Automated in situ brain imaging for mapping the Drosophila connectome.

Mapping the connectome, a wiring diagram of the entire brain, requires large-scale imaging of numerous single neurons with diverse morphology. It is a formidable challenge to reassemble these neurons into a virtual brain and correlate their structural networks with neuronal activities, which are measured in different experiments to analyze the informational flow in the brain. Here, we report an in situ brain imaging technique called Fly Head Array Slice Tomography (FHAST), which permits the reconstruction of structural and functional data to generate an integrative connectome in Drosophila. Using FHAST, the head capsules of an array of flies can be opened with a single vibratome sectioning to expose the brains, replacing the painstaking and inconsistent brain dissection process. FHAST can reveal in situ brain neuroanatomy with minimal distortion to neuronal morphology and maintain intact neuronal connections to peripheral sensory organs. Most importantly, it enables the automated 3D imaging of 100 intact fly brains in each experiment. The established head model with in situ brain neuroanatomy allows functional data to be accurately registered and associated with 3D images of single neurons. These integrative data can then be shared, searched, visualized, and analyzed for understanding how brain-wide activities in different neurons within the same circuit function together to control complex behaviors.

Feature selection and combination criteria for improving accuracy in protein structure prediction.

The classification of protein structures is essential for their function determination in bioinformatics. At present, a reasonably high rate of prediction accuracy has been achieved in classifying proteins into four classes in the SCOP database according to their primary amino acid sequences. However, for further classification into fine-grained folding categories, especially when the number of possible folding patterns as those defined in the SCOP database is large, it is still quite a challenge. In our previous work, we have proposed a two-level classification strategy called hierarchical learning architecture (HLA) using neural networks and two indirect coding features to differentiate proteins according to their classes and folding patterns, which achieved an accuracy rate of 65.5%. In this paper, we use a combinatorial fusion technique to facilitate feature selection and combination for improving predictive accuracy in protein structure classification. When applying various criteria in combinatorial fusion to the protein fold prediction approach using neural networks with HLA and the radial basis function network (RBFN), the resulting classification has an overall prediction accuracy rate of 87% for four classes and 69.6% for 27 folding categories. These rates are significantly higher than the accuracy rate of 56.5% previously obtained by Ding and Dubchak. Our results demonstrate that data fusion is a viable method for feature selection and combination in the prediction and classification of protein structure.

Homogenous distribution of fast response action potentials in canine pulmonary vein sleeves: a contradictory report.

Pulmonary veins may serve as source of ectopic focus (or foci) in initiating atrial tachyarrhythmias in human beings. However, the animal model for such focal atrial fibrillation is still lacking and cellular mechanism for arrhythmias remains to be studied. Recently, a series of reports of cellular electrophysiological characterization of pulmonary vein sleeves demonstrated an extremely high incidence of automaticity (varied from 40 to 76%) and triggered activity (from 0 to 44%) in normal healthy control dogs and rabbits. The present study was therefore designed to re-investigate the cellular electrophysiological properties of canine pulmonary veins. Intracellular action potentials were characterized in pulmonary vein sleeves in 50 normal healthy dogs. Conventional glass microelectrode recording technique was used. Experiments were focused on the incidence of automaticity and triggered activity in pulmonary vein sleeve tissues. Surprisingly, our results showed that all pulmonary vein sleeves tissues in these dogs displayed fast-response action potentials under the well-controlled experimental condition (100%, n=50). No spontaneous pacemaking activities, early or delayed afterdepolarisations were observed (0%, n=50). No high-frequency spikes or irregular rhythm could be recorded in all experiments (0%, n=50). Both the frequency response and membrane responsiveness of the pulmonary vein action potentials were characterized. No electrophysiological inhomogeneity between the distal and the proximal part of the sleeves was observed. In conclusion, canine pulmonary vein sleeves do not display arrhythmogenic activities under normal physiological conditions. The possible explanations for the controversy in pulmonary veins electrophysiology were discussed.

Developing a stereotypical Drosophila brain atlas.

Brain research requires a standardized brain atlas to describe both the variance and invariance in brain anatomy and neuron connectivity. In this study, we propose a system to construct a standardized 3D Drosophila brain atlas by integrating labeled images from different preparations. The 3D fly brain atlas consists of standardized anatomical global and local reference models, e.g., the inner and external brain surfaces and the mushroom body. The averaged global and local reference models are generated by the model averaging procedure, and then the standard Drosophila brain atlas can be compiled by transferring the averaged neuropil models into the averaged brain surface models. The main contribution and novelty of our study is to determine the average 3D brain shape based on the isosurface suggested by the zero-crossings of a 3D accumulative signed distance map. Consequently, in contrast with previous approaches that also aim to construct a stereotypical brain model based on the probability map and a user-specified probability threshold, our method is more robust and thus capable to yield more objective and accurate results. Moreover, the obtained 3D average shape is useful for defining brain coordinate systems and will be able to provide boundary conditions for volume registration methods in the future. This method is distinguishable from those focusing on 2D + Z image volumes because its pipeline is designed to process 3D mesh surface models of Drosophila brains.

Retention of features on a mapped Drosophila brain surface using a Bézier-tube-based surface model averaging technique.

Model averaging is a widely used technique in biomedical applications. Two established model averaging methods, iterative shape averaging (ISA) method and virtual insect brain (VIB) method, have been applied to several organisms to generate average representations of their brain surfaces. However, without sufficient samples, some features of the average Drosophila brain surface obtained using the above methods may disappear or become distorted. To overcome this problem, we propose a Bézier-tube-based surface model averaging strategy. The proposed method first compensates for disparities in position, orientation, and dimension of input surfaces, and then evaluates the average surface by performing shape-based interpolation. Structural features with larger individual disparities are simplified with half-ellipse-shaped Bézier tubes, and are unified according to these tubes to avoid distortion during the averaging process. Experimental results show that the average model yielded by our method could preserve fine features and avoid structural distortions even if only a limit amount of input samples are used. Finally, we qualitatively compare our results with those obtained by ISA and VIB methods by measuring the surface-to-surface distances between input surfaces and the averaged ones. The comparisons show that the proposed method could generate a more representative average surface than both ISA and VIB methods.

Three-dimensional reconstruction of brain-wide wiring networks in Drosophila at single-cell resolution.

BACKGROUND: Animal behavior is governed by the activity of interconnected brain circuits. Comprehensive brain wiring maps are thus needed in order to formulate hypotheses about information flow and also to guide genetic manipulations aimed at understanding how genes and circuits orchestrate complex behaviors. RESULTS: To assemble this map, we deconstructed the adult Drosophila brain into approximately 16,000 single neurons and reconstructed them into a common standardized framework to produce a virtual fly brain. We have constructed a mesoscopic map and found that it consists of 41 local processing units (LPUs), six hubs, and 58 tracts covering the whole Drosophila brain. Despite individual local variation, the architecture of the Drosophila brain shows invariance for both the aggregation of local neurons (LNs) within specific LPUs and for the connectivity of projection neurons (PNs) between the same set of LPUs. An open-access image database, named FlyCircuit, has been constructed for online data archiving, mining, analysis, and three-dimensional visualization of all single neurons, brain-wide LPUs, their wiring diagrams, and neural tracts. CONCLUSION: We found that the Drosophila brain is assembled from families of multiple LPUs and their interconnections. This provides an essential first step in the analysis of information processing within and between neurons in a complete brain.