Accelerated　brain　aging　(ABA)　intricately　links　with　age-associated　neurodegenerative　and　neuropsychiatric　diseases,　emphasizing　the　critical　need　for　a　nuanced　exploration　of　heterogeneous　ABA　patterns.　This　investigation　leveraged　data　from　the　UK　Biobank　(UKB)　for　a　comprehensive　analysis,　utilizing　structural　magnetic　resonance　imaging　(sMRI),　diffusion　magnetic　resonance　imaging　(dMRI),　and　resting-state　functional　magnetic　resonance　imaging　(rsfMRI)　from　31,621　participants.　Pre-processing　employed　tools　from　the　FMRIB　Software　Library　(FSL,　version　5.0.10),　FreeSurfer,　DTIFIT,　and　MELODIC,　seamlessly　integrated　into　the　UKB　imaging　processing　pipeline.　The　Lasso　algorithm　was　employed　for　brain-age　prediction,　utilizing　derived　phenotypes　obtained　from　brain　imaging　data.　Subpopulations　of　accelerated　brain　aging　(ABA)　and　resilient　brain　aging　(RBA)　were　delineated　based　on　the　error　between　actual　age　and　predicted　brain　age.　The　ABA　subgroup　comprised　1949　subjects　(experimental　group),　while　the　RBA　subgroup　comprised　3203　subjects　(control　group).　Semi-supervised　heterogeneity　through　discriminant　analysis　(HYDRA)　refined　and　characterized　the　ABA　subgroups　based　on　distinctive　neuroimaging　features.　HYDRA　systematically　stratified　ABA　subjects　into　three　subtypes:　SubGroup　2　exhibited　extensive　gray-matter　atrophy,　distinctive　white-matter　patterns,　and　unique　connectivity　features,　displaying　lower　cognitive　performance;　SubGroup　3　demonstrated　minimal　atrophy,　superior　cognitive　performance,　and　higher　physical　activity;　and　SubGroup　1　occupied　an　intermediate　position.　This　investigation　underscores　pronounced　structural　and　functional　heterogeneity　in　ABA,　revealing　three　subtypes　and　paving　the　way　for　personalized　neuroprotective　treatments　for　age-related　neurological,　neuropsychiatric,　and　neurodegenerative　diseases.