R
Richard Dinga
Researcher at Radboud University Nijmegen
Publications - 36
Citations - 1595
Richard Dinga is an academic researcher from Radboud University Nijmegen. The author has contributed to research in topics: Major depressive disorder & Normative. The author has an hindex of 13, co-authored 31 publications receiving 998 citations. Previous affiliations of Richard Dinga include VU University Medical Center & VU University Amsterdam.
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Journal ArticleDOI
Subcortical brain alterations in major depressive disorder : findings from the ENIGMA Major Depressive Disorder working group
Tiffany C. Ho,Boris A. Gutman,Elena Pozzi,Hans J. Grabe,Norbert Hosten,Katharina Wittfeld,Henry Völzke,Bernhard T. Baune,Bernhard T. Baune,Udo Dannlowski,Katharina Förster,Dominik Grotegerd,Ronny Redlich,Andreas Jansen,Tilo Kircher,Axel Krug,Susanne Meinert,Igor Nenadic,Nils Opel,Richard Dinga,Dick J. Veltman,Knut Schnell,Ilya M. Veer,Henrik Walter,Ian H. Gotlib,Matthew D. Sacchet,Matthew D. Sacchet,André Aleman,Nynke A. Groenewold,Dan J. Stein,Meng Li,Martin Walter,Christopher R.K. Ching,Neda Jahanshad,Anjanibhargavi Ragothaman,Dmitry Isaev,Artemis Zavaliangos-Petropulu,Paul M. Thompson,Philipp G. Sämann,Lianne Schmaal +39 more
TL;DR: Three-dimensional brain magnetic resonance imaging data was meta-analyzed to identify subcortical brain volumes that robustly discriminate major depressive disorder patients from healthy controls and showed robust smaller hippocampal volumes in MDD patients, moderated by age of onset and first episode versus recurrent episode status.
Journal ArticleDOI
Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group
Laura K.M. Han,Richard Dinga,Richard Dinga,Tim Hahn,Christopher R.K. Ching,Lisa T. Eyler,Lisa T. Eyler,Lyubomir I. Aftanas,Moji Aghajani,André Aleman,Bernhard T. Baune,Bernhard T. Baune,Klaus Berger,Ivan V. Brak,Geraldo Busatto Filho,Angela Carballedo,Colm G. Connolly,Baptiste Couvy-Duchesne,Kathryn R. Cullen,Udo Dannlowski,Christopher G. Davey,Danai Dima,Danai Dima,Fábio L.S. Duran,Verena Enneking,Elena Filimonova,Stefan Frenzel,Thomas Frodl,Thomas Frodl,Thomas Frodl,Cynthia H.Y. Fu,Cynthia H.Y. Fu,Beata R. Godlewska,Ian H. Gotlib,Hans J. Grabe,Hans J. Grabe,Nynke A. Groenewold,Nynke A. Groenewold,Dominik Grotegerd,Oliver Gruber,Geoffrey B. Hall,Ben J. Harrison,Sean N. Hatton,Sean N. Hatton,Marco Hermesdorf,Ian B. Hickie,Tiffany C. Ho,Norbert Hosten,Andreas Jansen,Claas Kähler,Tilo Kircher,Bonnie Klimes-Dougan,Bernd Kramer,Axel Krug,Axel Krug,Jim Lagopoulos,Jim Lagopoulos,Ramona Leenings,Frank P. MacMaster,Glenda MacQueen,Andrew M. McIntosh,Quinn McLellan,Quinn McLellan,Katie L. McMahon,Sarah E. Medland,Bryon A. Mueller,Benson Mwangi,Evgeny Osipov,Maria J. Portella,Elena Pozzi,Elena Pozzi,Liesbeth Reneman,Jonathan Repple,Pedro G.P. Rosa,Matthew D. Sacchet,Philipp G. Sämann,Knut Schnell,Anouk Schrantee,Egle Simulionyte,Jair C. Soares,Jens Sommer,Dan J. Stein,Olaf Steinsträter,Lachlan T. Strike,Sophia I. Thomopoulos,Marie-José van Tol,Ilya M. Veer,Robert Vermeiren,Henrik Walter,Nic J.A. van der Wee,Steven J.A. van der Werff,Heather C. Whalley,Nils R. Winter,Katharina Wittfeld,Katharina Wittfeld,Margaret J. Wright,Mon-Ju Wu,Henry Völzke,Tony T. Yang,Vasileios Zannias,Greig I. de Zubicaray,Giovana Zunta-Soares,Christoph Abé,Martin Alda,Ole A. Andreassen,Erlend Bøen,Caterina del Mar Bonnín,Erick J. Canales-Rodríguez,Dara M. Cannon,Xavier Caseras,Tiffany M. Chaim-Avancini,Torbjørn Elvsåshagen,Pauline Favre,Pauline Favre,Sonya Foley,Janice M. Fullerton,Janice M. Fullerton,Jose Manuel Goikolea,Bartholomeus C M Haarman,Tomas Hajek,Chantal Henry,Josselin Houenou,Josselin Houenou,Fleur M. Howells,Martin Ingvar,Rayus Kuplicki,Beny Lafer,Mikael Landén,Mikael Landén,Rodrigo Machado-Vieira,Ulrik Fredrik Malt,Colm McDonald,Philip B. Mitchell,Leila Nabulsi,Maria Concepcion Garcia Otaduy,Bronwyn Overs,Mircea Polosan,Mircea Polosan,Edith Pomarol-Clotet,Joaquim Radua,Maria M. Rive,Gloria Roberts,Henricus G. Ruhé,Henricus G. Ruhé,Raymond Salvador,Salvador Sarró,Theodore D. Satterthwaite,Jonathan Savitz,Jonathan Savitz,Aart H. Schene,Peter R. Schofield,Peter R. Schofield,Mauricio H. Serpa,Kang Sim,Márcio Gerhardt Soeiro-de-Souza,Ashley N. Sutherland,Henk Temmingh,Garrett M. Timmons,Anne Uhlmann,Eduard Vieta,Daniel H. Wolf,Marcus V. Zanetti,Neda Jahanshad,Paul M. Thompson,Dick J. Veltman,Brenda W.J.H. Penninx,Andre F. Marquand,James H. Cole,James H. Cole,Lianne Schmaal +169 more
TL;DR: This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD, and substantial within-group variance and overlap between groups were observed.
Journal ArticleDOI
Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data : a machine learning approach
Richard Dinga,Andre F. Marquand,Andre F. Marquand,Dick J. Veltman,Aartjan T.F. Beekman,Robert A. Schoevers,Albert M. van Hemert,Brenda W.J.H. Penninx,Lianne Schmaal +8 more
TL;DR: Among the large set of variables considered, only the IDS provided predictive value for course prediction on an individual level, although this analysis represents only one possible methodological approach.
Journal ArticleDOI
From pattern classification to stratification: towards conceptualizing the heterogeneity of Autism Spectrum Disorder.
Thomas Wolfers,Dorothea L. Floris,Richard Dinga,Daan van Rooij,Christina Isakoglou,Seyed Mostafa Kia,Mariam Zabihi,Alberto Llera,Rajanikanth Chowdanayaka,Vinod Kumar,Han Peng,Charles Laidi,Dafnis Batalle,Ralica Dimitrova,Tony Charman,Eva Loth,Meng-Chuan Lai,Emily J.H. Jones,Sarah Baumeister,Caroline Moessnang,Tobias Banaschewski,Christine Ecker,Guillaume Dumas,Jonathan O'Muircheartaigh,Declan G. Murphy,Jan K. Buitelaar,Andre F. Marquand,Christian F. Beckmann +27 more
TL;DR: Large variance is observed across pattern classification studies in terms of predictive performance from about 60% to 98% accuracy, which is among other factors likely linked to sampling bias, different validation procedures across studies, the heterogeneity of ASD and differences in data quality.
Posted ContentDOI
Controlling for effects of confounding variables on machine learning predictions
TL;DR: This work proposes controlling for confounds post-hoc on the level of machine learning predictions, which allows partitioning of the predictive performance into the performance that can be explained by confounds and performance independent of confounds.