{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:LGSWBKQFXVKN22NQBUTV7LILTC","short_pith_number":"pith:LGSWBKQF","schema_version":"1.0","canonical_sha256":"59a560aa05bd54dd69b00d275fad0b98bf1d5534df7378e18c39997dcb3f33b7","source":{"kind":"arxiv","id":"1208.3766","version":1},"attestation_state":"computed","paper":{"title":"A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.QM"],"primary_cat":"q-bio.NC","authors_text":"D. Marinazzo, G. Wu, H. Chen, J. Ding, S. Stramaglia, W.Liao","submitted_at":"2012-08-18T17:02:26Z","abstract_excerpt":"A great improvement to the insight on brain function that we can get from fMRI data can come from effective connectivity analysis, in which the flow of information between even remote brain regions is inferred by the parameters of a predictive dynamical model. As opposed to biologically inspired models, some techniques as Granger causality (GC) are purely data-driven and rely on statistical prediction and temporal precedence. While powerful and widely applicable, this approach could suffer from two main limitations when applied to BOLD fMRI data: confounding effect of hemodynamic response func"},"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":"1208.3766","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.NC","submitted_at":"2012-08-18T17:02:26Z","cross_cats_sorted":["q-bio.QM"],"title_canon_sha256":"adc9c8158fedab06c1284210cb7cbe8d6ba9d27183698440a8679e04330175e9","abstract_canon_sha256":"3c1be4cf451baf4fe6b8489064ccf2b1785c8be699229f8d86eecbfa3cb5e8a3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:48:26.305012Z","signature_b64":"sSOTWV6VIFt1cC1apbQVNQ7UCWnF/ThiMXNprtE+n+N3xErxG7ezaYOOMv1JgqIC5Zb8C4y0v5quKzY3RAbvAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"59a560aa05bd54dd69b00d275fad0b98bf1d5534df7378e18c39997dcb3f33b7","last_reissued_at":"2026-05-18T03:48:26.304206Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:48:26.304206Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.QM"],"primary_cat":"q-bio.NC","authors_text":"D. Marinazzo, G. Wu, H. Chen, J. Ding, S. Stramaglia, W.Liao","submitted_at":"2012-08-18T17:02:26Z","abstract_excerpt":"A great improvement to the insight on brain function that we can get from fMRI data can come from effective connectivity analysis, in which the flow of information between even remote brain regions is inferred by the parameters of a predictive dynamical model. As opposed to biologically inspired models, some techniques as Granger causality (GC) are purely data-driven and rely on statistical prediction and temporal precedence. While powerful and widely applicable, this approach could suffer from two main limitations when applied to BOLD fMRI data: confounding effect of hemodynamic response func"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1208.3766","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1208.3766","created_at":"2026-05-18T03:48:26.304336+00:00"},{"alias_kind":"arxiv_version","alias_value":"1208.3766v1","created_at":"2026-05-18T03:48:26.304336+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1208.3766","created_at":"2026-05-18T03:48:26.304336+00:00"},{"alias_kind":"pith_short_12","alias_value":"LGSWBKQFXVKN","created_at":"2026-05-18T12:27:14.488303+00:00"},{"alias_kind":"pith_short_16","alias_value":"LGSWBKQFXVKN22NQ","created_at":"2026-05-18T12:27:14.488303+00:00"},{"alias_kind":"pith_short_8","alias_value":"LGSWBKQF","created_at":"2026-05-18T12:27:14.488303+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/LGSWBKQFXVKN22NQBUTV7LILTC","json":"https://pith.science/pith/LGSWBKQFXVKN22NQBUTV7LILTC.json","graph_json":"https://pith.science/api/pith-number/LGSWBKQFXVKN22NQBUTV7LILTC/graph.json","events_json":"https://pith.science/api/pith-number/LGSWBKQFXVKN22NQBUTV7LILTC/events.json","paper":"https://pith.science/paper/LGSWBKQF"},"agent_actions":{"view_html":"https://pith.science/pith/LGSWBKQFXVKN22NQBUTV7LILTC","download_json":"https://pith.science/pith/LGSWBKQFXVKN22NQBUTV7LILTC.json","view_paper":"https://pith.science/paper/LGSWBKQF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1208.3766&json=true","fetch_graph":"https://pith.science/api/pith-number/LGSWBKQFXVKN22NQBUTV7LILTC/graph.json","fetch_events":"https://pith.science/api/pith-number/LGSWBKQFXVKN22NQBUTV7LILTC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LGSWBKQFXVKN22NQBUTV7LILTC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LGSWBKQFXVKN22NQBUTV7LILTC/action/storage_attestation","attest_author":"https://pith.science/pith/LGSWBKQFXVKN22NQBUTV7LILTC/action/author_attestation","sign_citation":"https://pith.science/pith/LGSWBKQFXVKN22NQBUTV7LILTC/action/citation_signature","submit_replication":"https://pith.science/pith/LGSWBKQFXVKN22NQBUTV7LILTC/action/replication_record"}},"created_at":"2026-05-18T03:48:26.304336+00:00","updated_at":"2026-05-18T03:48:26.304336+00:00"}