{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:FET2TJQUQS262HCVPN7YQZJDPK","short_pith_number":"pith:FET2TJQU","schema_version":"1.0","canonical_sha256":"2927a9a61484b5ed1c557b7f8865237a98b0a3514936a2cdccf6e12c94fc7bea","source":{"kind":"arxiv","id":"2211.06161","version":1},"attestation_state":"computed","paper":{"title":"Spatial Temporal Graph Convolution with Graph Structure Self-learning for Early MCI Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.LG","authors_text":"Bin Guo, Bo Liu, Fugen Zhou, Yunpeng Zhao","submitted_at":"2022-11-11T12:29:00Z","abstract_excerpt":"Graph neural networks (GNNs) have been successfully applied to early mild cognitive impairment (EMCI) detection, with the usage of elaborately designed features constructed from blood oxygen level-dependent (BOLD) time series. However, few works explored the feasibility of using BOLD signals directly as features. Meanwhile, existing GNN-based methods primarily rely on hand-crafted explicit brain topology as the adjacency matrix, which is not optimal and ignores the implicit topological organization of the brain. In this paper, we propose a spatial temporal graph convolutional network with a no"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2211.06161","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-11T12:29:00Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"4f188a745a77260e773555690aa0cab9b9a1461e0458e566ecc89360909a5db4","abstract_canon_sha256":"d6ca4e576b90d7654d73c9f85330216d0c52e13e04da5e3cf5008687d310a727"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:15:17.925632Z","signature_b64":"rP+Zs700RDzO687KLs6h+s2JvQKfoQJfvkWFTosiC7sUDlwB1JxSr/1DR8mB/adDZObgtBGWse3Q4Ua8XRKyAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2927a9a61484b5ed1c557b7f8865237a98b0a3514936a2cdccf6e12c94fc7bea","last_reissued_at":"2026-07-05T05:15:17.925221Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:15:17.925221Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spatial Temporal Graph Convolution with Graph Structure Self-learning for Early MCI Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.LG","authors_text":"Bin Guo, Bo Liu, Fugen Zhou, Yunpeng Zhao","submitted_at":"2022-11-11T12:29:00Z","abstract_excerpt":"Graph neural networks (GNNs) have been successfully applied to early mild cognitive impairment (EMCI) detection, with the usage of elaborately designed features constructed from blood oxygen level-dependent (BOLD) time series. However, few works explored the feasibility of using BOLD signals directly as features. Meanwhile, existing GNN-based methods primarily rely on hand-crafted explicit brain topology as the adjacency matrix, which is not optimal and ignores the implicit topological organization of the brain. In this paper, we propose a spatial temporal graph convolutional network with a no"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.06161","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2211.06161/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2211.06161","created_at":"2026-07-05T05:15:17.925274+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.06161v1","created_at":"2026-07-05T05:15:17.925274+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.06161","created_at":"2026-07-05T05:15:17.925274+00:00"},{"alias_kind":"pith_short_12","alias_value":"FET2TJQUQS26","created_at":"2026-07-05T05:15:17.925274+00:00"},{"alias_kind":"pith_short_16","alias_value":"FET2TJQUQS262HCV","created_at":"2026-07-05T05:15:17.925274+00:00"},{"alias_kind":"pith_short_8","alias_value":"FET2TJQU","created_at":"2026-07-05T05:15:17.925274+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FET2TJQUQS262HCVPN7YQZJDPK","json":"https://pith.science/pith/FET2TJQUQS262HCVPN7YQZJDPK.json","graph_json":"https://pith.science/api/pith-number/FET2TJQUQS262HCVPN7YQZJDPK/graph.json","events_json":"https://pith.science/api/pith-number/FET2TJQUQS262HCVPN7YQZJDPK/events.json","paper":"https://pith.science/paper/FET2TJQU"},"agent_actions":{"view_html":"https://pith.science/pith/FET2TJQUQS262HCVPN7YQZJDPK","download_json":"https://pith.science/pith/FET2TJQUQS262HCVPN7YQZJDPK.json","view_paper":"https://pith.science/paper/FET2TJQU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.06161&json=true","fetch_graph":"https://pith.science/api/pith-number/FET2TJQUQS262HCVPN7YQZJDPK/graph.json","fetch_events":"https://pith.science/api/pith-number/FET2TJQUQS262HCVPN7YQZJDPK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FET2TJQUQS262HCVPN7YQZJDPK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FET2TJQUQS262HCVPN7YQZJDPK/action/storage_attestation","attest_author":"https://pith.science/pith/FET2TJQUQS262HCVPN7YQZJDPK/action/author_attestation","sign_citation":"https://pith.science/pith/FET2TJQUQS262HCVPN7YQZJDPK/action/citation_signature","submit_replication":"https://pith.science/pith/FET2TJQUQS262HCVPN7YQZJDPK/action/replication_record"}},"created_at":"2026-07-05T05:15:17.925274+00:00","updated_at":"2026-07-05T05:15:17.925274+00:00"}