Grey and white matter metrics demonstrate distinct and complementary prediction of differences in cognitive performance in children: Findings from ABCD (N= 11 876)

Journal of Neuroscience, vol. 44, pp. e0465232023

Abstract

Individual differences in cognitive performance in childhood are a key predictor of significant life outcomes such as educational attainment and mental health. Differences in cognitive ability are governed in part by variations in brain structure. However, studies commonly focus on either grey or white matter metrics in humans, leaving open the key question as to whether grey or white matter microstructure play distinct or complementary roles supporting cognitive performance. To compare the role of grey and white matter in supporting cognitive performance, we used regularized structural equation models to predict cognitive performance with grey and white matter measures. Specifically, we compared how grey matter (volume, cortical thickness and surface area) and white matter measures (volume, fractional anisotropy and mean diffusivity) predicted individual differences in cognitive performance. The models were tested in 11,876 children (ABCD Study, 5680 female; 6196 male) at 10 years old. We found that grey and white matter metrics bring partly non-overlapping information to predict cognitive performance. The models with only grey or white matter explained respectively 15.4% and 12.4% of the variance in cognitive performance, while the combined model explained 19.0%. Zooming in we additionally found that different metrics within grey and white matter had different predictive power, and that the tracts/regions that were most predictive of cognitive performance differed across metric. These results show that studies focusing on a single metric in either grey or white matter to study the link between brain structure and cognitive performance are missing a key part of the equation. Significance Statement This paper enriches the recent debates on the challenges of linking variation in brain structure to phenotypic differences (Marek et al., 2022). We demonstrate that using latent variables (to improve power), structural equation modelling (to allow greater flexibility in linking brain to behaviour), and by simultaneously incorporating multiple measures of grey and white matter in a large sample, we demonstrate relatively strong and robust brain-behaviour associations, which highlight the complementarity of grey and white matter metrics in predicting cognitive performance as well as the importance of incorporating the full complexity of these associations over 1-to-1 linkages. This finding should lead researchers to consider integrating both grey and white matter measures when demonstrating a more comprehensive picture of brain-cognition relationships.

Authors 3

1. Lea C. Michel

   Radboud University Medical Center · Radboud University Nijmegen

   Affiliation as printed

   Cognitive Neuroscience Department, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands lea.michel@radboudumc.nl

   Cognitive Neuroscience Department, Radboud University Medical Center

2. Ethan Michael McCormick Aachen Methodology and Statistics Institute of Psychology, Leiden University

   Leiden University · Radboud University Medical Center · Radboud University Nijmegen · University of North Carolina at Chapel Hill

   Affiliation as printed

   Cognitive Neuroscience Department, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands

   Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, North Carolina 27599-3270

   Methodology and Statistics, Institute of Psychology, Leiden University, Leiden 2333 AK, The Netherlands

   Cognitive Neuroscience Department, Radboud University Medical Center

   Department of Psychology and Neuroscience, University of North Carolina

   Institute of Psychology, Leiden University

3. Rogier Kievit

   Radboud University Medical Center · Radboud University Nijmegen

   Affiliation as printed

   Cognitive Neuroscience Department, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands

   Cognitive Neuroscience Department, Radboud University Medical Center