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Gorbach, T. (2019). Methods for longitudinal brain imaging studies with dropout. (Doctoral dissertation). Umeå: Umeå universitet
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
Gorbach, T. & de Luna, X. (2018). Inference for partial correlation when data are missing not at random. Statistics and Probability Letters, 141, 82-89
Open this publication in new window or tab >>Inference for partial correlation when data are missing not at random
2018 (English)In: Statistics and Probability Letters, ISSN 0167-7152, E-ISSN 1879-2103, Vol. 141, p. 82-89Article in journal (Refereed) Published
Abstract [en]

We introduce uncertainty regions to perform inference on partial correlations when data are missing not at random. These uncertainty regions are shown to have a desired asymptotic coverage. Their finite sample performance is illustrated via simulations and real data example. (C) 2018 Elsevier B.V. All rights reserved.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV, 2018
Keywords
Nonignorable dropout, Uncertainty region, Change-change analysis, Brain markers, Cognition
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:umu:diva-151035 (URN)10.1016/j.spl.2018.05.027 (DOI)000440961600011 ()2-s2.0-85048717692 (Scopus ID)
Available from: 2018-09-04 Created: 2018-09-04 Last updated: 2019-04-26Bibliographically approved
Gorbach, T., Pudas, S., Lundquist, A., Orädd, G., Josefsson, M., Salami, A., . . . Nyberg, L. (2017). Longitudinal association between hippocampus atrophy and episodic-memory decline. Neurobiology of Aging, 51, 167-176
Open this publication in new window or tab >>Longitudinal association between hippocampus atrophy and episodic-memory decline
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2017 (English)In: Neurobiology of Aging, ISSN 0197-4580, E-ISSN 1558-1497, Vol. 51, p. 167-176Article in journal (Refereed) Published
Abstract [en]

There is marked variability in both onset and rate of episodic-memory decline in aging. Structural magnetic resonance imaging studies have revealed that the extent of age-related brain changes varies markedly across individuals. Past studies of whether regional atrophy accounts for episodic-memory decline in aging have yielded inconclusive findings. Here we related 15-year changes in episodic memory to 4-year changes in cortical and subcortical gray matter volume and in white-matter connectivity and lesions. In addition, changes in word fluency, fluid IQ (Block Design), and processing speed were estimated and related to structural brain changes. Significant negative change over time was observed for all cognitive and brain measures. A robust brain-cognition change-change association was observed for episodic-memory decline and atrophy in the hippocampus. This association was significant for older (65-80 years) but not middle-aged (55-60 years) participants and not sensitive to the assumption of ignorable attrition. Thus, these longitudinal findings highlight medial-temporal lobe system integrity as particularly crucial for maintaining episodic-memory functioning in older age. 

Keywords
Aging, cognitive decline, episodic memory, hippocampus, longitudinal changes, non-ignorable attrition
National Category
Probability Theory and Statistics Neurosciences
Identifiers
urn:nbn:se:umu:diva-128725 (URN)10.1016/j.neurobiolaging.2016.12.002 (DOI)000397168600018 ()28089351 (PubMedID)
Funder
Swedish Research CouncilKnut and Alice Wallenberg FoundationRagnar Söderbergs stiftelse
Available from: 2016-12-15 Created: 2016-12-13 Last updated: 2019-01-25Bibliographically approved
Gorbach, T., Lundquist, A., de Luna, X., Nyberg, L. & Salami, A.A Hierarchical Bayesian Mixture Modeling Approach for Analysis of Resting-State Functional Brain Connectivity: An Alternative to Thresholding.
Open this publication in new window or tab >>A Hierarchical Bayesian Mixture Modeling Approach for Analysis of Resting-State Functional Brain Connectivity: An Alternative to Thresholding
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(English)Manuscript (preprint) (Other academic)
National Category
Probability Theory and Statistics Neurosciences
Identifiers
urn:nbn:se:umu:diva-155614 (URN)
Available from: 2019-01-24 Created: 2019-01-24 Last updated: 2019-04-25
Gorbach, T., Lundquist, A., de Luna, X., Nyberg, L. & Salami, A.Bayesian mixture modeling for longitudinal fMRI connectivity studies with dropout.
Open this publication in new window or tab >>Bayesian mixture modeling for longitudinal fMRI connectivity studies with dropout
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(English)Manuscript (preprint) (Other academic)
National Category
Probability Theory and Statistics Neurosciences
Identifiers
urn:nbn:se:umu:diva-155616 (URN)
Available from: 2019-01-24 Created: 2019-01-24 Last updated: 2019-04-26
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-2135-9963

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