{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/121262"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/121262","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Brain network modularity predicts cognitive flexibility","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-08-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-08-01","abstract_has_math":false,"creators":["Angebrandt, Alexanndra"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Psychology","degree_department":null,"school":null,"contributors":["Barbey, Aron"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-08","date_published":"2023-08","updated_at":"2026-07-22T22:24:57Z","subjects":["Fmri","Graph Theory","Cognitive Neuroscience","Modularity, Learning","Executive Function"],"languages":["en","eng"],"rights":["Copyright 2023 Alexanndra Angebrandt"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/121262","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Barbey, Aron"]},{"key":"dc:creator","label":"Author","values":["Angebrandt, Alexanndra"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-08","2023-07-17"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Psychology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Fmri","Graph Theory","Cognitive Neuroscience","Modularity, Learning","Executive Function"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Alexanndra Angebrandt"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/121262"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-08-01","The student, Alexanndra Angebrandt, accepted the attached license on 2023-07-12 at 12:49.","The student, Alexanndra Angebrandt, submitted this Thesis for approval on 2023-07-12 at 12:54.","This Thesis was approved for publication on 2023-07-17 at 16:17.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19655 on 2023-12-04 at 17:35:09","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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Brain network modularity predicts cognitive flexibility"]}]}],"canonical_facts":{"dc:contributor":["Barbey, Aron"],"dc:creator":["Angebrandt, Alexanndra"],"dc:date":["2023-08","2023-07-17"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-08-01","The student, Alexanndra Angebrandt, accepted the attached license on 2023-07-12 at 12:49.","The student, Alexanndra Angebrandt, submitted this Thesis for approval on 2023-07-12 at 12:54.","This Thesis was approved for publication on 2023-07-17 at 16:17.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19655 on 2023-12-04 at 17:35:09","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."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/121262"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Alexanndra Angebrandt"],"dc:subject":["Fmri","Graph Theory","Cognitive Neuroscience","Modularity, Learning","Executive Function"],"dc:title":["Brain network modularity predicts cognitive flexibility"],"dc:type":["text"],"thesis:degree_discipline":["Psychology"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:57Z"}