| Literature DB >> 26822360 |
Scott Mackey1, Kees-Jan Kan2, Bader Chaarani2, Nelly Alia-Klein3, Albert Batalla4, Samantha Brooks5, Janna Cousijn5, Alain Dagher6, Michiel de Ruiter7, Sylvane Desrivieres8, Sarah W Feldstein Ewing9, Rita Z Goldstein3, Anna E Goudriaan10, Mary M Heitzeg11, Kent Hutchison12, Chiang-Shan R Li13, Edythe D London14, Valentina Lorenzetti15, Maartje Luijten16, Rocio Martin-Santos17, Angelica M Morales18, Martin P Paulus19, Tomas Paus20, Godfrey Pearlson13, Renée Schluter21, Reza Momenan22, Lianne Schmaal23, Gunter Schumann8, Rajita Sinha13, Zsuzsika Sjoerds24, Dan J Stein5, Elliot A Stein25, Nadia Solowij26, Susan Tapert27, Anne Uhlmann5, Dick Veltman23, Ruth van Holst21, Henrik Walter28, Margaret J Wright29, Murat Yucel15, Deborah Yurgelun-Todd30, Derrek P Hibar31, Neda Jahanshad31, Paul M Thompson31, David C Glahn13, Hugh Garavan2, Patricia Conrod32.
Abstract
Since the sample size of a typical neuroimaging study lacks sufficient statistical power to explore unknown genomic associations with brain phenotypes, several international genetic imaging consortia have been organized in recent years to pool data across sites. The challenges and achievements of these consortia are considered here with the goal of leveraging these resources to study addiction. The authors of this review have joined together to form an Addiction working group within the framework of the ENIGMA project, a meta-analytic approach to multisite genetic imaging data. Collectively, the Addiction working group possesses neuroimaging and genomic data obtained from over 10,000 subjects. The deadline for contributing data to the first round of analyses occurred at the beginning of May 2015. The studies performed on this data should significantly impact our understanding of the genetic and neurobiological basis of addiction.Entities:
Keywords: Addiction; ENIGMA; Genetic imaging; Neuroimaging
Mesh:
Year: 2015 PMID: 26822360 PMCID: PMC4820288 DOI: 10.1016/bs.pbr.2015.07.026
Source DB: PubMed Journal: Prog Brain Res ISSN: 0079-6123 Impact factor: 2.453