{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T02:26:44Z","timestamp":1785292004978,"version":"3.55.0"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T00:00:00Z","timestamp":1708992000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T00:00:00Z","timestamp":1708992000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Alz Res Therapy"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n \n The pathophysiology of Alzheimer\u2019s disease (AD) involves\n \n \n $$\\beta$$<\/jats:tex-math>\n \n \u03b2<\/mml:mi>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n -amyloid (A\n \n \n $$\\beta$$<\/jats:tex-math>\n \n \u03b2<\/mml:mi>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n ) accumulation. Early identification of individuals with abnormal\n \n \n $$\\beta$$<\/jats:tex-math>\n \n \u03b2<\/mml:mi>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n -amyloid levels is crucial, but A\n \n \n $$\\beta$$<\/jats:tex-math>\n \n \u03b2<\/mml:mi>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n quantification with positron emission tomography (PET) and cerebrospinal fluid (CSF) is invasive and expensive.\n <\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n We propose a machine learning framework using standard non-invasive (MRI, demographics, APOE, neuropsychology) measures to predict future A\n \n \n $$\\beta$$<\/jats:tex-math>\n \n \u03b2<\/mml:mi>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n -positivity in A\n \n \n $$\\beta$$<\/jats:tex-math>\n \n \u03b2<\/mml:mi>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n -negative individuals. We separately study A\n \n \n $$\\beta$$<\/jats:tex-math>\n \n \u03b2<\/mml:mi>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n -positivity defined by PET and CSF.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Cross-validated AUC for 4-year A\n \n \n $$\\beta$$<\/jats:tex-math>\n \n \u03b2<\/mml:mi>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n conversion prediction was 0.78 for the CSF-based and 0.68 for the PET-based A\n \n \n $$\\beta$$<\/jats:tex-math>\n \n \u03b2<\/mml:mi>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n definitions. Although not trained for the clinical status-change prediction, the CSF-based model excelled in predicting future mild cognitive impairment (MCI)\/dementia conversion in cognitively normal\/MCI individuals (AUCs, respectively, 0.76 and 0.89 with a separate dataset).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n \n Standard measures have potential in detecting future A\n \n \n $$\\beta$$<\/jats:tex-math>\n \n \u03b2<\/mml:mi>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n -positivity and assessing conversion risk, even in cognitively normal individuals. The CSF-based definition led to better predictions than the PET-based definition.\n <\/jats:p>\n <\/jats:sec>","DOI":"10.1186\/s13195-024-01415-w","type":"journal-article","created":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T08:02:31Z","timestamp":1709020951000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Machine learning prediction of future amyloid beta positivity in amyloid-negative individuals"],"prefix":"10.1186","volume":"16","author":[{"given":"Elaheh","family":"Moradi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mithilesh","family":"Prakash","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anette","family":"Hall","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alina","family":"Solomon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bryan","family":"Strange","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jussi","family":"Tohka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"for the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,27]]},"reference":[{"issue":"4","key":"1415_CR1","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 CR Jr, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA research framework: toward a biological definition of Alzheimer\u2019s disease. Alzheimers Dement. 2018;14(4):535\u201362.","journal-title":"Alzheimers Dement."},{"issue":"10284","key":"1415_CR2","doi-asserted-by":"publisher","first-page":"1577","DOI":"10.1016\/S0140-6736(20)32205-4","volume":"397","author":"P Scheltens","year":"2021","unstructured":"Scheltens P, De Strooper B, Kivipelto M, Holstege H, Ch\u00e9telat G, Teunissen CE, et al. Alzheimer\u2019s disease. Lancet. 2021;397(10284):1577\u201390.","journal-title":"Lancet."},{"key":"1415_CR3","doi-asserted-by":"crossref","unstructured":"Villemagne VL, Dor\u00e9 V, Burnham SC, Masters CL, Rowe CC. Imaging tau and amyloid-$$\\beta$$ proteinopathies in Alzheimer disease and other conditions. Nat Rev Neurol. 2018;14(4):225\u201336.","DOI":"10.1038\/nrneurol.2018.9"},{"issue":"3","key":"1415_CR4","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1093\/brain\/awu387","volume":"138","author":"AM Fagan","year":"2015","unstructured":"Fagan AM. What does it mean to be \u2018amyloid-positive\u2019? Brain. 2015;138(3):514\u20136.","journal-title":"Brain."},{"issue":"3","key":"1415_CR5","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.jalz.2011.03.005","volume":"7","author":"GM McKhann","year":"2011","unstructured":"McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr, Kawas CH, 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. 2011;7(3):263\u20139.","journal-title":"Alzheimers Dement."},{"issue":"8","key":"1415_CR6","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1038\/nrneurol.2015.119","volume":"11","author":"JM Tarasoff-Conway","year":"2015","unstructured":"Tarasoff-Conway JM, Carare RO, Osorio RS, Glodzik L, Butler T, Fieremans E, et al. Clearance systems in the brain\u2013implications for Alzheimer disease. Nat Rev Neurol. 2015;11(8):457\u201370.","journal-title":"Nat Rev Neurol."},{"issue":"12","key":"1415_CR7","doi-asserted-by":"publisher","first-page":"1136","DOI":"10.1097\/nen.0b013e31815c5efb","volume":"66","author":"PT Nelson","year":"2007","unstructured":"Nelson PT, Jicha GA, Schmitt FA, Liu H, Davis DG, Mendiondo MS, et al. Clinicopathologic correlations in a large Alzheimer disease center autopsy cohort: neuritic plaques and neurofibrillary tangles\" do count\" when staging disease severity. J Neuropathol Exp Neurol. 2007;66(12):1136\u201346.","journal-title":"J Neuropathol Exp Neurol."},{"issue":"3","key":"1415_CR8","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1001\/jamaneurol.2021.5225","volume":"79","author":"CB Young","year":"2022","unstructured":"Young CB, Mormino EC. Prevalence rates of amyloid positivity\u2013updates and relevance. JAMA Neurol. 2022;79(3):225\u20137.","journal-title":"JAMA Neurol."},{"issue":"3","key":"1415_CR9","doi-asserted-by":"publisher","first-page":"801","DOI":"10.3233\/JAD-132561","volume":"41","author":"M Zwan","year":"2014","unstructured":"Zwan M, van Harten A, Ossenkoppele R, Bouwman F, Teunissen C, Adriaanse S, et al. Concordance between cerebrospinal fluid biomarkers and [11C] PIB PET in a memory clinic cohort. J Alzheimers Dis. 2014;41(3):801\u20137.","journal-title":"J Alzheimers Dis."},{"issue":"1","key":"1415_CR10","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/s00406-022-01439-z","volume":"273","author":"J Contador","year":"2023","unstructured":"Contador J, Vargas-Mart\u00ednez AM, Sanchez-Valle R, Trapero-Bertran M, Llad\u00f3 A. Cost-effectiveness of Alzheimer\u2019s disease CSF biomarkers and amyloid-PET in early-onset cognitive impairment diagnosis. Eur Arch Psychiatry Clin Neurosci. 2023;273(1):243\u201352.","journal-title":"Eur Arch Psychiatry Clin Neurosci."},{"key":"1415_CR11","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1016\/j.trci.2019.06.001","volume":"5","author":"R Wittenberg","year":"2019","unstructured":"Wittenberg R, Knapp M, Karagiannidou M, Dickson J, Schott JM. Economic impacts of introducing diagnostics for mild cognitive impairment Alzheimer\u2019s disease patients. Alzheimers Dement Transl Res Clin Interv. 2019;5:382\u20137.","journal-title":"Alzheimers Dement Transl Res Clin Interv."},{"key":"1415_CR12","doi-asserted-by":"crossref","unstructured":"Park CJ, Seo Y, Choe YS, Jang H, Lee H, Kim JP, et al. Predicting conversion of brain $$\\beta$$-amyloid positivity in amyloid-negative individuals. Alzheimers Res Ther. 2022;14(1):129.","DOI":"10.1186\/s13195-022-01067-8"},{"issue":"4","key":"1415_CR13","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1002\/ana.21610","volume":"65","author":"LM Shaw","year":"2009","unstructured":"Shaw LM, Vanderstichele H, Knapik-Czajka M, Clark CM, Aisen PS, Petersen RC, et al. Cerebrospinal fluid biomarker signature in Alzheimer\u2019s disease neuroimaging initiative subjects. Ann Neurol. 2009;65(4):403\u201313.","journal-title":"Ann Neurol."},{"issue":"1","key":"1415_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s42466-023-00262-8","volume":"5","author":"D Yosypyshyn","year":"2023","unstructured":"Yosypyshyn D, Ku\u010dikien\u0117 D, Ramakers I, Schulz JB, Reetz K, Costa AS. Clinical characteristics of patients with suspected Alzheimer\u2019s disease within a CSF A\u00df-ratio grey zone. Neurol Res Pract. 2023;5(1):1\u201311.","journal-title":"Neurol Res Pract."},{"key":"1415_CR15","doi-asserted-by":"crossref","unstructured":"Hansson O, Seibyl J, Stomrud E, Zetterberg H, Trojanowski JQ, Bittner T, et al. CSF biomarkers of Alzheimer\u2019s disease concord with amyloid-$$\\beta$$ PET and predict clinical progression: a study of fully automated immunoassays in BioFINDER and ADNI cohorts. Alzheimers Dement. 2018;14(11):1470\u201381.","DOI":"10.1016\/j.jalz.2018.01.010"},{"issue":"10","key":"1415_CR16","doi-asserted-by":"publisher","first-page":"5888","DOI":"10.1038\/s41380-021-01263-2","volume":"26","author":"M Bucci","year":"2021","unstructured":"Bucci M, Chiotis K, Nordberg A, Initiative ADN. Alzheimer\u2019s disease profiled by fluid and imaging markers: tau PET best predicts cognitive decline. Mol Psychiatry. 2021;26(10):5888\u201398.","journal-title":"Mol Psychiatry."},{"issue":"15","key":"1415_CR17","doi-asserted-by":"publisher","first-page":"1193","DOI":"10.1212\/WNL.0b013e3181bc010c","volume":"73","author":"WJ Jagust","year":"2009","unstructured":"Jagust WJ, Landau S, Shaw L, Trojanowski J, Koeppe R, Reiman E, et al. Relationships between biomarkers in aging and dementia. Neurology. 2009;73(15):1193\u20139.","journal-title":"Neurology."},{"issue":"3","key":"1415_CR18","doi-asserted-by":"publisher","first-page":"378","DOI":"10.2967\/jnumed.111.090340","volume":"53","author":"AD Joshi","year":"2012","unstructured":"Joshi AD, Pontecorvo MJ, Clark CM, Carpenter AP, Jennings DL, Sadowsky CH, et al. Performance characteristics of amyloid PET with florbetapir F 18 in patients with Alzheimer\u2019s disease and cognitively normal subjects. J Nucl Med. 2012;53(3):378\u201384.","journal-title":"J Nucl Med."},{"key":"1415_CR19","doi-asserted-by":"publisher","first-page":"1398","DOI":"10.1007\/s00259-014-2753-3","volume":"41","author":"S Landau","year":"2014","unstructured":"Landau S, Thomas B, Thurfjell L, Schmidt M, Margolin R, Mintun M, et al. Amyloid PET imaging in Alzheimer\u2019s disease: a comparison of three radiotracers. Eur J Nucl Med Mol Imaging. 2014;41:1398\u2013407.","journal-title":"Eur J Nucl Med Mol Imaging."},{"issue":"2","key":"1415_CR20","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","volume":"62","author":"B Fischl","year":"2012","unstructured":"Fischl B. FreeSurfer. Neuroimage. 2012;62(2):774\u201381.","journal-title":"Neuroimage"},{"key":"1415_CR21","volume-title":"UCSF freesurfer methods","author":"M Hartig","year":"2014","unstructured":"Hartig M, Truran-Sacrey D, Raptentsetsang S, Simonson A, Mezher A, Schuff N, et al. UCSF freesurfer methods. San Francisco: ADNI Alzheimers Disease Neuroimaging Initiative; 2014."},{"issue":"11","key":"1415_CR22","doi-asserted-by":"publisher","first-page":"0277322","DOI":"10.1371\/journal.pone.0277322","volume":"17","author":"BK Karaman","year":"2022","unstructured":"Karaman BK, Mormino EC, Sabuncu MR, Initiative ADN. Machine learning based multi-modal prediction of future decline toward Alzheimer\u2019s disease: an empirical study. PLoS ONE. 2022;17(11):0277322.","journal-title":"PLoS ONE."},{"issue":"7","key":"1415_CR23","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1016\/j.jalz.2015.05.002","volume":"11","author":"CR Jack Jr","year":"2015","unstructured":"Jack CR Jr, Barnes J, Bernstein MA, Borowski BJ, Brewer J, Clegg S, et al. Magnetic resonance imaging in Alzheimer\u2019s disease neuroimaging initiative 2. Alzheimers Dement. 2015;11(7):740\u201356.","journal-title":"Alzheimers Dement."},{"key":"1415_CR24","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.mri.2018.03.003","volume":"50","author":"M Gomez-Sancho","year":"2018","unstructured":"Gomez-Sancho M, Tohka J, Gomez-Verdejo V, Initiative ADN, et al. Comparison of feature representations in MRI-based MCI-to-AD conversion prediction. Magn Reson Imaging. 2018;50:84\u201395.","journal-title":"Magn Reson Imaging."},{"issue":"5","key":"1415_CR25","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1016\/j.jalz.2018.12.009","volume":"15","author":"EC Edmonds","year":"2019","unstructured":"Edmonds EC, McDonald CR, Marshall A, Thomas KR, Eppig J, Weigand AJ, et al. Early versus late MCI: improved MCI staging using a neuropsychological approach. Alzheimers Dement. 2019;15(5):699\u2013708.","journal-title":"Alzheimers Dement."},{"issue":"1","key":"1415_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v033.i01","volume":"33","author":"J Friedman","year":"2010","unstructured":"Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33(1):1.","journal-title":"J Stat Softw."},{"issue":"1","key":"1415_CR27","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1002\/wics.14","volume":"1","author":"GC McDonald","year":"2009","unstructured":"McDonald GC. Ridge regression. Wiley Interdiscip Rev Comput Stat. 2009;1(1):93\u2013100.","journal-title":"Wiley Interdiscip Rev Comput Stat."},{"issue":"1","key":"1415_CR28","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1093\/biostatistics\/kxj037","volume":"8","author":"WE Johnson","year":"2007","unstructured":"Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8(1):118\u201327.","journal-title":"Biostatistics."},{"key":"1415_CR29","doi-asserted-by":"publisher","first-page":"116956","DOI":"10.1016\/j.neuroimage.2020.116956","volume":"218","author":"J Radua","year":"2020","unstructured":"Radua J, Vieta E, Shinohara R, Kochunov P, Quid\u00e9 Y, Green MJ, et al. Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA. NeuroImage. 2020;218:116956.","journal-title":"NeuroImage."},{"issue":"2","key":"1415_CR30","doi-asserted-by":"publisher","first-page":"172","DOI":"10.2967\/jnumed.121.262464","volume":"63","author":"F Orlhac","year":"2022","unstructured":"Orlhac F, Eertink JJ, Cottereau AS, Zijlstra JM, Thieblemont C, Meignan M, et al. A guide to ComBat harmonization of imaging biomarkers in multicenter studies. J Nucl Med. 2022;63(2):172\u20139.","journal-title":"J Nucl Med."},{"key":"1415_CR31","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.neuroimage.2017.11.024","volume":"167","author":"JP Fortin","year":"2018","unstructured":"Fortin JP, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA, et al. Harmonization of cortical thickness measurements across scanners and sites. Neuroimage. 2018;167:104\u201320.","journal-title":"Neuroimage."},{"issue":"4","key":"1415_CR32","doi-asserted-by":"publisher","first-page":"0121945","DOI":"10.1371\/journal.pone.0121945","volume":"10","author":"B Diedenhofen","year":"2015","unstructured":"Diedenhofen B, Musch J. cocor: a comprehensive solution for the statistical comparison of correlations. PLoS ONE. 2015;10(4):0121945.","journal-title":"PLoS ONE."},{"key":"1415_CR33","unstructured":"Hastie T, Qian J, Tay K. An Introduction to glmnet. CRAN R Repositary; 2021."},{"key":"1415_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v028.i05","volume":"28","author":"M Kuhn","year":"2008","unstructured":"Kuhn M. Building predictive models in R using the caret package. J Stat Softw. 2008;28:1\u201326.","journal-title":"J Stat Softw."},{"issue":"6","key":"1415_CR35","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1093\/bioinformatics\/bts034","volume":"28","author":"JT Leek","year":"2012","unstructured":"Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28(6):882\u20133.","journal-title":"Bioinformatics."},{"key":"1415_CR36","unstructured":"Diedenhofen B, Diedenhofen MB. Package \u2018cocor\u2019. Comprehensive R Archive Network; 2016. https:\/\/cran.r-project.org\/web\/packages\n. Accessed 24 Mar 2022."},{"key":"1415_CR37","unstructured":"Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, et\u00a0al. Package \u201cpROC\u2019. Technical Report. 2017. https:\/\/cran.r-project.org\/web."},{"issue":"3","key":"1415_CR38","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1016\/j.csda.2008.09.023","volume":"53","author":"S Potapov","year":"2009","unstructured":"Potapov S, Adler W, Hofner B, Lausen B. Package \u2018daim\u2019. Comput Stat Data Anal. 2009;53(3):718\u201329.","journal-title":"Comput Stat Data Anal."},{"issue":"2","key":"1415_CR39","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1002\/wics.147","volume":"3","author":"H Wickham","year":"2011","unstructured":"Wickham H. ggplot2. Wiley Interdiscip Rev Comput Stat. 2011;3(2):180\u20135.","journal-title":"Wiley Interdiscip Rev Comput Stat."},{"issue":"18","key":"1415_CR40","doi-asserted-by":"publisher","first-page":"2847","DOI":"10.1093\/bioinformatics\/btw313","volume":"32","author":"Z Gu","year":"2016","unstructured":"Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics. 2016;32(18):2847\u20139.","journal-title":"Bioinformatics."},{"issue":"5","key":"1415_CR41","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1016\/j.tips.2015.03.002","volume":"36","author":"K Blennow","year":"2015","unstructured":"Blennow K, Mattsson N, Sch\u00f6ll M, Hansson O, Zetterberg H. Amyloid biomarkers in Alzheimer\u2019s disease. Trends Pharmacol Sci. 2015;36(5):297\u2013309.","journal-title":"Trends Pharmacol Sci."},{"issue":"1","key":"1415_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12987-022-00318-y","volume":"19","author":"Y Li","year":"2022","unstructured":"Li Y, Rusinek H, Butler T, Glodzik L, Pirraglia E, Babich J, et al. Decreased CSF clearance and increased brain amyloid in Alzheimer\u2019s disease. Fluids Barriers CNS. 2022;19(1):1\u20139.","journal-title":"Fluids Barriers CNS."},{"key":"1415_CR43","doi-asserted-by":"crossref","unstructured":"Sala A, Nordberg A, Rodriguez-Vieitez E, Initiative ADN. Longitudinal pathways of cerebrospinal fluid and positron emission tomography biomarkers of amyloid-$$\\beta$$ positivity. Mol Psychiatry. 2021;26(10):5864\u201374.","DOI":"10.1038\/s41380-020-00950-w"},{"key":"1415_CR44","unstructured":"Jagust WJ, Landau SM, Initiative ADN, et al. Temporal dynamics of $$\\beta$$-amyloid accumulation in aging and Alzheimer disease. Neurology. 2021;96(9):e1347\u201357."},{"key":"1415_CR45","doi-asserted-by":"crossref","unstructured":"Elman JA, Panizzon MS, Gustavson DE, Franz CE, Sanderson-Cimino ME, Lyons MJ, et al. Amyloid-$$\\beta$$ positivity predicts cognitive decline but cognition predicts progression to amyloid-$$\\beta$$ positivity. Biol Psychiatry. 2020;87(9):819\u201328.","DOI":"10.1016\/j.biopsych.2019.12.021"},{"issue":"9","key":"1415_CR46","doi-asserted-by":"publisher","first-page":"1687","DOI":"10.1002\/alz.12618","volume":"18","author":"H Zetterberg","year":"2022","unstructured":"Zetterberg H. Biofluid-based biomarkers for Alzheimer\u2019s disease-related pathologies: an update and synthesis of the literature. Alzheimers Dement. 2022;18(9):1687\u201393.","journal-title":"Alzheimers Dement."},{"key":"1415_CR47","doi-asserted-by":"crossref","unstructured":"Palmqvist S, Insel PS, Zetterberg H, Blennow K, Brix B, Stomrud E, et al. Accurate risk estimation of $$\\beta$$-amyloid positivity to identify prodromal Alzheimer\u2019s disease: cross-validation study of practical algorithms. Alzheimers Dement. 2019;15(2):194\u2013204.","DOI":"10.1016\/j.jalz.2018.08.014"},{"key":"1415_CR48","doi-asserted-by":"crossref","unstructured":"Insel PS, Palmqvist S, Mackin RS, Nosheny RL, Hansson O, Weiner MW, et al. Assessing risk for preclinical $$\\beta$$-amyloid pathology with APOE, cognitive, and demographic information. Alzheimers Dement Diagn Assess Dis Monit. 2016;4:76\u201384.","DOI":"10.1016\/j.dadm.2016.07.002"},{"key":"1415_CR49","doi-asserted-by":"crossref","unstructured":"Ezzati A, Harvey D, Golzar A, Habeck C, Qureshi I, Zammit A, et\u00a0al. Risk estimation of Amyloid Beta positivity on PET with Cognitive, CSF Biomarkers, volumetric MRI, APOE, and Demographic information, vol 515. AAN Enterprises; 2020.","DOI":"10.1212\/WNL.94.15_supplement.515"},{"key":"1415_CR50","doi-asserted-by":"crossref","unstructured":"Tosun D, Veitch D, Aisen P, Jack\u00a0Jr CR, Jagust WJ, Petersen RC, et\u00a0al. Detection of $$\\beta$$-amyloid positivity in Alzheimer\u2019s Disease Neuroimaging Initiative participants with demographics, cognition, MRI and plasma biomarkers. Brain Commun. 2021;3(2):fcab008.","DOI":"10.1093\/braincomms\/fcab008"},{"key":"1415_CR51","doi-asserted-by":"crossref","unstructured":"Li C, Liu M, Xia J, Mei L, Yang Q, Shi F, et\u00a0al. Predicting Brain Amyloid-$$\\beta$$ PET Grades with Graph Convolutional Networks Based on Functional MRI and Multi-Level Functional Connectivity. J Alzheimers Dis. 2022;(Preprint):1\u201315.","DOI":"10.1101\/2021.08.26.21262325"},{"key":"1415_CR52","doi-asserted-by":"crossref","unstructured":"Palmqvist S, Sch\u00f6ll M, Strandberg O, Mattsson N, Stomrud E, Zetterberg H, et al. Earliest accumulation of $$\\beta$$-amyloid occurs within the default-mode network and concurrently affects brain connectivity. Nat Commun. 2017;8(1):1214.","DOI":"10.1038\/s41467-017-01150-x"},{"key":"1415_CR53","doi-asserted-by":"publisher","unstructured":"Strange BA, Zhang L, Sierra-Marcos A, Alfayate E, Tohka J, Medina M. Predicting the future development of mild cognitive impairment in the cognitively healthy elderly. bioRxiv. 2020:2020\u201307. https:\/\/doi.org\/10.1101\/2020.07.30.227496.","DOI":"10.1101\/2020.07.30.227496"},{"key":"1415_CR54","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1016\/j.neuroimage.2014.10.002","volume":"104","author":"E Moradi","year":"2015","unstructured":"Moradi E, Pepe A, Gaser C, Huttunen H, Tohka J, Initiative ADN, et al. Machine learning framework for early MRI-based Alzheimer\u2019s conversion prediction in MCI subjects. Neuroimage. 2015;104:398\u2013412.","journal-title":"Neuroimage."},{"issue":"6","key":"1415_CR55","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.1038\/s41591-021-01348-z","volume":"27","author":"S Palmqvist","year":"2021","unstructured":"Palmqvist S, Tideman P, Cullen N, Zetterberg H, Blennow K, Initiative ADN, et al. Prediction of future Alzheimer\u2019s disease dementia using plasma phospho-tau combined with other accessible measures. Nat Med. 2021;27(6):1034\u201342.","journal-title":"Nat Med."},{"issue":"10","key":"1415_CR56","doi-asserted-by":"publisher","first-page":"3136","DOI":"10.1093\/brain\/awaa248","volume":"143","author":"CR Jack Jr","year":"2020","unstructured":"Jack CR Jr, Wiste HJ, Weigand SD, Therneau TM, Lowe VJ, Knopman DS, et al. Predicting future rates of tau accumulation on PET. Brain. 2020;143(10):3136\u201350.","journal-title":"Brain."},{"issue":"14","key":"1415_CR57","doi-asserted-by":"publisher","first-page":"1240","DOI":"10.1212\/WNL.0000000000001991","volume":"85","author":"S Palmqvist","year":"2015","unstructured":"Palmqvist S, Zetterberg H, Mattsson N, Johansson P, Minthon L, Blennow K, et al. Detailed comparison of amyloid PET and CSF biomarkers for identifying early Alzheimer disease. Neurology. 2015;85(14):1240\u20139.","journal-title":"Neurology."},{"key":"1415_CR58","doi-asserted-by":"crossref","unstructured":"Palmqvist S, Mattsson N, Hansson O, Initiative ADN. Cerebrospinal fluid analysis detects cerebral amyloid-$$\\beta$$ accumulation earlier than positron emission tomography. Brain. 2016;139(4):1226\u201336.","DOI":"10.1093\/brain\/aww015"}],"container-title":["Alzheimer's Research & Therapy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13195-024-01415-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13195-024-01415-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13195-024-01415-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T11:08:36Z","timestamp":1709032116000},"score":1,"resource":{"primary":{"URL":"https:\/\/alzres.biomedcentral.com\/articles\/10.1186\/s13195-024-01415-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,27]]},"references-count":58,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1415"],"URL":"https:\/\/doi.org\/10.1186\/s13195-024-01415-w","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2023.07.17.547202","asserted-by":"object"}]},"ISSN":["1758-9193"],"issn-type":[{"value":"1758-9193","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,27]]},"assertion":[{"value":"25 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The ADNI study was approved by the Institutional Review Board (IRB) of each participating site and was conducted in accordance with Federal Regulations. For participants in ADNI protocols, written informed consent was obtained and the study was conducted in accordance with the Declaration of Helsinki.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"46"}}