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Spatial collinearity constrains multivariate molecular-enriched network estimation
Biorxiv : the Preprint Server for Biology
|June 22, 2026
Summary
Spatial overlap in brain receptor maps poses analytical challenges. A new study shows that modeling multiple receptors simultaneously reduces network reliability, favoring a simpler, single-receptor approach for accurate brain connectivity analysis.
Area of Science:
- Neuroimaging
- Neuroscience
- Computational Psychiatry
Background:
- Positron Emission Tomography (PET) receptor maps integrate micro- and macro-scale brain function.
- High spatial overlap (collinearity) among PET receptor maps presents analytical and interpretive challenges.
- Receptor-Enriched Analysis of functional Connectivity by Targets (REACT) uses receptor maps to define molecular-enriched functional networks.
Purpose of the Study:
- To systematically investigate the impact of spatial collinearity among PET receptor maps on multivariate REACT models.
- To compare the reliability of multivariate versus univariate modeling approaches for molecular-enriched functional connectivity.
- To evaluate the utility of univariate modeling in a pharmacological neuroimaging study.
Main Methods:
- Combinatorial analysis of 19 neurotransmitter receptor and transporter maps to assess collinearity.
- Test-retest functional Magnetic Resonance Imaging (fMRI) data from the Human Connectome Project were used to evaluate network reliability.
- Comparison of multivariate and univariate REACT models using simulated and real neuroimaging data, including a study of LSD effects.
Main Results:
- Spatial collinearity among PET receptor maps increases rapidly with the number of simultaneously modeled receptors.
- Modeling more receptors in multivariate REACT analyses significantly degrades the reliability of molecular-enriched functional networks.
- Univariate modeling, analyzing each receptor independently, produced more reliable networks and accurately identified 5-HT2A receptor involvement in LSD's effects.
Conclusions:
- Spatial collinearity is a fundamental limitation for multivariate molecular-enriched network estimation using PET data.
- Univariate modeling offers a more robust and reliable approach for analyzing molecular-enriched functional connectivity compared to multivariate methods.
- The findings support the use of univariate modeling as a default strategy for this class of neuroimaging analysis.
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