{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:H7MED3EKD3WFQRESAUC7N3TWCZ","short_pith_number":"pith:H7MED3EK","canonical_record":{"source":{"id":"2103.11695","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2021-03-22T09:48:34Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"f0451e56ffc0018595b6b16c9d052fa7ed0162b48cce63f0106e3d040ea24623","abstract_canon_sha256":"b4b1f66eee8ff39659f3d00c4c01c549fdca11ef79cfacfb1cd0981281e389a5"},"schema_version":"1.0"},"canonical_sha256":"3fd841ec8a1eec5844920505f6ee7616556fa0504325ea9e0c84ab9d95032a7e","source":{"kind":"arxiv","id":"2103.11695","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.11695","created_at":"2026-07-05T02:25:42Z"},{"alias_kind":"arxiv_version","alias_value":"2103.11695v1","created_at":"2026-07-05T02:25:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.11695","created_at":"2026-07-05T02:25:42Z"},{"alias_kind":"pith_short_12","alias_value":"H7MED3EKD3WF","created_at":"2026-07-05T02:25:42Z"},{"alias_kind":"pith_short_16","alias_value":"H7MED3EKD3WFQRES","created_at":"2026-07-05T02:25:42Z"},{"alias_kind":"pith_short_8","alias_value":"H7MED3EK","created_at":"2026-07-05T02:25:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:H7MED3EKD3WFQRESAUC7N3TWCZ","target":"record","payload":{"canonical_record":{"source":{"id":"2103.11695","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2021-03-22T09:48:34Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"f0451e56ffc0018595b6b16c9d052fa7ed0162b48cce63f0106e3d040ea24623","abstract_canon_sha256":"b4b1f66eee8ff39659f3d00c4c01c549fdca11ef79cfacfb1cd0981281e389a5"},"schema_version":"1.0"},"canonical_sha256":"3fd841ec8a1eec5844920505f6ee7616556fa0504325ea9e0c84ab9d95032a7e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:25:42.260562Z","signature_b64":"Tq8eIrxkJ+UIP1y2hwUPuWlwykmKietqjgBfog9N0Yu1R/uN4INOgPNGKdjVyJrEkEh45UZqNlvX37ssdxPCCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3fd841ec8a1eec5844920505f6ee7616556fa0504325ea9e0c84ab9d95032a7e","last_reissued_at":"2026-07-05T02:25:42.260137Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:25:42.260137Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2103.11695","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:25:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JvC9hOrtAQoFEN5oHbYi9+DoC/BbKUeOEZc3g09lX7tRSHKOhepESOmJVWrWPTabxBbYrB0RTVsUU2K8+IxcDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T14:52:47.935885Z"},"content_sha256":"6487fa310a2371680a02a6a37ea188031635ee84f822abb903d8126cf4e96097","schema_version":"1.0","event_id":"sha256:6487fa310a2371680a02a6a37ea188031635ee84f822abb903d8126cf4e96097"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:H7MED3EKD3WFQRESAUC7N3TWCZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Predicting brain-age from raw T 1 -weighted Magnetic Resonance Imaging data using 3D Convolutional Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Annette Peters, Beate Endemann, Benjamin Risse, Bernhard T. Baune, B\\\"orge Schmidt, Daniel Emden, Dominik Grotegerd, Fabian Bamberg, German National Cohort Study Center Consortium, Harald Kugel, Heike Minnerup, Henry V\\\"olzke, Jan Ernsting, Jochen G. Hirsch, Jonathan Repple, Kelvin Sarink, Klaus Berger, Lale Umutlu, Lukas Fisch, Marie Beisemann, Niklas Wulms, Nils Opel, Nils R. Winter, Oyunbileg von Stackelberg, Ramona Felizitas Sowade, Ramona Leenings, Robin B\\\"ulow, Ronny Redlich, Susanne Meinert, Svenja Caspers, Thomas Kr\\\"oncke, Thoralf Niendorf, Tilo Kircher, Tim Hahn, Udo Dannlowski, Vincent Holstein","submitted_at":"2021-03-22T09:48:34Z","abstract_excerpt":"Age prediction based on Magnetic Resonance Imaging (MRI) data of the brain is a biomarker to quantify the progress of brain diseases and aging. Current approaches rely on preparing the data with multiple preprocessing steps, such as registering voxels to a standardized brain atlas, which yields a significant computational overhead, hampers widespread usage and results in the predicted brain-age to be sensitive to preprocessing parameters. Here we describe a 3D Convolutional Neural Network (CNN) based on the ResNet architecture being trained on raw, non-registered T$_ 1$-weighted MRI data of N="},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.11695","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/2103.11695/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:25:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"thp6ARg2JNY1fdVN6W9wST1ti/3aMZsCN7yvlNpx3iESgCcT9h5W3Z52+skGDO1B6vgFv38qYMR1JNgKvEnWCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T14:52:47.936503Z"},"content_sha256":"9a1ff0cfaad8ca273781505f76eeca1fb2493309a323c235357abb2455e8d5bb","schema_version":"1.0","event_id":"sha256:9a1ff0cfaad8ca273781505f76eeca1fb2493309a323c235357abb2455e8d5bb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/H7MED3EKD3WFQRESAUC7N3TWCZ/bundle.json","state_url":"https://pith.science/pith/H7MED3EKD3WFQRESAUC7N3TWCZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/H7MED3EKD3WFQRESAUC7N3TWCZ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-05T14:52:47Z","links":{"resolver":"https://pith.science/pith/H7MED3EKD3WFQRESAUC7N3TWCZ","bundle":"https://pith.science/pith/H7MED3EKD3WFQRESAUC7N3TWCZ/bundle.json","state":"https://pith.science/pith/H7MED3EKD3WFQRESAUC7N3TWCZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/H7MED3EKD3WFQRESAUC7N3TWCZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:H7MED3EKD3WFQRESAUC7N3TWCZ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"b4b1f66eee8ff39659f3d00c4c01c549fdca11ef79cfacfb1cd0981281e389a5","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2021-03-22T09:48:34Z","title_canon_sha256":"f0451e56ffc0018595b6b16c9d052fa7ed0162b48cce63f0106e3d040ea24623"},"schema_version":"1.0","source":{"id":"2103.11695","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.11695","created_at":"2026-07-05T02:25:42Z"},{"alias_kind":"arxiv_version","alias_value":"2103.11695v1","created_at":"2026-07-05T02:25:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.11695","created_at":"2026-07-05T02:25:42Z"},{"alias_kind":"pith_short_12","alias_value":"H7MED3EKD3WF","created_at":"2026-07-05T02:25:42Z"},{"alias_kind":"pith_short_16","alias_value":"H7MED3EKD3WFQRES","created_at":"2026-07-05T02:25:42Z"},{"alias_kind":"pith_short_8","alias_value":"H7MED3EK","created_at":"2026-07-05T02:25:42Z"}],"graph_snapshots":[{"event_id":"sha256:9a1ff0cfaad8ca273781505f76eeca1fb2493309a323c235357abb2455e8d5bb","target":"graph","created_at":"2026-07-05T02:25:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2103.11695/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Age prediction based on Magnetic Resonance Imaging (MRI) data of the brain is a biomarker to quantify the progress of brain diseases and aging. Current approaches rely on preparing the data with multiple preprocessing steps, such as registering voxels to a standardized brain atlas, which yields a significant computational overhead, hampers widespread usage and results in the predicted brain-age to be sensitive to preprocessing parameters. Here we describe a 3D Convolutional Neural Network (CNN) based on the ResNet architecture being trained on raw, non-registered T$_ 1$-weighted MRI data of N=","authors_text":"Annette Peters, Beate Endemann, Benjamin Risse, Bernhard T. Baune, B\\\"orge Schmidt, Daniel Emden, Dominik Grotegerd, Fabian Bamberg, German National Cohort Study Center Consortium, Harald Kugel, Heike Minnerup, Henry V\\\"olzke, Jan Ernsting, Jochen G. Hirsch, Jonathan Repple, Kelvin Sarink, Klaus Berger, Lale Umutlu, Lukas Fisch, Marie Beisemann, Niklas Wulms, Nils Opel, Nils R. Winter, Oyunbileg von Stackelberg, Ramona Felizitas Sowade, Ramona Leenings, Robin B\\\"ulow, Ronny Redlich, Susanne Meinert, Svenja Caspers, Thomas Kr\\\"oncke, Thoralf Niendorf, Tilo Kircher, Tim Hahn, Udo Dannlowski, Vincent Holstein","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2021-03-22T09:48:34Z","title":"Predicting brain-age from raw T 1 -weighted Magnetic Resonance Imaging data using 3D Convolutional Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.11695","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:6487fa310a2371680a02a6a37ea188031635ee84f822abb903d8126cf4e96097","target":"record","created_at":"2026-07-05T02:25:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"b4b1f66eee8ff39659f3d00c4c01c549fdca11ef79cfacfb1cd0981281e389a5","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2021-03-22T09:48:34Z","title_canon_sha256":"f0451e56ffc0018595b6b16c9d052fa7ed0162b48cce63f0106e3d040ea24623"},"schema_version":"1.0","source":{"id":"2103.11695","kind":"arxiv","version":1}},"canonical_sha256":"3fd841ec8a1eec5844920505f6ee7616556fa0504325ea9e0c84ab9d95032a7e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3fd841ec8a1eec5844920505f6ee7616556fa0504325ea9e0c84ab9d95032a7e","first_computed_at":"2026-07-05T02:25:42.260137Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:25:42.260137Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Tq8eIrxkJ+UIP1y2hwUPuWlwykmKietqjgBfog9N0Yu1R/uN4INOgPNGKdjVyJrEkEh45UZqNlvX37ssdxPCCA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:25:42.260562Z","signed_message":"canonical_sha256_bytes"},"source_id":"2103.11695","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6487fa310a2371680a02a6a37ea188031635ee84f822abb903d8126cf4e96097","sha256:9a1ff0cfaad8ca273781505f76eeca1fb2493309a323c235357abb2455e8d5bb"],"state_sha256":"f7a788e40d03d8cc0bec06df2dd305611d553cbe6bd49df28a8c87baedd5ad7b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SniRz8IaCWNdTNM+lO/GgBF0AiEGKKW6NwCqD5Umr0l+oh206A7SqEHAf1KS5r57mRFP14/hd/bBBzgvvRnjBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T14:52:47.940624Z","bundle_sha256":"d53c5166a7cc466fa60a12c8a069c04e24a64e0c9bbfe3f1cf1ed3765ca6f053"}}