We introduce TempoCave, a novel visualization application for analyzing dynamic brain networks, or connectomes. TempoCave provides a range of functionality to explore metrics related to the activity patterns and modular affiliations of different regions in the brain. These patterns are calculated by processing raw data retrieved functional magnetic resonance imaging (fMRI) scans, which creates a network of weighted edges between each brain region, where the weight indicates how likely these regions are to activate synchronously. In particular, we support the analysis needs of clinical psychologists, who examine these modular affiliations and weighted edges and their temporal dynamics, utilizing them to understand relationships between neurological disorders and brain activity, which could have a significant impact on the way in which patients are diagnosed and treated. We summarize the core functionality of TempoCave, which supports a range of comparative tasks, and runs both in a desktop mode and in an immersive mode. Furthermore, we present a real-world use case that analyzes pre- and post-treatment connectome datasets from 27 subjects in a clinical study investigating the use of cognitive behavior therapy to treat major depression disorder, indicating that TempoCave can provide new insight into the dynamic behavior of the human brain.
@cite_27 investigate visual encodings of edge weights within an adjacency matrix to support the comparisons of brain connectivity patterns. further emphasizes comparisons of different modularity affiliation metrics that summarize the edge weights in the network. Other recent brain visualization tools focus on neurobiological tasks, such as 's @cite_21 , which provides tools to conduct integrated analyses of genomic data and mesoscale neuroscience datasets at the level of individual neurons. aims to support tasks - relevant to diagnosing and treating patients with neurological disorders, extending previous work that focused on static connectomes @cite_6 , which also presents a 3D network view and enables a user to explore data immersively @cite_26 .
@cite_27 @cite_21 @cite_6 @cite_26
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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. 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. Abstract In recent years, big brain-initiatives and consortia have created vast resources of publicly available brain data that can be used by neuroscientists for their own research experiments. This includes microscale connectivity data—brain-network graphs with billions of edges—and vast spatial gene expression resources—the representation of tens of thousands genes in brain space. Their joint analysis for higher order relations in structural or functional neuroanatomy would enable the genetic dissection of brain networks on a genome-wide scale. Current experimental workflows involve only time-consuming manual aggregation and extensive graph theoretical analysis of data from different sources, which rarely provide spatial context to operate continuously on different scales. In this paper, we propose BrainTrawler, a task-driven, web-based framework that incorporates visual analytics methods to explore heterogeneous neurobiological data. It facilitates spatial indexing to query large-scale voxel-level connectivity data and gene expression collections in real-time. Relating data to the hierarchical structure of common anatomical atlases enables the retrieval on different anatomical levels. Together with intuitive network visualization, iterative visual queries, and quantitative information this allows the genetic dissection of multimodal networks on local global scales in a spatial context. We demonstrate the relevance of our approach for neuroscience by exploring social-behavior and memory learning related functional neuroanatomy in mice. 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.