Differential patterns of age‐related cortical and subcortical functional connectivity in 6‐to‐10 year old children: A connectome‐wide association study

Abstract

IntroductionTypical brain development is characterized by specific patterns of maturation of functional networks. Cortico‐cortical connectivity generally increases, whereas subcortico‐cortical connections often decrease. Little is known about connectivity changes amongst different subcortical regions in typical development.MethodsThis study examined age‐ and gender‐related differences in functional connectivity between and within cortical and subcortical regions using two different approaches. The participants included 411 six‐ to ten‐year‐old typically developing children sampled from the population‐based Generation R study. Functional connectomes were defined in native space using regions of interest from subject‐specific FreeSurfer segmentations. Connections were defined as: (a) the correlation between regional mean time‐series; and (b) the focal maximum of voxel‐wise correlations within FreeSurfer regions. The association of age and gender with each functional connection was determined using linear regression. The preprocessing included the exclusion of children with excessive head motion and scrubbing to reduce the influence of minor head motion during scanning.ResultsCortico‐cortical associations echoed previous findings that connectivity shifts from short to long‐range with age. Subcortico‐cortical associations with age were primarily negative in the focal network approach but were both positive and negative in the mean time‐series network approach. Between subcortical regions, age‐related associations were negative in both network approaches. Few connections had significant associations with gender.ConclusionsThe present study replicates previously reported age‐related patterns of connectivity in a relatively narrow age‐range of children. In addition, we extended these findings by demonstrating decreased connectivity within the subcortex with increasing age. Lastly, we show the utility of a more focal approach that challenges the spatial assumptions made by the traditional mean time series approach.

Profiling intra- and inter-individual differences in brain development across early adolescence

“…These include decreases, on average, in gray matter volume (Bethlehem et al, 2022; Lenroot et al, 2007), cortical thickness (Tamnes et al, 2017; Wierenga et al, 2014b), gray-to-white matter contrast (Norbom et al, 2019; Paus et al, 2001), mean diffusivity (Schmithorst and Yuan, 2010), and transverse diffusivity (Asato et al, 2010) In addition, we also replicated increases in white matter volume, fractional anisotropy (Lebel and Deoni, 2018; Schmithorst and Yuan, 2010; Tamnes et al, 2018), white and gray matter isotropic intracellular diffusion (Palmer et al, 2022), white matter directional intracellular diffusion (ibid), and within-network functional connectivity (Fair et al, 2007; Satterthwaite et al, 2012). Also consistent with prior findings, we identified both increases and decreases in gray matter directional intracellular diffusion (Palmer et al, 2022), subcortical-network functional connectivity (Duijvenvoorde et al, 2019; Ji et al, 2019; Langen et al, 2018), and BOLD variance (Nomi et al, 2017; Wang et al, 2021), depending on the brain region or network. Larger decreases in gray matter volume were identified in parietal regions than in frontal, temporal, or occipital regions, consistent with prior findings (Lenroot et al, 2007).…”

Age‐related change of auditory functional connectivity in Human Connectome Project data and tinnitus patients

“…For example, cortical gray matter thickness peaks in children and generally decreases after adolescence . Synaptogenesis and arborization can result in increasing cross talk between brain regions . After adolescence, the localized brain functions lose specialized characteristics, but other regions provide something useful to neutralize the disruption.…”

Dissociation of reliability, heritability, and predictivity in coarse- and fine-scale functional connectomes during development

“…While computing connectivity profiles to be hyperaligned, it is often desirable to use individualized connectivity targets to account for topographic idiosyncrasies in target regions. Individualized connectivity targets can be generated with individualized parcellations (Glasser et al, 2016; Langen et al, 2018; Kong et al, 2019, 2021, 2023; Anderson et al, 2021) or by iterating the hyperalignment algorithm (Busch et al, 2021; Jiahui et al, 2023). In our implementation for this study, we used parcels from the Glasser cortical parcellation (Glasser et al, 2016) as the cortical fields and targets to be hyperaligned.…”