{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:V6R5HZINPSOACVBLD6D5X4UXT4","short_pith_number":"pith:V6R5HZIN","schema_version":"1.0","canonical_sha256":"afa3d3e50d7c9c01542b1f87dbf2979f08b4756d1d64e70d960dc052c3abfb60","source":{"kind":"arxiv","id":"2410.05341","version":3},"attestation_state":"computed","paper":{"title":"NeuroBOLT: Resting-state EEG-to-fMRI Synthesis with Multi-dimensional Feature Mapping","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"eess.IV","authors_text":"Ange Lou, Catie Chang, Daniel Moyer, Dario J. Englot, Roza G. Bayrak, Shengchao Zhang, Shiyu Wang, Soheil Kolouri, Yamin Li, Ziyuan Xu","submitted_at":"2024-10-07T02:47:55Z","abstract_excerpt":"Functional magnetic resonance imaging (fMRI) is an indispensable tool in modern neuroscience, providing a non-invasive window into whole-brain dynamics at millimeter-scale spatial resolution. However, fMRI is constrained by issues such as high operation costs and immobility. With the rapid advancements in cross-modality synthesis and brain decoding, the use of deep neural networks has emerged as a promising solution for inferring whole-brain, high-resolution fMRI features directly from electroencephalography (EEG), a more widely accessible and portable neuroimaging modality. Nonetheless, the c"},"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":"2410.05341","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-10-07T02:47:55Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"752369cdf1b6b8c21408f49aa883358ba730dc4b94ee68f537aa2be6df62aacb","abstract_canon_sha256":"dcc66fead25546e284476013a887df2affc6824144b009948c676eb097a717b4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:06:02.254292Z","signature_b64":"yiAVGNWYLlcJGyU28KX3H15M25nRcVL49koCWUJnt+6iCZGOTThVEIVrvp6KlzCuVfvvo++enRKJBW3YNYtWBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"afa3d3e50d7c9c01542b1f87dbf2979f08b4756d1d64e70d960dc052c3abfb60","last_reissued_at":"2026-07-05T12:06:02.253804Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:06:02.253804Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"NeuroBOLT: Resting-state EEG-to-fMRI Synthesis with Multi-dimensional Feature Mapping","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"eess.IV","authors_text":"Ange Lou, Catie Chang, Daniel Moyer, Dario J. Englot, Roza G. Bayrak, Shengchao Zhang, Shiyu Wang, Soheil Kolouri, Yamin Li, Ziyuan Xu","submitted_at":"2024-10-07T02:47:55Z","abstract_excerpt":"Functional magnetic resonance imaging (fMRI) is an indispensable tool in modern neuroscience, providing a non-invasive window into whole-brain dynamics at millimeter-scale spatial resolution. However, fMRI is constrained by issues such as high operation costs and immobility. With the rapid advancements in cross-modality synthesis and brain decoding, the use of deep neural networks has emerged as a promising solution for inferring whole-brain, high-resolution fMRI features directly from electroencephalography (EEG), a more widely accessible and portable neuroimaging modality. Nonetheless, the c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.05341","kind":"arxiv","version":3},"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/2410.05341/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":"2410.05341","created_at":"2026-07-05T12:06:02.253862+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.05341v3","created_at":"2026-07-05T12:06:02.253862+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.05341","created_at":"2026-07-05T12:06:02.253862+00:00"},{"alias_kind":"pith_short_12","alias_value":"V6R5HZINPSOA","created_at":"2026-07-05T12:06:02.253862+00:00"},{"alias_kind":"pith_short_16","alias_value":"V6R5HZINPSOACVBL","created_at":"2026-07-05T12:06:02.253862+00:00"},{"alias_kind":"pith_short_8","alias_value":"V6R5HZIN","created_at":"2026-07-05T12:06:02.253862+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/V6R5HZINPSOACVBLD6D5X4UXT4","json":"https://pith.science/pith/V6R5HZINPSOACVBLD6D5X4UXT4.json","graph_json":"https://pith.science/api/pith-number/V6R5HZINPSOACVBLD6D5X4UXT4/graph.json","events_json":"https://pith.science/api/pith-number/V6R5HZINPSOACVBLD6D5X4UXT4/events.json","paper":"https://pith.science/paper/V6R5HZIN"},"agent_actions":{"view_html":"https://pith.science/pith/V6R5HZINPSOACVBLD6D5X4UXT4","download_json":"https://pith.science/pith/V6R5HZINPSOACVBLD6D5X4UXT4.json","view_paper":"https://pith.science/paper/V6R5HZIN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.05341&json=true","fetch_graph":"https://pith.science/api/pith-number/V6R5HZINPSOACVBLD6D5X4UXT4/graph.json","fetch_events":"https://pith.science/api/pith-number/V6R5HZINPSOACVBLD6D5X4UXT4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V6R5HZINPSOACVBLD6D5X4UXT4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V6R5HZINPSOACVBLD6D5X4UXT4/action/storage_attestation","attest_author":"https://pith.science/pith/V6R5HZINPSOACVBLD6D5X4UXT4/action/author_attestation","sign_citation":"https://pith.science/pith/V6R5HZINPSOACVBLD6D5X4UXT4/action/citation_signature","submit_replication":"https://pith.science/pith/V6R5HZINPSOACVBLD6D5X4UXT4/action/replication_record"}},"created_at":"2026-07-05T12:06:02.253862+00:00","updated_at":"2026-07-05T12:06:02.253862+00:00"}