Bridging big data in the ENIGMA consortium to combine non-equivalent cognitive measures.

Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE

Original Publication: London : Nature Publishing Group, copyright 2011-

Big Data* ; Cognition*/physiology; Humans ; Male ; Female ; Middle Aged ; Adult ; Aged ; Reproducibility of Results ; Verbal Learning/physiology ; Neuropsychological Tests ; Memory/physiology ; Young Adult

Investigators in neuroscience have turned to Big Data to address replication and reliability issues by increasing sample size. These efforts unveil new questions about how to integrate data across distinct sources and instruments. The goal of this study was to link scores across common auditory verbal learning tasks (AVLTs). This international secondary analysis aggregated multisite raw data for AVLTs across 53 studies totaling 10,505 individuals. Using the ComBat-GAM algorithm, we isolated and removed the component of memory scores associated with site effects while preserving instrumental effects. After adjustment, a continuous item response theory model used multiple memory items of varying difficulty to estimate each individual's latent verbal learning ability on a single scale. Equivalent raw scores across AVLTs were then found by linking individuals through the ability scale. Harmonization reduced total cross-site score variance by 37% while preserving meaningful memory effects. Age had the largest impact on scores overall (- 11.4%), while race/ethnicity variable was not significant (p > 0.05). The resulting tools were validated on dually administered tests. The conversion tool is available online so researchers and clinicians can convert memory scores across instruments. This work demonstrates that global harmonization initiatives can address reproducibility challenges across the behavioral sciences.
(© 2024. The Author(s).)

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Investigator: M Adamson; M Alda; S Alonso-Lana; S Ambrogi; TJ Anderson; C Arango; RF Asarnow; M Avram; R Ayesa-Arriola; T Babikian; N Banaj; LJ Bird; S Borgwardt; A Brodtmann; K Brosch; K Caeyenberghs; VD Calhoun; ND Chiaravalloti; DX Cifu; B Crespo-Facorro; JC Dalrymple-Alford; K Dams-O'Connor; U Dannlowski; D Darby; N Davenport; J DeLuca; CM Diaz-Caneja; SG Disner; E Dobryakova; S Ehrlich; C Esopenko; F Ferrarelli; LE Frank; C Franz; P Fuentes-Claramonte; H Genova; CC Giza; J Goltermann; D Grotegerd; M Gruber; A Gutierrez-Zotes; M Ha; J Haavik; C Hinkin; KR Hoskinson; D Hubl; A Irimia; A Jansen; M Kaess; X Kang; K Kenney; B Keřková; MS Khlif; M Kim; J Kindler; T Kircher; K Knížková; KK Kolskår; D Krch; WS Kremen; T Kuhn; V Kumari; JS Kwon; R Langella; S Laskowitz; J Lee; J Lengenfelder; SW Liebel; V Liou-Johnson; SM Lippa; M Løvstad; A Lundervold; C Marotta; CA Marquardt; P Mattos; A Mayeli; CR McDonald; S Meinert; TR Melzer; J Merchán-Naranjo; C Michel; RA Morey; B Mwangi; DJ Myall; I Nenadić; MR Newsome; A Nunes; T O'Brien; V Oertel; J Ollinger; A Olsen; VOG de la Foz; M Ozmen; H Pardoe; M Parent; F Piras; F Piras; E Pomarol-Clotet; J Repple; G Richard; J Rodriguez; M Rodriguez; K Rootes-Murdy; J Rowland; NP Ryan; R Salvador; AM Sanders; A Schmidt; JC Soares; G Spalleta; F Španiel; A Stasenko; F Stein; B Straube; A Thames; F Thomas-Odenthal; SI Thomopoulos; E Tone; I Torres; M Troyanskaya; JA Turner; KM Ulrichsen; G Umpierrez; E Vilella; L Vivash; WC Walker; E Werden; LT Westlye; K Wild; A Wroblewski; MJ Wu; GR Wylie; LN Yatham; GB Zunta-Soares
Keywords: Harmonization; Item response theory; Mega analysis; Traumatic brain injury; Verbal learning

Date Created: 20241016 Date Completed: 20241016 Latest Revision: 20241207

20241209

PMC11484938

10.1038/s41598-024-72968-x

39414844