Individual Variation in Functional Topography of Association Networks in Youth

Abstract

The spatial distribution of large-scale functional networks on the cerebral cortex differs between individuals and is particularly variable in association networks that are responsible for higher-order cognition. However, it remains unknown how this functional topography evolves in development and supports cognition. Capitalizing on advances in machine learning and a large sample imaged with 27 min of high-quality functional MRI (fMRI) data (n = 693, ages 8-23 years), we delineate how functional topography evolves during youth. We found that the functional topography of association networks is refined with age, allowing accurate prediction of unseen individuals' brain maturity. The cortical representation of association networks predicts individual differences in executive function. Finally, variability of functional topography is associated with fundamental properties of brain organization, including evolutionary expansion, cortical myelination, and cerebral blood flow. Our results emphasize the importance of considering the plasticity and diversity of functional neuroanatomy during development and suggest advances in personalized therapeutics.

Keywords: MRI; adolescence; cognition; cognitive control; development; executive function; functional MRI; network; parcellation; topography.

Figure 1.

Group atlas of 17 networks. The networks in the group atlas include visual networks (numbers 6 and 10); somatomotor networks (2: foot motor; 4: face motor; 11 and 13: hand motor); an auditory network (16); dorsal attention networks (5 and 14); cingulo-opercular/ventral attention networks (7 and 9); fronto-parietal control networks (3, 15, and 17), and default mode networks (1, 8, and 12). In this atlas, there are 17 loadings for each vertex, which quantify the extent it belongs to each network. For each loading map, brighter colors indicate greater loadings. Vertices can be assigned to the network with the highest loading, yielding a discrete network parcellation (center).

Figure 2.

Individuals display distinct functional network topography. The group network parcellation and four example network parcellations are displayed. The four participants include a child with low executive function (EF), a child with high EF, an adult with low EF, and an adult with high EF. The topmost row represents the whole-brain discrete network parcellation, while the subsequent five rows represent the fronto-parietal, cingulo-opercular/ventral attention, default mode, visual, and somatomotor networks. For each network, both the loading map and the corresponding discrete networks are displayed. While the gross spatial distribution of networks was consistent across participants, distinct person-specific topographic features could be readily observed. In particular, heterogeneity in the spatial distribution of networks was apparent in association networks including fronto-parietal, cingulo-opercular/ventral attention, and default mode networks. In contrast, participant-level representations of visual and somatomotor networks appeared to be more consistent across individuals.

Figure 3.

Across-subject variability of functional network topography is highest in association cortex. (A) A non-parametric measure of variability revealed that functional topography was most variable across individuals in association cortex and least variable in sensorimotor cortex. (B) Summarizing variability by network revealed that across-subject variability was highest in association networks including fronto-parietal, dorsal attention, default mode, and cingulo-opercular/ventral attention networks. (C) Variability of functional topography was nearly identical in each of three age-based tertiles (n = 231 each). FP: fronto-parietal; VA: cingulo-opercular/ventral attention; DA: dorsal attention; DM: default mode; AU: auditory; SM: somatomotor; VS: visual.

Figure 4.

Associations between total cortical representation and age in youth. (A) The total cortical representation of two networks – the face motor network (network 4) and occipital pole visual network (network 10) – declined with age (P_Bonf < 0.05; dashed lines indicate networks with non-significant age effects). (B) Scatter plot of the relationship between age and the total cortical representation of the face motor network.

Figure 5.

Functional topography evolves with age in youth and predicts unseen individuals’ brain maturity. (A) The complex pattern of functional topography could be used to predict brain maturity in unseen data. Data points represent predicted brain maturity of subjects in a model trained on independent data; inset histogram represents the null distribution of prediction accuracy from a permutation test. The 2-fold cross-validation was implemented by splitting all participants into two subsets that were matched on age. (B) Repeated random 2-fold cross-validation (100 runs) provided evidence of stable prediction accuracy, which was far better than a null distribution with permuted data (inset). (C) Examining the sum of the model weights of all positively-weighted and negatively-weighted vertices separately within each network revealed that association networks contributed the most in predicting brain maturity. Both positive and negative associations with age were present within each network. (D) Model feature weights driving prediction were highest at network edges for network 17; the top 25% of vertices in terms of feature importance are displayed. (E) Feature weight was negatively correlated with group network loadings across positively-weighted vertices (panel D) for network 17; inset displays spatial association compared to null distribution from permutation test. (F) At each location on the cortex, the absolute contribution weight of each network was summed, revealing that association cortex contributed the most to the multivariate model. (G) Functional network maturation is constrained by topographic variability. Vertices that contributed the most to the multivariate model predicting brain maturity were also those that varied the most across subjects. Inset histograms represent spatial association compared to a null distribution obtained from spatial permutation testing.

Figure 6.

The total cortical representation of fronto-parietal networks is associated with executive function. (A) Executive function was positively correlated with the total cortical representation of two fronto-parietal networks, including one lateral fronto-parietal network (network 17) and the medial parietal network (network 15). In contrast, executive function was negatively correlated with the total cortical representation of the foot motor network (P_Bonf < 0.05; dashed lines indicate networks with non-significant age effects). Scatter plot of the relationship between executive function and the total cortical representation of both network 17 (B) and network 15 (C).

Figure 7.

Functional topography of association networks predicts individual differences in executive function. (A) The complex pattern of functional network topography predicted executive function in unseen data (data points represent predicted executive function of subjects by a model trained on independent data; inset histogram represents the distribution of prediction accuracy from a permutation test). The 2-fold cross-validation was implemented by splitting all participants into two subsets that were matched on executive function. (B) Repeated random 2-fold cross-validation (100 runs) provided evidence of stable prediction accuracy, which was far better than a null distribution with permuted data (inset). (C) The most important topographic features in this model were found in association cortex critical for executive function, and were maximal in the cingulo-opercular/ventral attention and fronto-parietal control networks. (D) Functional topography within association cortex drives prediction of executive function. At each location on the cortex, the absolute contribution weight of each network was summed. (E) The vertices that contributed the most in this multivariate model were those that varied most across participants; inset histograms represent spatial association compared to a null distribution obtained from spatial permutation testing.

Figure 8.

Variability in functional topography aligns with fundamental properties of brain organization. Higher variability in network topography was associated with greater evolutionary expansion (A), lower myelin content (B), and higher cerebral blood flow (CBF, C). Inset histograms represent spatial association compared to a null distribution obtained from spatial permutation testing.