| Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications S Vieira, WHL Pinaya, A Mechelli Neuroscience & Biobehavioral Reviews 74, 58-75, 2017 | 710 | 2017 |
| Autoencoders WHL Pinaya, S Vieira, R Garcia-Dias, A Mechelli Machine learning, 193-208, 2020 | 261 | 2020 |
| Machine learning for brain age prediction: Introduction to methods and clinical applications L Baecker, R Garcia-Dias, S Vieira, C Scarpazza, A Mechelli EBioMedicine 72, 2021 | 159 | 2021 |
| Convolutional neural networks WHL Pinaya, S Vieira, R Garcia-Dias, A Mechelli Machine learning, 173-191, 2020 | 118 | 2020 |
| Machine learning: methods and applications to brain disorders A Mechelli, S Vieira Academic Press, 2019 | 106 | 2019 |
| Using machine learning and structural neuroimaging to detect first episode psychosis: reconsidering the evidence S Vieira, Q Gong, WHL Pinaya, C Scarpazza, S Tognin, B Crespo-Facorro, ... Schizophrenia bulletin 46 (1), 17-26, 2020 | 105 | 2020 |
| Brain age prediction: A comparison between machine learning models using region‐and voxel‐based morphometric data L Baecker, J Dafflon, PF Da Costa, R Garcia‐Dias, S Vieira, C Scarpazza, ... Human brain mapping 42 (8), 2332-2346, 2021 | 96 | 2021 |
| Introduction to machine learning S Vieira, WHL Pinaya, A Mechelli Machine learning, 1-20, 2020 | 86 | 2020 |
| Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners R Garcia-Dias, C Scarpazza, L Baecker, S Vieira, WHL Pinaya, A Corvin, ... Neuroimage 220, 117127, 2020 | 82 | 2020 |
| Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual D Lei, WHL Pinaya, J Young, T Van Amelsvoort, M Marcelis, G Donohoe, ... Human brain mapping 41 (5), 1119-1135, 2020 | 82 | 2020 |
| A transdiagnostic neuroanatomical signature of psychiatric illness Q Gong, C Scarpazza, J Dai, M He, X Xu, Y Shi, B Zhou, S Vieira, ... Neuropsychopharmacology 44 (5), 869-875, 2019 | 81 | 2019 |
| Detecting schizophrenia at the level of the individual: relative diagnostic value of whole-brain images, connectome-wide functional connectivity and graph-based metrics D Lei, WHL Pinaya, T Van Amelsvoort, M Marcelis, G Donohoe, ... Psychological medicine 50 (11), 1852-1861, 2020 | 80 | 2020 |
| Towards precision medicine in psychosis: benefits and challenges of multimodal multicenter studies—PSYSCAN: translating neuroimaging findings from research into clinical practice S Tognin, HH van Hell, K Merritt, I Winter-van Rossum, MG Bossong, ... Schizophrenia bulletin 46 (2), 432-441, 2020 | 78 | 2020 |
| Clustering analysis R Garcia-Dias, S Vieira, WHL Pinaya, A Mechelli machine learning, 227-247, 2020 | 68 | 2020 |
| Using normative modelling to detect disease progression in mild cognitive impairment and Alzheimer’s disease in a cross-sectional multi-cohort study WHL Pinaya, C Scarpazza, R Garcia-Dias, S Vieira, L Baecker, ... Scientific reports 11 (1), 15746, 2021 | 67* | 2021 |
| Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders C Scarpazza, M Ha, L Baecker, R Garcia-Dias, WHL Pinaya, S Vieira, ... Translational Psychiatry 10 (1), 107, 2020 | 47 | 2020 |
| Graph convolutional networks reveal network-level functional dysconnectivity in schizophrenia D Lei, K Qin, WHL Pinaya, J Young, T Van Amelsvoort, M Marcelis, ... Schizophrenia Bulletin 48 (4), 881-892, 2022 | 46 | 2022 |
| Can we predict who will benefit from cognitive-behavioural therapy? A systematic review and meta-analysis of machine learning studies S Vieira, X Liang, R Guiomar, A Mechelli Clinical Psychology Review 97, 102193, 2022 | 42 | 2022 |
| Neuroanatomical abnormalities in first-episode psychosis across independent samples: a multi-centre mega-analysis S Vieira, Q Gong, C Scarpazza, S Lui, X Huang, B Crespo-Facorro, ... Psychological medicine 51 (2), 340-350, 2021 | 34 | 2021 |
| Machine learning S Vieira, A Mechelli Academic Press, 2019 | 31 | 2019 |
