Emotions　can　be　perceived　from　both　facial　and　bodily　expressions.　Our　previous　study　has　found　the　successful　decoding　of　facial　expressions　based　on　the　functional　connectivity　(FC)　patterns.　However,　the　role　of　the　FC　patterns　in　the　recognition　of　bodily　expressions　remained　unclear,　and　no　neuroimaging　studies　have　adequately　addressed　the　question　of　whether　emotions　perceiving　from　facial　and　bodily　expressions　are　processed　rely　upon　common　or　different　neural　networks.　To　address　this,　the　present　study　collected　functional　magnetic　resonance　imaging　(fMRI)　data　from　a　block　design　experiment　with　facial　and　bodily　expression　videos　as　stimuli　(three　emotions:　anger,　fear,　and　joy),　and　conducted　multivariate　pattern　classification　analysis　based　on　the　estimated　FC　patterns.　We　found　that　in　addition　to　the　facial　expressions,　bodily　expressions　could　also　be　successfully　decoded　based　on　the　large-scale　FC　patterns.　The　emotion　classification　accuracies　for　the　facial　expressions　were　higher　than　that　for　the　bodily　expressions.　Further　contributive　FC　analysis　showed　that　emotion-discriminative　networks　were　widely　distributed　in　both　hemispheres,　containing　regions　that　ranged　from　primary　visual　areas　to　higher-level　cognitive　areas.　Moreover,　for　a　particular　emotion,　discriminative　FCs　for　facial　and　bodily　expressions　were　distinct.　Together,　our　findings　highlight　the　key　role　of　the　FC　patterns　in　the　emotion　processing,　indicating　how　large-scale　FC　patterns　reconfigure　in　processing　of　facial　and　bodily　expressions,　and　suggest　the　distributed　neural　representation　for　the　emotion　recognition.　Furthermore,　our　results　also　suggest　that　the　human　brain　employs　separate　network　representations　for　facial　and　bodily　expressions　of　the　same　emotions.　This　study　provides　new　evidence　for　the　network　representations　for　emotion　perception　and　may　further　our　understanding　of　the　potential　mechanisms　underlying　body　language　emotion　recognition.