Poorer white matter microstructure predicts slower and more variable reaction time performance: evidence for a neural noise hypothesis in a large lifespan cohort

Abstract

Most prior research in the neural and behavioral sciences has been focused on characterizing averages in cognition, brain characteristics, or behavior, and attempting to predict differences in these averages among individuals. However, this overwhelming focus on mean levels may leave us with an incomplete picture of what drives individual differences in behavioral phenotypes by ignoring the variability of behavior around an individual’s mean. In particular, better white matter (WM) structural microstructure has been hypothesized to support consistent behavioral performance by decreasing gaussian noise in signal transfer. In contrast, lower indices of white matter microstructure have been associated with greater within-subject variance in the ability to deploy performance-related resources, especially in clinical samples. We tested this ‘neural noise’ hypothesis in a large adult lifespan cohort (Cam-CAN) with over 2500 individuals in a (2681 behavioral sessions with 708 scans in adults aged 18–102) using measures of WM tract microstructure to predict mean levels and variability in reaction time performance on a simple behavioral task using a dynamic structural equation model (DSEM). We found broad support for neural noise hypothesis, such that lower WM microstructure predicted individual differences in separable components of behavioral performance estimated using DSEM, including slower mean responses and increased variability. These effects were robust when including age in the model, suggesting consistent effects of WM microstructure across the adult lifespan above and beyond concurrent effects of ageing. Crucially, these results demonstrate the utility of DSEM for modeling and predicting behavioral variability directly, and the promise of studying variability for understanding cognitive processes.

Authors 2

1. Ethan Michael McCormick corresponding Aachen Methodology and Statistics Department Institute of Psychology, Leiden University

   Leiden University · University of Cambridge · Radboud University Medical Center · Radboud University Nijmegen · University of North Carolina at Chapel Hill · Donders Institute for Brain, Cognition and Behaviour · MRC Cognition and Brain Sciences Unit

   Affiliation as printed

   University of North Carolina at Chapel Hill

   Cognitive Neuroscience Department, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands;

   Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, United States;

   MRC Cognition and Brain Sciences Unit, University of Cambridge, United Kingdom

   Methodology and Statistics Department, Institute of Psychology, Leiden University, Leiden, Netherlands;

2. Rogier Kievit

   University of Cambridge · Radboud University Medical Center · Radboud University Nijmegen · Donders Institute for Brain, Cognition and Behaviour · MRC Cognition and Brain Sciences Unit

   Affiliation as printed

   Cognitive Neuroscience Department , Donders Institute for Brain, Cognition and Behavior ,

   MRC Cognition and Brain Sciences Unit , University of Cambridge , United Kingdom

   Cognitive Neuroscience Department, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands;

   MRC Cognition and Brain Sciences Unit, University of Cambridge, United Kingdom