Automatic search for fMRI connectivity mapping: an alternative to Granger causality testing using formal equivalences among SEM path modeling, VAR, and unified SEM
Abstract
Modeling the relationships among brain regions of interest (ROIs) carries unique potential to explicate how the brain orchestrates information processing. However, hurdles arise when using functional MRI data. Variation in ROI activity contains sequential dependencies and shared influences on synchronized activation. Consequently, both lagged and contemporaneous relationships must be considered for unbiased statistical parameter estimation. Identifying these relationships using a data-driven approach could guide theory-building regarding integrated processing. The present paper demonstrates how the unified SEM attends to both lagged and contemporaneous influences on ROI activity. Additionally, this paper offers an approach akin to Granger causality testing, Lagrange multiplier testing, for statistically identifying directional influence among ROIs and employs this approach using an automatic search procedure to arrive at the optimal model. Rationale for this equivalence is offered by explicating the formal relationships among path modeling, vector autoregression, and unified SEM. When applied to simulated data, biases in estimates which do not consider both lagged and contemporaneous paths become apparent. Finally, the use of unified SEM with the automatic search procedure is applied to an empirical data example.
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Unified SEM contains both lagged and contemporaneous directed relations among regions, making it ideal for biological systems that likely contain these effects.
“Unified SEM contains both lagged and contemporaneous directed relations among regions, making it ideal for biological systems that likely contain these effects as detailed in [18].”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [18]
Including lagged relations via uSEM ensures unbiased estimates of contemporaneous relations.
“Including the lagged relations via uSEM ensures unbiased estimates of these contemporaneous relations [18].”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [18]
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Self-citation
- Authors
- Gates, K. M., Molenaar, P. C. M., Hillary, F. G., Ram, N., & Rovine, M. J.
- Affiliations
- Pennsylvania State University (US)
- Corresponding author
- Gates, Kathleen M.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2010-04
- Volume
- 50
- Issue
- 3
- Pages
- 1118-1125
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2136626859
- Cited by
- 171
- References
- 26
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 9 · 2023: 12 · 2022: 15 · 2021: 13 · 2020: 10 · 2019: 18 · 2018: 14 · 2017: 11 · 2016: 10 · 2015: 14 · 2014: 5 · 2013: 11 · 2012: 8
- FWCI
- 5.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (0852147)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- Granger causality, vector autoregression, path modeling, score test, estimation bias, brain information processing
- MeSH terms
- Adult, Algorithms, Automation, Brain, Brain Mapping, Computer Simulation, Feasibility Studies, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neural Pathways, Regression Analysis, Signal Processing, Computer-Assisted, Models, Statistical, Young Adult
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