On Visualization and Interpretation of Complex Connectomic Results
Poster No:
2141
Submission Type:
Abstract Submission
Authors:
Javid Dadashkarimi1, Stephanie Noble1, Abigail Greene1, R Todd Constable1, xenophon papademetris1, Dustin Scheinost1
Institutions:
1Yale University, New Haven, CT
First Author:
Co-Author(s):
Introduction:
Structural and functional connectomes are widely used to characterize differences between individuals and groups (Finn, 2015; Turk, 2019; Hong, 2019). However, visualizing and interpreting these results is challenging, in part due to the large number of connections. Here, we provide a toolkit, as part of the BioImage Suite Web project (https://bioimagesuiteweb.github.io/webapp/connviewer.html), to visualize complex connectome-based results across multiple levels of feature summarization, improving interpretability.
Methods:
Our toolkit provides a variety of methods for visualizing connectome-based results at different levels of summarization (e.g. the edge, node, and network levels) to maximize interpretability. For the node level, the most popular technique for connectome visualization are variations of circle plots and ball-and-stick plots. These techniques show significant connection (or edges) and do not require summarization to demonstrate node-to-node interactions. In this sense, they are the most accurate representation of the results. However, visualizing and interpreting them can be difficult particularly with large numbers of edges. For the node level, surface visualization of nodes can be color coded based the number of significant edges that connect to that node (i.e., the graph theory measure of degree). This visualization is intuitive to neuroimagers as it mimics visualization of "blobs" other imaging results, such as fMRI activation or cortical thickness results. However, information at the edge level is lost through this summarization, including information about which regions are interacting. For the network level, network-network summary and cord plots similarly summarize the number of significant edges between canonical functional networks such as the default mode network and frontoparietal network. This further summarization permits easy visualization of the involvement of large-scale networks. In contrast to the surface plots, as within and between network connections are considered, information about how these networks interact is retained. In contrast to the edge level visualizations, network level visualizations are the least representative of the underlying results due to the large amount of data summarization.
Results:
The visualization toolkit in BioImage Suite Web is fully implemented in JavaScript, operates on browser, requires no installation and is platform independent. The only requirement to use the software is a modern web-browser such as Chrome, Firefox, or Safari. To encourage users to use multiple levels of visualization when interpreting results, all visualizations are available in a single tool and "result" connectomes only need to be loaded into the tool once to use all visualizations. We provide a YouTube channel with "how-to" videos on using the connectome viewer (https://www.youtube.com/watch?v=7t3VXDqHzJ4). Source code and developer information can be found at: https://github.com/bioimagesuiteweb/bisweb and https://github.com/bioimagesuiteweb/bisweb/blob/master/docs/README.md. We offer native visualizations for two predefined atlases: the Shen 268 atlas (Shen, 2013) and Automated Anatomical Labeling (AAL; Tzourio-Mazoyer, 2002) and functionality for importing any other atlases.
·Screenshot of the visualization toolkit in BioImage Suite Web.
·Different levels of summarization in visualizing a complex connectome. All modules are implemented in JavaScript.
Conclusions:
The connectome viewer in BioImage Suite Web can be used to visualize any connectome-based results. across descending levels of dimensionality, maximizing the interpretability of this traditionally challenging data type.
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling
Methods Development 2
Neuroinformatics and Data Sharing:
Workflows
Novel Imaging Acquisition Methods:
BOLD fMRI 1
Keywords:
Development
FUNCTIONAL MRI
1|2Indicates the priority used for review
My abstract is being submitted as a Software Demonstration.
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.
Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Was any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
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For human MRI, what field strength scanner do you use?
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Provide references using author date format
2. Turk, Elise, et al. "Functional connectome of the fetal brain." Journal of Neuroscience 39.49 (2019): 9716-9724.
3. Hong, Seok-Jun, et al. "Atypical functional connectome hierarchy in autism." Nature communications 10.1 (2019): 1022.
4. Shen, Xilin, et al. "Groupwise whole-brain parcellation from resting-state fMRI data for network node identification." Neuroimage 82 (2013): 403-415.
5. Tzourio-Mazoyer, Nathalie, et al. "Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain." Neuroimage15.1 (2002): 273-289.