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. Author manuscript; available in PMC: 2020 May 13.
Published in final edited form as: Mind Brain Educ. 2018 Jun 19;12(1):49–60. doi: 10.1111/mbe.12168

Longer Fixation Times During Reading Are Correlated With Decreased Connectivity in Cognitive-Control Brain Regions During Rest in Children

Tzipi Horowitz-Kraus 1,2,3, Christopher DiCesare 4,5, Adam W Kiefer 4,5
PMCID: PMC7220207  NIHMSID: NIHMS1010613  PMID: 32405320

Abstract

Dyslexia, or reading difficulty (RD), is characterized by slow, inaccurate reading and accompanied by deficit in executive functions (EF) and altered functional connectivity (FC) in the related networks (i.e., cingulo-opercular). Individuals with RD also present with altered oculomotor gaze patterns that include longer fixation times. The researchers examined the relationship between fixation times and FC of neural circuits related to EF during rest in children with RD and typical readers. Nineteen children participated in a 10-min resting-state scan. FC analysis was performed with the anterior cingulate cortex (ACC), related to cognitive control, chosen as a seed. Fixation time during word reading was used as a covariate of interest. Results demonstrated that FC between the ACC and the left inferior frontal cortex pars triangularis and left inferior prefrontal cortex during rest were negatively correlated with fixation times during word reading. These exploratory results support the critical role for the cingulo-opercular network, which is related to cognitive control, in the reading process, and likely also in reading impairment.

Dyslexia, or reading difficulty (RD), is characterized by slow and inaccurate reading that persists into adulthood despite remedial interventions and exposure to written language, with a prevalence rate of 5%−20% in school-age children (IDA, 2011; Shaywitz, 2003). Deficits in phonological processing have been shown as the main cause for RD (Scarborough, 1998). However, recent research examining RD-associated reading deficits has also suggested a deficit in executive functions (EF) for individuals with RD (Altemeier, Abbott, & Berninger, 2008; Brosnan et al., 2002; Gooch, Snowling, & Hulme, 2011; Helland & Asbjornsen, 2000; Menghini, Carlesimo, Marotta, Finzi, & Vicari, 2010; Reiter, Tucha, & Lange, 2005; Tiffin-Richards, Hasselhorn, Woerner, Rothenberger, & Banaschewski, 2008). Thus, RD is considered a multifaceted disorder that requires a variety of investigative technologies and methodologies to uncover the various mechanisms driving RD-associated reading impairments.

The term EF encompasses a variety of conscious processes that monitor and optimize performance, which are essential for goal setting, stimuli synthesis, preparatory actions, and execution of behavior (Baddeley, Logie, Bressi, Della Sala, & Spinnler, 1986; Luria, 1973). Several studies have determined difficulties in various aspects of EF in children with RD: allocating the attention from one stimulus to the other (or switching) (Facoetti, Paganoni, & Lorusso, 2000; Facoetti, Paganoni, Turatto, Marzola, & Mascetti, 2000; Shaywitz & Shaywitz, 2008), attentional difficulty (i.e., focusing the attention in one stimulus; Facoetti, Paganoni, Turatto, et al., 2000; Shaywitz & Shaywitz, 2008), inhibition (Brosnan et al., 2002), and working memory (Ackerman & Dykman, 1993; Helland & Asbjornsen, 2000). Importantly, error self-monitoring deficits have also been identified in nonlinguistic and linguistic domains (Horowitz-Kraus & Breznitz, 2008, 2009). Despite the large number of studies that have examined EF impairments in children with RD, a lack of consensus exists as to the involvement of this difficulty in their inability to read effectively (Barkley, Grodzin- sky, & DuPaul, 1992; Stoet & Snyder, 2007).

One approach to deepen our understanding of the involvement of impaired EF during the reading process in children with RD is the use of neuroimaging tools to clearly define the involvement of altered neural circuits related to EF in reading among this population. In this approach, cognitive control networks are related to as several regions, functionally connected, that is, with a synchronized time series. The “dual-networks top-down model” is a functional connectivity (FC)-based model for the assessment of cognitive control networks (Power et al., 2011). This model proposes two cognitive-control/EF networks with different neuroanatomical correlates. The first is the frontoparietal network consisting of a rapid adaptive-control network that promotes attention to perceptual cues (Dosenbach, Fair, Cohen, Schlaggar, & Petersen, 2008). The second is the cingulo-opercular network, which is a set-maintenance network responsible for task goals, adjustments of feedback control, and error monitoring (Dosenbach et al., 2008). It is widely accepted that FC within these two net-works increases throughout cognitive development (Fair, Dosenbach, Petersen, & Schlaggar, 2012) and, importantly, that both of these networks are engaged during reading (Ihnen, Petersen, & Schlaggar, 2013). Specifically, research has determined that altered EF forms the basis of reading deficits in individuals with RD, such as slower processing speed (Breznitz & Misra, 2003), impaired visual attention (Gabrieli & Norton, 2012), and error monitoring (Horowitz-Kraus & Breznitz, 2008). Relative to these findings, there is an increasing interest in examining FC differences in cognitive-control networks in these individuals. For example, we have demonstrated that children with RD exhibit decreased FC in their cingulo-opercular network (Horowitz-Kraus, Toro-Serey, & DiFrancesco, 2015). Similar work has demonstrated that during error monitoring in individuals with RD, the anterior cingulate cortex (ACC), which is a component of the cingulo-opercular network, exhibited decreased activation compared to typical readers (TRs; Horowitz-Kraus etal., 2014). Smaller event-related potential amplitudes, which were originated from the ACC (i.e., the error-related negativity) during reading, were also found in children with RD and was correlated with their low reading scores (Horowitz-Kraus & Breznitz, 2008, 2014). Hence, these smaller amplitudes reflect decreased activation of the ACC in these readers.

Reading deficits that result from RD are accompanied by altered oculomotor gaze patterns as these readers exhibit longer fixations, shorter saccades, and more regressions compared to their more skilled counterparts (Jainta & Kapoula, 2011; Rayner, 1998). Individuals with RD are also known to exhibit multiple fixations on a greater number of words, with gaze duration (i.e., total fixation time) positively related to word length for both Italian and German readers (Rayner, 1998). It has also been demonstrated that individuals with RD can process only a few letters at each fixation, and that those with reduced visual attention are incapable of increasing the number of letters processed in a reading task (Prado, Dubois, & Valdois, 2007). Initial fixation position during word recognition is also suboptimal in readers with RD, and this leads to a larger number of refixations compared to TRs (Hawelka, Gagl, & Wimmer, 2010). While these types of deficits are consistently demonstrated, it is not yet clear whether fixation performance deficits are related to impaired cognitive control in individuals with RD (Bellocchi, Muneaux, Bastien-Toniazzo, & Ducrot, 2013), which can result in some intervention strategies for this population. Eye-movement patterns have been shown previously to be suitable for observing several key EF involved in reading (planning, working memory, attention to task-relevant and task-irrelevant information, error monitoring, and shifting attention) (Hodgson, Bajwa, Owen, & Kennard, 2000; Mennie, Hayhoe, & Sullivan, 2007; Wass, Porayska-Pomsta, & Johnson, 2011), a connection which was not verified in the RD group yet. However, different eye-movement patterns are likely associated with different types of cognitive processing, and therefore it is difficult to uncover the relationship between oculomotor and specific behavioral deficits (Holmqvist etal., 2011). One method to better understand the cognitive mechanisms underlying these deficits is to examine the relationship between neural FC and fixation performance as negative or reduced FC of cognitive-control regions (or key components of these) in combination with fixation deficits. This method would reveal the critical correspondence between eye-movement patterns and neural circuits related to cognitive control (see also Horowitz-Kraus et al., 2015, for the role of the cognitive-control networks in reading).

As the official definition for RD does not include EF despite the findings pointing at an additional difficulty in EF in this population, the aim of the current study was to strengthen and extend this point even more by connecting physiological data during reading (i.e., eye movement data) to (1) EF and reading ability and (2) neurobiology. We will do that by examining the relationship between fixation times and FC of neural circuits related to EF during rest in both children with RD and TRs. Because we aim to validate the existence of challenges in EF in children with RD, we hypothesized that children with RD will demonstrate lower EF scores than TRs and that longer fixation times during reading would be correlated with decreased (i.e., negative) FC between a key region of the cingulo-opercular network, the ACC, and left-lateralized regions related to cognitive control. Because FC during a resting state data is reflective of an actual task, without being affected from eye movement derived from a specific external stimuli (Tavor et al., 2016), we chose to answer these questions using a resting state data.

METHODS

Participants

Children with RD (n = 10; mean age: 9.91 years, SD = 0.982, eight males, nine right-handed) and TRs (n = 9; mean age: 10.18 years, SD = 0.714, five males, all right handed; t(18) = −1.142, p = .2) participated in the current study. Children were recruited by posted ads in the hospital and clinics, libraries, and schools around the city. The participants in the RD group had been previously diagnosed with RD from external clinics in the community. We also verified the existence of RD using several standardized reading tests: RD was defined as scoring below the 25th percentile on at least two tests from a battery of standardized reading measures (see Behavioral Measures section). All participants were within the normal range of nonverbal intelligence quotient (IQ), and none had a history of neurological or emotional disorders. Children with report of attention deficit hyperactivity disorder (ADHD) as well as those with a moderate and higher probability for attention (as measured by the Conners questionnaires) were excluded from the study (Conners, 1989). No differences were found between the two reading groups in attention ability as measured by the Conners questionnaires (Conners, 1989; t(18) = 2.267, ns).

All participants were native monolingual English speakers and screened for compatibility for participation in the magnetic resonance imaging (MRI) scan (no dental work or other implants to affect safety or image quality). Participants had no history of neurological or psychiatric impairments. Informed consent and assent were signed by parents and participants, respectively. The study was reviewed and approved by the appropriate institutional review board.

Behavioral Measures

All children were administered the Test of Nonverbal Intelligence (TONI-3; Brown, Sherbenou, & Johnsen, 1997) and the Peabody Picture Vocabulary Test (PPVT-4; Dunn & Dunn, 2007) to verify normal nonverbal IQ and verbal IQ, respectively. Reading ability, fluency and comprehension, and phonological abilities were assessed using the letter-word and word-attack subtests from the Woodcock-Johnson-III (WJ-III; Woodcock & Johnson, 1989), the Test of Silent Reading Efficiency and Comprehension (TOSREC; Wagner, Torgesen, Rashotte, & Pearson, 2010), and the Elision Subtest (from Comprehensive Test of Phonological Processing [CTOPP]; Wagner, Torgesen, & Rashotte, 1999), respectively. We also tested EF in the following domains: shifting (color-word Stroop task; Dellis, Kaplan, & Kramer, 2001), visual attention (i.e., Sky Search task; Manly, Robertson, Anderson, & Nimmo-Smith, 1999), working memory (digit-span subtest; Wechsler, 1999), and learning (Wisconsin test; Nyhus & Barcelo, 2009). Attention difficulties were ruled out by the administration of the Conners child reports (Conners, 1989).

Eye-Movement Data Collection

Lexical Decision Task

Stimuli for the lexical decision task consisted of 80 randomized items, either words or pseudowords (modeled after Van der Mark et al., 2009), and participants indicated whether the stimuli were real words or not through button-pressing. Word stimuli were 40 high-frequency words (four to six letters long) matched for imageability and concreteness (adapted from Van der Mark et al., 2009). The 40 pseudowords were created by substituting one or two letters in real words. The stimuli were presented horizontally in the center of the screen using Tobii Pro Studio software (Tobii Technologies, Stockholm, Sweden). Following the presentation of each word/pseudoword, participants were provided with a Yes/No screen corresponding to a “thumb up, happy face” and a “thumb down, sad face”, respectively, and were requested to push the “Yes” button for a real word and the “No” button for a nonword. Each stimulus was presented for 800 ms, and subsequently an interstimulus interval of 200 ms followed by a “Yes/No” screen presented for 1,500 ms. Each eye-tracking session started with a practice session of 10 stimuli.

Gaze Data

Gaze data were collected at 60 Hz using a Tobii X2–60 eye tracker (Tobii Technologies, Stockholm, Sweden). The tracker was mounted to the bottom center of a laptop computer that was situated on top of a desk. Participants were instructed to sit upright, approximately 65 cm from the computer, such that the center of the monitor was at eye level. Initially, participants were instructed to follow a moving target using a standardized 5-point calibration. Participants were instructed to restrict their movement as much as possible while performing the reading tasks. If at any point throughout the assessment the participant moved beyond any normal postural sway necessary to maintain an upright seated position (i.e., shifting of weight or an involuntary movement such as coughing or sneezing), the participant was realigned with the screen and recalibrated. Following a practice session of 10 words, data was recorded for analysis purposes. Gaze data were postprocessed and fixations were determined in Tobii Studio using a proprietary algorithm (Olsen, 2012). The processed data were then exported to Matlab (Mathworks, Natick, MA), where reading time and fixation number and duration were parsed from each stimulus presentation. Only data for correct word reading were included in the analysis.

Correlation Analysis of Behavioral Reading Measures and Fixation Duration

Before determining the relations between eye-movement patterns and neuroimaging data, we calculated the relationship between reading ability, EF, and eye-movement patterns on the behavioral level using a Pearson correlation. To determine the relations between eye-movement patterns and reading ability we used the word and non-word reading (Woodcock & Johnson, 1989), phonological awareness (Wagner et al., 1999), fluency, and comprehension (Torgesen, Wagner, & Rashotte, 1999). To examine the relations between the eye-movement patterns and EF we used attention shifting (Dellis et al., 2001), visual attention (Manly et al., 1999), working memory (Wechsler, 1999), and learning (Nyhus & Barcelo, 2009), and fixation duration was performed. Data was corrected using Bonferroni correction.

Neuroimaging Procedure

A resting-state condition was administered using a Philips Achieva 3 T MRI scanner (Philips Medical Systems, Best, The Netherlands). Participants were asked to look at a gray cross in the center of a projector screen for 10 min and avoid sleeping or closing their eyes other than blinking durng the scan. A T2-weighted, gradient echo, echo planar imaging or EPI sequence was used with functional MRI (fMRI) parameters: time repetition/time echo = 2000/38 ms, matrix size = 64 × 64, and slice thickness = 5 mm, resulting in a voxel size = 4 × 4 × 5 mm3. During the resting-state scan, 300 whole-brain volumes were acquired for a total imaging time of 10 min. The initial 10 time points acquired were discarded to allow for T1 relaxation equilibrium. In addition, a high-resolution T1-weighted three-dimensional anatomical scan was acquired using an inversion recovery or inversion recovery-prepared turbo gradient-echo acquisition protocol with a spatial resolution of 1× 1×1 mm3. Participants were acclimated and desensitized to the scanner to condition them for comfort during imaging (see Byars etal., 2002, for details). Head motions were controlled using elastic straps that were attached to either side of the head-coil apparatus.

MRI Data Analysis

During image reconstruction, a multi-echo reference scan was initially used to correct for Nyquist ghosts and geometric distortion due to B0 field inhomogeneity. Reconstructed fMRI data were then spatially preprocessed using SPM8 software (www.fil.ion.cl.ac.uk/spm/), including slice-timing correction, realignment for motion correction, coregistration of the anatomical image to the mean aligned functional image, segmentation by gray matter, white matter, and cerebrospinal fluid tissue classes, normalization of all images to the Montreal Neurological Institute or MNI space, and spatial smoothing with an 8-mm full width at half-maximum or FWHM Gaussian kernel. Motion was corrected using pyramid coregistration (Thevenaz, Rutti- mann, & Unser, 1998) in SPM12. Using the ART toolbox for motion correction implemented in the CONN toolbox, we performed three-dimensional affine transformation to align the volumes, which resulted in six motion parameters (three translational and three rotational). Time points with excessive motion were rejected from the postprocessing pipeline (>0.02 mm). Data was visually inspected for artifacts such as missing voxels, stripes, ghosting, or intensity differences. All data frames survived these criteria and are included in the analysis. All data met the criterion of median voxel displacement <0.02 mm in the center of the brain. See online Supporting Information for the nascence of differences in motion between the two groups.

Following the spatial preprocessing described, the neuroimaging data were fed into a FC toolbox for Matlab (CONN) (Whitfield-Gabrieli & Nieto-Castanon, 2012). Additional preprocessing under the anatomical component-based noise-correction framework or aComp-Cor (Behzadi, Restom, Liau, & Liu, 2007) was performed and included extraction of the first five principle eigenvariates of the BOLD time-courses from white-matter and cerebral spinal fluid regions for use as regressors in the analysis to remove signal variation associated with these noncortical regions. In addition, the six motion parameters for each session, together with their first derivatives, were regressed out of the voxel time series. Finally, the voxel time-series data were band-pass filtered between 0.008 and 0.2 Hz, as recommended by Baria, Baliki, Parrish, and Apkarian (2011).

FC Analysis

FC between the chosen cognitive control region (the left and right ACC [Brodmann area (BA) 24]), and the regions in the entire brain (left and right) as target regions of interest (ROI) was calculated as the correlation coefficient for the average voxel signal for each ROI pair.

Correlation of Reading and EF Behavioral Measures

To determine the relationship between EF and reading measures, Pearson correlation was conducted. Data were corrected for multiple comparisons.

Correlation of Neuroimaging Data and Eye-Movement Measures

In order to have a full array of the shortest and longest fixation times in the analysis, the calculated FC was correlated with the fixation time during correct word reading (in milliseconds) for each child, the entire study cohort (both children with RD and TRs), and each group separately. Threshold was set to p< .005 (uncorrected).

RESULTS

Behavioral Measures

Verbal and nonverbal general ability and attention performance were within the average range for all participants. Children with RD showed nonverbal ability and attention scores similar to those for TRs, but demonstrated significantly lower reading scores in all reading domains measured (word and nonword reading, phonological awareness, fluency, and reading comprehension), as well as in verbal ability. Children with RD also showed lower scores than TRs in all EF measured, but were significantly lower only in shifting, working memory, and learning (see Table 1).

Table 1.

Behavioral Measures for Children With Reading Difficulties (RDs) and Typical Readers (TRs)

Ability Measure RD, mean
(SD) (A) 		TR, mean
(SD) (B) 		Contrast, t
(17), p		 Mean
difference 		Standard
error
difference 		Effect
size (d)
Nonverbal ability TONI (scaled score) 104.20 (6.52) 105.22 (8.467)    A< B, −0.296 ns   −1.022   3.448 0.09
Verbal ability PPVT (scaled score)   99.1 (7.53) 114.55 (10.8)    A <B,−3.648** −15.455   4.236 1.15
Attention Conner’s Child (percent)   52.70 (27.79)   32.89 (8)    A> B, 2.146 ns   −2.756   1.135 0.67
Reading fluency and comprehension TOSREC (index)   85.33 (6.12) 112.77 (16.63)    A < B, −4.645** −27.444   5.9 1.46
Phonological awareness CTOPP, Elision (scaled score)     7.80 (2.15)   13.56(1.74)    A <B, −6.366***   −5.756 −7.663 2.01
Word reading Woodcock Johnson Letter-Word (scaled score)   94.70 (6.881) 108.78 (8.074)    A < B, −4.104** −25.889   3.292 1.29
Decoding Woodcock Johnson Word-Attack (scaled score)   91.00 (6.11) 116.89 (8.192)    A <B,−7.863*** −14.078   3.43 2.48
Attention shifting (switching) Stroop Color-Word, (scaled score)     9.8 (3.04)   12.56 (1.59)    A <B,−2.427*   19.811   9.231 0.76
Visual attention Sky Search (standard score)     6.6 (3.06)      9 (3.24)    A < B, −1.654, ns   −2.4   1.446 0.52
Working memory Digit Span (standard score)     7.8 (2.89)   10.56 (1.13)    A <B, −2.781*   −2.756     .991 0.87
Learning Wisconsin (total errors, percent)   23.25 (12.49)   14.33 (6.2)    A> B, 1.898*     8.917   4.697 0.59
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Note. CTOPP, Comprehensive Test of Phonological Processing; SD, standard deviation.

*

p < .05.

**

p< .01.

***

p < .001. ns, not significant.

Eye-Movement Patterns

Longer fixation times were observed for children with RD (X = 128.0 ms, SD = 100.0) compared to TRs (X = 56.19 ms, SD= 17.95); t(17) = −2.04, p < .05, d = 0.646, mean difference =−71.857, standard error difference = 37.073. The average fixation time was 90.23 ms (SD = 82.99) during correct word reading for the lexical decision task. Children with RD and TRs did not differ in their reaction time (RD: X= 810.88 ms, SD= 12.3; TR: X = 804.82 ms, SD = 5.42, t(17) = −1.417, ns, d = 0.447, mean difference =−6.065, standard error difference = 4.28) and accuracy rate (RD: X= 86.43%, SD= 10.42; TR: X = 89.61%, SD= 10.99, t(17) = 0.644, ns, d = 0.2, mean difference = 3.173, standard error difference = 4.93) for correct word reading.

Correlational Analyses

The Correlation of Reading and EF Measures

To verify the relations between cognitive control (or EF) and reading measures, Pearson correlation analysis was conducted. Significant correlation between reading and EF in both groups (contextual fluency and word reading with working memory; r(19) = 0.507, p < .05; r(19) = 0.625, p < .01, respectively) and between phonological processing and working memory, attention shifting, visual attention, and learning abilities, r(19) = 0.552, p < .05; r(19) = 0.494, p < .05; r(19) = 0.494, p < .05; r(19) = −0.457, p < .05, was found. Greater fluency, word reading, and phonological processing were correlated with higher EF scores.

The Correlation of Behavioral Measures and Eye-Movement Measures

Eye-movement data was found to be related to the behavioral data. Significant negative correlation between fixation time and letter word reading or attention shifting was found, r(19) = 0.788, p < .05 and r(19) = −0.421, p < .05, grespectively, and longer fixation times were correlated with lower word-reading and attention-shifting scores.

Correlation of FC and Eye-Movement Measures

Children with RD

Significant correlation occurred between the cognitive control region (ACC) and the left inferior frontal cortex pars triangularis (BA 45; t(9) = −5.17, p < .005 uncorrected) in children with RD, and fixation times during word reading were observed (Figure 1). Results suggest that lower negative FC between these regions was correlated with longer fixation times during reading.

Fig. 1.

Fig. 1.

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Correlation coefficient matrices for decreased functional connectivity in children with reading difficulty (RD). (a) The anterior cingulate cortex (BA24) (in green) is negatively correlated with the left inferior frontal gyrus (BA45, in blue), presented in sagittal axis. Connectivity strength is reflected in the line thickness. (b) Anterior cingulate cortex (black circle) and regions related to cognitive control (left inferior frontal gyrus) (blue circle) and longer fixation time during reading in children with RDs (n = 10), p < .005, uncorrected. Figures are presented in an axial display. (c) Effect size of the functional connectivity analysis (effect size is noted in the y-axis).

Typical Readers

Significant correlation occurred between the cognitive control region (ACC) and the left posterior entorhinal cortex (BA28; t(7) = −5.89, p < .005 uncorrected) and left superior temporal gyrus (BA22; t(7) = −4.14,p < .005 uncorrected) in TRs, and fixation times during word reading were observed (Figure 2). Results suggest that lower negative FC between these regions was correlated with longer fixation times during reading.

Fig. 2.

Fig. 2.

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Correlation coefficient matrices for decreased functional connectivity in typical readers. (a) The anterior cingulate cortex (BA24) (in green) is negatively correlated with the left superior temporal gyrus (BA 22) and left posterior entorhinal cortex (BA 28), both in blue, presented in sagittal axis. Connectivity strength is reflected in the line thickness. (b) Anterior cingulate cortex (black circle) and regions related to language processing (left superior temporal gyrus) and cognitive control (left posterior entorhinal cortex) (blue circles) and longer fixation time during reading in typical readers (n = 9), p < .005, uncorrected. Figures are presented in an axial display. (c) Effect size of the functional connectivity analysis (effect size is noted in the y-axis).

Both Reading Groups

Significant correlation occurred between the cognitive control region (ACC) and the left inferior frontal cortex pars triangularis (BA 45; t(17) = −3.25,p < .005 uncorrected) and left inferior prefrontal cortex (BA10; t(17) = −3.03, p < .005 uncorrected) in all study participants, and fixation times during word reading were observed (Figure 3). Results suggest that lower negative FC between these regions was correlated with longer fixation times during reading.

Fig. 3.

Fig. 3.

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Correlation coefficient matrices for decreased functional connectivity in both groups. (a) The anterior cingulate cortex (BA24) (in green) and the left inferior frontal gyrus (BA45) and the inferior prefrontal cortex (BA 47), both in blue, presented in sagittal axis. Connectivity strength is reflected in the line thickness. (b) Anterior cingulate cortex (black circle) and regions related to cognitive control (left inferior frontal gyrus and inferior prefrontal cortex) (blue circles) and longer fixation time during reading in the entire study sample (n = 19), p < .005, uncorrected. Figures are presented in an axial display. (c) Effect size of the functional connectivity analysis (effect size is noted in the y-axis).

DISCUSSION

The aim of the current study was to strengthen the existence of an EF difficulty in children with RD and to define the neural circuits underpinning altered eye-movement patterns related to reading and EF during rest in children with RD. Resting-state data was acquired from children with RD and TRs, and data were correlated with fixation times during word reading. In support of our hypotheses, children with RD demonstrated lower reading and EF abilities than TRs. Lower word-reading scores were positively related to lower shifting abilities. In addition, children with RD showed longer fixation times during word reading compared to TRs. Overall, longer fixation time was negatively correlated with reading ability, attention shifting, and decreased FC between the ACC and cognitive-control-related regions (inferior frontal gyrus and inferior prefrontal cortex). Specifically, children with RD showed negative FC between the cognitive control region (ACC) and additional cognitive control-related regions (inferior frontal gyrus), whereas the negative FC also involved language regions in TRs (superior temporal gyrus and entorhinal cortex). These results are discussed in the context of the involvement of EF and EF-related neural circuits for both typical and atypical reading.

Longer Fixation Associated with Impaired Reading—Why?

The results of the negative correlations between fixation duration and word-reading ability and attention shifting and the significant differences in fixation times between children with RD and TRs confirm that impaired reading is associated with longer fixation times. As Rayner has suggested, the reader obtains new visual information during fixations in the written text (Rayner, 1998). Hutzler and Wimmer compared fixation duration in children with RD in German versus Italian and demonstrated longer fixations in German, which shares similarities to English syllables (Hutzler & Wimmer, 2004). English and German languages contain initial and/or final consonant clusters, which makes it more challenging to assemble the syllables in reading in these languages. Due to the well-described phonological deficit in individuals with RD, the letter-sound correspondence does not come as natural for the RD reader as it does for TRs. Therefore, longer fixations are needed to match the correct sound to the letter, especially when the word involves complex syllables (Hutzler & Wimmer, 2004).

Is There an Association Between Cognitive Control and Longer Fixations?

Rayner and colleagues outline the cognitive processes involved in the decision-making process that underlies eye movements during reading (Rayner, 1998; Rayner & Pollatsek, 1989; Rayner, Schotter, Masson, Potter, & Treiman, 2016). The authors suggest that eye movements are basically motor responses that require time to plan and execute and describe several processes that are all happening simultaneously: The reader processes the fixated word, plans to move the eyes forward, and processes the upcoming word using parafoveal information. This concept has been recently supported by Luthi and colleagues, who report that eye-movement patterns involve key EF and that a agnetic stimulation of key neural circuits related to EF (i.e., dorsolateral prefrontal cortex) affects EF (Luthi et al., 2014). We suggest that the reading process specifically involves several key EF that can be reflected in eye-movement patterns: visual attention (the ability to ignore irrelevant visual information), working memory that enables the update of new information to that previously read, planning of the next step in the process (i.e., should the reader gaze forward or backward?), and decision making. Therefore, it is not surprising that key regions involved in the described abilities show decreased FC in relationship to altered eye movements during reading (i.e., longer fixation times). In the current study, we observed a decreased FC between the ACC (decision making and planning as part of the cingulo-opercular network; Dosenbach et al., 2008), inferior prefrontal cortex (working memory and visual attention; Burgess, Dumontheil, & Gilbert, 2007; Gilbert et al., 2006), and inferior frontal gyrus (interference and response selection, part of working memory abilities; see Badre, Poldrack, Pare-Blagoev, Insler, & Wagner, 2005; Jonides & Nee, 2006; see Nee et al., 2013, for review).

The link between low FC (i.e., decreased cognitive control) and longer fixation time indicates that the fixation behavior, which was previously reported for readers with RD, is potentially due to underdeveloped or maladaptive cognitive-control processes. Importantly, different fixation behaviors are associated with different types of cognitive processes (Holmqvist etal., 2011). During reading, for example, initial-word fixations are linked to lexical activation, while later fixations are associated with discourse integrative processes (Rayner, 1998). It has been suggested that the duration of fixations can differentiate between these different processes, and fixations shorter than 140 ms are associated with lexical properties (McConkie, Reddix, & Zola, 1992). In the current study, the average fixation time was 90.23 ms—a time duration under the 140 ms threshold, which suggests that lexical processes did occur. However, longer fixation time at the word level may suggest an impaired and nonautomatic process of attention shifting of perceiving the word and then looking for a semantic meaning in working memory, as has been previously suggested (Rayner, 1998) and which is also reflected by a reduced FC of neural circuits related to cognitive control. These findings are in line with previous studies suggesting an overall altered eye-movement patterns in readers with RD (Bednarek, Tarnowski, & Grabowska, 2006; Prado et al., 2007). Based on the current study’s results, if there are significant relations between altered eye-movement patterns and EF and neural circuits supporting it, it is not surprising that the altered eye movement characteristics extend beyond the linguistic domain. An additional study should examine if the same neural circuits support this eye movement alteration.

The results of the current study support the critical role for cognitive control, and specifically of the cingulo-opercular network in both the reading process and reading impairment. These results echo our previous findings of the overall lower FC (i.e., global efficiency) of this network in children with RD during rest (Horowitz-Kraus et al., 2015). The cingulo-opercular network is related to a set-maintenance that maintains task goals, sustains adjustments for feedback control, and monitors errors, all of which are critical for reading (Horowitz-Kraus et al., 2015). Since the described cognitive abilities are all part of the reading process, it is only natural that the critical role of this network was also evident in reading remediation, as was previously observed (Horowitz-Kraus etal., 2015). In that study, an EF-based reading remediation program resulted in increased FC of the cingulo-opercular network in children with RD (Horowitz-Kraus et al., 2015). Future studies should examine whether the change in FC in this network among children with RD following training may also be reflected in eye-movement patterns, specifically with shorter fixation times. In support of these suggestions, an Italian study demonstrated how repetitive exposure to video games resulted in better reading in adults with RD (Franceschini et al., 2013). The authors suggested that the positive effect on reading ability was mediated through the effect of the exposure to the video games on attention and reaction times, which was “leaking” to the reading. Future studies should also examine the effect of cognitive-control training on both reading ability and the relationship between eye movements and neural circuits related to reading and cognitive control.

Interestingly, differences between the regions involved in FC and the cognitive control region (ACC) were found between children with RD and TRs. Whereas TRs showed a relationship between longer fixations and decreased FC between the ACC and language (superior temporal gyrus) and cognitive-abilities (entorhinal cortex) regions, children with RD showed a relationship between the ACC and only cognitive regions (inferior frontal gyrus). The literature suggests a critical role of the entorhinal cortex as a hub in a widespread network for memory and navigation (Moscovitch et al., 2005). The entorhinal cortex interacts with the hippocampus, which is critical for semantic and spatial memory, both of which are critical for reading (Moscovitch et al., 2005), and also controls the association of impulses from the eye and the ear (Freeman, 2004). The reading process involves synchronized visual and auditory information (Breznitz, 2006). Therefore, it may be that in TRs longer fixations are due to a synchronization failure, whereas longer fixations in children with RD are mainly due to a failure in the executive system. Further studies should be performed to map the association of different eye-movement measures (e.g., dilation, regressions, saccades) with neural circuits related to reading ability (i.e., cognitive control, language, and visual processing) (see Horowitz-Kraus et al., 2014, for ROI related to these abilities). This approach would potentially reveal the underlying neural circuits for reading challenges in both children with RD and TRs and the eye-movement patterns reflecting the activation and FC of these brain regions.

Limitations of the Study

The results of the current study should be considered taking into account the following limitations. First, despite the relatively high threshold used in this analysis (p < .005), the number of participants in this study was relatively small and therefore the results did not survive multiple comparisons when including all ROI in the brain (right and left BAs) and the repressors of interest (eye-movement measures) and no interest (six motion parameters) included in the analysis. Therefore these results should be considered exploratory and an additional study should employ a larger number of participants. Second, the analysis for the study was based on an a priori chosen ROI as a seed reflecting a key region related to cognitive control (i.e., the ACC), which therefore limited the results to regions correlated with this specific ROI. Future study should examine the FC with both the whole brain and the various cognitive-control networks (cingulo-opercular, frontoparietal, and default-mode networks). Third, we used single-word reading and not sentences reading, preventing the interpretation of our results at the contextual level, which is a more natural way to evaluate reading ability. Future study should compare eye-movement patterns and the FC related to these measures for both word reading and contextual reading in both children with RD and TRs.

CONCLUSIONS AND FUTURE STUDY

The current study is one of the first studies to associate FC during rest with eye-movement patterns in reading. In line with Pennington’s studies looking at profiles of children with RD with ADHD (Pennington, 2006; Pennington, Groisser, & Welsh, 1993; Welsh, Pennington, & Groisser, 1991; Willcutt et al., 2001; Willcutt et al., 2010), the population of children with RD might be subgrouped also into profiles of children suffering from challenges in different types of EF, which can be characterized by different profiles of eye-movement patterns and may require different clinical approach. This point should be examined in depth. Our results strengthen previous behavioral studies connecting eye-movement patterns with EF and provide neurobiological support for this connection in children. Additional research mapping the neural correlates for additional eye-movement measures may allow the use of fixation time as a biomarker for difficulties with reading in the future.

Supplementary Material

Table S1

Acknowledgments—

This study was supported by the Board of Trustee Award, Cincinnati Children’s Hospital Medical Center. The authors acknowledge J. Denise Wetzel for editing of the manuscript.

Footnotes

SUPPORTING INFORMATION

Additional supporting information may be found online in the Supporting Information section at the end of the article. Table S1 Motion parameters for typical readers and children with reading difficulties (independent t test results).

REFERENCES

  1. Ackerman PT, & Dykman RA (1993). Phonological processes, confrontational naming, and immediate memory in dyslexia. Journal ofLearningDisabilities, 26, 597–609. [DOI] [PubMed] [Google Scholar]
  2. Altemeier LE, Abbott RD, & Berninger VW (2008). Executive functions for reading and writing in typical literacy development and dyslexia. Journal of Clinical and Experimental Neuropsychology, 30, 588–606. 10.1080/13803390701562818 [DOI] [PubMed] [Google Scholar]
  3. Baddeley A, Logie R, Bressi S, Della Sala S, & Spinnler H (1986). Dementia and working memory. Quarterly Journal of Experimental Psychology Section A, 38, 603–618. [DOI] [PubMed] [Google Scholar]
  4. Badre D, Poldrack RA, Pare-Blagoev EJ, Insler RZ, & Wagner AD (2005). Dissociable controlled retrieval and generalized selection mechanisms in ventrolateral prefrontal cortex. Neuron, 47,907–918. 10.1016/j.neuron.2005.07.023 [DOI] [PubMed] [Google Scholar]
  5. Baria AT, Baliki MN, Parrish T, & Apkarian AV (2011). Anatomical and functional assemblies of brain BOLD oscillations. Journal of Neuroscience, 31, 7910–7919. 10.1523/jneurosci.1296-11.2011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Barkley RA, Grodzinsky G, & DuPaul GJ (1992). Frontal lobe functions in attention deficit disorder with and without hyperactivity: A review and research report. Journal of Abnormal Child Psychology, 20(2), 163–188. [DOI] [PubMed] [Google Scholar]
  7. Bednarek DB, Tarnowski A, & Grabowska A (2006). Latencies of stimulus-driven eye movements are shorter in dyslexic subjects. Brain and Cognition, 60, 64–69. [DOI] [PubMed] [Google Scholar]
  8. Behzadi Y, Restom K, Liau J, & Liu TT (2007). A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage, 37(1), 90–101. 10.1016/j.neuroimage.2007.04.042 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Bellocchi S, Muneaux M, Bastien-Toniazzo M, & Ducrot S (2013). I can read it in your eyes: What eye movements tell us about visuo-attentional processes in developmental dyslexia. Research in Developmental Disabilities, 34(1), 452–460. 10.1016/j.ridd.2012.09.002 [DOI] [PubMed] [Google Scholar]
  10. Breznitz Z (2006) Fluency in reading: Synchronization of processes.Mahwah, NJ: Lawrence Erlbaum. [Google Scholar]
  11. Breznitz Z, & Misra M (2003). Speed of processing of the visual-orthographic and auditory-phonological systems in adult dyslexics: The contribution of “asynchrony” to word recognition deficits. Brain and Language, 85, 486–502. [DOI] [PubMed] [Google Scholar]
  12. Brosnan M, Demetre J, Hamill S, Robson K, Shepherd H, & Cody G (2002). Executive functioning in adults and children with developmental dyslexia. Neuropsychologia, 40, 2144–2155. [DOI] [PubMed] [Google Scholar]
  13. Brown L, Sherbenou R, & Johnsen S (1997) Test of Nonverbal Intelligence (3rd ed). Austin, TX: Pro-Ed. [Google Scholar]
  14. Burgess PW, Dumontheil I, & Gilbert SJ (2007). The gateway hypothesis of rostral prefrontal cortex (area 10) function. Trends in Cognitive Sciences, 11(7), 290–298. 10.1016/j.tics.2007.05.004 [DOI] [PubMed] [Google Scholar]
  15. Byars AW, Holland SK, Strawsburg RH, Bommer W, Dunn RS, Schmithorst VJ, & Plante E (2002). Practical aspects of conducting large-scale functional magnetic resonance imaging studies in children. Journal ofChild Neurology, 17, 885–890. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Conners CK (1989) Conners Rating Scales manual. North Tonawanda, NY: Multi-Health Systems. [Google Scholar]
  17. Dellis DC, Kaplan E, & Kramer JH (2001) Delis-Kaplan Executive Function System. San Antonio, TX: Psychological Corporation. [Google Scholar]
  18. Dosenbach NU, Fair DA, Cohen AL, Schlaggar BL, & Petersen SE (2008). A dual-networks architecture of top-down control. Trends in Cognitive Sciences, 12(3), 99–105. 10.1016/j.tics.2008.01.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Dunn LM, & Dunn DM (2007) Peabody Picture Vocabulary Test. Bloomington, MN: NCS Pearson. [Google Scholar]
  20. Facoetti A, Paganoni P, & Lorusso ML (2000). The spatial distribution of visual attention in developmental dyslexia. Experimental Brain Research, 132, 531–538. [DOI] [PubMed] [Google Scholar]
  21. Facoetti A, Paganoni P, Turatto M, Marzola V, & Mascetti GG (2000). Visual-spatial attention in developmental dyslexia. Cortex, 36,109–123. [DOI] [PubMed] [Google Scholar]
  22. Fair DM, Dosenbach NU, Petersen SE, & Schlaggar BL (2012). Resting state studies on the development of control systems. In Posner MI (Ed.), Cognitive neuroscience of attention. (2nd ed, pp. 291–311). New York, NY: Guilford Press. [Google Scholar]
  23. Franceschini S, Gori S, Ruffino M, Viola S, Molteni M, & Facoetti A (2013). Action video games make dyslexic children read better. Current Biology, 23, 462–466. 10.1016/j.cub.2013.01.044 [DOI] [PubMed] [Google Scholar]
  24. Freeman WJ (2004). How and why brains create MEANING-T from sensory information. Dynamics of Intentionality, 14(2), 1–9. [Google Scholar]
  25. Gabrieli JD, & Norton ES (2012). Reading abilities: Importance ofvisual-spatial attention. Current Biology, 22(9), R298–R299. 10.1016/j.cub.2012.03.041 [DOI] [PubMed] [Google Scholar]
  26. Gilbert SJ, Spengler S, Simons JS, Steele JD, Lawrie SM, Frith CD, & Burgess PW (2006). Functional specialization within rostral prefrontal cortex (area 10): A meta-analysis. Journal of Cognitive Neuroscience, 18, 932–948. 10.1162/jocn.2006.18.6.932 [DOI] [PubMed] [Google Scholar]
  27. Gooch D, Snowling M, & Hulme C (2011). Time perception, phonological skills and executive function in children with dyslexia and/or ADHD symptoms. Journal of Child Psychology and Psychiatry, 52(2), 195–203. 10.1111/)1469-7610.2010.02312.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hawelka S, Gagl B, & Wimmer H (2010). A dual-route perspective on eye movements of dyslexic readers. Cognition, 115, 367–379. 10.1016/j.cognition.2009.11.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Helland T, & Asbjornsen A (2000). Executive functions in dyslexia. Child Neuropsychology, 6(1), 37–48. 10.1076/0929-7049(200003)6:1;1-B;FT037 [DOI] [PubMed] [Google Scholar]
  30. Hodgson TL, Bajwa A, Owen AM, & Kennard C (2000). The strategic control of gaze direction in the Tower-of-London task. Journal of Cognitive Neuroscience, 12, 894–907. [DOI] [PubMed] [Google Scholar]
  31. Holmqvist K, Nystrom M, Andersson R, Dewhurst R, Jarodzka H, & van de Weijer J (2011) Eye tracking: A comprehensive guide to methods and measures. Oxford, UK: Oxford University Press. [Google Scholar]
  32. Horowitz-Kraus T, & Breznitz Z (2008). An error-detection mechanism in reading among dyslexic and regular readers—An ERP study. Clinical Neurophysiology, 119, 2238–2246. 10.1016/j.clinph.2008.06.009 [DOI] [PubMed] [Google Scholar]
  33. Horowitz-Kraus T, & Breznitz Z (2009). Can the error detection mechanism benefit from training the working memory? A comparison between dyslexics and controls—An ERP study. PLoS One, 4(9), e7141 10.1371/journal.pone.0007141 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Horowitz-Kraus T, & Breznitz Z (2014). Can reading rate acceleration improve error monitoring and cognitive abilities underlying reading in adolescents with reading difficulties and in typical readers? Brain Research, 1544, 1–14. 10.1016/j.brainres.2013.11.027 [DOI] [PubMed] [Google Scholar]
  35. Horowitz-Kraus T, Toro-Serey C, & DiFrancesco M (2015). Increased resting-state functional connectivity in the cingulo-opercular cognitive-control network after intervention in children with reading difficulties. PLoS One, 10, e0133762. 10.1371/journal.pone.0133762 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Horowitz-Kraus T, Vannest JJ, Kadis D, Cicchino N, Wang YY, & Holland SK (2014). Reading acceleration training changes brain circuitry in children with reading difficulties. Brain and Behavior: A Cognitive Neuroscience Perspective, 4, 886–902. 10.1002/brb3.281 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Hutzler F, & Wimmer H (2004). Eye movements of dyslexic children when reading in a regular orthography. Brain and Language, 89(1), 235–242. 10.1016/s0093-934x(03)00401-2 [DOI] [PubMed] [Google Scholar]
  38. Ihnen SK, Petersen SE, & Schlaggar BL (2013). Separable roles for attentional control sub-systems in reading tasks: A combined behavioral and fMRI study. Cerebral Cortex, 25, 1198–1218. 10.1093/cercor/bht313 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. International Dyslexia Association (IDA). (2011, November). Definition of dyslexia—Based on the initial definition of the Research Committee of the Orton Dyslexia Society, former name of the IDA, done in 1994. Paper presented at the International Dyslexia Association, Chicago, IL: Retrieved from http://interdys.org/DyslexiaDefinition.html [Google Scholar]
  40. Jainta S, & Kapoula Z (2011). Dyslexic children are confronted with unstable binocular fixation while reading. PLoS One, 6(4), e18694. 10.1371/journal.pone.0018694 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Jonides J, & Nee DE (2006). Brain mechanisms of proactive interference in working memory. Neuroscience, 139(1), 181–193. 10.1016/j.neuroscience.2005.06.042 [DOI] [PubMed] [Google Scholar]
  42. Luria AR (1973) The working brain. New York, NY: Basic Books. [Google Scholar]
  43. Luthi M, Henke K, Gutbrod K, Nyffeler T, Chaves S, & Muri RM (2014). In your eyes only: Deficits in executive functioning after frontal TMS reflect in eye movements. Frontiers in Behavioral Neuroscience, 8, 7 10.3389/fnbeh.2014.00007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Manly T, Robertson IH, Anderson V, & Nimmo-Smith I (1999) TEA-Ch: The Test of Everyday Attention for Children manual. Bury St Edmunds, UK: Thames Valley Test Company Limited. [Google Scholar]
  45. McConkie GW, Reddix MD, & Zola D (1992). Perception and cognition in reading: Where is the meeting point? In Rayner K (Ed.), Eye movements and visual cognition (pp. 293–303). New York, NY: Springer. [Google Scholar]
  46. Menghini D, Carlesimo GA, Marotta L, Finzi A, & Vicari S (2010). Developmental dyslexia and explicit long-term memory. Dyslexia, 16(3), 213–225. 10.1002/dys.410 [DOI] [PubMed] [Google Scholar]
  47. Mennie N, Hayhoe M, & Sullivan B (2007). Look-ahead fixations: Anticipatory eye movements in natural tasks. Experimental Brain Research, 179(3), 427–442. 10.1007/s00221-006-0804-0 [DOI] [PubMed] [Google Scholar]
  48. Moscovitch M, Rosenbaum RS, Gilboa A, Addis DR, Westmacott R, Grady C, … Nadel L. (2005). Functional neuroanatomy of remote episodic, semantic and spatial memory: A unified account based on multiple trace theory. Journal of Anatomy, 207(1), 35–66. 10.1111/j.1469-7580.2005.00421.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Nee DE, Brown JW, Askren MK , Berman MG, Demiralp E, Krawitz A, & Jonides J. (2013). A meta-analysis of executive components of working memory. Cerebral Cortex, 23, 264–282. 10.1093/cercor/bhs007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Nyhus E, & Barcelo F (2009). The Wisconsin Card Sorting Test and the cognitive assessment of prefrontal executive functions: A critical update. Brain and Cognition, 71, 437–451. 10.1016/j.bandc.2009.03.005 [DOI] [PubMed] [Google Scholar]
  51. Olsen A (2012). The Tobii I-VTFixation Filter: Algorithm description: Tobii eye tracking research web site. Retrieved from http://www.tobii.com/en/eye-tracking-research/global/library/white-papers/the-tobii-i-vt-fixation-filter/
  52. Pennington BF (2006). From single to multiple deWcit models of developmental disorders. Cognition, 101, 385–413. [DOI] [PubMed] [Google Scholar]
  53. Pennington BF, Groisser D, & Welsh MC (1993). Contrasting cognitive deficits in attention deficit hyperactivity disorder versus reading disability. Developmental Psychology, 29, 511–523. [Google Scholar]
  54. Power JD, Cohen AL, Nelson SM, Wig GS, Barnes KA, Church JA, … Petersen SE. (2011). Functional net-work organization of the human brain. Neuron, 72, 665–678. 10.1016/j.neuron.2011.09.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Prado C, Dubois M, & Valdois S (2007). The eye movements of dyslexic children during reading and visual search: Impact of the visual attention span. Vision Research, 47, 2521–2530. 10.1016/j.visres.2007.06.001 [DOI] [PubMed] [Google Scholar]
  56. Rayner K (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124, 372–422. [DOI] [PubMed] [Google Scholar]
  57. Rayner K, & Pollatsek A (1989) The psychology of reading. Engle-wood Cliffs, NJ: Prentice-Hall. [Google Scholar]
  58. Rayner K, Schotter ER, Masson ME, Potter MC, & Treiman R (2016). So much to read, so little time: How do we read, and can speed reading help? Psychological Science in the Public Interest, 17(1), 4–34. 10.1177/1529100615623267 [DOI] [PubMed] [Google Scholar]
  59. Reiter A, Tucha O, & Lange KW (2005). Executive functions in children with dyslexia. Dyslexia, 11(2), 116–131. https://doiorg/10.1002/dys.289 [DOI] [PubMed] [Google Scholar]
  60. Scarborough HS (1998). Early identification of children at risk for reading disabilities: Phonological awareness and some other promising predictors In Shapiro BK, Accardo PJ, & Capute AJ (Eds.), Specific reading disability: A view of the spectrum (pp. 75–119). Timonium, MD: York Press. [Google Scholar]
  61. Shaywitz SE (2003) Overcoming dyslexia: A new and complete science-based program for reading problems at any level. New York, NY: Alfred A. Knopf. [Google Scholar]
  62. Shaywitz SE, & Shaywitz BA (2008). Paying attention to reading: The neurobiology of reading and dyslexia. Development and Psychopathology, 20,1329–1349. 10.1017/S0954579408000631 [DOI] [PubMed] [Google Scholar]
  63. Stoet G, & Snyder LH (2007). Correlates of stimulus-response congruence in the posterior parietal cortex. Journal of Cognitive Neuroscience, 19(2), 194–203. 10.1162/jocn.2007.19.2.194 [DOI] [PubMed] [Google Scholar]
  64. Tavor I, Parker JO , Mars RB, Smith SM , Behrens TE, & Jbabdi S. (2016). Task-free MRI predicts individual differences in brain activity during task performance. Science, 352(6282), 216–220. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Thevenaz P, Ruttimann UE, & Unser M (1998). A pyramid approach to subpixel registration based on intensity. IEEE Transactions on Image Processing, 7(1), 27–41. 10.1109/83.650848 [DOI] [PubMed] [Google Scholar]
  66. Tiffin-Richards MC, Hasselhorn M, Woerner W, Rothenberger A, & Banaschewski T (2008). Phonological short-term memory and central executive processing in attention-deficit/hyperactivity disorder with/without dyslexia—Evidence of cognitive overlap. Journal of Neural Transmission, 115(2), 227–234. 10.1007/s00702-007-0816-3 [DOI] [PubMed] [Google Scholar]
  67. Torgesen JK, Wagner RK, & Rashotte CA (1999) Test of Word Reading Efficiency (TOWRE). Austin, TX: Pro-Ed. [Google Scholar]
  68. Van der Mark S, Bucher K, Maurer U, Schulz E, Brem S, Buck- elmuller J, … Brandeis D (2009). Children with dyslexialack multiple specializations along the visual word-form (VWF) system. Neuroimage, 47, 1940–1949. 10.1016/j.neuroimage.2009.05.021 [DOI] [PubMed] [Google Scholar]
  69. Wagner RK, Torgesen JK, & Rashotte CA (1999) Comprehensive Test of Phonological Processing (CTOPP). Austin, TX: Pro-Ed. [Google Scholar]
  70. Wagner RK, Torgesen JK, Rashotte CA, & Pearson NA (2010) Test of Silent Reading Efficiency and Comprehension. Austin, TX: Pro-Ed. [Google Scholar]
  71. Wass S, Porayska-Pomsta K, & Johnson MH (2011). Training attentional control in infancy. Current Biology, 21,1543–1547. 10.1016/j.cub.2011.08.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Wechsler D (1999) Wechsler Intelligence Scale for Children—Third edition (WISC-III). New York, NY: Psychological Corporation. [Google Scholar]
  73. Welsh M, Pennington B, & Groisser D (1991). A normative developmental-study of executive function—A window on prefrontal function in children. Developmental Neuropsychology, 7(2), 131–149. [Google Scholar]
  74. Whitfield-Gabrieli S, & Nieto-Castanon A (2012). CONN: A functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connectivity, 2(3), 125–141. 10.1089/brain.2012.0073 [DOI] [PubMed] [Google Scholar]
  75. Willcutt EG, Betjemann RS, McGrath LM, Chhabildas NA, Olson RK, DeFries JC, & Pennington BF (2010). Etiology and neuropsychology of comorbidity between RD and ADHD: The case for multiple-deficit models. Cortex, 46, 1345–1361. 10.1016/j.cortex.2010.06.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Willcutt EG, Pennington BF, Boada R, Ogline JS, Tunick RA, Chhabildas NA, & Olson RK (2001). A comparison of the cognitive deficits in reading disability and attention-deficit/hyperactivity disorder. Journal of Abnormal Psychology, 110, 157–172. [DOI] [PubMed] [Google Scholar]
  77. Woodcock RW, & Johnson MB (1989) Woodcock-Johnson Psycho-Educational Battery-Revised (WJ-R). Allen, TX: Developmental Learning Materials. [Google Scholar]

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Supplementary Materials

Table S1
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