Longitudinal developmental trajectories of inhibition and white-matter maturation of the fronto-basal-ganglia circuits

Abstract

Response inhibition refers to the cancelling of planned (or restraining of ongoing) actions and is required in much of our everyday life. Response inhibition appears to improve dramatically in early development and plateau in adolescence. The fronto-basal-ganglia network has long been shown to predict individual differences in the ability to enact response inhibition. In the current study, we examined whether developmental trajectories of fiber-specific white matter properties of the fronto-basal-ganglia network was predictive of parallel developmental trajectories of response inhibition. 138 children aged 9-14 completed the stop-signal task (SST). A subsample of 73 children underwent high-angular resolution diffusion MRI data for up to three time points. Performance on the SST was assessed using a parametric race modelling approach. White matter organization of the fronto-basal-ganglia circuit was estimated using fixel-based analysis. Contrary to predictions, we did not find any significant associations between maturational trajectories of fronto-basal-ganglia white matter and developmental improvements in SST performance. Findings suggest that the development of white matter organization of the fronto-basal-ganglia and development of stopping performance follow distinct maturational trajectories.

Keywords: Childhood Development; Diffusion MRI; Fixel-based analysis; Longitudinal analyses; Response inhibition; Stop-signal task.

Fig. 1

(A) Distribution of scores for each subject in the behavioral sample (N = 138); (B) Distribution of scores for each subject in the neuroimaging sample (N = 73). Subjects are ordered by age (years).

Fig. 2

Basic flow diagram of the analysis procedures undertaken in the present study. Detailed information on each step of the process is presented in the Methods and the Supplementary Materials (Appendix A). Note: SST: Stop-signal Task; QC: Quality Control; GAMMs: Generalized additive mixed-models; dMRI: Diffusion MRI; SS3TCSD: Single-shelled three-tissue constrained spherical deconvolution; FBA: Fixel-based Analysis; FD: Fiber density; FC: Fiber cross-section.

Fig. 3

Tracts of the fronto-basal-ganglia circuit. (A) Bilateral IFG-preSMA pathway; (B) bilateral preSMA-STN pathway; (C) bilateral IFG-STN pathway. Voxel position of each coronal slice in millimetres: x = 0, y = −8.5, z = 18.

Fig. 4

Developmental trajectories of stop-signal performance across ages 9–14. Panels A-D present the best-fitting models for SSRT, muS, sigmaS and tauS respectively. Fitted lines with 95 % confidence intervals were modelled by the best fitting GAMMs for each variable of interest and overlaid on the raw data points. SSRT: Stop-signal reaction time; muS: Mu of the stop-signal reaction time distribution; sigmaS: Sigma of the stop-signal reaction time distribution; tauS: Tau of the stop-signal reaction time distribution. ns: non-significant. Age in years, SST metrics in milliseconds.

Fig. 5

Developmental trajectories of logFC underlying the fronto-basal-ganglia circuit. Panels A-C presents the best-fitting models for the left IFG-preSMA, right preSMA-STN & right IFG-STN pathways. Panels D-F presents the best-fitting models for the right IFG-preSMA, left preSMA-STN & left IFG-STN pathways. Fitted lines with 95 % confidence intervals were modelled by the best fitting GAMMs for each variable of interest and overlaid on the raw data points. Left/Right IFG-preSMA (logFC): Log-transformed fiber cross-section of the left/right inferior frontal-gyrus to presupplementary motor area connection; Left/Right preSMA-STN (logFC): Log-transformed fiber cross-section of the left/right presupplementary motor area to subthalamic nucleus connection; Left/Right IFG-STN (logFC): Log-transformed fiber cross-section of the left/right inferior frontal-gyrus to subthalamic nucleus connection. ns: non-significant.