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Predicting learning success from patterns of pre-training magnetic resonance images

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Title: Predicting learning success from patterns of pre-training magnetic resonance images
Author(s): Vo, Loan
Director of Research: Wang, Michelle Y.
Doctoral Committee Chair(s): Liang, Zhi-Pei
Doctoral Committee Member(s): Wang, Michelle Y.; Kramer, Arthur F.; Ahuja, Narendra; Coleman, Todd P.
Department / Program: Electrical and Computer Engineering
Discipline: Electrical and Computer Engineering
Degree Granting Institution: University of Illinois at Urbana-Champaign
Degree: Ph.D.
Genre: Dissertation
Subject(s): nonheme iron T2* time-averaged T2* Magnetic resonance imaging (MRI) learning striatum caudate nucleus putamen nucleus accumbens
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.
Issue Date: 2012-06-27
URI: http://hdl.handle.net/2142/31976
Rights Information: Copyright 2012 Loan Vo
Date Available in IDEALS: 2012-06-27
Date Deposited: 2012-05
 

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