Using Machine Learning for Prognostication of Diagnosis and Identifying Neural Correlates of Impulse Dyscontrol in Preclinical and Prodromal Dementia
Date
2019-08-22
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Introduction: Mild Behavioural impairment (MBI) is a validated syndrome that describes neuropsychiatric symptoms (NPS) in preclinical and prodromal dementia. This thesis uses machine learning (ML) and traditional statistical models to: 1) Explore the utility of NPS for predicting diagnostic status 2) Identify the neural correlates of MBI impulse dyscontrol (ID) domain. Methods: Data from the Alzheimer’s Disease Neuroimaging (ADNI) database were extracted. All subjects enrolled in ADNI were between the age of 55-90 years, English or Spanish speakers, and accompanied by study partners who completed the NPI-Q 1) To address the first objective, the logistic model tree classifier combined with an information gain feature selection was trained to predict follow-up diagnosis (normal cognition [NC], MCI, or AD-dementia) using baseline neuroimaging, neuropsychiatric, and demographic data. 2) To address the second objective, ID was identified as behavioural symptoms of agitation/aggression, irritability, and aberrant motor behaviour. Linear mixed effect models were used to assess if ID was related to regional diffusion tensor imaging (DTI) and volumetric parameters. Additionally, ML modeling used a rule-based classification algorithm combined with an information gain feature selector to predict ID using neuroimaging variables. Results: 1) MBI total scores and volume of the left hippocampus were identified as the most important features to predict follow-up diagnostic status. 2) Cingulum, fornix, inferior/superior fronto-occipital fasciculus, superior cerebellar peduncle, and corpus callosum, were the white matter tracts associated with ID. Grey matter regions associated with ID included the parahippocampal gyrus supramarginal gyrus, superior frontal regions, and hippocampus. Conclusion: NPS are early indicators of neurodegenerative disease and can be used predict cognitive decline and dementia.
Description
Keywords
Aging, Dementia, Mild Behavioural Impairment, Machine Learning, Early detection, Neuroimaging, Diffusion Tensor Imaging, Cognitive decline, Pre-dementia risk states, Neuropsychiatric symptoms
Citation
Gill, S. C. (2019). Using Machine Learning for Prognostication of Diagnosis and Identifying Neural Correlates of Impulse Dyscontrol in Preclinical and Prodromal Dementia (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.