{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:45GIANDWCPNCA43XHNOIQNUSF7","short_pith_number":"pith:45GIANDW","schema_version":"1.0","canonical_sha256":"e74c80347613da2073773b5c8836922fe27d844379c2796ccf35a472dbc96a19","source":{"kind":"arxiv","id":"1903.09412","version":1},"attestation_state":"computed","paper":{"title":"A constrained ICA-EMD Model for Group Level fMRI Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"q-bio.NC","authors_text":"Ana Maria Tom\\'e, Elmar W. Lang, Markus Goldhacker, Mark W. Greenlee, Simon Wein","submitted_at":"2019-03-22T09:15:23Z","abstract_excerpt":"Independent component analysis (ICA), as a data driven method, has shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is, that it is not compatible to the analysis of group data in general. Therefore various techniques have been proposed in order to overcome this limitation of ICA. In this paper a novel ICA-based work-flow for extracting resting state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used to generate reference signals in a data driven manner, which can be in"},"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":"1903.09412","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.NC","submitted_at":"2019-03-22T09:15:23Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"8e0f5e1bd8fe6365cebaf10c5c16b79c8da64b28e8d5daa353796d7fb2c1b722","abstract_canon_sha256":"2233487a68364ff9b73cb415253363b92035afd4c87e3fa2b676c2a4ca8b417b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:50:39.706267Z","signature_b64":"29l79eyOEFXmNt1HLz5oMAEwiA2G93OLxmkCBAT9pvhfJkCjl3XXND6+G6c5d56KFs6Ts3/Ly7WlS3ftu+1VBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e74c80347613da2073773b5c8836922fe27d844379c2796ccf35a472dbc96a19","last_reissued_at":"2026-05-17T23:50:39.705801Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:50:39.705801Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A constrained ICA-EMD Model for Group Level fMRI Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"q-bio.NC","authors_text":"Ana Maria Tom\\'e, Elmar W. Lang, Markus Goldhacker, Mark W. Greenlee, Simon Wein","submitted_at":"2019-03-22T09:15:23Z","abstract_excerpt":"Independent component analysis (ICA), as a data driven method, has shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is, that it is not compatible to the analysis of group data in general. Therefore various techniques have been proposed in order to overcome this limitation of ICA. In this paper a novel ICA-based work-flow for extracting resting state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used to generate reference signals in a data driven manner, which can be in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.09412","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":"1903.09412","created_at":"2026-05-17T23:50:39.705869+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.09412v1","created_at":"2026-05-17T23:50:39.705869+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.09412","created_at":"2026-05-17T23:50:39.705869+00:00"},{"alias_kind":"pith_short_12","alias_value":"45GIANDWCPNC","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_16","alias_value":"45GIANDWCPNCA43X","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_8","alias_value":"45GIANDW","created_at":"2026-05-18T12:33:10.108867+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/45GIANDWCPNCA43XHNOIQNUSF7","json":"https://pith.science/pith/45GIANDWCPNCA43XHNOIQNUSF7.json","graph_json":"https://pith.science/api/pith-number/45GIANDWCPNCA43XHNOIQNUSF7/graph.json","events_json":"https://pith.science/api/pith-number/45GIANDWCPNCA43XHNOIQNUSF7/events.json","paper":"https://pith.science/paper/45GIANDW"},"agent_actions":{"view_html":"https://pith.science/pith/45GIANDWCPNCA43XHNOIQNUSF7","download_json":"https://pith.science/pith/45GIANDWCPNCA43XHNOIQNUSF7.json","view_paper":"https://pith.science/paper/45GIANDW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.09412&json=true","fetch_graph":"https://pith.science/api/pith-number/45GIANDWCPNCA43XHNOIQNUSF7/graph.json","fetch_events":"https://pith.science/api/pith-number/45GIANDWCPNCA43XHNOIQNUSF7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/45GIANDWCPNCA43XHNOIQNUSF7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/45GIANDWCPNCA43XHNOIQNUSF7/action/storage_attestation","attest_author":"https://pith.science/pith/45GIANDWCPNCA43XHNOIQNUSF7/action/author_attestation","sign_citation":"https://pith.science/pith/45GIANDWCPNCA43XHNOIQNUSF7/action/citation_signature","submit_replication":"https://pith.science/pith/45GIANDWCPNCA43XHNOIQNUSF7/action/replication_record"}},"created_at":"2026-05-17T23:50:39.705869+00:00","updated_at":"2026-05-17T23:50:39.705869+00:00"}