VisualNeuro: A Hypothesis Formation and Reasoning Application for Multi‐Variate Brain Cohort Study Data

Abstract

We present an application, and its development process, for interactive visual analysis of brain imaging data and clinical measurements. The application targets neuroscientists interested in understanding the correlations between active brain regions and physiological or psychological factors. The application has been developed in a participatory design process and has subsequently been released as the free software ‘VisualNeuro’. From initial observations of the neuroscientists' workflow, we concluded that while existing tools provide powerful analysis options, they lack effective interactive exploration requiring the use of many tools side by side. Consequently, our application has been designed to simplify the workflow combining statistical analysis with interactive visual exploration. The resulting environment comprises parallel coordinates for effective overview and selection, Welch's t‐test to filter out brain regions with statistically significant differences and multiple visualizations for comparison between brain regions and clinical parameters. These exploration concepts enable neuroscientists to interactively explore the complex bidirectional interplay between clinical and brain measurements and easily compare different patient groups. A qualitative user study has been performed with three neuroscientists from different domains. The study shows that the developed environment supports simultaneous analysis of more parameters, provides rapid pathways to insights and is an effective tool for hypothesis formation.

Where did my Lines go? Visualizing Missing Data in Parallel Coordinates

“…Section 2.2). These concepts are information removal [JBF∗19], adding a missing values axis [ST17], and imputed values [TAF12, CCH15]. The concepts will be detailed in the following and are illustrated in Figure 2, left.…”

“…It is not uncommon for attributes of high‐dimensional datapoints to be missing in real‐world datasets, as exemplified in recent works [SS18, JBF∗19,MGU∗21]. The reasons for missing values can be myriad, e.g., broken sensors, incomplete forms, or physical measurements that could not be collected during a clinical study.…”

“…Despite the prevalence of missing values, they are often disregarded by removing the datapoint [JBF∗19]. However, removing an entire line obfuscates that there are missing values in the data.…”

“…However, removing an entire line obfuscates that there are missing values in the data. As this clearly is undesirable, there have been several works that represent missing values in parallel coordinates, e.g., by introducing a missing values axis [MGU∗21] or using information removal [JBF∗19]. However, little is known about the value of these techniques, as their performance with respect to missing value discovery, or interference with common parallel coordinates analysis tasks, has not yet been evaluated.…”

PRAGMA: Interactively Constructing Functional Brain Parcellations

“…A different approach to analyzing functional modules using NodeTrix [14] representations is proposed by Yang et al [35]. VisualNeuro [15] offers a tool for hypothesis formation and reasoning about cohort study data. Brain Modulyzer [22] is an interactive visual exploration tool for investigating regional correlation and hierarchical network structures.…”