Introduction

Developmental dyslexia (DD) is a reading-skill impairment with a strong and multifactorial genetic component1, which may emerge irrespective of adequate intelligence and reading instruction2. It is the most common neurodevelopmental disorder having a prevalence reported to range between 5–17.5%3 and 5–10%4. Due to being common and having a devastating influence on the individual’s academic achievements, career, and coping5 make it pertinent to understand its neural basis. Yet, this task is very challenging due to the heterogeneity of its phenotype6,7 and the complexity of the neural network underlying reading8,9. According to current leading theories, DD is primarily based on phonological deficits4,10 and associated with significant implicit learning problems11, and working-memory dysfunctions12.

The endeavor to find anomalies in the neural reading circuitry in DD has continued for over 50 years, yet with relatively few replicated results on the neuroanatomical abnormalities in DD and their association with reading-related skills (e.g.13,14). Meta-analyses summarizing the heterogenous voxel-based morphometry (VBM) findings have reported grey matter (GM) anomalies mainly in the left occipito-temporal and bilateral superior temporal and parietal areas as well as the cerebellum bilaterally15,16,17. The most recent meta-analysis, including 1164 participants across 18 studies, concluded, however, that even large-scale studies highlight a range of inconsistencies and limitations14. Furthermore, according to this analysis the most robust finding in DD is reduced total brain volume, rendering the cortical anomalies specifically associated with DD unsettled.

Besides VBM, a promising approach for searching more subtle neuroanatomical markers18 is surface-based morphometry (SBM), which has been, however, scarcely used in DD research. Of the few studies carried out so far, a region of interest analysis found diminished cortical areas in adults with DD in inferior frontal and fusiform regions and abnormal cortical thickness lateralization in the supramarginal area19. However, these findings could not be replicated in studies with larger sample sizes20, 21.

In addition to the unusually challenging complex geno- and phenotypes of DD, a range of methodological issues have led to a lack of consensus on the GM anomalies in DD and their contribution to DD symptoms. The variation in preprocessing methods, statistical thresholding, and study populations as well as the lack of consistency in adjusting the analyses for confounding effects, like brain size, across the studies may partly explain this, and has given rise to methodological recommendations for more reliable research14. On this account, we set out to evaluate the critical GM volume and cortical surface abnormalities in adults with DD, employing neuropsychological testing of functions vital for reading and a combination of up-to-date whole-brain VBM22 and SBM23, using recommended methods, statistical thresholding, and systematically controlling for relevant covariates. VBM and SBM were chosen (i) to evaluate different levels of GM anomalies in dyslexia, (ii) to complement each other, and (iii) aim to overcome the methodological limitations involved in either of the methods used alone24. In addition, whole-brain data-driven analyses were deliberately chosen given the lack of consensus over grey matter anomalies in dyslexia14. Based on data discussed above, we expected to find GM anomalies in DD in left reading-related network, and their association with skills essential for reading. Due to lack of consistent results in the few existing SBM studies on DD, no specific hypotheses could be made, but we expected the cortical SBM and VBM findings to overlap.

Materials and methods

Participants

Forty-five right-handed Finnish-speaking participants completed the MR imaging, the final sample consisting of 22 typically reading and 23 dyslexic participants with no history of neurological or psychiatric diseases. The groups were balanced in age, years of education and music education, and sex (Table 1), but significantly differed in the reading-skill measures and composite scores of phonological processing, reading skills, and working memory (Table 2). However, they differed in all IQ indices. Verbal IQ (VIQ), but not performance IQ (PIQ), is expected to be lower than normal in DD and, therefore, PIQ was used as a covariate. No group differences were found in total GM, white matter (WM), CSF, total intracranial volume (TIV), or total brain volume (Table 1).

Table 1 Demographic and morphological data.
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Table 2 Neuropsychological data.
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A participant was classified as dyslexic if either a recent statement on dyslexia diagnosis was available from a health-care professional (e.g., psychologist), or he/she had reading-related problems in childhood based on the Adult Reading History Questionnaire (ARHQ; cut-off at 43% for the childhood-related items;25), confirmed in a clinical interview, combined with a performance of at least one standard deviation (SD) below the average of age-matched standardized control data26 in at least two reading subtests (word list reading, pseudoword list reading, text reading) in speed or accuracy (Table 2). Control-group participants (1) had no language-related problems and neither did their parents nor siblings, (2) reported no childhood problems in reading or writing in ARHQ or interview, and (3) performed within norm in at least two out of three reading subtests in both speed and accuracy.

The exclusion criteria were as follows (self-reported in questionnaires and clinical interview except for IQ, which was tested): attention deficit evaluated with the Adult ADHD Self-Report Scale ASRS-v1.1 questionnaire27, developmental or other language impairment, other neurological or psychiatric disorders, substance abuse, medication affecting the brain, uncorrected visual deficit, an individualized school curriculum, early bilingualism, PIQ below 80, and non-detachable metal in the body or pregnancy. The study, performed according to the Declaration of Helsinki, was approved by the Coordinating Ethics Committee of The Hospital District of Helsinki and Uusimaa. A signed informed consent was obtained from all participants.

Neuropsychological tests

The neuropsychological test battery assessed IQ, working memory functions, reading, and phonological processing, combined into four composite scores. They were averages over the z-transformed test scores for reading and phonological processing, and averages of the standardized test scores according to the Working Memory Index in Wechsler Memory Scale (WMS-III) for working memory and according to PIQ, VIQ, and full IQ in Wechsler Adult Intelligent Scale (WAIS-IV) for IQ (Table 2). Reading skills (accuracy and speed; Cronbach’s α = 0.87) were assessed with word and pseudoword list reading tests28. The phonological processing composite (Cronbach’s α = 0.69) included ‘Pig Latin’28, non-word span length29, and rapid alternating stimulus naming30, measuring phonological awareness, phonological short-term memory, and rapid access of phonological information, respectively31. Working memory functions were evaluated with WMS-III subtests letter-number sequencing and spatial span32. Verbal IQ was assessed with WAIS-IV subtests similarities and vocabulary and performance IQ with subtests block design and matrix reasoning.

In the analyses, composite scores were used instead of the individual single-task variables to reduce the number of analyses and the error variance related to single task performance. Due to the data size no factor analysis could be run using, therefore, the classifications based on previous theoretical and factor-analytic studies31 and checking the internal consistency of our domain variables with Cronbach’s α (see above).

MRI data acquisition

A 3 T MAGNETOM Skyra MRI scanner (Siemens Healthcare, Germany) with a 32-channel head coil (AMI center, Aalto University, Espoo, Finland; duration 30 min) was used. High-resolution magnetization prepared rapid acquisition gradient-recalled T1 images were obtained (176 slices, slice thickness 1 mm, flip angle = 7°, TR = 2530 ms, TE = 3.3 ms, voxel size = 1.0 × 1.0 × 1.0 mm3). A physician checked the MRIs for incidental findings.

Voxel-based morphometry

Morphometric analysis was carried out using the Statistical Parametric Mapping (SPM12, Wellcome Department of Cognitive Neurology, UCL) under MATLAB 9.4.0 (The MathWorks Inc., Natick, MA, USA, version R2018a). After reorienting the T1 images using the anterior commissure as origin, the new segmentation algorithm with default parameters, except affine regularization set to the International Consortium for Brain Mapping (ICBM) template for the brains of European participants, was applied to the T1 images, segmenting them precisely into GM, WM, and CSF probability maps. Tissue probability maps were then normalized to the Montreal Neurological Institute (MNI) space using the Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) registration process implemented in SPM12. During the process, the imaging data were resampled to 1.5 × 1.5 × 1.5 mm3 voxel size and modulated, allowing evaluation of regional volumetric differences. Images were smoothed with an isotropic Gaussian kernel of 8 mm full width at half maximum (FMWH). During each step, the images were visually checked for potential segmentation and registration errors. The TIV was calculated by combining the GM, WM, and CSF images generated during the segmentation.

Surface-based morphometry

Brain-surface group differences were analyzed using the CAT12 toolbox (C. Gaser, Structural Brain Mapping Group, Jena University Hospital, Jena, Germany; http://dbm.neuro.uni-jena.de/cat/) under SPM12. Default parameters in standard-protocol accordance (http://www.neuro.uni-jena.de/cat12/CAT12-Manual.pdf) were used in segmentation, surface estimation, data resampling, and smoothing. Extracted surface parameters included thickness, gyrification measuring surface complexity in 3D33, sulcus depth, and cortical complexity (fractal dimension34). As recommended, smoothing filter size in FWHM was 15 mm for thickness data and 20 mm for folding data (e.g. gyrification). The surface data were visually inspected for artefacts and homogeneity and the overall image quality was checked in statistical quality control.

Statistical analyses

In VBM analysis, one independent-sample t-test with two different contrasts (Controls > Dyslexics, Dyslexics > Controls) was calculated. The results were thresholded using the “Threshold and transform spmT-maps” function in CAT12 toolbox at a default cluster-forming threshold (uncorrected p < 0.001) and a familywise error rate (FWE) corrected p < 0.05 at the cluster level (alpha-level) and corrected for non-isotropic smoothness35. All VBM analyses were adjusted for age, sex, and TIV36. In addition, to follow recent recommendations14 and to take the group difference into account, PIQ was also added as a covariate of no-interest in the VBM analyses. Neuroanatomical regions were identified using the Automated Anatomical Labeling Atlas37 included in the xjView toolbox (http://www.alivelearn.net/xjview/).

In SBM, four independent-samples t-tests (cortical thickness, gyrification, sulcus depth, complexity) with two different contrasts (Controls > Dyslexics, Dyslexics > Controls) were calculated. Like VBM analyses, SBM analyses were thresholded at a default whole-brain threshold (uncorrected p < 0.001) and a FWE corrected p < 0.05 at the cluster-level and corrected for non-isotropic smoothness. SBM analyses were adjusted for age, sex, and PIQ, but not for TIV as it is not recommended for surface analyses. SBM results were corrected for the total number of carried out surface analyses, that is, alpha-level was set to 0.05/4 = 0.0125.

Partial correlations (two-tailed) were calculated between each individual significant VBM and SBM result and the three composite z-scores (reading score, phonological processing, working memory; Table 2) over the whole sample using SPSS (IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 24.0. Armonk, NY: IBM Corp.) whilst controlling for age, sex, TIV, and PIQ. To control for multiple comparisons, false discovery rate (FDR) approach was used and only significant results are reported.

Results

Volumetric group differences (VBM)

First, group differences in global brain measurements (total GM, WM, CSF, TIV, and total brain volume) were evaluated with five independent-sample t-tests, and no statistically significant volumetric group differences were found (p = 0.168–0.644); see Table 1). In whole-brain VBM analyses, controls had greater GM volume than dyslexic participants in one cluster comprising the left insula, superior temporal gyrus, putamen, globus pallidus, and parahippocampal gyrus. Greater GM volume in this cluster (both groups included) correlated significantly with higher reading (R = 0.434, p = 0.009) and phonological processing composite scores (R = 0.347, p = 0.030; Fig. 1, Table 3).

Figure 1
figure 1

VBM and SBM group differences (see also Table 3). (A) Grey matter volume anomalies in dyslexia (Controls > Dyslexics). (B) Cortical thickness anomalies in dyslexia (Controls > Dyslexics). N = 45. Statistical maps are thresholded at a cluster-level FWE-corrected p < 0.05 threshold. Mean adjusted cluster grey matter volume and mean adjusted cluster cortical thickness correlations to reading-related skills are shown with scatter plots. Bar plots for mean adjusted grey matter volume and mean cortical thickness in significant clusters (Table 3) are shown: bar = mean, error-bar = standard error of mean, d Cohen’s d, GP globus pallidus, INS insula, PUT putamen, STG superior temporal gyrus.

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Table 3 Whole-brain VBM and SBM group comparison results.
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Cortical group differences (SBM)

The control participants had greater cortical thickness in the left insula than the dyslexic participants. Greater thickness in this area (both groups included) correlated significantly with higher reading (R = 0.342, p = 0.020) and phonological processing composite scores (R = 0.547, p < 0.001; Fig. 1, Table 3). Gyrification, sulcus depth, and cortical complexity analyses yielded no significant results.

Discussion

There is an obvious need to understand the neural underpinnings of DD, which is highly prevalent and can have devastating academic, psychosocial, and psychiatric effects on the individual affected (e.g.5). Yet, brain abnormalities in DD have remained unsettled due to its heterogenous pheno- and genotypes1, 7 and the great methodological variability of previous studies, the most robust finding so far being a lowered total brain volume14. By implementing two converging GM analysis methods following up on recent recommendations, combined with careful neuropsychological testing, we compared DD and control samples without total brain volume differences. Furthermore, we determined how reading-related skills are associated with our neuroanatomical findings. Our results showed: (1) diminished GM volume and cortical thickness overlapping in left insula in DD, (2) decreased GM volume in left superior temporal and subcortical areas in DD, and (3) an association between a lower GM volume in all these areas and lower reading and phonological test scores (both groups included in the analysis). Our data pinpoint converging areas for reading-related skills and GM abnormalities in DD in the absence of significant total brain volume differences between the studied groups. This suggests that the occurrence of DD does not (only) rely on brain volume reduction as a predisposing factor or as a de rigueur developmental consequence (see also19).

The GM anomalies in our DD sample originated in the left hemisphere where the neural network involved in reading is preponderant8, 9. Also, the most consistent functional and structural abnormalities in DD have been found in the left hemisphere15,16,17, 38,39,40, although they are not limited to it15. Our cortical GM volume reduction findings in participants with DD comprised a cluster including superior temporal and insular areas. The involvement of superior temporal areas in reading-related tasks and their lower activation in such tasks as well as diminished volumes in DD have been frequently reported (e.g.16, 40). However, the exact area identified by different studies varies, including superior, middle, and inferior temporal gyri, as well as superior temporal sulcus (e.g.15,16,17, 41,42,43). The present study revealed GM volume reductions in DD in the left superior temporal pole in which previous studies have shown both functional44 and structural41 anomalies in participants with DD. The left temporal pole is connected with left inferior frontal areas via left uncinate fasciculus, which has previously been implicated in dyslexia45, potentially belonging to the temporal-frontal network proposed to underlie the phonological access deficits in DD10, 46.

Additionally, our study pinpointed the role of left insula in DD, GM abnormalities in which we found with two complementary methods (VBM, SBM). Previous studies showing structural anomalies in DD in insula are rare47 and lack evaluation of the relationship between reading skills and brain structures. The scarcity of previous structural anomaly findings in the left insula in DD might owe partially to the lack of systematical use of relevant covariates. Here, in addition to age and gender, the analyses were controlled for PIQ and brain volume differences (VBM), both of which have been shown to affect volumes of brain regions, including the insula48, 49. Consistent with our results, a recent analysis on functional brain networks identified the left insula as a critical hub in DD50. Insula is highly connected with the adjacent fronto-temporal, parietal, and subcortical regions, including anterior and posterior language areas51, 52. Left insula has an important mediating role in speech production53 and phonological processing54, 55, and its posterior part is particularly active in the post-articulatory period during both reading and naming56. Moreover, consistent with our results, insular dysfunctions have been uncovered in individuals having DD and a phonological deficit57. It was also shown to underlie deficient temporal processing of speech and non-speech sounds in DD58. Left insula in DD also shares fewer connections with other nodes in a left-hemispheric reading network comprising temporo-parietal and occipital regions59. This is compatible with the suggestion that DD might be a disconnection syndrome, with poor neural communication between key brain areas involved in reading and, therefore, vitally contributing to this disorder54, 60. Moreover, evidence from lesion studies suggests that damage to the left insula underpins acquired dyslexia61. Whereas previous studies reporting left insular structural anomalies in DD are scarce, taken together, these findings suggest that the left insula plays a role in reading, and its structural and functional anomalies in DD should be confirmed and explored further in future studies.

Subcortical structures, so far scarcely studied in DD, have recently been proposed to have a role in this and related developmental language disorders11, 62. We found diminished GM volume in DD in left striatum (globus pallidus, putamen) and parahippocampal gyrus. Corticostriatal and hippocampal learning systems are implicated in language and procedural learning, impairments of which have been associated with reading deficits11, 62. Consistent with our results, few previous studies have revealed GM anomalies in the left striatum in DD41, 55. It has been shown that the connectivity between the left striatum and insula are important in reading, especially in children, suggesting its essential role in early reading acquisition63. Moreover, the connectivity between left striatum and insula is altered in DD and left striatum (putamen) has been suggested to contribute to phonological dysfunctions in DD55.

Neuroanatomical studies on DD combining VBM and SBM are so far scarce. While VBM has remained as one of the most widely used automatic computational neuroanatomy techniques, it has its own limitations concerning used preprocessing parameters and, for example, sample size, that can contribute to the heterogeneity of previously reported GM findings in DD. Unequal sized groups in a VBM study can produce an inflated false positive rate whereas with groups of equal size (in the present study 22 vs. 23), false positive rate has been shown to be at the expected rate (i.e., about 5%)64. However, the interpretation of volumetric GM anomalies in DD, even when following best practices, remains difficult, given that GM volume arises from cortical thickness and area. Here, using both VBM and SBM in concert allows more accurate evaluation of GM anomalies in DD while overcoming limitations involved in either of the methods used alone. The surface-based coordinate system is more accurate than the volumetric one, providing the opportunity to study subtle neuroanatomical anomalies associated with DD24. Importantly, the present results revealed overlapping GM volumetric and cortical thickness anomalies in DD in the left insula, suggesting that the decreased cortical thickness gives rise to the observed volumetric anomaly as well. Future studies on DD combining volumetric and surface-based analyses in a large sample of participants with DD are needed as they might reveal other cortical anomalies in DD, for example in gyrification, which the present study failed to find.

The most pertinent issue in studying neuroanatomical anomalies in DD has been the lack of consistency across the reported brain regions. This can be a consequence of the complexity and phenotypic heterogeneity of DD6, 7. The most extensive and recent meta-analysis did not find consistent evidence for local GM abnormalities in DD, reporting a reduced total brain volume as the most systematic finding14. Lowered total brain volume may result from or be associated with a wide range of confounding issues which could underlie the current inconsistent picture on the neuroanatomical origins of DD. Possibly having groups not significantly differing in total brain volume and controlling for relevant confounding factors at least partly explains our results, which converge with a number of neurofunctional studies on DD, but share only little overlap with previous meta-analytical neuroanatomical reports.

In conclusion, we found GM anomalies in the left superior temporal, insular, and striatal-hippocampal areas in DD. These areas subserve phonological and implicit learning functions, the deficits of which are thought to vitally contribute to DD4, 11. Previous anatomical studies linking the structure of these areas with DD is scarce, but especially functional evidence supports our findings. However, future studies with similarly rigorous methodology and groups with matched total brain volumes as here, but including larger participant samples, should further evaluate these brain regions and their contribution to phonological and implicit learning functions in DD as well as their functional and structural connectivity with the reading network. Furthermore, in order to disentangle the effects of inherited factors leading to DD and those caused by this disorder (for example, less exposure to print, atypical reading strategies), longitudinal studies determining brain structure abnormalities prior to and after reading-skill acquisition are needed.