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• W2156826612 abstract "Abstract Previous studies have investigated patterns of volumetric covariance (i.e. intercorrelation) among brain regions. Methodological issues, however, have limited the validity and generalizability of findings from these prior studies. Additionally, patterns of volumetric covariance have often been assumed to reflect the presence of structural networks, but this assumption has never been tested formally. We identified patterns of volumetric covariance, correlated these patterns with behavioral measures, and tested the hypothesis that the observed patterns of covariance reflect the presence of underlying networks. Specifically, we performed factor analysis on regional brain volumes of 99 healthy children and adults, and we correlated factor scores with scores on the Stroop Word‐Color Interference Test. We identified four latent volumetric systems in each hemisphere: dorsal cortical, limbic, posterior, and basal ganglia. The positive correlation of the right posterior system with Stroop scores suggested that larger latent volumes are detrimental to inhibitory control. We also applied Structural Equation Modeling (SEM) to our dataset ( n = 107) to test whether a model based on the anatomical pathways within cortico‐striatal‐thalamic‐cortical (CSTC) circuits accounts for the covariances observed in our sample. The degree to which SEM predicted volumetric covariance in the CSTC circuit depended on whether we controlled for age and whole brain volume in the analyses. Removing the effects of age worsened the fit of the model, pointing to a possible developmental component in establishing connections within CSTC circuits. These modeling techniques may prove useful in the future for the study of structural networks in disease populations. Hum Brain Mapp 2008. © 2007 Wiley‐Liss, Inc." @default.
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• W2156826612 date "2007-10-12" @default.
• W2156826612 modified "2023-10-13" @default.
• W2156826612 title "Latent volumetric structure of the human brain: Exploratory factor analysis and structural equation modeling of gray matter volumes in healthy children and adults" @default.
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• W2156826612 doi "https://doi.org/10.1002/hbm.20466" @default.
• W2156826612 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/2656433" @default.
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