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, ...
The well-aligned ZnO nanorod arrays were fabricated through a simple chemical process under low temperature. The nanorod arrays were characterized by XRD, SEM, TEM, HRTEM and SAED, and their optical performances were investigated with Raman and PL spectroscopy. The results show that the as-prepared ZnO nanorod arrays are dense, the individual ZnO nanorod is hexagonal shape and grows along c-axis perpendicular to the substrate surface with good single crystal in nature. The Raman property of nanorod arrays displays change with the altering the preparation conditions. Moreover, the PL performances of ZnO nanorod arrays exhibit tunable character with adjusting the synthesis conditions.
Precisely how the anatomical structure of the brain supports a wide range of complex functions remains a question of marked importance in both basic and clinical neuroscience. Progress has been hampered by the lack of theoretical frameworks explaining how a structural network of relatively rigid inter-areal connections can produce a diverse repertoire of functional neural dynamics. Here, we address this gap by positing that the brain’s structural network architecture determines the set of accessible functional connectivity patterns according to predictions of network control theory. In a large developmental cohort of 823 youths aged 8 to 23 years, we found that the flexibility of a brain region’s functional connectivity was positively correlated with the proportion of its structural links extending to different cognitive systems. Notably, this relationship was mediated by nodes’ boundary controllability, suggesting that a region’s strategic location on the boundaries of modules may underpin the capacity to integrate information across different cognitive processes. Broadly, our study provides a mechanistic framework that illustrates how temporal flexibility observed in functional networks may be mediated by the controllability of the underlying structural connectivity.AUTHOR SUMMARYPrecisely how the relatively rigid white matter wiring of the human brain gives rise to a diverse repertoire of functional neural dynamics is not well understood. In this work, we combined tools from network science and control theory to address this question. Capitalizing on a large developmental cohort, we demonstrated that the ability of a brain region to flexibly change its functional module allegiance over time (i.e., its modular flexibility), was positively correlated with its proportion of anatomical edges projecting to multiple cognitive networks (i.e., its structural participation coefficient). Moreover, this relationship was strongly mediated by the region’s boundary controllability, a metric capturing its capacity to integrate information across multiple cognitive domains.
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