We introduce time curves as a general approach for visualizing patterns of evolution in temporal data. Examples of such patterns include slow and regular progressions, large sudden changes, and reversals to previous states. These patterns can be of interest in a range of domains, such as collaborative document editing, dynamic network analysis, and video analysis. Time curves employ the metaphor of folding a timeline visualization into itself so as to bring similar time points close to each other. This metaphor can be applied to any dataset where a similarity metric between temporal snapshots can be defined, thus it is largely datatype-agnostic. We illustrate how time curves can visually reveal informative patterns in a range of different datasets.

Abstract We introduce MultiPiles, a visualization to explore time‐series of dense, weighted networks. MultiPiles is based on the physical analogy of piling adjacency matrices, each one representing a single temporal snapshot. Common interfaces for visualizing dynamic networks use techniques such as: flipping/animation; small multiples; or summary views in isolation. Our proposed ‘piling’ metaphor presents a hybrid of these techniques, leveraging each one's advantages, as well as offering the ability to scale to networks with hundreds of temporal snapshots. While the MultiPiles technique is applicable to many domains, our prototype was initially designed to help neuroscientists investigate changes in brain connectivity networks over several hundred snapshots. The piling metaphor and associated interaction and visual encodings allowed neuroscientists to explore their data, prior to a statistical analysis. They detected high‐level temporal patterns in individual networks and this helped them to formulate and reject several hypotheses.

Connectivity information derived from diffusion MRI can be used to parcellate the cerebral cortex into anatomically and functionally meaningful subdivisions. Acquisition and processing parameters can significantly affect parcellation results, and there is no consensus on best practice protocols. We propose a novel approach for evaluating parcellation based on measuring the degree to which parcellation conforms to known principles of brain organization, specifically cortical field homogeneity and interhemispheric homology. The proposed metrics are well behaved on morphologically generated whole-brain parcels, where they correctly identify contralateral homologies and give higher scores to anatomically versus arbitrarily generated parcellations. The measures show that individual cortical fields have characteristic connectivity profiles that are compact and separable, and that the topological arrangement of such fields is strongly conserved between hemispheres and individuals. The proposed metrics can be used to evaluate the quality of parcellations at the subject and group levels and to improve acquisition and data processing for connectivity-based cortical parcellation.

Multi-voxel pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data is an emerging approach for probing the neural correlates of cognition. MVPA allows cognitive states to be modeled as distributed patterns of neural activity and classified according to stimulus conditions. In practice, building a robust, generalizable classification model can be challenging because the number of voxels (features) far exceeds the number of stimulus instances/data observations. To avoid model overfitting, there is a need to select informative voxels before building a classification model. In this paper, we propose a robust feature (voxel) selection framework using mutual information (MI) and partial least square regression (PLS) to establish an informativeness index for prioritizing selection of voxels based on the degree of their association to the experimental conditions. We evaluated the robustness of our proposed framework by assessing performance of standard classification algorithms, when combined with our feature selection approach, in a publicly-available fMRI dataset of object-level representation widely used to benchmark MVPA performance (Haxby, 2001). The computational results suggest that our feature selection framework based on MI and PLS drastically improves the classification accuracy relative to those previously reported in the literature. Our results also suggest that highly informative voxels may provide meaningful insight into the functional-anatomic relationship of brain activity and stimulus conditions.