Recently,　functional　brain　network　analysis　via　graph　neural　networks　has　achieved　state-of-the-art　results　as　it　can　directly　extract　information　from　irregular　graphs　without　any　approximation.　However,　current　methods　remain　limited　in　exploring　the　high-order　structural　information　of　brain　networks.　To　address　this　issue,　we　propose　a　hypergraph　attention　network　for　functional　brain　network　classification　(FC-HAT).　First,　we　build　a　dynamic　hypergraph　generation　phase　and　model　each　brain　network　as　a　hypergraph　to　preserve　the　high-order　information.　The　pair-wise　and　community-wise　similarities　in　functional　brain　networks　are　separately　captured　by　k　nearest　neighbors　and　k-means.　Theoretical　analysis　shows　that　the　constructed　hypergraph　exhibits　superior　spectral　properties.　Then,　we　design　a　hypergraph　attention　aggregation　phase　to　further　extract　information　in　hypergraphs.　This　includes　node　and　hyperedge　attention　layers　that　can　separately　aggregate　features　among　nodes　and　hyperedges.　Finally,　the　two　phases　are　jointly　optimized　in　an　end-to-end　manner,　which　can　dynamically　update　hypergraphs　and　node　embeddings　along　with　the　training　process.　Experimental　results　on　ABIDE-I　and　ADHD-200　demonstrate　the　effectiveness　of　FC-HAT　in　cerebral　disease　classification.　Moreover,　the　abnormal　connectivity　patterns　and　brain　regions　identified　by　FC-HAT　are　found　to　be　more　likely　to　become　biomarkers　associated　with　cerebral　diseases.(c)　2022　Elsevier　Inc.　All　rights　reserved.