Individual Differences in Reading Comprehension: A Resting-State Functional Connectivity Approach
Open Access
- Author:
- Yu, Anya
- Graduate Program:
- Psychology
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- April 30, 2018
- Committee Members:
- Ping Li, Thesis Advisor/Co-Advisor
Janet van Hell, Committee Member
Chaleece Wyatt Sandberg, Committee Member
Xiao Liu, Committee Member - Keywords:
- reading
text comprehension
executive function
functional connectivity
resting-state fMRI - Abstract:
- Reading is one of the fundamental methods through which we acquire new knowledge and skills(Hanh et al., 2007; Macabasco-O’Connell et al., 2011), and reading comprehension has been shown to be a strong predictor of individual’s quality of life as well as future success (Baker, Parker, Williams, Clark & Nurss, 1997; Ritchie & Bates, 2013). Individual executive function (EF) skills have been reported to be a significant factor that influences reading comprehension success in both children and adults (Cartwright, 2015). Text reading comprehension is a complex cognitive process, and relies on a distributed network of brain regions (Li & Clariana, 2018). It is therefore very likely that text reading comprehension is better captured by an interconnected and interactive neural network. There has been ample literature investigating reading, however most neurocognitive investigations of language comprehension are limited to word-level rather than text-level reading (see reviews by Ferstl, 2010 and Mason & Just, 2013). Furthermore, most neuroimaging studies of text comprehension have been focused on investigating reading-related patterns via functional magnetic resonance imaging (fMRI), and evidence showing that resting-state functional connectivity (RSFC) can capture text reading comprehension is lacking. Our study aims to clarify the relationship between RSFC in the language network and reading comprehension performance as well as individual EF skills. A step-wise algorithm was used to explore whether one or more two-way interactions could better explain variation in the reading comprehension scores. To address concerns about not adequately controlling for multiple comparisons and overfitting the data, we also used a model based on the decision regression tree algorithm (Breiman, 2001) that has been applied in functional connectivity studies (Richiardi, Eryilmaz, Schwartz, Vuilleumier & Van De Ville, 2010; Venkataraman, Whitford, Westin, Golland & Kubicki, 2012). All of the interactions that explained a significant amount of variance in the data are entered in a leave-one-subject-out cross-validation analysis. The behavioral results confirmed a significant positive correlation between EF task performances and our reading task performance. While no single predictor had significant main effects with reading and EF indices, the decision tree model revealed significant effects in the temporoparietal connectivity interaction that had above chance predicting power on reading performance (Spearman’s =.37, p=.01). These patterns suggest that the temporoparietal connectivity can act as a reliable classifier distinguishing lower-ability and better-ability readers. This is convergent with DTI findings correlating temporoparietal white-matter tract integrity with reading performance (Kingberg et al., 2000), suggesting that the temporoparietal connectivity is particularly engaged in text comprehension.