Biological brain-age prediction using machine learning on neuroimaging data: links with pathophysiological mechanisms, dementia risk factors and cognitive decline
This thesis explores the links between neuroimaging-derived brain-age, which is computed using machine learning techniques, and different pathophysiological mechanisms, dementia risk factors and cognitive decline in asymptomatic individuals in the early stage of preclinical AD and in individuals wit...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/691460 |
| Acceso en línea: | http://hdl.handle.net/10803/691460 |
| Access Level: | acceso abierto |
| Palabra clave: | Brain aging Machine learning Preclinical Alzheimer’s Disease Neuroimaging CSF biomarkers Cognitive decline Dementia risk factors Pathophysiological mechanisms Envejecimiento cerebral Aprendizaje automático Enfermedad de Alzheimer Preclínica Neuroimagen Biomarcadores de LCR Deterioro cognitivo Factores de riesgo de demencia Mecanismos fisiopatológicos Envelliment cerebral Aprenentatge automàtic Malaltia d'Alzheimer Preclínica Neuroimatge Biomarcadors de LCR Deteriorament cognitiu Factors de risc de demència Mecanismes fisiopatològics 616.8 |
| Sumario: | This thesis explores the links between neuroimaging-derived brain-age, which is computed using machine learning techniques, and different pathophysiological mechanisms, dementia risk factors and cognitive decline in asymptomatic individuals in the early stage of preclinical AD and in individuals with mild cognitive impairment. We showed that we can develop a neuroimaging-based biomarker for biological brain age (the so-called brain-age), which is robust and generalizable across participants from different cohorts. We further found that having an older-appearing brain is associated with higher neuronal loss measured with plasma neurofilament light (NfL), more advanced stages of amyloid and tau pathology and carrying the APOE-ε34 allele, and higher white matter hyperintensities. Additionally, brain-age might be able to show sex differences in brain aging. Moreover, findings showed that brain-age captures the association between modifiable risk factors and longitudinal cognitive decline, particularly in individuals that do not have Aβ pathology. Furthermore, our findings showed that higher TREM2-mediated microglial reactivity, as measured with CSF sTREM2, was associated with a younger brain-age after adjusting for AD pathology. These findings contribute to the growing field of brain-age as a biomarker of biological brain aging. Our results help in understanding of the mechanisms underlying biological brain aging, cognitive decline, and different physiological brain process such as neurodegeneration, glial activation, AD pathology and cerebrovascular disease. This highlights the potential of brain-age for preventive interventions targeting cognitive decline and providing insights into aging-related mechanisms. |
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