Journal Article10.1007/7854_2024_462
Large-Scale Neuroimaging of Mental Illness.
Christopher R.K. Ching,Melody J Y Kang,Paul M Thompson +2 more
TL;DR: Large-scale neuroimaging research aims to develop replicable and generalizable brain markers for mental illness, leveraging international collaborations and data pooling to achieve precision psychiatry and improve treatment outcomes through transdiagnostic brain signature mapping.
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Abstract: Neuroimaging has provided important insights into the brain variations related to mental illness. Inconsistencies in prior studies, however, call for methods that lead to more replicable and generalizable brain markers that can reliably predict illness severity, treatment course, and prognosis. A paradigm shift is underway with large-scale international research teams actively pooling data and resources to drive consensus findings and test emerging methods aimed at achieving the goals of precision psychiatry. In parallel with large-scale psychiatric genomics studies, international consortia combining neuroimaging data are mapping the transdiagnostic brain signatures of mental illness on an unprecedented scale. This chapter discusses the major challenges, recent findings, and a roadmap for developing better neuroimaging-based tools and markers for mental illness.
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References
Highly accurate protein structure prediction with AlphaFold
John M. Jumper,Richard O. Evans,Alexander Pritzel,Tim Green,Michael Figurnov,Olaf Ronneberger,Kathryn Tunyasuvunakool,Russell Bates,Augustin Žídek,Anna Potapenko,Alex Bridgland,Clemens Meyer,Simon A. A. Kohl,Andrew J. Ballard,Andrew Cowie,Bernardino Romera-Paredes,Stanislav Nikolov,R. D. Jain,Jonas Adler,Trevor Back,Stig Petersen,David Reiman,Ellen Clancy,Michal Zielinski,Martin Steinegger,Michalina Pacholska,Tamas Berghammer,Sebastian Bodenstein,David L. Silver,Oriol Vinyals,Andrew W. Senior,Koray Kavukcuoglu,Pushmeet Kohli,Demis Hassabis +33 more
TL;DR: For example, AlphaFold as mentioned in this paper predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture. But the accuracy is limited by the fact that no homologous structure is available.
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Cathie Sudlow,John Gallacher,Naomi E. Allen,Valerie Beral,Paul Burton,John Danesh,Paul Downey,Paul Elliott,Jane Green,Martin J Landray,Bette Liu,Paul M. Matthews,Giok Ong,Jill P. Pell,Alan J. Silman,Alan Young,Tim Sprosen,Tim Peakman,Rory Collins +18 more
TL;DR: The UK Biobank is described, a large population-based prospective study, established to allow investigation of the genetic and non-genetic determinants of the diseases of middle and old age.
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TL;DR: In this paper, a technique for automatically assigning a neuroanatomical label to each voxel in an MRI volume based on probabilistic information automatically estimated from a manually labeled training set is presented.
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The WU-Minn Human Connectome Project: An Overview
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5.9K