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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker
James H. Cole, Rudra P. K. Poudel, Dimosthenis Tsagkrasoulis, et al.
NeuroImage (2017) Vol. 163, pp. 115-124
Open Access | Times Cited: 760

Showing 1-25 of 760 citing articles:

Medical Image Analysis using Convolutional Neural Networks: A Review
Syed Muhammad Anwar, Muhammad Majid, Adnan Qayyum, et al.
Journal of Medical Systems (2018) Vol. 42, Iss. 11
Closed Access | Times Cited: 1135
An overview of deep learning in medical imaging focusing on MRI
Alexander Selvikvåg Lundervold, Arvid Lundervold
Zeitschrift für Medizinische Physik (2018) Vol. 29, Iss. 2, pp. 102-127
Open Access | Times Cited: 980
Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers
James H. Cole, Katja Franke
Trends in Neurosciences (2017) Vol. 40, Iss. 12, pp. 681-690
Open Access | Times Cited: 752
A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises
S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos, et al.
Proceedings of the IEEE (2021) Vol. 109, Iss. 5, pp. 820-838
Open Access | Times Cited: 683
Common brain disorders are associated with heritable patterns of apparent aging of the brain
Tobias Kaufmann, Dennis van der Meer, Nhat Trung Doan, et al.
Nature Neuroscience (2019) Vol. 22, Iss. 10, pp. 1617-1623
Open Access | Times Cited: 486
Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide
Shelly Soffer, Avi Ben-Cohen, Orit Shimon, et al.
Radiology (2019) Vol. 290, Iss. 3, pp. 590-606
Closed Access | Times Cited: 432
Brain age and other bodily ‘ages’: implications for neuropsychiatry
James H. Cole, Riccardo E. Marioni, Sarah E. Harris, et al.
Molecular Psychiatry (2018) Vol. 24, Iss. 2, pp. 266-281
Open Access | Times Cited: 382
Accurate brain age prediction with lightweight deep neural networks
Han Peng, Weikang Gong, Christian F. Beckmann, et al.
Medical Image Analysis (2020) Vol. 68, pp. 101871-101871
Open Access | Times Cited: 369
Estimation of brain age delta from brain imaging
Stephen M. Smith, Diego Vidaurre, Fidel Alfaro‐Almagro, et al.
NeuroImage (2019) Vol. 200, pp. 528-539
Open Access | Times Cited: 355
Brain age prediction using deep learning uncovers associated sequence variants
Benedikt A. Jónsson, Gyða Björnsdóttir, Thorgeir E. Thorgeirsson, et al.
Nature Communications (2019) Vol. 10, Iss. 1
Open Access | Times Cited: 343
Multimodality neuroimaging brain-age in UK biobank: relationship to biomedical, lifestyle, and cognitive factors
James H. Cole
Neurobiology of Aging (2020) Vol. 92, pp. 34-42
Open Access | Times Cited: 306
MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide
Vishnu Bashyam, Güray Erus, Jimit Doshi, et al.
Brain (2020) Vol. 143, Iss. 7, pp. 2312-2324
Open Access | Times Cited: 281
Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises
Jing Sui, Rongtao Jiang, Juan Bustillo, et al.
Biological Psychiatry (2020) Vol. 88, Iss. 11, pp. 818-828
Open Access | Times Cited: 268
Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality
Ye Tian, Vanessa Cropley, Andrea B. Maier, et al.
Nature Medicine (2023) Vol. 29, Iss. 5, pp. 1221-1231
Open Access | Times Cited: 264
Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics
Tong He, Ru Kong, Avram J. Holmes, et al.
NeuroImage (2019) Vol. 206, pp. 116276-116276
Open Access | Times Cited: 253
Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets
Marc‐Andre Schulz, B.T. Thomas Yeo, Joshua T Vogelstein, et al.
Nature Communications (2020) Vol. 11, Iss. 1
Open Access | Times Cited: 234
Deep neural networks in psychiatry
Daniel Durstewitz, Georgia Koppe, Andreas Meyer‐Lindenberg
Molecular Psychiatry (2019) Vol. 24, Iss. 11, pp. 1583-1598
Open Access | Times Cited: 232
Optimising network modelling methods for fMRI
Usama Pervaiz, Diego Vidaurre, Mark W. Woolrich, et al.
NeuroImage (2020) Vol. 211, pp. 116604-116604
Open Access | Times Cited: 222
Commentary: Correction procedures in brain-age prediction
Ann‐Marie G. de Lange, James H. Cole
NeuroImage Clinical (2020) Vol. 26, pp. 102229-102229
Open Access | Times Cited: 200
Biomarkers of aging
Hainan Bao, Jiani Cao, Mengting Chen, et al.
Science China Life Sciences (2023) Vol. 66, Iss. 5, pp. 893-1066
Open Access | Times Cited: 194
Investigating systematic bias in brain age estimation with application to post‐traumatic stress disorders
Hualou Liang, Fengqing Zhang, Xin Niu
Human Brain Mapping (2019) Vol. 40, Iss. 11, pp. 3143-3152
Open Access | Times Cited: 188
Gray Matter Age Prediction as a Biomarker for Risk of Dementia
Johnny Wang, Maria J. Knol, Aleksei Tiulpin, et al.
Proceedings of the National Academy of Sciences (2019) Vol. 116, Iss. 42, pp. 21213-21218
Open Access | Times Cited: 187
Artificial intelligence for aging and longevity research: Recent advances and perspectives
Alex Zhavoronkov, Polina Mamoshina, Quentin Vanhaelen, et al.
Ageing Research Reviews (2018) Vol. 49, pp. 49-66
Open Access | Times Cited: 182
Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations
Stephen M. Smith, Lloyd T. Elliott, Fidel Alfaro‐Almagro, et al.
eLife (2020) Vol. 9
Open Access | Times Cited: 174
Brain Imaging Generation with Latent Diffusion Models
Walter Hugo Lopez Pinaya, Petru-Daniel Tudosiu, Jessica Dafflon, et al.
Lecture notes in computer science (2022), pp. 117-126
Closed Access | Times Cited: 170
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