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  Voxel-based morphometry studies of personality: Issue of statistical model specification-effect of nuisance covariates

Hu, X., Erb, M., Ackermann, A., Martin, J., Grodd, W., & Reiterer, S. (2011). Voxel-based morphometry studies of personality: Issue of statistical model specification-effect of nuisance covariates. NeuroImage, 54(3), 1994-2005. doi:10.1016/j.neuroimage.2010.10.024.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0001-BCB8-C Version Permalink: http://hdl.handle.net/21.11116/0000-0001-BCB9-B
Genre: Journal Article

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Hu, X, Author
Erb, M1, Author              
Ackermann, A, Author
Martin, JA, Author
Grodd, W1, Author              
Reiterer, SM, Author
Affiliations:
1Department of Neuroradiology, University of Tübingen, ou_persistent22              

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 Abstract: There are an increasing number of studies on the localization of personality using voxel-based morphometry. Due to the complex analytic challenge in volumetric studies, the specification and treatment of the nuisance covariate (such as age, gender, and global measures) is currently not consistent. Here, we present a study in which we conducted voxel-based morphometry with Five-Factor Model (FFM) of personality traits (extraversion, neuroticism, openness to experience, agreeableness, and conscientiousness) that aimed to test the influence of NC specification in the determination of the results. In this study, 62 healthy subjects underwent MRI investigation and completed a German version of the FFM personality questionnaire. Voxel-based morphometry was used to investigate the correlation between the FFM personality traits and subtle brain structure. Different NC combinations were used during the model specification. Significant clusters were found only under the condition of some of the NC combinations but not under the others. In addition, we use the structure equation modeling (automated specification search from AMOS) to narrow down the possible choices of NC combinations according to a set of goodness-of-fit indices to identify well-fitted statistic models. As a final step, theoretical implications of the results are discussed, before accepting the selected model.

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 Dates: 2011-02
 Publication Status: Published in print
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1016/j.neuroimage.2010.10.024
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Title: NeuroImage
Source Genre: Journal
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Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 54 (3) Sequence Number: - Start / End Page: 1994 - 2005 Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166