Extended unified SEM approach for modeling event-related fMRI data

Research output: Contribution to journalArticle

63 Citations (Scopus)

Abstract

There has been increasing emphasis in fMRI research on the examination of how regions covary in a distributed neural network. Event-related data designs present a unique challenge to modeling how couplings among regions change in the presence of experimental manipulations. The present paper presents the extended unified SEM (euSEM), a novel approach for acquiring effective connectivity maps with event-related data. The euSEM adds to the unified SEM, which models both lagged and contemporaneous effects, by estimating the direct effects that experimental manipulations have on blood-oxygen-level dependent activity as well as the modulating effects the manipulations have on couplings among regions. Monte Carlos simulations included in this paper offer support for the model's ability to recover covariance patterns used to estimate data. Next, we apply the model to empirical data to demonstrate feasibility. Finally, the results of the empirical data are compared to those found using dynamic causal modeling. The euSEM provides a flexible approach for modeling event-related data as it may be employed in an exploratory, partially exploratory, or entirely confirmatory manner.

Original languageEnglish (US)
Pages (from-to)1151-1158
Number of pages8
JournalNeuroImage
Volume54
Issue number2
DOIs
StatePublished - Jan 15 2011

Fingerprint

Magnetic Resonance Imaging
Oxygen
Research

All Science Journal Classification (ASJC) codes

  • Neurology
  • Cognitive Neuroscience

Cite this

@article{bbf2f8244bbf4047a05f020e61ff7920,
title = "Extended unified SEM approach for modeling event-related fMRI data",
abstract = "There has been increasing emphasis in fMRI research on the examination of how regions covary in a distributed neural network. Event-related data designs present a unique challenge to modeling how couplings among regions change in the presence of experimental manipulations. The present paper presents the extended unified SEM (euSEM), a novel approach for acquiring effective connectivity maps with event-related data. The euSEM adds to the unified SEM, which models both lagged and contemporaneous effects, by estimating the direct effects that experimental manipulations have on blood-oxygen-level dependent activity as well as the modulating effects the manipulations have on couplings among regions. Monte Carlos simulations included in this paper offer support for the model's ability to recover covariance patterns used to estimate data. Next, we apply the model to empirical data to demonstrate feasibility. Finally, the results of the empirical data are compared to those found using dynamic causal modeling. The euSEM provides a flexible approach for modeling event-related data as it may be employed in an exploratory, partially exploratory, or entirely confirmatory manner.",
author = "Gates, {Kathleen M.} and Peter Molenaar and Hillary, {Frank Gerard} and Semyon Slobounov",
year = "2011",
month = "1",
day = "15",
doi = "10.1016/j.neuroimage.2010.08.051",
language = "English (US)",
volume = "54",
pages = "1151--1158",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press Inc.",
number = "2",

}

Extended unified SEM approach for modeling event-related fMRI data. / Gates, Kathleen M.; Molenaar, Peter; Hillary, Frank Gerard; Slobounov, Semyon.

In: NeuroImage, Vol. 54, No. 2, 15.01.2011, p. 1151-1158.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Extended unified SEM approach for modeling event-related fMRI data

AU - Gates, Kathleen M.

AU - Molenaar, Peter

AU - Hillary, Frank Gerard

AU - Slobounov, Semyon

PY - 2011/1/15

Y1 - 2011/1/15

N2 - There has been increasing emphasis in fMRI research on the examination of how regions covary in a distributed neural network. Event-related data designs present a unique challenge to modeling how couplings among regions change in the presence of experimental manipulations. The present paper presents the extended unified SEM (euSEM), a novel approach for acquiring effective connectivity maps with event-related data. The euSEM adds to the unified SEM, which models both lagged and contemporaneous effects, by estimating the direct effects that experimental manipulations have on blood-oxygen-level dependent activity as well as the modulating effects the manipulations have on couplings among regions. Monte Carlos simulations included in this paper offer support for the model's ability to recover covariance patterns used to estimate data. Next, we apply the model to empirical data to demonstrate feasibility. Finally, the results of the empirical data are compared to those found using dynamic causal modeling. The euSEM provides a flexible approach for modeling event-related data as it may be employed in an exploratory, partially exploratory, or entirely confirmatory manner.

AB - There has been increasing emphasis in fMRI research on the examination of how regions covary in a distributed neural network. Event-related data designs present a unique challenge to modeling how couplings among regions change in the presence of experimental manipulations. The present paper presents the extended unified SEM (euSEM), a novel approach for acquiring effective connectivity maps with event-related data. The euSEM adds to the unified SEM, which models both lagged and contemporaneous effects, by estimating the direct effects that experimental manipulations have on blood-oxygen-level dependent activity as well as the modulating effects the manipulations have on couplings among regions. Monte Carlos simulations included in this paper offer support for the model's ability to recover covariance patterns used to estimate data. Next, we apply the model to empirical data to demonstrate feasibility. Finally, the results of the empirical data are compared to those found using dynamic causal modeling. The euSEM provides a flexible approach for modeling event-related data as it may be employed in an exploratory, partially exploratory, or entirely confirmatory manner.

UR - http://www.scopus.com/inward/record.url?scp=78649663945&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78649663945&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2010.08.051

DO - 10.1016/j.neuroimage.2010.08.051

M3 - Article

C2 - 20804852

AN - SCOPUS:78649663945

VL - 54

SP - 1151

EP - 1158

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

IS - 2

ER -