In this paper, we present an approach for medical ultrasound (US) image enhancement. It is based on a novel perceptual saliency measure which favors smooth, long curves with constant curvature. The perceptual salient boundaries of tissues in US images are enhanced by computing the saliency of directional vectors in the image space, via a local searching algorithm. Our measure is generally determined by curvature changes, intensity gradient and the interaction of neighboring vectors. To restrain speckle noise during the enhancement process, an adaptive speckle suspension term is also combined into the proposed saliency measure. The results obtained on both simulated images and medical US data reveal superior performance of the novel approach over a number of commonly used speckle filters. Applications of US image segmentation show that although the proposed algorithm cannot remove the speckle noise completely and may discard weak anatomical structures in some case, it still provides a considerable gain to US image processing for computer-aided diagnosis.

Recent studies have shown that machine learning can improve the accuracy of detecting object boundaries in images. In the standard approach, a boundary detector is trained by minimizing its pixel-level disagreement with human boundary tracings. This naive metric is problematic because it is overly sensitive to boundary locations. This problem is solved by metrics provided with the Berkeley Segmentation Dataset, but these can be insensitive to topological differences, such as gaps in boundaries. Furthermore, the Berkeley metrics have not been useful as cost functions for supervised learning. Using concepts from digital topology, we propose a new metric called the warping error that tolerates disagreements over boundary location, penalizes topological disagreements, and can be used directly as a cost function for learning boundary detection, in a method that we call Boundary Learning by Optimization with Topological Constraints (BLOTC). We trained boundary detectors on electron microscopic images of neurons, using both BLOTC and standard training. BLOTC produced substantially better performance on a 1.2 million pixel test set, as measured by both the warping error and the Rand index evaluated on segmentations generated from the boundary labelings. We also find our approach yields significantly better segmentation performance than either gPb-OWT-UCM or multiscale normalized cut, as well as Boosted Edge Learning trained directly on our data.

Inside the cell, proteins essential for signaling, morphogenesis, and migration navigate the complex, ever-changing environment through vesicular trafficking or microtubule-driven mechanisms. However, the mechanisms by which soluble proteins reach their target destinations remain unknown. Here, we show that soluble proteins are directed toward the cell’s advancing edge by advection, diffusion facilitated by fluid flow. The advective transport mechanism operates in a compartment at the front of the cell isolated from the rest of the cytoplasm by a semi-permeable actin-myosin barrier that restricts protein mixing between the compartment and the rest of the cytoplasm. Contraction at the barrier generates a molecularly non-specific fluid flow that propels treadmilling actin monomer, actin-binding, adhesion, and even inert proteins forward. Changes in the dynamic local curvature of the barrier direct the flow, targeting proteins toward the protruding regions of the leading edge, effectively coordinating the distribution of proteins needed for local changes in cellular dynamics. Outside the compartment, diffusion is the primary mode of soluble protein transport. Our findings suggest that cells possess previously unrecognized organizational strategies for managing soluble protein concentration and distributing them efficiently for activities such as protrusion and adhesion.

Brain-derived neurotrophic factor (BDNF) plays an important role in hippocampus-dependent learning and memory. Canonically, this has been ascribed to an enhancing effect on neuronal excitability and synaptic plasticity in the CA1 region. However, it is the pyramidal neurons in the subiculum that form the primary efferent pathways conveying hippocampal information to other areas of the brain, and yet the effect of BDNF on these neurons has remained unexplored. We present new data that BDNF regulates neuronal excitability and cellular plasticity in a much more complex manner than previously suggested. Subicular pyramidal neurons can be divided into two major classes, which have different electrophysiological and morphological properties, different requirements for the induction of plasticity and different extra-hippocampal projections. We found that BDNF increases excitability in one class of subicular pyramidal neurons, yet decreases excitability of the other class. Further, while endogenous BDNF was necessary for the induction of synaptic plasticity in both cell types, BDNF enhanced intrinsic plasticity in one class of pyramidal neurons, yet suppressed intrinsic plasticity in the other. Taken together, these data suggest a novel role for BDNF signaling, as it appears to dynamically and bidirectionally regulate the output of hippocampal information to different regions of the brain.

Two-photon imaging and optogenetic stimulation rely on high illumination powers, particularly for state-of-the-art applications that target deeper structures, achieve faster measurements, or probe larger brain areas. However, little information is available on heating and resulting damage induced by high-power illumination in the brain. Here we used thermocouple probes and quantum dot nanothermometers to measure temperature changes induced by two-photon microscopy in the neocortex of awake and anaesthetized mice. We characterized heating as a function of wavelength, exposure time, and distance from the center of illumination. Although total power is highest near the surface of the brain, heating was most severe hundreds of microns below the focal plane, due to heat dissipation through the cranial window. Continuous illumination of a 1mm2 area produced a peak temperature increase of approximately 1.8°C/100mW. Continuous illumination with powers above 250 mW induced lasting damage, detected with immunohistochemistry against Iba1, GFAP, heat shock proteins, and activated Caspase-3. Higher powers were usable in experiments with limited duty ratios, suggesting an approach to mitigate damage in high-power microscopy experiments.

The microvasculature underlies the supply networks that support neuronal activity within heterogeneous brain regions. What are common versus heterogeneous aspects of the connectivity, density, and orientation of capillary networks? To address this, we imaged, reconstructed, and analyzed the microvasculature connectome in whole adult mice brains with sub-micrometer resolution. Graph analysis revealed common network topology across the brain that leads to a shared structural robustness against the rarefaction of vessels. Geometrical analysis, based on anatomically accurate reconstructions, uncovered a scaling law that links length density, i.e., the length of vessel per volume, with tissue-to-vessel distances. We then derive a formula that connects regional differences in metabolism to differences in length density and, further, predicts a common value of maximum tissue oxygen tension across the brain. Last, the orientation of capillaries is weakly anisotropic with the exception of a few strongly anisotropic regions; this variation can impact the interpretation of fMRI data.

BACKGROUND: Structural MRI has demonstrated brain alterations in depression pathology and antidepressants treatment. While synaptic plasticity has been previously proposed as the potential underlying mechanism of MRI findings at a cellular and molecular scale, there is still insufficient evidence to link the MRI findings and synaptic plasticity mechanisms in depression pathology.

METHODS: In this study, a Wistar-Kyoto (WKY) depression rat model was treated with antidepressants (citalopram or Jie-Yu Pills) and tested in a series of behavioral tests and a 7.0 MRI scanner. We then measured dendritic spine density within altered brain regions. We also examined expression of synaptic marker proteins (PSD-95 and SYP).

RESULTS: WKY rats exhibited depression-like behaviors in the sucrose preference test (SPT) and forced swim test (FST), and anxiety-like behaviors in the open field test (OFT). Both antidepressants reversed behavioral changes in SPT and OFT but not in FST. We found a correlation between SPT performance and brain volumes as detected by MRI. All structural changes were consistent with alterations of the corpus callosum (white matter), dendritic spine density, as well as PSD95 and SYP expression at different levels. Two antidepressants similarly reversed these macro- and micro-changes.

LIMITATIONS: The single dose of antidepressants was the major limitation of this study. Further studies should focus on the white matter microstructure changes and myelin-related protein alterations, in addition to comparing the effects of ketamine.

CONCLUSION: Translational evidence links structural MRI changes and synaptic plasticity alterations, which promote our understanding of SPT mechanisms and antidepressant response in WKY rats.

Advances in brain connectomics have demonstrated the extraordinary complexity of neural circuits. Developing neurons encounter the axons and dendrites of many different neuron types and form synapses with only a subset of them. During circuit assembly, neurons express cell-type-specific repertoires comprising many cell adhesion molecules (CAMs) that can mediate interactions between developing neurites. Many CAM families have been shown to contribute to brain wiring in different ways. It has been challenging, however, to identify receptor-ligand pairs directly matching neurons with their synaptic targets. Here, we integrated the synapse-level connectome of the neural circuit with the developmental expression patterns and binding specificities of CAMs on pre- and postsynaptic neurons in the Drosophila visual system. To overcome the complexity of neural circuits, we focus on pairs of genetically related neurons that make differential wiring choices. In the motion detection circuit, closely related subtypes of T4/T5 neurons choose between alternative synaptic targets in adjacent layers of neuropil. This choice correlates with the matching expression in synaptic partners of different receptor-ligand pairs of the Beat and Side families of CAMs. Genetic analysis demonstrated that presynaptic Side-II and postsynaptic Beat-VI restrict synaptic partners to the same layer. Removal of this receptor-ligand pair disrupts layers and leads to inappropriate targeting of presynaptic sites and postsynaptic dendrites. We propose that different Side/Beat receptor-ligand pairs collaborate with other recognition molecules to determine wiring specificities in the fly brain. Combining transcriptomes, connectomes, and protein interactome maps allow unbiased identification of determinants of brain wiring.

Whole-brain imaging allows for comprehensive functional mapping of distributed neural pathways, but neuronal perturbation experiments are usually limited to targeting predefined regions or genetically identifiable cell types. To complement whole-brain measures of activity with brain-wide manipulations for testing causal interactions, we introduce a system that uses measuredactivity patterns to guide optical perturbations of any subset of neurons in the same fictively behaving larval zebrafish. First, a light-sheet microscope collects whole-brain data that are rapidly analyzed by a distributed computing system to generate functional brain maps. On the basis of these maps, the experimenter can then optically ablate neurons and image activity changes across the brain. We applied this method to characterize contributions of behaviorally tuned populations to the optomotor response. We extended the system to optogenetically stimulate arbitrary subsets of neurons during whole-brain imaging. These open-source methods enable delineating the contributions of neurons to brain-wide circuit dynamics and behavior in individual animals.

In the absence of salient sensory cues to guide behavior, animals must still execute sequences of motor actions in order to forage and explore. How such successive motor actions are coordinated to form global locomotion trajectories is unknown. We mapped the structure of larval zebrafish swim trajectories in homogeneous environments and found that trajectories were characterized by alternating sequences of repeated turns to the left and to the right. Using whole-brain light-sheet imaging, we identified activity relating to the behavior in specific neural populations that we termed the anterior rhombencephalic turning region (ARTR). ARTR perturbations biased swim direction and reduced the dependence of turn direction on turn history, indicating that the ARTR is part of a network generating the temporal correlations in turn direction. We also find suggestive evidence for ARTR mutual inhibition and ARTR projections to premotor neurons. Finally, simulations suggest the observed turn sequences may underlie efficient exploration of local environments.