TY - JOUR
T1 - Automatic search for fMRI connectivity mapping
T2 - An alternative to Granger causality testing using formal equivalences among SEM path modeling, VAR, and unified SEM
AU - Gates, Kathleen M.
AU - Molenaar, Peter
AU - Hillary, Frank Gerard
AU - Ram, Nilam
AU - Rovine, Michael J.
N1 - Funding Information:
This work was supported by a National Science Foundation grant ( 0852147 ).
PY - 2010/4/15
Y1 - 2010/4/15
N2 - 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.
AB - 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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U2 - 10.1016/j.neuroimage.2009.12.117
DO - 10.1016/j.neuroimage.2009.12.117
M3 - Article
C2 - 20060050
AN - SCOPUS:77249142250
SN - 1053-8119
VL - 50
SP - 1118
EP - 1125
JO - NeuroImage
JF - NeuroImage
IS - 3
ER -