{"paper":{"title":"Diffeomorphic Cortical Alignment via Direct Warping of Streamline Endpoints","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Aligning cortical surfaces by directly warping white-matter tract endpoints on a product manifold improves fiber bundle correspondence.","cross_cats":["stat.ME"],"primary_cat":"cs.CV","authors_text":"Martin Cole, Yang Xiang, Zhengwu Zhang","submitted_at":"2026-05-16T01:46:32Z","abstract_excerpt":"Cortical surface registration is often driven by local geometric descriptors (e.g., sulcal depth and curvature). While this approach achieves geometric correspondence, it neglects the long-range wiring constraints imposed by white-matter anatomy. Diffusion MRI tractography offers these crucial constraints; however, prior connectivity-informed pipelines typically align precomputed connectivity matrices, making the optimization highly sensitive to connectivity estimation and its resolution. In this paper, we introduce a novel connectivity-based surface registration method that aligns cortical su"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Experiments on HCP data demonstrate improved tract-level correspondence, achieving higher connectivity-level overlap coefficients on major fiber bundles and stronger robustness across grid resolutions for Ω compared to state-of-the-art methods such as ENCORE and MSMAll.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That operating directly on tract endpoints modeled as a point cloud on the product manifold Ω × Ω and minimizing connectivity mismatch via iterative diffeomorphic warps produces anatomically valid alignments that respect long-range white-matter constraints without introducing artifacts from tractography estimation.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Presents a diffeomorphic cortical surface registration technique that iteratively warps streamline endpoints on the product manifold to optimize tract-level correspondence using HCP data.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Aligning cortical surfaces by directly warping white-matter tract endpoints on a product manifold improves fiber bundle correspondence.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"1b4b64b5062a11cfae57da847e1ac84b517067f834d1bbfc3befe59f4422c741"},"source":{"id":"2605.16742","kind":"arxiv","version":1},"verdict":{"id":"f63d3f3c-e510-402a-99c7-3525a2836060","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T21:27:32.810984Z","strongest_claim":"Experiments on HCP data demonstrate improved tract-level correspondence, achieving higher connectivity-level overlap coefficients on major fiber bundles and stronger robustness across grid resolutions for Ω compared to state-of-the-art methods such as ENCORE and MSMAll.","one_line_summary":"Presents a diffeomorphic cortical surface registration technique that iteratively warps streamline endpoints on the product manifold to optimize tract-level correspondence using HCP data.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That operating directly on tract endpoints modeled as a point cloud on the product manifold Ω × Ω and minimizing connectivity mismatch via iterative diffeomorphic warps produces anatomically valid alignments that respect long-range white-matter constraints without introducing artifacts from tractography estimation.","pith_extraction_headline":"Aligning cortical surfaces by directly warping white-matter tract endpoints on a product manifold improves fiber bundle correspondence."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.16742/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T22:01:19.850493Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T21:40:55.286145Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T19:01:56.333368Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.462867Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"6ee9d435deec9c4cfd7d8d9ff72adba2ace00d7e48bd6386c20382bf77d3ea08"},"references":{"count":29,"sample":[{"doi":"","year":null,"title":"arXiv preprint arXiv:2503.15830 , year=","work_id":"afbb5578-6009-47f1-be35-8d8d5e6ce8c2","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2018,"title":"Recognition of white matter bundles using local and global streamline-based registration and clustering , author=. 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