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[Preprint]. 2023 Apr 7:2023.01.16.524331.
doi: 10.1101/2023.01.16.524331.

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

Eamonn Kennedy  1   2   3 Shashank Vadlamani  1 Hannah M Lindsey  1   3 Pui-Wa Lei  4 Mary Jo-Pugh  1   2 Maheen Adamson  5   6 Martin Alda  7 Silvia Alonso-Lana  8   9 Sonia Ambrogi  10 Tim J Anderson  11   12   13 Celso Arango  14 Robert F Asarnow  15   16   17 Mihai Avram  18 Rosa Ayesa-Arriola  9   19 Talin Babikian  15   20 Nerisa Banaj  10 Laura J Bird  21 Stefan Borgwardt  18   22 Amy Brodtmann  23   24 Katharina Brosch  25 Karen Caeyenberghs  26 Vince D Calhoun  27 Nancy D Chiaravalloti  28   29 David X Cifu  30 Benedicto Crespo-Facorro  9   31 John C Dalrymple-Alford  11   12   32 Kristen Dams-O'Connor  33   34 Udo Dannlowski  35 David Darby  36   37   38 Nicholas Davenport  39   40 John DeLuca  29   41 Covadonga M Diaz-Caneja  14 Seth G Disner  39   40 Ekaterina Dobryakova  29   42 Stefan Ehrlich  43   44 Carrie Esopenko  33 Fabio Ferrarelli  45 Lea E Frank  46 Carol Franz  47   48 Paola Fuentes-Claramonte  8   9 Helen Genova  29   49 Christopher C Giza  20   50   51 Janik Goltermann  35 Dominik Grotegerd  35 Marius Gruber  35   52 Alfonso Gutierrez-Zotes  9   53   54 Minji Ha  55 Jan Haavik  56   57 Charles Hinkin  15 Kristen R Hoskinson  58   59 Daniela Hubl  60 Andrei Irimia  61   62   63 Andreas Jansen  25 Michael Kaess  64   65 Xiaojian Kang  5 Kimbra Kenney  66   67 Barbora Keřková  1   2   3   4   5   6   7   8   9   10   11   12   13   14   15   16   17   18   19   20   21   22   23   24   25   26   27   28   29   30   31   32   33   34   35   36   37   38   39   40   41   42   43   44   45   46   47   48   49   50   51   52   53   54   55   56   57   58   59   60   61   62   63   64   65   66   67   68   69   70   71   72   73   74   75   76   77   78   79   80   81   82   83   84   85   86   87   88   89   90   91   92   93   94   95   96   97   98   99   100   101   102   103   104   105   106   107   108   109   110   111   112   113   114   115   116 Mohamed Salah Khlif  23 Minah Kim  68   69 Jochen Kindler  64 Tilo Kircher  25 Karolina Knížková  70   71 Knut K Kolskår  72   73   74 Denise Krch  29   42 William S Kremen  47   48 Taylor Kuhn  15 Veena Kumari  75 Jun Soo Kwon  55   68   69 Roberto Langella  10 Sarah Laskowitz  76 Jungha Lee  55 Jean Lengenfelder  29   42 Spencer W Liebel  1   3 Victoria Liou-Johnson  5 Sara M Lippa  67   77 Marianne Løvstad  73   74 Astri Lundervold  78 Cassandra Marotta  36   37 Craig A Marquardt  39   40 Paulo Mattos  79 Ahmad Mayeli  45 Carrie R McDonald  80   81 Susanne Meinert  35   82 Tracy R Melzer  11   12   32 Jessica Merchán-Naranjo  14 Chantal Michel  64 Rajendra A Morey  76   83 Benson Mwangi  84 Daniel J Myall  12 Igor Nenadić  25 Mary R Newsome  85   86 Abraham Nunes  7   87 Terence O'Brien  88   89 Viola Oertel  90 John Ollinger  67 Alexander Olsen  91   92   93 Victor Ortiz García de la Foz  19 Mustafa Ozmen  94 Heath Pardoe  38 Marise Parent  95 Fabrizio Piras  10 Federica Piras  10 Edith Pomarol-Clotet  9 Jonathan Repple  35   52 Geneviève Richard  72 Jonathan Rodriguez  47 Mabel Rodriguez  70 Kelly Rootes-Murdy  27 Jared Rowland  96   97   98 Nicholas P Ryan  26   99 Raymond Salvador  9 Anne-Marthe Sanders  72   73   74 Andre Schmidt  100 Jair C Soares  84 Gianfranco Spalleta  10 Filip Španiel  70   101 Alena Stasenko  47   81 Frederike Stein  25 Benjamin Straube  25 April Thames  15 Florian Thomas-Odenthal  25 Sophia I Thomopoulos  102 Erin Tone  103 Ivan Torres  104   105 Maya Troyanskaya  85   86 Jessica A Turner  106 Kristine M Ulrichsen  72   73   74 Guillermo Umpierrez  107 Elisabet Vilella  9   53   54 Lucy Vivash  36   37 William C Walker  108   109 Emilio Werden  38 Lars T Westlye  72   73   110 Krista Wild  111 Adrian Wroblewski  25 Mon-Ju Wu  84 Glenn R Wylie  29   112 Lakshmi N Yatham  104 Giovana B Zunta-Soares  84 Paul M Thompson  102   113 David F Tate  1   3 Frank G Hillary  114   115   116 Emily L Dennis  1   3 Elisabeth A Wilde  1   3
Affiliations

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

Eamonn Kennedy et al. bioRxiv. .

Abstract

Investigators 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.

Keywords: Harmonization; Mega analysis; Tools; Verbal learning.

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Conflict of interest statement

Competing Interest Statement: Dr. Arango has been a consultant to or has received honoraria or grants from Acadia, Angelini, Biogen, Boehringer, Gedeon Richter, Janssen Cilag, Lundbeck, Medscape, Menarini, Minerva, Otsuka, Pfizer, Roche, Sage, Servier, Shire, Schering Plough, Sumitomo Dainippon Pharma, Sunovion and Takeda. Dr. Brodtmann serves on the editorial boards of Neurology and International Journal of Stroke. Dr. Diaz-Caneja has received honoraria from Exeltis and Angelinii. Dr. Giza: consultant for NBA, NFL, NHLPA, Los Angeles Lakers; Advisory Board: Highmark Interactive, Novartis, MLS, NBA, USSF; Medicolegal 1–2 cases annually. Dr. Soares: ALKERMES (Research Grant), ALLERGAN (Research Grant), ASOFARMA (Consultant), ATAI (Stock), BOEHRINGER Ingelheim (Consultant), COMPASS (Research Grant), JOHNSON & JOHNSON (Consultant), LIVANOVA (Consultant), PFIZER (Consultant), PULVINAR NEURO LLC (Consultant), RELMADA (Consultant), SANOFI (Consultant), SUNOVIAN (Consultant). Dr. Thompson received partial research support from Biogen, Inc., for research unrelated to this manuscript. Dr. Yatham has been on speaker or advisory boards for, or has received research grants from, Alkermes, Abbvie, Canadian Institutes of Health Research, Sumitomo Dainippon Pharma, GlaxoSmithKline, Intracellular Therapies, Merck, Sanofi, Sequiris, Servier, and Sunovion, over the past 3 years, all outside this work. The collection of this cohort was partially supported by an investigator-initiated research grant from Biogen (US). Biogen had no role in the analysis or writing of this manuscript. Eisai (JP) and Life Molecular Imaging for research unrelated to this manuscript. Dr. Wylie has received research support from the NJ Commission for brain injury research, from the Dept of Veterans’ Affairs, from Biogen, from Bristol, Myers, Squibb, from Genetech, and has served on advisory boards for the CDMRP and the VA. All of these activities are unrelated to this research. The views expressed in this article are those of the author(s) and do not reflect the official policy of the Department of Army/Navy/Air Force, Department of Defense, or U.S. Government.

Figures

Figure 1.
Figure 1.
Comparing proportions of memory items recalled before and after harmonization. Mean scores for each site (dots) are shown broken out by instrument (color) and item (Top: Trial 1 immediate free recall, Middle: Total sum of all Trials, Bottom: Long-delay free recall scores).
Figure 2.
Figure 2.
Visualizing covariate effects on scores across AVLTs. (a) Boxplots of scores stratified by group (TBI vs. control) and sex/gender indicated that males and those with history of TBI had significantly lower scores on average for all tests. Age-related declines (b) and the beneficial effects of education (c) on scores were consistent across all AVLTs.
Figure 3.
Figure 3.. Visualizing and Validating Conversions.
(a) Average raw scores as a function of individual ability are shown approximated as cubic polynomial fits for immediate, short, and long delay trials. Horizontal lines of equivalent ability connect equivalent raw scores across tests, which facilitates the construction of crosswalks. (b) Scatter plot and fit of the adjusted raw sum of learning Trial scores for a subset of cases who were administered both the CVLT and RAVLT (n=36). The confidence area of the dually assessed data is shown in blue, and agrees with the derived crosswalk for CVLT->RAVLT (n=9,362, black dotted line).

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