{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:FFBIPNHGKUIJO7N47V5DVB3ICV","short_pith_number":"pith:FFBIPNHG","schema_version":"1.0","canonical_sha256":"294287b4e65510977dbcfd7a3a87681552ac9cba645f54a26d7e776fd3420492","source":{"kind":"arxiv","id":"2501.00378","version":2},"attestation_state":"computed","paper":{"title":"STARFormer: A Novel Spatio-Temporal Aggregation Reorganization Transformer of FMRI for Brain Disorder Diagnosis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Hongjie Yan, Lei Chen, Nizhuan Wang, Wai Ting Siok, Weiming Zeng, Wenhao Dong, Yueyang Li","submitted_at":"2024-12-31T10:20:15Z","abstract_excerpt":"Many existing methods that use functional magnetic resonance imaging (fMRI) classify brain disorders, such as autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD), often overlook the integration of spatial and temporal dependencies of the blood oxygen level-dependent (BOLD) signals, which may lead to inaccurate or imprecise classification results. To solve this problem, we propose a Spatio-Temporal Aggregation eorganization ransformer (STARFormer) that effectively captures both spatial and temporal features of BOLD signals by incorporating three key modules. The r"},"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":"2501.00378","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-12-31T10:20:15Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"180e52a2df9ffcef9bfca005df5bfe24c4d8783cb6102dc569c90cc9783e5ac0","abstract_canon_sha256":"0a5f53a883f4b596777bb9359173ea48af99e9cedf84730771fb1700fe678e09"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:49:49.588782Z","signature_b64":"c7a+ArG4ZBxnxbAT+gK6oyq5gWocDKxktfoj257ewp90vRRl7RWpN7LJNYugAohZ5XCH6rjzp81r00tU8UaCAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"294287b4e65510977dbcfd7a3a87681552ac9cba645f54a26d7e776fd3420492","last_reissued_at":"2026-07-05T11:49:49.588246Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:49:49.588246Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"STARFormer: A Novel Spatio-Temporal Aggregation Reorganization Transformer of FMRI for Brain Disorder Diagnosis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Hongjie Yan, Lei Chen, Nizhuan Wang, Wai Ting Siok, Weiming Zeng, Wenhao Dong, Yueyang Li","submitted_at":"2024-12-31T10:20:15Z","abstract_excerpt":"Many existing methods that use functional magnetic resonance imaging (fMRI) classify brain disorders, such as autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD), often overlook the integration of spatial and temporal dependencies of the blood oxygen level-dependent (BOLD) signals, which may lead to inaccurate or imprecise classification results. To solve this problem, we propose a Spatio-Temporal Aggregation eorganization ransformer (STARFormer) that effectively captures both spatial and temporal features of BOLD signals by incorporating three key modules. The r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.00378","kind":"arxiv","version":2},"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/2501.00378/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":"2501.00378","created_at":"2026-07-05T11:49:49.588324+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.00378v2","created_at":"2026-07-05T11:49:49.588324+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.00378","created_at":"2026-07-05T11:49:49.588324+00:00"},{"alias_kind":"pith_short_12","alias_value":"FFBIPNHGKUIJ","created_at":"2026-07-05T11:49:49.588324+00:00"},{"alias_kind":"pith_short_16","alias_value":"FFBIPNHGKUIJO7N4","created_at":"2026-07-05T11:49:49.588324+00:00"},{"alias_kind":"pith_short_8","alias_value":"FFBIPNHG","created_at":"2026-07-05T11:49:49.588324+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2502.20769","citing_title":"Information Bottleneck-Guided Heterogeneous Graph Learning for Interpretable Neurodevelopmental Disorder Diagnosis","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FFBIPNHGKUIJO7N47V5DVB3ICV","json":"https://pith.science/pith/FFBIPNHGKUIJO7N47V5DVB3ICV.json","graph_json":"https://pith.science/api/pith-number/FFBIPNHGKUIJO7N47V5DVB3ICV/graph.json","events_json":"https://pith.science/api/pith-number/FFBIPNHGKUIJO7N47V5DVB3ICV/events.json","paper":"https://pith.science/paper/FFBIPNHG"},"agent_actions":{"view_html":"https://pith.science/pith/FFBIPNHGKUIJO7N47V5DVB3ICV","download_json":"https://pith.science/pith/FFBIPNHGKUIJO7N47V5DVB3ICV.json","view_paper":"https://pith.science/paper/FFBIPNHG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.00378&json=true","fetch_graph":"https://pith.science/api/pith-number/FFBIPNHGKUIJO7N47V5DVB3ICV/graph.json","fetch_events":"https://pith.science/api/pith-number/FFBIPNHGKUIJO7N47V5DVB3ICV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FFBIPNHGKUIJO7N47V5DVB3ICV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FFBIPNHGKUIJO7N47V5DVB3ICV/action/storage_attestation","attest_author":"https://pith.science/pith/FFBIPNHGKUIJO7N47V5DVB3ICV/action/author_attestation","sign_citation":"https://pith.science/pith/FFBIPNHGKUIJO7N47V5DVB3ICV/action/citation_signature","submit_replication":"https://pith.science/pith/FFBIPNHGKUIJO7N47V5DVB3ICV/action/replication_record"}},"created_at":"2026-07-05T11:49:49.588324+00:00","updated_at":"2026-07-05T11:49:49.588324+00:00"}