| SARS-CoV-2 is associated with changes in brain structure in UK Biobank G Douaud, S Lee, F Alfaro-Almagro, C Arthofer, C Wang, P McCarthy, ... Nature 604 (7907), 697-707, 2022 | 1410 | 2022 |
| Brain imaging before and after COVID-19 in UK Biobank G Douaud, S Lee, F Alfaro-Almagro, C Arthofer, C Wang, P McCarthy, ... MedRxiv, 2021.06. 11.21258690, 2021 | 117 | 2021 |
| Scalable prediction of acute myeloid leukemia using high-dimensional machine learning and blood transcriptomics S Warnat-Herresthal, K Perrakis, B Taschler, M Becker, K Baßler, M Beyer, ... Iscience 23 (1), 2020 | 71 | 2020 |
| The alzheimer's disease prediction of longitudinal evolution (TADPOLE) challenge: Results after 1 year follow-up RV Marinescu, NP Oxtoby, AL Young, EE Bron, AW Toga, MW Weiner, ... arXiv preprint arXiv:2002.03419, 2020 | 63 | 2020 |
| Alcohol consumption and telomere length: Mendelian randomization clarifies alcohol’s effects A Topiwala, B Taschler, KP Ebmeier, S Smith, H Zhou, DF Levey, V Codd, ... Molecular Psychiatry 27 (10), 4001-4008, 2022 | 57 | 2022 |
| MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry K Bendfeldt, B Taschler, L Gaetano, P Madoerin, P Kuster, ... Brain imaging and behavior 13, 1361-1374, 2019 | 43 | 2019 |
| Causal inference on neuroimaging data with Mendelian randomisation B Taschler, SM Smith, TE Nichols NeuroImage 258, 119385, 2022 | 34 | 2022 |
| Classifying individuals at high-risk for psychosis based on functional brain activity during working memory processing K Bendfeldt, R Smieskova, N Koutsouleris, S Klöppel, A Schmidt, A Walter, ... NeuroImage: Clinical 9, 555-563, 2015 | 30 | 2015 |
| Deep learning of causal structures in high dimensions under data limitations K Lagemann, C Lagemann, B Taschler, S Mukherjee Nature machine intelligence 5 (11), 1306-1316, 2023 | 29 | 2023 |
| Telomere length and brain imaging phenotypes in UK Biobank A Topiwala, TE Nichols, LZJ Williams, EC Robinson, F Alfaro-Almagro, ... PLoS One 18 (3), e0282363, 2023 | 24 | 2023 |
| Genetic architecture of brain age and its causal relations with brain and mental disorders EH Leonardsen, D Vidal-Piñeiro, JM Roe, O Frei, AA Shadrin, ... Molecular Psychiatry 28 (7), 3111-3120, 2023 | 19 | 2023 |
| Spatial modeling of multiple sclerosis for disease subtype prediction B Taschler, T Ge, K Bendfeldt, N Müller-Lenke, TD Johnson, TE Nichols Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: 17th …, 2014 | 17 | 2014 |
| Brain imaging before and after COVID-19 in UK Biobank. medRxiv 2021 G Douaud, S Lee, F Alfaro-Almagro, C Arthofer, C Wang, F Lange, ... Academic Search, 0 | 14 | |
| Reliability of multi-site UK Biobank MRI brain phenotypes for the assessment of neuropsychiatric complications of SARS-CoV-2 infection: The COVID-CNS travelling heads study E Duff, F Zelaya, FA Almagro, KL Miller, N Martin, TE Nichols, B Taschler, ... PLoS One 17 (9), e0273704, 2022 | 13 | 2022 |
| Amplitudes of resting-state functional networks–investigation into their correlates and biophysical properties S Lee, JD Bijsterbosch, FA Almagro, L Elliott, P McCarthy, B Taschler, ... NeuroImage 265, 119779, 2023 | 8 | 2023 |
| Model-based clustering in very high dimensions via adaptive projections B Taschler, F Dondelinger, S Mukherjee arXiv preprint arXiv:1902.08472, 2019 | 5 | 2019 |
| Premorbid brain structure influences risk of amyotrophic lateral sclerosis AG Thompson, B Taschler, SM Smith, MR Turner Journal of Neurology, Neurosurgery & Psychiatry 95 (4), 360-365, 2024 | 3 | 2024 |
| Classification of multiple sclerosis patients from the geometry and texture of white matter lesions B Taschler, K Bendfeldt, N Müller-Lenke, EW Radü, TE Nichols 20th Annual Meeting of the Organization for Human Brain Mapping (OHBM) 5, 2014 | 3 | 2014 |
| The Alzheimer’s disease prediction of longitudinal evolution (TADPOLE) challenge: results after 1 year follow-up RV Marinescu, NP Oxtoby, AL Young, EE Bron, AW Toga, MW Weiner, ... ArXiv Preprint ArXiv, 2002 | 3 | 2002 |
| Scalable prediction of acute myeloid leukemia using high-dimensional machine learning and blood transcriptomics. iScience S Warnat-Herresthal, K Perrakis, B Taschler, M Becker, K Baßler, M Beyer | 2 | 2020 |
Thomas E. NicholsUniversity of OxfordEmail được xác minh tại bdi.ox.ac.uk