{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T06:00:40Z","timestamp":1785477640685,"version":"3.56.0"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Spatial transcriptomics enables molecular mapping of ischemic stroke tissue, but spatial domain identification is challenging when injury disrupts normal tissue geometry. Methods relying on a single spatial-proximity graph cannot connect physically distant spots that share damage-associated transcriptional programs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed SpatialDomainAE, an unsupervised dual-graph attention autoencoder that constructs separate spatial-neighbor and expression-similarity graphs, processes each using graph attention, and combines their embeddings through learned per-spot fusion weights. The method was evaluated on a mouse middle cerebral artery occlusion 10\u00d7 Visium dataset comprising control, 1-, 3-, and 7-day post-injury sections, totaling 10,173 spots and 22 annotated domains. All methods were evaluated over 10 random seeds using a shared Leiden-resolution protocol.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>At 3 days post-injury, SpatialDomainAE achieved an ARI of 0.700 \u00b1 0.025 and significantly exceeded all external baselines, including the dual-view Spatial-MGCN. Across all four samples, its performance was competitive rather than uniformly superior, and it was robust under a fixed clustering resolution. No comparable advantage was observed on an external human dorsolateral prefrontal cortex benchmark. Controlled experiments showed that the long-range transcriptomic content of the feature graph, rather than edge length alone, accounted for the improvement. Fusion weights separated lesion-associated domains at the region level, while differential expression and pathway enrichment recovered inflammatory, complement, gliosis, and proliferative programs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>SpatialDomainAE is particularly useful in disrupted pathological tissue. Its fusion weights should be interpreted as an exploratory region-level model diagnostic rather than a validated spot-level biomarker.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnins.2026.1881087","type":"journal-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T05:37:16Z","timestamp":1785476236000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Dual-graph attention autoencoder for spatial domain identification in ischemic stroke"],"prefix":"10.3389","volume":"20","author":[{"given":"Yuan-Yuan","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, Anhui Public Health Clinical Center","place":["Hefei, Anhui, China"]},{"name":"Department of Rehabilitation Medicine, The First Affiliated Hospital of Anhui Medical University, Anhui Public Health Clinical Center","place":["Hefei, Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wang-Ting","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, Anhui Public Health Clinical Center","place":["Hefei, Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine, The First Affiliated Hospital of Anhui Medical University, Anhui Public Health Clinical Center","place":["Hefei, Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xian-Liang","family":"Rao","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine, The First Affiliated Hospital of Anhui Medical University, Anhui Public Health Clinical Center","place":["Hefei, Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, Anhui Public Health Clinical Center","place":["Hefei, Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,31]]},"reference":[{"key":"B1","article-title":"Layer normalization","author":"Ba","year":"2016","journal-title":"arXiv preprint arXiv:1607.06450"},{"key":"B2","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/S0140-6736(20)31179-X","article-title":"Stroke","volume":"396","author":"Campbell","year":"2020","journal-title":"Lancet"},{"key":"B3","doi-asserted-by":"publisher","first-page":"e2501812","DOI":"10.1002\/smtd.202501812","article-title":"M-stgcn: A position-aware multimodal graph convolutional framework for joint spatial domain identification and gene expression denoising","volume":"10","author":"Chen","year":"2026","journal-title":"Small Methods"},{"key":"B4","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1016\/S0166-2236(99)01401-0","article-title":"Pathobiology of ischaemic stroke: an integrated view","volume":"22","author":"Dirnagl","year":"1999","journal-title":"Trends Neurosci"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1739","DOI":"10.1038\/s41467-022-29439-6","article-title":"Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder","volume":"13","author":"Dong","year":"2022","journal-title":"Nat. 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