{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:4W5PHQMLYAV2YYBDINT4JMT2AL","short_pith_number":"pith:4W5PHQML","schema_version":"1.0","canonical_sha256":"e5baf3c18bc02bac60234367c4b27a02fbcc1f33e5ca8af9bf18d8272e899946","source":{"kind":"arxiv","id":"2608.07287","version":1},"attestation_state":"computed","paper":{"title":"A foundation-model approach to pediatric headache classification from rs-fMRI","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Allison Smith, Alyssa Lebel, Clara Moon, Guilherme S. Imai Aldeia, Julie Shulman, Navil Sethna, Scott Holmes, William G. La Cava","submitted_at":"2026-08-07T14:46:57Z","abstract_excerpt":"Headache is the most common neurological disorder in children and substantially affects quality of life. We investigated whether resting-state functional MRI (rs-fMRI) can support pediatric headache classification using machine learning. We encoded rs-fMRI data using NeuroSTORM, a recent foundation model, and fine-tuned it to distinguish healthy controls from children with headache and subsequently classify headache subtypes. We compared NeuroSTORM with a standard neuroscience approach using functional-connectivity (FC) matrices derived from brain activity as predictors. Using 189 rs-fMRI scan"},"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":"2608.07287","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-08-07T14:46:57Z","cross_cats_sorted":[],"title_canon_sha256":"fe6a32e9965a33228dc9806a7a401ca95600858516503360d66013e5d8ec9604","abstract_canon_sha256":"4eab8309078cb6f28ff5b38c1729bd281ead23b029bd9f51a39b6452d9ee7816"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-10T01:14:52.121172Z","signature_b64":"0r5aFVjQU8OdPDXXGX65UPl2FvIK09rKCozK2rvy9lzwGSTtlI8ySGB3lMMOFoHlWOgS4sO+SIWWPC0P5CRZCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e5baf3c18bc02bac60234367c4b27a02fbcc1f33e5ca8af9bf18d8272e899946","last_reissued_at":"2026-08-10T01:14:52.118737Z","signature_status":"signed_v1","first_computed_at":"2026-08-10T01:14:52.118737Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A foundation-model approach to pediatric headache classification from rs-fMRI","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Allison Smith, Alyssa Lebel, Clara Moon, Guilherme S. Imai Aldeia, Julie Shulman, Navil Sethna, Scott Holmes, William G. La Cava","submitted_at":"2026-08-07T14:46:57Z","abstract_excerpt":"Headache is the most common neurological disorder in children and substantially affects quality of life. We investigated whether resting-state functional MRI (rs-fMRI) can support pediatric headache classification using machine learning. We encoded rs-fMRI data using NeuroSTORM, a recent foundation model, and fine-tuned it to distinguish healthy controls from children with headache and subsequently classify headache subtypes. We compared NeuroSTORM with a standard neuroscience approach using functional-connectivity (FC) matrices derived from brain activity as predictors. Using 189 rs-fMRI scan"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.07287","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/2608.07287/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":"2608.07287","created_at":"2026-08-10T01:14:52.119760+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.07287v1","created_at":"2026-08-10T01:14:52.119760+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.07287","created_at":"2026-08-10T01:14:52.119760+00:00"},{"alias_kind":"pith_short_12","alias_value":"4W5PHQMLYAV2","created_at":"2026-08-10T01:14:52.119760+00:00"},{"alias_kind":"pith_short_16","alias_value":"4W5PHQMLYAV2YYBD","created_at":"2026-08-10T01:14:52.119760+00:00"},{"alias_kind":"pith_short_8","alias_value":"4W5PHQML","created_at":"2026-08-10T01:14:52.119760+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/4W5PHQMLYAV2YYBDINT4JMT2AL","json":"https://pith.science/pith/4W5PHQMLYAV2YYBDINT4JMT2AL.json","graph_json":"https://pith.science/api/pith-number/4W5PHQMLYAV2YYBDINT4JMT2AL/graph.json","events_json":"https://pith.science/api/pith-number/4W5PHQMLYAV2YYBDINT4JMT2AL/events.json","paper":"https://pith.science/paper/4W5PHQML"},"agent_actions":{"view_html":"https://pith.science/pith/4W5PHQMLYAV2YYBDINT4JMT2AL","download_json":"https://pith.science/pith/4W5PHQMLYAV2YYBDINT4JMT2AL.json","view_paper":"https://pith.science/paper/4W5PHQML","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.07287&json=true","fetch_graph":"https://pith.science/api/pith-number/4W5PHQMLYAV2YYBDINT4JMT2AL/graph.json","fetch_events":"https://pith.science/api/pith-number/4W5PHQMLYAV2YYBDINT4JMT2AL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4W5PHQMLYAV2YYBDINT4JMT2AL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4W5PHQMLYAV2YYBDINT4JMT2AL/action/storage_attestation","attest_author":"https://pith.science/pith/4W5PHQMLYAV2YYBDINT4JMT2AL/action/author_attestation","sign_citation":"https://pith.science/pith/4W5PHQMLYAV2YYBDINT4JMT2AL/action/citation_signature","submit_replication":"https://pith.science/pith/4W5PHQMLYAV2YYBDINT4JMT2AL/action/replication_record"}},"created_at":"2026-08-10T01:14:52.119760+00:00","updated_at":"2026-08-10T01:14:52.119760+00:00"}