Age- and gender-related differences in the cortical anatomical network

Abstract

Neuroanatomical differences attributable to aging and gender have been well documented, and these differences may be associated with differences in behaviors and cognitive performance. However, little is known about the dynamic organization of anatomical connectivity within the cerebral cortex, which may underlie population differences in brain function. In this study, we investigated age and sex effects on the anatomical connectivity patterns of 95 normal subjects ranging in age from 19 to 85 years. Using the connectivity probability derived from diffusion magnetic resonance imaging tractography, we characterized the cerebral cortex as a weighted network of connected regions. This approach captures the underlying organization of anatomical connectivity for each subject at a regional level. Advanced graph theoretical analysis revealed that the resulting cortical networks exhibited "small-world" character (i.e., efficient information transfer both at local and global scale). In particular, the precuneus and posterior cingulate gyrus were consistently observed as centrally connected regions, independent of age and sex. Additional analysis revealed a reduction in overall cortical connectivity with age. There were also changes in the underlying network organization that resulted in decreased local efficiency, and also a shift of regional efficiency from the parietal and occipital to frontal and temporal neocortex in older brains. In addition, women showed greater overall cortical connectivity and the underlying organization of their cortical networks was more efficient, both locally and globally. There were also distributed regional differences in efficiency between sexes. Our results provide new insights into the substrates that underlie behavioral and cognitive differences in aging and sex.

Figure 1.

The schematic image processing for the construction of the cortical weighted network. A, The AAL template masks in diffusion MRI space for one subject. Each color represents a cortical region. B, Connectivity probability using diffusion MRI tractography. The yellow–red color represents the resulting probability (yellow > red) from the left precuneus (marked as blue) to the other voxels. C, The regional probability matrix from the probabilistic tractography for the same subject. Each row or column represents one cortical region. The order of regions in the matrix is the same as in our previous study (Gong et al., 2009). For more details, see Materials and Methods.

Figure 2.

The small worldness of the cortical weighted networks. The weighted network is sparser as the probability threshold increases (A). The selected threshold range of 0.01∼0.1 approximately corresponds to a sparsity range of 8∼27%. The cortical network has much higher local efficiency than the matched random network (B) but similar global efficiency (C) over the entire sparsity range, indicating small-world character. The error bar indicates 1 SD of the network efficiency across subjects. Statistically, there are significant differences in both local and global efficiency between cortical and matched random networks, over the entire sparsity range.

Figure 3.

The age effect on the cost and efficiency of the cortical networks. A, Plots of the T statistic of the age effect on the network cost, local efficiency, and global efficiency as a function of the sparsity. Significant positive age effect on the cost was found over the entire sparsity. Local efficiency but not global efficiency showed significant age effect over the majority of the sparsity range. In accordance, significant age effect was observed on both the integrated network cost (B) and integrated local efficiency (C), but the integrated global efficiency showed no significant age effect (D). Notably, all results here were calculated after adjusting for the effects of brain size and sex, using a general linear model.

Figure 4.

The sex effect on the cost and efficiency of the cortical networks. A, Plots of the T statistic of sex effect on the network cost, local efficiency, and global efficiency as a function of the sparsity. A significant women less than men effect was found on the cost over the entire sparsity. Both local efficiency and global efficiency showed sex effect over a wide range of the sparsity. In accordance, the integrated network cost (B), integrated local efficiency (C), and integrated global efficiency (D) all showed significant sex effect. Notably, all results here were calculated after adjusting for the effects of brain size and age, using a general linear model.

Figure 5.

The integrated regional efficiency for all cortical regions. The cortical regions were ranked in the order of descending mean integrated regional efficiency across subjects. The gray bar represents the mean regional efficiency and each x mark corresponds to one subject. As shown, the PUN and PCG always have the highest regional efficiencies, regardless age and sex. For the abbreviations of cortical regions, see supplemental Table 1 (available at www.jneurosci.org as supplemental material).

Figure 6.

The spatial distribution of cortical regions showing significant age effect (p < 0.05, FDR corrected) on the integrated regional efficiency. The color represents t statistic of the age effect that was calculated from the general linear model. Each identified region was marked out. Notably, negative age effect was mainly distributed in the parietal and occipital cortex (12 of 15), whereas the positive age effect was localized only in the frontal and temporal cortex. Note that nine regions (the PUN, SPG, CUN, SOG, ORBmid, ORBsup, SFGdor, ITG, and TPOmid) appeared in a bilateral manner. For the abbreviations of cortical regions, see supplemental Table 1 (available at www.jneurosci.org as supplemental material).