Classification of Major Depressive Disorder via Multi-site Weighted LASSO Model. - Dajiang Zhu; Brandalyn C. Riedel; Neda Jahanshad; Nynke A. Groenewold; Dan J. Stein; Ian H. Gotlib; Matthew D. Sacchet; Danai Dima; James H. Cole; Cynthia H. Y. Fu; Henrik Walter; Ilya M. Veer; et al. (MICCAI (3), 2017)

Classification of Major Depressive Disorder via Multi-Site Weighted LASSO Model. - Dajiang Zhu; Brandalyn C. Riedel; Neda Jahanshad; Nynke A. Groenewold; Dan J. Stein; Ian H. Gotlib; Danai Dima; James H. Cole; Cynthia H. Y. Fu; Henrik Walter; Ilya M. Veer; Thomas Frodl; et al. (CoRR, 2017)

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; Matthan W. A. Caan; Claire J. Steves; Tim D. Spector; Giovanni Montana (NeuroImage, 2017)

NEURO-DRAM: a 3D recurrent visual attention model for interpretable neuroimaging classification. - David Wood; James H. Cole; Thomas C. Booth (CoRR, 2019)

Labelling Imaging Datasets on the Basis of Neuroradiology Reports: A Validation Study. - David A. Wood 004; Sina Kafiabadi; Aisha Al Busaidi; Emily Guilhem; Jeremy Lynch; Matthew Townend; Antanas Montvila; Juveria Siddiqui; Naveen Gadapa; Matthew Benger; Gareth J. Barker; Sébastien Ourselin; et al. (iMIMIC/MIL3ID/LABELS@MICCAI, 2020)

Automated Labelling using an Attention model for Radiology reports of MRI scans (ALARM). - David A. Wood 004; Jeremy Lynch; Sina Kafiabadi; Emily Guilhem; Aisha Al Busaidi; Antanas Montvila; Thomas Varsavsky; Juveria Siddiqui; Naveen Gadapa; Matthew Townend; Martin Kiik; Keena Patel; et al. (MIDL, 2020)

Hierarchical Gaussian Processes with Wasserstein-2 Kernels. - Sebastian Popescu; David J. Sharp; James H. Cole; Ben Glocker (CoRR, 2020)

Editorial: Deep Learning in Aging Neuroscience. - Javier Ramírez 001; Juan Manuel Górriz; Andrés Ortiz 001; James H. Cole; Martin Dyrba (Frontiers Neuroinformatics, 2020)

Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study. - Ann-Marie G. de Lange; Melis Anatürk; Sana Suri; Tobias Kaufmann 001; James H. Cole; Ludovica Griffanti; Eniko Zsoldos; Daria E. A. Jensen; Nicola Filippini; Archana Singh-Manoux; Mika Kivimäki; Lars T. Westlye; et al. (NeuroImage, 2020)

Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation. - Sebastian G. Popescu; David J. Sharp; James H. Cole; Konstantinos Kamnitsas; Ben Glocker (IPMI, 2021)

Automated triaging of head MRI examinations using convolutional neural networks. - David A. Wood 004; Sina Kafiabadi; Aisha Al Busaidi; Emily Guilhem; Antanas Montvila; Siddharth Agarwal; Jeremy Lynch; Matthew Townend; Gareth J. Barker; Sébastien Ourselin; James H. Cole; Thomas C. Booth (MIDL, 2021)

An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling. - Tim Hahn; Jan Ernsting; Nils R. Winter; Vincent Holstein; Ramona Leenings; Marie Beisemann; Lukas Fisch; Kelvin Sarink; Daniel Emden; Nils Opel; Ronny Redlich; Jonathan Repple; et al. (CoRR, 2021)

Distributional Gaussian Processes Layers for Out-of-Distribution Detection. - Sebastian G. Popescu; David J. Sharp; James H. Cole; Konstantinos Kamnitsas; Ben Glocker (CoRR, 2022)

Deep learning models for triaging hospital head MRI examinations. - David A. Wood 004; Sina Kafiabadi; Ayisha Al Busaidi; Emily Guilhem; Antanas Montvila; Jeremy Lynch; Matthew Townend; Siddharth Agarwal; Asif Mazumder; Gareth J. Barker; Sébastien Ourselin; James H. Cole; et al. (Medical Image Anal., 2022)

Accurate brain-age models for routine clinical MRI examinations. - David A. Wood 004; Sina Kafiabadi; Ayisha Al Busaidi; Emily Guilhem; Antanas Montvila; Jeremy Lynch; Matthew Townend; Siddharth Agarwal; Asif Mazumder; Gareth J. Barker; Sébastien Ourselin; James H. Cole; et al. (NeuroImage, 2022)

Brain Matters: Exploring Bias in AI for Neuroimaging Research. - Sophie A. Martin; Francesca Biondo; James H. Cole; Beatrice Taylor (CLIP/FAIMI/EPIMI@MICCAI, 2023)

A self-supervised text-vision framework for automated brain abnormality detection. - David A. Wood 004; Emily Guilhem; Sina Kafiabadi; Ayisha Al Busaidi; Kishan Dissanayake; Ahmed Hammam; Nina Mansoor; Matthew Townend; Siddharth Agarwal; Yiran Wei; Asif Mazumder; Gareth J. Barker; et al. (CoRR, 2024)

Artificial intelligence for abnormality detection in high volume neuroimaging: a systematic review and meta-analysis. - Siddharth Agarwal; David A. Wood 004; Mariusz Grzeda; Chandhini Suresh; Munaib Din; James H. Cole; Marc Modat; Thomas C. Booth (CoRR, 2024)

A perspective on brain-age estimation and its clinical promise. - Christian Gaser; Polona Kalc; James H. Cole (Nat. Comput. Sci., 2024)

Publisher Correction: A perspective on brain-age estimation and its clinical promise. - Christian Gaser; Polona Kalc; James H. Cole (Nat. Comput. Sci., 2024)

Brain Age Prediction Using a Lightweight Convolutional Neural Network. - Fatma El Zahraa Magdy; Mario Parreño Centeno; James H. Cole; João Paulo Papa; Fumie Costen (IEEE Access, 2025)

Does restrictive anorexia nervosa impact brain aging? A machine learning approach to estimate age based on brain structure. - Yubraj Gupta; Feliberto de la Cruz; Katrin Rieger; Monica di Giuliano; Christian Gaser; James H. Cole; Lauren Breithaupt; Laura M. Holsen; Kamryn T. Eddy; Jennifer J. Thomas; Suheyla Cetin Karayumak; Marek Kubicki; et al. (Comput. Biol. Medicine, 2025)

Explainable AI Methods for Neuroimaging: Systematic Failures of Common Tools, the Need for Domain-Specific Validation, and a Proposal for Safe Application. - Nys Tjade Siegel; James H. Cole; Mohamad Habes; Stefan Haufe; Kerstin Ritter; Marc-André Schulz (CoRR, 2025)