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Zhengdong Wang

Publications and source records attributed to Zhengdong Wang.

2 recordsLinked to original sources

Brain-wide spontaneous neural avalanches: Definition, functional dynamics and cognitive relevance.

Although spontaneous activity is ubiquitous across multiple spatiotemporal scales, its functional organization and cognitive relevance remain poorly understood. Following the classic neuronal avalanche framework, a spontaneous avalanche is defined as consecutively active frames separated by inactive time bins. Hence, multiple distinct avalanches may be considered as one avalanche, thereby ignoring their spatial and temporal distinguishability. Furthermore, group-level power-law fitting of such neural avalanches is often performed to evaluate brain criticality (referring to a system perched between order and disorder) due to the limited recording length of macroscale neuroimaging (such as functional magnetic resonance imaging), and the functional representation of brain-wide neural avalanches is largely unexplored. To address these issues, we proposed large-scale neural avalanches as a single, spatially consecutive cascade pattern and further investigated their functional dynamics, network propagation, and association with task-evoked activity. Compared with the conventional inactive-bin definition, our current approach is more favorable to power-law fitting of avalanche size and duration distributions at the individual level. We also demonstrated that participants whose brain activities were close to the critical point tend to have higher cognitive abilities. Notably, the ratio of neural avalanches that evolved from primary sensory to association networks negatively correlated with cognitive abilities. Moreover, the geometric distance between low-dimensional representations of task-evoked activity and spontaneous avalanches was associated with behavioral performance. This study not only provides a promising avenue for measuring avalanche criticality based on human whole-brain neuroimaging, but also suggests that spontaneous neural avalanches and their low-dimensional representations contribute to human cognitive abilities.

Humans

Exploring the transmission of cognitive task information through optimal brain pathways.

Understanding the large-scale information processing that underlies complex human cognition is the central goal of cognitive neuroscience. While emerging activity flow models demonstrate that cognitive task information is transferred by interregional functional or structural connectivity, graph-theory-based models typically assume that neural communication occurs via the shortest path of brain networks. However, whether the shortest path is the optimal route for empirical cognitive information transmission remains unclear. Based on a large-scale activity flow mapping framework, we found that the performance of activity flow prediction with the shortest path was significantly lower than that with the direct path. The shortest path routing was superior to other network communication strategies, including search information, path ensembles, and navigation. Intriguingly, the shortest path outperformed the direct path in activity flow prediction when the physical distance constraint and asymmetric routing contribution were simultaneously considered. This study not only challenges the shortest path assumption through empirical network models but also suggests that cognitive task information routing is constrained by the spatial and functional embedding of the brain network.

Humans