TemporalTracks: visual analytics for exploration of 4D fMRI time-series coactivation

M. de Ridder, K. Klein, J. Kim

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

4 Citations (Scopus)


Functional magnetic resonance imaging (fMRI) is a 4D medical imaging modality that depicts a proxy of neuronal activity in a series of temporal scans. Statistical processing of the modality shows promise in uncovering insights about the functioning of the brain, such as the default mode network, and characteristics of mental disorders. Current statistical processing generally summarises the temporal signals between brain regions into a single data point to represent the 'coactivation' of the regions. That is, how similar are their temporal patterns over the scans. However, the potential of such processing is limited by issues of possible data misrepresentation due to uncertainties, e.g. noise in the data. Moreover, it has been shown that brain signals are characterised by brief traces of coactivation, which are lost in the single value representations. To alleviate the issues, alternate statistical processes have been used, however creating effective techniques has proven difficult due to problems, e.g. issues with noise, which often require user input to uncover. Visual analytics, therefore, through its ability to interactively exploit human expertise, presents itself as an interesting approach of benefit to the domain. In this work, we present the conceptual design behind TemporalTracks, our visual analytics system for exploration of 4D fMRI time-series coactivation data, utilising a visual metaphor to effectively present coactivation data for easier understanding. We describe our design with a case study visually analysing Human Connectome Project data, demonstrating that TemporalTracks can uncover temporal events that would otherwise be hidden in standard analysis.

Original languageEnglish
Title of host publicationCGI'17 - Proceedings of the 2017 Computer Graphics International Conference
Subtitle of host publicationYokohama, Japan — June 27 - 30, 2017
EditorsXiaoyang Mao, Daniel Thalmann, Marina Gavrilova
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages6
ISBN (Electronic)9781450352284
Publication statusPublished - 2017
Externally publishedYes
EventComputer Graphics International 2017 - Yokohama, Japan
Duration: 27 Jun 201730 Jun 2017
Conference number: 34th


ConferenceComputer Graphics International 2017
Abbreviated title CGI 2017
Internet address


  • Coactivation analysis
  • Functional magnetic resonance imaging
  • Temporal data visualization

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