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

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2012

Author dc:creator

• Vo, Loan

Contributors dc:contributor

• Wang, Michelle Y.
• Liang, Zhi-Pei
• Kramer, Arthur F.
• Ahuja, Narendra
• Coleman, Todd P.

• nonheme iron
• T2*
• time-averaged T2*
• Magnetic resonance imaging (MRI)
• learning
• striatum
• caudate nucleus
• putamen
• nucleus accumbens

Statement dc:rights

• Copyright 2012 Loan Vo

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/31976

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/31976