{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T02:11:28Z","timestamp":1781921488993,"version":"3.54.5"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T00:00:00Z","timestamp":1745884800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T00:00:00Z","timestamp":1745884800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Medizinische Fakult\u00e4t Heidelberg der Universit\u00e4t Heidelberg"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Automated tools have been proposed to quantify brain volume for suspected dementia diagnoses. However, their robustness in longitudinal, real-life cohorts remains unexplored. This exploratory study examined if expert visual assessment (EVA) of atrophy progression is reflected by automated volumetric analyses (AVA) on sequential MR-imaging. We analyzed a random subset of 20 patients with two consecutive 3D T1-weighted examinations (median follow-up 4.0\u00a0years, LQ-UQ: 2.1\u20135.2, range: 0.2\u201310). Thirteen (65%) with cognitive decline, the remaining with other neuropsychiatric diseases. EVA was performed by two blinded neuroradiologists using a 3 or 5-point Likert scale for atrophy progression (scores <jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>$$\\pm$$<\/jats:tex-math>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>\u00b1<\/mml:mo>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula> 0\u20132: no, probable and certain progression or decrease, respectively) in dementia-relevant brain regions (frontal-, parietal-, temporal lobes, hippocampi, ventricles). Differences of AVA-volumes were normalized to baseline (delta). Inter-rater agreement of EVA scores was excellent (\u03ba\u2009=\u20090.92). AVA-delta and EVA showed significant global associations for the right hippocampus (p<jats:sub>KW<\/jats:sub>\u2009=\u20090.035), left temporal lobe (p<jats:sub>KW<\/jats:sub>\u2009=\u20090.0092), ventricle volume (p<jats:sub>KW<\/jats:sub>\u2009=\u20090.0091) and a weak association for the parietal lobe (p<jats:sub>KW<\/jats:sub>\u2009=\u20090.067). Post hoc testing revealed additional significant link for the left hippocampus (p<jats:sub>WSRT<\/jats:sub>\u2009=\u20090.039). In conclusion, the associations between volumetric deltas and EVA of atrophy progression showed promising results for certain brain regions. However, AVA-deltas exhibited unexpected variance, highlighting the need for caution and expert visual confirmation, particularly when scanners or acquisition protocols vary during follow-ups. Therefore, further validation, ideally in large prospective cohorts, is necessary before AVA can be recommended for routine clinical implementation in longitudinal follow-ups.<\/jats:p>","DOI":"10.1038\/s41598-025-98360-x","type":"journal-article","created":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T05:09:32Z","timestamp":1745903372000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Longitudinal automated brain volumetry versus expert visual assessment of atrophy progression on MRI: an exploratory study"],"prefix":"10.1038","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6244-6925","authenticated-orcid":false,"given":"Max","family":"Gebest","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christel","family":"Wei\u00df","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang-Gyu","family":"Cho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucrezia","family":"Hausner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lutz","family":"Fr\u00f6lich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex","family":"F\u00f6rster","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nandhini","family":"Santhanam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johann","family":"Fontana","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christoph","family":"Groden","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Holger","family":"Wenz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1589-8699","authenticated-orcid":false,"given":"M\u00e1t\u00e9 E.","family":"Maros","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,29]]},"reference":[{"key":"98360_CR1","doi-asserted-by":"publisher","first-page":"e105","DOI":"10.1016\/S2468-2667(21)00249-8","volume":"7","author":"GBDDF Collaborators","year":"2022","unstructured":"GBDDF Collaborators. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: An analysis for the Global Burden of Disease Study 2019. Lancet Public Health 7, e105\u2013e125. https:\/\/doi.org\/10.1016\/S2468-2667(21)00249-8 (2022).","journal-title":"Lancet Public Health"},{"key":"98360_CR2","doi-asserted-by":"publisher","first-page":"1143","DOI":"10.1212\/wnl.56.9.1143","volume":"56","author":"DS Knopman","year":"2001","unstructured":"Knopman, D. S. et al. Practice parameter: Diagnosis of dementia (an evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology 56, 1143\u20131153. https:\/\/doi.org\/10.1212\/wnl.56.9.1143 (2001).","journal-title":"Neurology"},{"key":"98360_CR3","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.jalz.2011.03.005","volume":"7","author":"GM McKhann","year":"2011","unstructured":"McKhann, G. M. et al. The diagnosis of dementia due to Alzheimer\u2019s disease: Recommendations from the National Institute on Aging-Alzheimer\u2019s Association workgroups on diagnostic guidelines for Alzheimer\u2019s disease. Alzheimers Dement. 7, 263\u2013269. https:\/\/doi.org\/10.1016\/j.jalz.2011.03.005 (2011).","journal-title":"Alzheimers Dement."},{"key":"98360_CR4","doi-asserted-by":"publisher","first-page":"822","DOI":"10.1016\/s1474-4422(13)70124-8","volume":"12","author":"JM Wardlaw","year":"2013","unstructured":"Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 12, 822\u2013838. https:\/\/doi.org\/10.1016\/s1474-4422(13)70124-8 (2013).","journal-title":"Lancet Neurol."},{"key":"98360_CR5","doi-asserted-by":"publisher","first-page":"2456","DOI":"10.1093\/brain\/awr179","volume":"134","author":"K Rascovsky","year":"2011","unstructured":"Rascovsky, K. et al. Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain 134, 2456\u20132477. https:\/\/doi.org\/10.1093\/brain\/awr179 (2011).","journal-title":"Brain"},{"key":"98360_CR6","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.1212\/WNL.0b013e31821103e6","volume":"76","author":"ML Gorno-Tempini","year":"2011","unstructured":"Gorno-Tempini, M. L. et al. Classification of primary progressive aphasia and its variants. Neurology 76, 1006\u20131014. https:\/\/doi.org\/10.1212\/WNL.0b013e31821103e6 (2011).","journal-title":"Neurology"},{"key":"98360_CR7","doi-asserted-by":"publisher","first-page":"1773","DOI":"10.1007\/s00234-021-02746-3","volume":"63","author":"HG Pemberton","year":"2021","unstructured":"Pemberton, H. G. et al. Technical and clinical validation of commercial automated volumetric MRI tools for dementia diagnosis-a systematic review. Neuroradiology 63, 1773\u20131789. https:\/\/doi.org\/10.1007\/s00234-021-02746-3 (2021).","journal-title":"Neuroradiology"},{"key":"98360_CR8","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/s1474-4422(19)30368-0","volume":"19","author":"F Jessen","year":"2020","unstructured":"Jessen, F. et al. The characterisation of subjective cognitive decline. Lancet Neurol. 19, 271\u2013278. https:\/\/doi.org\/10.1016\/s1474-4422(19)30368-0 (2020).","journal-title":"Lancet Neurol."},{"key":"98360_CR9","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1016\/j.cger.2013.07.003","volume":"29","author":"R Roberts","year":"2013","unstructured":"Roberts, R. & Knopman, D. S. Classification and epidemiology of MCI. Clin. Geriatr. Med. 29, 753\u2013772. https:\/\/doi.org\/10.1016\/j.cger.2013.07.003 (2013).","journal-title":"Clin. Geriatr. Med."},{"key":"98360_CR10","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1016\/j.jalz.2018.02.018","volume":"14","author":"CR Jack Jr","year":"2018","unstructured":"Jack, C. R. Jr. et al. NIA-AA research framework: Toward a biological definition of Alzheimer\u2019s disease. Alzheimers Dement 14, 535\u2013562. https:\/\/doi.org\/10.1016\/j.jalz.2018.02.018 (2018).","journal-title":"Alzheimers Dement"},{"key":"98360_CR11","doi-asserted-by":"publisher","first-page":"1459","DOI":"10.3233\/jad-180532","volume":"65","author":"DM Hedderich","year":"2018","unstructured":"Hedderich, D. M. et al. Increasing diagnostic accuracy of mild cognitive impairment due to Alzheimer\u2019s disease by user-independent Web-Based Whole-Brain Volumetry. J. Alzheimers Dis. 65, 1459\u20131467. https:\/\/doi.org\/10.3233\/jad-180532 (2018).","journal-title":"J. Alzheimers Dis."},{"key":"98360_CR12","doi-asserted-by":"publisher","first-page":"2821","DOI":"10.1007\/s00330-019-06602-0","volume":"30","author":"DM Hedderich","year":"2020","unstructured":"Hedderich, D. M. et al. Normative brain volume reports may improve differential diagnosis of dementing neurodegenerative diseases in clinical practice. Eur. Radiol. 30, 2821\u20132829. https:\/\/doi.org\/10.1007\/s00330-019-06602-0 (2020).","journal-title":"Eur. Radiol."},{"key":"98360_CR13","doi-asserted-by":"publisher","first-page":"5312","DOI":"10.1007\/s00330-020-07455-8","volume":"31","author":"HG Pemberton","year":"2021","unstructured":"Pemberton, H. G. et al. Automated quantitative MRI volumetry reports support diagnostic interpretation in dementia: A multi-rater, clinical accuracy study. Eur. Radiol. 31, 5312\u20135323. https:\/\/doi.org\/10.1007\/s00330-020-07455-8 (2021).","journal-title":"Eur. Radiol."},{"key":"98360_CR14","doi-asserted-by":"publisher","first-page":"1043","DOI":"10.1007\/s00330-020-07196-8","volume":"31","author":"J Caspers","year":"2021","unstructured":"Caspers, J., Heeger, A., Turowski, B. & Rubbert, C. Automated age- and sex-specific volumetric estimation of regional brain atrophy: Workflow and feasibility. Eur. Radiol. 31, 1043\u20131048. https:\/\/doi.org\/10.1007\/s00330-020-07196-8 (2021).","journal-title":"Eur. Radiol."},{"key":"98360_CR15","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/j.nicl.2018.08.004","volume":"20","author":"MW Vernooij","year":"2018","unstructured":"Vernooij, M. W. et al. Automatic normative quantification of brain tissue volume to support the diagnosis of dementia: A clinical evaluation of diagnostic accuracy. Neuroimage Clin. 20, 374\u2013379. https:\/\/doi.org\/10.1016\/j.nicl.2018.08.004 (2018).","journal-title":"Neuroimage Clin."},{"key":"98360_CR16","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1016\/j.nicl.2018.09.013","volume":"20","author":"S Kl\u00f6ppel","year":"2018","unstructured":"Kl\u00f6ppel, S. et al. Voxel-wise deviations from healthy aging for the detection of region-specific atrophy. Neuroimage Clin. 20, 851\u2013860. https:\/\/doi.org\/10.1016\/j.nicl.2018.09.013 (2018).","journal-title":"Neuroimage Clin."},{"key":"98360_CR17","doi-asserted-by":"publisher","first-page":"1906","DOI":"10.1212\/WNL.0b013e3181a82634","volume":"72","author":"I Driscoll","year":"2009","unstructured":"Driscoll, I. et al. Longitudinal pattern of regional brain volume change differentiates normal aging from MCI. Neurology 72, 1906\u20131913. https:\/\/doi.org\/10.1212\/WNL.0b013e3181a82634 (2009).","journal-title":"Neurology"},{"key":"98360_CR18","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1002\/alz.12105","volume":"16","author":"Z Ismail","year":"2020","unstructured":"Ismail, Z. et al. Recommendations of the 5th Canadian Consensus Conference on the diagnosis and treatment of dementia. Alzheimers Dement 16, 1182\u20131195. https:\/\/doi.org\/10.1002\/alz.12105 (2020).","journal-title":"Alzheimers Dement"},{"key":"98360_CR19","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.nicl.2014.11.001","volume":"7","author":"D Schmitter","year":"2015","unstructured":"Schmitter, D. et al. An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer\u2019s disease. NeuroImage: Clinical 7, 7\u201317 (2015).","journal-title":"NeuroImage: Clinical"},{"key":"98360_CR20","doi-asserted-by":"publisher","unstructured":"Kober, T., R, S. H. M. & Team, D. Letter to the Editor regarding article \u201cTechnical and clinical validation of commercial automated volumetric MRI tools for dementia diagnosis\u2014a systematic review\u201d(https:\/\/doi.org\/10.1007\/s00234-021-02818-4). Neuroradiology 64, 847\u2013848 (2022).","DOI":"10.1007\/s00234-021-02818-4"},{"key":"98360_CR21","doi-asserted-by":"publisher","first-page":"1225","DOI":"10.1136\/jnnp-2014-310090","volume":"86","author":"L Harper","year":"2015","unstructured":"Harper, L., Barkhof, F., Fox, N. C. & Schott, J. M. Using visual rating to diagnose dementia: A critical evaluation of MRI atrophy scales. J. Neurol. Neurosurg. Psychiatry 86, 1225\u20131233. https:\/\/doi.org\/10.1136\/jnnp-2014-310090 (2015).","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"98360_CR22","doi-asserted-by":"publisher","first-page":"967","DOI":"10.1136\/jnnp.55.10.967","volume":"55","author":"P Scheltens","year":"1992","unstructured":"Scheltens, P. et al. Atrophy of medial temporal lobes on MRI in \u201cprobable\u201d Alzheimer\u2019s disease and normal ageing: Diagnostic value and neuropsychological correlates. J. Neurol. Neurosurg. Psychiatry 55, 967\u2013972. https:\/\/doi.org\/10.1136\/jnnp.55.10.967 (1992).","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"98360_CR23","doi-asserted-by":"publisher","first-page":"2618","DOI":"10.1007\/s00330-011-2205-4","volume":"21","author":"EL Koedam","year":"2011","unstructured":"Koedam, E. L. et al. Visual assessment of posterior atrophy development of a MRI rating scale. Eur. Radiol. 21, 2618\u20132625. https:\/\/doi.org\/10.1007\/s00330-011-2205-4 (2011).","journal-title":"Eur. Radiol."},{"key":"98360_CR24","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1159\/000100973","volume":"23","author":"CM Kipps","year":"2007","unstructured":"Kipps, C. M. et al. Clinical significance of lobar atrophy in frontotemporal dementia: Application of an MRI visual rating scale. Dement Geriatr. Cogn. Disord. 23, 334\u2013342. https:\/\/doi.org\/10.1159\/000100973 (2007).","journal-title":"Dement Geriatr. Cogn. Disord."},{"key":"98360_CR25","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1212\/01.WNL.0000044400.11317.EC","volume":"60","author":"A Du","year":"2003","unstructured":"Du, A. et al. Atrophy rates of entorhinal cortex in AD and normal aging. Neurology 60, 481\u2013486 (2003).","journal-title":"Neurology"},{"key":"98360_CR26","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1080\/01621459.1952.10483441","volume":"47","author":"WH Kruskal","year":"1952","unstructured":"Kruskal, W. H. & Wallis, W. A. Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47, 583\u2013621 (1952).","journal-title":"J. Am. Stat. Assoc."},{"key":"98360_CR27","doi-asserted-by":"publisher","DOI":"10.3389\/pore.2021.643146","volume":"27","author":"ME Maros","year":"2021","unstructured":"Maros, M. E. et al. Cell cycle regulatory protein expression in multinucleated giant cells of giant cell tumor of bone: Do they proliferate?. Pathol. Oncol. Res. 27, 643146 (2021).","journal-title":"Pathol. Oncol. Res."},{"key":"98360_CR28","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0136054","volume":"10","author":"H Wenz","year":"2015","unstructured":"Wenz, H. et al. Image quality of 3rd generation spiral cranial dual-source CT in combination with an advanced model iterative reconstruction technique: A prospective intra-individual comparison study to standard sequential cranial CT using identical radiation dose. PLoS ONE 10, e0136054 (2015).","journal-title":"PLoS ONE"},{"key":"98360_CR29","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.ejro.2016.05.006","volume":"3","author":"H Wenz","year":"2016","unstructured":"Wenz, H. et al. Intra-individual diagnostic image quality and organ-specific-radiation dose comparison between spiral cCT with iterative image reconstruction and z-axis automated tube current modulation and sequential cCT. Eur. J. Radiol. Open 3, 182\u2013190 (2016).","journal-title":"Eur. J. Radiol. Open"},{"key":"98360_CR30","doi-asserted-by":"publisher","first-page":"9060","DOI":"10.1007\/s00330-021-08227-8","volume":"31","author":"HY Park","year":"2021","unstructured":"Park, H. Y., Park, C. R., Suh, C. H., Shim, W. H. & Kim, S. J. Diagnostic performance of the medial temporal lobe atrophy scale in patients with Alzheimer\u2019s disease: A systematic review and meta-analysis. Eur. Radiol. 31, 9060\u20139072. https:\/\/doi.org\/10.1007\/s00330-021-08227-8 (2021).","journal-title":"Eur. Radiol."},{"key":"98360_CR31","doi-asserted-by":"publisher","DOI":"10.1002\/brb3.1662","volume":"10","author":"V Velickaite","year":"2020","unstructured":"Velickaite, V. et al. Visual rating versus volumetry of regional brain atrophy and longitudinal changes over a 5-year period in an elderly population. Brain Behav. 10, e01662 (2020).","journal-title":"Brain Behav."},{"key":"98360_CR32","doi-asserted-by":"publisher","first-page":"9057","DOI":"10.1007\/s00330-021-08340-8","volume":"31","author":"A Rau","year":"2021","unstructured":"Rau, A. & Urbach, H. The MTA score-simple and reliable, the best for now?. Eur. Radiol. 31, 9057\u20139059. https:\/\/doi.org\/10.1007\/s00330-021-08340-8 (2021).","journal-title":"Eur. Radiol."},{"key":"98360_CR33","doi-asserted-by":"crossref","unstructured":"Rau, A. et al. SVM-based normal pressure hydrocephalus detection. Clin. Neuroradiol., 31, 1029\u20131035 (2021).","DOI":"10.1007\/s00062-020-00993-0"},{"key":"98360_CR34","doi-asserted-by":"publisher","first-page":"102310","DOI":"10.1016\/j.nicl.2020.102310","volume":"27","author":"G M\u00e5rtensson","year":"2020","unstructured":"M\u00e5rtensson, G. et al. Medial temporal atrophy in preclinical dementia: Visual and automated assessment during six year follow-up. NeuroImage: Clin 27, 102310 (2020).","journal-title":"NeuroImage: Clin"},{"key":"98360_CR35","doi-asserted-by":"publisher","first-page":"261","DOI":"10.3174\/ajnr.A7790","volume":"44","author":"F Rahmani","year":"2023","unstructured":"Rahmani, F. et al. Validity assessment of an automated brain morphometry tool for patients with de novo memory symptoms. AJNR Am. J. Neuroradiol. 44, 261\u2013267. https:\/\/doi.org\/10.3174\/ajnr.A7790 (2023).","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"98360_CR36","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1007\/s00234-016-1737-3","volume":"58","author":"S Haller","year":"2016","unstructured":"Haller, S. et al. Basic MR sequence parameters systematically bias automated brain volume estimation. Neuroradiology 58, 1153\u20131160 (2016).","journal-title":"Neuroradiology"},{"key":"98360_CR37","doi-asserted-by":"publisher","first-page":"1359","DOI":"10.1007\/s00234-022-02898-w","volume":"64","author":"LA Zaki","year":"2022","unstructured":"Zaki, L. A. et al. Comparing two artificial intelligence software packages for normative brain volumetry in memory clinic imaging. Neuroradiology 64, 1359\u20131366 (2022).","journal-title":"Neuroradiology"},{"key":"98360_CR38","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1007\/s10278-015-9847-8","volume":"29","author":"P Kalavathi","year":"2016","unstructured":"Kalavathi, P. & Prasath, V. B. Methods on skull stripping of MRI head scan images-a review. J. Digit Imag. 29, 365\u2013379. https:\/\/doi.org\/10.1007\/s10278-015-9847-8 (2016).","journal-title":"J. Digit Imag."},{"key":"98360_CR39","doi-asserted-by":"publisher","first-page":"10826","DOI":"10.1038\/s41598-022-14983-4","volume":"12","author":"L Pei","year":"2022","unstructured":"Pei, L. et al. A general skull stripping of multiparametric brain MRIs using 3D convolutional neural network. Sci. Rep. 12, 10826. https:\/\/doi.org\/10.1038\/s41598-022-14983-4 (2022).","journal-title":"Sci. Rep."},{"key":"98360_CR40","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1016\/j.neuroimage.2015.03.035","volume":"112","author":"MA Fraser","year":"2015","unstructured":"Fraser, M. A., Shaw, M. E. & Cherbuin, N. A systematic review and meta-analysis of longitudinal hippocampal atrophy in healthy human ageing. Neuroimage 112, 364\u2013374. https:\/\/doi.org\/10.1016\/j.neuroimage.2015.03.035 (2015).","journal-title":"Neuroimage"},{"key":"98360_CR41","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1148\/radiol.2482070938","volume":"248","author":"JD Sluimer","year":"2008","unstructured":"Sluimer, J. D. et al. Whole-brain atrophy rate and cognitive decline: Longitudinal MR study of memory clinic patients. Radiology 248, 590\u2013598. https:\/\/doi.org\/10.1148\/radiol.2482070938 (2008).","journal-title":"Radiology"},{"key":"98360_CR42","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.neubiorev.2015.02.008","volume":"52","author":"C Scarpazza","year":"2015","unstructured":"Scarpazza, C., Tognin, S., Frisciata, S., Sartori, G. & Mechelli, A. False positive rates in Voxel-based Morphometry studies of the human brain: Should we be worried?. Neurosci. Biobehav. Rev. 52, 49\u201355. https:\/\/doi.org\/10.1016\/j.neubiorev.2015.02.008 (2015).","journal-title":"Neurosci. Biobehav. Rev."},{"key":"98360_CR43","doi-asserted-by":"publisher","first-page":"1283","DOI":"10.1002\/jmri.24038","volume":"38","author":"H Takao","year":"2013","unstructured":"Takao, H., Hayashi, N. & Ohtomo, K. Effects of the use of multiple scanners and of scanner upgrade in longitudinal voxel-based morphometry studies. J. Magn. Reson. Imag. 38, 1283\u20131291. https:\/\/doi.org\/10.1002\/jmri.24038 (2013).","journal-title":"J. Magn. Reson. Imag."},{"key":"98360_CR44","doi-asserted-by":"publisher","first-page":"2216","DOI":"10.1016\/j.neuroimage.2009.10.066","volume":"49","author":"H-J Huppertz","year":"2010","unstructured":"Huppertz, H.-J., Kr\u00f6ll-Seger, J., Kl\u00f6ppel, S., Ganz, R. E. & Kassubek, J. Intra-and interscanner variability of automated voxel-based volumetry based on a 3D probabilistic atlas of human cerebral structures. Neuroimage 49, 2216\u20132224 (2010).","journal-title":"Neuroimage"},{"key":"98360_CR45","unstructured":"Richiardi, J. et al. Establishing and evaluating reference atrophy ranges from consecutive image pairs in brain MR volumetry."},{"key":"98360_CR46","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0044195","volume":"7","author":"DP Streitb\u00fcrger","year":"2012","unstructured":"Streitb\u00fcrger, D. P. et al. Investigating structural brain changes of dehydration using voxel-based morphometry. PLoS ONE 7, e44195. https:\/\/doi.org\/10.1371\/journal.pone.0044195 (2012).","journal-title":"PLoS ONE"},{"key":"98360_CR47","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1212\/01.Wnl.0000150542.16969.Cc","volume":"64","author":"T Duning","year":"2005","unstructured":"Duning, T. et al. Dehydration confounds the assessment of brain atrophy. Neurology 64, 548\u2013550. https:\/\/doi.org\/10.1212\/01.Wnl.0000150542.16969.Cc (2005).","journal-title":"Neurology"},{"key":"98360_CR48","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.nicl.2014.08.014","volume":"6","author":"K Nakamura","year":"2014","unstructured":"Nakamura, K., Brown, R. A., Araujo, D., Narayanan, S. & Arnold, D. L. Correlation between brain volume change and T2 relaxation time induced by dehydration and rehydration: Implications for monitoring atrophy in clinical studies. Neuroimage Clin. 6, 166\u2013170. https:\/\/doi.org\/10.1016\/j.nicl.2014.08.014 (2014).","journal-title":"Neuroimage Clin."},{"key":"98360_CR49","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1016\/j.neurobiolaging.2008.06.016","volume":"31","author":"JD Sluimer","year":"2010","unstructured":"Sluimer, J. D. et al. Whole-brain atrophy rate and CSF biomarker levels in MCI and AD: A longitudinal study. Neurobiol. Aging 31, 758\u2013764. https:\/\/doi.org\/10.1016\/j.neurobiolaging.2008.06.016 (2010).","journal-title":"Neurobiol. Aging"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-98360-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-98360-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-98360-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T05:09:35Z","timestamp":1745903375000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-98360-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,29]]},"references-count":49,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["98360"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-98360-x","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,29]]},"assertion":[{"value":"26 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"M.E.M. and H.W. report consultancy for Siemens Healthineers GmbH. M.E.M. reports unrelated consultancy for EppData GmbH. The remaining authors have no conflicts of interest to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"14968"}}