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For: 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: 41] [Cited by in F6Publishing: 18] [Article Influence: 20.5] [Reference Citation Analysis]
Number Citing Articles
1 Tinauer C, Heber S, Pirpamer L, Damulina A, Schmidt R, Stollberger R, Ropele S, Langkammer C. Interpretable brain disease classification and relevance-guided deep learning. Sci Rep 2022;12:20254. [PMID: 36424437 DOI: 10.1038/s41598-022-24541-7] [Reference Citation Analysis]
2 Thomas AW, Ré C, Poldrack RA. Interpreting mental state decoding with deep learning models. Trends in Cognitive Sciences 2022;26:972-986. [DOI: 10.1016/j.tics.2022.07.003] [Reference Citation Analysis]
3 Dinsdale NK, Bluemke E, Sundaresan V, Jenkinson M, Smith SM, Namburete AI. Challenges for machine learning in clinical translation of big data imaging studies. Neuron 2022. [DOI: 10.1016/j.neuron.2022.09.012] [Reference Citation Analysis]
4 Luo Y, Chen W, Qiu J, Jia T. Accelerated functional brain aging in major depressive disorder: evidence from a large scale fMRI analysis of Chinese participants. Transl Psychiatry 2022;12:397. [PMID: 36130921 DOI: 10.1038/s41398-022-02162-y] [Reference Citation Analysis]
5 Dartora C, Marseglia A, Mårtensson G, Rukh G, Dang J, Muehlboeck J, Wahlund L, Moreno R, Barroso J, Ferreira D, Schiöth HB, Westman E, the Alzheimer’s Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of ageing, the Japanese Alzheimer’s Disease Neuroimaging Initiative, the AddNeuroMed consortium. Predicting the Age of the Brain with Minimally Processed T1-weighted MRI Data.. [DOI: 10.1101/2022.09.06.22279594] [Reference Citation Analysis]
6 Tian YE, Cropley V, Maier AB, Lautenschlager NT, Breakspear M, Zalesky A. Biological aging of human body and brain systems.. [DOI: 10.1101/2022.09.03.22279337] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Mouches P, Wilms M, Bannister JJ, Aulakh A, Langner S, Forkert ND. An exploratory causal analysis of the relationships between the brain age gap and cardiovascular risk factors. Front Aging Neurosci 2022;14:941864. [DOI: 10.3389/fnagi.2022.941864] [Reference Citation Analysis]
8 Zhou Z, Srinivasan D, Li H, Abdulkadir A, Nasrallah I, Wen J, Doshi J, Erus G, Mamourian E, Bryan NR, Wolk DA, Beason-held L, Resnick SM, Satterthwaite TD, Davatzikos C, Shou H, Fan Y, the ISTAGING Consortium. Multiscale functional connectivity patterns of the aging brain learned from rsfMRI data of 4,259 individuals of the multi-cohort iSTAGING study.. [DOI: 10.1101/2022.07.27.501626] [Reference Citation Analysis]
9 Hofmann SM, Beyer F, Lapuschkin S, Goltermann O, Loeffler M, Müller KR, Villringer A, Samek W, Witte AV. Towards the Interpretability of Deep Learning Models for Multi-modal Neuroimaging: Finding Structural Changes of the Ageing Brain. Neuroimage 2022;:119504. [PMID: 35882272 DOI: 10.1016/j.neuroimage.2022.119504] [Reference Citation Analysis]
10 Poloni KM, Ferrari RJ. A deep ensemble hippocampal CNN model for brain age estimation applied to Alzheimer’s diagnosis. Expert Systems with Applications 2022;195:116622. [DOI: 10.1016/j.eswa.2022.116622] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
11 Battineni G, Hossain MA, Chintalapudi N, Amenta F. A Survey on the Role of Artificial Intelligence in Biobanking Studies: A Systematic Review. Diagnostics (Basel) 2022;12:1179. [PMID: 35626333 DOI: 10.3390/diagnostics12051179] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Leonardsen EH, Peng H, Kaufmann T, Agartz I, Andreassen OA, Celius EG, Espeseth T, Harbo HF, Høgestøl EA, Lange AM, Marquand AF, Vidal-Piñeiro D, Roe JM, Selbæk G, Sørensen Ø, Smith SM, Westlye LT, Wolfers T, Wang Y. Deep neural networks learn general and clinically relevant representations of the ageing brain. Neuroimage 2022;:119210. [PMID: 35462035 DOI: 10.1016/j.neuroimage.2022.119210] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
13 Le Goallec A, Diai S, Collin S, Prost JB, Vincent T, Patel CJ. Using deep learning to predict abdominal age from liver and pancreas magnetic resonance images. Nat Commun 2022;13:1979. [PMID: 35418184 DOI: 10.1038/s41467-022-29525-9] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Zhou Z, Srinivasan D, Li H, Abdulkadir A, Shou H, Davatzikos C, Fan Y. Harmonization of multi-site functional connectivity measures in tangent space improves brain age prediction. Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging 2022. [DOI: 10.1117/12.2611557] [Reference Citation Analysis]
15 Sundaresan V, Dinsdale NK, Jenkinson M, Griffanti L. Omni-Supervised Domain Adversarial Training for White Matter Hyperintensity Segmentation in the UK Biobank. 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) 2022. [DOI: 10.1109/isbi52829.2022.9761539] [Reference Citation Analysis]
16 Wrigglesworth J, Harding IH, Ward P, Woods RL, Storey E, Fitzgibbon B, Egan G, Murray A, Shah RC, Trevaks RE, Ward S, Mcneil JJ, Ryan J; on behalf of the ASPREE investigator group. Factors Influencing Change in Brain-Predicted Age Difference in a Cohort of Healthy Older Individuals. ADR 2022. [DOI: 10.3233/adr-220011] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 C Monte-Rubio G, Segura B, P Strafella A, van Eimeren T, Ibarretxe-Bilbao N, Diez-Cirarda M, Eggers C, Lucas-Jiménez O, Ojeda N, Peña J, Ruppert MC, Sala-Llonch R, Theis H, Uribe C, Junque C. Parameters from site classification to harmonize MRI clinical studies: Application to a multi-site Parkinson's disease dataset. Hum Brain Mapp 2022. [PMID: 35305545 DOI: 10.1002/hbm.25838] [Reference Citation Analysis]
18 Kim J, Lee J, Lee S. Investigation of Genetic Variants and Causal Biomarkers Associated with Brain Aging.. [DOI: 10.1101/2022.03.04.22271813] [Reference Citation Analysis]
19 Mouches P, Wilms M, Rajashekar D, Langner S, Forkert ND. Multimodal biological brain age prediction using magnetic resonance imaging and angiography with the identification of predictive regions. Hum Brain Mapp 2022. [PMID: 35138012 DOI: 10.1002/hbm.25805] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
20 Rhee J. Urban form analysis through morphometry and machine learning. Artificial Intelligence in Urban Planning and Design 2022. [DOI: 10.1016/b978-0-12-823941-4.00007-x] [Reference Citation Analysis]
21 Bardozzo F, Delli Priscoli M, Russo AG, Crescenzi D, Di Benedetto U, Esposito F, Tagliaferri R. Soft Brain Ageing Indicators Based on Light-Weight LeNet-Like Neural Networks and Localized 2D Brain Age Biomarkers. Computational Intelligence Methods for Bioinformatics and Biostatistics 2022. [DOI: 10.1007/978-3-031-20837-9_19] [Reference Citation Analysis]
22 Popescu SG, Glocker B, Sharp DJ, Cole JH. Local Brain-Age: A U-Net Model. Front Aging Neurosci 2021;13:761954. [PMID: 34966266 DOI: 10.3389/fnagi.2021.761954] [Cited by in Crossref: 6] [Cited by in F6Publishing: 8] [Article Influence: 6.0] [Reference Citation Analysis]
23 Hu G, Zhang Q, Yang Z, Li B. Accurate Brain Age Prediction Model for Healthy Children and Adolescents using 3D-CNN and Dimensional Attention. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2021. [DOI: 10.1109/bibm52615.2021.9669900] [Reference Citation Analysis]
24 O’connell S, Cannon DM, Ó Broin P. Predictive Modelling of Brain Disorders with Magnetic Resonance Imaging: A Systematic Review of Modelling Practices, Transparency, and Interpretability in the use of Convolutional Neural Networks.. [DOI: 10.1101/2021.11.20.21266620] [Reference Citation Analysis]
25 Leonardsen EH, Peng H, Kaufmann T, Agartz I, Andreassen OA, Celius EG, Espeseth T, Harbo HF, Høgestøl EA, de Lange A, Marquand AF, Vidal-piñeiro D, Roe JM, Selbæk G, Sørensen Ø, Smith SM, Westlye LT, Wolfers T, Wang Y. Deep neural networks learn general and clinically relevant representations of the ageing brain.. [DOI: 10.1101/2021.10.29.21265645] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
26 Wrigglesworth J, Yaacob N, Ward P, Woods RL, McNeil J, Storey E, Egan G, Murray A, Shah RC, Jamadar SD, Trevaks R, Ward S, Harding IH, Ryan J; ASPREE investigator group. Brain-predicted age difference is associated with cognitive processing in later-life. Neurobiol Aging 2021;109:195-203. [PMID: 34775210 DOI: 10.1016/j.neurobiolaging.2021.10.007] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
27 Tinauer C, Heber S, Pirpamer L, Damulina A, Schmidt R, Stollberger R, Ropele S, Langkammer C. Interpretable Brain Disease Classification and Relevance-Guided Deep Learning.. [DOI: 10.1101/2021.09.09.21263013] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
28 Fisch L, Leenings R, Winter NR, Dannlowski U, Gaser C, Cole JH, Hahn T. Editorial: Predicting Chronological Age From Structural Neuroimaging: The Predictive Analytics Competition 2019. Front Psychiatry 2021;12:710932. [PMID: 34421686 DOI: 10.3389/fpsyt.2021.710932] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
29 Hepp T, Blum D, Armanious K, Schölkopf B, Stern D, Yang B, Gatidis S. Uncertainty estimation and explainability in deep learning-based age estimation of the human brain: Results from the German National Cohort MRI study. Comput Med Imaging Graph 2021;92:101967. [PMID: 34392229 DOI: 10.1016/j.compmedimag.2021.101967] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
30 Shen X, Huang J, Sun Y, Li M, Pan B, Ding W. Parallel Pathway Convolutional Neural Network with Low-rank Fusion for Brain Age Prediction. 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI) 2021. [DOI: 10.1109/dtpi52967.2021.9540107] [Reference Citation Analysis]
31 Le Goallec A, Diai S, Collin S, Prost J, Vincent T, Patel CJ. Using deep learning to predict age from liver and pancreas magnetic resonance images allows the identification of genetic and non-genetic factors associated with abdominal aging.. [DOI: 10.1101/2021.06.24.21259492] [Reference Citation Analysis]
32 Hofmann SM, Beyer F, Lapuschkin S, Goltermann O, Loeffler M, Müller K, Villringer A, Samek W, Witte AV. Towards the Interpretability of Deep Learning Models for Multi-modal Neuroimaging: Finding Structural Changes of the Ageing Brain.. [DOI: 10.1101/2021.06.25.449906] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
33 Goallec AL, Diai S, Collin S, Vincent T, Patel CJ. Using deep learning to predict brain age from brain magnetic resonance images and cognitive tests reveals that anatomical and functional brain aging are phenotypically and genetically distinct.. [DOI: 10.1101/2021.06.22.21259280] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
34 Gong W, Beckmann CF, Vedaldi A, Smith SM, Peng H. Optimising a Simple Fully Convolutional Network for Accurate Brain Age Prediction in the PAC 2019 Challenge. Front Psychiatry 2021;12:627996. [PMID: 34040552 DOI: 10.3389/fpsyt.2021.627996] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 7.0] [Reference Citation Analysis]
35 [DOI: 10.1109/isbi48211.2021.9434081] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 9.0] [Reference Citation Analysis]
36 Ball G, Kelly CE, Beare R, Seal ML. Individual variation underlying brain age estimates in typical development. Neuroimage 2021;235:118036. [PMID: 33838267 DOI: 10.1016/j.neuroimage.2021.118036] [Cited by in Crossref: 13] [Cited by in F6Publishing: 15] [Article Influence: 13.0] [Reference Citation Analysis]
37 Zabihi M, Kia SM, Wolfers T, de Boer S, Fraza C, Soheili-nezhad S, Dinga R, Llera Arenas A, Bzdok D, Beckmann CF, Marquand A. Explanatory latent representation of heterogeneous spatial maps of task-fMRI in large-scale datasets.. [DOI: 10.1101/2021.03.10.434856] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
38 Wyburd MK, Hesse LS, Aliasi M, Jenkinson M, Papageorghiou AT, Haak MC, Namburete AIL. Assessment of Regional Cortical Development Through Fissure Based Gestational Age Estimation in 3D Fetal Ultrasound. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis 2021. [DOI: 10.1007/978-3-030-87735-4_23] [Reference Citation Analysis]
39 Bintsi K, Baltatzis V, Hammers A, Rueckert D. Voxel-Level Importance Maps for Interpretable Brain Age Estimation. Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data 2021. [DOI: 10.1007/978-3-030-87444-5_7] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
40 Ball G, Kelly CE, Beare R, Seal ML. Individual variation underlying brain age estimates in typical development.. [DOI: 10.1101/2020.11.30.405290] [Reference Citation Analysis]
41 Kolbeinsson A, Filippi S, Panagakis Y, Matthews PM, Elliott P, Dehghan A, Tzoulaki I. Accelerated MRI-predicted brain ageing and its associations with cardiometabolic and brain disorders. Sci Rep 2020;10:19940. [PMID: 33203906 DOI: 10.1038/s41598-020-76518-z] [Cited by in Crossref: 13] [Cited by in F6Publishing: 12] [Article Influence: 6.5] [Reference Citation Analysis]