{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T16:39:23Z","timestamp":1785515963539,"version":"3.56.0"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T00:00:00Z","timestamp":1761696000000},"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. Neurol."],"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To assess the predictive value of guanine nucleotide-binding protein, alpha-stimulating activity polypeptide (GNAS) and cerebral perfusion in various vascular regions for the severity of ischemic white matter lesions (WMLs).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Patients hospitalized at the Neurology Department of the Affiliated Hospital of Chengde Medical University between April and November 2023 were evaluated for ischemic cerebral WMLs using magnetic resonance imaging. In this retrospective cohort study, patients were classified into two groups: mild and severe, based on Fazekas scores. White matter perfusion was assessed using image segmentation of arterial spin labeling sequence images. Predictive variables were identified via machine learning (ML). GNAS levels in peripheral blood were measured to explore their association with WML severity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among 85 patients (43 mild [24 males and 19 females], 42\u202fsevere [27 males and 15 females]), significant differences were observed in age (64.00\u202f\u00b1\u202f8.47\u202fyears vs. 68.38\u202f\u00b1\u202f10.85\u202fyears,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.041), cerebral atrophy (37.2% vs. 71.4%,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.002), and history of hypertension (41.7% vs. 77.0%,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.002). Corpus callosum perfusion was lower in the severe group (35.84\u202f\u00b1\u202f6.34 vs. 31.73\u202f\u00b1\u202f8.60\u202fmL\/[min\u00b7100\u202fg],\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.037). ML yielded 77.27% model accuracy. Although no significant difference in GNAS levels was observed (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.375), a significant difference was noted in the Fazekas scores (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In patients with ischemic WMLs, factors such as age, sex, history of cerebral infarction, GNAS levels, and specific perfusion metrics are predictive of WML progression. Advanced imaging and ML improve detection. GNAS levels correlated with Fazekas scores, indicating their downregulation in the hypoperfused white matter.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fneur.2025.1612379","type":"journal-article","created":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T05:30:45Z","timestamp":1761715845000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Predictive value of cerebral perfusion and guanine nucleotide-binding protein, alpha-stimulating activity polypeptide in ischemic white matter lesions: a machine learning approach"],"prefix":"10.3389","volume":"16","author":[{"given":"Ning","family":"Yu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuai","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongkai","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijie","family":"Dou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengxian","family":"Jiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yajing","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hebo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,10,29]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1038\/s41591-023-02249-z","article-title":"Dementia research needs a global approach","volume":"29","year":"2023","journal-title":"Nat Med"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"105756","DOI":"10.1016\/j.phrs.2021.105756","article-title":"Autophagy in vascular dementia and natural products with autophagy regulating activity","volume":"170","author":"Wang","year":"2021","journal-title":"Pharmacol Res"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"1381","DOI":"10.3233\/JAD-201165","article-title":"Deciphering Alzheimer\u2019s disease pathogenic pathway: role of chronic brain hypoperfusion on p-tau and mTOR","volume":"79","author":"la De Torre","year":"2021","journal-title":"J Alzheimer's Dis"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.trsl.2022.12.003","article-title":"Vascular contributions to Alzheimer\u2019s disease","volume":"254","author":"Eisenmenger","year":"2023","journal-title":"Transl Res"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"11934","DOI":"10.1038\/ncomms11934","article-title":"Early role of vascular dysregulation on late-onset Alzheimer\u2019s disease based on multifactorial data-driven analysis","volume":"7","author":"Iturria-Medina","year":"2016","journal-title":"Nat Commun"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1186\/s40478-023-01590-1","article-title":"Chronic cerebral hypoperfusion: a critical feature in unravelling the etiology of vascular cognitive impairment","volume":"11","author":"Rajeev","year":"2023","journal-title":"Acta Neuropathol Commun"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"H1","DOI":"10.1152\/ajpheart.00581.2016","article-title":"Functional vascular contributions to cognitive impairment and dementia: mechanisms and consequences of cerebral autoregulatory dysfunction, endothelial impairment, and neurovascular uncoupling in aging","volume":"312","author":"Toth","year":"2017","journal-title":"Am J Physiol Heart Circ Physiol"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"e12530","DOI":"10.1111\/micc.12530","article-title":"The presence of cerebral white matter lesions and lower skin microvascular perfusion predicts lower cognitive performance in type 1 diabetes patients with retinopathy but not in healthy controls-a longitudinal study","volume":"26","author":"Emanuel","year":"2019","journal-title":"Microcirculation"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"101867","DOI":"10.1016\/j.compmedimag.2021.101867","article-title":"Automatic segmentation of white matter hyperintensities from brain magnetic resonance images in the era of deep learning and big data - 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