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Anne Uhlmann

Showing results (31-40 of 60) with videos related to

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Progress in Brain Research|January 30, 2016
Genetic imaging consortium for addiction medicine: From neuroimaging to genesScott Mackey, Kees-Jan Kan, Bader Chaarani, et al.
Frontiers in Psychiatry|September 8, 2022
Enhancing neuroimaging genetics through meta-analysis for Tourette syndrome (ENIGMA-TS): A worldwide platform for collaborationPeristera Paschou, Yin Jin, Kirsten Müller-Vahl, et al.
The American Journal of Psychiatry|October 20, 2018
Mega-Analysis of Gray Matter Volume in Substance Dependence: General and Substance-Specific Regional EffectsScott Mackey, Nicholas Allgaier, Bader Chaarani, et al.
Psychological Medicine|May 16, 2019
Interactive impact of childhood maltreatment, depression, and age on cortical brain structure: mega-analytic findings from a large multi-site cohortLeonardo Tozzi, Lisa Garczarek, Deborah Janowitz, et al.
Molecular Psychiatry|February 9, 2024
Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA studyFoivos Georgiadis, Sara Larivière, David Glahn, et al.
The American Journal of Psychiatry|February 26, 2025
Estimating Multimodal Structural Brain Variability in Schizophrenia Spectrum Disorders: A Worldwide ENIGMA StudyWolfgang Omlor, Finn Rabe, Simon Fuchs, et al.
Biorxiv : the Preprint Server for Biology|November 14, 2023
Estimating multimodal brain variability in schizophrenia spectrum disorders: A worldwide ENIGMA studyWolfgang Omlor, Finn Rabe, Simon Fuchs, et al.
Neuroimage|May 30, 2020
Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMAJoaquim Radua, Eduard Vieta, Russell Shinohara, et al.
Molecular Psychiatry|September 2, 2018
Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working GroupAbraham Nunes, Hugo G Schnack, Christopher R K Ching, et al.
Machine Learning in Medical Imaging. MLMI (Workshop)|July 24, 2018
Machine Learning for Large-Scale Quality Control of 3D Shape Models in NeuroimagingDmitry Petrov, Boris A Gutman, Shih-Hua Julie Yu, et al.
Pageof 6

Showing results (31-40 of 60) with videos related to

Sort By:
Pageof 6
Progress in Brain Research|January 30, 2016
Genetic imaging consortium for addiction medicine: From neuroimaging to genesScott Mackey, Kees-Jan Kan, Bader Chaarani, et al.
Frontiers in Psychiatry|September 8, 2022
Enhancing neuroimaging genetics through meta-analysis for Tourette syndrome (ENIGMA-TS): A worldwide platform for collaborationPeristera Paschou, Yin Jin, Kirsten Müller-Vahl, et al.
The American Journal of Psychiatry|October 20, 2018
Mega-Analysis of Gray Matter Volume in Substance Dependence: General and Substance-Specific Regional EffectsScott Mackey, Nicholas Allgaier, Bader Chaarani, et al.
Psychological Medicine|May 16, 2019
Interactive impact of childhood maltreatment, depression, and age on cortical brain structure: mega-analytic findings from a large multi-site cohortLeonardo Tozzi, Lisa Garczarek, Deborah Janowitz, et al.
Molecular Psychiatry|February 9, 2024
Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA studyFoivos Georgiadis, Sara Larivière, David Glahn, et al.
The American Journal of Psychiatry|February 26, 2025
Estimating Multimodal Structural Brain Variability in Schizophrenia Spectrum Disorders: A Worldwide ENIGMA StudyWolfgang Omlor, Finn Rabe, Simon Fuchs, et al.
Biorxiv : the Preprint Server for Biology|November 14, 2023
Estimating multimodal brain variability in schizophrenia spectrum disorders: A worldwide ENIGMA studyWolfgang Omlor, Finn Rabe, Simon Fuchs, et al.
Neuroimage|May 30, 2020
Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMAJoaquim Radua, Eduard Vieta, Russell Shinohara, et al.
Molecular Psychiatry|September 2, 2018
Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working GroupAbraham Nunes, Hugo G Schnack, Christopher R K Ching, et al.
Machine Learning in Medical Imaging. MLMI (Workshop)|July 24, 2018
Machine Learning for Large-Scale Quality Control of 3D Shape Models in NeuroimagingDmitry Petrov, Boris A Gutman, Shih-Hua Julie Yu, et al.
Pageof 6