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BIARAM: A process for analyzing correlated brain regions using association rule mining.
Pankush Kalgotra1, Ramesh Sharda2
1Graduate School of Management, Clark University, United States.
Computer Methods and Programs in Biomedicine
|June 16, 2018
Summary
Association rule mining (ARM) reveals simultaneous brain region activation. This network approach identified stronger frontal lobe and sublobar/insula correlations after interruptions, offering new insights into brain responses.
Area of Science:
- Neuroscience
- Data Mining
- Biomedical Engineering
Background:
- Analyzing correlated brain activity offers superior insights compared to individual region analysis.
- Network approaches for brain activity analysis are gaining interest.
- Association rule mining (ARM), a data mining technique, is proposed for analyzing simultaneous brain region activation.
Purpose of the Study:
- Introduce ARM for analyzing simultaneous brain region activation.
- Describe a process for converting neural images into analyzable datasets.
- Suggest applying ARM for insights into brain responses to stimuli like technological interruptions.
Main Methods:
- Analyzed electroencephalogram (EEG) data from 46 participants.
- Converted brain waves to images using standardized low-resolution brain electromagnetic tomography (sLORETA).
- Reorganized data into a transactional format for ARM analysis.
Main Results:
- Compared ARM results with conventional neuroimaging analysis methods.
- Observed a stronger correlation between frontal lobe and sublobar/insula regions post-interruption.
- This correlated activity was not apparent through independent region analysis.
Conclusions:
- Introduced ARM as a novel method for analyzing multiple brain images simultaneously.
- Suggests the biomedical community can utilize ARM for deeper understanding of stimuli-affected correlated brain regions.
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