Biology subjects

Ikeno, H.

Publications and source records attributed to Ikeno, H..

3 recordsLinked to original sources

bioRxiv2018

Parallelized analysis of spatial gene expression patterns by database integration

We developed a computational framework for automated integration of a large number of two-dimensional (2D) images with three-dimensional (3D) image datasets located in the standard 3D coordinate. We applied the framework to 2,810 para-sagittal sectioned mouse brain 2D images of in situ hybridization (ISH), archived in the BrainTx database (http://www.cdtdb.neuroinf.jp). We registered the ISH images into the mouse standard coordinate space for MR images, Waxholm space (WHS, https://www.nitrc.org/projects/incfwhsmouse) by linearly transforming them into each of a series of para-sagittal MR image slices, and identifying the best-fit slice by calculating the similarity metric value ({delta}). Transformed 2D images were compared with 3D gene expression image datasets, which were made using a microtomy-based microarray assay system, Transcriptome Tomography, and archived in the ViBrism DB (http://vibrism.neuroinf.jp): the 3D images are located in the WHS.\n\nWe first transformed ISH images of 10 regionally expressed genes and compared them to signals of corresponding 3D expression images in ViBrism DB for evaluating the integration schema: two types of data, produced with different modalities and originally located in different dimensions, were successfully compared after enhancing ISH signals against background noise. Then, for the massive transformation of BrainTx database images, we parallelized our framework, using the IPython cluster package, and implemented it on the PC cluster provided for the Brain Atlasing Hackathon activity hosted by Neuroinformatics Japan Center in Japan. We could identify the best-fit positions for all of the ISH images. All programs were made available through the GitHub repository, at the web site of neuroinformatics/bah2016_registration (https://github.com/neuroinformatics/bah2016_registration).

neuroscience↗

bioRxiv2018

A segmentation scheme for complex neuronal arbors and application to vibration sensitive neurons in the honeybee brain

The morphology of a neuron is strongly related to its physiological properties, and thus to information processing functions. Optical microscope images are widely used for extracting the structure of neurons. Although several approaches have been proposed to trace and extract complex neuronal structures from microscopy images, available methods remain prone to errors. In this study, we present a practical scheme for processing confocal microscope images and reconstructing neuronal structures. We evaluated this scheme using image data samples and associated gold standard reconstructions from the BigNeuron Project. In these samples, dendritic arbors belonging to multiple projection branches of the same neuron overlapped in space, making it difficult to automatically and accurately trace their structural connectivity. Our proposed scheme, which combines several software tools for image masking and filtering with an existing tool for dendritic segmentation and tracing, outperformed state-of-the-art automatic methods in reconstructing such neuron structures. For evaluating our scheme, we applied it to a honeybee local interneuron, DL-Int-1, which has complex arbors and is considered to be a critical neuron for encoding the information indicated in the waggle dance of the honeybee.

bioinformatics↗

bioRxiv2017

Network simulations of interneuron circuits in the honeybee primary auditory center

Processing of airborne vibration signals in the auditory system is essential for honeybee communication through the waggle dance language. Properties of neurons in the honeybee primary auditory center suggest a circuitry of excitatory and inhibitory neurons encoding these communication signals. To test this assumption, we simulated this network and analyzed the predicted responses for different types of inputs. In particular, we investigated the effect of specific inhibitory connections in the network. The results indicate that the experimentally observed responses of certain interneuron types are compatible with an inhibitory network of vibration processing in the primary auditory center of the honeybee.

bioinformatics↗