The spatial distribution of large-scale functional networks on the anatomic cortex differs between individuals, and is particularly variable in networks responsible for executive function.However, it remains unknown how this functional topography evolves in development and supports cognition. Capitalizing upon advances in machine learning and a large sample of youth (n=693, ages 8-23y) imaged with 27 minutes of high-quality fMRI data, 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 an unseen individual's brain maturity. Furthermore, the cortical representation of executive 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 both the plasticity and diversity of functional neuroanatomy during development, and suggest advances in personalized therapeutics.We conducted several supplementary analyses to confirm that our results were robust to methodological choices. In order to ensure that our matched split of the data was representative, we repeated this procedure with 100 random splits of the data, which returned highly consistent results and feature weights (mean partial r = 0.69, Pperm < 0.001; mean MAE = 1.93 years, Pperm < 0.001; Supplementary Figure 8). For comparison, we repeated this procedure using both the discrete network parcellation derived from NMF and also that from MS-HBM. While still highly significant (Pperm < 0.001), not considering network probability mildly degraded predictive accuracy (see Supplementary Figure 8). Taken together, these results demonstrate that functional network topography encodes brain maturity, is driven by refinement of higher-order association networks, and is constrained by the individual variability of these systems.
Control network topography predicts individual differences in executive functionHaving found that functional topography accurately encoded brain maturation, we next evaluated the implications of topographic variability for cognition. Specifically, we investigated whether variation in functional network topography predicted individual differences in executive function. Executive function was summarized using a previously-published factor analysis of the Penn Computerized Neurocognitive Battery (Moore et al., 2015). While controlling for age, sex, and motion, general additive models revealed that the improved executive performance was associated with a greater total cortical representation of bilateral fronto-parietal control networks and the cingulo-opercular ventral attention network (Figure 6A; left fronto-parietal: network 11, Z = 5.88, PBonf = 1.09 ´ 10 -7 , partial r = 0.22, CI = [0.15, 0.29]; right fronto-parietal: network 17, Z = 5.23, PBonf = 3.90 ´ 10 -6 , partial r = 0.19, ...