Ferreira, Francisca; (2024) Advanced computational anatomy approaches to understanding outcome variability in Functional Neurosurgery. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Functional Neurosurgery is a specialised surgical discipline that targets discrete brain circuits to interrupt or modulate abnormal neural signals, aiming to alleviate the physical manifestations of neurologic diseases. Whilst such techniques are most commonly applied in the treatment of movement disorders, there is a broad range of clinical indications and targets and successful intervention hinges on precisely targeting the right structure, in the right patient. Clinical outcomes can be variable and difficult to predict. This thesis seeks to investigate outcome variability after functional neurosurgery, using structural and quantitative Magnetic Resonance Imaging (MRI) techniques to: (1) Better localise surgical targets of interest that are typically invisible on standard MRI sequences, at the individual subject level, to allow more precise targeting compared to traditional“atlas-based targeting” approaches. (2) Define the structural brain changes linked to disease severity and response to surgery, to better characterise patient phenotype, allow outcome predictions, and ultimately help inform surgical candidate selection. This work demonstrates that connectivity-based approaches, using probabilistic tractography, allow better mapping of structures at the individual-subject level than traditional atlas-based approaches, and are largely robust to artificial sources of variance from methodological artefacts. Connectivitybased methods are combined with quantitative MRI to identify structural correlates of outcome variability across different levels of anatomical spatial scale: from surgical targeting sites or nearby changes in tissue microstructure, through patterns of connectivity, to mesoscopic changes at the whole brain level. This investigation shows that the latter is the key factor in Parkinson’s disease outcome following sub-thalamic nucleus deep brain stimulation, which suggests a role for disease sub-types in responsiveness to and timing of surgery. The same framework was used to identify a common structural basis for tremor severity that bridges different aetiological causes. Whilst the mechanisms underpinning outcome variability after functional neurosurgery are complex, I present a framework to help characterise this phenomenon, leveraging structural connectivity to better identify surgical targets in combination with advanced anatomical methods to measure brain changes at different levels of scale. These resulting properties can be exploited to inform timely surgical candidate selection, refine targeting, improve long term outcomes, and contribute to the further development of precision functional neurosurgery.

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