Modelling Structural Variations in Brain Aging

Abstract

The aging of the brain is a complex process shaped by a combination of genetic factors and environmental influences, exhibiting variations from one population to another. This thesis in- vestigates normative population-specific structural changes in the brain and explores variations in aging-related changes across different populations. The study gathers data from diverse groups, constructs individual models, and compares them through a thoughtfully designed framework. This thesis proposes as a comprehensive pipeline covering data collection, modeling, and the creation of an analysis framework. Finally, it offers an illustrative cross-population analysis, shedding light on the comparative aspects of brain aging. In our study, the Indian population is considered as the reference, and an effort is made to ad- dress gaps within this population through the creation of a population-specific database, an atlas, and an aging model to facilitate the study. Due to the challenges in data collection, we adopted a cross-sectional approach. A cross-sectional brain image database is meticulously curated for In- dian population. A sub-cortical structural atlas is created for the young population, enabling us to establish reference structural segmentation map for the Indian population. Age-specific, gender balanced, and high-resolution scans collected to create the first Indian brain aging model. Choosing cross-sectional data collection made sense because data from other populations were also mostly collected in a cross-sectional manner. Using the in-house database for Indian population and pub- licly available datasets for other populations, our inter-population analysis compares aging trends across Indian, Caucasian, Chinese, and Japanese populations. Developing an aging model from cross-sectional data presents challenges in distinguishing between cross-sectional variations and normative trends. In response, we proposed a method specifically tailored for cross-sectional data. We present a unique metric within our comprehensive aging comparison framework to differentiate between temporal and global anatomical variations across populations. This thesis has detailed a comprehensive process to compare the aspects of healthy aging across these diverse groups, ultimately concluding with a pilot study across four different populations. This framework can be readily adapted to study various research problems, exploring changes associated with different populations while considering factors beyond ethnicity, such as lifestyle, education, socio-economic factors, etc. Similar analysis frameworks and studies with multiple modalities and larger sample sizes will contribute to deriving more conclusive results.