The　advantages　of　structural　magnetic　resonance　imaging　(sMRI)-based　multidimen-sional　tensor　morphological　features　in　brain　disease　research　are　the　high　sensitivity　and　resolution　of　sMRI　to　comprehensively　capture　the　key　structural　information　and　quantify　the　structural　deformation.　However,　its　direct　application　to　regression　analysis　of　high-dimensional　small-sample　data　for　brain　age　prediction　may　cause　＂dimensional　catastrophe＂.　Therefore,　this　paper　develops　a　brain　age　prediction　method　for　high-dimensional　small-sample　data　based　on　sMRI　multidimen-sional　morphological　features　and　constructs　brain　age　gap　estimation　(BrainAGE)　biomarkers　to　quantify　abnormal　aging　of　key　subcortical　structures　by　extracting　subcortical　structural　features　for　brain　age　prediction,　which　can　then　establish　statistical　analysis　models　to　help　diagnose　Alzheimer＇s　disease　and　monitor　health　conditions,　intervening　at　the　preclinical　stage.