TempoCave: Visualizing Dynamic Connectome Datasets to Support Cognitive Behavioral Therapy

Abstract

Figure 1: A screenshot of the TempoCave application. Here, a user compares frames from pre-and post-treatment dynamic connectomes for an individual patient with major depression disorder. Using the option panels on either side of the application, a user can choose different visual encodings to accentuate features useful for understanding the activity of particular brain regions. The left and right coloring of the nodes indicates the modular affinity of a brain region for a patient's pre-and post-treatment connectome, respectively. Likewise, the styling of the connections indicates either the connectome they belong to or the strength of the correlation. A user can synchronize playback of the frames of the connectomes or compare selected frames on demand to gain insight into their dynamics, and during an analysis session a user can interactively toggle on or off brain regions of interest, or switch to alternative representations of the connectome defined using layouts based on dimensionality reduction techniques. ABSTRACTWe 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 significant impact on the way in which patients are diagnosed and treated. We summarize the core functionality of Tem-poCave, 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.

Monte Carlo Physarum Machine: Characteristics of Pattern Formation in Continuous Stochastic Transport Networks

“…As an illustrative example, we consider the problem of 3D edge bundling in complex network visualization (Bach et al, 2017;Ferreira et al, 2018;Holten & Van Wijk, 2009). We used MCPM to fit and visualize a human brain connectome (Xu et al, 2019), a set of 105 high-level nodes representing different subregions of the brain (so-called modes). To simplify this experiment (Figure 29), we disregard the connectome edges and simply weigh each node by its overall connectivity strength.…”

Visual Comparison of Networks in VR

“…Research on visual network comparison has mainly focused on the development of practical approaches, e.g. for specific types of networks [33,53,55,57,66], and for structural overviews by abstraction or aggregation, see e.g. [10,35,67].…”

“…Recent work includes both application-oriented research, e.g. in the context of connectome and brain activity analysis [50,66], and more fundamental investigations, e.g. on navigation [23,60], the influence of encodings [14], or the difference between immersive environments [17].…”

“…Research related to fMRI data comes with certain challenges and demands that can be mainly abstracted to different comparison tasks regarding weighted graphs [1,18,33,66]. Alper et al made use of a literature survey and individual expert interviews with seven experts to determine the main challenges that neuroscientists have to solve when dealing with fMRI comparison [1].…”

PRAGMA: Interactively Constructing Functional Brain Parcellations

“…Network-based neuroscience visualization tools have demonstrated how to gain insight from connectome datasets, some at the level of functional modules, e.g. NeuroCave [16] and Tem-poCave [33], and some at the level of individual neurons, e.g. Brain-Trawler [8].…”