{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/92741"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/92741","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Neural correlates of training and transfer","abstract":"This Dissertation was approved for publication on 2016-07-11 at 14:54.","abstract_html":"This Dissertation was approved for publication on 2016-07-11 at 14:54.","abstract_has_math":false,"creators":["Nikolaidis, George Aki"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Neuroscience","degree_department":null,"school":null,"contributors":["Kramer, Arthur","Barbey, Aron","Sutton, Brad","Smaragdis, Paris"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-11-10T17:50:00Z","date_published":"2016-11-10T17:50:00Z","updated_at":"2026-07-22T22:26:35Z","subjects":["Training","Machine learning","Cognitive training","Transfer","Functional connectivity","Magnetic resonance image (MRI)","Structural volume"],"languages":["en"],"rights":["Copyright 2016 George Nikolaidis"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/92741","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kramer, Arthur","Barbey, Aron","Sutton, Brad","Smaragdis, Paris"]},{"key":"dc:creator","label":"Author","values":["Nikolaidis, George Aki"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-11-10T17:50:00Z","2016-07-11","2016-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Neuroscience"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["Training","Machine learning","Cognitive training","Transfer","Functional connectivity","Magnetic resonance image (MRI)","Structural volume"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 George Nikolaidis"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/92741"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This Dissertation was approved for publication on 2016-07-11 at 14:54.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9733 on 2016-11-09 at 10:22:08","Made available in DSpace on 2016-11-10T17:50:00Z (GMT). No. of bitstreams: 3 NIKOLAIDIS-DISSERTATION-2016.pdf: 2656057 bytes, checksum: beddfd7782603fc509755189502178af (MD5) LICENSE.txt: 4214 bytes, checksum: 9038ddff49a2e10dbf64dec11a6ddebd (MD5) PROQUEST_LICENSE.txt: 4560 bytes, checksum: 096f3399d65cf4d483ed15d90dc80a34 (MD5) Previous issue date: 2016-07-11","Cognitive training holds promise to improve cognitive ability in many people, young, old, both healthy, and those with psychiatric or neurological illness, but this field largely lacks a mechanistic understanding of the process by which training demonstrates transfer to improve underlying cognitive abilities. In Chapter 1, we examine how mapping the neural correlates of training and transfer is critical for developing a mechanistic explanation of how training drives transfer. In the current study, we trained 45 young adults with Mind Frontiers, an adaptive cognitive training game that targets executive function, attention, and reasoning. We investigate how both brain structure and resting state networks are associated with training gain and transfer. In Chapter 2, we investigate how both pre-existing and training-induced differences in brain structure are predictive of training and transfer. In Chapter 3, we assess how both pre-existing, and training-induced differences in resting state network connectivity in the default mode, cingulo-opercular, frontal-parietal, and subcortical networks predict training gain and transfer. In Chapter 4, we examine the relationship of the structural and resting state data in predicting training and transfer. We assess the extent to which these predictors overlap and dissociate with one another over predictions of training gain and transfer. To make our predictions, we utilize a simple machine learning paradigm that we developed to maximize the reliability and interpretability of our findings. We found extensive overlap in structural predictions of training gain and transfer in low level visual and auditory areas, suggesting that greater fidelity in low level sensory systems may contribute to greater signal to noise ratios during training, enabling better training quality and transfer. Furthermore, our resting state results also highlight the importance of training quality through demonstrating the importance of the cingulo-opercular network, which is critical for both the regulation of the default mode network and deployment of sustained attention during training. These results suggest that greater training fidelity through lessened distraction may play an important role in maximizing the benefits of an intervention.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-11-09 without embargo terms","The student, George Nikolaidis, accepted the attached license on 2016-06-30 at 13:21.","The student, George Nikolaidis, submitted this Dissertation for approval on 2016-06-30 at 13:37."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Neural correlates of training and transfer"]}]}],"canonical_facts":{"dc:contributor":["Kramer, Arthur","Barbey, Aron","Sutton, Brad","Smaragdis, Paris"],"dc:creator":["Nikolaidis, George Aki"],"dc:date":["2016-11-10T17:50:00Z","2016-07-11","2016-08"],"dc:description":["This Dissertation was approved for publication on 2016-07-11 at 14:54.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9733 on 2016-11-09 at 10:22:08","Made available in DSpace on 2016-11-10T17:50:00Z (GMT). No. of bitstreams: 3 NIKOLAIDIS-DISSERTATION-2016.pdf: 2656057 bytes, checksum: beddfd7782603fc509755189502178af (MD5) LICENSE.txt: 4214 bytes, checksum: 9038ddff49a2e10dbf64dec11a6ddebd (MD5) PROQUEST_LICENSE.txt: 4560 bytes, checksum: 096f3399d65cf4d483ed15d90dc80a34 (MD5) Previous issue date: 2016-07-11","Cognitive training holds promise to improve cognitive ability in many people, young, old, both healthy, and those with psychiatric or neurological illness, but this field largely lacks a mechanistic understanding of the process by which training demonstrates transfer to improve underlying cognitive abilities. In Chapter 1, we examine how mapping the neural correlates of training and transfer is critical for developing a mechanistic explanation of how training drives transfer. In the current study, we trained 45 young adults with Mind Frontiers, an adaptive cognitive training game that targets executive function, attention, and reasoning. We investigate how both brain structure and resting state networks are associated with training gain and transfer. In Chapter 2, we investigate how both pre-existing and training-induced differences in brain structure are predictive of training and transfer. In Chapter 3, we assess how both pre-existing, and training-induced differences in resting state network connectivity in the default mode, cingulo-opercular, frontal-parietal, and subcortical networks predict training gain and transfer. In Chapter 4, we examine the relationship of the structural and resting state data in predicting training and transfer. We assess the extent to which these predictors overlap and dissociate with one another over predictions of training gain and transfer. To make our predictions, we utilize a simple machine learning paradigm that we developed to maximize the reliability and interpretability of our findings. We found extensive overlap in structural predictions of training gain and transfer in low level visual and auditory areas, suggesting that greater fidelity in low level sensory systems may contribute to greater signal to noise ratios during training, enabling better training quality and transfer. Furthermore, our resting state results also highlight the importance of training quality through demonstrating the importance of the cingulo-opercular network, which is critical for both the regulation of the default mode network and deployment of sustained attention during training. These results suggest that greater training fidelity through lessened distraction may play an important role in maximizing the benefits of an intervention.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-11-09 without embargo terms","The student, George Nikolaidis, accepted the attached license on 2016-06-30 at 13:21.","The student, George Nikolaidis, submitted this Dissertation for approval on 2016-06-30 at 13:37."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/92741"],"dc:language":["en"],"dc:rights":["Copyright 2016 George Nikolaidis"],"dc:subject":["Training","Machine learning","Cognitive training","Transfer","Functional connectivity","Magnetic resonance image (MRI)","Structural volume"],"dc:title":["Neural correlates of training and transfer"],"dc:type":["text"],"thesis:degree_discipline":["Neuroscience"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:35Z"}