|Institution:||University of California – Irvine|
|Keywords:||Neurosciences; brain connectivity; computational modeling; network analysis; neurology; spinocerebellar ataxia type 6; stroke|
|Full text PDF:||http://www.escholarship.org/uc/item/78p5463x|
Identifying biomarkers of brain disease is a crucial goal of neurological care as it could translate into improved diagnoses and targeting of individualized treatments. Such biomarkers at the cellular and molecular level are difficult to detect directly in humans with current imaging methods, precluding the identification of true biomarkers of disease. Recent advancements in network analyses and computational modeling have enabled novel ways to identify biophysical parameters in imaging data. The studies in this dissertation explored these methods using two models of neurological disease: Spinocerebellar Ataxia Type 6 (SCA6), a neurodegenerative disease, in which biomarkers can help determine pre-symptomatic changes, and stroke, in which biomarkers can be used to predict long-term recovery. The general objective was to examine physiological mechanisms subjacent to individualized clinical phenotype in SCA6 and stroke, to get closer to developing concrete cures. In Chapter 1, a combination of structural equation modeling, DTI, and functional activation maps were used to detect functional and structural brain changes at different stages of severity of SCA6. An increase in cerebellar activation as well as increased connectivity between cerebellum and cerebral cortex in pre-symptomatic patients was found. A concomitant structural change was seen in the cerebral and cerebellar peduncles. These results showed the sensitivity of using network analysis in detecting brain alterations prior to symptom onset in a degenerative disease. However, these changes seen were at a network level with no ties to the underlying physiology, limiting them from being true biomarkers of SCA6. Therefore, Chapters 4-7 used a novel modeling application, The Virtual Brain, to model brain dynamics associated with stroke using models dependent on the structural brain connectivity of individuals, plus local physiological parameters. Results showed that compared to controls, individuals with stroke showed differences in several physiological parameters indicating a hyper-excitable brain state. This hyper-excitable state was associated with poor motor recovery. Manually normalizing the identified parameters in stroke cases led to a normalization of brain activity, indicating the promise of these parameters in acting as biomarkers for stroke recovery, and the utility of The Virtual Brain in furthering the goal of individualized medicine. These studies in stroke comprise some of the first forays into a new type of brain analysis, in which multiple scales are explored to bridge the brain dynamics and global level connectivity changes associated with neurologic disease with their underlying mechanistic underpinnings in individuals. In summary, the results from the studies comprising this dissertation hopefully provide evidence that novel network analyses and advances in computational modeling can herald a new era of biomarker exploration to pave the way for individualized medicine.