ViBrism DB: an interactive search and viewer platform for 2D/3D anatomical images of gene expression and co-expression networks.

Understanding anatomical structures and biological functions based on gene expression is critical in a systemic approach to address the complexity of the mammalian brain, where >25 000 genes are expressed in a precise manner. Co-expressed genes are thought to regulate cell type- or region-specific brain functions. Thus, well-designed data acquisition and visualization systems for profiling combinatorial gene expression in relation to anatomical structures are crucial. To this purpose, using our techniques of microtomy-based gene expression measurements and WebGL-based visualization programs, we mapped spatial expression densities of genome-wide transcripts to the 3D coordinates of mouse brains at four post-natal stages, and built a database, ViBrism DB (http://vibrism.neuroinf.jp/). With the DB platform, users can access a total of 172 022 expression maps of transcripts, including coding, non-coding and lncRNAs in the whole context of 3D magnetic resonance (MR) images. Co-expression of transcripts is represented in the image space and in topological network graphs. In situ hybridization images and anatomical area maps are browsable in the same space of 3D expression maps using a new browser-based 2D/3D viewer, BAH viewer. Created images are shareable using URLs, including scene-setting parameters. The DB has multiple links and is expandable by community activity.

Reconstruction of root systems in Cryptomeria japonica using root point coordinates and diameters.

MAIN CONCLUSION: We developed simple algorithms for reconstructing tree root system architecture using only the root point coordinate and diameter, which can be systematically obtained without digging up the root systems. Root system architecture (RSA) is strongly related to various root functions of the tree. The aim of this study was to develop a three-dimensional (3D) RSA model using systematically obtained information on root locations and root diameters at the locations. We excavated root systems of Cryptomeria japonica and systematically obtained XYZ coordinates and root diameters using a 10-cm grid. We clarified the patterns of the root point connections and developed a reconstructed root system model. We found that the root diameters farther from the stump centre are smaller. Additionally, we found that the root lengths of the segments running between the base and the connected root point were smaller than those of other root segments, and the inner angle between the base and the stump and between the base and the connected root point was narrower than for the other pairs. The new RSA model developed according to these results had average accuracies of 0.64 and 0.80 for estimates of total volume and length, respectively. The developed model can estimate 3D RSA using only root point data, which can be obtained without digging up root systems. This suggests a wide applicability of this model in root function evaluation.

Interneurons in the Honeybee Primary Auditory Center Responding to Waggle Dance-Like Vibration Pulses.

Female honeybees use the "waggle dance" to communicate the location of nectar sources to their hive mates. Distance information is encoded in the duration of the waggle phase (von Frisch, 1967). During the waggle phase, the dancer produces trains of vibration pulses, which are detected by the follower bees via Johnston's organ located on the antennae. To uncover the neural mechanisms underlying the encoding of distance information in the waggle dance follower, we investigated morphology, physiology, and immunohistochemistry of interneurons arborizing in the primary auditory center of the honeybee (Apis mellifera). We identified major interneuron types, named DL-Int-1, DL-Int-2, and bilateral DL-dSEG-LP, that responded with different spiking patterns to vibration pulses applied to the antennae. Experimental and computational analyses suggest that inhibitory connection plays a role in encoding and processing the duration of vibration pulse trains in the primary auditory center of the honeybee.SIGNIFICANCE STATEMENT The waggle dance represents a form of symbolic communication used by honeybees to convey the location of food sources via species-specific sound. The brain mechanisms used to decipher this symbolic information are unknown. We examined interneurons in the honeybee primary auditory center and identified different neuron types with specific properties. The results of our computational analyses suggest that inhibitory connection plays a role in encoding waggle dance signals. Our results are critical for understanding how the honeybee deciphers information from the sound produced by the waggle dance and provide new insights regarding how common neural mechanisms are used by different species to achieve communication.

Spatial registration of neuron morphologies based on maximization of volume overlap.

BACKGROUND: Morphological features are widely used in the study of neuronal function and pathology. Invertebrate neurons are often structurally stereotypical, showing little variance in gross spatial features but larger variance in their fine features. Such variability can be quantified using detailed spatial analysis, which however requires the morphologies to be registered to a common frame of reference. RESULTS: We outline here new algorithms - Reg-MaxS and Reg-MaxS-N - for co-registering pairs and groups of morphologies, respectively. Reg-MaxS applies a sequence of translation, rotation and scaling transformations, estimating at each step the transformation parameters that maximize spatial overlap between the volumes occupied by the morphologies. We test this algorithm with synthetic morphologies, showing that it can account for a wide range of transformation differences and is robust to noise. Reg-MaxS-N co-registers groups of more than two morphologies by iteratively calculating an average volume and registering all morphologies to this average using Reg-MaxS. We test Reg-MaxS-N using five groups of morphologies from the Droshophila melanogaster brain and identify the cases for which it outperforms existing algorithms and produce morphologies very similar to those obtained from registration to a standard brain atlas. CONCLUSIONS: We have described and tested algorithms for co-registering pairs and groups of neuron morphologies. We have demonstrated their application to spatial comparison of stereotypic morphologies and calculation of dendritic density profiles, showing how our algorithms for registering neuron morphologies can enable new approaches in comparative morphological analyses and visualization.

Walking patterns induced by learned odors in the honeybee, Apis mellifera L.

The odor localization strategy induced by odors learned via differential conditioning of the proboscis extension response was investigated in honeybees. In response to reward-associated but not non-reward-associated odors, learners walked longer paths than non-learners and control bees. When orange odor reward association was learned, the path length and the body turn angles were small during odor stimulation and greatly increased after stimulation ceased. In response to orange odor, bees walked locally with alternate left and right turns during odor stimulation to search for the reward-associated odor source. After odor stimulation, bees walked long paths with large turn angles to explore the odor plume. For clove odor, learning-related modulations of locomotion were less pronounced, presumably due to a spontaneous preference for orange in the tested population of bees. This study is the first to describe how an odor-reward association modulates odor-induced walking in bees.

Waggle dance effect: dancing in autumn reduces the mass loss of a honeybee colony.

A honeybee informs her nestmates about the location of a profitable food source that she has visited by means of a waggle dance: a round dance and a figure-of-eight dance for a short- and long-distance food source, respectively. Consequently, the colony achieves an effective collection of food. However, it is still not fully understood how much effect the dance behavior has on the food collection, because most of the relevant experiments have been performed only in limited locations under limited experimental conditions. Here, we examined the efficacy of the waggle dances by physically preventing bees from dancing and then analyzing the changes in daily mass of the hive as an index of daily food collection. To eliminate place- and year-specific effects, the experiments were performed under fully natural conditions in three different cities in Japan from mid September to early October in three different years. Because the experiments were performed in autumn, all six of the tested colonies lost mass on most of the experimental days. When the dance was prevented, the daily reduction in mass change was greater than when the dance was allowed, i.e. the dance inhibited the reduction of the hive mass. This indicates that dance is effective for food collection. Furthermore, clear inhibition was observed on the first two days of the experiments; after that, inhibition was no longer evident. This result suggests that the bee colony adapted to the new environment.

Adaptations during Maturation in an Identified Honeybee Interneuron Responsive to Waggle Dance Vibration Signals.

Honeybees are social insects, and individual bees take on different social roles as they mature, performing a multitude of tasks that involve multi-modal sensory integration. Several activities vital for foraging, like flight and waggle dance communication, involve sensing air vibrations through their antennae. We investigated changes in the identified vibration-sensitive interneuron DL-Int-1 in the honeybee Apis mellifera during maturation by comparing properties of neurons from newly emerged adult and forager honeybees. Although comparison of morphological reconstructions of the neurons revealed no significant changes in gross dendritic features, consistent and region-dependent changes were found in dendritic density. Comparison of electrophysiological properties showed an increase in the firing rate differences between stimulus and nonstimulus periods in foragers compared with newly emerged adult bees. The observed differences in neurons of foragers compared with newly emerged adult honeybees suggest refined connectivity, improved signal propagation, and enhancement of response features possibly important for the network processing of air vibration signals relevant for the waggle dance communication of honeybees.

Neuroethology of the Waggle Dance: How Followers Interact with the Waggle Dancer and Detect Spatial Information.

Since the honeybee possesses eusociality, advanced learning, memory ability, and information sharing through the use of various pheromones and sophisticated symbol communication (i.e., the "waggle dance"), this remarkable social animal has been one of the model symbolic animals for biological studies, animal ecology, ethology, and neuroethology. Karl von Frisch discovered the meanings of the waggle dance and called the communication a "dance language." Subsequent to this discovery, it has been extensively studied how effectively recruits translate the code in the dance to reach the advertised destination and how the waggle dance information conflicts with the information based on their own foraging experience. The dance followers, mostly foragers, detect and interact with the waggle dancer, and are finally recruited to the food source. In this review, we summarize the current state of knowledge on the neural processing underlying this fascinating behavior.

Development and application of a neuroinformatics environment for neuroscience and neuroethology.

Insect brains are excellent models for analyzing neuronal function in moderately complex central nervous systems due to the vast potential they offer for revealing the intricate details of the workings of a biological neural network. For a systematic approach to understanding neuronal mechanisms, it is important to integrate research results from various fields, such as morphology, physiology and immunohistochemistry. We are developing a database system, the Bombyx Neuron Database (BoND) for assembling and sharing experimental and analytical data. The system is designed and developed based on experimental data, mostly obtained from intracellular recordings. A new WWW technology, CMS (Content Management System), was implemented in our system. That is, PHP-based CMS, XOOPS, provides several functions for web-based database management, for instance, user accounting, web page designing and data backup. The BoND was developed by our original database module of XOOPS, in order to deal with electrophysiological and anatomical data. Research resources from various fields are combined in the database for realizing a conjunction of experiments and analysis, which will assist progress in understanding neural network mechanisms as a virtual laboratory.

Inhibitory Pathways for Processing the Temporal Structure of Sensory Signals in the Insect Brain.

Insects have acquired excellent sensory information processing abilities in the process of evolution. In addition, insects have developed communication schemes based on the temporal patterns of specific sensory signals. For instance, male moths approach a female by detecting the spatiotemporal pattern of a pheromone plume released by the female. Male crickets attract a conspecific female as a mating partner using calling songs with species-specific temporal patterns. The dance communication of honeybees relies on a unique temporal pattern of vibration caused by wingbeats during the dance. Underlying these behaviors, neural circuits involving inhibitory connections play a critical common role in processing the exact timing of the signals in the primary sensory centers of the brain. Here, we discuss common mechanisms for processing the temporal patterns of sensory signals in the insect brain.

Measuring the Flight Ability of the Ambrosia Beetle, Platypus Quercivorus (Murayama), Using a Low-Cost, Small, and Easily Constructed Flight Mill.

The ambrosia beetle, Platypus quercivorus (Murayama), is the vector of a fungal pathogen that causes mass mortality of Fagaceae trees (Japanese oak wilt). Therefore, knowing the dispersal capacity may help inform trapping/tree removal efforts to prevent this disease more effectively. In this study, we measured the flight velocity and duration and estimated the flight distance of the beetle using a newly developed flight mill. The flight mill is low cost, small, and constructed using commonly available items. Both the flight mill arm and its vertical axis comprise a thin needle. A beetle specimen is glued to one tip of the arm using instant glue. The other tip is thick due to being covered with plastic, thus it facilitates the detection of rotations of the arm. The revolution of the arm is detected by a photo sensor mounted on an infrared LED, and is indicated by a change in the output voltage when the arm passed above the LED. The photo sensor is connected to a personal computer and the output voltage data are stored at a sampling rate of 1 kHz. By conducting experiments using this flight mill, we found that P. quercivorus can fly at least 27 km. Because our flight mill comprises cheap and small ordinary items, many flight mills can be prepared and used simultaneously in a small laboratory space. This enables experimenters to obtain a sufficient amount of data within a short period.

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, application of logical product 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 distance information indicated in the waggle dance of the honeybee.

Image analysis procedure for the optical scanning of fine-root dynamics: errors depending on the observer and root-viewing window size.

Clarifying the dynamics of fine roots is critical to understanding carbon and nutrient cycling in forest ecosystems. An optical scanner can potentially be used in studying fine-root dynamics in forest ecosystems. The present study examined image analysis procedures suitable for an optical scanner having a large (210 mm × 297 mm) root-viewing window. We proposed a protocol for analyzing whole soil images obtained by an optical scanner that cover depths of 0-210 mm. We tested our protocol using six observers with different experience in studying roots. The observers obtained data from the manual digitization of sequential soil images recorded for a Bornean tropical forest according to the protocol. Additionally, the study examined the potential tradeoff between the soil image size and accuracy of estimates of fine-root dynamics in a simple exercise. The six observers learned the protocol and obtained similar temporal patterns of fine-root growth and biomass with error of 10-20% regardless of their experience. However, there were large errors in decomposition owing to the low visibility of decomposed fine roots. The simple exercise revealed that a smaller root-viewing window (smaller than 60% of the original window) produces patterns of fine-root dynamics that are different from those for the original window size. The study showed the high applicability of our image analysis approach for whole soil images taken by optical scanners in estimating the fine-root dynamics of forest ecosystems.

Error in the honeybee waggle dance improves foraging flexibility.

The honeybee waggle dance communicates the location of profitable food sources, usually with a certain degree of error in the directional information ranging from 10-15° at the lower margin. We simulated one-day colonial foraging to address the biological significance of information error in the waggle dance. When the error was 30° or larger, the waggle dance was not beneficial. If the error was 15°, the waggle dance was beneficial when the food sources were scarce. When the error was 10° or smaller, the waggle dance was beneficial under all the conditions tested. Our simulation also showed that precise information (0-5° error) yielded great success in finding feeders, but also caused failures at finding new feeders, i.e., a high-risk high-return strategy. The observation that actual bees perform the waggle dance with an error of 10-15° might reflect, at least in part, the maintenance of a successful yet risky foraging trade-off.

Automated analysis of two-dimensional positions and body lengths of earthworms (Oligochaeta); MimizuTrack.

Earthworms are important soil macrofauna inhabiting almost all ecosystems. Their biomass is large and their burrowing and ingestion of soils alters soil physicochemical properties. Because of their large biomass, earthworms are regarded as an indicator of "soil heath". However, primarily because the difficulties in quantifying their behavior, the extent of their impact on soil material flow dynamics and soil health is poorly understood. Image data, with the aid of image processing tools, are a powerful tool in quantifying the movements of objects. Image data sets are often very large and time-consuming to analyze, especially when continuously recorded and manually processed. We aimed to develop a system to quantify earthworm movement from video recordings. Our newly developed program successfully tracked the two-dimensional positions of three separate parts of the earthworm and simultaneously output the change in its body length. From the output data, we calculated the velocity of the earthworm's movement. Our program processed the image data three times faster than the manual tracking system. To date, there are no existing systems to quantify earthworm activity from continuously recorded image data. The system developed in this study will reduce input time by a factor of three compared with manual data entry and will reduce errors involved in quantifying large data sets. Furthermore, it will provide more reliable measured values, although the program is still a prototype that needs further testing and improvement. Combined with other techniques, such as measuring metabolic gas emissions from earthworm bodies, this program could provide continuous observations of earthworm behavior in response to environmental variables under laboratory conditions. In the future, this standardized method will be applied to other animals, and the quantified earthworm movement will be incorporated into models of soil material flow dynamics or behavior in response to chemical substances present in the soil.

Development of a new method to track multiple honey bees with complex behaviors on a flat laboratory arena.

A computer program that tracks animal behavior, thereby revealing various features and mechanisms of social animals, is a powerful tool in ethological research. Because honeybee colonies are populated by thousands of bees, individuals co-exist in high physical densities and are difficult to track unless specifically tagged, which can affect behavior. In addition, honeybees react to light and recordings must be made under special red-light conditions, which the eyes of bees perceive as darkness. The resulting video images are scarcely distinguishable. We have developed a new algorithm, K-Track, for tracking numerous bees in a flat laboratory arena. Our program implements three main processes: (A) The object (bee's) region is detected by simple threshold processing on gray scale images, (B) Individuals are identified by size, shape and spatiotemporal positional changes, and (C) Centers of mass of identified individuals are connected through all movie frames to yield individual behavioral trajectories. The tracking performance of our software was evaluated on movies of mobile multi-artificial agents and of 16 bees walking around a circular arena. K-Track accurately traced the trajectories of both artificial agents and bees. In the latter case, K-track outperformed Ctrax, well-known software for tracking multiple animals. To investigate interaction events in detail, we manually identified five interaction categories; 'crossing', 'touching', 'passing', 'overlapping' and 'waiting', and examined the extent to which the models accurately identified these categories from bee's interactions. All 7 identified failures occurred near a wall at the outer edge of the arena. Finally, K-Track and Ctrax successfully tracked 77 and 60 of 84 recorded interactive events, respectively. K-Track identified multiple bees on a flat surface and tracked their speed changes and encounters with other bees, with good performance.

NeuronDepot: keeping your colleagues in sync by combining modern cloud storage services, the local file system, and simple web applications.

Neuroscience today deals with a "data deluge" derived from the availability of high-throughput sensors of brain structure and brain activity, and increased computational resources for detailed simulations with complex output. We report here (1) a novel approach to data sharing between collaborating scientists that brings together file system tools and cloud technologies, (2) a service implementing this approach, called NeuronDepot, and (3) an example application of the service to a complex use case in the neurosciences. The main drivers for our approach are to facilitate collaborations with a transparent, automated data flow that shields scientists from having to learn new tools or data structuring paradigms. Using NeuronDepot is simple: one-time data assignment from the originator and cloud based syncing-thus making experimental and modeling data available across the collaboration with minimum overhead. Since data sharing is cloud based, our approach opens up the possibility of using new software developments and hardware scalabitliy which are associated with elastic cloud computing. We provide an implementation that relies on existing synchronization services and is usable from all devices via a reactive web interface. We are motivating our solution by solving the practical problems of the GinJang project, a collaboration of three universities across eight time zones with a complex workflow encompassing data from electrophysiological recordings, imaging, morphological reconstructions, and simulations.

Effect of Olfactory Stimulus on the Flight Course of a Honeybee, Apis mellifera, in a Wind Tunnel.

It is known that the honeybee, Apis mellifera, uses olfactory stimulus as important information for orienting to food sources. Several studies on olfactory-induced orientation flight have been conducted in wind tunnels and in the field. From these studies, optical sensing is used as the main information with the addition of olfactory signals and the navigational course followed by these sensory information. However, it is not clear how olfactory information is reflected in the navigation of flight. In this study, we analyzed the detailed properties of flight when oriented to an odor source in a wind tunnel. We recorded flying bees with a video camera to analyze the flight area, speed, angular velocity and trajectory. After bees were trained to be attracted to a feeder, the flight trajectories with or without the olfactory stimulus located upwind of the feeder were compared. The results showed that honeybees flew back and forth in the proximity of the odor source, and the search range corresponded approximately to the odor spread area. It was also shown that the angular velocity was different inside and outside the odor spread area, and trajectories tended to be bent or curved just outside the area.

Development of a scheme and tools to construct a standard moth brain for neural network simulations.

Understanding the neural mechanisms for sensing environmental information and controlling behavior in natural environments is a principal aim in neuroscience. One approach towards this goal is rebuilding neural systems by simulation. Despite their relatively simple brains compared with those of mammals, insects are capable of processing various sensory signals and generating adaptive behavior. Nevertheless, our global understanding at network system level is limited by experimental constraints. Simulations are very effective for investigating neural mechanisms when integrating both experimental data and hypotheses. However, it is still very difficult to construct a computational model at the whole brain level owing to the enormous number and complexity of the neurons. We focus on a unique behavior of the silkmoth to investigate neural mechanisms of sensory processing and behavioral control. Standard brains are used to consolidate experimental results and generate new insights through integration. In this study, we constructed a silkmoth standard brain and brain image, in which we registered segmented neuropil regions and neurons. Our original software tools for segmentation of neurons from confocal images, KNEWRiTE, and the registration module for segmented data, NeuroRegister, are shown to be very effective in neuronal registration for computational neuroscience studies.

How do honeybees attract nestmates using waggle dances in dark and noisy hives?

It is well known that honeybees share information related to food sources with nestmates using a dance language that is representative of symbolic communication among non-primates. Some honeybee species engage in visually apparent behavior, walking in a figure-eight pattern inside their dark hives. It has been suggested that sounds play an important role in this dance language, even though a variety of wing vibration sounds are produced by honeybee behaviors in hives. It has been shown that dances emit sounds primarily at about 250-300 Hz, which is in the same frequency range as honeybees' flight sounds. Thus the exact mechanism whereby honeybees attract nestmates using waggle dances in such a dark and noisy hive is as yet unclear. In this study, we used a flight simulator in which honeybees were attached to a torque meter in order to analyze the component of bees' orienting response caused only by sounds, and not by odor or by vibrations sensed by their legs. We showed using single sound localization that honeybees preferred sounds around 265 Hz. Furthermore, according to sound discrimination tests using sounds of the same frequency, honeybees preferred rhythmic sounds. Our results demonstrate that frequency and rhythmic components play a complementary role in localizing dance sounds. Dance sounds were presumably developed to share information in a dark and noisy environment.