Cantoral Ceballos, José AntonioCárdenas Castro, Héctor Manuel2025-07-182025-05Cárdenas Castro, H. M. (2025). Multimodal neuroimaging and explainable deep learning for characterizing brain aging: insights into biomarkers of healthy and pathological aging [Tesis maestría]. Instituto Tecnológico y de Estudios Superiores de Monterrey. Recuperado de: https://hdl.handle.net/11285/703862https://hdl.handle.net/11285/703862https://orcid.org/0000-0001-5597-939XThe aging brain undergoes complex structural and functional transformations that differ- entiate healthy aging from pathological trajectories such as dementia. This study pioneers a multimodal neuroimaging and explainable deep learning framework to characterize brain aging, identify biomarkers of neurodegeneration, and elucidate the interplay between local anatomical changes and global network reorganization. Leveraging structural MRI-derived volumetrics and graph theory-based connectivity metrics extracted from resting-state fMRI from a heterogeneous cohort of cognitively healthy individuals and patients with Dementia attributed to Alzheimer’s and non-Alzheimer’s Disease, two predictive models were devel- oped: (1) a brain-age regression model to quantify deviations from normative aging patterns and (2) a dementia classification model to distinguish pathological from healthy aging. Both models achieved robust performance (mean absolute error = 0.68 years for controls in re- gression; F1-score = 0.93 for classification), with interpretable feature contributions revealed through SHAP (SHapley Additive exPlanations) analyses. Explainable AI (SHAP) analyses revealed non-linear feature interactions and highlighted established and novel neuroanatom- ical correlates of brain aging and dementia. By synthesizing computational innovation with clinical neuroimaging, this research provides actionable biomarkers for aging research, re- fines the conceptual framework of compensatory brain reorganization, and establishes a new contribution for AI-driven precision diagnostics in neurodegenerative disorders.TextoengopenAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0BIOLOGÍA Y QUÍMICA::CIENCIAS DE LA VIDA::NEUROCIENCIAS::NEUROFISIOLOGÍAINGENIERÍA Y TECNOLOGÍA::CIENCIAS TECNOLÓGICAS::TECNOLOGÍA DE LAS TELECOMUNICACIONES::DISPOSITIVOS DE RAYOS XMEDICINA Y CIENCIAS DE LA SALUD::CIENCIAS MÉDICAS::CIENCIAS CLÍNICAS::RADIOLOGÍATechnologyScienceMultimodal neuroimaging and explainable deep learning for characterizing brain aging: insights into biomarkers of healthy and pathological agingTesis de maestríaXAIDeep LearningNeuroimagingAgingDementia