Brain heterogeneity within aging and cognitive impairment : implications for precision medicine and prevention

<p dir="ltr">This doctoral thesis investigates the heterogeneity of grey matter (GM) atrophy patterns in aging, specifically focusing on cognitively unimpaired elderly individuals and those at risk of neurodegenerative diseases, including Alzheimer's disease (AD). Through the application of advanced clustering techniques, including unsupervised random forest and longitudinal Bayesian as well as regression models, we identified distinct GM trajectories and patterns across multiple cohorts.</p><p dir="ltr">In cognitively unimpaired individuals aged 60-85, longitudinal Bayesian clustering revealed four GM atrophy trajectories, particularly in frontoparietal regions. These trajectories were associated with varying degrees of cerebrovascular burden and cognitive decline, highlighting the heterogeneity of age-related brain changes and the potential for identifying at-risk populations before the onset of cognitive symptoms. Similarly, clustering analysis in dementia-free 70-year-olds revealed five distinct GM clusters, differentiated by cortical thickness and associated with cardiometabolic and cognitive factors, underscoring the role of vascular health in brain aging.</p><p dir="ltr">Further, this thesis explores the response to lifestyle interventions and pharmacological treatments based on patterns of atrophy. In the FINGER trial, six GM patterns were identified, each showing differential responses to a multidomain lifestyle intervention aimed at preventing cognitive decline in at-risk older adults. Additionally, in a randomized clinical trial of Donepezil in individuals with mild cognitive impairment (MCI), those with hippocampal-sparing or minimal atrophy subtypes exhibited slower atrophy rates compared to other subtypes, suggesting that brain heterogeneity may influence treatment efficacy.</p><p dir="ltr">Overall, this work emphasizes the importance of understanding GM heterogeneity in aging and its implications for the early detection and prevention of cognitive decline. By identifying distinct GM patterns, this thesis contributes to more personalized approaches for interventions, enhancing the potential for precision medicine in neurodegenerative disease prevention and treatment.</p><h3>List of scientific papers</h3><p dir="ltr">I. <b>Lorenzon G,</b> Poulakis K, Mohanty R, Kivipelto M, Eriksdotter M, Ferreira D, Westman E; Alzheimer's Disease Neuroimaging Initiative. Frontoparietal atrophy trajectories in cognitively unimpaired elderly individuals using longitudinal Bayesian clustering. Comput Biol Med. 2024 Oct 1;182:109190. <a href="https://doi.org/10.1016/j.compbiomed.2024.109190">https://doi.org/10.1016/j.compbiomed.2024.109190</a><br><br>II. <b>Lorenzon G,</b> Marseglia A, Poulakis K, Rydén L, Lindberg O, Shams S, Mohanty R, Ferreira D, Kivipelto M, Eriksdotter M, Kern S, Skoog I, Westman E; Risk and protective factors associated with grey matter patterns in older adults. [Manuscript]</p><p dir="ltr"><br>III. <b>Lorenzon G,</b> Marseglia A, Mohanty R, Lehtisalo J, Poulakis K, Ngandu T, Solomon A, Kivipelto M, Westman E; Brain patterns and risk factors influencing the effect of the FINGER multimodal intervention trial. [Manuscript]</p><p dir="ltr"><br>IV. Diaz-Galvan P, <b>Lorenzon G,</b> Mohanty R, Mårtensson G, Cavedo E, Lista S, Vergallo A, Kantarci K, Hampel H, Dubois B, Grothe MJ, Ferreira D, Westman E. Differential response to donepezil in MRI subtypes of mild cognitive impairment. Alzheimers Res Ther. 2023 Jun 23;15(1):117. <a href="https://doi.org/10.1186/s13195-023-01253-2" rel="noreferrer" target="_blank">https://doi.org/10.1186/s13195-023-01253-2</a></p>