Biomarker for tracking progression of Alzheimer's disease in clinical trials

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dc.contributor.advisorMarkey, Mia Kathleen
dc.contributor.committeeMemberCowperthwaite, Matthew C
dc.contributor.committeeMemberBovik, Alan C
dc.contributor.committeeMemberGrauman, Kristen
dc.contributor.committeeMemberGhosh, Joydeep
dc.creatorVerma, Nishant
dc.date.accessioned2017-05-05T17:25:21Z
dc.date.available2017-05-05T17:25:21Z
dc.date.issued2015-08
dc.date.submittedAugust 2015
dc.date.updated2017-05-05T17:25:21Z
dc.description.abstractCurrently, there are no treatments available for mitigating the neurological effects of Alzheimer's disease. All clinical trials of disease-modifying treatments, which showed promise in animal models, have failed to show a significant treatment effect in human trials. The lack of a sensitive outcome measure and the focus on the dementia stage for investigating treatments are believed to be the primary reasons behind the failure of all clinical trials till date. The currently used outcome measure, the Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog), suffers from low sensitivity in tracking progression of cognitive impairment in clinical trials. A shift in the focus to the prodromal mild cognitive impairment (MCI) stage may help improve the efficiency of clinical trials. However, even lower sensitivity of the ADAS-Cog and an inability to specifically select progressive MCI patients limit the efficiency of clinical trials in the MCI stage. Cerebral atrophy measured on structural magnetic resonance (MR) imaging is highly promising for tracking disease progression in clinical trials. However, cerebral atrophy has not been yet approved as a valid biomarker due to the lack of an understanding behind its relationship with cognitive impairment. The focus of this dissertation spans across the two research areas of (i) developing automatic algorithms for analysis of patients' brain MR volumes, and (ii) improving the efficiency of clinical trials of disease-modifying treatments. This dissertation presents a novel knowledge-driven decision theory approach for automatic tissue segmentation of brain MR volumes, which shows better segmentation performance than the existing approaches. The remaining dissertation contributions focus at improving the efficiency of clinical trials of disease-modifying treatments. An improved scoring methodology is presented for the ADAS-Cog outcome measure, which measures cognitive impairment with better accuracy and significantly improves the sensitivity of the ADAS-Cog in the mild-to-moderate Alzheimer's disease stage. However, the ADAS-Cog continues to suffers from low sensitivity in the MCI stage due to inherent limitations of its items. For improving the efficiency of clinical trials in the MCI stage, a biomarker has been developed that combines the ADAS-Cog with cerebral atrophy for more accurate tracking of Alzheimer's progression and facilitating selection of MCI patients in clinical trials.
dc.description.departmentBiomedical Engineering
dc.format.mimetypeapplication/pdf
dc.identifierdoi:10.15781/T21G0J095
dc.identifier.urihttp://hdl.handle.net/2152/46741
dc.language.isoen
dc.subjectAlzheimer's disease
dc.subjectClinical trials
dc.subjectAlzheimer's Disease Assessment Scale-Cognitive subscale
dc.subjectItem response theory
dc.subjectADAS-Cog
dc.subjectCognitive impairment
dc.subjectMCI
dc.subjectClinical trial efficiency
dc.subjectMCI stage
dc.subjectProdromal stage
dc.subjectCerebral atrophy
dc.subjectMR volumes
dc.subjectAutomatic tissue segmentation
dc.subjectBiomarkers
dc.subjectAlzheimer’s biomarkers
dc.subjectADAS-Cog scoring methodology
dc.titleBiomarker for tracking progression of Alzheimer's disease in clinical trials
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentBiomedical Engineering
thesis.degree.disciplineBiomedical Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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