{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:ASXPIP5U7ENSLFQIM4X36PB7GT","short_pith_number":"pith:ASXPIP5U","canonical_record":{"source":{"id":"1703.01842","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-06T12:45:39Z","cross_cats_sorted":["q-bio.NC","stat.ML"],"title_canon_sha256":"91a05653e1587d747897a70ab5edc88b4b13c976e1a20eda42c990deb6d2ec3a","abstract_canon_sha256":"66bd925f0abeb07db673b452e975acdb6364b03eb61b2a6a6f2cc276eb96e9d3"},"schema_version":"1.0"},"canonical_sha256":"04aef43fb4f91b259608672fbf3c3f34cc05bf208a28da22abeba7fc3633bec4","source":{"kind":"arxiv","id":"1703.01842","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.01842","created_at":"2026-05-18T00:36:38Z"},{"alias_kind":"arxiv_version","alias_value":"1703.01842v3","created_at":"2026-05-18T00:36:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.01842","created_at":"2026-05-18T00:36:38Z"},{"alias_kind":"pith_short_12","alias_value":"ASXPIP5U7ENS","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_16","alias_value":"ASXPIP5U7ENSLFQI","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_8","alias_value":"ASXPIP5U","created_at":"2026-05-18T12:31:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:ASXPIP5U7ENSLFQIM4X36PB7GT","target":"record","payload":{"canonical_record":{"source":{"id":"1703.01842","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-06T12:45:39Z","cross_cats_sorted":["q-bio.NC","stat.ML"],"title_canon_sha256":"91a05653e1587d747897a70ab5edc88b4b13c976e1a20eda42c990deb6d2ec3a","abstract_canon_sha256":"66bd925f0abeb07db673b452e975acdb6364b03eb61b2a6a6f2cc276eb96e9d3"},"schema_version":"1.0"},"canonical_sha256":"04aef43fb4f91b259608672fbf3c3f34cc05bf208a28da22abeba7fc3633bec4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:36:38.888212Z","signature_b64":"WUnw9BK692ruxUXg7cD4CS7z4K3d9SETYPSTbcSGARt4nCMHOjIaZGedTg4kmLbApzADx2s8hNis1EnPrKPGCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"04aef43fb4f91b259608672fbf3c3f34cc05bf208a28da22abeba7fc3633bec4","last_reissued_at":"2026-05-18T00:36:38.887639Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:36:38.887639Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1703.01842","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:36:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vzsVMtge6HnL5ycjdVlnmrz7H3P2CVSxaSx2XEUu6klAv4U8HfqgnrSZvZG4zDqURyQqfZw2fkCid1Hn5+xeDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T21:20:08.610570Z"},"content_sha256":"e79c53b5573983093d031a0966fbbc8044b63c434a6b57f2a07194cc0feb2d62","schema_version":"1.0","event_id":"sha256:e79c53b5573983093d031a0966fbbc8044b63c434a6b57f2a07194cc0feb2d62"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:ASXPIP5U7ENSLFQIM4X36PB7GT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.NC","stat.ML"],"primary_cat":"cs.CV","authors_text":"Bastien Pasdeloup, Mathilde M\\'enoret, Nicolas Farrugia, Vincent Gripon","submitted_at":"2017-03-06T12:45:39Z","abstract_excerpt":"Graph Signal Processing (GSP) is a promising framework to analyze multi-dimensional neuroimaging datasets, while taking into account both the spatial and functional dependencies between brain signals. In the present work, we apply dimensionality reduction techniques based on graph representations of the brain to decode brain activity from real and simulated fMRI datasets. We introduce seven graphs obtained from a) geometric structure and/or b) functional connectivity between brain areas at rest, and compare them when performing dimension reduction for classification. We show that mixed graphs "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.01842","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:36:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZEjzkcFSta7Y/PbVa7xAuecMIkk7wGf6VnvHn+rp3EDBlC7oXbeLnqL5PIGGcEz0XCQZM5oqKmJVJ5tWVN0mCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T21:20:08.611213Z"},"content_sha256":"6d2728d2503baa24df66a450eec8ae6527f2e2c2939e200951f0dbbacb29ced0","schema_version":"1.0","event_id":"sha256:6d2728d2503baa24df66a450eec8ae6527f2e2c2939e200951f0dbbacb29ced0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ASXPIP5U7ENSLFQIM4X36PB7GT/bundle.json","state_url":"https://pith.science/pith/ASXPIP5U7ENSLFQIM4X36PB7GT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ASXPIP5U7ENSLFQIM4X36PB7GT/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-24T21:20:08Z","links":{"resolver":"https://pith.science/pith/ASXPIP5U7ENSLFQIM4X36PB7GT","bundle":"https://pith.science/pith/ASXPIP5U7ENSLFQIM4X36PB7GT/bundle.json","state":"https://pith.science/pith/ASXPIP5U7ENSLFQIM4X36PB7GT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ASXPIP5U7ENSLFQIM4X36PB7GT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:ASXPIP5U7ENSLFQIM4X36PB7GT","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"66bd925f0abeb07db673b452e975acdb6364b03eb61b2a6a6f2cc276eb96e9d3","cross_cats_sorted":["q-bio.NC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-06T12:45:39Z","title_canon_sha256":"91a05653e1587d747897a70ab5edc88b4b13c976e1a20eda42c990deb6d2ec3a"},"schema_version":"1.0","source":{"id":"1703.01842","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.01842","created_at":"2026-05-18T00:36:38Z"},{"alias_kind":"arxiv_version","alias_value":"1703.01842v3","created_at":"2026-05-18T00:36:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.01842","created_at":"2026-05-18T00:36:38Z"},{"alias_kind":"pith_short_12","alias_value":"ASXPIP5U7ENS","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_16","alias_value":"ASXPIP5U7ENSLFQI","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_8","alias_value":"ASXPIP5U","created_at":"2026-05-18T12:31:08Z"}],"graph_snapshots":[{"event_id":"sha256:6d2728d2503baa24df66a450eec8ae6527f2e2c2939e200951f0dbbacb29ced0","target":"graph","created_at":"2026-05-18T00:36:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Graph Signal Processing (GSP) is a promising framework to analyze multi-dimensional neuroimaging datasets, while taking into account both the spatial and functional dependencies between brain signals. In the present work, we apply dimensionality reduction techniques based on graph representations of the brain to decode brain activity from real and simulated fMRI datasets. We introduce seven graphs obtained from a) geometric structure and/or b) functional connectivity between brain areas at rest, and compare them when performing dimension reduction for classification. We show that mixed graphs ","authors_text":"Bastien Pasdeloup, Mathilde M\\'enoret, Nicolas Farrugia, Vincent Gripon","cross_cats":["q-bio.NC","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-06T12:45:39Z","title":"Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.01842","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:e79c53b5573983093d031a0966fbbc8044b63c434a6b57f2a07194cc0feb2d62","target":"record","created_at":"2026-05-18T00:36:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"66bd925f0abeb07db673b452e975acdb6364b03eb61b2a6a6f2cc276eb96e9d3","cross_cats_sorted":["q-bio.NC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-06T12:45:39Z","title_canon_sha256":"91a05653e1587d747897a70ab5edc88b4b13c976e1a20eda42c990deb6d2ec3a"},"schema_version":"1.0","source":{"id":"1703.01842","kind":"arxiv","version":3}},"canonical_sha256":"04aef43fb4f91b259608672fbf3c3f34cc05bf208a28da22abeba7fc3633bec4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"04aef43fb4f91b259608672fbf3c3f34cc05bf208a28da22abeba7fc3633bec4","first_computed_at":"2026-05-18T00:36:38.887639Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:36:38.887639Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WUnw9BK692ruxUXg7cD4CS7z4K3d9SETYPSTbcSGARt4nCMHOjIaZGedTg4kmLbApzADx2s8hNis1EnPrKPGCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:36:38.888212Z","signed_message":"canonical_sha256_bytes"},"source_id":"1703.01842","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e79c53b5573983093d031a0966fbbc8044b63c434a6b57f2a07194cc0feb2d62","sha256:6d2728d2503baa24df66a450eec8ae6527f2e2c2939e200951f0dbbacb29ced0"],"state_sha256":"6173211b3a66b5e860eac00ac8775443af2e86745e9f85364bfb0c3ea6a1b952"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lDC6CJAnNtYQ76rwymNGpppY48vLNr8jJO551CeYq4EVFbyW2uGz/GfvQ+O6mbFcx5T4U9VrAlcndpys+213BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-24T21:20:08.615061Z","bundle_sha256":"1998e1eea77fd3d235906942a47ade34791147f9421e3e7a9989395b5957ef6e"}}