{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:16:32Z","timestamp":1786115792753,"version":"build-2736575974"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T00:00:00Z","timestamp":1785974400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Aging Neurosci."],"abstract":"\n Introduction<\/jats:title>\n \n Normative brain metabolite references are essential for evaluating metabolic change in rehabilitation patients with stroke, traumatic brain injury, or cognitive decline. We provide preliminary, cohort-specific reference estimates in cognitively healthy older adults using proton magnetic resonance spectroscopy (\n 1<\/jats:sup>\n H-MRS) and examine regional and age-related differences.\n <\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n Twenty healthy older adults (10 males, 10 females; mean age 71.0 \u00b1 9.5 years, range 56\u201384) underwent single-voxel\n \n 1<\/jats:sup>\n H\n <\/jats:italic>\n -MRS at 3T in three regions: dorsal anterior cingulate cortex (DACC), posterior cingulate cortex (PCC), and occipital cortex (OC). A Bayesian deep-learning convolutional neural network quantified 14 metabolite ratios normalized to total creatine (\n tCr<\/jats:italic>\n ). Because regions were acquired within each participant (20 independent observations), regional differences were evaluated primarily with the within-subject Friedman test (Kendall\u2019s\n W<\/jats:italic>\n ), with Kruskal\u2013Wallis as a secondary analysis; Benjamini\u2013Hochberg false discovery rate (FDR) correction was applied across the 14 metabolites. Age\u2013 and MMSE\u2013metabolite associations used Spearman\u2019s rank correlation with FDR applied both across all 42 tests and within each region.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Nine of 14 metabolites showed significant regional differences (seven also surviving the primary Friedman test after FDR correction), with glycerophosphocholine (GPC)\/tCr showing the largest effect (Friedman\n W<\/jats:italic>\n = 0.82; Kruskal\u2013Wallis \u03b5\n 2<\/jats:sup>\n = 0.55). \u03b3-aminobutyric acid (GABA)\/tCr was highest in the OC, whereas myo-inositol (mI) and choline compounds were highest in the DACC. In exploratory age analyses, the PCC showed negative correlations for aspartate (Asp)\/tCr (\u03c1 = \u20130.61,\n p<\/jats:italic>\n = 0.004) and glutathione (GSH)\/tCr (\u03c1 = \u20130.59,\n p<\/jats:italic>\n = 0.006), both surviving region-wise FDR correction (\n q<\/jats:italic>\n = 0.044), with glutamate (Glu)\/tCr nominal (\u03c1 = \u20130.45) and total N-acetylaspartate (tNAA)\/tCr at trend level (\u03c1 = \u20130.39); the DACC showed positive correlations for glucose (Glc)\/tCr (\u03c1 = +0.60) and lactate (Lac)\/tCr (\u03c1 = +0.50). No association survived the more conservative correction across all 42 tests. The OC showed no significant age associations.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n \n This study established preliminary, cohort-specific multi-regional\n \n 1<\/jats:sup>\n H\n <\/jats:italic>\n -MRS reference estimates for cognitively intact older adults, with robust regional profiles and large effect sizes. The PCC showed exploratory age-related change\u2014most consistently a coordinated aspartate\u2013glutathione decline\u2014requiring confirmation in larger cohorts, whereas the OC emerged as a candidate metabolically stable region.\n <\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/fnagi.2026.1896159","type":"journal-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T05:31:20Z","timestamp":1785994280000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Region-specific brain metabolite reference estimates and exploratory age-related changes in cognitively intact older adults: a preliminary deep-learning 1H-MRS study"],"prefix":"10.3389","volume":"18","author":[{"given":"Young-Ah","family":"Choi","sequence":"first","affiliation":[{"name":"Department of Rehabilitation Medicine, Incheon St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea","place":["Seoul, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyeong Hun","family":"Lee","sequence":"additional","affiliation":[{"name":"Research and Development Center, METLiT Inc.","place":["Seoul, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong-Kyu","family":"Jang","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Incheon St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea","place":["Seoul, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junghyeon","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine, Incheon St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea","place":["Seoul, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Si Won","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine, Incheon St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea","place":["Seoul, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min-Wook","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine, Incheon St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea","place":["Seoul, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,6]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"371","DOI":"10.3390\/metabo11060371","article-title":"Alterations of striato-thalamic metabolism in normal aging human brain\u2014an MR metabolic imaging study.","volume":"11","author":"Ahlswede","year":"2021","journal-title":"Metabolites"},{"key":"B2","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1002\/nbm.1940060114","article-title":"Quantitation of proton NMR spectra of the human brain using tissue water as an internal concentration reference.","volume":"6","author":"Barker","year":"1993","journal-title":"NMR Biomed."},{"key":"B3","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: A practical and powerful approach to multiple testing.","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. 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