Composite reaction time, not evidence accumulation efficiency, correlates with whole-brain white matter characteristics

The relationship between cognitive performance, often measured by processing speed, and brain network characteristics has been widely studied, yet the results remain inconsistent. While many studies have linked processing speed to the microstructure of white matter, discrepancies arise due to differences in the tasks used, behavioural measures assessed, and specific white matter tracts considered. To address these challenges, we present a pre-registered analysis using a large (N=159) dataset, incorporating state-of-the-art MRI data acquired from a high-gradient 3T Connectom scanner. We combine data from three reaction-time tasks to create composite measures of cognitive performance, mitigating the limitations of experiment-specific analyses. We applied the drift-diffusion model to derive key cognitive parameters—drift rate, boundary separation, and non-decision time— along with behavioural measures including mean reaction time, reaction time variability, and accuracy. Using general linear models, we explored the relationship between these parameters and the global and task-specific structural networks of the brain, weighted by volume-normalized streamline counts and myelin-water-fraction. Our results revealed negative associations between the global efficiency of streamline-weighted networks and both mean reaction time and reaction time variability (β=-0.18/- 0.21, p=0.025/0.01 and β=-0.18/-0.18, p=0.028/0.022 for whole-brain and task-specific networks, respectively). Contrary to our hypothesis, these effects were not captured by decision model parameters, and controlling for age did not alter the results. This analysis replicates the association between reaction time variability and the structural organization of the brain, but further research is needed to refine our understanding of the specific cognitive mechanisms underlying these relationships.