Parkinson＇s　disease　(PD)　is　a　serious　neurological　disease.　Many　studies　have　preseted　regions　of　interest　such　as　substantia　nigra　(SN)　for　PD　detection　from　magnetic　resonance　imaging　(MRI).　However,　the　SN　is　not　the　only　region　with　remarkable　tissue　changes　in　PD　MRIs.　Patients　with　Prodromal　Parkinson＇s　Disease　usually　present　with　non-motor　symptoms,　and　the　associated　brain　regions　may　show　varying　degrees　of　damage　on　imaging.　Therefore,　exploring　PD-related　regions　from　whole-brain　MRI　is　essential.　In　this　study,　we　proposed　an　interpretable　PD　detection　framework,　including　PD　classification　and　feature　region　visualization.　Specifically,　we　constructed　a　3D　ResNet　model　that　could　detect　PD　from　whole-brain　MRIs　and　discover　other　brain　regions　related　to　PD　through　3D　Gradient-weighted　Class　Activation　Mapping　(Grad-CAM)　and　Unified　Parkinson＇s　Disease　Rating　Scale　(UPDRS).　We　obtained　T1-Weighted　MRIs　from　the　Parkinson＇s　Progression　Markers　Initiative　(PPMI)　database.　The　average　classification　accuracy　of　the　5-fold　cross-validation　and　held-out　dataset　reached　96.1%　and　94.5%,　respectively.　In　addition,　we　used　the　3D　Grad-CAM　framework　to　extract　the　weight　of　the　feature　map　and　obtain　visual　interpretation.　The　heat　map　highlighted　the　regions　that　were　crucial　for　PD　classification　and　found　significant　differences　between　PD　and　HC　in　frontal　lobe　related　to　linguistic　semantic　disorders.　The　UPDRS　scores　of　PD　and　HC　on　the　linguistic　semantic　function　items　were　also　remarkably　different.　Combined　with　previous　studies,　this　work　verified　the　significance　of　the　frontal　lobe　and　proved　that　the　correlation　between　the　frontal　lobe　and　the　pathogenesis　of　PD　was　explanatory.