Task‐specific topology of brain networks supporting working memory and inhibition
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Abstract
Network neuroscience explores the brain's connectome, demonstrating that dynamic neural networks support cognitive functions. This study investigates how distinct cognitive abilities—working memory and cognitive inhibitory control—are supported by unique brain network configurations constructed by estimating whole‐brain networks using mutual information. The study involved 195 participants who completed the Sternberg Item Recognition task and Flanker tasks while undergoing electroencephalography recording. A mixed‐effects linear model analyzed the influence of network metrics on cognitive performance, considering individual differences and task‐specific dynamics. The findings indicate that working memory and cognitive inhibitory control are associated with different network attributes, with working memory relying on distributed networks and cognitive inhibitory control on more segregated ones. Our analysis suggests that both strong and weak connections contribute to cognitive processes, with weak connections potentially leading to a more stable and support networks of memory and cognitive inhibitory control. The findings indirectly support the network neuroscience theory of intelligence, suggesting different functional topology of networks inherent to various cognitive functions. Nevertheless, we propose that understanding individual variations in cognitive abilities requires recognizing both shared and unique processes within the brain's network dynamics.
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This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/14991035.
Summary:
This paper applied the Network Neuroscience Theory of intelligence to discover and classify task-specific functional networks involving working memory and inhibition. The study relied primarily on EEG data and frequency analysis before and during the Sternberg Task and Eriksen Flanker Test. While some overlapping activity was noted, task-specific networks primarily resulted in the different patterns between the tasks.
Pros:
Techniques are described thoroughly, and goals, with underlying theoretical reasoning, are clearly stated. Thoroughness of methods and procedure lead to high replicability of experiment.
Consistent citation use validates reasoning and theory.
Suggestions/Improvemen…
This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/14991035.
Summary:
This paper applied the Network Neuroscience Theory of intelligence to discover and classify task-specific functional networks involving working memory and inhibition. The study relied primarily on EEG data and frequency analysis before and during the Sternberg Task and Eriksen Flanker Test. While some overlapping activity was noted, task-specific networks primarily resulted in the different patterns between the tasks.
Pros:
Techniques are described thoroughly, and goals, with underlying theoretical reasoning, are clearly stated. Thoroughness of methods and procedure lead to high replicability of experiment.
Consistent citation use validates reasoning and theory.
Suggestions/Improvements:
Abstract
Could be enhanced by an example of a distributed network attribute and a mention of possible applications of understanding neural network dynamics.
Introduction
Consolidates explanation of key concepts, whose mention later could be useful.
The paper relies a lot on correlational data, meaning there is no definitive establishment of whether brain network reconfiguration directly causes differences in intelligence or if other factors influence it.
Some studies (Kruschwitz et al., 2018; Metzen et al., 2024) do not really showcase the link between general intelligence and network measures.
Methods
Exclusion criteria are not present.
Inter-frequency interactions are considered "beyond the scope of this study" without explanation.
Lacks reasoning for why incongruent tasks were used.
Grammar/Stylistic errors in Methods
Stylistically, (n=195) could be added after participant number.
Insert comma after "inhibition" for clarity in line 27.
Redundant repetition of "assessing working memory" in line 13.
Change "The participant presented" to "The participants were presented" for clarity in line 29.
The sentence "A total of 200 test trials were presented, 34 with half being congruent and the other half being incongruent" is redundant because the paragraph ends with this statement repeated.
Results
Self-referenced research to justify threshold value of 0.5. While it is good to note, relying heavily or frequently on self-published previous research can be unethical.
As stated previously, reiteration of network characterization metrics would improve comprehensibility.
Discussion
The study doesn't represent or discuss the entire spectrum of cognitive functions. It only focuses on two, meaning that these findings might not be able to be applied to real world decision making or scenarios where individuals multitask.
The study examines network states at rest and under cognitive load but not how networks evolve dynamically within a task.
The author briefly mentions that findings suggest a network active across various cognitive functions, however they fail to explain what results suggest this and how these interactions can affect cross task relationships.
Competing interests
The authors declare that they have no competing interests.
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