Bridging Big Data: Procedures for Combining Non-equivalent Cognitive Measures from the ENIGMA Consortium-Reference-Cited by-同舟云学术

Bridging Big Data: Procedures for Combining Non-equivalent Cognitive Measures from the ENIGMA Consortium

Published:2023-01-19 Issue: Volume: Page:
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Author:

Kennedy Eamonn^ORCID,Vadlamani Shashank,Lindsey Hannah M^ORCID,Lei Pui-Wa,Jo-Pugh Mary^ORCID,Adamson Maheen^ORCID,Alda Martin^ORCID,Alonso-Lana Silvia^ORCID,Ambrogi Sonia^ORCID,Anderson Tim J^ORCID,Arango Celso,Asarnow Robert F,Avram Mihai^ORCID,Ayesa-Arriola Rosa^ORCID,Babikian Talin,Banaj Nerisa^ORCID,Bird Laura J^ORCID,Borgwardt Stefan,Brodtmann Amy^ORCID,Brosch Katharina^ORCID,Caeyenberghs Karen,Calhoun Vince D^ORCID,Chiaravalloti Nancy D^ORCID,Cifu David X,Crespo-Facorro Benedicto,Dalrymple-Alford John C^ORCID,Dams-O’Connor Kristen,Dannlowski Udo^ORCID,Darby David^ORCID,Davenport Nicholas,DeLuca John^ORCID,Diaz-Caneja Covadonga M,Disner Seth G^ORCID,Dobryakova Ekaterina,Ehrlich Stefan^ORCID,Esopenko Carrie^ORCID,Ferrarelli Fabio,Frank Lea E^ORCID,Franz Carol^ORCID,Fuentes-Claramonte Paola^ORCID,Genova Helen^ORCID,Giza Christopher C^ORCID,Goltermann Janik^ORCID,Grotegerd Dominik^ORCID,Gruber Marius^ORCID,Gutierrez-Zotes Alfonso^ORCID,Ha Minji,Haavik Jan^ORCID,Hinkin Charles,Hoskinson Kristen R^ORCID,Hubl Daniela,Irimia Andrei^ORCID,Jansen Andreas^ORCID,Kaess Michael,Kang Xiaojian,Kenney Kimbra^ORCID,Keřková Barbora^ORCID,Khlif Mohamed Salah,Kim Minah^ORCID,Kindler Jochen^ORCID,Kircher Tilo^ORCID,Knížková Karolina^ORCID,Kolskår Knut K^ORCID,Krch Denise^ORCID,Kremen William S^ORCID,Kuhn Taylor^ORCID,Kumari Veena,Kwon Jun Soo^ORCID,Langella Roberto,Laskowitz Sarah,Lee Jungha,Lengenfelder Jean,Liebel Spencer W^ORCID,Liou-Johnson Victoria,Lippa Sara M,Løvstad Marianne^ORCID,Lundervold Astri,Marotta Cassandra,Marquardt Craig A^ORCID,Mattos Paulo^ORCID,Mayeli Ahmad^ORCID,McDonald Carrie R,Meinert Susanne^ORCID,Melzer Tracy R^ORCID,Merchán-Naranjo Jessica^ORCID,Michel Chantal^ORCID,Morey Rajendra A,Mwangi Benson,Myall Daniel J^ORCID,Nenadić Igor^ORCID,Newsome Mary R^ORCID,Nunes Abraham^ORCID,O’Brien Terence^ORCID,Oertel Viola,Ollinger John,Olsen Alexander^ORCID,de la Foz Victor Ortiz García^ORCID,Ozmen Mustafa^ORCID,Pardoe Heath^ORCID,Parent Marise,Piras Fabrizio^ORCID,Piras Federica^ORCID,Pomarol-Clotet Edith,Repple Jonathan^ORCID,Richard Geneviève^ORCID,Rodriguez Jonathan,Rodriguez Mabel,Rootes-Murdy Kelly,Rowland Jared,Ryan Nicholas P^ORCID,Salvador Raymond,Sanders Anne-Marthe^ORCID,Schmidt Andre^ORCID,Soares Jair C,Spalleta Gianfranco^ORCID,Španiel Filip,Stasenko Alena^ORCID,Stein Frederike^ORCID,Straube Benjamin^ORCID,Thames April,Thomas-Odenthal Florian^ORCID,Thomopoulos Sophia I^ORCID,Tone Erin^ORCID,Torres Ivan^ORCID,Troyanskaya Maya,Turner Jessica A,Ulrichsen Kristine M^ORCID,Umpierrez Guillermo,Vilella Elisabet^ORCID,Vivash Lucy,Walker William C,Werden Emilio,Westlye Lars T^ORCID,Wild Krista,Wroblewski Adrian^ORCID,Wu Mon-Ju^ORCID,Wylie Glenn R^ORCID,Yatham Lakshmi N,Zunta-Soares Giovana B,Thompson Paul M^ORCID,Tate David F^ORCID,Hillary Frank G,Dennis Emily L^ORCID,Wilde Elisabeth A^ORCID

Abstract

AbstractInvestigators in neuroscience have turned to Big Data to address replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. These efforts unveil new questions about integrating data arising from distinct sources and instruments. We focus on the most frequently assessed cognitive domain - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated global raw data from 53 studies totaling N = 10,505 individuals. A mega-analysis was conducted using empirical bayes harmonization to remove site effects, followed by linear models adjusting for common covariates. A continuous item response theory (IRT) model estimated each individual’s latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance while preserving covariate effects, and our conversion tool is freely available online. This demonstrates that large-scale data sharing and harmonization initiatives can address reproducibility and integration challenges across the behavioral sciences.TeaserWe present a global effort to devise harmonization procedures necessary to meaningfully leverage big data.

Publisher

Cold Spring Harbor Laboratory

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