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Beyond consensus: Embracing heterogeneity in curated neuroimaging meta-analysis
Gia H Ngo1, Simon B Eickhoff2, Minh Nguyen3
1Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore; School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA.
Neuroimage
|June 24, 2019
Summary
The author-topic model reveals latent patterns in brain activation meta-analyses, explaining heterogeneity in activation likelihood estimation (ALE) studies. This method identifies functional sub-domains and task-dependent co-activation patterns, offering a flexible approach for neuroimaging research.
Area of Science:
- Neuroimaging
- Cognitive Neuroscience
- Data Science
Background:
- Coordinate-based meta-analyses, such as Activation Likelihood Estimation (ALE), are crucial for understanding mind-brain relationships by identifying consistent brain activation patterns.
- ALE and Meta-Analytic Co-activation Modeling (MACM) typically treat experimental heterogeneity as noise, potentially overlooking important underlying structures.
- Heterogeneity in ALE or MACM analyses may indicate functional sub-domains or multiple task-dependent roles for specific brain regions.
Purpose of the Study:
- To introduce and validate the author-topic model as a tool for exploring and explaining heterogeneity within ALE-type meta-analyses.
- To demonstrate the model's ability to uncover latent patterns that account for variations in brain activation data.
- To assess the utility of the author-topic model in identifying functional sub-domains and task-dependent co-activation patterns.
Main Methods:
- Application of the author-topic model to a dataset of 179 experiments on self-generated thought to analyze the default network.
- Application of the author-topic model to a dataset of 323 experiments involving the left inferior frontal junction (IFJ) to study co-activation patterns.
- Comparison of author-topic model performance against spatial independent component analysis using both simulated and real neuroimaging data.
Main Results:
- Author-topic modeling of self-generated thought experiments revealed distinct cognitive components that fractionate the default network.
- Analysis of the left IFJ demonstrated its involvement in multiple, context-specific co-activation patterns.
- The author-topic model demonstrated comparable or superior performance to spatial independent component analysis in simulations and real data.
Conclusions:
- The author-topic model is a versatile method for dissecting heterogeneity in ALE-type meta-analyses.
- This approach can robustly identify functional sub-domains, subtypes of mental disorders, or task-dependent co-activation patterns.
- The findings suggest a powerful new avenue for exploring complex brain function through meta-analytic data.
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