Understanding Wellbeing Profiles According to White Matter Structural Connectivity Sub-types in Early Adolescents: The First Hundred Brains Cohort from the Longitudinal Adolescent Brain Study

Abstract

Wellbeing is protective against the emergence of psychopathology. Neurobiological markers associated with mental wellbeing during adolescence are important to understand. Limited research has examined neural networks (white matter tracts) and mental wellbeing in early adolescence specifically. A cross-sectional diffusion tensor imaging analysis approach was conducted, from the Longitudinal Adolescent Brain study, First Hundred Brains cohort (N = 99; 46.5% female; M_age = 13.01, SD = 0.55). Participants completed self-report measures including wellbeing, quality-of-life, and psychological distress. Potential neurobiological profiles using fractional anisotropy, axial, and radial diffusivity were determined via a whole brain voxel-wise approach, and hierarchical cluster analysis of fractional anisotropy values, obtained from 21 major white matter tracts. Three cluster groups with significantly different neurobiological profiles were distinguished. No significant differences were found between the three cluster groups and measures of wellbeing, but two left lateralized significant associations between white matter tracts and wellbeing measures were found. These results provide preliminary evidence for potential neurobiological markers of mental health and wellbeing in early adolescence and should be tracked longitudinally to provide more detailed and robust findings.

Keywords: Adolescence; Mental health; Wellbeing; White matter.

Fig. 1

a Standardized FA scores (mean with 95% confidence interval bars) across cluster groupings, derived from 21 white matter-tracts (JHU-ICBM atlas). b Visualization of the 21 JHU-ICBM atlas tracts included in the cluster analysis, overlaid on a standard space template. Axial slices depict inferior to superior moving from left to right. For greater distinction, bilateralized or single tracts are each color coded uniquely and correspond to the labels on the x-axis in subplot-a

Fig. 2

Correlation between FA of the SLF-L and K10 scores in cluster 1 (r = −0.462, p < 0.001; N = 48; triangles), compared to cluster 2 (no significant correlation; N = 28; squares) and cluster 3 (no significant correlation; N = 23; circles). FA fractional anisotropy; SLF–L superior longitudinal fasciculus, left; K10 scores = psychological distress, with higher scores meaning higher psychological distress

Fig. 3

Correlation between FA of the SCR-L and COMPAS-W total wellbeing scores in cluster 2 (r = −0.623, p < 0.001, N = 28; squares), compared to cluster 1 (no significant correlation; N = 48; triangles) and cluster 3 (no significant correlation; N = 23; circles). FA fractional anisotropy; SCR–L superior corona radiata, left; COMPAS-W Total Wellbeing, with higher scores meaning higher total wellbeing