In a voxel-wise analysis, Qiu et al found widespread age effects on FA across the cerebellum, temporal, frontal, and parietal lobes.47 Additionally, they found that reading scores (in Chinese and English) were associated with higher FA in a number of PXD101 regions. Lebel et al found that the developmental trajectory of measures of anisotropy and diffusivity across most tracts were best fit with an

exponential curve Inhibitors,research,lifescience,medical (Figure 2). 48 Echoing structural studies above, they found the last tracts to mature were frontotemporal connections. In one of the largest brain imaging studies to date, Kochunov et al detailed how 11 major tracts change over the lifespan (age 11 to 90) in 831 subjects.49 By charting the FA of these tracts across their subject pool, they reported the “age-at-peak” for each tract, as

well as the rate of increase/decrease, along with sex differences, in some cases. Figure 2. White matter maturation Inhibitors,research,lifescience,medical between ages 5 and 30. Age-related fractional anisotropy increases measured by tractography Inhibitors,research,lifescience,medical in 202 individuals across 10 tracts. Reproduced from ref 48: Lebel C, Walker L, Leemans A, Phillips L, Beaulieu C. Microstructural maturation … Using DTI-based connectivity analysis, Hagmann et al used graph theory to show that the efficiency of the brain’s anatomical network increased with age—as did the number of detectable connections for each brain region.50 Graph theory represents the brain as a set of nodes (brain regions) and edges (the connections Inhibitors,research,lifescience,medical between them). A number of standard parameters such as path length and modularity, to name a few, are used to describe network topology.51 Characteristic path length

measures the average path length in a network. It does not refer to the physical length of the tracts, Inhibitors,research,lifescience,medical but the number of edges, or individual “jumps,” between nodes in the network. Modularity is the degree to which a system may be subdivided into smaller networks. Graph theory can quantify more global features in brain connectivity patterns. These include network efficiency, or the degree to which the network is differentiated into modules. Using cortical connectivity matrices calculated from HARDI data, Dennis et al examined the developmental trajectory of graph theoretical measures of structural connectivity (Figure 3).52 mafosfamide Path length and modularity, among other measures, decreased with age, suggesting an increase in network integration. Interestingly, the left and right intrahemispheric networks, when analyzed separately, showed opposing age trends; some parameters increased with age in the left hemisphere, but decreased in the right. If this is corroborated in the future, it could point to different developmental processes in each hemisphere, perhaps due to the known structural asymmetry of the brain, which also increases with age.