Depression　is　one　of　the　most　common　mental　health　disorders　and　has　been　a　major　focus　of　research,　particularly　through　the　lens　of　automated　diagnostic　methods.　While　many　studies　have　explored　magnetic　resonance　imaging　techniques　separately,　the　integration　of　multiple　neuroimaging　modalities　has　received　less　attention.　To　address　this　gap,　we　introduce　a　multimodal　automatic　classification　method　that　leverages　both　resting-state　functional　magnetic　resonance　imaging　and　structural　magnetic　resonance　imaging.　Our　approach　employs　a　multi-stream　3D　Convolutional　Neural　Network　model　to　facilitate　joint　training　on　diverse　features　extracted　from　rs-fMRI　and　sMRI　data.　By　classifying　a　combined　group　of　830　MDD　patients　and　771　normal　controls　from　the　REST-meta-MDD　dataset,　our　model　achieves　an　impressive　accuracy　of　69.38%　using　a　feature　combination　of　CSF,　REHO,　and　fALFF.　This　result　signifies　a　notable　enhancement　in　classification　performance,　contributing　valuable　insights　into　the　capabilities　of　multimodal　imaging　in　MDD　diagnosis.　©　2024　SPIE.