{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:6CM25YA2QVB54ZE3G5YNTMO2YK","short_pith_number":"pith:6CM25YA2","schema_version":"1.0","canonical_sha256":"f099aee01a8543de649b3770d9b1dac29260b325f0d80c59d06b4b4664e8bc0f","source":{"kind":"arxiv","id":"2409.19407","version":1},"attestation_state":"computed","paper":{"title":"Brain-JEPA: Brain Dynamics Foundation Model with Gradient Positioning and Spatiotemporal Masking","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"q-bio.NC","authors_text":"Christopher Li Hsian Chen, Fang Ji, Joanna Su Xian Chong, Juan Helen Zhou, Nathanael Ren Jie Tong, Ruilin Li, Thuan Tinh Nguyen, Yilei Wu, Zijian Dong","submitted_at":"2024-09-28T17:06:06Z","abstract_excerpt":"We introduce Brain-JEPA, a brain dynamics foundation model with the Joint-Embedding Predictive Architecture (JEPA). This pioneering model achieves state-of-the-art performance in demographic prediction, disease diagnosis/prognosis, and trait prediction through fine-tuning. Furthermore, it excels in off-the-shelf evaluations (e.g., linear probing) and demonstrates superior generalizability across different ethnic groups, surpassing the previous large model for brain activity significantly. Brain-JEPA incorporates two innovative techniques: Brain Gradient Positioning and Spatiotemporal Masking. "},"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":"2409.19407","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"q-bio.NC","submitted_at":"2024-09-28T17:06:06Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"b2ccee90abf802b2b9170972604201853b0c873b64061f1ce6f6fea4f6e58ea1","abstract_canon_sha256":"d0e9a9fccea1433fa95437c2faf615d7659c094d101fdf5c302e376e3af23c3d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:13:15.092952Z","signature_b64":"3pw9e4BCMh/WD8INTKJKtfv+NkMmNeWte+309mNJm8Z7eNipx4hlV52nrcGTbDVpGo/vMOtFilT47PDNBuM7Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f099aee01a8543de649b3770d9b1dac29260b325f0d80c59d06b4b4664e8bc0f","last_reissued_at":"2026-07-05T09:13:15.092454Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:13:15.092454Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Brain-JEPA: Brain Dynamics Foundation Model with Gradient Positioning and Spatiotemporal Masking","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"q-bio.NC","authors_text":"Christopher Li Hsian Chen, Fang Ji, Joanna Su Xian Chong, Juan Helen Zhou, Nathanael Ren Jie Tong, Ruilin Li, Thuan Tinh Nguyen, Yilei Wu, Zijian Dong","submitted_at":"2024-09-28T17:06:06Z","abstract_excerpt":"We introduce Brain-JEPA, a brain dynamics foundation model with the Joint-Embedding Predictive Architecture (JEPA). This pioneering model achieves state-of-the-art performance in demographic prediction, disease diagnosis/prognosis, and trait prediction through fine-tuning. Furthermore, it excels in off-the-shelf evaluations (e.g., linear probing) and demonstrates superior generalizability across different ethnic groups, surpassing the previous large model for brain activity significantly. Brain-JEPA incorporates two innovative techniques: Brain Gradient Positioning and Spatiotemporal Masking. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.19407","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/2409.19407/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":"2409.19407","created_at":"2026-07-05T09:13:15.092514+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.19407v1","created_at":"2026-07-05T09:13:15.092514+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.19407","created_at":"2026-07-05T09:13:15.092514+00:00"},{"alias_kind":"pith_short_12","alias_value":"6CM25YA2QVB5","created_at":"2026-07-05T09:13:15.092514+00:00"},{"alias_kind":"pith_short_16","alias_value":"6CM25YA2QVB54ZE3","created_at":"2026-07-05T09:13:15.092514+00:00"},{"alias_kind":"pith_short_8","alias_value":"6CM25YA2","created_at":"2026-07-05T09:13:15.092514+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.26279","citing_title":"Beyond Single-Source Cognitive Taskonomy:Multi-Source Task Relations through fMRI Transfer Learning","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01240","citing_title":"Rhamba: Region-Aware Hybrid Attention-Mamba Framework for Self-Supervised Learning in Resting-State fMRI","ref_index":27,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01240","citing_title":"Rhamba: Region-Aware Hybrid Attention-Mamba Framework for Self-Supervised Learning in Resting-State fMRI","ref_index":27,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6CM25YA2QVB54ZE3G5YNTMO2YK","json":"https://pith.science/pith/6CM25YA2QVB54ZE3G5YNTMO2YK.json","graph_json":"https://pith.science/api/pith-number/6CM25YA2QVB54ZE3G5YNTMO2YK/graph.json","events_json":"https://pith.science/api/pith-number/6CM25YA2QVB54ZE3G5YNTMO2YK/events.json","paper":"https://pith.science/paper/6CM25YA2"},"agent_actions":{"view_html":"https://pith.science/pith/6CM25YA2QVB54ZE3G5YNTMO2YK","download_json":"https://pith.science/pith/6CM25YA2QVB54ZE3G5YNTMO2YK.json","view_paper":"https://pith.science/paper/6CM25YA2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.19407&json=true","fetch_graph":"https://pith.science/api/pith-number/6CM25YA2QVB54ZE3G5YNTMO2YK/graph.json","fetch_events":"https://pith.science/api/pith-number/6CM25YA2QVB54ZE3G5YNTMO2YK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6CM25YA2QVB54ZE3G5YNTMO2YK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6CM25YA2QVB54ZE3G5YNTMO2YK/action/storage_attestation","attest_author":"https://pith.science/pith/6CM25YA2QVB54ZE3G5YNTMO2YK/action/author_attestation","sign_citation":"https://pith.science/pith/6CM25YA2QVB54ZE3G5YNTMO2YK/action/citation_signature","submit_replication":"https://pith.science/pith/6CM25YA2QVB54ZE3G5YNTMO2YK/action/replication_record"}},"created_at":"2026-07-05T09:13:15.092514+00:00","updated_at":"2026-07-05T09:13:15.092514+00:00"}