No abstract

Age‐related changes in focal cortical morphology have been well documented in previous literature; however, how interregional coordination patterns of the focal cortical morphology reorganize with advancing age is not well established. In this study, we performed a comprehensive analysis of the topological changes in single‐subject morphological brain networks across the adult lifespan. Specifically, we constructed four types of single‐subject morphological brain networks for 650 participants (aged from 18 to 88 years old), and characterized their topological organization using graph‐based network measures. Age‐related changes in the network measures were examined via linear, quadratic, and cubic models. We found profound age‐related changes in global small‐world attributes and efficiency, local nodal centralities, and interregional similarities of the single‐subject morphological brain networks. The age‐related changes were mainly embodied in cortical thickness networks, involved in frontal regions and highly connected hubs, concentrated on short‐range connections, characterized by linear changes, and susceptible to connections between limbic, frontoparietal, and ventral attention networks. Intriguingly, nonlinear (i.e., quadratic or cubic) age‐related changes were frequently found in the insula and limbic regions, and age‐related cubic changes preferred long‐range morphological connections. Finally, we demonstrated that the morphological similarity in cortical thickness between two frontal regions mediated the relationship between age and cognition measured by Cattell scores. Taken together, these findings deepen our understanding of adaptive changes of the human brain with advancing age, which may account for interindividual variations in behaviors and cognition.

Although bottom-up attention can improve visual performance with and without awareness, whether they are governed by a common neural computation remains unclear. Using a modified Posner paradigm with backward masking, we found that both the attention-triggered cueing effect with and without awareness displayed a monotonic gradient profile (Gaussian-like). The scope of this profile, however, was significantly wider with than without awareness. Subsequently, for each subject, the stimulus size was manipulated as their respective mean scopes with and without awareness while stimulus contrast was varied in a spatial cueing task. By measuring the gain pattern of contrast-response functions, we observed changes in the cueing effect consonant with changes in contrast gain for bottom-up attention with awareness and response gain for bottom-up attention without awareness. Our findings indicate an awareness-dependent normalization framework of visual bottom-up attention, placing a necessary constraint, namely, awareness, on our understanding of the neural computations underlying visual attention.

Neuroimaging-based connectomics studies have long focused on the wiring patterns between gray matter regions. In recent years, increasing evidence emerges that neural activity in specific sets of white matter (WM) tracts dynamically fluctuates in a coordinated manner. However, the structural basis underlying the coordination is poorly understood largely due to the lack of approaches for estimating structural relations between WM regions. Here, we developed an approach to construct morphological WM networks based on structural magnetic resonance imaging. We found that the morphological WM networks exhibited nontrivial organizational principles, presented good to excellent short- and long-term reliability, accounted for phenotypic interindividual differences (Motor and Cognition), and were under genetic control. Interestingly, highly heritable edges contributed largely to interindividual differences in phenotype. Through integration with other multimodal and multiscale data, we further showed that the morphological WM networks were able to predict regional profiles of hamodynamic coherence, metabolic synchronization, gene co-expression and chemoarchitectonic covariance. Moreover, the prediction followed functional connectomic hierarchy of WM for hamodynamic coherence, was driven by genes enriched in the forebrain neuron development and differentiation for gene co-expression, and was attributed to serotonergic system-related receptors and transporters for chemoarchitectonic covariance. Finally, applying our approach to multiple sclerosis and neuromyelitis optica spectrum disorders, we found that both diseases were associated with morphological WM dysconnectivity, which was correlated with clinical variables and able to diagnose and differentiate the diseases. Altogether, our findings indicate that morphological WM networks provide a reliable and meaningful means to explore WM architecture in health and disease.