A Tool for Interactive Data Visualization: Application to Over 10,000 Brain Imaging and Phantom MRI Data Sets.

Area of Science:

• Neuroimaging
• Data Science
• Computational Neuroscience

Background:

• Big data in neuroimaging presents challenges for analysis and visualization.
• Existing methods may not efficiently handle the scale and complexity of brain MRI datasets.
• The COllaborative Imaging and Neuroinformatics Suite (COINS) hosts a large repository of MRI scans.

Purpose of the Study:

• To develop a web-based visualization approach for large-scale brain MRI data.
• To enable rapid identification of patterns and quality control insights from neuroimaging datasets.
• To leverage dimensionality reduction techniques for enhanced data exploration.

Main Methods:

• Utilized automated image capture and processing systems.
• Applied t-distributed stochastic neighbor embedding (t-SNE) for dimensionality reduction of MRI data.
• Developed interactive web-based visualizations using JavaScript (D3 library).
• Implemented two visualization approaches: one based on quality control (QC) metrics and another on computed variables of interest (e.g., brain volume).

Main Results:

• Demonstrated effective clustering of data based on QC metrics, identifying datasets with poor quality or specific site characteristics.
• Revealed patterns in structural MRI data, correlating brain volume and density with factors like scanner type, age, and gender.
• Successfully visualized over 10,000 MRI datasets, showcasing the scalability of the approach.

Conclusions:

• The proposed web-based approach facilitates rapid exploration and understanding of large neuroimaging datasets.
• The integration of t-SNE with interactive visualization aids in identifying meaningful patterns and assessing data quality.
• This tool is valuable for researchers dealing with growing volumes of brain MRI data.