{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T06:47:14Z","timestamp":1790750834823,"version":"4.1.0"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T00:00:00Z","timestamp":1717545600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T00:00:00Z","timestamp":1717545600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Only a third of individuals with mild cognitive impairment (MCI) progress to dementia of the Alzheimer\u2019s type (DAT). Identifying biomarkers that distinguish individuals with MCI who will progress to DAT (MCI-Converters) from those who will not (MCI-Non-Converters) remains a key challenge in the field. In our study, we evaluate whether the individual rates of loss of volumes of the Hippocampus and entorhinal cortex (EC) with age in the MCI stage can predict progression to DAT. Using data from 758 MCI patients in the Alzheimer\u2019s Disease Neuroimaging Database, we employ Linear Mixed Effects (LME) models to estimate individual trajectories of regional brain volume loss over 12\u00a0years on average. Our approach involves three key analyses: (1) mapping age-related volume loss trajectories in MCI-Converters and Non-Converters, (2) using logistic regression to predict progression to DAT based on individual rates of hippocampal and EC volume loss, and (3) examining the relationship between individual estimates of these volumetric changes and cognitive decline across different cognitive functions\u2014episodic memory, visuospatial processing, and executive function. We find that the loss of Hippocampal volume is significantly more rapid in MCI-Converters than Non-Converters, but find no such difference in EC volumes. We also find that the rate of hippocampal volume loss in the MCI stage is a significant predictor of conversion to DAT, while the rate of volume loss in the EC and other additional regions is not. Finally, individual estimates of rates of regional volume loss in both the Hippocampus and EC, and other additional regions, correlate strongly with individual rates of cognitive decline. Across all analyses, we find significant individual variation in the initial volumes and the rates of changes in volume with age in individuals with MCI. This study highlights the importance of personalized approaches in predicting AD progression, offering insights for future research and intervention strategies.<\/jats:p>","DOI":"10.1038\/s41598-024-63301-7","type":"journal-article","created":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T10:03:42Z","timestamp":1717581822000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Estimating individual trajectories of structural and cognitive decline in mild cognitive impairment for early prediction of progression to dementia of the Alzheimer\u2019s type"],"prefix":"10.1038","volume":"14","author":[{"given":"Shreya K.","family":"Rajagopal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adriene M.","family":"Beltz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin M.","family":"Hampstead","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thad A.","family":"Polk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,5]]},"reference":[{"issue":"1","key":"63301_CR1","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1111\/ene.13439","volume":"25","author":"CA Lane","year":"2018","unstructured":"Lane, C. A., Hardy, J. & Schott, J. M. Alzheimer\u2019s disease. Eur. J. Neurol. 25(1), 59\u201370. https:\/\/doi.org\/10.1111\/ene.13439 (2018).","journal-title":"Eur. J. Neurol."},{"key":"63301_CR2","doi-asserted-by":"publisher","first-page":"123","DOI":"10.2147\/DNND.S228939","volume":"9","author":"J Rasmussen","year":"2019","unstructured":"Rasmussen, J. & Langerman, H. Alzheimer\u2019s disease\u2014Why we need early diagnosis. Degener. Neurol. Neuromuscul. Dis. 9, 123\u2013130. https:\/\/doi.org\/10.2147\/DNND.S228939 (2019).","journal-title":"Degener. Neurol. Neuromuscul. Dis."},{"issue":"9518","key":"63301_CR3","doi-asserted-by":"publisher","first-page":"1262","DOI":"10.1016\/S0140-6736(06)68542-5","volume":"367","author":"S Gauthier","year":"2006","unstructured":"Gauthier, S. et al. mild cognitive impairment. Lancet 367(9518), 1262\u20131270. https:\/\/doi.org\/10.1016\/S0140-6736(06)68542-5 (2006).","journal-title":"Lancet"},{"issue":"2","key":"63301_CR4","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1212\/01.CON.0000267234.69379.4e","volume":"13","author":"RC Petersen","year":"2007","unstructured":"Petersen, R. C. Mild cognitive impairment. Continuum Lifelong Learn. Neurol. 13(2), 15. https:\/\/doi.org\/10.1212\/01.CON.0000267234.69379.4e (2007).","journal-title":"Continuum Lifelong Learn. Neurol."},{"key":"63301_CR5","unstructured":"Alzheimer\u2019s Association. Alzheimer\u2019s Disease Facts and Figures. https:\/\/www.alz.org\/media\/Documents\/alzheimers-facts-and-figures.pdf (2022)."},{"issue":"3","key":"63301_CR6","doi-asserted-by":"publisher","first-page":"994","DOI":"10.1523\/JNEUROSCI.23-03-00994.2003","volume":"23","author":"PM Thompson","year":"2003","unstructured":"Thompson, P. M. et al. Dynamics of gray matter loss in Alzheimer\u2019s disease. J. Neurosci. 23(3), 994\u20131005. https:\/\/doi.org\/10.1523\/JNEUROSCI.23-03-00994.2003 (2003).","journal-title":"J. Neurosci."},{"issue":"4","key":"63301_CR7","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/S0197-4580(97)00056-0","volume":"18","author":"H Braak","year":"1997","unstructured":"Braak, H. & Braak, E. Frequency of stages of Alzheimer-related lesions in different age categories. Neurobiol. Aging 18(4), 351\u2013357. https:\/\/doi.org\/10.1016\/S0197-4580(97)00056-0 (1997).","journal-title":"Neurobiol. Aging"},{"issue":"11","key":"63301_CR8","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1212\/01.wnl.0000256697.20968.d7","volume":"68","author":"DP Devanand","year":"2007","unstructured":"Devanand, D. P. et al. Hippocampal and entorhinal atrophy in mild cognitive impairment: Prediction of Alzheimer disease. Neurology 68(11), 828\u2013836 (2007).","journal-title":"Neurology"},{"issue":"1","key":"63301_CR9","first-page":"139","volume":"20","author":"K Juottonen","year":"1999","unstructured":"Juottonen, K., Laakso, M. P., Partanen, K. & Soininen, H. Comparative MR analysis of the entorhinal cortex and hippocampus in diagnosing Alzheimer disease. Am. J. Neuroradiol. 20(1), 139\u2013144 (1999).","journal-title":"Am. J. Neuroradiol."},{"key":"63301_CR10","doi-asserted-by":"publisher","first-page":"174524","DOI":"10.5402\/2013\/174524","volume":"2013","author":"A Vijayakumar","year":"2012","unstructured":"Vijayakumar, A. & Vijayakumar, A. Comparison of hippocampal volume in dementia subtypes. ISRN Radiol. 2013, 174524. https:\/\/doi.org\/10.5402\/2013\/174524 (2012).","journal-title":"ISRN Radiol."},{"issue":"5","key":"63301_CR11","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1001\/archneur.63.5.693","volume":"63","author":"LG Apostolova","year":"2006","unstructured":"Apostolova, L. G. et al. Conversion of mild cognitive impairment to Alzheimer disease predicted by hippocampal atrophy maps. Arch. Neurol. 63(5), 693\u2013699. https:\/\/doi.org\/10.1001\/archneur.63.5.693 (2006).","journal-title":"Arch. Neurol."},{"issue":"4","key":"63301_CR12","doi-asserted-by":"publisher","first-page":"1359","DOI":"10.3233\/JAD-170261","volume":"59","author":"E Lawrence","year":"2017","unstructured":"Lawrence, E. et al. A systematic review of longitudinal studies which measure Alzheimer\u2019s disease biomarkers. J. Alzheimer\u2019s Dis. 59(4), 1359\u20131379. https:\/\/doi.org\/10.3233\/JAD-170261 (2017).","journal-title":"J. Alzheimer\u2019s Dis."},{"issue":"1","key":"63301_CR13","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1037\/h0029382","volume":"74","author":"LJ Cronbach","year":"1970","unstructured":"Cronbach, L. J. & Furby, L. How we should measure \u201cchange\u201d Or should we? Psychol. Bull. 74(1), 68 (1970).","journal-title":"Psychol. Bull."},{"issue":"7902","key":"63301_CR14","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1038\/s41586-022-04492-9","volume":"603","author":"S Marek","year":"2022","unstructured":"Marek, S. et al. Reproducible brain-wide association studies require thousands of individuals. Nature 603(7902), 654\u2013660 (2022).","journal-title":"Nature"},{"issue":"9","key":"63301_CR15","doi-asserted-by":"publisher","first-page":"1305","DOI":"10.1016\/j.jalz.2018.07.219","volume":"16","author":"Y Stern","year":"2020","unstructured":"Stern, Y. et al. Whitepaper: Defining and investigating cognitive reserve, brain reserve, and brain maintenance. Alzheimer\u2019s Dementia 16(9), 1305\u20131311. https:\/\/doi.org\/10.1016\/j.jalz.2018.07.219 (2020).","journal-title":"Alzheimer\u2019s Dementia"},{"key":"63301_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neurobiolaging.2017.05.004","volume":"57","author":"MC Hoenig","year":"2017","unstructured":"Hoenig, M. C. et al. Tau pathology and cognitive reserve in Alzheimer\u2019s disease. Neurobiol. Aging 57, 1\u20137. https:\/\/doi.org\/10.1016\/j.neurobiolaging.2017.05.004 (2017).","journal-title":"Neurobiol. Aging"},{"issue":"11","key":"63301_CR17","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.1016\/S1474-4422(12)70191-6","volume":"11","author":"Y Stern","year":"2012","unstructured":"Stern, Y. Cognitive reserve in ageing and Alzheimer\u2019s disease. Lancet Neurol. 11(11), 1006\u20131012. https:\/\/doi.org\/10.1016\/S1474-4422(12)70191-6 (2012).","journal-title":"Lancet Neurol."},{"key":"63301_CR18","doi-asserted-by":"publisher","first-page":"101645","DOI":"10.1016\/j.nicl.2018.101645","volume":"21","author":"S Basaia","year":"2019","unstructured":"Basaia, S. et al. Automated classification of Alzheimer\u2019s disease and mild cognitive impairment using a single MRI and deep neural networks. NeuroImage Clin. 21, 101645 (2019).","journal-title":"NeuroImage Clin."},{"issue":"8","key":"63301_CR19","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1016\/j.jalz.2019.02.007","volume":"15","author":"H Li","year":"2019","unstructured":"Li, H., Habes, M., Wolk, D. A., Fan, Y., Alzheimer\u2019s Disease Neuroimaging Initiative. A deep learning model for early prediction of Alzheimer\u2019s disease dementia based on hippocampal magnetic resonance imaging data. Alzheimer\u2019s Dementia 15(8), 1059\u20131070 (2019).","journal-title":"Alzheimer\u2019s Dementia"},{"key":"63301_CR20","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.neuroimage.2019.01.031","volume":"189","author":"S Spasov","year":"2019","unstructured":"Spasov, S. et al. A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer\u2019s disease. Neuroimage 189, 276\u2013287 (2019).","journal-title":"Neuroimage"},{"key":"63301_CR21","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.media.2017.01.008","volume":"37","author":"HI Suk","year":"2017","unstructured":"Suk, H. I. et al. Deep ensemble learning of sparse regression models for brain disease diagnosis. Med. Image Anal. 37, 101\u2013113 (2017).","journal-title":"Med. Image Anal."},{"key":"63301_CR22","first-page":"1","volume":"32","author":"S Ajmera","year":"2019","unstructured":"Ajmera, S., Rajagopal, S., Rehman, R. & Sridharan, D. Infra-slow brain dynamics as a marker for cognitive function and decline. Adv. Neural Inf. Process. Syst. 32, 1 (2019).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"63301_CR23","doi-asserted-by":"publisher","first-page":"119541","DOI":"10.1016\/j.eswa.2023.119541","volume":"217","author":"M Bucholc","year":"2023","unstructured":"Bucholc, M., Titarenko, S., Ding, X., Canavan, C. & Chen, T. A hybrid machine learning approach for prediction of conversion from mild cognitive impairment to dementia. Expert Syst. Appl. 217, 119541 (2023).","journal-title":"Expert Syst. Appl."},{"key":"63301_CR24","doi-asserted-by":"publisher","first-page":"576029","DOI":"10.3389\/fneur.2020.576029","volume":"11","author":"AH Syaifullah","year":"2021","unstructured":"Syaifullah, A. H., Shiino, A., Ishida, M. & Tanigaki, K. Machine learning for diagnosis of AD and prediction of MCI progression from brain MRI using brain anatomical analysis using diffeomorphic deformation. Front. Neurol. 11, 576029 (2021).","journal-title":"Front. Neurol."},{"key":"63301_CR25","doi-asserted-by":"publisher","first-page":"688926","DOI":"10.3389\/fnagi.2021.688926","volume":"13","author":"T Zhang","year":"2021","unstructured":"Zhang, T. et al. Predicting MCI to AD conversation using integrated sMRI and rs-fMRI: Machine learning and graph theory approach. Front. Aging Neurosci. 13, 688926 (2021).","journal-title":"Front. Aging Neurosci."},{"issue":"1","key":"63301_CR26","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1038\/s41467-022-35712-5","volume":"14","author":"E Maheux","year":"2023","unstructured":"Maheux, E. et al. Forecasting individual progression trajectories in Alzheimer\u2019s disease. Nat. Commun. 14(1), 761 (2023).","journal-title":"Nat. Commun."},{"key":"63301_CR27","unstructured":"Lim, B. & van der Schaar, M. Disease-atlas: Navigating disease trajectories using deep learning. In Machine Learning for Healthcare Conference 137\u2013160 (PMLR, 2018)."},{"key":"63301_CR28","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.neuroscience.2023.01.029","volume":"514","author":"R Franciotti","year":"2023","unstructured":"Franciotti, R. et al. Comparison of machine learning-based approaches to predict the conversion to Alzheimer\u2019s disease from mild cognitive impairment. Neuroscience 514, 143\u2013152 (2023).","journal-title":"Neuroscience"},{"key":"63301_CR29","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.neurobiolaging.2017.04.025","volume":"57","author":"RK Olsen","year":"2017","unstructured":"Olsen, R. K. et al. Human anterolateral entorhinal cortex volumes are associated with cognitive decline in aging prior to clinical diagnosis. Neurobiol. Aging 57, 195\u2013205. https:\/\/doi.org\/10.1016\/j.neurobiolaging.2017.04.025 (2017).","journal-title":"Neurobiol. Aging"},{"issue":"1","key":"63301_CR30","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1111\/cns.12317","volume":"21","author":"G-P Peng","year":"2015","unstructured":"Peng, G.-P. et al. Correlation of hippocampal volume and cognitive performances in patients with either mild cognitive impairment or Alzheimer\u2019s disease. CNS Neurosci. Therap. 21(1), 15\u201322. https:\/\/doi.org\/10.1111\/cns.12317 (2015).","journal-title":"CNS Neurosci. Therap."},{"issue":"1","key":"63301_CR31","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1097\/WAD.0b013e3182163b62","volume":"26","author":"LG Apostolova","year":"2012","unstructured":"Apostolova, L. G. et al. Hippocampal atrophy and ventricular enlargement in normal aging, mild cognitive impairment and Alzheimer\u2019s disease. Alzheimer Dis. Assoc. Disord. 26(1), 17 (2012).","journal-title":"Alzheimer Dis. Assoc. Disord."},{"issue":"2","key":"63301_CR32","doi-asserted-by":"publisher","first-page":"647","DOI":"10.3233\/JAD-2010-1406","volume":"20","author":"BR Ott","year":"2010","unstructured":"Ott, B. R. et al. Brain ventricular volume and cerebrospinal fluid biomarkers of Alzheimer\u2019s disease. J. Alzheimer\u2019s Dis. 20(2), 647\u2013657. https:\/\/doi.org\/10.3233\/JAD-2010-1406 (2010).","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"63301_CR33","doi-asserted-by":"publisher","first-page":"101910","DOI":"10.1016\/j.compmedimag.2021.101910","volume":"90","author":"SA Mofrad","year":"2021","unstructured":"Mofrad, S. A. et al. A predictive framework based on brain volume trajectories enabling early detection of Alzheimer\u2019s disease. Comput. Med. Imaging Graph. 90, 101910 (2021).","journal-title":"Comput. Med. Imaging Graph."},{"issue":"1","key":"63301_CR34","doi-asserted-by":"publisher","first-page":"8020","DOI":"10.1038\/s41598-021-87434-1","volume":"11","author":"I Koval","year":"2021","unstructured":"Koval, I. et al. AD course map charts Alzheimer\u2019s disease progression. Sci. Rep. 11(1), 8020 (2021).","journal-title":"Sci. Rep."},{"issue":"4","key":"63301_CR35","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1007\/s00401-006-0127-z","volume":"112","author":"H Braak","year":"2006","unstructured":"Braak, H., Alafuzoff, I., Arzberger, T., Kretzschmar, H. & Del Tredici, K. Staging of Alzheimer disease-associated neurofibrillary pathology using paraffin sections and immunocytochemistry. Acta Neuropathol. 112(4), 389\u2013404 (2006).","journal-title":"Acta Neuropathol."},{"key":"63301_CR36","doi-asserted-by":"publisher","first-page":"e13159","DOI":"10.1111\/bpa.13159","volume":"33","author":"J Llamas-Rodr\u00edguez","year":"2023","unstructured":"Llamas-Rodr\u00edguez, J. et al. TDP-43 and tau concurrence in the entorhinal subfields in primary age-related tauopathy and preclinical Alzheimer\u2019s disease. Brain Pathol. 33, e13159 (2023).","journal-title":"Brain Pathol."},{"issue":"6259","key":"63301_CR37","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1126\/science.aac8128","volume":"350","author":"L Kunz","year":"2015","unstructured":"Kunz, L. et al. Reduced grid-cell\u2013like representations in adults at genetic risk for Alzheimer\u2019s disease. Science 350(6259), 430\u2013433 (2015).","journal-title":"Science"},{"key":"63301_CR38","doi-asserted-by":"publisher","first-page":"551829","DOI":"10.3389\/fnins.2020.00804","volume":"14","author":"S Kulason","year":"2020","unstructured":"Kulason, S. et al. Entorhinal and transentorhinal atrophy in preclinical Alzheimer\u2019s disease. Front. Neurosci. 14, 551829 (2020).","journal-title":"Front. Neurosci."},{"key":"63301_CR39","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.nicl.2014.04.009","volume":"5","author":"L Younes","year":"2014","unstructured":"Younes, L., Albert, M., Miller, M. I., BIOCARD Research Team. Inferring changepoint times of medial temporal lobe morphometric change in preclinical Alzheimer\u2019s disease. NeuroImage Clin. 5, 178\u2013187 (2014).","journal-title":"NeuroImage Clin."},{"issue":"5","key":"63301_CR40","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1016\/j.jalz.2010.12.008","volume":"7","author":"D Cabral","year":"2011","unstructured":"Cabral, D. et al. Frequency of Alzheimer\u2019s disease pathology at autopsy in patients with clinical normal pressure hydrocephalus. Alzheimer\u2019s Dementia 7(5), 509\u2013513 (2011).","journal-title":"Alzheimer\u2019s Dementia"},{"issue":"4","key":"63301_CR41","doi-asserted-by":"publisher","first-page":"1388","DOI":"10.1093\/cercor\/bhu238","volume":"26","author":"T Hedden","year":"2016","unstructured":"Hedden, T. et al. Multiple brain markers are linked to age-related variation in cognition. Cereb. Cortex 26(4), 1388\u20131400. https:\/\/doi.org\/10.1093\/cercor\/bhu238 (2016).","journal-title":"Cereb. Cortex"},{"issue":"2","key":"63301_CR42","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1037\/pag0000236","volume":"33","author":"JE Karr","year":"2018","unstructured":"Karr, J. E., Graham, R. B., Hofer, S. M. & Muniz-Terrera, G. When does cognitive decline begin? A systematic review of change point studies on accelerated decline in cognitive and neurological outcomes preceding mild cognitive impairment, dementia, and death. Psychol. Aging 33(2), 195 (2018).","journal-title":"Psychol. Aging"},{"issue":"2","key":"63301_CR43","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/S1474-4422(12)70291-0","volume":"12","author":"CR Jack","year":"2013","unstructured":"Jack, C. R. et al. Tracking pathophysiological processes in Alzheimer\u2019s disease: An updated hypothetical model of dynamic biomarkers. Lancet Neurol. 12(2), 207\u2013216. https:\/\/doi.org\/10.1016\/S1474-4422(12)70291-0 (2013).","journal-title":"Lancet Neurol."},{"issue":"3","key":"63301_CR44","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1093\/cercor\/bhab223","volume":"32","author":"K Kwak","year":"2022","unstructured":"Kwak, K. et al. Differential role for hippocampal subfields in Alzheimer\u2019s disease progression revealed with deep learning. Cereb. Cortex 32(3), 467\u2013478 (2022).","journal-title":"Cereb. Cortex"},{"issue":"6","key":"63301_CR45","doi-asserted-by":"publisher","first-page":"e27429","DOI":"10.1016\/j.heliyon.2024.e27429","volume":"10","author":"M Punzi","year":"2024","unstructured":"Punzi, M. et al. Atrophy of hippocampal subfields and amygdala nuclei in subjects with mild cognitive impairment progressing to Alzheimer\u2019s disease. Heliyon 10(6), e27429 (2024).","journal-title":"Heliyon"},{"issue":"1","key":"63301_CR46","doi-asserted-by":"publisher","first-page":"237","DOI":"10.3233\/JAD-200775","volume":"79","author":"Q Zeng","year":"2021","unstructured":"Zeng, Q. et al. Distinct atrophy pattern of hippocampal subfields in patients with progressive and stable mild cognitive impairment: A longitudinal MRI study. J. Alzheimer\u2019s Dis. 79(1), 237\u2013247 (2021).","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"63301_CR47","doi-asserted-by":"publisher","first-page":"746","DOI":"10.1007\/s10548-014-0415-1","volume":"28","author":"W Khan","year":"2015","unstructured":"Khan, W. et al. Automated hippocampal subfield measures as predictors of conversion from mild cognitive impairment to Alzheimer\u2019s disease in two independent cohorts. Brain Topogr. 28, 746\u2013759 (2015).","journal-title":"Brain Topogr."},{"key":"63301_CR48","doi-asserted-by":"publisher","first-page":"13","DOI":"10.3389\/fninf.2019.00013","volume":"13","author":"W Zhao","year":"2019","unstructured":"Zhao, W. et al. Trajectories of the hippocampal subfields atrophy in the Alzheimer\u2019s disease: A structural imaging study. Front. Neuroinform. 13, 13. https:\/\/doi.org\/10.3389\/fninf.2019.00013 (2019).","journal-title":"Front. Neuroinform."},{"key":"63301_CR49","doi-asserted-by":"crossref","unstructured":"Long, X., Jiang, C., & Zhang, L. Morphological biomarker differentiating MCI converters from nonconverters: longitudinal evidence based on hemispheric asymmetry. Behav Neurol. 2018, 2018","DOI":"10.1155\/2018\/3954101"},{"key":"63301_CR50","doi-asserted-by":"crossref","unstructured":"Mitolo, M., Stanzani-Maserati, M., Capellari, S., Testa, C., Rucci, P., Poda, R., ... & Liguori, R. Predicting conversion from mild cognitive impairment to Alzheimer's disease using brain 1H-MRS and volumetric changes: a two-year retrospective follow-up study. Neuroimage Clin. 23, 101843 (2019).","DOI":"10.1016\/j.nicl.2019.101843"},{"key":"63301_CR51","doi-asserted-by":"crossref","unstructured":"Sun, Y., Bi, Q., Wang, X., Hu, X., Li, H., Li, X., ... & Han, Y. Prediction of conversion from amnestic mild cognitive impairment to Alzheimer's disease based on the brain structural connectome. Front Neurol. 9, 1178 (2019)","DOI":"10.3389\/fneur.2018.01178"},{"issue":"3","key":"63301_CR52","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1093\/ageing\/27.3.399","volume":"27","author":"B Agrell","year":"1998","unstructured":"Agrell, B. & Dehlin, O. The clock-drawing test. Age Age. 27(3), 399\u2013404 (1998).","journal-title":"Age Age."},{"issue":"3","key":"63301_CR53","doi-asserted-by":"publisher","first-page":"271","DOI":"10.2466\/pms.1958.8.3.271","volume":"8","author":"RM Reitan","year":"1958","unstructured":"Reitan, R. M. Validity of the trail making test as an indicator of organic brain damage. Percept. Motor Skills 8(3), 271\u2013276. https:\/\/doi.org\/10.2466\/pms.1958.8.3.271 (1958).","journal-title":"Percept. Motor Skills"},{"key":"63301_CR54","volume-title":"L\u2019examen clinique en psychologie (The Clinical Psychological Examination)","author":"A Rey","year":"1964","unstructured":"Rey, A. L\u2019examen clinique en psychologie (The Clinical Psychological Examination) (Presses Universitaires de France, 1964)."},{"key":"63301_CR55","doi-asserted-by":"publisher","first-page":"71","DOI":"10.2307\/2530808","volume":"39","author":"JF Strenio","year":"1983","unstructured":"Strenio, J. F., Weisberg, H. I. & Bryk, A. S. Empirical Bayes estimation of individual growth-curve parameters and their relationship to covariates. Biometrics 39, 71\u201386 (1983).","journal-title":"Biometrics"},{"key":"63301_CR56","unstructured":"Bates, D. M. lme4: Mixed-Effects Modeling with R (2010)."},{"issue":"2","key":"63301_CR57","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1080\/15248371003699969","volume":"11","author":"PJ Curran","year":"2010","unstructured":"Curran, P. J., Obeidat, K. & Losardo, D. Twelve frequently asked questions about growth curve modeling. J. Cogn. Dev. 11(2), 121\u2013136 (2010).","journal-title":"J. Cogn. Dev."},{"key":"63301_CR58","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/978-3-319-20484-0_8","volume-title":"A Life Course Perspective on Health Trajectories and Transitions","author":"P Ghisletta","year":"2015","unstructured":"Ghisletta, P., Renaud, O., Jacot, N. & Courvoisier, D. Linear mixed-effects and latent curve models for longitudinal life course analyses. In A Life Course Perspective on Health Trajectories and Transitions (eds Burton-Jeangros, C. et al.) 155\u2013178 (Springer, 2015)."},{"issue":"1","key":"63301_CR59","first-page":"20","volume":"14","author":"C DiStefano","year":"2019","unstructured":"DiStefano, C., Zhu, M. & Mindrila, D. Understanding and using factor scores: Considerations for the applied researcher. Pract. Assess. Res. Eval. 14(1), 20 (2019).","journal-title":"Pract. Assess. Res. Eval."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-63301-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-63301-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-63301-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T10:08:54Z","timestamp":1717582134000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-63301-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,5]]},"references-count":59,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["63301"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-63301-7","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,5]]},"assertion":[{"value":"27 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"12906"}}