Influence of goals on modular brain network organization during working memory

Predicting working memory efficiency across adulthood: the role of enhancement and suppression attentional mechanisms, moderated by age and other factors

The global population of individuals aged 60 years and older now exceeds one billion and continues to grow, underscoring the necessity of addressing cognitive challenges associated with aging. The present study aimed to evaluate the enhancement and suppression of attentional mechanisms, as well as working memory efficiency (WME), across the adult lifespan. Additionally, it examined factors that may moderate these relationships. A total of 194 participants, aged between 20 and 80 years, completed questionnaires and participated in an experimental task designed to assess attentional mechanisms and WME. The results indicated that both enhancement and suppression mechanisms remained stable throughout adulthood; however, WME showed a decline with increasing age. Notwithstanding, several moderating variables influenced these outcomes. Cognitive reserve and current cognitive functioning were found to be positive predictors of WME, whereas depression and anxiety exerted negative effects. Furthermore, a low level of cognitive reserve uniquely predicted diminished suppression of irrelevant information with advancing age. WME was consistently lower across all ages among participants with high levels of depression, in contrast to those with low or moderate levels, in whom WME followed the expected age-related trajectory. Additionally, elevated anxiety levels and an average sleep duration of five hours were associated with reduced WME, particularly in relation to the enhancement mechanism. Conversely, low levels of depression were linked to improved WME via the enhancement mechanism. These findings underscore the importance of moderating factors that may mitigate cognitive decline or support cognitive functioning throughout the aging process.

MQGA: A quantitative analysis of brain network hubs using multi-graph theoretical indices

Recent advancements in large-scale network studies have shown that connector hubs and provincial hubs are vital for coordinating complex cognitive tasks by facilitating information transfer between and within specialized modules. However, current methods for identifying these hubs often lack standardized measurement criteria, hindering quantitative analysis. This study proposes a novel computational method utilizing multi-graph theoretical index calculations to quantitatively analyze hub attributes in brain networks. Using benchmark network, random simulation network (N = 100), resting fMRI data from the ADHD-200 NYU dataset (HC = 110, ADHD = 146), and the Peking dataset (HC = 120, ADHD = 83), we introduce the Multi-criteria Quantitative Graph Analysis (MQGA) method, which employs betweenness centrality, degree centrality, and participation coefficient to determine the connector (con) hub index and provincial (pro) hub index. The method's accuracy, reliability, and stability were validated through correlation analysis of hub indices and labels, vulnerability tests, and consistency analysis across subjects. Results indicate that as network sparsity increases, the con hub index increases while the pro hub index decreases, with the optimal hub node index at 4 % sparsity. Vulnerability tests revealed that removing con nodes had a greater impact on network integrity than removing pro nodes. Both con and pro exhibited stability in consistency analyses, but con was more stable. The stability of hub scores in disease groups was significantly lower than in the healthy control group. High con values were found in the precuneus, postcentral gyrus, and precentral gyrus, whereas high pro values were identified in the precentral gyrus, postcentral gyrus, superior parietal lobule, precuneus, and superior temporal gyrus. This approach enhances the accuracy and sensitivity of hub node identification, facilitating precise comparisons and producing consistent, replicable results, advancing our understanding of brain network hub nodes, their roles in cognitive processes, and their implications for brain disease research.

High-Order Resting-State Functional Connectivity is Predictive of Working Memory Decline After Brain Tumor Resection

Surgical resection is one of the main treatment options for brain tumors. However, there is a risk of postoperative cognitive deterioration associated with resective surgery. Recent studies suggest that pre-surgery brain dynamics captured using functional Magnetic Resonance Imaging (fMRI) could provide valuable information about the risk of post-surgery cognitive decline. However, most of these studies are based on simple regression analysis of the raw fMRI signals that do not capture the underlying complex brain dynamics. Here, we investigated the role of higher-order functional brain networks in predicting cognitive decline after surgical resection of brain tumors. More specifically, we looked at the predictive power of second-order functional brain networks in estimating post-surgery working memory (WM) performance. Our results show that the second-order functional brain networks can accurately predict the working memory decline in patients with glioma and meningioma tumors. These findings suggest that there is an interesting relationship between pre-surgical higher-order brain dynamics and the risk of cognitive decline after surgery, which could potentially yield a better prognostic marker for treatment planning of brain tumor patients.