{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/31976"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/31976","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Predicting learning success from patterns of pre-training magnetic resonance images","abstract":"Performance in most complex cognitive and psychomotor tasks improves with training, yet the extent of improvement varies among individuals. Is it possible to forecast the benefit that a person might reap from training? What is the mechanism underlying learning? Several behavioral measures have been used to predict individual differences in task improvement, but their predictive power is limited. Our multi-voxel pattern analysis (support vector regression) of the time-averaged blood oxygen level dependent (BOLD) brain activity in the dorsal but not the ventral striatum, recorded before training, predicts subsequent learning success with high accuracy. The fact that the high prediction accuracy of the data did not depend on the task subjects were performing during the recording might suggest that individual differences in neuroanatomy or persistent physiology predict whether and to what extent people will benefit from training in a complex task. To find out the physiology behind the possibility of predicting learning from time-averaged T2*-weighted images, a follow-up experiment was designed and performed with additional magnetic resonance (MR) measurements, including susceptibility-sensitive ones, such as susceptibility-weighted imaging (SWI), T2-, T2*-quantitative as well as diffusion tensor imaging (DTI) and arterial spin labeling (ASL). We then discovered that (patterns of) nonheme iron (not heme) is the underlying factor driving learning prediction. This discovery of the relationship between iron concentrations and learning ability in healthy young adults could not only guide the development of potential neuromarkers for a person's memory and executive control functions, but also help design customized learning-interventions to improve cognition or prevent its decline.","abstract_html":"Performance in most complex cognitive and psychomotor tasks improves with training, yet the extent of improvement varies among individuals. Is it possible to forecast the benefit that a person might reap from training? What is the mechanism underlying learning? Several behavioral measures have been used to predict individual differences in task improvement, but their predictive power is limited. Our multi-voxel pattern analysis (support vector regression) of the time-averaged blood oxygen level dependent (BOLD) brain activity in the dorsal but not the ventral striatum, recorded before training, predicts subsequent learning success with high accuracy. The fact that the high prediction accuracy of the data did not depend on the task subjects were performing during the recording might suggest that individual differences in neuroanatomy or persistent physiology predict whether and to what extent people will benefit from training in a complex task. To find out the physiology behind the possibility of predicting learning from time-averaged T2*-weighted images, a follow-up experiment was designed and performed with additional magnetic resonance (MR) measurements, including susceptibility-sensitive ones, such as susceptibility-weighted imaging (SWI), T2-, T2*-quantitative as well as diffusion tensor imaging (DTI) and arterial spin labeling (ASL). We then discovered that (patterns of) nonheme iron (not heme) is the underlying factor driving learning prediction. This discovery of the relationship between iron concentrations and learning ability in healthy young adults could not only guide the development of potential neuromarkers for a person's memory and executive control functions, but also help design customized learning-interventions to improve cognition or prevent its decline.","abstract_has_math":false,"creators":["Vo, Loan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":["Wang, Michelle Y.","Liang, Zhi-Pei","Kramer, Arthur F.","Ahuja, Narendra","Coleman, Todd P."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012-06-27T21:22:40Z","date_published":"2012-06-27T21:22:40Z","updated_at":"2026-07-22T22:25:30Z","subjects":["nonheme iron","T2*","time-averaged T2*","Magnetic resonance imaging (MRI)","learning","striatum","caudate nucleus","putamen","nucleus accumbens"],"languages":["en"],"rights":["Copyright 2012 Loan Vo"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/31976","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wang, Michelle Y.","Liang, Zhi-Pei","Kramer, Arthur F.","Ahuja, Narendra","Coleman, Todd P."]},{"key":"dc:creator","label":"Author","values":["Vo, Loan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2012-06-27T21:22:40Z","2014-06-28T10:00:23Z","2012-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"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":["nonheme iron","T2*","time-averaged T2*","Magnetic resonance imaging (MRI)","learning","striatum","caudate nucleus","putamen","nucleus accumbens"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2012 Loan Vo"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/31976"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Performance in most complex cognitive and psychomotor tasks improves with training, yet the extent of improvement varies among individuals. 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To find out the physiology behind the possibility of predicting learning from time-averaged T2*-weighted images, a follow-up experiment was designed and performed with additional magnetic resonance (MR) measurements, including susceptibility-sensitive ones, such as susceptibility-weighted imaging (SWI), T2-, T2*-quantitative as well as diffusion tensor imaging (DTI) and arterial spin labeling (ASL). We then discovered that (patterns of) nonheme iron (not heme) is the underlying factor driving learning prediction. 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To find out the physiology behind the possibility of predicting learning from time-averaged T2*-weighted images, a follow-up experiment was designed and performed with additional magnetic resonance (MR) measurements, including susceptibility-sensitive ones, such as susceptibility-weighted imaging (SWI), T2-, T2*-quantitative as well as diffusion tensor imaging (DTI) and arterial spin labeling (ASL). We then discovered that (patterns of) nonheme iron (not heme) is the underlying factor driving learning prediction. This discovery of the relationship between iron concentrations and learning ability in healthy young adults could not only guide the development of potential neuromarkers for a person's memory and executive control functions, but also help design customized learning-interventions to improve cognition or prevent its decline.","Item withdrawn by Katherine Eriksen (eriksen3@illinois.edu) on 2012-04-18T14:17:38Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 3 Vo_Loan.rar: 4799710 bytes, checksum: 9928d6b2e732558014dd980bdcc40e83 (MD5) Vo_Loan.pdf: 771182 bytes, checksum: cc3fcfcf26a4ecebee7bd460605afa7a (MD5) Vo_Loan.pdf: 766116 bytes, checksum: 65e5884005936ab87e32267c25c2b694 (MD5)","Made available in DSpace on 2012-06-27T21:22:40Z (GMT). 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