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Effects of parcellation and threshold on brainconnectivity measures
T C Lacy1,2, P A Robinson1,2
1School of Physics, University of Sydney, Sydney, NSW, Australia.
Plos One
|October 1, 2020
Summary
A simple physical connectivity model accurately reproduces brain network properties, enabling objective comparison and prediction across studies. This highlights how network analysis findings depend on data processing choices like thresholding and parcellation.
Area of Science:
- Neuroscience
- Computational Biology
- Network Science
Background:
- Brain connectivity studies often yield varying results due to differences in data processing, specifically parcellation and thresholding.
- Understanding these variations is crucial for objectively comparing and integrating findings from diverse neuroimaging studies.
Purpose of the Study:
- To demonstrate that a simple distance-based physical connectivity model can replicate the statistical properties of brain networks.
- To establish a framework for objectively interrelating studies with different parcellation and thresholding methods.
- To predict the outcomes of future studies based on network scale and processing parameters.
Main Methods:
- Development of a simple, distance-based physical connectivity model for brain regions.
- Analysis of statistical properties of brain networks generated by the model.
- Comparison of model-generated network properties with those reported in published neuroimaging data.
- Investigation of the impact of coarse-graining (parcellation) and thresholding on network measures.
Main Results:
- The proposed physical connectivity model successfully reproduces the statistical properties of brain connections observed in published data.
- The model demonstrates that network measures are highly dependent on the chosen parcellation and thresholding parameters.
- Apparent 'small-world' network properties can emerge in specific brain regions due to these processing choices, even if the underlying finer-scale network does not exhibit such properties.
Conclusions:
- A parsimonious, distance-based physical model can account for observed statistical properties of brain connectivity.
- The findings underscore the critical influence of methodological choices (parcellation, thresholding) on network analysis outcomes.
- Researchers must carefully consider these factors when interpreting and comparing results across different brain network studies.
