Brain Connectivity

This study investigated the association between psychological resilience and resting-state network functional connectivity of three major brain networks in pediatric concussion.

This was a substudy of a randomized controlled trial, recruiting children with concussion and orthopedic injury. Participants completed the Connor–Davidson Resilience 10 Scale and underwent magnetic resonance imaging at 72 h and 4-weeks postinjury. We explored associations between resilience and connectivity with the default mode network (DMN), central executive network (CEN), and salience network (SN) at both timepoints and also any change that occurred over time. We also explored associations between resilience and connectivity within each network.

A total of 67 children with a concussion (median age = 12.87 [IQR: 11.79–14.36]; 46% female) and 30 with orthopedic injury (median age = 12.27 [IQR: 11.19–13.94]; 40% female) were included. Seed-to-voxel analyses detected a positive correlation between 72-h resilience and CEN connectivity in the concussion group. Group moderated associations between resilience and SN connectivity at 72 h, as well as resilience and DMN connectivity over time. Regions-of-interest analyses identified group as a moderator of longitudinal resilience and within-DMN connectivity.

These results suggest that neural recovery from concussion could be reliant on resilience. Resilience was related to functional connectivity with three of the main networks in the brain that are often impacted by concussion. Improving resilience might be investigated as a modifiable variable in children as both a protective and restorative in the context of concussion.

Clinical Trial Registration Identifier: NCT05105802. PedCARE^+MRI team (see Supplementary Appendix S1)

This work contributes to recent evidence suggesting an association between psychological resilience and outcomes following pediatric concussion. Specifically, we identified several significant associations between resilience postconcussion and functional connectivity of major resting state networks in the brain. These results compliment findings reported in the literature that resilience is associated with postconcussion symptomatology, as well as quality of life. This study may serve to support future investigations of resilience-targeted interventions following pediatric concussion and its impact on neural and clinical outcomes.

: Age-related cognitive decline and mental health problems are accompanied by changes in resting-state functional connectivity (rsFC) indices, such as reduced brain network segregation. Meanwhile, exercise can improve cognition, mood, and neural network function in older adults. Studies on effects of exercise on rsFC outcomes in older adults have chiefly focused on changes after exercise training and suggest improved network segregation through enhanced within-network connectivity. However, effects of acute exercise on rsFC measures of neural network integrity in older adults, which presumably underlie changes observed after exercise training, have received less attention. In this study, we hypothesized that acute exercise in older adults would improve functional segregation of major cognition and affect-related brain networks.

To test this, we analyzed rsFC data from 37 healthy and physically active older adults after they completed 30 min of moderate-to-vigorous intensity cycling and after they completed a seated rest control condition. Conditions were performed in a counterbalanced order across separate days in a within-subject crossover design. We considered large-scale brain networks associated with cognition and affect, including the frontoparietal network (FPN), salience network (SAL), default mode network (DMN), and affect-reward network (ARN).

We observed that after acute exercise, there was greater segregation between SAL and DMN, as well as greater segregation between SAL and ARN.

These findings indicate that acute exercise in active older adults alters rsFC measures in key cognition and affect-related networks in a manner that opposes age-related dedifferentiation of neural networks that may be detrimental to cognition and mental health.

Our findings contribute novel insight on changes in large-scale functional brain network organization after acute exercise in healthy, active older adults. We argue that these effects of acute exercise may benefit cognition and mental health during older age by countering age-related rsFC changes in major functional brain networks (e.g., salience, default mode, and affect-reward networks). Our multilayered analysis approach of large-scale network rsFC (e.g., between-network segregation; within- and between-network connectivity) and novel between-network segregation index formula can feasibly be employed by the field in the future.

Essential tremor (ET) comprises motor and non-motor-related features, whereas the current neuro-pathogenetic basis is still insufficient to explain the etiologies of ET. Although cerebellum-associated circuits have been discovered, the large-scale cerebral network connectivity in ET remains unclear. This study aimed to characterize the ET in terms of functional connectivity as well as network. We hypothesized that the resting-state network (RSN) within cerebrum could be altered in patients with ET.

Resting-state functional magnetic resonance imaging (fMRI) was used to evaluate the inter- and intra-network connectivity as well as the functional activity in ET and normal control. Correlation analysis was performed to explore the relationship between RSN metrics and tremor features.

Comparison of inter-network connectivity indicated a decreased connectivity between default mode network and ventral attention network in the ET group (p < 0.05). Differences in functional activity (assessed by amplitude of low-frequency fluctuation, ALFF) were found in several brain regions participating in various RSNs (p < 0.05). The ET group generally has higher degree centrality over normal control. Correlation analysis has revealed that tremor features are associated with inter-network connectivity (|r| = 0.135–0.506), ALFF (|r| = 0.313–0.766), and degree centrality (|r| = 0.523–0.710).

Alterations in the cerebral network of ET were detected by using resting-state fMRI, demonstrating a potentially useful approach to explore the cerebral alterations in ET.

This study explores the alteration of functional connectivity and characteristics of resting-state networks in patients with essential tremor (ET), specifically investigating our hypothesis that the network within cerebrum could be altered in patients with ET. The results demonstrate several differences between normal control and ET subjects, indicating that cerebral network of ET could be of importance in addition to the cerebellum. Such finding also shows the potential of resting-state functional magnetic resonance imaging in exploring the cerebral basis in ET.

Resting-state fMRI analyses have been used to examine functional connectivity in the aging brain. Recently, fluctuations in the fMRI BOLD signal have been used as a potential marker of integrity in neural systems. Despite its increasing popularity, the results of BOLD variability analyses and traditional seed-based functional connectivity analyses have rarely been compared. The current study examined fMRI BOLD signal variability and default mode network seed-based analyses in healthy older and younger adults to better understand the unique contributions of these methodological approaches.

Thirty-four healthy participants were separated into a younger adult group (age 25–35, n = 17) and an older adult group (age 65+, n = 17). For each participant, a map of the standard deviation of the BOLD signal (SDBOLD) was derived. Group comparisons examined differences in resting-state SDBOLD in younger versus older adults. Seed-based analyses were used to examine differences between younger and older adults in the default mode network.

Between-group comparisons revealed significantly greater BOLD variability in widespread brain regions in older relative to younger adults. There were no significant differences between younger and older adults in the default mode network connectivity.

The current findings align with an increasing number of studies reporting greater BOLD variability in older relative to younger adults. The current results also suggest that the traditional resting state examination methods may not detect nuanced age-related differences. Further large-scale studies in an adult lifespan sample are needed to better understand the functional relevance of the BOLD variability in normative aging.

Examining functional connectivity helps us to better understand the healthy aging brain. The traditional means of examining functional connectivity have relied on a traditional seed-based analysis. More novel approaches, such as BOLD variability, have been gaining popularity; however, it has rarely been compared with other methods. By comparing the impact of both methodologies on the same dataset, we contribute to the growing number of studies that use BOLD variability and further the understanding of this methodology. Furthermore, we can directly examine the resulting differences between the two approaches and expand our understanding of functional connectivity across the lifespan.