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For: Doan NT, Engvig A, Zaske K, Persson K, Lund MJ, Kaufmann T, Cordova-Palomera A, Alnæs D, Moberget T, Brækhus A, Barca ML, Nordvik JE, Engedal K, Agartz I, Selbæk G, Andreassen OA, Westlye LT; Alzheimer's Disease Neuroimaging Initiative. Distinguishing early and late brain aging from the Alzheimer's disease spectrum: consistent morphological patterns across independent samples. Neuroimage 2017;158:282-95. [PMID: 28666881 DOI: 10.1016/j.neuroimage.2017.06.070] [Cited by in Crossref: 22] [Cited by in F6Publishing: 18] [Article Influence: 4.4] [Reference Citation Analysis]
Number Citing Articles
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3 Arbabyazd L, Shen K, Wang Z, Hofmann-Apitius M, Ritter P, McIntosh AR, Battaglia D, Jirsa V. Virtual Connectomic Datasets in Alzheimer's Disease and Aging Using Whole-Brain Network Dynamics Modelling. eNeuro 2021;8:ENEURO. [PMID: 34045210 DOI: 10.1523/ENEURO.0475-20.2021] [Cited by in F6Publishing: 5] [Reference Citation Analysis]
4 Norbom LB, Ferschmann L, Parker N, Agartz I, Andreassen OA, Paus T, Westlye LT, Tamnes CK. New insights into the dynamic development of the cerebral cortex in childhood and adolescence: Integrating macro- and microstructural MRI findings. Prog Neurobiol 2021;204:102109. [PMID: 34147583 DOI: 10.1016/j.pneurobio.2021.102109] [Cited by in F6Publishing: 11] [Reference Citation Analysis]
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6 Cox SR, Harris MA, Ritchie SJ, Buchanan CR, Valdés Hernández MC, Corley J, Taylor AM, Madole JW, Harris SE, Whalley HC, McIntosh AM, Russ TC, Bastin ME, Wardlaw JM, Deary IJ, Tucker-Drob EM. Three major dimensions of human brain cortical ageing in relation to cognitive decline across the eighth decade of life. Mol Psychiatry 2021. [PMID: 33398085 DOI: 10.1038/s41380-020-00975-1] [Cited by in Crossref: 13] [Cited by in F6Publishing: 11] [Article Influence: 13.0] [Reference Citation Analysis]
7 Dinsdale NK, Bluemke E, Smith SM, Arya Z, Vidaurre D, Jenkinson M, Namburete AIL. Learning patterns of the ageing brain in MRI using deep convolutional networks. Neuroimage 2021;224:117401. [PMID: 32979523 DOI: 10.1016/j.neuroimage.2020.117401] [Cited by in Crossref: 11] [Cited by in F6Publishing: 18] [Article Influence: 5.5] [Reference Citation Analysis]
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9 Maglanoc LA, Kaufmann T, Jonassen R, Hilland E, Beck D, Landrø NI, Westlye LT. Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis. Hum Brain Mapp 2020;41:241-55. [PMID: 31571370 DOI: 10.1002/hbm.24802] [Cited by in Crossref: 10] [Cited by in F6Publishing: 15] [Article Influence: 3.3] [Reference Citation Analysis]
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11 Trevisan K, Cristina-Pereira R, Silva-Amaral D, Aversi-Ferreira TA. Theories of Aging and the Prevalence of Alzheimer's Disease. Biomed Res Int 2019;2019:9171424. [PMID: 31317043 DOI: 10.1155/2019/9171424] [Cited by in Crossref: 23] [Cited by in F6Publishing: 30] [Article Influence: 7.7] [Reference Citation Analysis]
12 Smith S, Duff E, Groves A, Nichols TE, Jbabdi S, Westlye LT, Tamnes CK, Engvig A, Walhovd KB, Fjell AM, Johansen-Berg H, Douaud G. Structural Variability in the Human Brain Reflects Fine-Grained Functional Architecture at the Population Level. J Neurosci 2019;39:6136-49. [PMID: 31152123 DOI: 10.1523/JNEUROSCI.2912-18.2019] [Cited by in Crossref: 16] [Cited by in F6Publishing: 16] [Article Influence: 5.3] [Reference Citation Analysis]
13 Finnegan ME, Visanji NP, Romero-canelon I, House E, Rajan S, Mosselmans JFW, Hazrati L, Dobson J, Collingwood JF. Synchrotron XRF imaging of Alzheimer’s disease basal ganglia reveals linear dependence of high-field magnetic resonance microscopy on tissue iron concentration. Journal of Neuroscience Methods 2019;319:28-39. [DOI: 10.1016/j.jneumeth.2019.03.002] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
14 Høgestøl EA, Kaufmann T, Nygaard GO, Beyer MK, Sowa P, Nordvik JE, Kolskår K, Richard G, Andreassen OA, Harbo HF, Westlye LT. Cross-Sectional and Longitudinal MRI Brain Scans Reveal Accelerated Brain Aging in Multiple Sclerosis. Front Neurol 2019;10:450. [PMID: 31114541 DOI: 10.3389/fneur.2019.00450] [Cited by in Crossref: 27] [Cited by in F6Publishing: 26] [Article Influence: 9.0] [Reference Citation Analysis]
15 Shatte ABR, Hutchinson DM, Teague SJ. Machine learning in mental health: a scoping review of methods and applications. Psychol Med 2019;49:1426-48. [PMID: 30744717 DOI: 10.1017/S0033291719000151] [Cited by in Crossref: 212] [Cited by in F6Publishing: 162] [Article Influence: 70.7] [Reference Citation Analysis]
16 Sakai K, Yamada K. Machine learning studies on major brain diseases: 5-year trends of 2014–2018. Jpn J Radiol 2019;37:34-72. [DOI: 10.1007/s11604-018-0794-4] [Cited by in Crossref: 49] [Cited by in F6Publishing: 52] [Article Influence: 12.3] [Reference Citation Analysis]
17 Cho SH, Shin JH, Jang H, Park S, Kim HJ, Kim SE, Kim SJ, Kim Y, Lee JS, Na DL, Lockhart SN, Rabinovici GD, Seong JK, Seo SW; Alzheimer’s Disease Neuroimaging Initiative. Amyloid involvement in subcortical regions predicts cognitive decline. Eur J Nucl Med Mol Imaging 2018;45:2368-76. [PMID: 29980831 DOI: 10.1007/s00259-018-4081-5] [Cited by in Crossref: 20] [Cited by in F6Publishing: 25] [Article Influence: 5.0] [Reference Citation Analysis]
18 Chui K, Alhalabi W, Pang S, Pablos P, Liu R, Zhao M. Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications. Sustainability 2017;9:2309. [DOI: 10.3390/su9122309] [Cited by in Crossref: 53] [Cited by in F6Publishing: 34] [Article Influence: 10.6] [Reference Citation Analysis]