An Uncertainty Visual Analytics Framework for fMRI Functional Connectivity
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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.
KeywordsVisual Analytics Functional Magnetic Resonance Imaging Functional Connectivity Uncertainty Framework Visualization
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Conflict of Interest
- Angulo, D. A., Schneider, C., Oliver, J. H., Charpak, N., & Hernandez, J. T. (2016). A Multi-facetted Visual Analytics Tool for Exploratory Analysis of Human Brain and Function Datasets. Frontiers in neuroinformatics, 10.Google Scholar
- Arbabshirani, M., Castro, E., & Calhoun, V (2014). Accurate classification of schizophrenia patients based on novel resting-state fmri features. In EMBC, 6691–6694.Google Scholar
- Cui, W., Wang, X., & Riche, N. H. (2014). Let It Flow : a Static Method for Exploring Dynamic Graphs. 121–128, doi: https://doi.org/10.1109/PacificVis.2014.48.
- de Ridder, M., Klein, K., & Kim, J (2015). CereVA-Visual Analysis of Functional Brain Connectivity. In IVAPP, 131–138.Google Scholar
- Filippi, M. (2016). fMRI Techniques and Protocols: Springer.Google Scholar
- Filippi, M., & Filippi (2009). fMRI techniques and protocols: Springer.Google Scholar
- FMRIB Analysis Group, O. U. (2016). FSL. http://fsl.fmrib.ox.ac.uk/.
- Fujiwara, T., Chou, J.-K., McCullough, A. M., Ranganath, C., & Ma, K.-L (2017). A visual analytics system for brain functional connectivity comparison across individuals, groups, and time points. In Pacific Visualization Symposium (PacificVis), IEEE, 2017 (pp. 250-259): IEEE.Google Scholar
- Giraldo-Chica, M., & Woodward, N. D. (2016). Review of thalamocortical resting-state fmri studies in schizophrenia. Schizophrenia Research, 6.Google Scholar
- Irimia, A., Chambers, M. C., Torgerson, C. M., & Van Horn, J. D. (2012). Circular representation of human cortical networks for subject and population-level connectomic visualization. Neuroimage, 60(2), 1340–1351. https://doi.org/10.1016/j.neuroimage.2012.01.107.CrossRefPubMedPubMedCentralGoogle Scholar
- Jezzard, P., Matthews, P., & Smith, S. (2001). Functional MRI: an introduction to methods: Oxford University Press.Google Scholar
- Jie, B., Liu, M., Jiang, X., & Zhang, D. (2016) Sub-network Based Kernels for Brain Network Classification. In Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, (622–629): ACM.Google Scholar
- Liang, M., Zhou, Y., Jiang, T., Liu, Z., Tian, L., Liu, H., et al. (2006). Widespread functional disconnectivity in schizophrenia with resting-state functional magnetic resonance imaging. Neuroreport, 17(2), 209-213.Google Scholar
- Liu, Y., Wang, K., Chunshui, Y. U., He, Y., Zhou, Y., Liang, M., et al. (2008). Regional homogeneity, functional connectivity and imaging markers of Alzheimer's disease: A review of resting-state fMRI studies. Neuropsychologia, 46(6), 1648-1656.Google Scholar
- Liu, F., Xie, B., Wang, Y., Guo, W., Fouche, J.-P., Long, Z., et al. (2015). Characterization of post-traumatic stress disorder using resting-state fMRI with a multi-level parametric classification approach. Brain Topography, 28(2), 221-237.Google Scholar
- National Institute of Health (2016). AFNI. https://afni.nimh.nih.gov/.
- Peeters, R., & Sunaert, S. (2007). Clinical BOLD fMRI: artifacts, tips and tricks. In Clinical Functional MRI (pp. 227-249): Springer.Google Scholar
- Rashid, B., Arbabshirani, M. R., Damaraju, E., Cetin, M. S., Miller, R., Pearlson, G. D., et al. (2016). Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity. Neuroimage, 134, 645-657.Google Scholar
- Sarraf, S., & Tofighi, G. (2016). Classification of alzheimer's disease using fmri data and deep learning convolutional neural networks. arXiv Preprint arXiv, 1603, 08631.Google Scholar
- Sporns, O. (2010). Networks of the Brain: MIT Press.Google Scholar
- Swenson, R. (2006). Chapter 11: The Cerebral Cortex. In Review of Clinical and Functional Neuroscience (Vol. 1): Dartmouth Medical School.Google Scholar
- Wang, S., Zhang, Y., Lv, L., Wu, R., Fan, X., Zhao, J., et al. (2017). Abnormal regional homogeneity as a potential imaging biomarker for adolescent-onset schizophrenia: A resting-state fMRI study and support vector machine analysis. Schizophrenia Research.Google Scholar
- Woodward, N. D., Karbasforoushan, M. S., & Heckers, S. (2012). Thalamocortical dysconnectivity in schizophrenia. American Journal of Psychiatry, 169(10).Google Scholar
- Zeng, H., Ramos, C. G., Nair, V. A., Hu, Y., Liao, J., La, C., et al. (2015). Regional homogeneity (ReHo) changes in new onset versus chronic benign epilepsy of childhood with centrotemporal spikes (BECTS): A resting state fMRI study. Epilepsy Research, 116, 79-85.Google Scholar