Activity flow under the manipulation of cognitive load and training.

Flexible cognitive functions, such as working memory (WM), usually require a balance between localized and distributed information processing. However, it is challenging to uncover how local and distributed processing specifically contributes to task-induced activity in a region. Although the recently proposed activity flow mapping approach revealed the relative contribution of distributed processing, few studies have explored the adaptive and plastic changes that underlie cognitive manipulation. In this study, we recruited 51 healthy volunteers (31 females) and investigated how the activity flow and brain activation of the frontoparietal systems was modulated by WM load and training. While the activation of both executive control network (ECN) and dorsal attention network (DAN) increased linearly with memory load at baseline, the relative contribution of distributed processing showed a linear response only in the DAN, which was prominently attributed to within-network activity flow. Importantly, adaptive training selectively induced an increase in the relative contribution of distributed processing in the ECN and also a linear response to memory load, which were predominantly due to between-network activity flow. Furthermore, we demonstrated a causal effect of activity flow prediction through training manipulation on connectivity and activity. In contrast with classic brain activation estimation, our findings suggest that the relative contribution of distributed processing revealed by activity flow prediction provides unique insights into neural processing of frontoparietal systems under the manipulation of cognitive load and training. This study offers a new methodological framework for exploring information integration versus segregation underlying cognitive processing.

Dissociable functional responses along the posterior-anterior gradient of the frontal and parietal cortices revealed by parametric working memory and training.

While the storage capacity is limited, accumulating studies have indicated that working memory (WM) can be improved by cognitive training. However, understanding how exactly the brain copes with limited WM capacity and how cognitive training optimizes the brain remains inconclusive. Given the hierarchical functional organization of WM, we hypothesized that the activation profiles along the posterior-anterior gradient of the frontal and parietal cortices characterize WM load and training effects. To test this hypothesis, we recruited 51 healthy volunteers and adopted a parametric WM paradigm and training method. In contrast to exclusively strengthening the activation of posterior areas, a broader range of activation concurrently occurred in the anterior areas to cope with increased memory load for all subjects at baseline. Moreover, there was an imbalance in the responses of the posterior and anterior areas to the same increment of 1 item at different load levels. Although a general decrease in activation after adaptive training, the changes in the posterior and anterior areas were distinct at different memory loads. Particularly, we found that the activation gradient between the posterior and anterior areas was significantly increased at load 4-back after adaptive training, and the changes were correlated with improvement in WM performance. Together, our results demonstrate a shift in the predominant role of posterior and anterior areas in the frontal and parietal cortices when approaching WM capacity limits. Additionally, the training-induced performance improvement likely benefits from the elevated neural efficiency reflected in the increased activation gradient between the posterior and anterior areas.

Activity flow mapping over probabilistic functional connectivity.

Emerging evidence indicates that activity flow over resting-state network topology allows the prediction of task activations. However, previous studies have mainly adopted static, linear functional connectivity (FC) estimates as activity flow routes. It is unclear whether an intrinsic network topology that captures the dynamic nature of FC can be a better representation of activity flow routes. Moreover, the effects of between- versus within-network connections and tight versus loose (using rest baseline) task contrasts on the prediction of task-evoked activity across brain systems remain largely unknown. In this study, we first propose a probabilistic FC estimation derived from a dynamic framework as a new activity flow route. Subsequently, activity flow mapping was tested using between- and within-network connections separately for each region as well as using a set of tight task contrasts. Our results showed that probabilistic FC routes substantially improved individual-level activity flow prediction. Although it provided better group-level prediction, the multiple regression approach was more dependent on the length of data points at the individual-level prediction. Regardless of FC type, we consistently observed that between-network connections showed a relatively higher prediction performance in higher-order cognitive control than in primary sensorimotor systems. Furthermore, cognitive control systems exhibit a remarkable increase in prediction accuracy with tight task contrasts and a decrease in sensorimotor systems. This work demonstrates that probabilistic FC estimates are promising routes for activity flow mapping and also uncovers divergent influences of connectional topology and task contrasts on activity flow prediction across brain systems with different functional hierarchies.