{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:LGUNT6WECLWK7KIL7EOWKORC2Q","short_pith_number":"pith:LGUNT6WE","schema_version":"1.0","canonical_sha256":"59a8d9fac412ecafa90bf91d653a22d43acc14b05b0cd1331bcbff347f854b50","source":{"kind":"arxiv","id":"1310.3863","version":2},"attestation_state":"computed","paper":{"title":"Estimating Time-varying Brain Connectivity Networks from Functional MRI Time Series","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ML","authors_text":"Christoforos Anagnostopoulos, David Sharp, Giovanni Montana, Peter Hellyer, Ricardo Pio Monti, Robert Leech","submitted_at":"2013-10-14T21:37:55Z","abstract_excerpt":"Understanding the functional architecture of the brain in terms of networks is becoming increasingly common. In most fMRI applications functional networks are assumed to be stationary, resulting in a single network estimated for the entire time course. However recent results suggest that the connectivity between brain regions is highly non-stationary even at rest. As a result, there is a need for new brain imaging methodologies that comprehensively account for the dynamic (i.e., non-stationary) nature of the fMRI data. In this work we propose the Smooth Incremental Graphical Lasso Estimation ("},"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":"1310.3863","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2013-10-14T21:37:55Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"d5c72cabe144b7b5471f1420fe7a1c620fff36d789cd69e8ba3beed4924817d1","abstract_canon_sha256":"886acf6d2e42aa084a43d16ec43daff85f033d5c9cddee7745447f6e83f09d35"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:54:21.219769Z","signature_b64":"/opTqrtWZkcA8eIUo6BGbQ1CC7KsY96Mjo4VAajKmTQI6M97qXpkSYr/gdTV6a7VFq7DRv8qVg1Vls9QUg6IBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"59a8d9fac412ecafa90bf91d653a22d43acc14b05b0cd1331bcbff347f854b50","last_reissued_at":"2026-05-18T02:54:21.219333Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:54:21.219333Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Estimating Time-varying Brain Connectivity Networks from Functional MRI Time Series","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ML","authors_text":"Christoforos Anagnostopoulos, David Sharp, Giovanni Montana, Peter Hellyer, Ricardo Pio Monti, Robert Leech","submitted_at":"2013-10-14T21:37:55Z","abstract_excerpt":"Understanding the functional architecture of the brain in terms of networks is becoming increasingly common. In most fMRI applications functional networks are assumed to be stationary, resulting in a single network estimated for the entire time course. However recent results suggest that the connectivity between brain regions is highly non-stationary even at rest. As a result, there is a need for new brain imaging methodologies that comprehensively account for the dynamic (i.e., non-stationary) nature of the fMRI data. In this work we propose the Smooth Incremental Graphical Lasso Estimation ("},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1310.3863","kind":"arxiv","version":2},"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":"1310.3863","created_at":"2026-05-18T02:54:21.219401+00:00"},{"alias_kind":"arxiv_version","alias_value":"1310.3863v2","created_at":"2026-05-18T02:54:21.219401+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1310.3863","created_at":"2026-05-18T02:54:21.219401+00:00"},{"alias_kind":"pith_short_12","alias_value":"LGUNT6WECLWK","created_at":"2026-05-18T12:27:51.066281+00:00"},{"alias_kind":"pith_short_16","alias_value":"LGUNT6WECLWK7KIL","created_at":"2026-05-18T12:27:51.066281+00:00"},{"alias_kind":"pith_short_8","alias_value":"LGUNT6WE","created_at":"2026-05-18T12:27:51.066281+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/LGUNT6WECLWK7KIL7EOWKORC2Q","json":"https://pith.science/pith/LGUNT6WECLWK7KIL7EOWKORC2Q.json","graph_json":"https://pith.science/api/pith-number/LGUNT6WECLWK7KIL7EOWKORC2Q/graph.json","events_json":"https://pith.science/api/pith-number/LGUNT6WECLWK7KIL7EOWKORC2Q/events.json","paper":"https://pith.science/paper/LGUNT6WE"},"agent_actions":{"view_html":"https://pith.science/pith/LGUNT6WECLWK7KIL7EOWKORC2Q","download_json":"https://pith.science/pith/LGUNT6WECLWK7KIL7EOWKORC2Q.json","view_paper":"https://pith.science/paper/LGUNT6WE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1310.3863&json=true","fetch_graph":"https://pith.science/api/pith-number/LGUNT6WECLWK7KIL7EOWKORC2Q/graph.json","fetch_events":"https://pith.science/api/pith-number/LGUNT6WECLWK7KIL7EOWKORC2Q/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LGUNT6WECLWK7KIL7EOWKORC2Q/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LGUNT6WECLWK7KIL7EOWKORC2Q/action/storage_attestation","attest_author":"https://pith.science/pith/LGUNT6WECLWK7KIL7EOWKORC2Q/action/author_attestation","sign_citation":"https://pith.science/pith/LGUNT6WECLWK7KIL7EOWKORC2Q/action/citation_signature","submit_replication":"https://pith.science/pith/LGUNT6WECLWK7KIL7EOWKORC2Q/action/replication_record"}},"created_at":"2026-05-18T02:54:21.219401+00:00","updated_at":"2026-05-18T02:54:21.219401+00:00"}