fixed neuronal tissue: Mouse brains were made completely transparent by immersion in oil of the same refractive index as protein. By illuminating the brains with blue light (488 nm), neurons labeled with GFP were visualized by fluorescence. This way we could detect single neurons in hippocampi inside whole brains.
By surface rendering the shape and position of hippocampi relative to the brain surface could be depicted. In complete excised hippocampi subcellular resolution was obtained by 3D reconstruction from several hundred optical sections. The dendritic trees of CA1 hippocampal neurons with dendrites and dendritic spines could be visualized.
Already the very idea of awaking from anaesthesia during surgery is a horrible imagination for every patient. Indeed, intra-operative awareness still occurs in clinical practice and, if so, can lead to sequels like nightmares or even posttraumatic stress disorder and at least involves discomfort and pain for the patient. We work on a novel RQA-based anaesthesia monitoring device. RQA is a nonlinear mathematical data analysis method, which describes the complexity (predictability) of time series data like sampled EEG's. We have shown that this measure is strongly correlated with the state of consciousness and thus can be used to reliably detect intra-surgical awareness. Aim of our work is an apparatus that will warn an anaesthetist if a patient is about to awake during surgery.
