{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:44:38Z","timestamp":1760060678171,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T00:00:00Z","timestamp":1757462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MINCIENCIAS, COLOMBIA","award":["COL111089784907"],"award-info":[{"award-number":["COL111089784907"]}]},{"name":"Beneficiario proyecto de formaci\u00f3n de capital humano de alto nivel","award":["COL111089784907"],"award-info":[{"award-number":["COL111089784907"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Accurate correspondence of complex neuroanatomical surfaces under non-rigid deformations remains a formidable challenge in computational neuroimaging, owing to inter-subject topological variability, partial occlusions, and non-isometric distortions. Here, we introduce the Dynamic Graph Analyzer (DGA), a unified hybrid framework that integrates simplified structural descriptors with spatial constraints and formulates matching as a global linear assignment. Structurally, the DGA computes node-level metrics, degree weighted by betweenness centrality and local clustering coefficients, to capture essential topological patterns at a low computational cost. Spatially, it employs a two-stage scheme that combines global maximum distances and local rescaling of adjacent node separations to preserve geometric fidelity. By embedding these complementary measures into a single cost matrix solved via the Kuhn\u2013Munkres algorithm followed by a refinement of weak correspondences, the DGA ensures a globally optimal correspondence. In benchmark evaluations on the FAUST dataset, the DGA achieved a significant reduction in the mean geodetic reconstruction error compared to spectral graph convolutional netwworks (GCNs)\u2014which learn optimized spectral descriptors akin to classical approaches like heat\/wave kernel signatures (HKS\/WKS)\u2014and traditional spectral methods. Additional experiments demonstrate robust performance on partial matches in TOSCA and cross-species alignments in SHREC-20, validating resilience to morphological variation and symmetry ambiguities. These results establish the DGA as a scalable and accurate approach for brain shape correspondence, with promising applications in biomarker mapping, developmental studies, and clinical morphometry.<\/jats:p>","DOI":"10.3390\/make7030099","type":"journal-article","created":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T12:27:41Z","timestamp":1757507261000},"page":"99","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Graph Analysis: A Hybrid Structural\u2013Spatial Approach for Brain Shape Correspondence"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1260-7370","authenticated-orcid":false,"given":"Jonnatan","family":"Arias-Garc\u00eda","sequence":"first","affiliation":[{"name":"Automatics Research Group, Technologic University of Pereira, Pereira 660003, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2814-8838","authenticated-orcid":false,"given":"Hern\u00e1n Felipe","family":"Garc\u00eda","sequence":"additional","affiliation":[{"name":"SISTEMIC Research Group, University of Antioquia, Medell\u00edn 050010, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0061-4278","authenticated-orcid":false,"given":"Andr\u00e9s","family":"Escobar-Mej\u00eda","sequence":"additional","affiliation":[{"name":"Power Electronics Group, Department of Electrical Engineering, Universidad Tecnol\u00f3gica de Pereira, Pereira 660003, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0522-8683","authenticated-orcid":false,"given":"David","family":"C\u00e1rdenas-Pe\u00f1a","sequence":"additional","affiliation":[{"name":"Automatics Research Group, Technologic University of Pereira, Pereira 660003, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u00c1lvaro A.","family":"Orozco","sequence":"additional","affiliation":[{"name":"Automatics Research Group, Technologic University of Pereira, Pereira 660003, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1038\/s41591-021-01382-x","article-title":"Biomarkers for neurodegenerative diseases","volume":"27","author":"Hansson","year":"2021","journal-title":"Nat. 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