{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:46YMRLJI64CYWDOK264O42VOIM","short_pith_number":"pith:46YMRLJI","schema_version":"1.0","canonical_sha256":"e7b0c8ad28f7058b0dcad7b8ee6aae430eb30cb9c5a4f936dcac09221861f8f1","source":{"kind":"arxiv","id":"2501.05970","version":2},"attestation_state":"computed","paper":{"title":"A Brain Age Residual Biomarker (BARB): Leveraging MRI-Based Models to Detect Latent Health Conditions in U.S. Veterans","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Arthur Bousquet, Bita Aslrousta, Mark F. Conneely, Shahrzad Jamshidi, Sugata Banerji","submitted_at":"2025-01-10T13:56:03Z","abstract_excerpt":"Age prediction using brain imaging, such as MRIs, has achieved promising results, with several studies identifying the model's residual as a potential biomarker for chronic disease states. In this study, we developed a brain age predictive model using a dataset of 1,220 U.S. veterans (18--80 years) and convolutional neural networks (CNNs) trained on two-dimensional slices of axial T2-weighted fast spin-echo and T2-weighted fluid attenuated inversion recovery MRI images. The model, incorporating a degree-3 polynomial ensemble, achieved an $R^{2}$ of 0.816 on the testing set. Images were acquire"},"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":"2501.05970","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-10T13:56:03Z","cross_cats_sorted":[],"title_canon_sha256":"4c7d5d84554b801e0ae48741ee64c9728d8329d0a24d7e53633e40da4ce17cd6","abstract_canon_sha256":"c288240813a88ff16aa9cfc156c4c655b62077d940ce2103a3e658d5b2b00181"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:51:05.164733Z","signature_b64":"BUnq7KT2aXCwIh8hF9RxZM0BPg2cWPp5Vei5OeRex2e0nKR5X/P0YPLQmbG1N9nOkEZpomdy4YM0ugfOhkYmDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e7b0c8ad28f7058b0dcad7b8ee6aae430eb30cb9c5a4f936dcac09221861f8f1","last_reissued_at":"2026-07-05T10:51:05.164147Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:51:05.164147Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Brain Age Residual Biomarker (BARB): Leveraging MRI-Based Models to Detect Latent Health Conditions in U.S. Veterans","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Arthur Bousquet, Bita Aslrousta, Mark F. Conneely, Shahrzad Jamshidi, Sugata Banerji","submitted_at":"2025-01-10T13:56:03Z","abstract_excerpt":"Age prediction using brain imaging, such as MRIs, has achieved promising results, with several studies identifying the model's residual as a potential biomarker for chronic disease states. In this study, we developed a brain age predictive model using a dataset of 1,220 U.S. veterans (18--80 years) and convolutional neural networks (CNNs) trained on two-dimensional slices of axial T2-weighted fast spin-echo and T2-weighted fluid attenuated inversion recovery MRI images. The model, incorporating a degree-3 polynomial ensemble, achieved an $R^{2}$ of 0.816 on the testing set. Images were acquire"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.05970","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/2501.05970/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":"2501.05970","created_at":"2026-07-05T10:51:05.164206+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.05970v2","created_at":"2026-07-05T10:51:05.164206+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.05970","created_at":"2026-07-05T10:51:05.164206+00:00"},{"alias_kind":"pith_short_12","alias_value":"46YMRLJI64CY","created_at":"2026-07-05T10:51:05.164206+00:00"},{"alias_kind":"pith_short_16","alias_value":"46YMRLJI64CYWDOK","created_at":"2026-07-05T10:51:05.164206+00:00"},{"alias_kind":"pith_short_8","alias_value":"46YMRLJI","created_at":"2026-07-05T10:51:05.164206+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/46YMRLJI64CYWDOK264O42VOIM","json":"https://pith.science/pith/46YMRLJI64CYWDOK264O42VOIM.json","graph_json":"https://pith.science/api/pith-number/46YMRLJI64CYWDOK264O42VOIM/graph.json","events_json":"https://pith.science/api/pith-number/46YMRLJI64CYWDOK264O42VOIM/events.json","paper":"https://pith.science/paper/46YMRLJI"},"agent_actions":{"view_html":"https://pith.science/pith/46YMRLJI64CYWDOK264O42VOIM","download_json":"https://pith.science/pith/46YMRLJI64CYWDOK264O42VOIM.json","view_paper":"https://pith.science/paper/46YMRLJI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.05970&json=true","fetch_graph":"https://pith.science/api/pith-number/46YMRLJI64CYWDOK264O42VOIM/graph.json","fetch_events":"https://pith.science/api/pith-number/46YMRLJI64CYWDOK264O42VOIM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/46YMRLJI64CYWDOK264O42VOIM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/46YMRLJI64CYWDOK264O42VOIM/action/storage_attestation","attest_author":"https://pith.science/pith/46YMRLJI64CYWDOK264O42VOIM/action/author_attestation","sign_citation":"https://pith.science/pith/46YMRLJI64CYWDOK264O42VOIM/action/citation_signature","submit_replication":"https://pith.science/pith/46YMRLJI64CYWDOK264O42VOIM/action/replication_record"}},"created_at":"2026-07-05T10:51:05.164206+00:00","updated_at":"2026-07-05T10:51:05.164206+00:00"}