{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:2AYA3JHHOTOSTOU7EVS4XJYEKO","short_pith_number":"pith:2AYA3JHH","canonical_record":{"source":{"id":"1811.10520","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-26T17:23:28Z","cross_cats_sorted":[],"title_canon_sha256":"52c78f277481facf98e19df1d3a20aad498e1a5b204f4bfc6697b53de54f9ae3","abstract_canon_sha256":"8f1f4d0b9a94bf41558c36ffad4e015a510cd5facf3005e1430b634c5765a655"},"schema_version":"1.0"},"canonical_sha256":"d0300da4e774dd29ba9f2565cba704539bd2fa4cead62c2071aada43d4690f24","source":{"kind":"arxiv","id":"1811.10520","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.10520","created_at":"2026-05-17T23:59:47Z"},{"alias_kind":"arxiv_version","alias_value":"1811.10520v1","created_at":"2026-05-17T23:59:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.10520","created_at":"2026-05-17T23:59:47Z"},{"alias_kind":"pith_short_12","alias_value":"2AYA3JHHOTOS","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"2AYA3JHHOTOSTOU7","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"2AYA3JHH","created_at":"2026-05-18T12:31:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:2AYA3JHHOTOSTOU7EVS4XJYEKO","target":"record","payload":{"canonical_record":{"source":{"id":"1811.10520","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-26T17:23:28Z","cross_cats_sorted":[],"title_canon_sha256":"52c78f277481facf98e19df1d3a20aad498e1a5b204f4bfc6697b53de54f9ae3","abstract_canon_sha256":"8f1f4d0b9a94bf41558c36ffad4e015a510cd5facf3005e1430b634c5765a655"},"schema_version":"1.0"},"canonical_sha256":"d0300da4e774dd29ba9f2565cba704539bd2fa4cead62c2071aada43d4690f24","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:47.975026Z","signature_b64":"PNw8O/OWhOw8Of+8DLXV/xxovV72Sm/CmcEfJFz/LWVbjNZ/IPDNrc9LZg1Bm51oRb8y03subTZV28A59dSVAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d0300da4e774dd29ba9f2565cba704539bd2fa4cead62c2071aada43d4690f24","last_reissued_at":"2026-05-17T23:59:47.974412Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:47.974412Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.10520","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":"2026-05-17T23:59:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"u0Coo2cJg4joXa0pQULO0lhc5Pn+RgW+qDP3D9euLxoi3UOheq7JV/0UCbj/xVrAtgwQr5grpKAnhu3XpdsvBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T22:14:30.320581Z"},"content_sha256":"1f334af9f0850904d6f68da8168bea43b55f0abfae054da1e5d2d72d307ca9bc","schema_version":"1.0","event_id":"sha256:1f334af9f0850904d6f68da8168bea43b55f0abfae054da1e5d2d72d307ca9bc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:2AYA3JHHOTOSTOU7EVS4XJYEKO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Predicting Language Recovery after Stroke with Convolutional Networks on Stitched MRI","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cathy J. Price, Noor Sajid, Pranava Madhyastha, Thomas M. H. Hope, Yusuf H. Roohani","submitted_at":"2018-11-26T17:23:28Z","abstract_excerpt":"One third of stroke survivors have language difficulties. Emerging evidence suggests that their likelihood of recovery depends mainly on the damage to language centers. Thus previous research for predicting language recovery post-stroke has focused on identifying damaged regions of the brain. In this paper, we introduce a novel method where we only make use of stitched 2-dimensional cross-sections of raw MRI scans in a deep convolutional neural network setup to predict language recovery post-stroke. Our results show: a) the proposed model that only uses MRI scans has comparable performance to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.10520","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":""},"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":"2026-05-17T23:59:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rWjEkjxV3Y+B+pCFmWZpem+hNUi7cPu4St7J5mWAxpiki7moStIDQtUzy3moqkCVzqN9bXD4Qh36k4VB5lzRBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T22:14:30.321239Z"},"content_sha256":"59a0da307b07c2d979845a99cfa42b7c8da91ef7e2522090fad27b7fb944c4fc","schema_version":"1.0","event_id":"sha256:59a0da307b07c2d979845a99cfa42b7c8da91ef7e2522090fad27b7fb944c4fc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2AYA3JHHOTOSTOU7EVS4XJYEKO/bundle.json","state_url":"https://pith.science/pith/2AYA3JHHOTOSTOU7EVS4XJYEKO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2AYA3JHHOTOSTOU7EVS4XJYEKO/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":"2026-06-07T22:14:30Z","links":{"resolver":"https://pith.science/pith/2AYA3JHHOTOSTOU7EVS4XJYEKO","bundle":"https://pith.science/pith/2AYA3JHHOTOSTOU7EVS4XJYEKO/bundle.json","state":"https://pith.science/pith/2AYA3JHHOTOSTOU7EVS4XJYEKO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2AYA3JHHOTOSTOU7EVS4XJYEKO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:2AYA3JHHOTOSTOU7EVS4XJYEKO","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":"8f1f4d0b9a94bf41558c36ffad4e015a510cd5facf3005e1430b634c5765a655","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-26T17:23:28Z","title_canon_sha256":"52c78f277481facf98e19df1d3a20aad498e1a5b204f4bfc6697b53de54f9ae3"},"schema_version":"1.0","source":{"id":"1811.10520","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.10520","created_at":"2026-05-17T23:59:47Z"},{"alias_kind":"arxiv_version","alias_value":"1811.10520v1","created_at":"2026-05-17T23:59:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.10520","created_at":"2026-05-17T23:59:47Z"},{"alias_kind":"pith_short_12","alias_value":"2AYA3JHHOTOS","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"2AYA3JHHOTOSTOU7","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"2AYA3JHH","created_at":"2026-05-18T12:31:59Z"}],"graph_snapshots":[{"event_id":"sha256:59a0da307b07c2d979845a99cfa42b7c8da91ef7e2522090fad27b7fb944c4fc","target":"graph","created_at":"2026-05-17T23:59:47Z","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"},"paper":{"abstract_excerpt":"One third of stroke survivors have language difficulties. Emerging evidence suggests that their likelihood of recovery depends mainly on the damage to language centers. Thus previous research for predicting language recovery post-stroke has focused on identifying damaged regions of the brain. In this paper, we introduce a novel method where we only make use of stitched 2-dimensional cross-sections of raw MRI scans in a deep convolutional neural network setup to predict language recovery post-stroke. Our results show: a) the proposed model that only uses MRI scans has comparable performance to ","authors_text":"Cathy J. Price, Noor Sajid, Pranava Madhyastha, Thomas M. H. Hope, Yusuf H. Roohani","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-26T17:23:28Z","title":"Predicting Language Recovery after Stroke with Convolutional Networks on Stitched MRI"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.10520","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":"sha256:1f334af9f0850904d6f68da8168bea43b55f0abfae054da1e5d2d72d307ca9bc","target":"record","created_at":"2026-05-17T23:59:47Z","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":"8f1f4d0b9a94bf41558c36ffad4e015a510cd5facf3005e1430b634c5765a655","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-26T17:23:28Z","title_canon_sha256":"52c78f277481facf98e19df1d3a20aad498e1a5b204f4bfc6697b53de54f9ae3"},"schema_version":"1.0","source":{"id":"1811.10520","kind":"arxiv","version":1}},"canonical_sha256":"d0300da4e774dd29ba9f2565cba704539bd2fa4cead62c2071aada43d4690f24","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d0300da4e774dd29ba9f2565cba704539bd2fa4cead62c2071aada43d4690f24","first_computed_at":"2026-05-17T23:59:47.974412Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:59:47.974412Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PNw8O/OWhOw8Of+8DLXV/xxovV72Sm/CmcEfJFz/LWVbjNZ/IPDNrc9LZg1Bm51oRb8y03subTZV28A59dSVAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:59:47.975026Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.10520","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1f334af9f0850904d6f68da8168bea43b55f0abfae054da1e5d2d72d307ca9bc","sha256:59a0da307b07c2d979845a99cfa42b7c8da91ef7e2522090fad27b7fb944c4fc"],"state_sha256":"7f9c1e4a954d135a2726ca503ca43eab4d2b24da1af62842b40f9da3eb03266d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t18vl3D2+NKUCylcL4LlvYTmI/YSdaqFuCws5COHlLAyMSqHk/Skhyqv3ydpyR+0JZyboGhFYw5eFEFMhZleDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T22:14:30.324587Z","bundle_sha256":"c197fa0c7c7d9626f576a8c26531b30a7c9bef7dfe70f87e32513a833cea4ed7"}}