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Ian H Gotlib

Showing results (411-420 of 431) with videos related to

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Biological Psychiatry|June 26, 2021
Brain Correlates of Suicide Attempt in 18,925 Participants Across 18 International CohortsAdrian I Campos, Paul M Thompson, Dick J Veltman, et al.
Scientific Reports|January 11, 2024
Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measuresVladimir Belov, Tracy Erwin-Grabner, Moji Aghajani, et al.
Molecular Psychiatry|September 1, 2019
White matter disturbances in major depressive disorder: a coordinated analysis across 20 international cohorts in the ENIGMA MDD working groupLaura S van Velzen, Sinead Kelly, Dmitry Isaev, 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.
Molecular Psychiatry|September 7, 2022
Structural brain alterations associated with suicidal thoughts and behaviors in young people: results from 21 international studies from the ENIGMA Suicidal Thoughts and Behaviours consortiumLaura S van Velzen, Maria R Dauvermann, Lejla Colic, et al.
The American Journal of Psychiatry|July 30, 2019
No Alterations of Brain Structural Asymmetry in Major Depressive Disorder: An ENIGMA Consortium AnalysisCarolien G F de Kovel, Lyubomir Aftanas, André Aleman, et al.
Translational Psychiatry|May 31, 2020
ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharingLianne Schmaal, Elena Pozzi, Tiffany C Ho, et al.
Molecular Psychiatry|October 3, 2025
Classification of major depressive disorder using vertex-wise brain sulcal depth, curvature, and thickness with a deep and a shallow learning modelRoberto Goya-Maldonado, Tracy Erwin-Grabner, Ling-Li Zeng, et al.
Human Brain Mapping|June 3, 2024
Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesitySean R McWhinney, Jaroslav Hlinka, Eduard Bakstein, et al.
Arxiv|February 20, 2025
Classification of Major Depressive Disorder Using Vertex-Wise Brain Sulcal Depth, Curvature, and Thickness with a Deep and a Shallow Learning ModelRoberto Goya-Maldonado, Tracy Erwin-Grabner, Ling-Li Zeng, et al.
Pageof 44

Showing results (411-420 of 431) with videos related to

Sort By:
Pageof 44
Biological Psychiatry|June 26, 2021
Brain Correlates of Suicide Attempt in 18,925 Participants Across 18 International CohortsAdrian I Campos, Paul M Thompson, Dick J Veltman, et al.
Scientific Reports|January 11, 2024
Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measuresVladimir Belov, Tracy Erwin-Grabner, Moji Aghajani, et al.
Molecular Psychiatry|September 1, 2019
White matter disturbances in major depressive disorder: a coordinated analysis across 20 international cohorts in the ENIGMA MDD working groupLaura S van Velzen, Sinead Kelly, Dmitry Isaev, 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.
Molecular Psychiatry|September 7, 2022
Structural brain alterations associated with suicidal thoughts and behaviors in young people: results from 21 international studies from the ENIGMA Suicidal Thoughts and Behaviours consortiumLaura S van Velzen, Maria R Dauvermann, Lejla Colic, et al.
The American Journal of Psychiatry|July 30, 2019
No Alterations of Brain Structural Asymmetry in Major Depressive Disorder: An ENIGMA Consortium AnalysisCarolien G F de Kovel, Lyubomir Aftanas, André Aleman, et al.
Translational Psychiatry|May 31, 2020
ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharingLianne Schmaal, Elena Pozzi, Tiffany C Ho, et al.
Molecular Psychiatry|October 3, 2025
Classification of major depressive disorder using vertex-wise brain sulcal depth, curvature, and thickness with a deep and a shallow learning modelRoberto Goya-Maldonado, Tracy Erwin-Grabner, Ling-Li Zeng, et al.
Human Brain Mapping|June 3, 2024
Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesitySean R McWhinney, Jaroslav Hlinka, Eduard Bakstein, et al.
Arxiv|February 20, 2025
Classification of Major Depressive Disorder Using Vertex-Wise Brain Sulcal Depth, Curvature, and Thickness with a Deep and a Shallow Learning ModelRoberto Goya-Maldonado, Tracy Erwin-Grabner, Ling-Li Zeng, et al.
Pageof 44