Maturation and interhemispheric asymmetry in neurite density and orientation dispersion in early childhood

Functional connectomes become more longitudinally self-stable, but not more distinct from others, across early childhood

Functional connectomes, as measured with functional magnetic resonance imaging (fMRI), are highly individualized, and evidence suggests this individualization may increase across childhood. A connectome can become more individualized either by increasing self-stability or decreasing between-subject-similarity. Here we used a longitudinal early childhood dataset to investigate age associations with connectome self-stability, between-subject-similarity, and developmental individualization, defined as an individual's self-stability across a 12-month interval relative to their between-subject-similarity. fMRI data were collected during an 18-minute passive viewing scan from 73 typically developing children aged 4-7 years, at baseline and 12-month follow-up. We found that young children had highly individualized connectomes, with sufficient self-stability across 12-months for 98% identification accuracy. Linear models showed a significant relationship between age and developmental individualization across the whole brain and in most networks. This association appeared to be largely driven by an increase in self-stability with age, with only weak evidence for relationships between age and similarity across participants. Together our findings suggest that children's connectomes become more individualized across early childhood, and that this effect is driven by increasing self-stability rather than decreasing between-subject-similarity.

Age-Related Variations in Regional White Matter Volumetry and Microstructure During the Post-adolescence Period: A Cross-Sectional Study of a Cohort of 1,713 University Students

Human brain white matter undergoes a protracted maturation that continues well into adulthood. Recent advances in diffusion-weighted imaging (DWI) methods allow detailed characterizations of the microstructural architecture of white matter, and they are increasingly utilized to study white matter changes during development and aging. However, relatively little is known about the late maturational changes in the microstructural architecture of white matter during post-adolescence. Here we report on regional changes in white matter volume and microstructure in young adults undergoing university-level education. As part of the MRi-Share multi-modal brain MRI database, multi-shell, high angular resolution DWI data were acquired in a unique sample of 1,713 university students aged 18-26. We assessed the age and sex dependence of diffusion metrics derived from diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) in the white matter regions as defined in the John Hopkins University (JHU) white matter labels atlas. We demonstrate that while regional white matter volume is relatively stable over the age range of our sample, the white matter microstructural properties show clear age-related variations. Globally, it is characterized by a robust increase in neurite density index (NDI), and to a lesser extent, orientation dispersion index (ODI). These changes are accompanied by a decrease in diffusivity. In contrast, there is minimal age-related variation in fractional anisotropy. There are regional variations in these microstructural changes: some tracts, most notably cingulum bundles, show a strong age-related increase in NDI coupled with decreases in radial and mean diffusivity, while others, mainly cortico-spinal projection tracts, primarily show an ODI increase and axial diffusivity decrease. These age-related variations are not different between males and females, but males show higher NDI and ODI and lower diffusivity than females across many tracts. These findings emphasize the complexity of changes in white matter structure occurring in this critical period of late maturation in early adulthood.

The longitudinal relationship between BOLD signal variability changes and white matter maturation during early childhood

Intra-individual transient temporal fluctuations in brain signal, as measured by fMRI blood oxygenation level dependent (BOLD) variability, is increasingly considered an important signal rather than measurement noise. Evidence from computational and cognitive neuroscience suggests that signal variability is a good proxy-measure of brain functional integrity and information processing capacity. Here, we sought to explore across-participant and longitudinal relationships between BOLD variability, age, and white matter structure in early childhood. We measured standard deviation of BOLD signal, total white matter volume, global fractional anisotropy (FA) and mean diffusivity (MD) during passive movie viewing in a sample of healthy children (aged 2-8 years; N = 83). We investigated how age and white matter development related to changes in BOLD variability both across- and within-participants. Our across-participant analyses using behavioural partial least squares (bPLS) revealed that the influence of age and white matter maturation on BOLD variability was highly interrelated. BOLD variability increased in widespread frontal, temporal and parietal regions, and decreased in the hippocampus and parahippocampal gyrus with age and white matter development. Our longitudinal analyses using linear mixed effects modelling revealed significant associations between BOLD variability, age and white matter microstructure. Analyses using artificial neural networks demonstrated that BOLD variability and white matter micro and macro-structure at earlier ages were strong predictors of BOLD variability at later ages. By characterizing the across-participant and longitudinal features of the association between BOLD variability and white matter micro- and macrostructure in early childhood, our results provide a novel perspective to understand structure-function relationships in the developing brain.

Reliability on multiband diffusion NODDI models: A test retest study on children and adults

Neurite Orientation Dispersion and Density Imaging (NODDI) and Bingham-NODDI diffusion MRI models are nowadays very well-known models in the field of diffusion MRI as they represent powerful tools for the estimation of brain microstructure. In order to efficiently translate NODDI imaging findings into the diagnostic clinical practice, a test-retest approach would be useful to assess reproducibility and reliability of NODDI biomarkers, thus providing validation on precision of different fitting toolboxes. In this context, we conducted a test-retest study with the aim to assess the effects of different factors (i.e. fitting algorithms, multiband acceleration, shell configuration, age of subject and hemispheric side) on diffusion models reliability, assessed in terms of Intra-class Correlation Coefficient (ICC) and Variation Factor (VF). To this purpose, data from pediatric and adult subjects were acquired with Simultaneous-MultiSlice (SMS) imaging method with two different acceleration factor (AF) and four b-values, subsequently combined in seven shell configurations. Data were then fitted with two different GPU-based algorithms to speed up the analysis. Results show that each factor investigated had a significant effect on reliability of several diffusion parameters. Particularly, both datasets reveal very good ICC values for higher AF, suggesting that faster acquisitions do not jeopardize the reliability and are useful to decrease motion artifacts. Although very small reliability differences appear when comparing shell configurations, more extensive diffusion parameters variability results when considering shell configuration with lower b-values, especially for simple model like NODDI. Also fitting tools have a significant effect on reliability, but their difference occurs in both datasets and AF, so it appears to be independent from either misalignment and motion artifacts, or noise and SNR. The main achievement of the present study is to show how 10 min multi-shell diffusion MRI acquisition for NODDI acquisition can have reliable results in WM. More complex models do not appear to be more prone to less data acquisition as well as noisier data thus stressing the idea of Bingham-NODDI having greater sensitivity to true subject variability.