{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:NWKAWM5LTGCOL7E5A4UFFDLYZ7","short_pith_number":"pith:NWKAWM5L","schema_version":"1.0","canonical_sha256":"6d940b33ab9984e5fc9d0728528d78cffaeed389fba1e1a4d085b1b6b5bf9d6f","source":{"kind":"arxiv","id":"2203.04455","version":1},"attestation_state":"computed","paper":{"title":"Pruning Graph Convolutional Networks to select meaningful graph frequencies for fMRI decoding","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Bastien Pasdeloup, Giulia Lioi, Hugo Tessier, Nicolas Farrugia, Vincent Gripon, Yassine El Ouahidi","submitted_at":"2022-03-09T00:24:05Z","abstract_excerpt":"Graph Signal Processing is a promising framework to manipulate brain signals as it allows to encompass the spatial dependencies between the activity in regions of interest in the brain. In this work, we are interested in better understanding what are the graph frequencies that are the most useful to decode fMRI signals. To this end, we introduce a deep learning architecture and adapt a pruning methodology to automatically identify such frequencies. We experiment with various datasets, architectures and graphs, and show that low graph frequencies are consistently identified as the most importan"},"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":"2203.04455","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-09T00:24:05Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"32ae9b81b9e4ad7ecdce51a7d7cfd88be87df6551665a78e12c24dcd4d43dff5","abstract_canon_sha256":"a5905543876a1fcb29fef4347feed25b0572c0863568db0a75835ae9d5b65123"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:03:18.565244Z","signature_b64":"uWomkTPpHdro1L9bsRKCtd+fFyRlMqFiIltQu5mu9UON6l2lM4yBpSFJEjqgYLCIVtXeQLLjq2MUTTggpWgiBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6d940b33ab9984e5fc9d0728528d78cffaeed389fba1e1a4d085b1b6b5bf9d6f","last_reissued_at":"2026-07-05T04:03:18.564876Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:03:18.564876Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Pruning Graph Convolutional Networks to select meaningful graph frequencies for fMRI decoding","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Bastien Pasdeloup, Giulia Lioi, Hugo Tessier, Nicolas Farrugia, Vincent Gripon, Yassine El Ouahidi","submitted_at":"2022-03-09T00:24:05Z","abstract_excerpt":"Graph Signal Processing is a promising framework to manipulate brain signals as it allows to encompass the spatial dependencies between the activity in regions of interest in the brain. In this work, we are interested in better understanding what are the graph frequencies that are the most useful to decode fMRI signals. To this end, we introduce a deep learning architecture and adapt a pruning methodology to automatically identify such frequencies. We experiment with various datasets, architectures and graphs, and show that low graph frequencies are consistently identified as the most importan"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.04455","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/2203.04455/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":"2203.04455","created_at":"2026-07-05T04:03:18.564933+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.04455v1","created_at":"2026-07-05T04:03:18.564933+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.04455","created_at":"2026-07-05T04:03:18.564933+00:00"},{"alias_kind":"pith_short_12","alias_value":"NWKAWM5LTGCO","created_at":"2026-07-05T04:03:18.564933+00:00"},{"alias_kind":"pith_short_16","alias_value":"NWKAWM5LTGCOL7E5","created_at":"2026-07-05T04:03:18.564933+00:00"},{"alias_kind":"pith_short_8","alias_value":"NWKAWM5L","created_at":"2026-07-05T04:03:18.564933+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/NWKAWM5LTGCOL7E5A4UFFDLYZ7","json":"https://pith.science/pith/NWKAWM5LTGCOL7E5A4UFFDLYZ7.json","graph_json":"https://pith.science/api/pith-number/NWKAWM5LTGCOL7E5A4UFFDLYZ7/graph.json","events_json":"https://pith.science/api/pith-number/NWKAWM5LTGCOL7E5A4UFFDLYZ7/events.json","paper":"https://pith.science/paper/NWKAWM5L"},"agent_actions":{"view_html":"https://pith.science/pith/NWKAWM5LTGCOL7E5A4UFFDLYZ7","download_json":"https://pith.science/pith/NWKAWM5LTGCOL7E5A4UFFDLYZ7.json","view_paper":"https://pith.science/paper/NWKAWM5L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.04455&json=true","fetch_graph":"https://pith.science/api/pith-number/NWKAWM5LTGCOL7E5A4UFFDLYZ7/graph.json","fetch_events":"https://pith.science/api/pith-number/NWKAWM5LTGCOL7E5A4UFFDLYZ7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NWKAWM5LTGCOL7E5A4UFFDLYZ7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NWKAWM5LTGCOL7E5A4UFFDLYZ7/action/storage_attestation","attest_author":"https://pith.science/pith/NWKAWM5LTGCOL7E5A4UFFDLYZ7/action/author_attestation","sign_citation":"https://pith.science/pith/NWKAWM5LTGCOL7E5A4UFFDLYZ7/action/citation_signature","submit_replication":"https://pith.science/pith/NWKAWM5LTGCOL7E5A4UFFDLYZ7/action/replication_record"}},"created_at":"2026-07-05T04:03:18.564933+00:00","updated_at":"2026-07-05T04:03:18.564933+00:00"}