{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:7SIXFLL4YDYCWP64TNMHIS4QOA","short_pith_number":"pith:7SIXFLL4","schema_version":"1.0","canonical_sha256":"fc9172ad7cc0f02b3fdc9b58744b90700a987923cb6dd21b8a3fc3826484307d","source":{"kind":"arxiv","id":"2501.16282","version":1},"attestation_state":"computed","paper":{"title":"Brain-Adapter: Enhancing Neurological Disorder Analysis with Adapter-Tuning Multimodal Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Chao Cao, Dajiang Zhu, Jing Zhang, Lu Zhang, Minheng Chen, Tianming Liu, Tong Chen, Xiaowei Yu, Yanjun Lyu, Yan Zhuang","submitted_at":"2025-01-27T18:20:49Z","abstract_excerpt":"Understanding brain disorders is crucial for accurate clinical diagnosis and treatment. Recent advances in Multimodal Large Language Models (MLLMs) offer a promising approach to interpreting medical images with the support of text descriptions. However, previous research has primarily focused on 2D medical images, leaving richer spatial information of 3D images under-explored, and single-modality-based methods are limited by overlooking the critical clinical information contained in other modalities. To address this issue, this paper proposes Brain-Adapter, a novel approach that incorporates a"},"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":"2501.16282","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-01-27T18:20:49Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"162531aa0be59b5edb995893346fc679297400a617a579e752899bb5543e35cc","abstract_canon_sha256":"4a4079e53feb55d6c248c45106cbc5850591cffa46df1892330fd9fa60344469"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:13:19.274157Z","signature_b64":"sSebRS2gxsfJ/sdcztd1jngjhWp8naTk4DraLBuKzxa6fki33FZFdXkc7ZNOaxXD0eYoxX1FuNjzSOOB/i5gBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fc9172ad7cc0f02b3fdc9b58744b90700a987923cb6dd21b8a3fc3826484307d","last_reissued_at":"2026-07-05T11:13:19.273556Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:13:19.273556Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Brain-Adapter: Enhancing Neurological Disorder Analysis with Adapter-Tuning Multimodal Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Chao Cao, Dajiang Zhu, Jing Zhang, Lu Zhang, Minheng Chen, Tianming Liu, Tong Chen, Xiaowei Yu, Yanjun Lyu, Yan Zhuang","submitted_at":"2025-01-27T18:20:49Z","abstract_excerpt":"Understanding brain disorders is crucial for accurate clinical diagnosis and treatment. Recent advances in Multimodal Large Language Models (MLLMs) offer a promising approach to interpreting medical images with the support of text descriptions. However, previous research has primarily focused on 2D medical images, leaving richer spatial information of 3D images under-explored, and single-modality-based methods are limited by overlooking the critical clinical information contained in other modalities. To address this issue, this paper proposes Brain-Adapter, a novel approach that incorporates a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.16282","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/2501.16282/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":"2501.16282","created_at":"2026-07-05T11:13:19.273643+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.16282v1","created_at":"2026-07-05T11:13:19.273643+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.16282","created_at":"2026-07-05T11:13:19.273643+00:00"},{"alias_kind":"pith_short_12","alias_value":"7SIXFLL4YDYC","created_at":"2026-07-05T11:13:19.273643+00:00"},{"alias_kind":"pith_short_16","alias_value":"7SIXFLL4YDYCWP64","created_at":"2026-07-05T11:13:19.273643+00:00"},{"alias_kind":"pith_short_8","alias_value":"7SIXFLL4","created_at":"2026-07-05T11:13:19.273643+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.06565","citing_title":"Bridging Brain Connectomes and Clinical Reports for Early Alzheimer's Disease Diagnosis","ref_index":35,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7SIXFLL4YDYCWP64TNMHIS4QOA","json":"https://pith.science/pith/7SIXFLL4YDYCWP64TNMHIS4QOA.json","graph_json":"https://pith.science/api/pith-number/7SIXFLL4YDYCWP64TNMHIS4QOA/graph.json","events_json":"https://pith.science/api/pith-number/7SIXFLL4YDYCWP64TNMHIS4QOA/events.json","paper":"https://pith.science/paper/7SIXFLL4"},"agent_actions":{"view_html":"https://pith.science/pith/7SIXFLL4YDYCWP64TNMHIS4QOA","download_json":"https://pith.science/pith/7SIXFLL4YDYCWP64TNMHIS4QOA.json","view_paper":"https://pith.science/paper/7SIXFLL4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.16282&json=true","fetch_graph":"https://pith.science/api/pith-number/7SIXFLL4YDYCWP64TNMHIS4QOA/graph.json","fetch_events":"https://pith.science/api/pith-number/7SIXFLL4YDYCWP64TNMHIS4QOA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7SIXFLL4YDYCWP64TNMHIS4QOA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7SIXFLL4YDYCWP64TNMHIS4QOA/action/storage_attestation","attest_author":"https://pith.science/pith/7SIXFLL4YDYCWP64TNMHIS4QOA/action/author_attestation","sign_citation":"https://pith.science/pith/7SIXFLL4YDYCWP64TNMHIS4QOA/action/citation_signature","submit_replication":"https://pith.science/pith/7SIXFLL4YDYCWP64TNMHIS4QOA/action/replication_record"}},"created_at":"2026-07-05T11:13:19.273643+00:00","updated_at":"2026-07-05T11:13:19.273643+00:00"}