An Uncertainty Visual Analytics Framework for fMRI Functional Connectivity

M de Ridder; Karsten Klein; Jean Yang; Pengyi Yang; Jim Lagopoulos; Ian Hickie; Maxwell Bennett; Jinman Kim

doi:10.1007/s12021-018-9395-8

Back

An Uncertainty Visual Analytics Framework for fMRI Functional Connectivity

Journal article

Peer reviewed

An Uncertainty Visual Analytics Framework for fMRI Functional Connectivity

M de Ridder, Karsten Klein, Jean Yang, Pengyi Yang, Jim Lagopoulos, Ian Hickie, Maxwell Bennett and Jinman Kim

Neuroinformatics, Vol.17(2), pp.211-223

2019

DOI: https://doi.org/10.1007/s12021-018-9395-8

Appears in Thompson Institute Research Collection

Files and links (1)

url

https://doi.org/10.1007/s12021-018-9395-8View

Published Version

Abstract

Biochemistry and Cell Biology

Neurosciences

visual analytics

functional magnetic resonance imaging

functional connectivity

uncertainty

framework visualization

Other Collaborations

Thompson Institute Special Collection

Analysis and interpretation of functional magnetic resonance imaging (fMRI) has been used to characterise many neuronal diseases, such as schizophrenia, bipolar disorder and Alzheimer's disease. Functional connectivity networks (FCNs) are widely used because they greatly reduce the amount of data that needs to be interpreted and they provide a common network structure that can be directly compared. However, FCNs contain a range of data uncertainties stemming from inherent limitations, e.g. during acquisition, as well as the loss of voxel-level data, and the use of thresholding in data abstraction. Additionally, human uncertainties arise during interpretation due to the complexity in understanding the data. While existing FCN visual analytics tools have begun to mitigate the human ambiguities, reducing the impact of data limitations is an open problem. In this paper, we propose a novel visual analytics framework with three linked, purpose-designed components to evoke deeper interpretation of the fMRI data: (i) an enhanced FCN abstraction; (ii) a temporal signal viewer; and (iii) the anatomical context. Each component has been specifically designed with novel visual cues and interaction to expose the impact of uncertainties on the data. We augment this with two methods designed for comparing subjects, by using a small multiples and a marker approach. We demonstrate the enhancements enabled by our framework on three case studies of common research scenarios, using clinical schizophrenia data, which highlight the value in interpreting fMRI FCN data with an awareness of the uncertainties. Finally, we discuss our framework in the context of fMRI visual analytics and the extensibility of our approach.

Details

Title: An Uncertainty Visual Analytics Framework for fMRI Functional Connectivity
Authors: M de Ridder (Author) - University of Sydney
Karsten Klein (Author) - University of Konstanz, Germany
Jean Yang (Author) - University of Sydney
Pengyi Yang (Author) - University of Sydney
Jim Lagopoulos (Author) - University of Sydney
Ian Hickie (Author) - University of Sydney
Maxwell Bennett (Author) - University of Sydney
Jinman Kim (Author) - University of Sydney
Publication details: Neuroinformatics, Vol.17(2), pp.211-223
Publisher: Springer
Date published: 2019
DOI: 10.1007/s12021-018-9395-8
ISSN: 1539-2791
Organisation Unit: University of the Sunshine Coast, Queensland; Thompson Institute
Language: English
Record Identifier: 99450876602621
Output Type: Journal article

Metrics

463 Record Views

2 Times Cited - Web of Science

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Collaboration types: Domestic collaboration; International collaboration
Web Of Science research areas: Computer Science, Interdisciplinary Applications; Neurosciences

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

Source: InCites