Reading difficulties (RDs) are characterized by slow and inaccurate reading as well as additional challenges in cognitive control (i.e., executive functions, especially in working memory, inhibition, and visual attention). Despite evidence demonstrating differences in these readers’ language and visual processing abilities, white matter differences associated with executive functions (EFs) difficulties in children with RDs are scarce. Structural correlates for reading and EFs in 8- to 12-year-old children with RDs versus typical readers (TRs) were examined using diffusion tensor imaging (DTI) data. Results suggest that children with RDs showed significantly lower reading and EF abilities versus TRs. Lower fractional anisotropy (FA) in left temporo-parietal tracts was found in children with RDs, who also showed positive correlations between reading and working memory and switching/inhibition scores and FA in the left superior longitudinal fasciculus (SLF). FA in the left SLF predicted working memory performance mediated by reading ability in children with RDs but not TRs. Our findings support alterations in white matter tracts related to working memory, switching/inhibition, and overall EF challenges in children with RDs and the linkage between working memory difficulties and FA alterations in the left SLF in children with RDs via reading.

Our findings support alterations in white matter tracts related to working memory, a component of executive functions, and reading in children with reading difficulties as well as the differences in association between white matter alterations in tracts associated with reading deficiency and working memory, switching/inhibition, and overall executive function challenges in children with reading difficulties.

Reading Difficulty: Definition and Characteristics

Reading difficulty (RD) is defined as a specific reading disorder that affects individuals with average and above-average intelligence; cannot be attributed to environmental factors or neurological, psychiatric conditions, or brain damage; and is prevalent in about 10% of children in the United States (Lyon et al., 2003). The prevailing theory for the cause of RD is the phonological processing deficit, which stands for impairment in awareness of the spoken sounds in language and mentally mapping letters to representations of the corresponding speech sounds (phonemes) (Snowling, 1995). Moreover, several studies have recently pointed to additional challenges in cognitive control, or executive functions (EFs), in this group of readers (Horowitz-Kraus & Breznitz, 2014; Levinson et al., 2018; Meiri et al., 2019; Mercedes & Cutting, 2020; Pennington et al., 1993; Wang et al., 2012). More specifically, challenges in working memory (Fostick & Revah, 2018; Ram-Tsur et al., 2008; Smith-Spark & Fisk, 2007; Zhao et al., 2015), switching/inhibition (Levinson et al., 2018), and visual attention (Facoetti & Molteni, 2001) were reported. These findings are supported by the extension of the Simple View of Reading model (Cutting et al., 2015), suggesting that in addition to linguistic (phonological) and decoding/reading abilities, EFs play a central role in the reading comprehension process. More specifically, working memory was found to affect the linguistic and word decoding domains (Cirino et al., 2019; Kim, 2020; Spencer et al., 2020; Taboada Barber et al., 2021) with additional work supporting the role of switching/inhibition in word decoding as well (Spencer et al., 2020).

Executive Functions: Definition and Relation to Reading Ability

EF is an umbrella term describing a set of high-order cognitive abilities that control and regulate functions and behaviors (Anderson, 2002). Basic EFs are involved in cognitive processes such as flexibility, working memory, attention control, and cognitive inhibition (Diamond, 2013; Lehto et al., 2003; Miyake et al., 2000). Studies have shown that children with RDs have deficits in working memory tasks in both verbal and visual domains and impairments in inhibition and shifting (Horowitz-Kraus, 2014; Reiter et al., 2005; Varvara et al., 2014). Reports suggest that the challenges in EFs in children with RDs continue into adulthood (Brosnan et al., 2002; Smith-Spark et al., 2016), interfering with their everyday routine and reading (Smith-Spark et al., 2016). Mechanistically, the ability to read newly encountered words relies on the ability to decode letters visually (i.e., utilizing visual attention abilities) to their corresponding sounds, maintain them in working memory, assemble them into a word, and match the semantic meaning to it in an automatic manner (see also Horowitz-Kraus, 2016). Switching/shifting between decoding and word recognition is also essential for fluent reading and reading comprehension (Spencer et al., 2020), which may explain how difficulties in these EFs contribute to reading challenges in children with RDs.

Neurobiological Correlates of Reading and EF Dysfunction in Individuals With RDs

It is traditionally suggested that the neural reading network consists of three discrete left hemisphere regions: the temporo-parietal, inferior-frontal (inferior-frontal gyrus), and occipito-temporal (fusiform gyrus, aka the Visual Word Form Area) cortices and is associated with word recognition (Martin et al., 2015; Norton et al., 2015). The temporo-parietal region is related to language and phonological processing and comprehension, whereas the inferior-frontal regions are related to production and semantic processing (Dehaene, 2009). Interestingly enough, recent studies have pointed at the participation of frontal cortices related to EFs in reading: the dorsal-anterior cingulate cortex and the dorsolateral prefrontal cortex related to error monitoring and working memory, respectively, where greater activation was related to increased reading abilities (Buchweitz et al., 2019). This increased activation was extended to EF networks related to these regions (i.e., cingulo-opercular and fronto-parietal), which showed increased networks connectivity related to increased reading performance (Horowitz-Kraus & Holland, 2015; Patael et al., 2018; Turkeltaub et al., 2003). It was suggested that an engagement of frontal cortices and networks associated with EFs during reading was related to a compensatory mechanism for these readers (Horowitz-Kraus, 2014; Horowitz-Kraus et al., 2015a, 2015b; Horowitz-Kraus & Holland, 2015; Horowitz-Kraus et al., 2014).

Traditionally, the left hemisphere played a central role in intact reading abilities. Previous research suggested that typical reading (TR) involves activation of the aforementioned regions, mainly in the left hemisphere (Backes et al., 2002; Horowitz-Kraus et al., 2013; Shaywitz et al., 2002; Silani et al., 2005; Simos et al., 2002), whereas individuals with RDs showed activation in the right homologous regions (Backes et al., 2002; Shaywitz et al., 2002; Simos et al., 2002; Waldie et al., 2013). Additionally, an altered activation in the left hemisphere has also been consistently reported in individuals with RDs. Specifically, decreased activation of the left inferior parietal, superior temporal, middle and inferior temporal, and fusiform regions in adults (Richlan et al., 2009; Turkeltaub et al., 2003), children (Richlan et al., 2011), and prereaders at risk for RDs (Vandermosten et al., 2016) were related to lower reading skills (Hoeft et al., 2007).

Studies using diffusion tensor imaging (DTI) provide complementary information to these functional neuroimaging studies. The “classical” reading tracts that connect the reading-related gray matter regions mentioned above include the arcuate fasciculus (AF)—with studies reporting reduced fractional anisotropy (FA) in the left AF—negatively correlated with single-word reading skills (Gullick & Booth, 2015) and the left superior longitudinal fasciculus (SLF)—with findings of positive correlation between FA in the SLF and reading scores (Carter et al., 2009; Klingberg et al., 2000). In addition to the AF and SLF, the left inferior longitudinal fasciculus (ILF) connecting the posterior inferior temporal gyrus to the anterior and medial temporal lobe areas, plays a crucial part in organizing visual stimuli about words according to their lexical meaning (Anwander et al., 2007; Cummine et al., 2015; Qi et al., 2015; Yeatman et al., 2012a, 2012b; Yeatman et al., 2013). This tract has also been strongly linked to language and reading (Hoeft et al., 2011; Myers et al., 2014; Saygin et al., 2013; Yeatman et al., 2012a, 2012b).

The AF and SLF have also been linked to cognitive abilities in healthy children and adolescents, including attention and spatial working memory (Urger et al., 2015; Vestergaard et al., 2011). Traditionally, the left SLF had been implicated in working memory abilities (Vestergaard et al., 2011), with reported associations between spatial working memory and FA in the left SLF in healthy children 7–13 years old (Vestergaard et al., 2011). Similarly, FA in the left SLF was predictive of verbal working memory in healthy children 8–19 years old (østby et al., 2011) and healthy adults (Koshiyama et al., 2020). Taken together, the existing evidence shows altered structural connectivity related to reading in children with RDs. However, it is unclear whether there are shared white matter tracts for reading and EFs in general and working memory, visual attention and shifting/inhibition in particular in RDs.

Therefore, the current study aims to determine the structural differences in children with RDs versus TR related to their reading and EF abilities, focusing on tracts associated with both. Structural connectivity studies examining the neurobiological correlates for EFs have pointed to the left SLF, AF, and the ILF as the key structures associated with reading alterations (Farah et al., 2020; Muetzel et al., 2008; Pavuluri et al., 2009; Peters et al., 2014; Tamnes et al., 2012), and therefore these tracts were chosen in the current study. We hypothesized that children with RDs will demonstrate decreased reading and EF abilities (including working memory, switching/inhibition and visual attention) and alterations in tracts related to these abilities. We also hypothesized that variability in white matter microstructure pertaining to reading and EF abilities, more specifically the left SLF in children with RDs (per Wang et al., 2016), would predict working memory ability mediated by reading ability, indicating shared structural components. We suspect that better working memory ability in children with RDs will be influenced by better reading, echoing previous reports (Demoulin & Kolinsky, 2016).

Participants

Children with RDs (n = 22, 10 females) and typical readers (TRs) (n = 24, 12 females) participated in the study, ages 8–12 years old with no significant mean age difference between the groups (t(44) = −1.183, p = 0.243). All participants were monolingual native English speakers with no neurological impairments or psychiatric history. Attention difficulties were exclusionary for this study and were determined using the Conners questionnaires (Goyette et al., 1978). Children in the RD group were diagnosed with RDs prior to study participation and demonstrated a standard score of −1 and below in at least two reading tasks from the reading tests in the “behavioral measures” list (following Kovelman et al., 2012; also see Behavioral Measures section). Both groups participated in the behavioral and neuroimaging sessions. Informed consents and assents were signed by parents and participants. The Institutional Review Board reviewed and approved the study in Cincinnati Children’s Hospital Medical Center, Ohio, USA.

Behavioral Measures

General abilities.

General nonverbal abilities were measured using the Test of Nonverbal Intelligence (TONI) (Brown et al., 2010), and verbal abilities were measured using the Peabody Picture Vocabulary Test (PPVT) (Dunn & Dunn, 2007).

Reading measures.

Reading and reading-related abilities were assessed using the following reading assessments: (1) phonological processing, using the Elision subtest, Comprehensive Test of Phonological Processing (CTOPP; Wagner et al., 2013); (2) automatic word reading using the sight word efficiency (SWE), Test of Word Reading Efficiency (TOWRE; Torgesen et al., 1999); (3) automatic phonological decoding efficiency subtest (TOWRE; Torgesen et al., 1999); (4) nontimed word reading using the orthographical subtest (letter-word) (WJ III; Woodcock et al., 2001); and (5) nontimed decoding using the word-attack subtest (WJ III; Woodcock et al., 2001).

Executive functions measures.

EF abilities were assessed using the following age-normalized measures: (1) working memory (Digit Span, the Wechsler Intelligence Scale for Children, WISC; Wechsler, 2012); (2) switching/inhibition (DKEF Stroop, Color-Word Condition 3; Delis et al., 2001); (3) visual-spatial attention (Test of Everyday Attention for Children, TEA-Ch, Sky Search subtest; Manly et al., 1999); (4) overall EF skills (the Behavior Rating Inventory of Executive Function, BRIEF; Gioia et al., 2000).

Behavioral Data Analysis

Independent samples t tests were used examine differences on the reading and EF tests between the two reading groups.

Neuroimaging Data Acquisition and Procedures

Data were acquired using a 3-Tesla Philips Achieva scanner. A three-dimensional T1-weighted inversion recovery prepared anatomical whole-brain scan, MPRAGE sequence was acquired with the following parameters: TR/TE = 8.1/3.7 ms; matrix 256 × 224; 160 slices in the sagittal direction, 1.0-mm isotropic voxels; scan time: 5 m, 15 s. Diffusion data was acquired using a single-shot spin-echo, echo planar imaging with TR/TE = 6,652.446/82.60 ms, 61 gradient directions plus 7 b0 images, and b-value of 1,000 s/mm2, slice thickness = 2 mm, voxel size = 2 × 2 × 2 mm, field of view = 224 × 120 × 224 mm, for a total scan time of 7 m, 25 s.

Participants were acclimated and desensitized to prepare for comfortable testing inside the MRI Philips scanner (for the desensitization procedure, see Kraus & Horowitz-Kraus, 2022). Elastic straps were attached to either side of the head-coil apparatus, and a headband strap was put on the child’s forehead to control head motion. Headphones equipped with a built-in microphone were used to establish verbal communication between the child and the study coordinator, and video monitoring was used to assess the child’s state and movement inside the scanner.

Neuroimaging Data Analyses

Preprocessing of the T1-weighted structural image included bias correction using the N4 algorithm (Tustison et al., 2010) as executed in the Advanced Normalization Tools toolbox. Removal of nonbrain tissue was completed using the Oxford Center for Functional MRI of the Brain brain extraction tool (Smith, 2002), after which the brain mask was applied to the original structural volume and bias correction using the N4 algorithm was repeated on the brain-extracted volume. Segmentation was performed using the FSL FIRST to produce a 3-class tissue (Zhang et al., 2001).

DTI data were processed using the Vistalab diffusion MRI software suite (Stanford Vision and Imaging Science and Technology) as part of the open-source mrDiffusion package: https://white.stanford.edu/software. DTI images were aligned to the motion-corrected mean of the nondiffusion-weighted (b = 0) images by using a rigid body algorithm. Following realignment, DTI images were then resampled to 2-mm isotropic voxels with eddy current and motion correction using a seventh-order b-spline algorithm based on statistical parameter mapping. Finally, the diffusion tensors were fitted to the resampled DTI data by using a least-squares fit and the RESTORE (robust estimation of tensors by outlier rejection) algorithm (Chang et al., 2005). The diffusion tensor model produces measures describing the diffusion characteristics of each voxel. Eigenvalues (λ1, λ2, λ3) from the diffusion tensor were used to compute FA (√(1/2)√((λ1 − λ2)2 + (λ3 − λ2)2 + (λ3 − λ1)2)/√(λ12 + λ22 + λ32) (Pierpaoli et al., 1996).

Using an in-house pipeline utilizing automatic fiber quantification (AFQ) software tool (Yeatman et al., 2012a, 2012b), the target white matter tracts in the brain of each participant were identified. Target white matter tracts included the AF, SLF, and ILF. Several processing steps were applied for each of the tracts including (1) whole-brain tractography, (2) region-of-interest (ROI)-based fiber tract segmentation and cleaning using a statistical outlier rejection algorithm, and (3) FA quantification. For tracking of target fasciculi, an initial seed point within the white matter mask was detected, and streamlines in both directions along the principal diffusion axes were traced. Tracing was terminated under two standard criteria: (1) if the FA at the current location was less than 0.2 and (2) if the minimum angle between the last path segment and next step direction is greater than 30 degrees (Yeatman et al., 2012a, 2012b). Each fiber tract was sampled to 99 equidistant nodes, and the spread of fibers at each node was represented as a three-dimensional Gaussian distribution. Fibers that were more than 5 standard deviations from the mean of the tract were removed. This procedure was repeated until no fiber outliers existed. Next, a quantification phase was conducted following the initial AFQ processes, where the diffusion properties were calculated by interpolating the FA values along the trajectory of the fiber group. Finally, mean and variance were calculated ‘within’ and ‘between’ groups for FA in each tract in the current analysis. The characteristics evaluated for each node and tract were later used to compare the groups.

Diffusion Data Analysis: Group Comparison

Fractional anisotropy tract profile comparison between the groups.

For the statistical analyses of FA, cluster-based analyses were conducted for the 99 nodes between each tract’s defining ROIs (Yeatman et al., 2012a, 2012b). The analyses were performed using independent samples t tests comparing RD and TR groups.

To compare tract profiles between the RD and TR groups, multiple two-tailed t tests were conducted, and a permutation-based multiple comparisons correction (Nichols & Holmes, 2002) was used to calculate clusters with adjacent t tests with significant differences between the groups. Significance was corrected for multiple comparisons, and the corrected alpha was set to 0.05 (Nichols & Holmes, 2002; Yeatman et al., 2012a, 2012b). The clusters that were reported showed the following criteria: (1) all neighboring nodes were significantly different (p < 0.05, uncorrected) between the groups and (2) the cluster of significant values was larger than the critical cluster size generated by the permutation-based multiple comparisons correction (Nichols & Holmes, 2002; Yeatman et al., 2012a, 2012b).

Correlations Between Diffusion and Behavioral Measures in the Reading and EF-Related Tracts

Using a Statistical Package for the Social Sciences (SPSS for Windows, version 24), correlation analyses were conducted between the average FA in clusters showing significant group differences and the participants’ (1) reading measure (TOWRE-SWE) and (2) EFs for each group separately. Normality was assessed based on Kolmogorov–Smirnoff (Corder & Foreman, 2009). Pearson correlation was reported when both correlation variables had normal distribution, and Spearman correlation was used for cases where the measures were not normally distributed in a specific group. Multiple correlations were corrected using a Bonferroni correction (p < 0.05). Fisher z-transformations assessed the significance of differences between the correlation coefficients measured in each group separately and the differences in correlations between the groups (Sheskin, 2004).

Moderated Mediation Analysis

A moderated mediation analysis was conducted to test if the relationship between FA in the left SLF and working memory performance was mediated by reading ability and moderated by group (RD vs. TR) (Hayes, 2013). Therefore, our hypothetical model links FA in the left SLF to working memory via an indirect path that includes reading ability (as a mediator), as a function of group (moderator), resulting in a conditional indirect effect. Bootstrapping bias-corrected confidence intervals were used with 10,000 bootstrap samples to test the null hypothesis (i.e., the indirect effect of FA in the left SLF on working memory is not significant). When zero falls within the confidence intervals, the null hypothesis is accepted. PROCESS macro (version 3.4) for SPSS was used for data analysis (Hayes, 2013). The index of moderated mediation was used (Hayes, 2015), and bootstrapping bias-corrected intervals was used to test its significance. Figure 1 illustrates the second stage moderated mediation model to be tested.

Figure 1.

Conceptual moderated mediation model for the predicting role of white matter microstructure on working memory ability mediated by reading ability and moderated by group. Predictor: FA of the left SLF, Outcome: working memory, mediator: reading ability, moderator: reading group.

Figure 1.

Conceptual moderated mediation model for the predicting role of white matter microstructure on working memory ability mediated by reading ability and moderated by group. Predictor: FA of the left SLF, Outcome: working memory, mediator: reading ability, moderator: reading group.

Close modal

Behavioral Measures

General abilities.

No significant differences between children with RDs and TRs were observed in general cognitive abilities and attention abilities (measured by the Conners). Decreased general language ability (as measured by the PPVT test) was found in children with RDs versus TRs (see Table 1; data was corrected for multiple comparisons using a Bonferroni correction p < 0.05).

Table 1.

Baseline behavioral reading and executive functions scores for children with reading difficulties and typical readers

Cognitive ability  Children with RDs Mean (SD) (A)TR Mean (SD) (B)Contrast (direction of results)T score
General ability General verbal ability Language ability (PPVT, standard score) 100.59 (8.87) 110.95 (12.79) A < B 3.05* 
General nonverbal ability Non-linguistic ability (TONI, percentile) 53.19 (22.69) 54.87 (21.77) A < B −0.25 
Reading Word-level reading Word reading (TOWRE, scaled score) 79.41 (13.12) 105.96 (13.07) A < B 6.87*** 
Nonword reading (TOWRE, scaled score) 78.86 (11.13) 108.79 (11.57) A < B 8.92*** 
Word reading, nontimed (WJ, letter-word, standard score) 86.09 (12.61) 112 (9.98) A < B 7.76*** 
Phonological processing Phonological processing (CTOPP, Ellison, scaled score) 7.27 (2.1) 11.83 (2.33) A < B 6.95*** 
Executive functions Working memory WISC Digit Span (standard score) 8.77 (2.11) 9.96 (1.79) A < B 2.03* 
Visual-Spatial/auditory Attention Teach TEA-Ch Sky Search Attention Test (scaled score) 6.77 (2.62) 9.67 (3.52) A < B 3.14** 
Switching/inhibition DKEF Color-Word Condition 3 (standard score) 8.55 (3.02) 14.63 (3.52) A < B 4.37*** 
General EF score BRIEF General Cognitive (parental reported) (t score) 68.57 (9.69) 42.54 (10.65) A > B 3.1** 
Cognitive ability  Children with RDs Mean (SD) (A)TR Mean (SD) (B)Contrast (direction of results)T score
General ability General verbal ability Language ability (PPVT, standard score) 100.59 (8.87) 110.95 (12.79) A < B 3.05* 
General nonverbal ability Non-linguistic ability (TONI, percentile) 53.19 (22.69) 54.87 (21.77) A < B −0.25 
Reading Word-level reading Word reading (TOWRE, scaled score) 79.41 (13.12) 105.96 (13.07) A < B 6.87*** 
Nonword reading (TOWRE, scaled score) 78.86 (11.13) 108.79 (11.57) A < B 8.92*** 
Word reading, nontimed (WJ, letter-word, standard score) 86.09 (12.61) 112 (9.98) A < B 7.76*** 
Phonological processing Phonological processing (CTOPP, Ellison, scaled score) 7.27 (2.1) 11.83 (2.33) A < B 6.95*** 
Executive functions Working memory WISC Digit Span (standard score) 8.77 (2.11) 9.96 (1.79) A < B 2.03* 
Visual-Spatial/auditory Attention Teach TEA-Ch Sky Search Attention Test (scaled score) 6.77 (2.62) 9.67 (3.52) A < B 3.14** 
Switching/inhibition DKEF Color-Word Condition 3 (standard score) 8.55 (3.02) 14.63 (3.52) A < B 4.37*** 
General EF score BRIEF General Cognitive (parental reported) (t score) 68.57 (9.69) 42.54 (10.65) A > B 3.1** 

Note. PPVT, Peabody Picture Vocabulary Test; TONI, Test of Nonverbal Intelligence; TOWRE, Test of Word Reading Efficiency; WJ, Woodcock-Johnson; CTOPP, Comprehensive Test of Phonological Processing; DKEF, Delis-Kaplan Executive Function System; BRIEF, Behavior Rating Inventory of Executive Function; TEA-Ch, Test of Everyday Attention for Children. Results are presented as mean (standard deviation). *p < 0.05; **, p < 0.01; ***, p < 0.001. Note that a higher score in the BRIEF represents lower abilities.

Reading abilities.

Children with RDs demonstrated significantly decreased reading abilities versus TR (timed and nontimed word and nonword reading subtests, reading fluency subtests, and phonological processing tests). See Table 1 for these results.

Executive functions.

Children with RDs showed significantly decreased EF abilities in several subdomains: switching/inhibition (DKEF subtest), working memory (WISC Digit Span test), and overall EF skills (BRIEF), and visual attention (Sky Search visual attention test from the TEA-Ch battery). Note that for the BRIEF questionnaire, a higher score is related to a lower EF ability. See Table 1.

Correlations Between Reading Abilities and Behavioral Executive Functions Across Both Groups

Overall, greater reading scores were associated with greater EF abilities across both groups. Specifically, significant correlations were found between automatic reading ability (TOWRE-SWE) and inhibition (r = 0.598, p < 0.001, measured by the Color-Word subtest, DKEF); working memory (r = 0.55, p < 0.001, measured by Digit Span test, WISC); General EF score (r = −0.5, p < 0.001, GEC test, BRIEF); and visual-spatial attention (r = 0.398, p = 0.006, Sky Search Attention test, TEA-Ch). Data were corrected for multiple comparisons using a Bonferroni correction at p < 0.05.

Diffusion Tensor Imaging Results—Comparison of Fractional Anisotropy Values in Reading and EF-Related Tracts Between the Groups

Fractional Anisotropy: Overall, children with RDs showed significantly lower FA in the left AF, ILF, and SLF, compared to TRs. See Table 2 and Figure 2 for the number of significant nodes per tract and the comparisons between the groups.

Table 2.

Node cluster-based t test analysis for white matter tract DTI measures in children with reading difficulties and typical readers

TractNumber of nodes with significant (p < 0.05) difference between children with RDs and TRs (location of cluster by node number)Contrast (direction of results)
Left AF 20 (79–99) TR > RD 
Left SLF 9 (91–99) TR > RD 
Left ILF 28 (26–53) TR > RD 
TractNumber of nodes with significant (p < 0.05) difference between children with RDs and TRs (location of cluster by node number)Contrast (direction of results)
Left AF 20 (79–99) TR > RD 
Left SLF 9 (91–99) TR > RD 
Left ILF 28 (26–53) TR > RD 

Note. Location of node clusters with a significant difference in fractional anisotropy values between children with reading difficulties (RDs) and typical readers (TRs). Results are shown as a total number of nodes with significant differences (specific location along the tract denoted by the node numbers). The contrast column shows the directionality between the groups in these clusters. AF, arcuate fasciculus; SLF, superior longitudinal fasciculus; ILF, inferior longitudinal fasciculus.

Figure 2.

Location of significant node clusters with significant difference between children with reading difficulties and typical readers (red) in the left arcuate fasciculus (A), left superior longitudinal fasciculus (B) and left inferior longitudinal fasciculus (C).

Figure 2.

Location of significant node clusters with significant difference between children with reading difficulties and typical readers (red) in the left arcuate fasciculus (A), left superior longitudinal fasciculus (B) and left inferior longitudinal fasciculus (C).

Close modal

Correlations of Reading Ability and Fractional Anisotropy—Correlations Between Reading (TOWRE-SWE) and Fractional Anisotropy Cluster Values

Higher reading ability was associated with higher FA values in the left SLF in children with RDs (r = 0.712, p < 0.001). No significant correlation was found between FA in the left SLF and reading in TRs (r = −0.132, p = 0.270). See Table 3.

Table 3.

Correlations between reading, executive functions, and fractional anisotropy cluster values in children with reading difficulties and typical readers in temporo-parietal white matter tracts

 Behavioral abilityBehavioral MeasureLeft AF r (p)Left SLF r (p)Left ILF r (p)
Reading Difficulties Reading TOWRE-SWE −0.34 (0.06) 0.712 (<0.0010.04 (0.42) 
Switching/inhibition DKEF Color-Word Condition 3 −0.36 (0.05) 0.31 (0.08) 0.32 (0.07) 
Working Memory WISC Digit Span −0.06 (0.39) 0.68 (0.002−0.23 (0.15) 
General EF BRIEF GEC 0.012 (0.47) 0.14 (0.28) −0.19 (0.21) 
Visual-Spatial Attention TEA-Ch Sky Search −0.16 (0.24) 0.24 (0.14) 0.12 (0.29) 
Typical Readers Reading TOWRE-SWE 0.02 (0.46) −0.13 (0.27) 0.16 (0.23) 
Switching/inhibition DKEF Color-Word Condition 3 −0.02 (0.46) −0.3 (0.08) 0.14 (0.25) 
Working Memory WISC Digit Span −0.06 (0.39) 0.18 (0.19) 0.02 (0.46) 
General EF BRIEF GEC 0.09 (0.33) −0.16 (0.22) 0.36 (0.04) 
Visual-Spatial Attention TEA-Ch Sky Search 0.21 (0.16) 0.29 (0.08) 0.32 (0.07) 
 Behavioral abilityBehavioral MeasureLeft AF r (p)Left SLF r (p)Left ILF r (p)
Reading Difficulties Reading TOWRE-SWE −0.34 (0.06) 0.712 (<0.0010.04 (0.42) 
Switching/inhibition DKEF Color-Word Condition 3 −0.36 (0.05) 0.31 (0.08) 0.32 (0.07) 
Working Memory WISC Digit Span −0.06 (0.39) 0.68 (0.002−0.23 (0.15) 
General EF BRIEF GEC 0.012 (0.47) 0.14 (0.28) −0.19 (0.21) 
Visual-Spatial Attention TEA-Ch Sky Search −0.16 (0.24) 0.24 (0.14) 0.12 (0.29) 
Typical Readers Reading TOWRE-SWE 0.02 (0.46) −0.13 (0.27) 0.16 (0.23) 
Switching/inhibition DKEF Color-Word Condition 3 −0.02 (0.46) −0.3 (0.08) 0.14 (0.25) 
Working Memory WISC Digit Span −0.06 (0.39) 0.18 (0.19) 0.02 (0.46) 
General EF BRIEF GEC 0.09 (0.33) −0.16 (0.22) 0.36 (0.04) 
Visual-Spatial Attention TEA-Ch Sky Search 0.21 (0.16) 0.29 (0.08) 0.32 (0.07) 

Note. Reading, TOWRE sight word reading, inhibition – DKEF condition 3, working memory – WISC Digit Span, general EFs – BRIEF GEC, visual attention – TEA-Ch Sky Search. Results are presented as correlation coefficients (p value). AF, arcuate fasciculus; SLF, superior longitudinal fasciculus; ILF, inferior longitudinal fasciculus. Significant results are bolded. Note that a negative correlation coefficient for the correlation of FA with the BRIEF test was noted as a positive correlation with ability since lower BRIEF scores suggest less EF difficulties.

Children with RDs versus TRs: The correlation coefficients calculated in the two groups differed significantly (Fisher’s z = 3.43, p < 0.001); that is, children with RDs showed a greater positive correlation between FA in the left SLF and reading compared to TRs. See Table 4.

Table 4.

Fisher z-transformations of correlation coefficients in fractional anisotropy between children with reading difficulties (r1) and typical readers (r2)

Behavioral ability/tractBehavioral MeasureLeft AF z (p)Left SLF z (p)Left ILF z (p)
Reading TOWRE-SWE −1.2 (0.11) 3.23 (0.001) −0.37 (0.35) 
Working Memory WISC Digit Span −0.00 (0.5) 2.14 (0.016) −0.8 (0.21) 
Switching/inhibition DKEF Color-Word Condition 3 −1.11 (0.13) 1.99 (0.02) 0.61 (0.27) 
Visual attention TEA-Ch Sky Search −1.19 (0.12) −0.19 (0.42) −0.64 (0.26) 
General EF BRIEF GEC −0.24 (0.4) 0.96 (0.17) −1.79 (0.037) 
Behavioral ability/tractBehavioral MeasureLeft AF z (p)Left SLF z (p)Left ILF z (p)
Reading TOWRE-SWE −1.2 (0.11) 3.23 (0.001) −0.37 (0.35) 
Working Memory WISC Digit Span −0.00 (0.5) 2.14 (0.016) −0.8 (0.21) 
Switching/inhibition DKEF Color-Word Condition 3 −1.11 (0.13) 1.99 (0.02) 0.61 (0.27) 
Visual attention TEA-Ch Sky Search −1.19 (0.12) −0.19 (0.42) −0.64 (0.26) 
General EF BRIEF GEC −0.24 (0.4) 0.96 (0.17) −1.79 (0.037) 

Note. Reading, Test of word reading efficiency (TOWRE), working memory – Wechsler Intelligence Scale for Children (WISC), Digit Span task. Results are presented as z-score (p value). AF, arcuate fasciculus; SLF, superior longitudinal fasciculus; ILF, inferior longitudinal fasciculus. Significant results surviving multiple comparisons are bolded.

Correlations Between Executive Functions and Fractional Anisotropy—Correlations Between Executive Functions Measures and Fractional Anisotropy Cluster Values

A significant positive correlation was found between working memory and FA in the left SLF cluster in children with RDs (r = 0.612, p = 0.005) but not in TRs (r = 0.184, p = 0.195). In the left ILF, a trend was observed; however, it did not reach significance following the control for false discovery rate. See Table 3.

For children with RDs versus TRs, A direct comparison of the correlation coefficient values between the groups using Fisher’s z-transformation was conducted. The results suggest that the magnitude of the correlation coefficient for children with RDs significantly exceeded that of TRs in working memory (left SLF cluster) and cognitive flexibility (left ILF cluster). See Table 4.

Moderated Mediation Analysis

Table 5 summarizes the overall model (regression coefficients, standard errors, t value, and significance).

Table 5.

Statistics of the moderated mediation model

 bSETP
Outcome variable: Reading variable 
Constant 49.18 5.23 9.38 >0.001 
FA, left SLF (predictor) 298.71 140.04 2.04 0.042 
Group (moderator) 29.83 3.26 9.12 >0.001 
FA, left SLF × Group (interaction) −176.89 84.88 −2.08 0.043 
F(3, 42) = 29.40, P = 0.00, R2 = 0.67 
Outcome variable: Working memory 
Constant 4.42 1.24 3.56 0.0009 
FA, left SLF (predictor) 4.05 5.94 0.68 0.498 
Reading (predictor) 0.053 0.012 4.14 0.0002 
F(2, 43) = 8.96, P = 0.0006, R2 = 0.29 
 bSETP
Outcome variable: Reading variable 
Constant 49.18 5.23 9.38 >0.001 
FA, left SLF (predictor) 298.71 140.04 2.04 0.042 
Group (moderator) 29.83 3.26 9.12 >0.001 
FA, left SLF × Group (interaction) −176.89 84.88 −2.08 0.043 
F(3, 42) = 29.40, P = 0.00, R2 = 0.67 
Outcome variable: Working memory 
Constant 4.42 1.24 3.56 0.0009 
FA, left SLF (predictor) 4.05 5.94 0.68 0.498 
Reading (predictor) 0.053 0.012 4.14 0.0002 
F(2, 43) = 8.96, P = 0.0006, R2 = 0.29 

Higher FA in the left SLF was associated with higher reading score (b = 298.71, p < 0.05). Higher FA in the left SLF was not directly associated with higher working memory scores (direct effect; b = 4.05, p = 0.498). The group variable significantly moderated the relationship between SLF and reading (b = −176.89, p = 0.043). However, the interaction only estimated the effect of FA in left SLF on reading by group and did not quantify the relationship between the moderator and the indirect effect.

Therefore, a formal test of moderated mediation was conducted, given by the index of moderated mediation (Hayes, 2015). The indirect effect of FA in the left SLF on working memory through timed word reading was dependent on the group and proved significant, as the bootstrap confidence interval (CI) of the index of moderated mediation did not contain zero (index = −9.46, S.E. = 5.22; 95% CI −.02, −1.78).

Table 6 presents the conditional indirect effect at two values of the dichotomous moderator: zero (RD group) and 1 (TR group). The findings indicated that higher FA in the left SLF led to higher working memory scores through higher reading scores only in the RD group.

Table 6.

Conditional indirect effects of FA on working memory through reading ability at values of the moderator (group)

GroupEffectBoot SE95% CISignificance
0 (RD) 6.51 3.71 1.34, 15.76 Significant 
1 (TR) −2.94 3.07 −9.79, 2.401 Nonsignificant 
GroupEffectBoot SE95% CISignificance
0 (RD) 6.51 3.71 1.34, 15.76 Significant 
1 (TR) −2.94 3.07 −9.79, 2.401 Nonsignificant 

The goal of the current study was to determine the shared structural alterations associated with EFs and reading in children with RDs. Per our hypotheses, the results demonstrated that children with RDs showed lower reading and EF abilities associated with decreased FA in the left SLF and ILF. Furthermore, our results support previous studies pointing at reduced FA in the left AF and SLF (dorsal pathway, phonology, and working memory-related) and ILF (ventral pathway, semantic/orthography related) (Rimrodt et al., 2010; Steinbrink et al., 2008; Su et al., 2018; Vandermosten et al., 2012, 2015). Support for the inferiority of children with RDs utilizing their left hemisphere was also found in the current study: children with RDs have shown lower FA in left temporo-parietal regions compared to TR. However, those with RDs who read better utilized their left hemisphere more: a higher positive correlation of reading and EFs with FA in the left SLF and ILF. These results will be discussed in depth in the context of the “extended” Simple View of Reading model (Cutting et al., 2015), outlining the involvement of EFs, and especially working memory, and switching/inhibition in the reading process in these readers.

The Relations Between Executive Functions and Reading in Children with RDs

Children with RDs have shown lower EF abilities correlated with their reading. This echoes previous studies that examined the role of EFs in children with RDs in general and in reading in particular (Brady et al., 1983; Jorm, 1979; Reiter et al., 2005; Varvara et al., 2014). Our study was built upon previous findings, which demonstrated that reading abilities in children with RDs are related to decreased EF ability compared to their TR counterparts (Bailey et al., 2018; Farah et al., 2019; Haft et al., 2019; Horowitz-Kraus et al., 2014; Mercedes & Cutting, 2020). The findings in the current study strengthen the “extended” Simple View of Reading model (Cutting et al., 2015), outlining the involvement of EFs in the reading process, especially the connection between working memory, switching/inhibition, and word decoding/recognition (Spencer et al., 2020). A recent study has demonstrated how an EF-based reading training had a positive effect on EFs, reading, and increased functional connectivity between neural circuits, supporting both reading and EF (fusiform gyrus and the dorsal part of the anterior cingulate cortex) in children with RDs (Horowitz-Kraus & Holland, 2015). This further supports the reliance of word reading on EFs in children with RDs and their close association to their reading difficulties. Future studies are warranted to examine the effect of an EF-based reading intervention on tracts related to reading and EFs in children with RDs and TRs.

Reduced Engagement of the Left Hemisphere in Children With RDs

Our results suggest that children with RDs demonstrated lower FA in left temporo-parietal tracts compared to children with TR. In support of our results, the findings of alteration in structural connectivity in the corpus callosum, forceps major, and vertical occipital fasciculus in participants with RDs versus TRs were previously reported in Finnish as well as in Chinese speakers (specifically in the left inferior fronto-occipital fasciculus, cerebellar pathways, and thalamo-pontine tracts and the posterior isthmus and anterior splenium of the corpus callosum) (Sihvonen et al., 2021; Wang et al., 2019) . However, whereas these studies examined the correlations between these alterations with phonological processing (English) and Chinese characters and auditory processing in the Chinese cohort, our study extends these findings also to include the correlation between SLF and working memory and switching/inhibition abilities.

It is important to mention, though, that a recent activation likelihood estimation meta-analysis on adult and pediatric populations focusing on voxel-based analysis and employing more drastic corrections for multiple comparisons reported no reliable differences between children with RDs and TRs in FA (Moreau et al., 2018). In addition to the rigor and correction resulting from a whole-brain analysis, this study also included a wide range of age groups, which have variable reading abilities and disorders and might have diminished the difference between groups in that study and reported in the current one. Previous studies documented decreased activation/hypoactivation in left temporo-parietal and right prefrontal gray matter regions associated with the reading network in individuals with RDs (Hoeft et al., 2011; Waldie et al., 2013). In addition to brain activation, individuals of all ages with RDs also showed decreased gray matter volume and altered sulci patterns in left occipito-temporal and temporo-parietal brain regions compared with TRs (Hoeft et al., 2007; Im et al., 2016; Pernet et al., 2009; Richlan et al., 2009). These reported findings might be related to the reduced FA in the left hemisphere found in the current study in readers with RDs; however, this should be further examined. A joint functional MRI-DTI study is needed to verify this point.

Our results also show a higher positive correlation between reading, working memory, switching/inhibition, and FA in the left SLF in children with RDs compared to TRs. The correlation comparisons provide structural support to the previously suggested role of the left hemisphere in reading and EF abilities (Barbey et al., 2012; Gonzalez et al., 2014; Hunter & Sparrow, 2012; Illingworth & Bishop, 2009; Leonard & Eckert, 2008; Shaywitz et al., 1998). Our findings suggest that higher FA in left temporo-parietal tracts (ILF and SLF) among children with RDs is related to better reading and EF abilities in this population. These results are complemented by previous studies showing adjacent gray matter volume indices in the left occipito-temporal and temporo-parietal areas in children with RDs, which correlate positively with reading and reading-related skills (He et al., 2013; Kronbichler et al., 2008). The correlations found between the FA in the SLF and working memory scores is supported by other findings related to the role of the left inferior parietal lobule in working memory/verbal memory processing (e.g., Borst & Anderson, 2013). The researchers reported a correspondence between updating working memory and activation in the inferior parietal lobule. As the SLF interconnects the inferior parietal regions with the frontal lobe and subserves fronto-parietal network crucial for efficient working memory (and EFs), our results correspond with Borst and Anderson regarding the relations of the SLF to working memory and switching/inhibition abilities. Moreover, the connection between the ILF and EFs, echoes previous findings (Fjell et al., 2016) and can be explained by the anatomy of the ILF connecting the occipital and more anterior brain regions through the temporal lobe, all associated with parts of the reading network (Cohen & Dehaene, 2009; Dehaene, 2009) and EF networks (Dosenbach et al., 2008). It would be interesting to conduct a multimodal functional-structural MRI study to confirm the functional alterations in reading and EF networks with the structural data in children with RDs. Importantly, the lack of association between the selected white matter tracts and EFs and reading in TRs, may point at alternative network recruitment needed to excel in these abilities among this population. It might be that for these readers, word reading level is relatively automatic and therefore does not demand the recruitment of EFs. However, the utilization of EF and reading-related neural circuits might be needed for contextual reading and reading comprehension (Meri et al., 2020). This was also observed in Meri et al. (2020) in an fMRI-based study focusing on reading comprehension and EF networks in TRs and children with RDs.

Mediating Effect of Reading on the Relation Between FA in the Left SLF and Working Memory in Children with RDs

The data of the current study provide support for poor reading ability as a common neuropsychological deficit that links FA in the left SLF and working memory ability among children with RDs. The results indicated that FA in the left SLF was related to reading ability, which in turn influenced working memory ability, and that this indirect effect was moderated by the reading group (RD vs. TR). More specifically, the indirect effect of FA in the left SLF on working memory through reading was significant only in children with RDs but not in the TR group. Per our hypothesis, better working memory ability in children with RDs was influenced by better reading, as learning to read might shape immediate memory (Demoulin & Kolinsky, 2016), also suggested by Nick Ellis about 30 years ago (Ellis, 1990). In beginner TRs, the intensive practice of decoding might enhance cumulative rehearsal (a strategy used in verbal memory), which in turn might lead to better sequential order memory performance. Additionally, the emergence of phonemic awareness and of orthographic representations might enhance the quality and precision of the language representations, which, in turn, would improve the encoding and retrieval of item information (Demoulin & Kolinsky, 2016). It is possible that children with RDs in the current study might be using strategies of beginning readers. Hence, better reading ability influenced better working memory predicted by FA in the left SLF. In contrast, TRs in the current study did not show this mediating effect of reading on the relationship between FA in the left SLF and working memory ability. The mediation analysis was also conducted while using SLF, working memory, and reading in other directions to test whether working memory mediated the relationship between FA in the SLF and reading ability, but no significant results were found. Hence, we can conclude that the only meaningful relations between the variables is when reading ability is a mediator for the relationship between FA in the left SLF and working memory ability.

Limitations

Our results should be considered with the following limitation. Even though AFQ provides a method to assess variance within tracts, using the tensors model in AFQ entails discarding the small branches in the tract and crossing fibers in a voxel. Future studies utilizing more sophisticated methods for diffusivity analysis algorithms such as neurite orientation dispersion and density imaging (NORDI) (Barritt et al., 2018), as well as CHARMED, AxCaliber, or ActiveAx (Assaf & Alexander, 2014) are warranted.

Conclusions

In summary, our results show the localized white matter tract differences between children with RDs and TRs overall and in relation to EFs and reading. These findings provide structural support to the involvement of EFs and especially of working memory, switching/inhibition in the extended Simple View of Reading model, and specifically in relation to single-word reading.

The authors would like to thank the families participating in the current study.

Rola Farah: Formal analysis; Visualization; Writing – original draft; Writing – review & editing. Noam Glukhovsky: Writing – original draft; Writing – review & editing. Keri Rosch: Writing – review & editing. Tzipi Horowitz-Kraus: Conceptualization; Data curation; Funding acquisition; Methodology; Project administration; Resources; Supervision; Writing – review & editing.

Tzipi Horowitz-Kraus, National Institute of Child Health and Human Development (https://dx.doi.org/10.13039/100000071), Award ID: HD086011.

     
  • Diffusion tensor imaging (DTI):

    An advanced, noninvasive magnetic resonance imaging modality focused on the study of water diffusion in white matter fibers; provides information about the microstructure of the fibers, including orientation, degree of myelination and axonal density.

  •  
  • Fractional anisotropy (FA):

    Fractional anisotropy is a common measure often used in diffusion imaging and often used to quantify white matter integrity; is thought to reflect fiber density, axonal diameter, and fiber myelination with values that range from 0 (highly isotropic—poor white matter integrity) to 1 (highly anisotropic—good white matter integrity).

  •  
  • Automatic fiber quantification (AFQ):

    A popular software that identifies, automatically, major fiber tracts in the brain and quantifies tissue properties at multiple locations along the tract length, hence creating a Tract Profile.

  •  
  • Permutation-based multiple comparisons correction:

    A nonparametric permutation approach that provides a flexible approach to account for the multiple comparisons problem implicit in the standard neuroimaging data analysis by utilizing a locally pooled (smoothed) variance estimate.

  •  
  • Fisher z-transformation:

    The Fisher z-transformation is a formula to transform the sampling distribution of Pearson’s r (i.e., the correlation coefficient) so that it becomes normally distributed and can be used to calculate a confidence interval for Pearson’s correlation coefficient.

  •  
  • Moderated mediation analysis:

    The process of uncovering the relationship between a dependent (X) and independent (Y) variable that is transmitted through a mediator (M) variable and is conditional on values of a moderating variable (W).

Anderson
,
P.
(
2002
).
Assessment and development of executive function (EF) during childhood
.
Child Neuropsychology
,
8
(
2
),
71
82
. ,
[PubMed]
Anwander
,
A.
,
Tittgemeyer
,
M.
,
von Cramon
,
D. Y.
,
Friederici
,
A. D.
, &
Knosche
,
T. R.
(
2007
).
Connectivity-based parcellation of Broca’s area
.
Cerebral Cortex
,
17
(
4
),
816
825
. ,
[PubMed]
Assaf
,
Y.
, &
Alexander
,
D. C.
(
2014
).
Advanced methods to study white matter microstructure
. In
Quantitative MRI of the spinal cord
(pp.
156
163
).
Amsterdam, the Netherlands
:
Elsevier
.
Backes
,
W.
,
Vuurman
,
E.
,
Wennekes
,
R.
,
Spronk
,
P.
,
Wuisman
,
M.
,
van Engelshoven
,
J.
, &
Jolles
,
J.
(
2002
).
Atypical brain activation of reading processes in children with developmental dyslexia
.
Journal of Child Neurology
,
17
(
12
),
867
871
. ,
[PubMed]
Bailey
,
S. K.
,
Aboud
,
K. S.
,
Nguyen
,
T. Q.
, &
Cutting
,
L. E.
(
2018
).
Applying a network framework to the neurobiology of reading and dyslexia
.
Journal of Neurodevelopmental Disorders
,
10
(
1
),
37
. ,
[PubMed]
Barbey
,
A. K.
,
Colom
,
R.
,
Solomon
,
J.
,
Krueger
,
F.
,
Forbes
,
C.
, &
Grafman
,
J.
(
2012
).
An integrative architecture for general intelligence and executive function revealed by lesion mapping
.
Brain
,
135
(
Pt 4
),
1154
1164
. ,
[PubMed]
Barritt
,
A. W.
,
Gabel
,
M. C.
,
Cercignani
,
M.
, &
Leigh
,
P. N.
(
2018
).
Emerging magnetic resonance imaging techniques and analysis methods in amyotrophic lateral sclerosis
.
Frontiers in Neurology
,
9
,
1065
. ,
[PubMed]
Borst
,
J. P.
, &
Anderson
,
J. R.
(
2013
).
Using model-based functional MRI to locate working memory updates and declarative memory retrievals in the fronto-parietal network
.
Proceedings of the National Academy of Sciences
,
110
(
5
),
1628
1633
. ,
[PubMed]
Brady
,
S.
,
Shankweiler
,
D.
, &
Mann
,
V.
(
1983
).
Speech perception and memory coding in relation to reading ability
.
Journal of Experimental Child Psychology
,
35
(
2
),
345
367
. ,
[PubMed]
Brosnan
,
M.
,
Demetre
,
J.
,
Hamill
,
S.
,
Robson
,
K.
,
Shepherd
,
H.
, &
Cody
,
G.
(
2002
).
Executive functioning in adults and children with developmental dyslexia
.
Neuropsychologia
,
40
(
12
),
2144
2155
. ,
[PubMed]
Brown
,
L.
,
Sherbenou
,
R. J.
, &
Johnsen
,
S. K.
(
2010
).
Test of nonverbal intelligence: TONI-4
.
Austin, TX
:
Pro-ed
.
Buchweitz
,
A.
,
Costa
,
A. C.
,
Toazza
,
R.
,
de Moraes
,
A. B.
,
Cara
,
V. M.
,
Esper
,
N. B.
, …
Franco
,
A. R.
(
2019
).
Decoupling of the occipitotemporal cortex and the brain’s default-mode network in dyslexia and a role for the cingulate cortex in good readers: A brain imaging study of brazilian children
.
Developmental Neuropsychology
,
44
(
1
),
146
157
. ,
[PubMed]
Carter
,
J. C.
,
Lanham
,
D. C.
,
Cutting
,
L. E.
,
Clements-Stephens
,
A. M.
,
Chen
,
X.
,
Hadzipasic
,
M.
, …
Kaufmann
,
W. E.
(
2009
).
A dual DTI approach to analyzing white matter in children with dyslexia
.
Psychiatry Research
,
172
(
3
),
215
219
. ,
[PubMed]
Chang
,
L. C.
,
Jones
,
D. K.
, &
Pierpaoli
,
C.
(
2005
).
RESTORE: Robust estimation of tensors by outlier rejection
.
Magnetic Resonance in Medicine
,
53
(
5
),
1088
1095
. ,
[PubMed]
Cirino
,
P. T.
,
Miciak
,
J.
,
Ahmed
,
Y.
,
Barnes
,
M. A.
,
Taylor
,
W. P.
, &
Gerst
,
E. H.
(
2019
).
Executive function: Association with multiple reading skills
.
Reading and Writing
,
32
(
7
),
1819
1846
. ,
[PubMed]
Cohen
,
L.
, &
Dehaene
,
S.
(
2009
).
Ventral and dorsal contributions to word reading
. In
E. B. M S
Gazzaniga
et al
(Eds.),
The cognitive neurosciences
(pp.
789
804
).
Cambridge, MA
:
Massachusetts Institute of Technology Press
.
Corder
,
G. W.
, &
Foreman
,
D. I.
(
2009
).
Nonparametric statistics for non-statisticians: A step-by-step approach
.
Hoboken, NJ
:
Wiley
.
Cummine
,
J.
,
Dai
,
W.
,
Borowsky
,
R.
,
Gould
,
L.
,
Rollans
,
C.
, &
Boliek
,
C.
(
2015
).
Investigating the ventral-lexical, dorsal-sublexical model of basic reading processes using diffusion tensor imaging
.
Brain Structure and Function
,
220
(
1
),
445
455
. ,
[PubMed]
Cutting
,
L.
,
Bailey
,
S.
,
Swett
,
K.
, &
Barquero
,
L. A.
(
2015
).
Neurobiological basis of word recognition and reading comprehension: Distinctions, overlaps, and implications for instruction and intervention
. In
C. C.
McCardle
(Ed.),
Reading intervention: From research to practice to research
.
Baltimore, MD
:
Brookes Publishing
.
Dehaene
,
S.
(
2009
).
Reading in the brain: The new science of how we read
.
New York, NY
:
Penguin
.
Delis
,
D. C.
,
Kaplan
,
E.
, &
Kramer
,
J. H.
(
2001
).
Delis-Kaplan executive function system (D-KEFS): Examiner’s manual: Flexibility of thinking, concept formation, problem solving, planning, creativity, impluse control, inhibition
.
Toronto, Canada
:
Pearson
.
Demoulin
,
C.
, &
Kolinsky
,
R.
(
2016
).
Does learning to read shape verbal working memory?
Psychonomic Bulletin & Review
,
23
(
3
),
703
722
. ,
[PubMed]
Diamond
,
A.
(
2013
).
Executive functions
.
Annual Review of Psychology
,
64
,
135
168
. ,
[PubMed]
Dosenbach
,
N. U.
,
Fair
,
D. A.
,
Cohen
,
A. L.
,
Schlaggar
,
B. L.
, &
Petersen
,
S. E.
(
2008
).
A dual-networks architecture of top-down control
.
Trends in Cognitive Sciences
,
12
(
3
),
99
105
. ,
[PubMed]
Dunn
,
L. M.
, &
Dunn
,
D. M.
(
2007
).
PPVT-4: Peabody picture vocabulary test
.
Minneapolis, MN
:
Pearson Assessments
.
Ellis
,
N.
(
1990
).
Reading, phonological skills and short-term memory: Interactive tributaries of development
.
Journal of Research in Reading
,
13
(
2
),
107
122
.
Facoetti
,
A.
, &
Molteni
,
M.
(
2001
).
The gradient of visual attention in developmental dyslexia
.
Neuropsychologia
,
39
(
4
),
352
357
. ,
[PubMed]
Farah
,
R.
,
Coalson
,
R. S.
,
Petersen
,
S. E.
,
Schlaggar
,
B. L.
, &
Horowitz-Kraus
,
T.
(
2019
).
Children use regions in the visual processing and executive function networks during a subsequent memory reading task
.
Cerebral Cortex
,
29
(
12
),
5180
5189
. ,
[PubMed]
Farah
,
R.
,
Tzafrir
,
H.
, &
Horowitz-Kraus
,
T.
(
2020
).
Association between diffusivity measures and language and cognitive-control abilities from early toddler’s age to childhood
.
Brain Structure and Function
,
225
,
1103
1122
. ,
[PubMed]
Fjell
,
A. M.
,
Sneve
,
M. H.
,
Grydeland
,
H.
,
Storsve
,
A. B.
, &
Walhovd
,
K. B.
(
2016
).
The disconnected brain and executive function decline in aging
.
Cerebral Cortex
,
27
(
3
),
2303
2317
. ,
[PubMed]
Fostick
,
L.
, &
Revah
,
H.
(
2018
).
Dyslexia as a multi-deficit disorder: Working memory and auditory temporal processing
.
Acta Psychologica
,
183
,
19
28
. ,
[PubMed]
Gioia
,
G. A.
,
Isquith
,
P. K.
,
Guy
,
S. C.
, &
Kenworthy
,
L. J.
(
2000
).
Test review: Behavior rating inventory of executive function
.
Child Neuropsychology
,
6
(
3
),
235
238
. ,
[PubMed]
Gonzalez
,
C. L.
,
Mills
,
K. J.
,
Genee
,
I.
,
Li
,
F.
,
Piquette
,
N.
,
Rosen
,
N.
, &
Gibb
,
R.
(
2014
).
Getting the right grasp on executive function
.
Frontiers in Psychology
,
5
,
285
. ,
[PubMed]
Goyette
,
C. H.
,
Conners
,
C. K.
, &
Ulrich
,
R. F.
(
1978
).
Normative data on revised Conners Parent and Teacher Rating Scales
.
Journal of Abnormal Child Psychology
,
6
(
2
),
221
236
. ,
[PubMed]
Gullick
,
M. M.
, &
Booth
,
J. R.
(
2015
).
The direct segment of the arcuate fasciculus is predictive of longitudinal reading change
.
Developmental Cognitive Neuroscience
,
13
,
68
74
. ,
[PubMed]
Haft
,
S.
,
Caballero
,
J. N.
,
Tanak
,
H.
,
Zekelman
,
N.
,
Cutting
,
L. E.
,
Uchiko
,
Y.
, &
Hoeft
,
F.
(
2019
).
Direct and indirect contributions of executive function to word decoding and reading comprehension in kindergarten
.
Learning and Individual Differences
,
76
,
101783
. ,
[PubMed]
Hayes
,
A. F.
(
2013
).
Introduction to mediation, moderation, and conditional process analysis: A regression-based approach
.
New York, NY
:
The Guilford Press
.
Hayes
,
A. F.
(
2015
).
An index and test of linear moderated mediation
.
Multivariate Behavioral Research
,
50
(
1
),
1
22
. ,
[PubMed]
He
,
Q.
,
Xue
,
G.
,
Chen
,
C.
,
Chen
,
C.
,
Lu
,
Z. L.
, &
Dong
,
Q.
(
2013
).
Decoding the neuroanatomical basis of reading ability: A multivoxel morphometric study
.
Journal of Neuroscience
,
33
(
31
),
12835
12843
. ,
[PubMed]
Hoeft
,
F.
,
McCandliss
,
B. D.
,
Black
,
J. M.
,
Gantman
,
A.
,
Zakerani
,
N.
,
Hulme
,
C.
, …
Gabrieli
,
J. D.
(
2011
).
Neural systems predicting long-term outcome in dyslexia
.
Proceedings of the National Academy of Sciences of the United States of America
,
108
(
1
),
361
366
. ,
[PubMed]
Hoeft
,
F.
,
Meyler
,
A.
,
Hernandez
,
A.
,
Juel
,
C.
,
Taylor-Hill
,
H.
,
Martindale
,
J. L.
, …
Gabrieli
,
J. D.
(
2007
).
Functional and morphometric brain dissociation between dyslexia and reading ability
.
Proceedings of the National Academy of Sciences of the United States of America
,
104
(
10
),
4234
4239
. ,
[PubMed]
Horowitz-Kraus
,
T.
(
2014
).
Pinpointing the deficit in executive functions in adolescents with dyslexia performing the Wisconsin card sorting test: An ERP study
.
Journal of Learning Disabilities
,
47
(
3
),
208
223
. ,
[PubMed]
Horowitz-Kraus
,
T.
(
2016
).
The role of executive functions in the reading process
. In
Reading fluency
(pp.
51
63
).
Cham, Switzerland
:
Springer
.
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
. ,
[PubMed]
Horowitz-Kraus
,
T.
, &
Holland
,
S. K.
(
2015
).
Greater functional connectivity between reading and error-detection regions following training with the reading acceleration program in children with reading difficulties
.
Annals of Dyslexia
,
65
(
1
),
1
23
. ,
[PubMed]
Horowitz-Kraus
,
T.
,
DiFrancesco
,
M.
,
Kay
,
B.
,
Wang
,
Y.
, &
Holland
,
S. K.
(
2015a
).
Increased functional connectivity of specific brain networks after reading training in dyslexic children
.
Clinical NeuroImage
,
8
,
619
630
. ,
[PubMed]
Horowitz-Kraus
,
T.
,
Toro-Serey
,
C.
, &
DiFrancesco
,
M.
(
2015b
).
Increased resting-state functional connectivity in the cingulo-opercular cognitive-control network after intervention in children with reading difficulties
.
PLoS One
,
10
(
7
),
e0133762
. ,
[PubMed]
Horowitz-Kraus
,
T.
,
Vannest
,
J. J.
,
Gozdas
,
E.
, &
Holland
,
S. K.
(
2014
).
Greater utilization of neural-circuits related to executive functions is associated with better reading: A longitudinal fMRI study using the verb generation task
.
Frontiers in Human Neuroscience
,
8
,
447
. ,
[PubMed]
Horowitz-Kraus
,
T.
,
Vannest
,
J. J.
, &
Holland
,
S. K.
(
2013
).
Overlapping neural circuitry for narrative comprehension and proficient reading in children and adolescents
.
Neuropsychologia
,
51
(
13
),
2651
2662
. ,
[PubMed]
Hunter
,
S. J.
, &
Sparrow
,
E. P.
(
2012
).
Executive function and dysfunction: Identification, assessment, and treatment
.
Cambridge, UK
:
Cambridge University Press
.
Illingworth
,
S.
, &
Bishop
,
D. V.
(
2009
).
Atypical cerebral lateralisation in adults with compensated developmental dyslexia demonstrated using functional transcranial Doppler ultrasound
.
Brain and Language
,
111
(
1
),
61
65
. ,
[PubMed]
Im
,
K.
,
Raschle
,
N. M.
,
Smith
,
S. A.
,
Ellen Grant
,
P.
, &
Gaab
,
N.
(
2016
).
Atypical sulcal pattern in children with developmental dyslexia and at-risk kindergarteners
.
Cerebral Cortex
,
26
(
3
),
1138
1148
. ,
[PubMed]
Jorm
,
A. F.
(
1979
).
The cognitive and neurological basis of developmental dyslexia: A theoretical framework and review
.
Cognition
,
7
(
1
),
19
33
. ,
[PubMed]
Kim
,
Y.-S. G.
(
2020
).
Simple but not simplistic: The simple view of reading unpacked and expanded
.
The Reading League Journal
,
1
(
2
),
15
34
.
Klingberg
,
T.
,
Hedehus
,
M.
,
Temple
,
E.
,
Salz
,
T.
,
Gabrieli
,
J. D.
,
Moseley
,
M. E.
, &
Poldrack
,
R. A.
(
2000
).
Microstructure of temporo-parietal white matter as a basis for reading ability: Evidence from diffusion tensor magnetic resonance imaging
.
Neuron
,
25
(
2
),
493
500
. ,
[PubMed]
Koshiyama
,
D.
,
Fukunaga
,
M.
,
Okada
,
N.
,
Morita
,
K.
,
Nemoto
,
K.
,
Yamashita
,
F.
, …
Hashimoto
,
R.
(
2020
).
Association between the superior longitudinal fasciculus and perceptual organization and working memory: A diffusion tensor imaging study
.
Neuroscience Letters
,
738
,
135349
. ,
[PubMed]
Kovelman
,
I.
,
Norton
,
E. S.
,
Christodoulou
,
J. A.
,
Gaab
,
N.
,
Lieberman
,
D. A.
,
Triantafyllou
,
C.
, …
Gabrieli
,
J. D.
(
2012
).
Brain basis of phonological awareness for spoken language in children and its disruption in dyslexia
.
Cerebral Cortex
,
22
(
4
),
754
764
. ,
[PubMed]
Kraus
,
D.
, &
Horowitz-Kraus
,
T.
(
2022
).
Functional MRI research involving healthy children: Ethics, safety, and recommended procedures
.
Acta Paediatrica
,
111
(
4
),
741
749
. ,
[PubMed]
Kronbichler
,
M.
,
Wimmer
,
H.
,
Staffen
,
W.
,
Hutzler
,
F.
,
Mair
,
A.
, &
Ladurner
,
G.
(
2008
).
Developmental dyslexia: Gray matter abnormalities in the occipitotemporal cortex
.
Human Brain Mapping
,
29
(
5
),
613
625
. ,
[PubMed]
Lehto
,
J. E.
,
Juujärvi
,
P.
,
Kooistra
,
L.
, &
Pulkkinen
,
L.
(
2003
).
Dimensions of executive functioning: Evidence from children
.
British Journal of Developmental Psychology
,
21
(
1
),
59
80
.
Leonard
,
C. M.
, &
Eckert
,
M. A.
(
2008
).
Asymmetry and dyslexia
.
Developmental Neuropsychology
,
33
(
6
),
663
681
. ,
[PubMed]
Levinson
,
O.
,
Hershey
,
A.
,
Farah
,
R.
, &
Horowitz-Kraus
,
T.
(
2018
).
Altered functional connectivity of the executive functions network during a stroop task in children with reading difficulties
.
Brain Connectivity
,
8
(
8
),
516
525
. ,
[PubMed]
Lyon
,
G. R.
,
Shaywitz
,
S. E.
, &
Shaywitz
,
B. A.
(
2003
).
A definition of dyslexia
.
Annals of Dyslexia
,
53
,
1
14
.
Manly
,
T.
,
Robertson
,
I.
,
Anderson
,
V.
, &
Nimmo-Smith
,
I.
(
1999
).
The test of everyday attention for children (TEA-Ch)
.
Chelmsford, UK
:
Thames Valley Test Company
.
Martin
,
A.
,
Schurz
,
M.
,
Kronbichler
,
M.
, &
Richlan
,
F.
(
2015
).
Reading in the brain of children and adults: A meta-analysis of 40 functional magnetic resonance imaging studies
.
Human Brain Mapping
,
36
(
5
),
1963
1981
. ,
[PubMed]
Meiri
,
R.
,
Levinson
,
O.
, &
Horowitz-Kraus
,
T.
(
2019
).
Altered association between executive functions and reading and math fluency tasks in children with reading difficulties compared with typical readers
.
Dyslexia
,
25
(
3
),
267
283
. ,
[PubMed]
Mercedes
,
S.
, &
Cutting
,
L. E.
(
2020
).
Relations among executive function, decoding, and reading comprehension: An investigation of sex differences
.
Discourse Processes
,
58
(
1
),
42
59
. ,
[PubMed]
Meri
,
R.
,
Farah
,
R.
, &
Horowitz-Kraus
,
T.
(
2020
).
Children with dyslexia utilize both top-down and bottom-up networks equally in contextual and isolated word reading
.
Neuropsychologia
,
147
,
107574
. ,
[PubMed]
Miyake
,
A.
,
Friedman
,
N. P.
,
Emerson
,
M. J.
,
Witzki
,
A. H.
,
Howerter
,
A.
, &
Wager
,
T. D.
(
2000
).
The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis
.
Cognitive Psychology
,
41
(
1
),
49
100
. ,
[PubMed]
Moreau
,
D.
,
Stonyer
,
J. E.
,
McKay
,
N. S.
, &
Waldie
,
K. E.
(
2018
).
No evidence for systematic white matter correlates of dyslexia: An activation likelihood estimation meta-analysis
.
Brain Research
,
1683
,
36
47
. ,
[PubMed]
Muetzel
,
R. L.
,
Collins
,
P. F.
,
Mueller
,
B. A.
,
Schissel
,
A. M.
,
Lim
,
K. O.
, &
Luciana
,
M.
(
2008
).
The development of corpus callosum microstructure and associations with bimanual task performance in healthy adolescents
.
NeuroImage
,
39
(
4
),
1918
1925
. ,
[PubMed]
Myers
,
C. A.
,
Vandermosten
,
M.
,
Farris
,
E. A.
,
Hancock
,
R.
,
Gimenez
,
P.
,
Black
,
J. M.
, …
Hoeft
,
F.
(
2014
).
White matter morphometric changes uniquely predict childrenʼs reading acquisition
.
Psychological Science
,
25
(
10
),
1870
1883
. ,
[PubMed]
Nichols
,
T. E.
, &
Holmes
,
A. P.
(
2002
).
Nonparametric permutation tests for functional neuroimaging: A primer with examples
.
Human Brain Mapping
,
15
(
1
),
1
25
. ,
[PubMed]
Norton
,
E. S.
,
Beach
,
S. D.
, &
Gabrieli
,
J. D. E.
(
2015
).
Neurobiology of dyslexia
.
Current Opinion in Neurobiology
,
30
,
73
78
. ,
[PubMed]
østby
,
Y.
,
Tamnes
,
C. K.
,
Fjell
,
A. M.
, &
Walhovd
,
K. B.
(
2011
).
Morphometry and connectivity of the fronto-parietal verbal working memory network in development
.
Neuropsychologia
,
49
(
14
),
3854
3862
. ,
[PubMed]
Patael
,
S. Z.
,
Farris
,
E. A.
,
Black
,
J. M.
,
Hancock
,
R.
,
Gabrieli
,
J. D. E.
,
Cutting
,
L. E.
, &
Hoeft
,
F.
(
2018
).
Brain basis of cognitive resilience: Prefrontal cortex predicts better reading comprehension in relation to decoding
.
PLoS One
,
13
(
6
),
e0198791
. ,
[PubMed]
Pavuluri
,
M. N.
,
Yang
,
S.
,
Kamineni
,
K.
,
Passarotti
,
A. M.
,
Srinivasan
,
G.
,
Harral
,
E. M.
, …
Zhou
,
X. J.
(
2009
).
Diffusion tensor imaging study of white matter fiber tracts in pediatric bipolar disorder and attention-deficit/hyperactivity disorder
.
Biological Psychiatry
,
65
(
7
),
586
593
. ,
[PubMed]
Pennington
,
B. F.
,
Groisser
,
D.
, &
Welsh
,
M. C.
(
1993
).
Contrasting cognitive deficits in attention deficit hyperactivity disorder versus reading disability
.
Developmental Psychology
,
29
,
511
523
.
Pernet
,
C.
,
Andersson
,
J.
,
Paulesu
,
E.
, &
Demonet
,
J. F.
(
2009
).
When all hypotheses are right: A multifocal account of dyslexia
.
Human Brain Mapping
,
30
(
7
),
2278
2292
. ,
[PubMed]
Peters
,
B. D.
,
Ikuta
,
T.
,
DeRosse
,
P.
,
John
,
M.
,
Burdick
,
K. E.
,
Gruner
,
P.
, …
Malhotra
,
A. K.
(
2014
).
Age-related differences in white matter tract microstructure are associated with cognitive performance from childhood to adulthood
.
Biological Psychiatry
,
75
(
3
),
248
256
. ,
[PubMed]
Pierpaoli
,
C.
,
Jezzard
,
P.
,
Basser
,
P. J.
,
Barnett
,
A.
, &
Di Chiro
,
G.
(
1996
).
Diffusion tensor MR imaging of the human brain
.
Radiology
,
201
(
3
),
637
648
. ,
[PubMed]
Qi
,
Z.
,
Han
,
M.
,
Garel
,
K.
,
San Chen
,
E.
, &
Gabrieli
,
J. D. E.
(
2015
).
White-matter structure in the right hemisphere predicts Mandarin Chinese learning success
.
Journal of Neurolinguistics
,
33
,
14
28
.
Ram-Tsur
,
R.
,
Faust
,
M.
, &
Zivotofsky
,
A. Z.
(
2008
).
Poor performance on serial visual tasks in persons with reading disabilities: Impaired working memory?
Journal of Learning Disabilities
,
41
(
5
),
437
450
. ,
[PubMed]
Reiter
,
A.
,
Tucha
,
O.
, &
Lange
,
K. W.
(
2005
).
Executive functions in children with dyslexia
.
Dyslexia
,
11
(
2
),
116
131
. ,
[PubMed]
Richlan
,
F.
,
Kronbichler
,
M.
, &
Wimmer
,
H.
(
2009
).
Functional abnormalities in the dyslexic brain: A quantitative meta-analysis of neuroimaging studies
.
Human Brain Mapping
,
30
(
10
),
3299
3308
. ,
[PubMed]
Richlan
,
F.
,
Kronbichler
,
M.
, &
Wimmer
,
H.
(
2011
).
Meta-analyzing brain dysfunctions in dyslexic children and adults
.
NeuroImage
,
56
(
3
),
1735
1742
. ,
[PubMed]
Rimrodt
,
S. L.
,
Peterson
,
D. J.
,
Denckla
,
M. B.
,
Kaufmann
,
W. E.
, &
Cutting
,
L. E.
(
2010
).
White matter microstructural differences linked to left perisylvian language network in children with dyslexia
.
Cortex
,
46
(
6
),
739
749
. ,
[PubMed]
Saygin
,
Z. M.
,
Norton
,
E. S.
,
Osher
,
D. E.
,
Beach
,
S. D.
,
Cyr
,
A. B.
,
Ozernov-Palchik
,
O.
, …
Gabrieli
,
J. D.
(
2013
).
Tracking the roots of reading ability: White matter volume and integrity correlate with phonological awareness in prereading and early-reading kindergarten children
.
Journal of Neuroscience
,
33
(
33
),
13251
13258
. ,
[PubMed]
Shaywitz
,
B. A.
,
Shaywitz
,
S. E.
,
Pugh
,
K. R.
,
Mencl
,
W. E.
,
Fulbright
,
R. K.
,
Skudlarski
,
P.
, …
Gore
,
J. C.
(
2002
).
Disruption of posterior brain systems for reading in children with developmental dyslexia
.
Biological Psychiatry
,
52
(
2
),
101
110
. ,
[PubMed]
Shaywitz
,
S. E.
,
Shaywitz
,
B. A.
,
Pugh
,
K. R.
,
Fulbright
,
R. K.
,
Constable
,
R. T.
,
Mencl
,
W. E.
, …
Gore
,
J. C.
(
1998
).
Functional disruption in the organization of the brain for reading in dyslexia
.
Proceedings of the National Academy of Sciences of the United States of America
,
95
(
5
),
2636
2641
. ,
[PubMed]
Sheskin
,
D.
(
2004
).
Handbook of parametric and nonparametric statistical procedures
(3rd ed.).
Boca Raton, FL
:
Chapman & Hall
.
Sihvonen
,
A. J.
,
Virtala
,
P.
,
Thiede
,
A.
,
Laasonen
,
M.
, &
Kujala
,
T.
(
2021
).
Structural white matter connectometry of reading and dyslexia
.
NeuroImage
,
241
,
118411
. ,
[PubMed]
Silani
,
G.
,
Frith
,
U.
,
Demonet
,
J. F.
,
Fazio
,
F.
,
Perani
,
D.
,
Price
,
C.
, …
Paulesu
,
E.
(
2005
).
Brain abnormalities underlying altered activation in dyslexia: A voxel based morphometry study
.
Brain
,
128
(
Pt 10
),
2453
2461
. ,
[PubMed]
Simos
,
P. G.
,
Fletcher
,
J. M.
,
Bergman
,
E.
,
Breier
,
J. I.
,
Foorman
,
B. R.
,
Castillo
,
E. M.
, …
Papanicolaou
,
A. C.
(
2002
).
Dyslexia-specific brain activation profile becomes normal following successful remedial training
.
Neurology
,
58
(
8
),
1203
1213
. ,
[PubMed]
Smith-Spark
,
J. H.
, &
Fisk
,
J. E.
(
2007
).
Working memory functioning in developmental dyslexia
.
Memory
,
15
(
1
),
34
56
. ,
[PubMed]
Smith-Spark
,
J. H.
,
Henry
,
L. A.
,
Messer
,
D. J.
,
Edvardsdottir
,
E.
, &
Ziecik
,
A. P.
(
2016
).
Executive functions in adults with developmental dyslexia
.
Research in Developmental Disabilities
,
53–54
,
323
341
. ,
[PubMed]
Smith
,
S. M.
(
2002
).
Fast robust automated brain extraction
.
Human Brain Mapping
,
17
(
3
),
143
155
. ,
[PubMed]
Snowling
,
M. J.
(
1995
).
Phonological processing and developmental dyslexia
.
Journal of Research in Reading
,
18
(
2
),
132
138
.
Spencer
,
M.
,
Richmond
,
M. C.
, &
Cutting
,
L. E.
(
2020
).
Considering the role of executive function in reading comprehension: A structural equation modeling approach
.
Scientific Studies of Reading
,
24
(
3
),
179
199
. ,
[PubMed]
Steinbrink
,
C.
,
Vogt
,
K.
,
Kastrup
,
A.
,
Müller
,
H.-P.
,
Juengling
,
F.
,
Kassubek
,
J.
, &
Riecker
,
A.
(
2008
).
The contribution of white and gray matter differences to developmental dyslexia: Insights from DTI and VBM at 3.0 T
.
Neuropsychologia
,
46
(
13
),
3170
3178
. ,
[PubMed]
Su
,
M.
,
Zhao
,
J.
,
Thiebaut de Schotten
,
M.
,
Zhou
,
W.
,
Gong
,
G.
,
Ramus
,
F.
, &
Shu
,
H.
(
2018
).
Alterations in white matter pathways underlying phonological and morphological processing in Chinese developmental dyslexia
.
Developmental Cognitive Neuroscience
,
31
,
11
19
. ,
[PubMed]
Taboada Barber
,
A.
,
Cartwright
,
K. B.
,
Hancock
,
G. R.
, &
Klauda
,
S. L.
(
2021
).
Beyond the simple view of reading: The role of executive functions in emergent bilinguals’ and English monolinguals’ reading comprehension
.
Reading Research Quarterly
,
56
,
S45
S64
.
Tamnes
,
C. K.
,
Fjell
,
A. M.
,
Westlye
,
L. T.
,
østby
,
Y.
, &
Walhovd
,
K. B.
(
2012
).
Becoming consistent: Developmental reductions in intraindividual variability in reaction time are related to white matter integrity
.
Journal of Neuroscience
,
32
(
3
),
972
982
. ,
[PubMed]
Torgesen
,
J. K.
,
Rashotte
,
C. A.
, &
Wagner
,
R. K.
(
1999
).
TOWRE: Test of word reading efficiency
.
Toronto, Canada
:
Psychological Corporation
.
Turkeltaub
,
P. E.
,
Gareau
,
L.
,
Flowers
,
D. L.
,
Zeffiro
,
T. A.
, &
Eden
,
G. F.
(
2003
).
Development of neural mechanisms for reading
.
Nature Neuroscience
,
6
(
7
),
767
773
. ,
[PubMed]
Tustison
,
N. J.
,
Avants
,
B. B.
,
Cook
,
P. A.
,
Zheng
,
Y.
,
Egan
,
A.
,
Yushkevich
,
P. A.
, &
Gee
,
J. C.
(
2010
).
N4ITK: improved N3 bias correction
.
IEEE Transactions on Medical Imaging
,
29
(
6
),
1310
1320
. ,
[PubMed]
Urger
,
S. E.
,
De Bellis
,
M. D.
,
Hooper
,
S. R.
,
Woolley
,
D. P.
,
Chen
,
S. D.
, &
Provenzale
,
J.
(
2015
).
The superior longitudinal fasciculus in typically developing children and adolescents: Diffusion tensor imaging and neuropsychological correlates
.
Journal of Child Neurology
,
30
(
1
),
9
20
. ,
[PubMed]
Vandermosten
,
M.
,
Boets
,
B.
,
Poelmans
,
H.
,
Sunaert
,
S.
,
Wouters
,
J.
, &
Ghesquiere
,
P.
(
2012
).
A tractography study in dyslexia: Neuroanatomic correlates of orthographic, phonological and speech processing
.
Brain
,
135
(
Pt 3
),
935
948
. ,
[PubMed]
Vandermosten
,
M.
,
Hoeft
,
F.
, &
Norton
,
E. S.
(
2016
).
Integrating MRI brain imaging studies of pre-reading children with current theories of developmental dyslexia: A review and quantitative meta-analysis
.
Current Opinion in Behavioral Sciences
,
10
,
155
161
. ,
[PubMed]
Vandermosten
,
M.
,
Vanderauwera
,
J.
,
Theys
,
C.
,
De Vos
,
A.
,
Vanvooren
,
S.
,
Sunaert
,
S.
, …
Ghesquière
,
P.
(
2015
).
A DTI tractography study in pre-readers at risk for dyslexia
.
Developmental Cognitive Neuroscience
,
14
,
8
15
. ,
[PubMed]
Varvara
,
P.
,
Varuzza
,
C.
,
Sorrentino
,
A. C.
,
Vicari
,
S.
, &
Menghini
,
D.
(
2014
).
Executive functions in developmental dyslexia
.
Frontiers in Human Neuroscience
,
8
,
120
. ,
[PubMed]
Vestergaard
,
M.
,
Madsen
,
K. S.
,
Baare
,
W. F.
,
Skimminge
,
A.
,
Ejersbo
,
L. R.
,
Ramsoy
,
T. Z.
, …
Jernigan
,
T. L.
(
2011
).
White matter microstructure in superior longitudinal fasciculus associated with spatial working memory performance in children
.
Journal of Cognitive Neuroscience
,
23
(
9
),
2135
2146
. ,
[PubMed]
Wagner
,
R. K.
,
Torgesen
,
J. K.
,
Rashotte
,
C. A.
, &
Pearson
,
N. A.
(
2013
).
CTOPP-2: Comprehensive test of phonological processing
.
Austin, TX
:
Pro-Ed
.
Waldie
,
K. E.
,
Haigh
,
C. E.
,
Badzakova-Trajkov
,
G.
,
Buckley
,
J.
, &
Kirk
,
I. J.
(
2013
).
Reading the wrong way with the right hemisphere
.
Brain Science
,
3
(
3
),
1060
1075
. ,
[PubMed]
Wang
,
H.-L. S.
,
Wang
,
N. Y.-H.
, &
Yeh
,
F.-C.
(
2019
).
Specifying the diffusion MRI connectome in Chinese-speaking children with developmental dyslexia and auditory processing deficits
.
Pediatrics & Neonatology
,
60
(
3
),
297
304
. ,
[PubMed]
Wang
,
L. C.
,
Tasi
,
H. J.
, &
Yang
,
H. M.
(
2012
).
Cognitive inhibition in students with and without dyslexia and dyscalculia
.
Research in Developmental Disabilities
,
33
(
5
),
1453
1461
. ,
[PubMed]
Wang
,
Y.
,
Mauer
,
M. V.
,
Raney
,
T.
,
Peysakhovich
,
B.
,
Becker
,
B. L.
,
Sliva
,
D. D.
, &
Gaab
,
N.
(
2016
).
Development of tract-specific white matter pathways during early reading development in at-risk children and typical controls
.
Cerebral Cortex
,
27
(
4
),
2469
2485
. ,
[PubMed]
Wechsler
,
D.
(
2012
).
Wechsler preschool and primary scale of intelligence
(4th ed.).
San Antonio, TX
:
The Psychological Corporation
.
Woodcock
,
R. W.
,
McGrew
,
K. S.
, &
Mather
,
N.
(
2001
).
Woodcock-Johnson III tests of cognitive abilities
.
Itasca, IL
:
Riverside
.
Yeatman
,
J. D.
,
Dougherty
,
R. F.
,
Ben-Shachar
,
M.
, &
Wandell
,
B. A.
(
2012a
).
Development of white matter and reading skills
.
Proceedings of the National Academy of Sciences of the United States of America
,
109
(
44
),
E3045
3053
. ,
[PubMed]
Yeatman
,
J. D.
,
Dougherty
,
R. F.
,
Myall
,
N. J.
,
Wandell
,
B. A.
, &
Feldman
,
H. M.
(
2012b
).
Tract profiles of white matter properties: Automating fiber-tract quantification
.
PLoS One
,
7
(
11
),
e49790
. ,
[PubMed]
Yeatman
,
J. D.
,
Rauschecker
,
A. M.
, &
Wandell
,
B. A.
(
2013
).
Anatomy of the visual word form area: Adjacent cortical circuits and long-range white matter connections
.
Brain and Language
,
125
(
2
),
146
155
. ,
[PubMed]
Zhang
,
Y.
,
Brady
,
M.
, &
Smith
,
S.
(
2001
).
Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm
.
IEEE Transactions on Medical Imaging
,
20
(
1
),
45
57
. ,
[PubMed]
Zhao
,
J.
,
Yang
,
Y.
,
Song
,
Y.-W.
, &
Bi
,
H.-Y.
(
2015
).
Verbal short-term memory deficits in Chinese children with dyslexia may not be a problem with the activation of phonological representations
.
Dyslexia
,
21
(
4
),
304
322
. ,
[PubMed]

Author notes

Competing Interests: The authors have declared that no competing interests exist.

Handling Editor: Patric Hagmann

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/legalcode.