BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets.

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

Vaa3D-x for cross-platform teravoxel-scale immersive exploration of multidimensional image data.

SUMMARY: Vaa3D is a software package that has been widely used to visualize and analyze multidimensional microscopic images in a number of cutting edge bioimage informatics applications. However, due to many recent updates of both software development environments and operating systems, it was highly requested to maintain Vaa3D and disseminate it on latest operating systems. In addition, there has never been a showcase about how to use Vaa3D's cross-platform visualization and immersive exploration functions for multidimensional and teravoxel-scale images. Here, we introduce a newly developed version of the software, called Vaa3D-x, to address all the above issues. AVAILABILITY AND IMPLEMENTATION: Vaa3D-x is released in both binary and Open-Source available at vaa3d.org and GitHub (https://github.com/Vaa3D). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Functional Dissection of Protein Kinases in Sexual Development and Female Receptivity of Drosophila.

Protein phosphorylation is crucial for a variety of biological functions, but how it is involved in sexual development and behavior is rarely known. In this study, we performed a screen of RNA interference targeting 177 protein kinases in Drosophila and identified 13 kinases involved in sexual development in one or both sexes. We further identified that PKA and CASK promote female sexual behavior while not affecting female differentiation. Knocking down PKA or CASK in about five pairs of pC1 neurons in the central brain affects the fine projection but not cell number of these pC1 neurons and reduces virgin female receptivity. We also found that PKA and CASK signaling is required acutely during adulthood to promote female sexual behavior. These results reveal candidate kinases required for sexual development and behaviors and provide insights into how kinases would regulate neuronal development and physiology to fine tune the robustness of sexual behaviors.

Petabyte-Scale Multi-Morphometry of Single Neurons for Whole Brains.

Recent advances in brain imaging allow producing large amounts of 3-D volumetric data from which morphometry data is reconstructed and measured. Fine detailed structural morphometry of individual neurons, including somata, dendrites, axons, and synaptic connectivity based on digitally reconstructed neurons, is essential for cataloging neuron types and their connectivity. To produce quality morphometry at large scale, it is highly desirable but extremely challenging to efficiently handle petabyte-scale high-resolution whole brain imaging database. Here, we developed a multi-level method to produce high quality somatic, dendritic, axonal, and potential synaptic morphometry, which was made possible by utilizing necessary petabyte hardware and software platform to optimize both the data and workflow management. Our method also boosts data sharing and remote collaborative validation. We highlight a petabyte application dataset involving 62 whole mouse brains, from which we identified 50,233 somata of individual neurons, profiled the dendrites of 11,322 neurons, reconstructed the full 3-D morphology of 1,050 neurons including their dendrites and full axons, and detected 1.9 million putative synaptic sites derived from axonal boutons. Analysis and simulation of these data indicate the promise of this approach for modern large-scale morphology applications.

Morphological diversity of single neurons in molecularly defined cell types.

Dendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types1,2, yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes. This diversity follows a set of generalizable principles that govern long-range axonal projections at different levels, including molecular correspondence, divergent or convergent projection, axon termination pattern, regional specificity, topography, and individual cell variability. Although clear concordance with transcriptomic profiles is evident at the level of major projection type, fine-grained morphological diversity often does not readily correlate with transcriptomic subtypes derived from unsupervised clustering, highlighting the need for single-cell cross-modality studies. Overall, our study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single-cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits.

TeraVR empowers precise reconstruction of complete 3-D neuronal morphology in the whole brain.

Neuron morphology is recognized as a key determinant of cell type, yet the quantitative profiling of a mammalian neuron's complete three-dimensional (3-D) morphology remains arduous when the neuron has complex arborization and long projection. Whole-brain reconstruction of neuron morphology is even more challenging as it involves processing tens of teravoxels of imaging data. Validating such reconstructions is extremely laborious. We develop TeraVR, an open-source virtual reality annotation system, to address these challenges. TeraVR integrates immersive and collaborative 3-D visualization, interaction, and hierarchical streaming of teravoxel-scale images. Using TeraVR, we have produced precise 3-D full morphology of long-projecting neurons in whole mouse brains and developed a collaborative workflow for highly accurate neuronal reconstruction.

Detecting abnormalities of corpus callosum connectivity in autism using magnetic resonance imaging and diffusion tensor tractography.

The corpus callosum (CC) has emerged as one of the primary targets of autism research. To detect aberrant CC interhemispheric connectivity in autism, we performed T1-weighted magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI)-based tractography in 18 children with high functioning autism (HFA) and 16 well-matched typically developing (TD) children. We compared global and regional T1 measures (CC volume, and CC density), and the DTI measures [fractional anisotropy (FA), apparent diffusion coefficient (ADC), average fiber length (AFL), and fiber number (FN)] of transcallosal fibers, between the two groups. We also evaluated the relationships between scores on the Childhood Autism Rating Scale (CARS) and CC T1 or DTI measurements. Significantly less white matter density in the anterior third of the CC, and higher ADC and lower FN values of the anterior third transcallosal fiber tracts were found in HFA patients compared to TD children. These results suggested that the anterior third CC density and transcallosal fiber connectivity were affected in HFA children.

Automatic reconstruction of 3D neuron structures using a graph-augmented deformable model.

MOTIVATION: Digital reconstruction of 3D neuron structures is an important step toward reverse engineering the wiring and functions of a brain. However, despite a number of existing studies, this task is still challenging, especially when a 3D microscopic image has low single-to-noise ratio and discontinued segments of neurite patterns. RESULTS: We developed a graph-augmented deformable model (GD) to reconstruct (trace) the 3D structure of a neuron when it has a broken structure and/or fuzzy boundary. We formulated a variational problem using the geodesic shortest path, which is defined as a combination of Euclidean distance, exponent of inverse intensity of pixels along the path and closeness to local centers of image intensity distribution. We solved it in two steps. We first used a shortest path graph algorithm to guarantee that we find the global optimal solution of this step. Then we optimized a discrete deformable curve model to achieve visually more satisfactory reconstructions. Within our framework, it is also easy to define an optional prior curve that reflects the domain knowledge of a user. We investigated the performance of our method using a number of challenging 3D neuronal image datasets of different model organisms including fruit fly, Caenorhabditis elegans, and mouse. In our experiments, the GD method outperformed several comparison methods in reconstruction accuracy, consistency, robustness and speed. We further used GD in two real applications, namely cataloging neurite morphology of fruit fly to build a 3D 'standard' digital neurite atlas, and estimating the synaptic bouton density along the axons for a mouse brain. AVAILABILITY: The software is provided as part of the V3D-Neuron 1.0 package freely available at http://penglab.janelia.org/proj/v3d.

V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets.

The V3D system provides three-dimensional (3D) visualization of gigabyte-sized microscopy image stacks in real time on current laptops and desktops. V3D streamlines the online analysis, measurement and proofreading of complicated image patterns by combining ergonomic functions for selecting a location in an image directly in 3D space and for displaying biological measurements, such as from fluorescent probes, using the overlaid surface objects. V3D runs on all major computer platforms and can be enhanced by software plug-ins to address specific biological problems. To demonstrate this extensibility, we built a V3D-based application, V3D-Neuron, to reconstruct complex 3D neuronal structures from high-resolution brain images. V3D-Neuron can precisely digitize the morphology of a single neuron in a fruitfly brain in minutes, with about a 17-fold improvement in reliability and tenfold savings in time compared with other neuron reconstruction tools. Using V3D-Neuron, we demonstrate the feasibility of building a 3D digital atlas of neurite tracts in the fruitfly brain.

Segmentation of center brains and optic lobes in 3D confocal images of adult fruit fly brains.

Automatic alignment (registration) of 3D images of adult fruit fly brains is often influenced by the significant displacement of the relative locations of the two optic lobes (OLs) and the center brain (CB). In one of our ongoing efforts to produce a better image alignment pipeline of adult fruit fly brains, we consider separating CB and OLs and align them independently. This paper reports our automatic method to segregate CB and OLs, in particular under conditions where the signal to noise ratio (SNR) is low, the variation of the image intensity is big, and the relative displacement of OLs and CB is substantial. We design an algorithm to find a minimum-cost 3D surface in a 3D image stack to best separate an OL (of one side, either left or right) from CB. This surface is defined as an aggregation of the respective minimum-cost curves detected in each individual 2D image slice. Each curve is defined by a list of control points that best segregate OL and CB. To obtain the locations of these control points, we derive an energy function that includes an image energy term defined by local pixel intensities and two internal energy terms that constrain the curve's smoothness and length. Gradient descent method is used to optimize this energy function. To improve both the speed and robustness of the method, for each stack, the locations of optimized control points in a slice are taken as the initialization prior for the next slice. We have tested this approach on simulated and real 3D fly brain image stacks and demonstrated that this method can reasonably segregate OLs from CBs despite the aforementioned difficulties.

White matter impairments in autism, evidence from voxel-based morphometry and diffusion tensor imaging.

This study explored white matter abnormalities in a group of Chinese children with high functioning autism (HFA). Twelve male children with HFA and ten matched typically developing children underwent diffusion tensor imaging (DTI) as well three-dimensional T1-weighted MRI for voxel-based morphometry (VBM). We found a significant decrease of the white matter density in the right frontal lobe, left parietal lobe and right anterior cingulate and a significant increase in the right frontal lobe, left parietal lobe and left cingulate gyrus in the HFA group compared with the control group. The HFA group also had decreased FA in the frontal lobe and left temporal lobe. By combining DT-MRI FA and MRI volumetric analyses based on the VBM model, the results showed consistent white matter abnormalities in a group of Chinese children with HFA.

Voxel-based morphometry study on brain structure in children with high-functioning autism.

Earlier studies have suggested abnormal brain volumes in autism, but inconsistencies exist. Using voxel-based morphometry, we compared global and regional brain volumes in 17 high-functioning autistic children with 15 matched controls. We identified significant reduction in left white matter volume and white/gray matter ratio in autism. Regional brain volume reductions were detected for right anterior cingulate, left superior parietal lobule white matter volumes, and right parahippocampal gyrus gray matter volume, whereas enlargements in bilateral supramarginal gyrus, right postcentral gyrus, right medial frontal gyrus, and right posterior lobe of cerebellum gray matter in autism. Our findings showed global and regional brain volumes abnormality in high-functioning autism.

Multicontext wavelet-based thresholding segmentation of brain tissues in magnetic resonance images.

A novel segmentation method based on wavelet transform is presented for gray matter, white matter and cerebrospinal fluid in thin-sliced single-channel brain magnetic resonance (MR) scans. On the basis of the local image model, multicontext wavelet-based thresholding segmentation (MCWT) is proposed to classify 2D MR data into tissues automatically. In MCWT, the wavelet multiscale transform of local image gray histogram is done, and the gray threshold is gradually revealed from large-scale to small-scale coefficients. Image segmentation is independently performed in each local image to calculate the degree of membership of a pixel to each tissue class. Finally, a strategy is adopted to integrate the intersected outcomes from different local images. The result of the experiment indicates that MCWT outperforms other traditional segmentation methods in classifying brain MR images.

Gender difference in hemodynamic responses of prefrontal area to emotional stress by near-infrared spectroscopy.

Presentation of negative pictures was used as emotional stress to assess gender differences in prefrontal area activation in a functional near-infrared spectroscopy (NIRS) study. Compared with neutral condition, the response of oxy-HB for men yielded no significant difference during stress period, but the response induced by stress pictures for women showed significant enhancement. It was indicated that it is crucial to take gender difference into account when negative stimuli are used in functional brain imaging.