Cortical activity and network organization underlying physical and cognitive exertion in active young adult athletes: Implications for concussion

Porter, Shaun; Silverberg, Noah D.; Virji‐Babul, Naznin

doi:10.1016/j.jsams.2018.09.233

Cited by 11 publications

(17 citation statements)

References 25 publications

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“…An increase in partial theta coherence has been observed in the frontal region during working memory tasks associated with physical exertion. An interesting U-shaped pattern was initially observed in the CC, where the CC of the theta band increased during both physical exertion tasks and mental tasks and decreased significantly when the tasks became more difficult [250]. The study limited its investigation to the frontal brain region; however, future studies should investigate the topological properties across the entire brain.…”

Section: E Connectivity Studies Of Physical Exertionmentioning

confidence: 97%

“…Discrimination between different levels of difficulty is reflected in the functional connectivity of the brain network, mainly in the prefrontal and parieto-occipital regions. Furthermore, a decrease in functional connectivity has been shown to indicate a reduction in human accuracy during difficult tasks [250]. A lower PLV has been demonstrated in parieto-occipital regions during highly difficult tasks in comparison with tasks of lower difficulty levels [251].…”

Section: B Connectivity Studies Of Mental Workloadmentioning

confidence: 99%

“…Working memory tasks require a high degree of cognitive effort, resulting in lower clustering and modular configurations but a higher Eglobal for the alpha, beta, and gamma bands [269]. When comparing a working memory task with a resting task, a high degree of functional integration in the theta band [250] and low functional segregation in the alpha band have been observed [16]. Thus, a small-world topology is evident with respect to the storage and retrieval of memories in all frequency bands [268].…”

Section: Connectivity Studies Of Working Memorymentioning

confidence: 99%

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A Graph Theory-Based Modeling of Functional Brain Connectivity Based on EEG: A Systematic Review in the Context of Neuroergonomics

Ismail

Karwowski

2020

IEEE Access

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Graph theory analysis, a mathematical approach, has been applied in brain connectivity studies to explore the organization of network patterns. The computation of graph theory metrics enables the characterization of the stationary behavior of electroencephalogram (EEG) signals that cannot be explained by simple linear methods. The main purpose of this study was to systematically review the graph theory applications for mapping the functional connectivity of the EEG data in neuroergonomics. Moreover, this article proposes a pipeline for constructing an unweighted functional brain network from EEG data using both source and sensor methods. Out of 57 articles, our results show that graph theory metrics used to characterize EEG data have attracted increasing attention since 2006, with the highest frequency of publications in 2018. Most studies have focused on cognitive tasks in comparison with motor tasks. The mean phase coherence method, based on the "phase-locking value," was the most frequently used functional estimation technique in the reviewed studies. Furthermore, the unweighted functional brain network has received substantially more attention in the literature than the weighted network. The global clustering coefficient and characteristic path length were the most prevalent metrics for differentiating between global integration and local segregation, and the small-worldness property emerged as a compelling metric for the characterization of information processing. This review provides insight into the use of graph theory metrics to model functional brain connectivity in the context of neuroergonomics research.

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Section: E Connectivity Studies Of Physical Exertionmentioning

confidence: 97%

Section: B Connectivity Studies Of Mental Workloadmentioning

confidence: 99%

Section: Connectivity Studies Of Working Memorymentioning

confidence: 99%

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A Graph Theory-Based Modeling of Functional Brain Connectivity Based on EEG: A Systematic Review in the Context of Neuroergonomics

Ismail

Karwowski

2020

IEEE Access

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“…Several graph theoretical indices were computed, and the analysis revealed significant changes only for the clustering coefficient, which is a measure of the prevalence of local connectivity within a functional network. Specifically, Porter et al (2019) found that local connectivity increased during the first three blocks of the exercise and decreased toward the end of the exercise, when the task became more difficult. However, these results are limited because the graph theory analysis was applied to the frontal region only: as a consequence, the overall properties of the complete functional brain network underpinning the cycling task remained unexplored.…”

Section: Introductionmentioning

confidence: 98%

“…According to a graph theoretical approach, the brain is defined as a network containing nodes and edges, which respectively, represent brain regions and the connecting pathways between those regions ( Bullmore and Sporns, 2009 ; Rubinov and Sporns, 2010 ). Very recently, Porter et al (2019) used EEG electrodes space graph theoretical analysis to examine Theta band functional connectivity during a combined graded working memory and exercise task on a stationary bicycle. Several graph theoretical indices were computed, and the analysis revealed significant changes only for the clustering coefficient, which is a measure of the prevalence of local connectivity within a functional network.…”

Section: Introductionmentioning

confidence: 99%

Modulation of Brain Functional Connectivity and Efficiency During an Endurance Cycling Task: A Source-Level EEG and Graph Theory Approach

Tamburro

Fronso

Robazza

et al. 2020

Front. Hum. Neurosci.

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Various methods have been employed to investigate different aspects of brain activity modulation related to the performance of a cycling task. In our study, we examined how functional connectivity and brain network efficiency varied during an endurance cycling task. For this purpose, we reconstructed EEG signals at source level: we computed current densities in 28 anatomical regions of interest (ROIs) through the eLORETA algorithm, and then we calculated the lagged coherence of the 28 current density signals to define the adjacency matrix. To quantify changes of functional network efficiency during an exhaustive cycling task, we computed three graph theoretical indices: local efficiency (LE), global efficiency (GE), and density (D) in two different frequency bands, Alpha and Beta bands, that indicate alertness processes and motor binding/fatigue, respectively. LE is a measure of functional segregation that quantifies the ability of a network to exchange information locally. GE is a measure of functional integration that quantifies the ability of a network to exchange information globally. D is a global measure of connectivity that describes the extent of connectivity in a network. This analysis was conducted for six different task intervals: pre-cycling; initial, intermediate, and final stages of cycling; and active recovery and passive recovery. Fourteen participants performed an incremental cycling task with simultaneous EEG recording and rated perceived exertion monitoring to detect the participants’ exhaustion. LE remained constant during the endurance cycling task in both bands. Therefore, we speculate that fatigue processes did not affect the segregated neural processing. We observed an increase of GE in the Alpha band only during cycling, which could be due to greater alertness processes and preparedness to stimuli during exercise. Conversely, although D did not change significantly over time in the Alpha band, its general reduction in the Beta bands during cycling could be interpreted within the framework of the neural efficiency hypothesis, which posits a reduced neural activity for expert/automated performances. We argue that the use of graph theoretical indices represents a clear methodological advancement in studying endurance performance.

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Exploring intensity-dependent modulations in EEG resting-state network efficiency induced by exercise

2021

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Purpose Exhaustive cardiovascular load can affect neural processing and is associated with decreases in sensorimotor performance. The purpose of this study was to explore intensity-dependent modulations in brain network efficiency in response to treadmill running assessed from resting-state electroencephalography (EEG) measures. Methods Sixteen trained participants were tested for individual peak oxygen uptake (VO2 peak) and performed an incremental treadmill exercise at 50% (10 min), 70% (10 min) and 90% speed VO2 peak (all-out) followed by cool-down running and active recovery. Before the experiment and after each stage, borg scale (BS), blood lactate concentration (BLa), resting heartrate (HRrest) and 64-channel EEG resting state were assessed. To analyze network efficiency, graph theory was applied to derive small world index (SWI) from EEG data in theta, alpha-1 and alpha-2 frequency bands. Results Analysis of variance for repeated measures revealed significant main effects for intensity on BS, BLa, HRrest and SWI. While BS, BLa and HRrest indicated maxima after all-out, SWI showed a reduction in the theta network after all-out. Conclusion Our explorative approach suggests intensity-dependent modulations of resting-state brain networks, since exhaustive exercise temporarily reduces brain network efficiency. Resting-state network assessment may prospectively play a role in training monitoring by displaying the readiness and efficiency of the central nervous system in different training situations.

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Cortical activity and network organization underlying physical and cognitive exertion in active young adult athletes: Implications for concussion

Cited by 11 publications

References 25 publications

A Graph Theory-Based Modeling of Functional Brain Connectivity Based on EEG: A Systematic Review in the Context of Neuroergonomics

A Graph Theory-Based Modeling of Functional Brain Connectivity Based on EEG: A Systematic Review in the Context of Neuroergonomics

Modulation of Brain Functional Connectivity and Efficiency During an Endurance Cycling Task: A Source-Level EEG and Graph Theory Approach

Exploring intensity-dependent modulations in EEG resting-state network efficiency induced by exercise

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