Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data

Kim, Jieun; Zhu, Wei; Chang, Linda; Bentler, Peter M.; Ernst, Thomas

doi:10.1002/hbm.20259

Cited by 237Functional Brain Connectivity StudiesAdvanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and Applications

Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data

Open access

Kim, Jieun, Zhu, Wei, Chang, Linda, Bentler, Peter M., Ernst, Thomas·2007·Human Brain Mapping

DOI 10.1002/hbm.20259PMC PMC6871502

Open this paper in CoChat — free account Open PDF

Abstract

The ultimate goal of brain connectivity studies is to propose, test, modify, and compare certain directional brain pathways. Path analysis or structural equation modeling (SEM) is an ideal statistical method for such studies. In this work, we propose a two‐stage unified SEM plus GLM (General Linear Model) approach for the analysis of multisubject, multivariate functional magnetic resonance imaging (fMRI) time series data with subject‐level covariates. In Stage 1, we analyze the fMRI multivariate time series for each subject individually via a unified SEM model by combining longitudinal pathways represented by a multivariate autoregressive (MAR) model, and contemporaneous pathways represented by a conventional SEM. In Stage 2, the resulting subject‐level path coefficients are merged with subject‐level covariates such as gender, age, IQ, etc., to examine the impact of these covariates on effective connectivity via a GLM. Our approach is exemplified via the analysis of an fMRI visual attention experiment. Furthermore, the significant path network from the unified SEM analysis is compared to that from a conventional SEM analysis without incorporating the longitudinal information as well as that from a Dynamic Causal Modeling (DCM) approach. Hum. Brain Mapp, 2007. © 2006 Wiley‐Liss, Inc.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1002/hbm.20259

Cites

48 of 48 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Variation in connection weights of specific connections between two ROIs has long been a focal point of fMRI research.
“Variation in the connection weights of specific connections between two given ROIs has long been a focal point of fMRI research [17].”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [17]
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 [17]
Support for this notion is found in fMRI data, where individual differences in brain patterns relating to indices such as performance and gender have been found in normative samples.
“Support for this notion is found in fMRI data, where individual differences in brain patterns relating to indices such as performance [17], [52] and gender [13] have been found in normative samples.”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [17]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Alternative Ways of Assessing Model Fit
Browne, M. W., & Cudeck, R. (1992) · Sociological Methods & Research · doi:10.1177/0049124192021002005 · Cited by 27,200 · see citing works
Structural Equations with Latent Variables
Bollen, K. A. (1989) · Wiley series in probability and statistics · doi:10.1002/9781118619179 · Cited by 17,512 · see citing works
Structural Equations with Latent Variables.
Clogg, C. C., & Bollen, K. A. (1991) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2072165 · Cited by 17,471 · see citing works
Testing Structural Equation Models.
Mueller, C. W., Bollen, K. A., & Long, J. S. (1994) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2074955 · Cited by 14,211 · see citing works
EQS : structural equations program manual
Bentler, P. M. (1989) · Medical Entomology and Zoology · Cited by 9,669 · see citing works
Self-citation
Testing Structural Equation Models.
Clogg, C. C., Bollen, K. A., & Long, J. S. (1995) · Social Forces · doi:10.2307/2580595 · Cited by 8,244 · see citing works
LISREL 7 : user's reference guide
Jöreskog, K. G., & Sörbom, D. (1989) · Medical Entomology and Zoology · Cited by 6,783 · see citing works
Dynamic causal modelling
Friston, K. J., Harrison, L., & Penny, W. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00202-7 · Cited by 4,583 · see citing works
Latent variable models: An introduction to factor, path, and structural analysis
John, C. (1987) · Medical Entomology and Zoology · Cited by 2,128 · see citing works
Asymptotically distribution-free methods for the analysis of covariance structures.
Browne, M. (1984) · British Journal of Mathematical & Statistical Psychology · doi:10.1111/j.2044-8317.1984.tb00789.x · Cited by 1,890 · see citing works
Advanced Structural Equation Modeling: Issues and Techniques
Rigdon, E. E. (1998) · Applied Psychological Measurement · doi:10.1177/01466216980221008 · Cited by 1,631 · see citing works
Selective and divided attention during visual discriminations of shape, color, and speed: functional anatomy by positron emission tomography
Corbetta, M., Miezin, F. M., Dobmeyer, S., Shulman, G. L., & Petersen, S. (1991) · Journal of Neuroscience · doi:10.1523/jneurosci.11-08-02383.1991 · Cited by 1,590 · see citing works
Mapping directed influence over the brain using Granger causality and fMRI
Roebroeck, A. F., Formisano, E., & Goebel, R. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.11.017 · Cited by 1,012 · see citing works
Comparing dynamic causal models.
Penny, W. D., Stephan, K. E., Mechelli, A., & Friston, K. J. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.03.026 · Cited by 848 · see citing works
Structural equation modeling and its application to network analysis in functional brain imaging
McLntosh, A. R., & Gonzalez-Lima, F. (1994) · Human Brain Mapping · doi:10.1002/hbm.460020104 · Cited by 838 · see citing works
Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI
Büchel, C. (1997) · Cerebral Cortex · doi:10.1093/cercor/7.8.768 · Cited by 794 · see citing works
Latent Variable Models: An Introduction to Factor, Path, and Structural Analysis
Wardrop, J. L., & Loehlin, J. C. (1987) · Journal of Educational Statistics · doi:10.2307/1165058 · Cited by 739 · see citing works
Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping
Goebel, R., Roebroeck, A. F., Kim, D., & Formisano, E. (2003) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2003.08.026 · Cited by 678 · see citing works
Visual attention: Insights from brain imaging
Kanwisher, N., & Wojciulik, E. (2000) · Nature reviews. Neuroscience · doi:10.1038/35039043 · Cited by 627 · see citing works
Network analysis of cortical visual pathways mapped with PET
McIntosh, A. R., Grady, C., Ungerleider, L. G., Haxby, J., Rapoport, S. I., & Horwitz, B. (1994) · Journal of Neuroscience · doi:10.1523/jneurosci.14-02-00655.1994 · Cited by 493 · see citing works
A Dynamic Factor Model for the Analysis of Multivariate Time Series
Molenaar, P. C. M. (1985) · Psychometrika · doi:10.1007/bf02294246 · Cited by 480 · see citing works
The Predictive Value of Changes in Effective Connectivity for Human Learning
Büchel, C., Coull, J. T., & Friston, K. J. (1999) · Science · doi:10.1126/science.283.5407.1538 · Cited by 447 · see citing works
Multivariate autoregressive modeling of fMRI time series
Harrison, L. M., Penny, W. D., & Friston, K. J. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00160-5 · Cited by 378 · see citing works
Visual attention as a multilevel selection process
Kästner, S., & Pinsk, M. A. (2004) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/cabn.4.4.483 · Cited by 258 · see citing works
How Good Is Good Enough in Path Analysis of fMRI Data?
Bullmore, E. T., Horwitz, B., Honey, G. D., Brammer, M. J., Williams, S., & Sharma, T. (2000) · NeuroImage · doi:10.1006/nimg.2000.0544 · Cited by 241 · see citing works
New approaches for exploring anatomical and functional connectivity in the human brain
Ramnani, N., Behrens, T. E. J., Penny, W. D., & Matthews, P. M. (2004) · Biological Psychiatry · doi:10.1016/j.biopsych.2004.02.004 · Cited by 229 · see citing works
Attentional modulation of effective connectivity from V2 to V5/MT in humans
Friston, K. J., & Büchel, C. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.13.7591 · Cited by 225 · see citing works
Brain Areas Specific for Attentional Load in a Motion-Tracking Task
Jovicich, J., Peters, R. J., Koch, C., Braun, J., Chang, L., & Ernst, T. (2001) · Journal of Cognitive Neuroscience · doi:10.1162/089892901753294347 · Cited by 199 · see citing works
Self-citation
Effects of Verbal Working Memory Load on Corticocortical Connectivity Modeled by Path Analysis of Functional Magnetic Resonance Imaging Data
Honey, G. D., Fu, C. H. Y., Kim, J., Brammer, M. J., Croudace, T. J., John Suckling, A., Pich, E. M., Williams, S., & Bullmore, E. T. (2002) · NeuroImage · doi:10.1006/nimg.2002.1193 · Cited by 184 · see citing works
Self-citation
Learning-related Neuronal Responses in Prefrontal Cortex Studied with Functional Neuroimaging
Fletcher, P. C. (1999) · Cerebral Cortex · doi:10.1093/cercor/9.2.168 · Cited by 177 · see citing works
Integrating electrophysiological and anatomical experimental data to create a large-scale model that simulates a delayed match-to-sample human brain imaging study
Tagamets, M.-A. (1998) · Cerebral Cortex · doi:10.1093/cercor/8.4.310 · Cited by 175 · see citing works
Structural modeling of functional neural pathways mapped with 2-deoxyglucose: effects of acoustic startle habituation on the auditory system
McIntosh, A. R., & Gonzalez-Lima, F. (1991) · Brain Research · doi:10.1016/0006-8993(91)90974-z · Cited by 158 · see citing works
Functional Magnetic Resonance Image Analysis of a Large-Scale Neurocognitive Network
Bullmore, E. T., Rabe‐Hesketh, S., Morris, R. G., Williams, S., Gregory, L. J., Gray, J. A., & Brammer, M. J. (1996) · NeuroImage · doi:10.1006/nimg.1996.0026 · Cited by 148 · see citing works
Neural modeling and functional brain imaging: an overview
Horwitz, B., Friston, K. J., & Taylor, J. G. (2000) · Neural Networks · doi:10.1016/s0893-6080(00)00062-9 · Cited by 146 · see citing works
Adaptation of the attention network in human immunodeficiency virus brain injury
Chang, L., Tomasi, D. G., Yakupov, R., Lozar, C., Anteraper, S. A., Caparelli, E. C., & Ernst, T. (2004) · Annals of Neurology · doi:10.1002/ana.20190 · Cited by 130 · see citing works
Self-citation
Interindividual Differences in Functional Interactions among Prefrontal, Parietal and Parahippocampal Regions during Working Memory
Glabus, M. F. (2003) · Cerebral Cortex · doi:10.1093/cercor/bhg082 · Cited by 118 · see citing works
Dynamic Factor Analysis of Nonstationary Multivariate Time Series
Molenaar, P. C. M., Gooijer, J. G. D., & Schmitz, B. (1992) · Psychometrika · doi:10.1007/bf02295422 · Cited by 115 · see citing works
Network interactions among limbic cortices, basal forebrain, and cerebellum differentiate a tone conditioned as a Pavlovian excitor or inhibitor: fluorodeoxyglucose mapping and covariance structural modeling
McIntosh, A. R., & Gonzalez-Lima, F. (1994) · Journal of Neurophysiology · doi:10.1152/jn.1994.72.4.1717 · Cited by 114 · see citing works
Biophysical models of fMRI responses
Stephan, K. E., Harrison, L. M., Penny, W. D., & Friston, K. J. (2004) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2004.08.006 · Cited by 96 · see citing works
Network analysis of motor system connectivity in Parkinson's disease: Modulation of thalamocortical interactions after pallidotomy
Grafton, S. T., Sutton, J. P., Couldwell, W. T., Lew, M., & Waters, C. (1994) · Human Brain Mapping · doi:10.1002/hbm.460020106 · Cited by 67 · see citing works
Understanding Neural Interactions in Learning and Memory Using Functional Neuroimaging
McIntosh, A. R. (1998) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.1998.tb10625.x · Cited by 64 · see citing works
Brain areas specific for attentional load in a motion tracking task
Jovicich, J., Peters, R. J., Christof Koch, A., Braun, J., Chang, L., & Ernst, T. (2010) · Journal of Vision · doi:10.1167/1.3.348 · Cited by 44 · see citing works
Self-citation
k-Space based summary motion detection for functional magnetic resonance imaging
Caparelli, E. C., Tomasi, D. G., Arnold, S., Chang, L., & Ernst, T. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00339-2 · Cited by 43 · see citing works
Self-citation
Statistical Procedures for Functional MRI
LANGE, N. D. (2000) · Medical radiology · doi:10.1007/978-3-642-58716-0_27 · Cited by 37 · see citing works
Mapping Neural Interactivity onto Regional Activity: An Analysis of Semantic Processing and Response Mode Interactions
Jennings, J. M., McIntosh, A. R., & Kapur, S. (1998) · NeuroImage · doi:10.1006/nimg.1998.0325 · Cited by 31 · see citing works
A hierarchy of univariate and multivariate structural time series models
Hershberger, S. L., Molenaar, P. C. M., & Corneal, S. E. (1996) · UvA-DARE (University of Amsterdam) · Cited by 19 · see citing works
Solo Version 6.0 for Windows. BMDP Statistical Software, Cork Technology Park, Model Farm Road, Cork, Ireland
Wyness, D. (1996) · European Journal of Orthodontics · doi:10.1093/ejo/18.6.672-a · Cited by 2 · see citing works
Untitled work
Xiong (1999) · doi:10.1002/(sici)1097-0193(1999)8:2/3

Flagged first, then by citations

Sorted by citations

Lack of group-to-individual generalizability is a threat to human subjects research
Fisher, A. J., Medaglia, J. D., & Jeronimus, B. F. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1711978115 · Cited by 1,099 · see citing works
Assessing functional connectivity in the human brain by fMRI
Rogers, B. P., Morgan, V. L., Newton, A. T., & Gore, J. C. (2007) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2007.03.007 · Cited by 477 · see citing works
Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples
Gates, K. M., & Molenaar, P. C. M. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.026 · Cited by 478 · see citing works
Time-structured and net intraindividual variability: Tools for examining the development of dynamic characteristics and processes.
Ram, N., & Gerstorf, D. (2009) · Psychology and Aging · doi:10.1037/a0017915 · Cited by 417 · see citing works
A complex network perspective on clinical science
Hofmann, S. G., Curtiss, J., & McNally, R. J. (2016) · Perspectives on Psychological Science · doi:10.1177/1745691616639283 · Cited by 366 · see citing works
An SEM approach to continuous time modeling of panel data: Relating authoritarianism and anomia.
Voelkle, M. C., Oud, J. H. L., Davidov, E., & Schmidt, P. (2012) · Psychological Methods · doi:10.1037/a0027543 · Cited by 320 · see citing works
Bridging the Nomothetic and Idiographic Approaches to the Analysis of Clinical Data
Beltz, A. M., Wright, A. G. C., Sprague, B. N., & Molenaar, P. C. M. (2016) · Assessment · doi:10.1177/1073191116648209 · Cited by 293 · see citing works
Personalized Models of Psychopathology
Wright, A. G. C., & Woods, W. C. (2020) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-102419-125032 · Cited by 281 · see citing works
Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity
Hyvärinen, A., Zhang, K., Shimizu, S., & Hoyer, P. O. (2010) · doi:10.5555/1756006.1859907 · Cited by 259 · see citing works
Using Structural Equation Modeling to Assess Functional Connectivity in the Brain: Power and Sample Size Considerations
Sideridis, G., Simos, P., Papanicolaou, A., & Fletcher, J. (2014) · Educational and Psychological Measurement · doi:10.1177/0013164414525397 · Cited by 246 · see citing works
Multivariate Granger causality analysis of fMRI data
Deshpande, G., LaConte, S., James, G. A., Peltier, S., & Hu, X. P. (2008) · Human Brain Mapping · doi:10.1002/hbm.20606 · Cited by 238 · see citing works
A simple view of the brain through a frequency-specific functional connectivity measure
Salvador, R., Martínez, A., Pomarol-Clotet, E., Gomar, J., Vila, F., Sarró, S., Capdevila, A., & Bullmore, E. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2007.08.018 · Cited by 225 · see citing works
Partial Granger causality—Eliminating exogenous inputs and latent variables
Guo, S., Seth, A. K., Kendrick, K. M., Zhou, C., & Feng, J. (2008) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2008.04.011 · Cited by 205 · see citing works
Automatic search for fMRI connectivity mapping: an alternative to Granger causality testing using formal equivalences among SEM path modeling, VAR, and unified SEM
Gates, K. M., Molenaar, P. C. M., Hillary, F. G., Ram, N., & Rovine, M. J. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.117 · Cited by 171 · see citing works
Investigating the biological and clinical significance of human dysbioses
Frank, D. N., Zhu, W., Sartor, R. B., & Li, E. (2011) · Trends in Microbiology · doi:10.1016/j.tim.2011.06.005 · Cited by 174 · see citing works
Self-citation
Foundations of idiographic methods in psychology and applications for psychotherapy
Piccirillo, M. L., & Rodebaugh, T. L. (2019) · Clinical Psychology Review · doi:10.1016/j.cpr.2019.01.002 · Cited by 160 · see citing works
Altered effective connectivity and anomalous anatomy in the basal ganglia-thalamocortical circuit of stuttering speakers
Lu, C., Peng, D., Chen, C., Ning, N., Ding, G., Li, K., Yang, Y., & Lin, C. (2009) · Cortex · doi:10.1016/j.cortex.2009.02.017 · Cited by 167 · see citing works
Network Mapping with GIMME
Beltz, A. M., & Gates, K. M. (2017) · Multivariate Behavioral Research · doi:10.1080/00273171.2017.1373014 · Cited by 161 · see citing works
The current status of temporal network analysis for clinical science: Considerations as the paradigm shifts?
Jordan, D. G., Winer, E. S., & Salem, T. (2020) · Journal of Clinical Psychology · doi:10.1002/jclp.22957 · Cited by 153 · see citing works
Neural changes underlying successful second language word learning: An fMRI study
Yang, J., Gates, K. M., Molenaar, P. C. M., & Li, P. (2014) · Journal of Neurolinguistics · doi:10.1016/j.jneuroling.2014.09.004 · Cited by 148 · see citing works
Effective connectivity of a reward network in obese women
Stoeckel, L. E., Kim, J., Weller, R. E., Cox, J. E., Cook, E. W., & Horwitz, B. (2009) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2009.05.016 · Cited by 147 · see citing works
Self-citation
Investigating Effective Brain Connectivity from fMRI Data: Past Findings and Current Issues with Reference to Granger Causality Analysis
Deshpande, G., & Hu, X. P. (2012) · Brain Connectivity · doi:10.1089/brain.2012.0091 · Cited by 142 · see citing works
Prefrontal Control of the Amygdala during Real-Time fMRI Neurofeedback Training of Emotion Regulation
Zotev, V. S., Phillips, R., Young, K. D., Drevets, W. C., & Bodurka, J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0079184 · Cited by 145 · see citing works
Uncovering general, shared, and unique temporal patterns in ambulatory assessment data.
Lane, S., Gates, K. M., Pike, H., Beltz, A. M., & Wright, A. G. C. (2018) · Psychological Methods · doi:10.1037/met0000192 · Cited by 136 · see citing works
Unsupervised Classification During Time-Series Model Building
Gates, K. M., Lane, S., Varangis, E. M. L., Giovanello, K. S., & Guiskewicz, K. (2016) · Multivariate Behavioral Research · doi:10.1080/00273171.2016.1256187 · Cited by 136 · see citing works
Brain Networks of Explicit and Implicit Learning
Yang, J., & Li, P. (2012) · PLoS ONE · doi:10.1371/journal.pone.0042993 · Cited by 138 · see citing works
Extended unified SEM approach for modeling event-related fMRI data
Gates, K. M., Molenaar, P. C. M., Hillary, F. G., & Slobounov, S. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.08.051 · Cited by 125 · see citing works
Functional brain interactions that serve cognitive–affective processing during pain and placebo analgesia
Craggs, J. G., Price, D. D., Verne, G. N., Perlstein, W. M., & Robinson, M. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.057 · Cited by 126 · see citing works
Cognitive Neuroscience Approaches to Individual Differences in Working Memory and Executive Control: Conceptual and Methodological Issues
Yarkoni, T., & Braver, T. S. (2010) · Plenum series on human exceptionality · doi:10.1007/978-1-4419-1210-7_6 · Cited by 140 · see citing works
Precision behavioral phenotyping as a strategy for uncovering the biological correlates of psychopathology
Tiego, J., Martin, E. A., DeYoung, C. G., Hagan, K. E., Cooper, S. E., Pasion, R., Satchell, L. P., Shackman, A. J., Bellgrove, M. A., Fornito, A., et al. (2023) · Nature Mental Health · doi:10.1038/s44220-023-00057-5 · Cited by 118 · see citing works
Second language lexical development and cognitive control: A longitudinal fMRI study.
Grant, A., Fang, S.-Y., & Li, P. (2015) · Brain and Language · doi:10.1016/j.bandl.2015.03.010 · Cited by 92 · see citing works
Parsing Heterogeneity in the Brain Connectivity of Depressed and Healthy Adults During Positive Mood
Price, R. B., Lane, S., Gates, K., Kraynak, T. E., Horner, M. S., Thase, M. E., & Siegle, G. J. (2017) · Biological Psychiatry · doi:10.1016/j.biopsych.2016.06.023 · Cited by 106 · see citing works
Focusing personality assessment on the person: Modeling general, shared, and person specific processes in personality and psychopathology.
Wright, A. G. C., Gates, K. M., Arizmendi, C., Lane, S., Woods, W. C., & Edershile, E. A. (2019) · Psychological Assessment · doi:10.1037/pas0000617 · Cited by 107 · see citing works
Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks
Gates, K. M., Molenaar, P. C. M., Iyer, S. P., Nigg, J. T., & Fair, D. A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0091322 · Cited by 107 · see citing works
Vector autoregression, structural equation modeling, and their synthesis in neuroimaging data analysis
Chen, G., Glen, D., Saad, Z. S., Hamilton, J. P., Thomason, M. E., Gotlib, I. H., & Cox, R. W. (2011) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2011.09.004 · Cited by 107 · see citing works
Data-Driven Subgroups in Depression Derived from Directed Functional Connectivity Paths at Rest
Price, R. B., Gates, K., Kraynak, T. E., Thase, M. E., & Siegle, G. J. (2017) · Neuropsychopharmacology · doi:10.1038/npp.2017.97 · Cited by 98 · see citing works
Inventory management, managerial competence and financial performance of small businesses
Orobia, L. A., Nakibuuka, J., Bananuka, J., & Akisimire, R. (2020) · Journal of Accounting in Emerging Economies · doi:10.1108/jaee-07-2019-0147 · Cited by 93 · see citing works
Dynamic Bayesian network modeling of fMRI: A comparison of group-analysis methods
Li, J., Wang, Z. J., Palmer, S. J., & McKeown, M. J. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.01.068 · Cited by 89 · see citing works
Connectivity analysis is essential to understand neurological disorders
Rowe, J. B. (2010) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2010.00144 · Cited by 89 · see citing works
State Space Modeling in an Open Source, Modular, Structural Equation Modeling Environment
Hunter, M. D. (2018) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2017.1369354 · Cited by 82 · see citing works
Uncovering Interactions in the Frequency Domain
Guo, S., Wu, J., Ding, M., & Feng, J. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000087 · Cited by 83 · see citing works
Systems Methods for Developmental Research
Molenaar, P. C. M., & Nesselroade, J. R. (2015) · Handbook of Child Psychology and Developmental Science · doi:10.1002/9781118963418.childpsy117 · Cited by 80 · see citing works
Large‐scale neural model validation of partial correlation analysis for effective connectivity investigation in functional MRI
Marrelec, G., Kim, J. J. H., Doyon, J., & Horwitz, B. (2008) · Human Brain Mapping · doi:10.1002/hbm.20555 · Cited by 79 · see citing works
Self-citation
Successful Emotion Regulation Is Predicted by Amygdala Activity and Aspects of Personality: A Latent Variable Approach
Morawetz, C., Alexandrowicz, R., & Heekeren, H. (2017) · Emotion · doi:10.1037/emo0000215 · Cited by 75 · see citing works
Semantic association investigated with functional MRI and independent component analysis
Kim, K. K., Karunanayaka, P., Privitera, M., Holland, S. K., & Szaflarski, J. P. (2011) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2010.11.010 · Cited by 77 · see citing works
Using Raw VAR Regression Coefficients to Build Networks can be Misleading
Bulteel, K., Tuerlinckx, F., Brose, A., & Ceulemans, E. (2016) · Multivariate Behavioral Research · doi:10.1080/00273171.2016.1150151 · Cited by 75 · see citing works
Examining working memory task acquisition in a disrupted neural network
Hillary, F. G., Medaglia, J. D., Gates, K., Molenaar, P. C., Slocomb, J., Peechatka, A., & Good, D. C. (2011) · Brain · doi:10.1093/brain/awr043 · Cited by 74 · see citing works
Automated Selection of Robust Individual-Level Structural Equation Models for Time Series Data
Lane, S., & Gates, K. M. (2017) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2017.1309978 · Cited by 66 · see citing works
Functional Imaging of Autonomic Regulation: Methods and Key Findings
Macey, P. M., Ogren, J. A., Kumar, R., & Harper, R. M. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00513 · Cited by 74 · see citing works
Interactive brains, social minds
Sänger, J., Lindenberger, U., & Müller, V. (2011) · Communicative & Integrative Biology · doi:10.4161/cib.17934 · Cited by 70 · see citing works
Examining the Dynamic Structure of Daily Internalizing and Externalizing Behavior at Multiple Levels of Analysis
Wright, A. G. C., Beltz, A. M., Gates, K. M., Molenaar, P. C. M., & Simms, L. J. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.01914 · Cited by 82 · see citing works
Brain network reconfiguration for language and domain-general cognitive control in bilinguals
Wu, J., Yang, J., Chen, M., Li, S., Zhang, Z., Kang, C., Ding, G., & Guo, T. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.06.022 · Cited by 70 · see citing works
Causal modelling combining instantaneous and lagged effects
Hyvärinen, A., Shimizu, S., & Hoyer, P. O. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390210 · Cited by 68 · see citing works
Randomized controlled trial of emotionally focused couple therapy compared to treatment as usual for depression: Outcomes and mechanisms of change
Wittenborn, A. K., Liu, T., Ridenour, T. A., Lachmar, E. M., Mitchell, E. A., & Seedall, R. B. (2018) · Journal of Marital and Family Therapy · doi:10.1111/jmft.12350 · Cited by 68 · see citing works
Functional Neuroimaging in Traumatic Brain Injury: From Nodes to Networks
Medaglia, J. D. (2017) · Frontiers in Neurology · doi:10.3389/fneur.2017.00407 · Cited by 63 · see citing works
Mild cognitive impairment and fMRI studies of brain functional connectivity: the state of the art
Farràs-Permanyer, L., Guàrdia‐Olmos, J., & Peró, M. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.01095 · Cited by 63 · see citing works
The role of large-scale neural interactions for developmental stuttering
Lu, C., Ning, N., Peng, D., Ding, G., Li, K., Yang, Y., & Lin, C. (2009) · Neuroscience · doi:10.1016/j.neuroscience.2009.04.020 · Cited by 63 · see citing works
Toward empirical process-based case conceptualization: An idionomic network examination of the process-based assessment tool
Sanford, B. T., Ciarrochi, J., Hofmann, S. G., Chin, F. T., Gates, K. M., & Hayes, S. C. (2022) · Journal of Contextual Behavioral Science · doi:10.1016/j.jcbs.2022.05.006 · Cited by 62 · see citing works
The use of machine learning and deep learning algorithms in functional magnetic resonance imaging—A systematic review
Rashid, M. U., Singh, H., & Goyal, V. (2020) · Expert Systems · doi:10.1111/exsy.12644 · Cited by 64 · see citing works
Data driven pathway analysis and forecast of global warming and sea level rise
Song, J., Tong, G., Chao, J., Chung, J., Zhang, M., Lin, W., Zhang, T., Bentler, P. M., & Zhu, W. (2023) · Scientific Reports · doi:10.1038/s41598-023-30789-4 · Cited by 60 · see citing works
Self-citation
Dynamic causal modeling of load-dependent modulation of effective connectivity within the verbal working memory network
Dima, D., Jogia, J., & Frangou, S. (2013) · Human Brain Mapping · doi:10.1002/hbm.22382 · Cited by 60 · see citing works
A Survey on Brain Effective Connectivity Network Learning
Ji, J., Zou, A., Liu, J., Yang, C., Zhang, X., & Song, Y. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3106299 · Cited by 56 · see citing works
Neuroimaging of individual differences: A latent variable modeling perspective
Cooper, S. R., Jackson, J. J., Barch, D. M., & Braver, T. S. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.12.022 · Cited by 57 · see citing works
Investigating the neural basis for fMRI-based functional connectivity in a blocked design: application to interregional correlations and psycho-physiological interactions
Kim, J., & Horwitz, B. (2008) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2007.10.011 · Cited by 56 · see citing works
Self-citation
Networks involved in olfaction and their dynamics using independent component analysis and unified structural equation modeling
Karunanayaka, P., Eslinger, P. J., Wang, J., Weitekamp, C. W., Molitoris, S., Gates, K. M., Molenaar, P. C. M., & Yang, Q. X. (2013) · Human Brain Mapping · doi:10.1002/hbm.22312 · Cited by 52 · see citing works
Testing the structure and process of personality using ambulatory assessment data: An overview of within-person and person-specific techniques.
Hofmans, J., Clercq, B. J. D., Kuppens, P., Verbeke, L., & Widiger, T. A. (2019) · Psychological Assessment · doi:10.1037/pas0000562 · Cited by 50 · see citing works
Biomarkers for Traumatic Brain Injury: Data Standards and Statistical Considerations
Huie, J. R., Mondello, S., Lindsell, C. J., Antiga, L., Yuh, E. L., Zanier, E. R., Masson, S., Rosario, B. L., Ferguson, A. R., Adeoye, O. M., et al. (2020) · Journal of Neurotrauma · doi:10.1089/neu.2019.6762 · Cited by 50 · see citing works
Multi-area neural mass modeling of EEG and MEG signals
Abbas Babajani-feremi, A., & Hamid Soltanian-zadeh, A. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.01.034 · Cited by 50 · see citing works
Methods for Quantifying Patterns of Dynamic Interactions in Dyads
Gates, K. M., & Liu, S. (2016) · Assessment · doi:10.1177/1073191116641508 · Cited by 50 · see citing works
Personalized models of psychopathology as contextualized dynamic processes: An example from individuals with borderline personality disorder.
Woods, W. C., Arizmendi, C., Gates, K. M., Stepp, S. D., Pilkonis, P. A., & Wright, A. G. C. (2020) · Journal of Consulting and Clinical Psychology · doi:10.1037/ccp0000472 · Cited by 45 · see citing works
Comparing directed functional connectivity between groups with confirmatory subgrouping GIMME
Henry, T. R., Feczko, E., Cordova, M., Earl, E., Williams, S., Nigg, J. T., Fair, D. A., & Gates, K. M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.12.040 · Cited by 45 · see citing works
Latent variable GIMME using model implied instrumental variables (MIIVs).
Gates, K. M., Fisher, Z. F., & Bollen, K. A. (2019) · Psychological Methods · doi:10.1037/met0000229 · Cited by 46 · see citing works
Dealing with Multiple Solutions in Structural Vector Autoregressive Models
Beltz, A. M., & Molenaar, P. C. M. (2016) · Multivariate Behavioral Research · doi:10.1080/00273171.2016.1151333 · Cited by 45 · see citing works
Insights from Personalized Models of Brain and Behavior for Identifying Biomarkers in PsychiatryPreprint
Kraus, B. T., Zinbarg, R. E., Braga, R. M., Nusslock, R., Mittal, V. A., & Gratton, C. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105259 · Cited by 43 · see citing works
Hierarchical syntactic processing is beyond mere associating: Functional magnetic resonance imaging evidence from a novel artificial grammar
Chen, L., Goucha, T., Männel, C., Friederici, A. D., & Zaccarella, E. (2021) · Human Brain Mapping · doi:10.1002/hbm.25432 · Cited by 39 · see citing works
A neuroimaging study of semantic representation in first and second languages
Zhang, X., Yang, J., Wang, R., & Li, P. (2020) · Language Cognition and Neuroscience · doi:10.1080/23273798.2020.1738509 · Cited by 42 · see citing works
Analyzing relationships among ARMA processes based on non-Gaussianity of external influences
Kawahara, Y., Shimizu, S., & Washio, T. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.02.008 · Cited by 41 · see citing works
Inferring functional connectivity in MRI using Bayesian network structure learning with a modified PC algorithm
Iyer, S., Izhak Shafran, J., Grayson, D. S., Gates, K. M., Nigg, J. T., & Fair, D. A. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.02.054 · Cited by 40 · see citing works
The first day is always the hardest: Functional connectivity during cue exposure and the ability to resist smoking in the initial hours of a quit attempt
Zelle, S. L., Gates, K. M., Fiez, J. A., Sayette, M. A., & Wilson, S. J. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.03.015 · Cited by 40 · see citing works
An Idiographic Examination of Day-to-Day Patterns of Substance Use Craving, Negative Affect, and Tobacco Use Among Young Adults in Recovery
Zheng, Y., Wiebe, R. P., Cleveland, H. H., Molenaar, P. C. M., & Harris, K. S. (2013) · Multivariate Behavioral Research · doi:10.1080/00273171.2013.763012 · Cited by 39 · see citing works
Inferring functional interaction and transition patterns via dynamic bayesian variable partition models
Zhang, J., Li, X., Li, C., Lian, Z., Huang, X., Zhong, G., Zhu, D., Li, K., Jin, C., Hu, X., et al. (2013) · Human Brain Mapping · doi:10.1002/hbm.22404 · Cited by 38 · see citing works
Principle of structural equation modeling for exploring functional interactivity within a putative network of interconnected brain areas
de Marco, G., Vrignaud, P., Destrieux, C., de Marco, D., Testelin, S., Devauchelle, B., & Berquin, P. C. (2008) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2008.05.003 · Cited by 38 · see citing works
Heterogeneity in suicide risk: Evidence from personalized dynamic models
Coppersmith, D. D. L., Kleiman, E. M., Millner, A. J., Wang, S. B., Arizmendi, C., Bentley, K. H., DeMarco, D., Fortgang, R. G., Zuromski, K. L., Maimone, J. S., et al. (2024) · Behaviour Research and Therapy · doi:10.1016/j.brat.2024.104574 · Cited by 41 · see citing works
Changes in alcohol-related brain networks across the first year of college: a prospective pilot study using fMRI effective connectivity mapping
Beltz, A. M., Gates, K. M., Engels, A. S., Molenaar, P. C. M., Pulido, C., Turrisi, R., Berenbaum, S. A., Gilmore, R. O., & Wilson, S. J. (2013) · Addictive Behaviors · doi:10.1016/j.addbeh.2012.12.023 · Cited by 36 · see citing works
The challenge of non-ergodicity in network neuroscience
Medaglia, J. D., Ramanathan, D. M., Venkatesan, U. M., & Hillary, F. G. (2011) · Network Computation in Neural Systems · doi:10.3109/09638237.2011.639604 · Cited by 35 · see citing works
Mapping Temporal Dynamics in Social Interactions With Unified Structural Equation Modeling: A Description and Demonstration Revealing Time-Dependent Sex Differences in Play Behavior
Beltz, A. M., Beekman, C. R., Molenaar, P. C. M., & Buss, K. A. (2013) · Applied Developmental Science · doi:10.1080/10888691.2013.805953 · Cited by 35 · see citing works
Evaluating the organizational structure and specificity of network topology within the face processing system
Elbich, D., Molenaar, P. C. M., & Scherf, K. S. (2019) · Human Brain Mapping · doi:10.1002/hbm.24546 · Cited by 34 · see citing works
How well does structural equation modeling reveal abnormal brain anatomical connections? An fMRI simulation study
Kim, J., & Horwitz, B. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.01.006 · Cited by 34 · see citing works
Self-citation
The effects of left or right hemispheric epilepsy on language networks investigated with semantic decision fMRI task and independent component analysis
Karunanayaka, P., Kim, K. K., Holland, S. K., & Szaflarski, J. P. (2011) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2010.12.029 · Cited by 33 · see citing works
Personalized models of personality disorders: using a temporal network method to understand symptomatology and daily functioning in a clinical sample
Dotterer, H. L., Beltz, A. M., Foster, K. T., Simms, L. J., & Wright, A. G. C. (2019) · Psychological Medicine · doi:10.1017/s0033291719002563 · Cited by 31 · see citing works
Rapidly acquired multisensory association in the olfactory cortex
Karunanayaka, P., Wilson, D. A., Vasavada, M. M., Wang, J., Martinez, B., Tobia, M. J., Kong, L., Eslinger, P. J., & Yang, Q. X. (2015) · Brain and Behavior · doi:10.1002/brb3.390 · Cited by 32 · see citing works
Huntington's disease: Brain imaging in Huntington's disease
Johnson, E. B., & Gregory, S. (2019) · Progress in molecular biology and translational science · doi:10.1016/bs.pmbts.2019.04.004 · Cited by 32 · see citing works
Learning Discriminative Bayesian Networks from High-Dimensional Continuous Neuroimaging Data
Zhou, L., Wang, L., Liu, L., Ogunbona, P., & Shen, D. G. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2511754 · Cited by 31 · see citing works
Greater BOLD activity but more efficient connectivity is associated with better cognitive performance within a sample of nicotine‐deprived smokers
Nichols, T. T., Gates, K. M., Molenaar, P. C. M., & Wilson, S. J. (2013) · Addiction Biology · doi:10.1111/adb.12060 · Cited by 29 · see citing works
Dynamic GSCA (Generalized Structured Component Analysis) with Applications to the Analysis of Effective Connectivity in Functional Neuroimaging Data
Jung, K., Takane, Y., Hwang, H., & Woodward, T. S. (2012) · Psychometrika · doi:10.1007/s11336-012-9284-2 · Cited by 29 · see citing works
Causal search procedures for fMRI: review and suggestions
Henry, T. R., & Gates, K. M. (2016) · Behaviormetrika · doi:10.1007/s41237-016-0010-8 · Cited by 28 · see citing works
From persons to general principles: Methodological decisions for idiographic and nomothetic research
Kuper, N., Andresen, P. K., Beck, E. D., Costantini, G., Hamaker, E. L., Wright, A. G. C., & Zimmermann, J. (2024) · European Journal of Personality · doi:10.1177/08902070241278020 · Cited by 28 · see citing works
Unsupervised classification reveals consistency and degeneracy in neural network patterns of emotion
Doyle, C. M., Lane, S., Brooks, J. A., Wilkins, R. W., Gates, K. M., & Lindquist, K. A. (2022) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsac028 · Cited by 25 · see citing works
Using functional magnetic resonance imaging in psychotherapy research: A brief introduction to concepts, methods, and task selection
Carrig, M. M., Kolden, G. G., & Strauman, T. J. (2009) · Psychotherapy Research · doi:10.1080/10503300902735864 · Cited by 26 · see citing works
A Tutorial on Estimating Time-Varying Vector Autoregressive Models
Haslbeck, J. M. B., Bringmann, L. F., & Waldorp, L. (2020) · Multivariate Behavioral Research · doi:10.1080/00273171.2020.1743630 · Cited by 46 · see citing works

Automatic search for fMRI connectivity mapping: an alternative to Granger causality testing using formal equivalences among SEM path modeling, VAR, and unified SEM
Gates, K. M., Molenaar, P. C. M., Hillary, F. G., Ram, N., & Rovine, M. J. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.117 · Cited by 171 · see citing works
Structural Equation Modeling with Time Series Data
Zhang, T. (2010) · SUNY Digital Repository Support (State University of New York System) · Cited by 2 · see citing works
Graphical models for brain connectivity from functional imaging data
Zheng, X., & Rajapakse, J. C. (2005) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2004.1379965 · Cited by 2 · see citing works
Multivariate statistical analysis in fMRI
Rowe, D. B., & Hoffmann, R. G. (2006) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/memb.2006.1607670 · Cited by 24 · see citing works
Path analysis of multivariate time series fMRI data with subject-level covariates
Zhang, Y. (2007) · SUNY Digital Repository Support (State University of New York System) · Cited by 3 · see citing works

Authors: Kim, J., Zhu, W., Chang, L., Bentler, P. M., & Ernst, T.
Affiliations: State University of New York (US)
University of Hawaiʻi at Mānoa (US)
University of California, Los Angeles (US)
ORCID: Kim, Jieun
Zhu, Wei
Bentler, Peter M.
Venue: Human Brain Mapping
Publisher: Wiley
Published: 2006-05-22
Volume: 28
Issue: 2
Pages: 85-93
ISSN: 1065-9471 (ISSN-L)
1097-0193
DOI: 10.1002/hbm.20259
PMC: PMC6871502
OpenAlex: W2118791853
Cited by: 236
References: 48
Citations by year: 2026: 3 · 2025: 8 · 2024: 15 · 2023: 14 · 2022: 12 · 2021: 15 · 2020: 19 · 2019: 15 · 2018: 11 · 2017: 17 · 2016: 19 · 2015: 15 · 2014: 5 · 2013: 11 · 2012: 8
FWCI: 4.78
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Human Brain Mapping (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Funders: U.S. Department of Energy
National Institutes of Health
National Institute on Aging (2 P30 AG08051-11, P30AG008051)
National Institute on Drug Abuse (K24DA016170, DA01070, DA-00017, K02DA016991, P01DA001070)
National Center for Research Resources (3M01 RR0101710-06S1)
Biological and Environmental Research
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Keywords: structural equation modeling, functional MRI, brain connectivity, effective connectivity, multivariate autoregressive model, path analysis, visual attention, dynamic causal modeling, multivariate time series
MeSH terms: Attention, Brain, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Neural Pathways, Oxygen, Photic Stimulation, Time Factors, Models, Statistical, Multivariate Analysis, Linear Models, Nonlinear Dynamics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data

Abstract

Integrity signals

Claims the paper makes

Claims other papers make about this one

Self-check

Cites

Cited by

Related

Record

About these checks