SwordPlots: Exploring Neuron Behavior within Dynamic Communities of Brain Networks
Study of the behavior of individual members in communities of dynamic networks can help neuroscientists to understand how interactions between neurons in brain networks change over time. Visualization of those temporal features is challenging, especially for networks embedded within spatial structures, such as brain networks. In this article, the authors present the design of SwordPlots, an interactive multi view visualization system to assist neuroscientists in their exploration of dynamic brain networks from multiple perspectives. Their visualization helps neuroscientists to understand how the functional behavior of the brain changes over time, how such behaviors are related to the spatial structure of the brain, and how communities of neurons with similar functionality evolve over time. To evaluate their application, they asked neuroscientists to use SwordPlots to examine four different mouse brain data sets. Based on feedback, their visualization design can provide neuroscientists with the ability to gain new insights into the properties of dynamic brain networks. c (c) 2016 Society for Imaging Science and Technology. [DOI: 10.2352/J.ImagingSci.Technol.2016.60.1.010405] INTRODUCTION Application of the approach of social network analysis (SNA) to mining and analyzing neurobiology data sets can be valuable in helping neuroscientists to understand and explore their data from different perspectives. The application of SNA to the study of brain networks has nodes mapped to a single neuron or a group of neurons, while edges between nodes represent a functional connection between neuronal sites. Neurons communicate through electrochemical signals. They process incoming chemical and electrical signals and transmit these signals to other neurons and other types of cells. A neural network is a group of connected neurons that performs a certain function. A community is defined as a group of nodes that tend to interact with other nodes in the same group more often than the nodes outside the group. However, SNA is not adequate to measure brain functional connectivity over time Received June 30, 2015; accepted for publication Nov. 4, 2015; published online Dec. 09, 2015. Associate Editor: Thomas Wischgoll. 1062 3701/2016/60(1)/010405/13/$25.00 since it lacks the ability to capture dynamic changes in the connectivity of these networks. Recently, there have been attempts to ameliorate this problem, one of which is dynamic network analysis (DNA).1 Detection of the evolution of neural communities using DNA can be helpful for understanding important changes in network structure and individual node behavior over time. We worked closely with neuroscientists who use the DNA method to explore changes in functional behaviors and their resulting effects on community identities of neurons over time. The research goal of our domain experts is to utilize social network modeling algorithms to analyze mouse brain time series imaging data to uncover interactions among neurons across time and space. The brain networks can be identified through the application of a linear correlation method and then passing these correlation metrics to a community analysis algorithm. Given the large numbers of potential nodes >20,000) in a typical brain imaging experiment, effective visual representations are necessarily needed to help domain scientists to study the mouse brain using themodel of DNA. Although a number of visualization tools have been developed during the last few years, the development of applications for the visualization and visual analytics of dynamic brain networks still requires further effort. A major challenge in visualizing dynamic brain networks is to reveal temporal features while at the same time accounting for additional constraints imposed by brain architecture, i.e. neuron location. Spatial and non spatial visual integration challenges are common across domains, from geospatial applications to bioinformatics to neuroscience applications.2 In this article, we present SwordPlots, an interactive multi view visualization application which assists the domain scientist to explore neuron behaviors within dynamic communities in the mouse brain. Our visualization design also provides the ability to filter data in a variety of ways in order to make it easier for the user to explore the data. The user can apply these capabilities to focus on a single node of interest. A single node encapsulates many neurons, but neuroscientists assume that the neurons within a single node J. Imaging Sci. Technol. 010405 1 Jan. Feb. 2016 (c)2016 Society for Imaging Science and Technology DOI: 10.2352/ISSN.2470 1173.2016.16HVEI 134 IS&T International Symposium on Electronic Imaging 2016 Human Vision and Electronic Imaging 2016 HVEI 134.1 Ma et al. SwordPlots: Exploring neuron behavior within dynamic communities of brain networks have similar functionality since they are all in close proximity to the same brain region. Once a node of interest has been identified, the user has the ability to highlight the node to gain more detailed information. We also create an additional visualization view which displays some characteristics of the communitymodel structure in space.Weuse two case studies to illustrate the effectiveness of our visualization tool. The visualization has the overall effect of reducing the data load on the scientists by allowing them to pare down a large number of neural connections within an experiment to those areas of interest and examine their behavior in time. The visualization has the contributions of allowing neuroscientists to choose any one of the nodes to track its behavior within dynamic brain communities during a selected time range, providing sufficient interactions at both the individual community level and the node level which can offer the user an overview of the network and the ability to focus on nodes of interest, and being designed for a very new research topic which combines the DNA with neuroscience. The remainder of this article is organized as follows. Background knowledge is introduced in the second section. The third section discusses the related work. In the fourth section we present the implementation of our visualization system in detail. The fifth section presents two case studies, followed by a qualitative evaluation in the sixth section. The seventh section discusses some limitations and future work, and the eighth section summarizes the contributions of the article. BACKGROUND Brain Network Analysis Network analysis of brain imaging data has proven to be a particularly challenging computational problem. For example, functional magnetic resonance imaging (fMRI) data using blood oxygen level dependent signals involves the analysis of tens of thousands of voxels, each with their own time series. Analysis of pairwise correlations across all of these voxels produces millions of potential interactions between voxels, leading to difficulties in producing a coherent understanding of the patterns of activity. Early efforts to analyze these data sets were focussed on seed based correlation approaches, whereby a time series in a region of interestwas correlated to all other voxels in the brain.3 5 Such methods have uncovered multiple different large scale brain networks, but are susceptible to biases based on the initial seeds chosen. Multivariate approaches, such as independent component analysis (ICA) have also been used and are free from the biases inherent in visualizing networks based on an initial seed.6,7 Most recently, graph theory based methods have been applied to brain imaging data and have revealed not only the presence of underlying networks but also the logic underlying the organization of networks by characterizing certain nodes as hubs, and by revealing small world behavior in certain brain networks.8 A potential problem with these approaches to brain network analysis is their limited ability to extract the natural dynamics of a network's organization. That is, generally these techniques are used to generate a single static network map using a window of many minutes of the obtained data. However, brain activity is intrinsically highly dynamic, with functional associations between neurons and brain regions that ebb and flow as the organism's level of arousal, focus of attention or topic of thought changes. Recently, neuroscientists have applied DNA tools initially developed for the study of social dynamics9,10 to the analysis of mouse brain imaging data. Among the analysis tools commonly used to understand the relationship between structure and function in social networks is community inference. The definition of 'community' in the analysis of brain networks is analogous to that of a neural assembly,11 whichwe define as a group of neurons that act collectively and are functionally connected. Community Analysis In network analysis, communities are generally defined as groups of nodes that tend to have more and stronger connections with each other than with nodes outside. This concept is derived from social networks, and can be applied in a variety of fields including biological networks.12 Communities appear in networks where nodes join together in tight groups that have few connections between them. Several methods have been proposed to find the structure, of which the best known is called modularity optimization. Modularity is a standard metric for finding and evaluating communities.13 Community detection by modularity optimization on large networks is a computationally challenging problem. Therefore, it needs to use algorithms that find highmodularity partitions within large networks in a short time. The Louvain algorithm14 is ideally suited to finding communities in such cases. Besides the advantage of being extremely fast, its steps are easy to implement. In each static network, the nodes within the same cluster have more edges connected to each other than to nodes outside the cluster. When dealing
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