Xu, Tianbo;
(2020)
Large-scale inference in the focally damaged human brain.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
Clinical outcomes in focal brain injury reflect the interactions between two distinct anatomically distributed patterns: the functional organisation of the brain and the structural distribution of injury. The challenge of understanding the functional architecture of the brain is familiar; that of understanding the lesion architecture is barely acknowledged. Yet, models of the functional consequences of focal injury are critically dependent on our knowledge of both. The studies described in this thesis seek to show how machine learning-enabled high-dimensional multivariate analysis powered by large-scale data can enhance our ability to model the relation between focal brain injury and clinical outcomes across an array of modelling applications. All studies are conducted on internationally the largest available set of MR imaging data of focal brain injury in the context of acute stroke (N=1333) and employ kernel machines at the principal modelling architecture. First, I examine lesion-deficit prediction, quantifying the ceiling on achievable predictive fidelity for high-dimensional and low-dimensional models, demonstrating the former to be substantially higher than the latter. Second, I determine the marginal value of adding unlabelled imaging data to predictive models within a semi-supervised framework, quantifying the benefit of assembling unlabelled collections of clinical imaging. Third, I compare high- and low-dimensional approaches to modelling response to therapy in two contexts: quantifying the effect of treatment at the population level (therapeutic inference) and predicting the optimal treatment in an individual patient (prescriptive inference). I demonstrate the superiority of the high-dimensional approach in both settings.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Large-scale inference in the focally damaged human brain |
| Event: | UCL (University College London) |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2020. Original content in this thesis is licensed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10114993 |
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