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Using functional magnetic resonance imaging to plan surgical resections of brain tumours


Gorgolewski,  Krzysztof J.
Max Planck Research Group Neuroanatomy and Connectivity, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Gorgolewski, K. J. (2013). Using functional magnetic resonance imaging to plan surgical resections of brain tumours. PhD Thesis.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0014-4F2B-A
Brain 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 postoperative 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 imag- ing 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 surgerieswere 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.