Background　Studies　have　identified　imaging　markers　of　binge　drinking.　Functional　connectivity　during　both　task　challenges　and　resting　state　was　shown　to　distinguish　binge　and　nonbinge　drinkers.　However,　no　studies　have　compared　the　efficacy　of　task　and　resting　data　in　the　classification.　Hypothesis　Task　outperforms　resting-state　functional　magnetic　resonance　imaging　(fMRI)　data　in　the　differentiation　of　binge　and　nonbinge　drinkers.　We　tested　the　hypothesis　via　multiple　deep　learning　algorithms.　Study　Type　Cross-sectional;　retrospective.　Population　A　total　of　149　binge　(107　men)　and　151　demographically　matched,　nonbinge　(92　men)　drinkers　curated　from　the　Human　Connectome　Project,　with　80%　randomly　selected　for　model　development　and　20%　for　validation/test.　Field　Strength/Sequence　A　3　T;　fMRI　with　a　blood　oxygen　level-dependent　(BOLD)　gradient-echo　echo-planar　sequence.　Assessment　FMRI　data　of　resting　state　and　seven　behavioral　tasks　were　acquired.　Graph　convolutional　network　(GCN),　long　short-term　memory,　convolutional,　and　recurrent　neural　network　models　were　built　to　distinguish　bingers　and　nonbingers　using　connectivity　matrices　of　8,　116,　and　268　regions　of　interest　(ROI).　Nodal　metrics　including　betweenness　centrality,　degree　centrality,　clustering　coefficient,　efficiency,　local　efficiency,　and　shortest　path　length　were　calculated　from　the　GCN　model.　Statistical　Tests　Model　performance　was　quantified　by　the　area　under　the　curve　(AUC)　in　receiver　operating　characteristic　analysis.　A　P　value　＜　0.05　was　considered　statistically　significant.　Results　Task　outperformed　resting　data　in　classification　by　approximately　8%　by　AUC　in　the　test　set.　Across　models　and　ROI　sets,　the　gambling,　motor,　language　and　working　memory　tasks,　each　with　AUC　of　0.614,　0.612,　0.605,　and　0.603,　performed　better　than　resting　data　(AUC　=　0.548).　Models　with　116　ROIs　(AUC　=　0.602)　consistently　outperformed　those　with　8　ROIs　(AUC　=　0.569).　Task　data　performed　best　with　GCN　(AUC　=　0.619).　Nodal　metrics　of　left　supplementary　motor　area　and　right　cuneus　showed　significant　group　main　effect　across　tasks.　Conclusion　Neural　responses　to　cognitive　challenges　relative　to　resting　state　better　characterize　binge　drinking.　The　performance　of　different　network　models　may　depend　on　behavioral　tasks　and　the　number　of　ROIs.　Evidence　Level　3　Technical　Efficacy　Stage　2