A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learning

Autor: Georgios V. Thomaidis, Konstantinos Papadimitriou, Sotirios Michos, Evangelos Chartampilas, Ioannis Tsamardinos

Publikováno v: IBRO Neuroscience Reports, Vol 15, Iss , Pp 77-89 (2023)

Background: Transcriptomic profile differences between patients with bipolar disorder and healthy controls can be identified using machine learning and can provide information about the potential role of the cerebellum in the pathogenesis of bipolar

Externí odkaz: https://doaj.org/article/5c5a69a1b30b48a091f1a845534ec1d9

What we learn about bipolar disorder from large-scale neuroimaging

Autor: Christian K. Tamnes, Bartholomeus C M Haarman, Jair C. Soares, Ole A. Andreassen, Viola Oertel, Theodore D. Satterthwaite, G. Tronchin, Michael Stäblein, Bradley J. MacIntosh, Melissa Pauling, Christopher R.K. Ching, Daniel H. Wolf, Dick J. Veltman, Ingrid Agartz, Bernhard T. Baune, Salvador Sarró, Mon-Ju Wu, Scott C Fears, Eduard Vieta, Melissa J. Green, Neeltje E.M. van Haren, Yann Quidé, Erlend Bøen, Yash Patel, Igor Nenadic, Martin Alda, Lisa T. Eyler, Arnaud Pouchon, Danai Dima, Tomáš Paus, Irene Bollettini, Torbjørn Elvsåshagen, Rachel M. Brouwer, Lakshmi N. Yatham, Michael Bauer, Caterina del Mar Bonnín, C. McDonald, Udo Dannlowski, Bronwyn Overs, Edith Pomarol-Clotet, Cristian Vargas Upegui, Oliver Gruber, Henricus G. Ruhé, Márcio Gerhardt Soeiro-de-Souza, Edouard Duchesnay, Hilary P. Blumberg, Tilo Kircher, Miho Ota, Michael Berk, Christoph Abé, Andreas Jansen, Kang Sim, Heather C. Whalley, Derrek P. Hibar, Roel A. Ophoff, Georgios V Thomaidis, Henrik Walter, Sophia Frangou, Michèle Wessa, Dara M. Cannon, Cara M. Altimus, Allison C. Nugent, Rodrigo Machado-Vieira, Orwa Dandash, Marcella Bellani, Unn K. Haukvik, Philip B. Mitchell, Ling-Li Zeng, Christian Knöchel, Jose Manuel Goikolea, Sonja M C de Zwarte, Francesco Benedetti, Sara Poletti, Janice M. Fullerton, Carlos A. Zarate, Aart H. Schene, Dan J. Stein, Chantal Henry, Tristram A. Lett, Mikael Landén, Daniel L Pham, Paolo Brambilla, Silvia Alonso-Lana, Sophia I. Thomopoulos, Carlos López-Jaramillo, Tomas Hajek, Bernd Kramer, G. Delvecchio, Maria M. Rive, Lars T. Westlye, Erick J. Canales-Rodríguez, Victoria L. Ives-Deliperi, Dominik Grotegerd, Beny Lafer, Abraham Nunes, Carrie E. Bearden, Raymond Salvador, Joaquim Radua, Amy C Bilderbeck, Xavier Caseras, Paul M. Thompson, Jorge R. C. Almeida, Pauline Favre, Gloria Roberts, David C. Glahn, Dag Alnæs, Julian A Pineda-Zapata, Tiril P. Gurholt, Mircea Polosan, Josselin Houenou, Fabiano G. Nery, Leila Nabulsi, Mary L. Phillips, Fleur M. Howells, Ana M. Díaz-Zuluaga, Elisa M T Melloni

Publikováno v: Human Brain Mapping
Dipòsit Digital de la UB
Universidad de Barcelona
ENIGMA Bipolar Disorder Working Group 2022, ' What we learn about bipolar disorder from large-scale neuroimaging : Findings and future directions from the ENIGMA Bipolar Disorder Working Group ', Human Brain Mapping, vol. 43, no. 1, pp. 56-82 . https://doi.org/10.1002/hbm.25098
Human Brain Mapping, 43, 56-82
Human Brain Mapping, 43, 1, pp. 56-82
2020, ' What we learn about bipolar disorder from large-scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group ', Human Brain Mapping . https://doi.org/10.1002/hbm.25098

MRI‐derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty re

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::403d82c99ed236f9ee19573815886cfb
http://www.scopus.com/inward/record.url?scp=85088655322&partnerID=8YFLogxK

A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learning

Autor: Georgios V. Thomaidis, Konstantinos Papadimitriou, Sotirios Michos, Evangelos Chartampilas, Ioannis Tsamardinos

BackroundTranscriptomic profile differences between patients with bipolar disorder and healthy controls can be identified using machine learning and can provide information about the potential role of the cerebellum in the pathogenesis of bipolar dis

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2f029a9a1b68bab3045cc916def42e80
https://doi.org/10.1101/2022.01.22.22269384

E-Poster Viewing

Autor: Bárbara Moura, Sofia Von Humboldt, Katarzyna Małgorzata Lion, Krzysztof Ostaszewski, Rachel Parker, Maria Łukasik, Georgios V. Thomaidis, Ivan Danilin, Filipa Barros, Eduardo Fernández-Jiménez

Publikováno v: European Psychiatry. 56:S322-S553

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::612e7f9f0bdca329909ad4be75544269
https://doi.org/10.1016/j.eurpsy.2019.01.002

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.