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- Gorbach, Tetiana, 1991-
(författare)
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Methods for longitudinal brain imaging studies with dropout
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 2. |
- Koch, Elise, 1980-
(författare)
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The genetics of schizophrenia : sex, drugs, and cognition
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cognitive impairment constitutes an important predictor of general functional outcomes in schizophrenia. Polygenic risk for schizophrenia has been linked to cognitive ability as well as brain activation during cognitive processing. Although sex differences have long been observed in schizophrenia patients, it is not known if genetic effects on cognitive phenotypes differ between males and females. Despite attempts to develop drugs that address the cognitive symptoms in schizophrenia or to investigate existing drugs with potential procognitive effects, there are currently no available medications that efficiently treat these symptoms in schizophrenia. The aim of this PhD project was to explore the genetic underpinnings of cognitive symptoms in schizophrenia, and to identify existing drugs that potentially could be used for repurposing to address these symptoms. We identified male-specific effects of polygenic risk for schizophrenia on lifespan cognitive functioning as well as brain activation during cognitive processing. Within gene networks, we identified a significant overlap between schizophrenia risk genes and genes associated with cognitive performance, and identified novel schizophrenia risk genes that are related to cognitive functioning. Utilizing gene networks incorporating gene expression data, we identified eight existing drugs that could potentially be used for repurposing to address the cognitive symptoms in schizophrenia, most of which have anti-inflammatory and neuroprotective effects. Using sex-specific gene expression data, we identified different repurposing candidates for male and female schizophrenia patients. In conclusion, the findings of this PhD project indicate that the effects of schizophrenia genetics on cognitive functioning are dependent on biological processes that differ between the sexes, and suggest that the cognitive symptoms in schizophrenia should be addressed by sex-specific pharmacological treatments.
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| 3. |
- Lundquist, Anders, 1978-
(författare)
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Contributions to the theory of unequal probability sampling
- 2009
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis consists of five papers related to the theory of unequal probability sampling from a finite population. Generally, it is assumed that we wish to make modelassisted inference, i.e. the inclusion probability for each unit in the population is prescribed before the sample is selected. The sample is then selected using some random mechanism, the sampling design. Mostly, the thesis is focused on three particular unequal probability sampling designs, the conditional Poisson (CP-) design, the Sampford design, and the Pareto design. They have different advantages and drawbacks: The CP design is a maximum entropy design but it is difficult to determine sampling parameters which yield prescribed inclusion probabilities, the Sampford design yields prescribed inclusion probabilities but may be hard to sample from, and the Pareto design makes sample selection very easy but it is very difficult to determine sampling parameters which yield prescribed inclusion probabilities. These three designs are compared probabilistically, and found to be close to each other under certain conditions. In particular the Sampford and Pareto designs are probabilistically close to each other. Some effort is devoted to analytically adjusting the CP and Pareto designs so that they yield inclusion probabilities close to the prescribed ones. The result of the adjustments are in general very good. Some iterative procedures are suggested to improve the results even further. Further, balanced unequal probability sampling is considered. In this kind of sampling, samples are given a positive probability of selection only if they satisfy some balancing conditions. The balancing conditions are given by information from auxiliary variables. Most of the attention is devoted to a slightly less general but practically important case. Also in this case the inclusion probabilities are prescribed in advance, making the choice of sampling parameters important. A complication which arises in the context of choosing sampling parameters is that certain probability distributions need to be calculated, and exact calculation turns out to be practically impossible, except for very small cases. It is proposed that Markov Chain Monte Carlo (MCMC) methods are used for obtaining approximations to the relevant probability distributions, and also for sample selection. In general, MCMC methods for sample selection does not occur very frequently in the sampling literature today, making it a fairly novel idea.
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