{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:FTTPSK2W4DUO4LF77CHSUC5LEL","short_pith_number":"pith:FTTPSK2W","schema_version":"1.0","canonical_sha256":"2ce6f92b56e0e8ee2cbff88f2a0bab22ffd1f213d8a75aaad2e241039b11d70e","source":{"kind":"arxiv","id":"2508.19482","version":1},"attestation_state":"computed","paper":{"title":"MRExtrap: Longitudinal Aging of Brain MRIs using Linear Modeling in Latent Space","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.IV","authors_text":"Christian F. Baumgartner, Jaivardhan Kapoor, Jakob H. Macke","submitted_at":"2025-08-26T23:59:10Z","abstract_excerpt":"Simulating aging in 3D brain MRI scans can reveal disease progression patterns in neurological disorders such as Alzheimer's disease. Current deep learning-based generative models typically approach this problem by predicting future scans from a single observed scan. We investigate modeling brain aging via linear models in the latent space of convolutional autoencoders (MRExtrap). Our approach, MRExtrap, is based on our observation that autoencoders trained on brain MRIs create latent spaces where aging trajectories appear approximately linear. We train autoencoders on brain MRIs to create lat"},"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":"2508.19482","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2025-08-26T23:59:10Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"7d68af79dc1f87569e950e54f5d74e70210d7da628e44a0eee57e44f64835018","abstract_canon_sha256":"fe564a8c1f6133b7fbc5786a2a8c2b8485832698b6a6fbaf1fc5b6c65b272444"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:00:12.291214Z","signature_b64":"sEr4DHO1PpS+1WJU8LdN35MvyHjfeLd5znO2Crgd84Rywh1QdFmjwR6S3mRKiTdY5wQLWvQHltsEpgIdaJ3IDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2ce6f92b56e0e8ee2cbff88f2a0bab22ffd1f213d8a75aaad2e241039b11d70e","last_reissued_at":"2026-07-05T12:00:12.290752Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:00:12.290752Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MRExtrap: Longitudinal Aging of Brain MRIs using Linear Modeling in Latent Space","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.IV","authors_text":"Christian F. Baumgartner, Jaivardhan Kapoor, Jakob H. Macke","submitted_at":"2025-08-26T23:59:10Z","abstract_excerpt":"Simulating aging in 3D brain MRI scans can reveal disease progression patterns in neurological disorders such as Alzheimer's disease. Current deep learning-based generative models typically approach this problem by predicting future scans from a single observed scan. We investigate modeling brain aging via linear models in the latent space of convolutional autoencoders (MRExtrap). Our approach, MRExtrap, is based on our observation that autoencoders trained on brain MRIs create latent spaces where aging trajectories appear approximately linear. We train autoencoders on brain MRIs to create lat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.19482","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/2508.19482/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":"2508.19482","created_at":"2026-07-05T12:00:12.290833+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.19482v1","created_at":"2026-07-05T12:00:12.290833+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.19482","created_at":"2026-07-05T12:00:12.290833+00:00"},{"alias_kind":"pith_short_12","alias_value":"FTTPSK2W4DUO","created_at":"2026-07-05T12:00:12.290833+00:00"},{"alias_kind":"pith_short_16","alias_value":"FTTPSK2W4DUO4LF7","created_at":"2026-07-05T12:00:12.290833+00:00"},{"alias_kind":"pith_short_8","alias_value":"FTTPSK2W","created_at":"2026-07-05T12:00:12.290833+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/FTTPSK2W4DUO4LF77CHSUC5LEL","json":"https://pith.science/pith/FTTPSK2W4DUO4LF77CHSUC5LEL.json","graph_json":"https://pith.science/api/pith-number/FTTPSK2W4DUO4LF77CHSUC5LEL/graph.json","events_json":"https://pith.science/api/pith-number/FTTPSK2W4DUO4LF77CHSUC5LEL/events.json","paper":"https://pith.science/paper/FTTPSK2W"},"agent_actions":{"view_html":"https://pith.science/pith/FTTPSK2W4DUO4LF77CHSUC5LEL","download_json":"https://pith.science/pith/FTTPSK2W4DUO4LF77CHSUC5LEL.json","view_paper":"https://pith.science/paper/FTTPSK2W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.19482&json=true","fetch_graph":"https://pith.science/api/pith-number/FTTPSK2W4DUO4LF77CHSUC5LEL/graph.json","fetch_events":"https://pith.science/api/pith-number/FTTPSK2W4DUO4LF77CHSUC5LEL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FTTPSK2W4DUO4LF77CHSUC5LEL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FTTPSK2W4DUO4LF77CHSUC5LEL/action/storage_attestation","attest_author":"https://pith.science/pith/FTTPSK2W4DUO4LF77CHSUC5LEL/action/author_attestation","sign_citation":"https://pith.science/pith/FTTPSK2W4DUO4LF77CHSUC5LEL/action/citation_signature","submit_replication":"https://pith.science/pith/FTTPSK2W4DUO4LF77CHSUC5LEL/action/replication_record"}},"created_at":"2026-07-05T12:00:12.290833+00:00","updated_at":"2026-07-05T12:00:12.290833+00:00"}