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dc.contributor.advisorvan Rossum, Marken
dc.contributor.advisorPernet, Cyrilen
dc.contributor.advisorStorkey, Amosen
dc.contributor.advisorBastin, Marken
dc.contributor.authorGorgolewski, Krzysztof Jaceken
dc.date.accessioned2013-09-25T12:52:59Z
dc.date.available2013-09-25T12:52:59Z
dc.date.issued2013-07-02
dc.identifier.urihttp://hdl.handle.net/1842/7861
dc.description.abstractBrain tumours, even though rare, are one of the deadliest types of cancer. The five year survival rate for the most malignant type of brain tumours is below 5%. Modern medicine provides many options for treating brain cancer such as radiotherapy and chemotherapy. However, one of the most effective ways of fighting the disease is surgical resection. During such a procedure the tumour is partially or completely removed. Unfortunately, even after a complete resection some tumourous tissue is left behind and can grow back or metastasise to a different location in the brain. It has been shown, however, that more aggressive resections lead to longer life expectancy. This does not come without risks. Depending on tumour location, extensive resections can lead to transient or permanent post-operative neurological deficits. Therefore, when planning a procedure, the neurosurgeon needs to find balance between extending patients life and maintaining its quality. Recent developments in Magnetic Resonance Imaging (MRI) fueled by the field of human cognitive neuroscience have led to improved methods of non-invasive imaging of the brain function. Such methods allow the creation of functional brain maps of populations or individual subjects. Adapting this technique to the clinical environment enables the assessment of the risks and to plan surgical procedures. The following work aims at improving the use of functional MRI with a specific clinical goal in mind. The thesis begins with description of etiology, epidemiology and treatment options for brain tumours. This is followed by a description of MRI and related data processing methods, which leads to introduction of a new technique for thresholding statistical maps which improves upon existing solutions by adapting to the nature of the problem at hand. In contrast to methods used in cognitive neuroscience our approach is optimized to work on single subjects and maintain a balance between false positive and false negative errors. This balance is crucial for accurate assessment of the risk of a surgical procedure. Using this method a test-retest reliability study was performed to assess five different behavioural paradigms and scanning parameters. This experiment was performed on healthy controls and was aimed at selecting which paradigms produce reliable results and therefore can be used for presurgical planning. This allowed the creation of a battery of task that was applied to glioma patients. Functional maps created before the surgeries were compared with electrocortical stimulation performed during the surgeries. The final contribution of this work focuses on technical aspects of performing neuroimaging data analysis. A novel data processing framework which provides means for rapid prototyping and easy translation and adaptation of already existing methods taken from cognitive neuroscience field is introduced. The framework enables fully automatic processing of patient data and therefore greatly reduced costs while maintaining quality control. A discussion of future directions and challenges in using functional MRI for presurgical planning concludes the thesis.en
dc.contributor.sponsorEngineering and Physical Sciences Research Council (EPSRC)en
dc.language.isoen
dc.publisherThe University of Edinburghen
dc.relation.hasversionGhosh, S., Burns, C., Clark, D., Gorgolewski, K., Halchenko, Y., Madison, C., Tungaraza, R., and Millman, K. J. (2010). Nipype: Opensource platform for unified and replicable interaction with existing neuroimaging tools. In 16th Annual Meeting of the Organization for Human Brain Mapping.en
dc.relation.hasversionGorgolewski, K., Bastin, M., Rigolo, L., Soleiman, H. A., Pernet, C., Storkey, A., and Golby, A. J. (2011a). Pitfalls of Thresholding Statistical Maps in Presurgical fMRI Mapping. In Proceedings 19th Scientific Meeting, International Society for Magnetic Resonance in Medicine, Montreal, Canada.en
dc.relation.hasversionGorgolewski, K., Burns, C. D., Madison, C., Clark, D., Halchenko, Y. O., Waskom, M. L., and Ghosh, S. S. (2011b). Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python. Frontiers in Neuroinformatics, 5(August):13.en
dc.relation.hasversionGorgolewski, K., Halchenko, Y., Notter, M., Varoquaux, G., Waskom, M., Ziegler, E., and Ghosh, S. (2012a). Nipype 2012: more packages, reusable workflows and reproducible science. In 18th Annual Meeting of the Organization for Human Brain Mapping, Beijing, China.en
dc.relation.hasversionGorgolewski, K., Storkey, A., Bastin, M., and Pernet, C. (2011c). Using a Combination of a Mixture Model and Topological FDR in the Context of Presurgical Planning. In 17th Annual Meeting of the Organization for Human Brain Mapping, Quebec, Canada.en
dc.relation.hasversionGorgolewski, K., Storkey, A., Bastin, M., and Pernet, C. (2012b). Reliability of single subject fMRI in the context of presurgical planning. In 18th Annual Meeting of the Organization for Human Brain Mapping, Beijing, China.en
dc.relation.hasversionGorgolewski, K. J., Storkey, A. J., Bastin, M. E., and Pernet, C. (2012c). Adaptive thresholding for reliable topological inference in single subject fMRI analysis. In ICML Workshop on Statistics, Machine Learning and Neuroscience, Edinburgh, UK.en
dc.relation.hasversionGorgolewski, K. J., Storkey, A. J., Bastin, M. E., and Pernet, C. R. (2012d). Adaptive thresholding for reliable topological inference in single subject fMRI analysis. Frontiers in Human Neuroscience, 6(August):1–14.en
dc.relation.hasversionGorgolewski, K. J., Storkey, A. J., Bastin, M. E., Whittle, I., and Pernet, C. (2013). Single subject fMRI test-retest reliability metrics and confounding factors. NeuroImage, 69:231–243.en
dc.subjectneuroimagingen
dc.subjectFMRIen
dc.subjectMRIen
dc.subjectbrain tumoursen
dc.subjectcanceren
dc.titleUsing functional magnetic resonance imaging to plan surgical resections of brain tumoursen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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