Spectral clustering based on structural magnetic resonance imaging and its relationship with major depressive disorder and cognitive ability

Autor: Gordon D. Waiter, Laura de Nooij, Simon R. Cox, Anca-Larisa Sandu, Mathew A. Harris, Andrew M. McIntosh, Alison D. Murray, Hon Wah Yeung, Archie Campbell, Colin R. Buchanan, J. Douglas Steele, Heather C. Whalley, Stephen M. Lawrie, David J. Porteous, Aleks Stolicyn, Liana Romaniuk, Xueyi Shen, Christopher J. McNeil, Keith Smith

Publikováno v: Yeung, H W, Shen, X, Stolicyn, A, De Nooij, L, Harris, M A, Romaniuk, L, Buchanan, C R, Waiter, G D, Sandu, A, Mcneil, C J, Murray, A, Steele, J D, Campbell, A, Porteous, D, Lawrie, S M, Mcintosh, A M, Cox, S R, Smith, K M & Whalley, H C 2021, ' Spectral Clustering Based on Structural Magnetic Resonance Imaging and its Relationship with Major Depressive Disorder and Cognitive Ability ', European Journal of Neuroscience, vol. 54, no. 6, pp. 6281-6303 . https://doi.org/10.1111/ejn.15423

There is increasing interest in using data-driven unsupervised methods to identify structural underpinnings of common mental illnesses, including Major Depressive Disorder (MDD) and associated traits such as cognition. However, studies are often limi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4f5cf0156c2525bf22f30161df489ea1
https://doi.org/10.1111/ejn.15423

Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes

Autor: Hon Wah Yeung, Aleks Stolicyn, Colin R. Buchanan, Elliot M. Tucker‐Drob, Mark E. Bastin, Saturnino Luz, Andrew M. McIntosh, Heather C. Whalley, Simon R. Cox, Keith Smith

Publikováno v: Yeung, H W, Stolicyn, A, Buchanan, C R, Tucker-Drob, E M, Bastin, M E, Luz, S, McIntosh, A M, Whalley, H C, Cox, S R & Smith, K 2023, ' Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes ', Human Brain Mapping, vol. 44, no. 5, pp. 1913-1933 . https://doi.org/10.1002/hbm.26182

There is increasing expectation that advanced, computationally expensive machine learning techniques, when applied to large population-wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::10b830db6630fd3fd522d489a0a25068
https://pubmed.ncbi.nlm.nih.gov/36541441

Automated classification of depression from structural brain measures across two independent community‐based cohorts

Autor: Emma L. Hawkins, Laura de Nooij, Heather C. Whalley, Miruna C. Barbu, J. Douglas Steele, Alison D. Murray, Mark Adams, Hon Wah Yeung, Xueyi Shen, Aleks Stolicyn, Mathew A. Harris, Andrew M. McIntosh, Stephen M. Lawrie

Publikováno v: Stolicyn, A, Harris, M A, Shen, X, Barbu, M C, Adams, M J, Hawkins, E L, Nooij, L, Yeung, H W, Murray, A D, Lawrie, S M, Steele, J D, Mcintosh, A M & Whalley, H C 2020, ' Automated classification of depression from structural brain measures across two independent community-based cohorts ', Human Brain Mapping . https://doi.org/10.1002/hbm.25095
Human Brain Mapping

Major depressive disorder (MDD) has been the subject of many neuroimaging case–control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically‐ascertained, currently sy

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5bd7790ac98341a827d49e8af78034f5
https://hdl.handle.net/20.500.11820/4bda7821-bea0-428f-9b07-73314d0ebb6b