During human brain development through infancy and childhood, microstructural and macrostructural changes take place to reshape the brain's structural networks and better adapt them to sophisticated functional and cognitive requirements. However, structural topological configuration of the human brain during this specific development period is not well understood. In this study, diffusion magnetic resonance image (dMRI) of 25 neonates, 13 toddlers, and 25 preadolescents were acquired to characterize network dynamics at these 3 landmark cross-sectional ages during early childhood. dMRI tractography was used to construct human brain structural networks, and the underlying topological properties were quantified by graph-theory approaches. Modular organization and small-world attributes are evident at birth with several important topological metrics increasing monotonically during development. Most significant increases of regional nodes occur in the posterior cingulate cortex, which plays a pivotal role in the functional default mode network. Positive correlations exist between nodal efficiencies and fractional anisotropy of the white matter traced from these nodes, while correlation slopes vary among the brain regions. These results reveal substantial topological reorganization of human brain structural networks through infancy and childhood, which is likely to be the outcome of both heterogeneous strengthening of the major white matter tracts and pruning of other axonal fibers.

Human brain functional networks are topologically organized with nontrivial connectivity characteristics such as small-worldness and densely linked hubs to support highly segregated and integrated information processing. However, how they emerge and change at very early developmental phases remains poorly understood. Here, we used resting-state functional MRI and voxel-based graph theory analysis to systematically investigate the topological organization of whole-brain networks in 40 infants aged around 31 to 42 postmenstrual weeks. The functional connectivity strength and heterogeneity increased significantly in primary motor, somatosensory, visual, and auditory regions, but much less in high-order default-mode and executive-control regions. The hub and rich-club structures in primary regions were already present at around 31 postmenstrual weeks and exhibited remarkable expansions with age, accompanied by increased local clustering and shortest path length, indicating a transition from a relatively random to a more organized configuration. Moreover, multivariate pattern analysis using support vector regression revealed that individual brain maturity of preterm babies could be predicted by the network connectivity patterns. Collectively, we highlighted a gradually enhanced functional network segregation manner in the third trimester, which is primarily driven by the rapid increases of functional connectivity of the primary regions, providing crucial insights into the topological development patterns prior to birth.

Fractal analysis methods are used to quantify the complexity of the human cerebral cortex. Many recent studies have focused on high resolution three-dimensional reconstructions of either the outer (pial) surface of the brain or the junction between the grey and white matter, but ignore the structure between these surfaces. This study uses a new method to incorporate the entire cortical thickness. Data were obtained from the Alzheimer's Disease (AD) Neuroimaging Initiative database (Control N=35, Mild AD N=35). Image segmentation was performed using a semi-automated analysis program. The fractal dimensions of three cortical models (the pial surface, grey/white surface and entire cortical ribbon) were calculated using a custom cube-counting triangle-intersection algorithm. The fractal dimension of the cortical ribbon showed highly significant differences between control and AD subjects (p<0.001). The inner surface analysis also found smaller but significant differences (p< 0.05). The pial surface dimensionality was not significantly different between the two groups. All three models had a significant positive correlation with the cortical gyrification index (r > 0.55, p<0.001). Only the cortical ribbon had a significant correlation with cortical thickness (r = 0.832, p< 0.001) and the Alzheimer's Disease Assessment Scale cognitive battery (r = −0.513, p = 0.002). The cortical ribbon dimensionality showed a larger effect size (d=1.12) in separating control and mild AD subjects than cortical thickness (d=1.01) or gyrification index (d=0.84). The methodological change shown in this paper may allow for further clinical application of cortical fractal dimension as a biomarker for structural changes that accrue with neurodegenerative diseases.

Cerebral metabolic rate of oxygen (CMRO2) is the rate of oxygen consumption by the brain and is thought to be a direct index of energy homeostasis and brain health. However, in vivo measurement of CMRO2 has been challenging, in particular for neonatal population in whom conventional radiotracer methods are not applicable due to safety concerns. In this study, we propose a method to quantify global CMRO2 in neonates based on arteriovenous differences in oxygen content and employ separate measurements of oxygenation and CBF parameters. Specifically, arterial and venous oxygenation levels were determined with pulse oximetry and a novel T2-Relaxation-Under-Spin-Tagging (TRUST) MRI, respectively. Global CBF was measured with a phase-contrast (PC) flow velocity MRI. The proposed method was implemented on a standard 3T MRI without the need of any exogenous tracers and the total scan duration was less than 5 minutes. We demonstrated the feasibility of this method in twelve healthy neonates within an age range of 35–42 gestational weeks. CMRO2 values were successfully obtained from ten neonates. It was found that average CMRO2 in this age range was 38.3±17.7 μmol/100g/min and was positively correlated with age (p=0.007, slope 5.2 μmol/100g/min per week), although the highest CMRO2 value in this age range was still less than half of the adult level. Test-retest studies showed a coefficient of variation of 5.8±2.2 % between repeated CMRO2 measurements. Additionally, given the highly variable blood flow velocity within this age range, it is recommended that the TRUST labeling thickness and position should be determined on a subject-by-subject basis, and an automatic algorithm was developed for this purpose. Although this method provides a global CMRO2 measure only, the clinical significance of an energy consumption marker and the convenience of this technique may make it a useful tool in functional assessment of neonatal population.