Brain network modularity predicts cognitive flexibility

Angebrandt, Alexanndra

Permalink

https://hdl.handle.net/2142/121262 Copy

Description

Title

Brain network modularity predicts cognitive flexibility

Author(s)

Angebrandt, Alexanndra

Issue Date

2023-07-17

Director of Research (if dissertation) or Advisor (if thesis)

Barbey, Aron

Department of Study

Psychology

Discipline

Psychology

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• fMRI
• graph theory
• cognitive neuroscience
• modularity, learning
• executive function

Abstract

Research in network neuroscience has advanced our understanding of the network organization of human intelligence, demonstrating that brain network modularity predicts individual differences in the flexible acquisition of new skills and knowledge. Despite the importance of this discovery, limitations in the scope and fidelity of prior research have overshadowed progress in the field. Indeed, previous studies have often narrowly focused on specific cognitive tasks and have only examined performance before and after interventions. Furthermore, prior research has failed to investigate the role of brain network modularity in predicting the temporal dynamics and rate of learning. To address these limitations, the present study investigated trial-level cognitive training data to examine the role of baseline association network modularity in predicting learning. We specifically examined performance across a comprehensive suite of seven executive function tasks. Evaluating trial-level data allowed us to assess both the initial limits and overall rate of learning during cognitive training. Our findings reveal a significant relationship between the average modularity of association networks and two important aspects of learning: the initial limits of learning (β = 0.554, p = 0.0318, partial R2 = 0.059) and the overall learning rate (β = 2.89, p = 0.019, partial R2 = 0.069). Crucially, our research demonstrates that the learning rate mediates the effect of modularity on intervention-induced changes in fluid intelligence (ACME = 14.929, p = 0.035). Furthermore, we explore the role of modularity within individual association networks and its relation to measures of learning. Notably, our results highlight the critical role of modularity within the frontoparietal control network in driving the relationship between baseline modularity and learning. In summary, this study provides novel insights into the underlying mechanisms of cognitive plasticity facilitated by brain network modularity. By analyzing trial-level cognitive training data, we demonstrate that baseline association network modularity predicts both the initial limits of learning and the overall rate of learning during cognitive training. Moreover, our findings suggest that the modularity of the frontoparietal control network plays a key role in the association between baseline modularity and cognitive plasticity. Overall, this research significantly contributes to our understanding of how modularity enables learning and holds promise for the development of targeted interventions to enhance cognitive plasticity.

Graduation Semester

2023-08

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/121262

Copyright and License Information

Copyright 2023 Alexanndra Angebrandt

Owning Collections