Visually comparing brain networks, or connectomes, is an essential task in the field of neuroscience. Especially relevant to the field of clinical neuroscience, group studies that examine differences between populations or changes over time within a population enable neuroscientists to reason about effective diagnoses and treatments for a range of neuropsychiatric disorders. In this paper, we specifically explore how visual analytics tools can be used to facilitate various clinical neuroscience tasks, in which observation and analysis of meaningful patterns in the connectome can support patient diagnosis and treatment. We conduct a survey of visualization tasks that enable clinical neuroscience activities, and further explore how existing connectome visualization tools support or fail to support these tasks. Based on our investigation of these tasks, we introduce a novel visualization tool, NeuroCave, to support group studies analyses. We discuss how our design decisions (the use of immersive visualization, the use of hierarchical clustering and dimensionality reduction techniques, and the choice of visual encodings) are motivated by these tasks. We evaluate NeuroCave through two use cases that illustrate the utility of interactive connectome visualization in clinical neuroscience contexts. In the first use case, we study sex differences using functional connectomes and discover hidden connectome patterns associated with well-known cognitive differences in spatial and verbal abilities. In the second use case, we show how the utility of visualizing the brain in different topological space coupled with clustering information can reveal the brain's intrinsic structure.
The connectogram @cite_50 , a recent connectome visualization technique, is a node-link diagram in a circular layout (see fig:case2 ). Names of brain regions are positioned along the perimeter of a circle, which is split into two halves according to their hemispheric affiliation. Each hemisphere is further divided into the different brain lobes. The inner sphere contains multiple colored rings, each representing a specific metric. The regions are interconnected within the circle using curved lines. This technique prevents some of the clutter that is found in other network visualizations. However, it is harder to correlate anatomical structures with connectivity using a circular diagram like the connectogram, as the multiple rings may make it difficult for a user to make sense of the data @cite_43 . tab:table1 contains a list of different visualization tools in the field of connectomics and their capabilities. Although previous efforts have surveyed visualization in connectomics, such as @cite_51 and @cite_20 , our survey is the first to comprehensively explore recent connectomics visualization software in terms of their ability to support group studies analysis tasks. Moreover, we catalog these software tools by the primary connectome dataset types they support.
@cite_43 @cite_20 @cite_51 @cite_50
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Cortical network architecture has predominantly been investigated visually using graph theory representations. In the context of human connectomics, such representations are not however always satisfactory because canonical methods for vertex–edge relationship representation do not always offer optimal insight regarding functional and structural neural connectivity. This article introduces an innovative framework for the depiction of human connectomics by employing a circular visualization method which is highly suitable to the exploration of central nervous system architecture. This type of representation, which we name a ‘connectogram’, has the capability of classifying neuroconnectivity relationships intuitively and elegantly. A multimodal protocol for MRI DTI neuroimaging data acquisition is here combined with automatic image segmentation to (1) extract cortical and non-cortical anatomical structures, (2) calculate associated volumetrics and morphometrics, and (3) determine patient-specific connectivity profiles to generate subject-level and population-level connectograms. The scalability of our approach is demonstrated for a population of 50 adults. Two essential advantages of the connectogram are (1) the enormous potential for mapping and analyzing the human connectome, and (2) the unconstrained ability to expand and extend this analysis framework to the investigation of clinical populations and animal models. In analysis of the human connectome, the connectivity of the human brain is collected from multiple imaging modalities and analyzed using graph theoretical techniques. The dimensionality of human connectivity data is high, and making sense of the complex networks in connectomics requires sophisticated visualization and analysis software. The current availability of software packages to analyze the human connectome is limited. The Connectome Visualization Utility (CVU) is a new software package designed for the visualization and network analysis of human brain networks. CVU complements existing software packages by offering expanded interactive analysis and advanced visualization features, including the automated visualization of networks in three different complementary styles and features the special visualization of scalar graph theoretical properties and modular structure. By decoupling the process of network creation from network visualization and analysis, we ensure that CVU can visualize networks from any imaging modality. CVU offers a graphical user interface, interactive scripting, and represents data uses transparent neuroimaging and matrix-based file types rather than opaque application-specific file formats. Reconstructing a synaptic wiring diagram, or connectome, from electron microscopy (EM) images of brain tissue currently requires many hours of manual annotation or proofreading (Kasthuri and Lichtman, 2010; Lichtman and Sanes, 2008; Seung, 2009). The desire to reconstruct ever larger and more complex networks has pushed the collection of ever larger EM datasets. A cubic millimeter of raw imaging data would take up 1 PB of storage and present an annotation project that would be impractical without relying heavily on automatic segmentation methods. The RhoanaNet image processing pipeline was developed to automatically segment large volumes of EM data and ease the burden of manual proofreading and annotation. Based on (, 2015), we updated every stage of the software pipeline to provide better throughput performance and higher quality segmentation results. We used state of the art deep learning techniques to generate improved membrane probability maps, and Gala (Nunez-, 2014) was used to agglomerate 2D segments into 3D objects. We applied the RhoanaNet pipeline to four densely annotated EM datasets, two from mouse cortex, one from cerebellum and one from mouse lateral geniculate nucleus (LGN). All training and test data is made available for benchmark comparisons. The best segmentation results obtained gave @math scores of 0.9054 and 09182 for the cortex datasets, 0.9438 for LGN, and 0.9150 for Cerebellum. The RhoanaNet pipeline is open source software. All source code, training data, test data, and annotations for all four benchmark datasets are available at this http URL. This paper describes novel methods for constructing the intrinsic geometry of the human brain connectome using dimensionality-reduction techniques. We posit that the high-dimensional, complex geometry that represents this intrinsic topology can be mathematically embedded into lower dimensions using coupling patterns encoded in the corresponding brain connectivity graphs. We tested both linear and nonlinear dimensionality-reduction techniques using the diffusion-weighted structural connectome data acquired from a sample of healthy subjects. Results supported the nonlinearity of brain connectivity data, as linear reduction techniques such as the multidimensional scaling yielded inferior lower-dimensional embeddings. To further validate our results, we demonstrated that for tractography-derived structural connectome more influential regions such as rich-club members of the brain are more centrally mapped or embedded. Further, abnormal brain connectivity can be visually understood by inspecting the altered geometry of these three-dimensional (3D) embeddings that represent the topology of the human brain, as illustrated using simulated lesion studies of both targeted and random removal. Last, in order to visualize brain’s intrinsic topology we have developed software that is compatible with virtual reality technologies, thus allowing researchers to collaboratively and interactively explore and manipulate brain connectome data. The human brain is a complex system whose topological organization can be represented using connectomics. Recent studies have shown that human connectomes can be constructed using various neuroimaging technologies and further characterized using sophisticated analytic strategies, such as graph theory. These methods reveal the intriguing topological architectures of human brain networks in healthy populations and explore the changes throughout normal development and aging and under various pathological conditions. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. Here, using MATLAB with a graphical user interface (GUI), we developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges. In addition, display properties, such as the color and size of network elements or the layout of the figure, can be adjusted within a comprehensive but easy-to-use settings panel. Moreover, BrainNet Viewer draws the brain surface, nodes and edges in sequence and displays brain networks in multiple views, as required by the user. The figure can be manipulated with certain interaction functions to display more detailed information. Furthermore, the figures can be exported as commonly used image file formats or demonstration video for further use. BrainNet Viewer helps researchers to visualize brain networks in an easy, flexible and quick manner, and this software is freely available on the NITRC website (www.nitrc.org projects bnv ). Abstract Advanced neuroinformatics tools are required for methods of connectome mapping, analysis and visualization. The inherent multi-modality of connectome datasets poses new challenges for data organization, integration and sharing. We have designed and implemented the Connectome Viewer Toolkit --- a set of free and extensible open-source neuroimaging tools written in Python. The key components of the toolkit are as follows: 1. The Connectome File Format is an XML-based container format to standardize multi-modal data integration and structured metadata annotation. 2. The Connectome File Format Library enables management and sharing of connectome files. 3. The Connectome Viewer is an integrated research and development environment for visualization and analysis of multi-modal connectome data. The Connectome Viewer's plugin architecture supports extensions with network analysis packages and an interactive scripting shell, to enable easy development and community contributions. Integration with tools from the scientific Python community allows the leveraging of numerous existing libraries for powerful connectome data mining, exploration and comparison. We demonstrate the applicability of the Connectome Viewer Toolkit using Diffusion MRI datasets processed by the Connectome Mapper. The Connectome Viewer Toolkit is available from http: www.cmtk.org . Thanks to advances in non-invasive technologies such as functional Magnetic Resonance Imaging (fMRI) and Diffusion Tensor Imaging (DTI), highly-detailed maps of brain structure and function can now be collected. In this context, brain connectomics have emerged as a fast growing field that aims at understanding these comprehensive maps of brain connectivity using sophisticated computational models. In this paper we present BRAINtrinsic, an innovative web-based 3D visual analytics tool that allows users to intuitively and iteratively interact with connectome data. Moreover, BRAINtrinsic implements a novel visualization platform that reconstructs connectomes’ intrinsic geometry, i.e., the topological space as informed by brain connectivity, via dimensionality reduction. BRAINtrinsic is implemented with virtual reality in mind and is fully compatible with the Oculus Rift technology. Last, we demonstrate its effectiveness through a series of case studies involving both structural and resting-state MR imaging data. Connectomics is a field of neuroscience that analyzes neuronal connections. A connectome is a complete map of a neuronal system, comprising all neuronal connections between its structures. The term "connectome" is close to the word "genome" and implies completeness of all neuronal connections, in the same way as a genome is a complete listing of all nucleotide sequences. The goal of connectomics is to create a complete representation of the brain's wiring. Such a representation is believed to increase our understanding of how functional brain states emerge from their underlying anatomical structure. Furthermore, it can provide important information for the cure of neuronal dysfunctions like schizophrenia or autism. In this paper, we review the current state-of-the-art of visualization and image processing techniques in the field of connectomics and describe some remaining challenges. In recent years, many new cortical areas have been identified in the macaque monkey. The number of identified connections between areas has increased even more dramatically. We report here on (1) a summary of the layout of cortical areas associated with vision and with other modalities, (2) a computerized database for storing and representing large amounts of information on connectivity patterns, and (3) the application of these data to the analysis of hierarchical organization of the cerebral cortex. Our analysis concentrates on the visual system, which includes 25 neocortical areas that are predominantly or exclusively visual in function, plus an additional 7 areas that we regard as visual-association areas on the basis of their extensive visual inputs. A total of 305 connections among these 32 visual and visual-association areas have been reported. This represents 31 of the possible number of pathways it each area were connected with all others. The actual degree of connectivity is likely to be closer to 40 . The great majority of pathways involve reciprocal connections between areas. There are also extensive connections with cortical areas outside the visual system proper, including the somatosensory cortex, as well as neocortical, transitional, and archicortical regions in the temporal and frontal lobes. In the somatosensory motor system, there are 62 identified pathways linking 13 cortical areas, suggesting an overall connectivity of about 40 . Based on the laminar patterns of connections between areas, we propose a hierarchy of visual areas and of somato sensory motor areas that is more comprehensive than those suggested in other recent studies. The current version of the visual hierarchy includes 10 levels of cortical processing. Altogether, it contains 14 levels if one includes the retina and lateral geniculate nucleus at the bottom as well as the entorhinal cortex and hippocampus at the top. Within this hierarchy, there are multiple, intertwined processing streams, which, at a low level, are related to the compartmental organization of areas V1 and V2 and, at a high level, are related to the distinction between processing centers in the temporal and pariet al lobes. However, there are some pathways and relationships (about 10 of the total) whose descriptions do not fit cleanly into this hierarchical scheme for one reason or another. In most instances, though, it is unclear whether these represent genuine exceptions to a strict hierarchy rather than inaccuracies or uncertainties in the reported assignment. Abstract Can semantic corpora be coupled to dynamical simulations in such a way so as to extract new associations from the data that were hitherto unapparent? We attempt to do this within neuroscience as an application domain, by introducing the notion of the semantome and coupling it to the connectome of the human brain network. This is implemented using BrainX3, a virtual reality simulation cum data mining platform that can be used for visualization, analysis and feature extraction of neuroscience data. We use this system to explore anatomical, functional and symptomatic semantics associated to simulated neuronal activity of a healthy brain, one with stroke and one perturbed by transcranial magnetic stimulation. In particular, we find that pariet al and occipital lesions in stroke affect the visual processing pathway leading to symptoms such as visual neglect, depression and photo-sensitivity seizures. Integrating semantomics with connectomics thus generates hypotheses about symptoms, functions and brain activity that supplement existing tools for diagnosis of mental illness. Our results suggest a new approach to big data with potential applications to other domains. The analysis of brain connectivity is a vast field in neuroscience with a frequent use of visual representations and an increasing need for visual analysis tools. Based on an in-depth literature review and interviews with neuroscientists, we explore high-level brain connectivity analysis tasks that need to be supported by dedicated visual analysis tools. A significant example of such a task is the comparison of different connectivity data in the form of weighted graphs. Several approaches have been suggested for graph comparison within information visualization, but the comparison of weighted graphs has not been addressed. We explored the design space of applicable visual representations and present augmented adjacency matrix and node-link visualizations. To assess which representation best support weighted graph comparison tasks, we performed a controlled experiment. Our findings suggest that matrices support these tasks well, outperforming node-link diagrams. These results have significant implications for the design of brain connectivity analysis tools that require weighted graph comparisons. They can also inform the design of visual analysis tools in other domains, e.g. comparison of weighted social networks or biological pathways. Connectomics is an emerging field in neuroscience that aims to reconstruct the 3-dimensional morphology of neurons from electron microscopy (EM) images. Recent studies have successfully demonstrated the use of convolutional neural networks (ConvNets) for segmenting cell membranes to individuate neurons. However, there has been comparatively little success in high-throughput identification of the intercellular synaptic connections required for deriving connectivity graphs. In this study, we take a compositional approach to segmenting synapses, modeling them explicitly as an intercellular cleft co-located with an asymmetric vesicle density along a cell membrane. Instead of requiring a deep network to learn all natural combinations of this compositionality, we train lighter networks to model the simpler marginal distributions of membranes, clefts and vesicles from just 100 electron microscopy samples. These feature maps are then combined with simple rules-based heuristics derived from prior biological knowledge. Our approach to synapse detection is both more accurate than previous state-of-the-art (7 higher recall and 5 higher F1-score) and yields a 20-fold speed-up compared to the previous fastest implementations. We demonstrate by reconstructing the first complete, directed connectome from the largest available anisotropic microscopy dataset (245 GB) of mouse somatosensory cortex (S1) in just 9.7 hours on a single shared-memory CPU system. We believe that this work marks an important step toward the goal of a microscope-pace streaming connectomics pipeline. Electron microscopic connectomics is an ambitious research direction with the goal of studying comprehensive brain connectivity maps by using high-throughput, nano-scale microscopy. One of the main challenges in connectomics research is developing scalable image analysis algorithms that require minimal user intervention. Recently, deep learning has drawn much attention in computer vision because of its exceptional performance in image classification tasks. For this reason, its application to connectomic analyses holds great promise, as well. In this paper, we introduce a novel deep neural network architecture, FusionNet, for the automatic segmentation of neuronal structures in connectomics data. FusionNet leverages the latest advances in machine learning, such as semantic segmentation and residual neural networks, with the novel introduction of summation-based skip connections to allow a much deeper network architecture for a more accurate segmentation. We demonstrate the performance of the proposed method by comparing it with state-of-the-art electron microscopy (EM) segmentation methods from the ISBI EM segmentation challenge. We also show the segmentation results on two different tasks including cell membrane and cell body segmentation and a statistical analysis of cell morphology. We introduce NeuroCave, a novel immersive visualization system that facilitates the visual inspection of structural and functional connectome datasets. The representation of the human connectome as a graph enables neuroscientists to apply network-theoretic approaches in order to explore its complex characteristics. With NeuroCave, brain researchers can interact with the connectome— either in a standard desktop environment or while wearing portable VR headsets (such as Oculus Rift, Samsung Gear, or Google Daydream VR platforms)— in any coordinate system or topological space, as well as cluster brain regions into different modules on demand. Furthermore, a default side-by-side layout enables simultaneous, synchronized manipulation in 3D, utilizing modern GPU hardware architecture, and facilitates comparison tasks across different subjects or diagnostic groups or longitudinally within the same subject. Visual clutter is mitigated using a state-of-the-art edge bundling technique and through an interactive lay... Mapping the connectivity of neurons in the brain (i.e., connectomics) is a challenging problem due to both the number of connections in even the smallest organisms and the nanometer resolution required to resolve them. Because of this, previous connectomes contain only hundreds of neurons, such as in the C.elegans connectome. Recent technological advances will unlock the mysteries of increasingly large connectomes (or partial connectomes). However, the value of these maps is limited by our ability to reason with this data and understand any underlying motifs. To aid connectome analysis, we introduce algorithms to cluster similarly-shaped neurons, where 3D neuronal shapes are represented as skeletons. In particular, we propose a novel location-sensitive clustering algorithm. We show clustering results on neurons reconstructed from the Drosophila medulla that show high-accuracy. The goal of connectomics is to manifest the interconnections of neural system with the Electron Microscopy (EM) images. However, the formidable size of EM image data renders human annotation impractical, as it may take decades to fulfill the whole job. An alternative way to reconstruct the connectome can be attained with the computerized scheme that can automatically segment the neuronal structures. The segmentation of EM images is very challenging as the depicted structures can be very diverse. To address this difficult problem, a deep contextual network is proposed here by leveraging multi-level contextual information from the deep hierarchical structure to achieve better segmentation performance. To further improve the robustness against the vanishing gradients and strengthen the capability of the back-propagation of gradient flow, auxiliary classifiers are incorporated in the architecture of our deep neural network. It will be shown that our method can effectively parse the semantic meaning from the images with the underlying neural network and accurately delineate the structural boundaries with the reference of low-level contextual cues. Experimental results on the benchmark dataset of 2012 ISBI segmentation challenge of neuronal structures suggest that the proposed method can outperform the state-of-the-art methods by a large margin with respect to different evaluation measurements. Our method can potentially facilitate the automatic connectome analysis from EM images with less human intervention effort. The study of neural circuit reconstruction, i.e., connectomics, is a challenging problem in neuroscience. Automated and semi-automated electron microscopy (EM) image analysis can be tremendously helpful for connectomics research. In this paper, we propose a fully automatic approach for intra-section segmentation and inter-section reconstruction of neurons using EM images. A hierarchical merge tree structure is built to represent multiple region hypotheses and supervised classification techniques are used to evaluate their potentials, based on which we resolve the merge tree with consistency constraints to acquire final intra-section segmentation. Then, we use a supervised learning based linking procedure for the inter-section neuron reconstruction. Also, we develop a semi-automatic method that utilizes the intermediate outputs of our automatic algorithm and achieves intra-segmentation with minimal user intervention. The experimental results show that our automatic method can achieve close-to-human intra-segmentation accuracy and state-of-the-art inter-section reconstruction accuracy. We also show that our semi-automatic method can further improve the intra-segmentation accuracy. Connectomics based on high resolution ssTEM imagery requires reconstruction of the neuron geometry from histological slides. We present an approach for the automatic membrane segmentation in anisotropic stacks of electron microscopy brain tissue sections. The ambiguities in neuronal segmentation of a section are resolved by using the context from the neighboring sections. We find the global dense correspondence between the sections by SIFT Flow algorithm, evaluate the features of the corresponding pixels and use them to perform the segmentation. Our method is 3.6 and 6.4 more accurate in two different accuracy metrics than the algorithm with no context from other sections. Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intrapariet al area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of pariet al and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition. In the field of connectomics, neuroscientists seek to identify cortical connectivity comprehensively. Neuronal boundary detection from the Electron Microscopy (EM) images is often done to assist the automatic reconstruction of neuronal circuit. But the segmentation of EM images is a challenging problem, as it requires the detector to be able to detect both filament-like thin and blob-like thick membrane, while suppressing the ambiguous intracellular structure. In this paper, we propose multi-stage multi-recursive-input fully convolutional networks to address this problem. The multiple recursive inputs for one stage, i.e., the multiple side outputs with different receptive field sizes learned from the lower stage, provide multi-scale contextual boundary information for the consecutive learning. This design is biologically-plausible, as it likes a human visual system to compare different possible segmentation solutions to address the ambiguous boundary issue. Our multi-stage networks are trained end-to-end. It achieves promising results on two public available EM segmentation datasets, the mouse piriform cortex dataset and the ISBI 2012 EM dataset.