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MAPBOT: Meta-analytic parcellation based on text, and its application to the human thalamus
Rui Yuan1, Paul A Taylor2, Tara L Alvarez3
1Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA; Department of Electrical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA.
This study introduces MAPBOT (Meta-Analytic Parcellation Based On Text), a novel method using text analysis from neuroimaging studies to map brain regions. It effectively parcellates brain areas, demonstrated by its application to understanding thalamus subdivisions.
Area of Science:
- Neuroimaging
- Computational Neuroscience
- Brain Mapping
Background:
- Meta-analysis of neuroimaging data is crucial for understanding brain function.
- Current meta-analyses primarily rely on activation coordinates, largely ignoring valuable textual information.
- Textual data within studies offers potential for enhanced brain region parcellation and characterization.
Purpose of the Study:
- To introduce a novel method, MAPBOT (Meta-Analytic Parcellation Based On Text), for brain region parcellation using textual features from neuroimaging studies.
- To demonstrate the utility of MAPBOT by applying it to understand the subdivisions of the thalamus.
- To highlight the potential of leveraging text data for more comprehensive brain mapping.
Main Methods:
- Developed MAPBOT, a method utilizing document clustering techniques on text from neuroimaging studies.
- Applied MAPBOT to a large corpus of studies to parcellate brain regions based on textual content.
- Used the thalamus as a case study due to extensive research, analyzing its functional and structural subdivisions.
Main Results:
- MAPBOT successfully parcellates brain regions using text features, offering a new approach beyond coordinate-based meta-analysis.
- The method provided insights into the subdivisions of the thalamus, showcasing its practical application.
- Demonstrated that textual information from neuroimaging literature is a powerful resource for brain mapping.
Conclusions:
- MAPBOT offers a powerful and generalizable method for brain parcellation by incorporating textual data from neuroimaging studies.
- This approach complements traditional coordinate-based meta-analysis, enabling a richer understanding of brain regions and their characteristics.
- Leveraging text analysis in meta-analysis opens new avenues for exploring brain structure and function across diverse populations.
