White matter microstructural variability linked to differential attentional skills and impulsive behavior in a pediatric population

Abstract

Structural and functional magnetic resonance imaging (MRI) studies have suggested a neuroanatomical basis that may underly attention-deficit–hyperactivity disorder (ADHD), but the anatomical ground truth remains unknown. In addition, the role of the white matter (WM) microstructure related to attention and impulsivity in a general pediatric population is still not well understood. Using a state-of-the-art structural connectivity pipeline based on the Brainnetome atlas extracting WM connections and its subsections, we applied dimensionality reduction techniques to obtain biologically interpretable WM measures. We selected the top 10 connections-of-interests (located in frontal, parietal, occipital, and basal ganglia regions) with robust anatomical and statistical criteria. We correlated WM measures with psychometric test metrics (Conner’s Continuous Performance Test 3) in 171 children (27 Dx ADHD, 3Dx ASD, 9–13 years old) from the population-based GESTation and Environment cohort. We found that children with lower microstructural complexity and lower axonal density show a higher impulsive behavior on these connections. When segmenting each connection in subsections, we report WM alterations localized in one or both endpoints reflecting a specific localization of WM alterations along each connection. These results provide new insight in understanding the neurophysiology of attention and impulsivity in a general population.

A Role for the Fornix in Temporal Sequence Memory

“…PC1 accounted for 59% of the variance, and MD, RD and AD provided major negative contributions. PC1 resembled the first component found in Chamberland et al (2019) and the second component of Geeraert et al (2020) (see also Gagnon et al, 2022). Therefore, PC1 was interpreted as positively relating to a ‘diffusion restriction’ property of the fibres (presumably related to myelin density and axonal packing; henceforth referred to as ‘diffusion restriction’) (Beaulieu, 2014).…”

“…Considering our tractography methods, it should be noted that although the diffusion metrics used here have been histologically validated separately (Sato et al, 2017), components resulting from their reduction with PCA have not (Geeraert et al, 2020, Gagnon et al, 2022. It is not clear, therefore, that any one underlying tract property would be wholly reflected in one component (i.e., complexity could influence PC2, but it could also influence PC1 to a lesser extent).…”

“…In this study, extending previous work from our lab, that focused on DTI measures, we adopted a recently developed dimensionality reduction framework to take advantage of redundancies in dMRI measures (Chamberland et al, 2019; Geeraert et al, 2020, see also Guberman et al, 2022; Gagnon et al, 2022). By combining multiple measures through PCA (Chamberland et al, 2019; Geeraert et al, 2020), we found that the two major components, PC1 and PC2, were contributed to mostly by MD, RD and AD; and FA, NDI and ODI, respectively.…”

“…We have no predictions about sex differences in temporal memory, so we anticipate that inclusion of male participants would result in similar findings, and consistent with this, previous work examining fornix microstructure and memory performance in young healthy adults reported no effects of participant sex (Hodgetts et al, 2020; see also Cahn et al, 2021). Considering our tractography methods, it should be noted that although the diffusion metrics used here have been histologically validated separately (Sato et al, 2017), components resulting from their reduction with PCA have not (Geeraert et al, 2020, Gagnon et al, 2022). It is not clear, therefore, that any one underlying tract property would be wholly reflected in one component (i.e., complexity could influence PC2, but it could also influence PC1 to a lesser extent).…”

“…This resulted in six microstructure metrics for three tracts for all 51 participants. These diffusion measures were reduced through PCA into microstructurally informative features (Chamberland et al, 2019; Geeraert et al, 2020; see also Gagnon et al, 2022; Guberman et al, 2022; Vaher et al, 2022). The tract microstructure data were combined in a single table.…”