Modelling Structural Variations in Brain Aging

Abstract

The aging of the brain is a complex process shaped by a combination of genetic factors and newlineenvironmental influences, exhibiting variations from one population to another. This thesis investigates newlinenormative population-specific structural changes in the brain and explores variations in newlineaging-related changes across different populations. The study gathers data from diverse groups, newlineconstructs individual models, and compares them through a thoughtfully designed framework. This newlinethesis proposes as a comprehensive pipeline covering data collection, modeling, and the creation of newlinean analysis framework. Finally, it offers an illustrative cross-population analysis, shedding light on newlinethe comparative aspects of brain aging. newlineIn our study, the Indian population is considered as the reference, and an effort is made to address newlinegaps within this population through the creation of a population-specific database, an atlas, newlineand an aging model to facilitate the study. Due to the challenges in data collection, we adopted newlinea cross-sectional approach. A cross-sectional brain image database is meticulously curated for Indian newlinepopulation. A sub-cortical structural atlas is created for the young population, enabling us newlineto establish reference structural segmentation map for the Indian population. Age-specific, gender newlinebalanced, and high-resolution scans collected to create the first Indian brain aging model. Choosing newlinecross-sectional data collection made sense because data from other populations were also mostly newlinecollected in a cross-sectional manner. Using the in-house database for Indian population and publicly newlineavailable datasets for other populations, our inter-population analysis compares aging trends newlineacross Indian, Caucasian, Chinese, and Japanese populations. Developing an aging model from newlinecross-sectional data presents challenges in distinguishing between cross-sectional variations and newlinenormative trends. In response, we proposed a method specifically tailored for cross-sectional data. newlineWe present a unique metric within our comprehensive aging c