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Title: Improvements in task performance after practice are associated with scale-free dynamics of brain activity
Authors: Kardan, O
Stier, AJ
Layden, EA
Choe, KW
Lyu, MA 
Zhang, XH
Beilock, SL
Rosenberg, MD
Berman, MG
Issue Date: 2023
Source: Network neuroscience, 2023, v. 7, no. 3, p. 1129-1152
Abstract: Abstract Although practicing a task generally benefits later performance on that same task, there are individual differences in practice effects. One avenue to model such differences comes from research showing that brain networks extract functional advantages from operating in the vicinity of criticality, a state in which brain network activity is more scale-free. We hypothesized that higher scale-free signal from fMRI data, measured with the Hurst exponent (H), indicates closer proximity to critical states. We tested whether individuals with higher H during repeated task performance would show greater practice effects. In Study 1, participants performed a dual-n-back task (DNB) twice during MRI (n = 56). In Study 2, we used two runs of n-back task (NBK) data from the Human Connectome Project sample (n = 599). In Study 3, participants performed a word completion task (CAST) across six runs (n = 44). In all three studies, multivariate analysis was used to test whether higher H was related to greater practice-related performance improvement. Supporting our hypothesis, we found patterns of higher H that reliably correlated with greater performance improvement across participants in all three studies. However, the predictive brain regions were distinct, suggesting that the specific spatial H↑ patterns are not task-general.
Author Summary Individuals vary in the degree to which they improve on a task upon repeating it, referred to as practice effects. Research has shown a relationship between scale-free brain activity and advantageous functional properties for the brain networks. Therefore, we asked if practice effects are related to these scale-free brain dynamics. We hypothesized that individuals with more scale-free fMRI activity, measured with Hurst exponent (H), when repeating tasks would show larger practice effects. Across three datasets including different tasks and individuals, we found that higher fMRI H was associated with greater task performance improvement. We conclude that exhibiting more efficient information processing when performing a cognitive task may be indexed by higher fMRI scale-free activity and predict further improvement in task performance.
Keywords: Scale-free activity
Learning and performance
Practice effects
Working memory tasks
Publisher: MIT Press
Journal: Network neuroscience 
EISSN: 2472-1751
DOI: 10.1162/netn_a_00319
Rights: © 2023 Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/legalcode.
The following publication Omid Kardan, Andrew J. Stier, Elliot A. Layden, Kyoung Whan Choe, Muxuan Lyu, Xihan Zhang, Sian L. Beilock, Monica D. Rosenberg, Marc G. Berman; Improvements in task performance after practice are associated with scale-free dynamics of brain activity. Network Neuroscience 2023; 7 (3): 1129–1152 is available at https://dx.doi.org/10.1162/netn_a_00319.
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