- Title
- Predicting recovery after stroke using neuroimaging
- Creator
- Visser, Milanka Michaëla
- Relation
- University of Newcastle Research Higher Degree Thesis
- Resource Type
- thesis
- Date
- 2019
- Description
- Research Doctorate - Doctor of Philosophy (PhD)
- Description
- Background and purposes: Stroke is the leading cause of adult disability in the developed world. Outside of the first 24 hours of symptom onset, there are limited treatment options to improve the recovery of stroke survivors. A significant hindrance to the development of post stroke interventions has been the enormous heterogeneity between patients which translates into different stroke recovery trajectories and outcomes that are difficult to predict. Neuroimaging is playing an increasingly important role in the characterisation of recovery after stroke since imaging offers a direct measurement of the stroke affected cerebral tissue and can characterise the structure and function of surrounding tissue. Such insights have been shown to improve the performance of statistical models looking to predict patient recovery. For example, it has been shown that white matter integrity may play a vital role in the recovery of motor function, as those patients with reduced white matter integrity do not show similar pattern of motor recovery as those with limited or no reductions in white matter integrity of white matter tracts. Currently, most studies have investigated neural changes in respect to motor recovery and language recovery and to a lesser extent in cognition and spatial neglect. While neuroimaging has been shown to improve the characterisation of post stroke motor deficits, there is potential for imaging to provide insights into other and more common post-stroke deficient such as the onset of fatigue and cognitive decline. Post-stroke fatigue is the most common symptom of stroke survivors, yet there is currently a limited understanding of the mechanism of action behind fatigue and there are currently no available and validate treatment options. Therefore, neuroimaging may provide insights into the complex syndrome of post-stroke fatigue, similar to the motor recovery literature. The aim of this thesis is to characterise neural changes after stroke in relation to neurological deficits across multiple domains, including motor recovery, recovery of language, and visual impairments. Additionally, this thesis aimed at investigating whether neuroimaging can be used to explore potential underlying neural mechanisms of post-stroke symptom of fatigue, which has been poorly understood to date. Furthermore, this thesis aimed at investigating which stroke survivors responded positively to pharmacological treatment of post-stroke fatigue with modafinil. Methods: To identify changes in neural markers across brain regions associated with different neurological deficits, diffusion tensor imaging (DTI) was used to quantify aspects of white matter after stroke. DTI parameters and neurological impairments (measured with the National Institutes of Health Stroke Scale [NIHSS]) were collected within the first week after stroke onset, and at 1 and 3 months after stroke. Mixed-effect linear regression modelling was used to investigate changes over time, as well as differences in DTI parameter between patients with a neurological deficit and those without. To investigate potential neural markers of post-stroke fatigue, stroke survivors with varying levels of fatigue underwent clinical measurements and MRI scanning which included resting state functional imaging and high resolution T1 for structural analysis. MRI was used to obtain neural markers ranging from brain area volumes, stroke lesion characteristics and functional connectivity. Linear regression modelling was used to identify clinical and neuroimaging correlates of post-stroke fatigue. To identify whether neuroimaging could play a role in the exploration of mechanisms underlying pharmacological intervention within modafinil for post-stroke fatigue two separate studies were conducted. The effects of modafinil treatment and an associated reduction of fatigue were investigated using independent component analysis (with permutation tests) as well as a region-of-interest based analysis of the fronto-striato-thalamic network. Additionally, a predictive model of treatment response to modafinil for the reduction of fatigue was developed using neural and clinical variables. Results: In a study of 14 participants (mean age: 71, median NIHSS-score: 9 [interquartile range=5-13.3]), DTI parameters showed significant changes over time, however their direction was not consistent across brain regions. Additionally, DTI parameter changes did not consistently differ between patients with a neurological deficit and those without. Severe post-stroke fatigue (N=50) was associated with reduced functional connectivity of the default mode network (β=-0.343, p=0.002) and salience network (β=-0.340, p=0.003). Alleviation of fatigue induced by modafinil treatment resulted in a decrease of functional connectivity in the fronto-parietal, sensory network and mesolimbic network as well as increased functional connectivity was observed within the fronto-striato-thalamic network (N=12; p<0.05). Low functional connectivity within the fronto-striato-thalamic network, together with high baseline levels of fatigue were significant predictors of modafinil-induced alleviation of fatigue (connectivity: β=-0.424, p=0.008, baseline fatigue: β=0.576, p=0.006, R²=0.53; N=20). Conclusion: Varying patterns were observed during the recovery of neurological deficits after stroke and these findings were not modulated by the presence of neurological deficits, indicating that inferences regarding a global pattern of recovery cannot be made using DTI imaging as they can reflect opposing mechanisms such as atrophy and compensatory strategies. Therefore, DTI may not be an ideal method to assess general post stroke deficient such as motor weakness or visual impairment in a board cohort. However, neuroimaging (specifically functional connectivity could aid in the identification of processes that underlie post-stroke fatigue, and the alleviation of post-stroke fatigue with modafinil treatment. Additionally, neural markers were identified that could improve the prediction of those stroke survivors that respond well to modafinil for the treatment of post-stroke fatigue. These studies demonstrate that neuroimaging could play an important role in the identification of neural markers of post-stroke symptoms which can be used to predict treatment gains, however what assessment and when they are performed requires a robust understanding of both MRI and stroke recovery.
- Subject
- stroke; neuroimaging; magnetic resonance imaging; prediction models; thesis by publication
- Identifier
- http://hdl.handle.net/1959.13/1403436
- Identifier
- uon:35172
- Rights
- Copyright 2019 Milanka Michaëla Visser
- Language
- eng
- Full Text
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| View Details Download | ATTACHMENT01 | Thesis | 9 MB | Adobe Acrobat PDF | View Details Download | ||
| View Details Download | ATTACHMENT02 | Abstract | 268 KB | Adobe Acrobat PDF | View Details Download |