Forkert, Nils D.Mouches, Pauline2023-02-112022-06-30Mouches, P. (2022). Age-dependent analysis of cerebral structures and arteries in a large database (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.http://hdl.handle.net/1880/115851https://dx.doi.org/10.11575/PRISM/40745Aging of the population is expected to lead to a rapid increase of neurological diseases. Such diseases can progress quickly and detrimentally affect the daily life of patients. Prognosis improves with early diagnosis, but early detection is diffcult. It is crucial to be able to differentiate early stage pathological alteration from normal age-related changes. Thus, there is a need for a better understanding of brain aging and reliable biomarkers. Within that context, the overarching aim of this work is to study normal aging patterns in brain tissues and arteries using a large database of magnetic resonance imaging and angiography data, as well as cardiovascular risk factors from the whole adult life span. To do so, the objectives of this thesis are: (1) to quantify artery morphology variability among adults and identify the impact of age, sex and cardiovascular risk factors on cerebrovascular structures; (2) to combine brain tissue and artery information for biological brain age prediction; (3) to explore the impact of cardiovascular risk factors on the brain age gap, which is a biomarker representing the difference between the biological brain age and chronological age. To achieve these objectives, first, a statistical cerebrovascular atlas is generated from multi-centre adult data. Image analyses and multivariate regression methods are then employed to find associations between brain artery morphology and aging. Second, multi-modal explainable deep learning models are used to accurately estimate the biological brain age and identify predictive brain regions. Third, an exploratory causal analysis is performed to isolate the effects of individual factors on the brain age gap. The results of this work offer a novel insight on brain tissue and artery aging patterns. An in-depth analysis of the brain age gap biomarker is carried out. Novel approaches are proposed to improve brain age prediction models in terms of accuracy and explainability. Finally, innovative methods are used to study cause and effects relationships between brain aging and cardiovascular risk factors. This work aims to uncover clinically relevant findings and represents valuable methodological advancements that could be used in other neuroimaging clinical applications, for instance, to ameliorate predictive models for decision-support.enUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Engineering--BiomedicalArtificial Intelligencedoctoral thesis