In this paper, we present an automatic method for estimating the trajectories of Escherichia coli bacteria from in vivo phase-contrast microscopy. To address the low-contrast boundaries in cellular images, an adaptive kernel-based technique is applied to detect cells in each frame. In addition to intensity features, region homogeneity measure and class uncertainty are also applied in this detection technique. To track cells with complex motion, a novel matching gain measure is introduced to cope with the challenges, particularly the presence of low-contrast boundary, the variations of appearance, and the frequent overlapping and occlusion. For multicell tracking over time, an optimal matching strategy is introduced to improve the handling of cell collision and broken trajectories. The results of successful tracking of Escherichia coli from various phase-contrast sequences are reported and compared with manually determined trajectories, as well as those obtained from existing tracking schemes. The stability of the algorithm with different parameter values is also analyzed and discussed.

We describe an approach for automation of the process of reconstruction of neural tissue from serial section transmission electron micrographs. Such reconstructions require 3D segmentation of individual neuronal processes (axons and dendrites) performed in densely packed neuropil. We first detect neuronal cell profiles in each image in a stack of serial micrographs with multi-scale ridge detector. Short breaks in detected boundaries are interpolated using anisotropic contour completion formulated in fuzzy-logic framework. Detected profiles from adjacent sections are linked together based on cues such as shape similarity and image texture. Thus obtained 3D segmentation is validated by human operators in computer-guided proofreading process. Our approach makes possible reconstructions of neural tissue at final rate of about 5 microm3/manh, as determined primarily by the speed of proofreading. To date we have applied this approach to reconstruct few blocks of neural tissue from different regions of rat brain totaling over 1000microm3, and used these to evaluate reconstruction speed, quality, error rates, and presence of ambiguous locations in neuropil ssTEM imaging data.

Subiculum, the primary efferent pathway of hippocampus, participates in memory for spatial tasks, relapse to drug abuse, and temporal lobe seizures. Subicular pyramidal neurons exhibit low-threshold burst firing driven by a spike afterdepolarization. Here we report that burst firing can be regulated by stimulation of afferent projections to subiculum. Unlike synaptic plasticity, burst plasticity did not require synaptic depolarization, activation of AMPA or NMDA receptors, or action potential firing. Rather, enhancement of burst firing required synergistic activation of group I, subtype 1 metabotropic glutamate receptors (mGluRs) and muscarinic acetylcholine receptors (mAChR). When either of these receptors was blocked, a suppression of bursting was revealed, which in turn was blocked by antagonists of group I, subtype 5 mGluRs. These results indicate that the output of subiculum can be strongly and bidirectionally regulated by activation of glutamatergic inputs within the hippocampus and cholinergic afferents from the medial septum.

Spine growth and retraction with synapse formation and elimination plays an important role in shaping brain circuits during development and in the adult brain, yet the temporal relationship between spine morphogenesis and the formation of functional synapses remains poorly defined. We imaged hippocampal pyramidal neurons to identify spines of different ages. We then used two-photon glutamate uncaging, whole-cell recording, and Ca(2+) imaging to analyze the properties of nascent spines and their older neighbors. New spines expressed glutamate-sensitive currents that were indistinguishable from mature spines of comparable volumes. Some spines exhibited negligible AMPA receptor-mediated responses, but the occurrence of these "silent" spines was uncorrelated with spine age. In contrast, NMDA receptor-mediated Ca(2+) accumulations were significantly lower in new spines. New spines reconstructed using electron microscopy made synapses. Our data support a model in which outgrowth and enlargement of nascent spines is tightly coupled to formation and maturation of glutamatergic synapses.

SmY RNAs are a family of approximately 70-90 nt small nuclear RNAs found in nematodes. In C. elegans, SmY RNAs copurify in a small ribonucleoprotein (snRNP) complex related to the SL1 and SL2 snRNPs that are involved in nematode mRNA trans-splicing. Here we describe a comprehensive computational analysis of SmY RNA homologs found in the currently available genome sequences. We identify homologs in all sequenced nematode genomes in class Chromadorea. We are unable to identify homologs in a more distantly related nematode species, Trichinella spiralis (class: Dorylaimia), and in representatives of non-nematode phyla that use trans-splicing. Using comparative RNA sequence analysis, we infer a conserved consensus SmY RNA secondary structure consisting of two stems flanking a consensus Sm protein binding site. A representative seed alignment of the SmY RNA family, annotated with the inferred consensus secondary structure, has been deposited with the Rfam RNA families database.

Ambulation after spinal cord injury is possible with the aid of neuroprosthesis employing functional electrical stimulation (FES). Individuals with incomplete spinal cord injury (iSCI) retain partial volitional control of muscles below the level of injury, necessitating careful integration of FES with intact voluntary motor function for efficient walking. In this study, the intramuscular electromyogram (iEMG) was used to detect the intent to step and trigger FES-assisted walking in a volunteer with iSCI via an implanted neuroprosthesis consisting of two channels of bipolar iEMG signal acquisition and 12 independent channels of stimulation. The detection was performed with two types of classifiers- a threshold-based classifier that compared the running mean of the iEMG with a discrimination threshold to generate the trigger and a pattern recognition classifier that compared the time-history of the iEMG with a specified template of activity to generate the trigger whenever the cross-correlation coefficient exceeded a discrimination threshold. The pattern recognition classifier generally outperformed the threshold-based classifier, particularly with respect to minimizing False Positive triggers. The overall True Positive rates for the threshold-based classifier were 61.6% and 87.2% for the right and left steps with overall False Positive rates of 38.4% and 33.3%. The overall True Positive rates for the left and right step with the pattern recognition classifier were 57.2% and 93.3% and the overall False Positive rates were 11.9% and 24.4%. The subject showed no preference for either the threshold or pattern recognition-based classifier as determined by the Usability Rating Scale (URS) score collected after each trial and both the classifiers were perceived as moderately easy to use.

Imaging informatics has emerged as a major research theme in biomedicine in the last few decades. Currently, personalised, predictive and preventive patient care is believed to be one of the top priorities in biomedical research and practice. Imaging informatics plays a major role in biomedicine studies. This paper reviews main applications and challenges of imaging informatics in biomedicine.

Drosophila is a marvelous system to study the underlying principles that govern how neural circuits govern behaviors. The scale of the fly brain (approximately 100,000 neurons) and the complexity of the behaviors the fly can perform make it a tractable experimental model organism. In addition, 100 years and hundreds of labs have contributed to an extensive array of tools and techniques that can be used to dissect the function and organization of the fly nervous system. This review discusses both the conceptual challenges and the specific tools for a neurogenetic approach to circuit mapping in Drosophila.

One of the central problems in neuroscience is reconstructing synaptic connectivity in neural circuits. Synapses onto a neuron can be probed by sequentially stimulating potentially pre-synaptic neurons while monitoring the membrane voltage of the post-synaptic neuron. Reconstructing a large neural circuit using such a "brute force" approach is rather time-consuming and inefficient because the connectivity in neural circuits is sparse. Instead, we propose to measure a post-synaptic neuron's voltage while stimulating sequentially random subsets of multiple potentially pre-synaptic neurons. To reconstruct these synaptic connections from the recorded voltage we apply a decoding algorithm recently developed for compressive sensing. Compared to the brute force approach, our method promises significant time savings that grow with the size of the circuit. We use computer simulations to find optimal stimulation parameters and explore the feasibility of our reconstruction method under realistic experimental conditions including noise and non-linear synaptic integration. Multineuronal stimulation allows reconstructing synaptic connectivity just from the spiking activity of post-synaptic neurons, even when sub-threshold voltage is unavailable. By using calcium indicators, voltage-sensitive dyes, or multi-electrode arrays one could monitor activity of multiple postsynaptic neurons simultaneously, thus mapping their synaptic inputs in parallel, potentially reconstructing a complete neural circuit.