Advances in statistical methods for large-scale binary-valued neuroimaging data
<p>White matter lesions are common in the ageing brain and their size, location, and evolution have been shown to be informative of diagnosis, treatment, or prevention of neurological conditions such as multiple sclerosis. The work presented in this thesis explores the use of voxel-based appro...
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
2021
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| _version_ | 1797107488324059136 |
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| author | Kindalova, P |
| author2 | Nichols, T |
| author_facet | Nichols, T Kindalova, P |
| author_sort | Kindalova, P |
| collection | OXFORD |
| description | <p>White matter lesions are common in the ageing brain and their size, location, and
evolution have been shown to be informative of diagnosis, treatment, or prevention of
neurological conditions such as multiple sclerosis. The work presented in this thesis explores the use of voxel-based approaches for modelling binary lesion data obtained from
brain Magnetic Resonance Imaging scans. We seek to develop methods that are computationally efficient and stable for massive datasets with very low lesion incidence, and
demonstrate the value of these methods with a real dataset to disentangle contributing
risk factors of lesion incidence.</p>
<p>Our contributions are spread across three main chapters including two published
articles and one preprint, and they could be summarised as</p>
<p><em>Chapter 2</em></p> <p>Kindalova et al. (2021a) explore whether the potential gains in estimator accuracy justify the use of a more computationally intensive spatial
modelling approach as opposed to a mass-univariate approach to modelling voxel-wise binary lesion data. A method comparison of three crosssectional lesion mapping approaches is facilitated through the development of a novel simulation framework of artificial lesion masks, which
mimics features of real lesion masks.</p>
<p><em>Chapter 3</em></p> <p>Veldsman et al. (2020) use data on 13,680 healthy ageing UK Biobank
participants at one time point to explore the effects of the individual
cerebrovascular risk factors (e.g. waist-to-hip ratio and smoking) on lesion
load and on lesion probability, which has been an obstacle in the literature
so far due to the dominating effect of hypertension and the presence of
comorbidities.</p>
<p><em>Chapter 4</em></p> <p>Kindalova et al. (2021b) adopt a generalized estimating equations approach to modelling longitudinal binary-valued outcomes. By adding a
Jeffreys-prior penalty to log-link generalized estimating equations for relative risk regression, finiteness of the estimates along with superior convergence rate are demonstrated in an extensive simulation study as well
as in a UK Biobank application on 1,578 participants with data from 2
visits.</p>
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| first_indexed | 2024-03-07T07:16:50Z |
| format | Thesis |
| id | oxford-uuid:72197588-aa42-44b1-94e7-866f782ce1c0 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:16:50Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | oxford-uuid:72197588-aa42-44b1-94e7-866f782ce1c02022-08-16T16:01:39ZAdvances in statistical methods for large-scale binary-valued neuroimaging dataThesishttp://purl.org/coar/resource_type/c_db06uuid:72197588-aa42-44b1-94e7-866f782ce1c0EnglishHyrax Deposit2021Kindalova, PNichols, TKosmidis, I<p>White matter lesions are common in the ageing brain and their size, location, and evolution have been shown to be informative of diagnosis, treatment, or prevention of neurological conditions such as multiple sclerosis. The work presented in this thesis explores the use of voxel-based approaches for modelling binary lesion data obtained from brain Magnetic Resonance Imaging scans. We seek to develop methods that are computationally efficient and stable for massive datasets with very low lesion incidence, and demonstrate the value of these methods with a real dataset to disentangle contributing risk factors of lesion incidence.</p> <p>Our contributions are spread across three main chapters including two published articles and one preprint, and they could be summarised as</p> <p><em>Chapter 2</em></p> <p>Kindalova et al. (2021a) explore whether the potential gains in estimator accuracy justify the use of a more computationally intensive spatial modelling approach as opposed to a mass-univariate approach to modelling voxel-wise binary lesion data. A method comparison of three crosssectional lesion mapping approaches is facilitated through the development of a novel simulation framework of artificial lesion masks, which mimics features of real lesion masks.</p> <p><em>Chapter 3</em></p> <p>Veldsman et al. (2020) use data on 13,680 healthy ageing UK Biobank participants at one time point to explore the effects of the individual cerebrovascular risk factors (e.g. waist-to-hip ratio and smoking) on lesion load and on lesion probability, which has been an obstacle in the literature so far due to the dominating effect of hypertension and the presence of comorbidities.</p> <p><em>Chapter 4</em></p> <p>Kindalova et al. (2021b) adopt a generalized estimating equations approach to modelling longitudinal binary-valued outcomes. By adding a Jeffreys-prior penalty to log-link generalized estimating equations for relative risk regression, finiteness of the estimates along with superior convergence rate are demonstrated in an extensive simulation study as well as in a UK Biobank application on 1,578 participants with data from 2 visits.</p> |
| spellingShingle | Kindalova, P Advances in statistical methods for large-scale binary-valued neuroimaging data |
| title | Advances in statistical methods for large-scale binary-valued neuroimaging data |
| title_full | Advances in statistical methods for large-scale binary-valued neuroimaging data |
| title_fullStr | Advances in statistical methods for large-scale binary-valued neuroimaging data |
| title_full_unstemmed | Advances in statistical methods for large-scale binary-valued neuroimaging data |
| title_short | Advances in statistical methods for large-scale binary-valued neuroimaging data |
| title_sort | advances in statistical methods for large scale binary valued neuroimaging data |
| work_keys_str_mv | AT kindalovap advancesinstatisticalmethodsforlargescalebinaryvaluedneuroimagingdata |