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Bayesian mixture modeling for longitudinal fMRI connectivity studies with dropout
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. (Stat4Reg)ORCID iD: 0000-0003-2135-9963
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI).ORCID iD: 0000-0003-1524-0851
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. (Stat4Reg)ORCID iD: 0000-0003-3187-1987
Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI). Umeå University, Faculty of Medicine, Department of Radiation Sciences. Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB).
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(English)Manuscript (preprint) (Other academic)
National Category
Probability Theory and Statistics Neurosciences
Identifiers
URN: urn:nbn:se:umu:diva-155616OAI: oai:DiVA.org:umu-155616DiVA, id: diva2:1282406
Available from: 2019-01-24 Created: 2019-01-24 Last updated: 2019-04-26
In thesis
1. Methods for longitudinal brain imaging studies with dropout
Open this publication in new window or tab >>Methods for longitudinal brain imaging studies with dropout
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Metoder för longitudinella hjärnavbildningsstudier med bortfall
Abstract [en]

One of the challenges in aging research is to understand the brain mechanisms that underlie cognitive development in older adults. Such aging processes are investigated in longitudinal studies, where the within-individual changes over time are observed. However, several methodological issues exist in longitudinal analyses.  One of them is loss of participants to follow-up, which occurs when individuals drop out from the study. Such dropout should be taken into account for valid conclusions from longitudinal investigations, and this is the focus of this thesis. The developed methods are used to explore brain aging and its relation to cognition within the Betula longitudinal study of aging.

Papers I and II consider the association between changes in brain structure and cognition. In the first paper, regression analysis is used to establish the statistical significance of brain-cognition associations while accounting for dropout. Paper II develops interval estimators directly for an association as measured by partial correlation, when some data are missing. The estimators of Paper II may be used in longitudinal as well as cross-sectional studies and are not limited to brain imaging. 

Papers III and IV study functional brain connectivity, which is the statistical dependency between the functions of distinct brain regions. Typically, only brain regions with associations stronger than a predefined threshold are considered connected. However, the threshold is often arbitrarily set and does not reflect the individual differences in the overall connectivity patterns.  Paper III proposes a mixture model for brain connectivity without explicit thresholding of associations and suggests an alternative connectivity measure. Paper IV extends the mixture modeling of Paper III to a longitudinal setting with dropout and investigates the impact of ignoring the dropout mechanism on the quality of the inferences made on longitudinal connectivity changes.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2019. p. 20
Series
Statistical studies, ISSN 1100-8989 ; 54
Keywords
Missing data, nonignorable dropout, sensitivity analysis, uncertainty intervals, pattern-mixture models, aging, cognition, MRI, brain structure, resting-state functional connectivity
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:umu:diva-155680 (URN)978-91-7855-011-1 (ISBN)
Public defence
2019-02-22, Hörsal 1031, Norra Beteendevetarhuset, Umeå University, Umeå, 10:15 (English)
Opponent
Supervisors
Available from: 2019-02-01 Created: 2019-01-25 Last updated: 2019-04-26Bibliographically approved

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Gorbach, TetianaLundquist, Andersde Luna, XavierNyberg, LarsSalami, Alireza

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Gorbach, TetianaLundquist, Andersde Luna, XavierNyberg, LarsSalami, Alireza
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StatisticsUmeå Centre for Functional Brain Imaging (UFBI)Department of Radiation SciencesDepartment of Integrative Medical Biology (IMB)Wallenberg Centre for Molecular Medicine at Umeå University (WCMM)
Probability Theory and StatisticsNeurosciences

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