Voxel and surface based whole brain analysis shows reading skill associated grey matter abnormalities in dyslexia

Teija Kujala, Aleksi J Sihvonen, Anja Thiede, Peter Palo-Oja, Paula Virtala, Jussi Numminen, Marja Laasonen, Teija Kujala, Aleksi J Sihvonen, Anja Thiede, Peter Palo-Oja, Paula Virtala, Jussi Numminen, Marja Laasonen

Abstract

Developmental dyslexia (DD) is the most prevalent neurodevelopmental disorder with a substantial negative influence on the individual's academic achievement and career. Research on its neuroanatomical origins has continued for half a century, yielding, however, inconsistent results, lowered total brain volume being the most consistent finding. We set out to evaluate the grey matter (GM) volume and cortical abnormalities in adult dyslexic individuals, employing a combination of whole-brain voxel- and surface-based morphometry following current recommendations on analysis approaches, coupled with rigorous neuropsychological testing. Whilst controlling for age, sex, total intracranial volume, and performance IQ, we found both decreased GM volume and cortical thickness in the left insula in participants with DD. Moreover, they had decreased GM volume in left superior temporal gyrus, putamen, globus pallidus, and parahippocampal gyrus. Higher GM volumes and cortical thickness in these areas correlated with better reading and phonological skills, deficits of which are pivotal to DD. Crucially, total brain volume did not influence our results, since it did not differ between the groups. Our findings demonstrating abnormalities in brain areas in individuals with DD, which previously were associated with phonological processing, are compatible with the leading hypotheses on the neurocognitive origins of DD.

Conflict of interest statement

The authors declare no competing interests.

Figures

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.

References

    1. Kere J. The molecular genetics and neurobiology of developmental dyslexia as model of a complex phenotype. Biochem. Biophys. Res. Commun. 2014;452:236–243. doi: 10.1016/j.bbrc.2014.07.102.
    1. American Psychiatric Association . Diagnostic and Statistical Manual Of Mental Disorders. 4. Washington, DC: American Psychiatric Association; 2000.
    1. Shaywitz SE. Dyslexia. N. Engl. J. Med. 1998;338:307–312. doi: 10.1056/NEJM199801293380507.
    1. Snowling MJ, Melby-Lervåg M. Oral language deficits in familial dyslexia: A meta-analysis and review. Psychol. Bull. 2016;142:498–545. doi: 10.1037/bul0000037.
    1. Eissa M. Behavioral and emotional problems associated with dyslexia in adolescence. Curr. Psychiatry. 2010;17:17–25.
    1. Perry C, Zorzi M, Ziegler JC. Understanding dyslexia through personalized large-scale computational models. Psychol. Sci. 2019;30:386–395. doi: 10.1177/0956797618823540.
    1. Zoubrinetzky R, Bielle F, Valdois S. New insights on developmental dyslexia subtypes: Heterogeneity of mixed reading profiles. PLoS ONE. 2014;9:e99337. doi: 10.1371/journal.pone.0099337.
    1. Kujala J, et al. Phase coupling in a cerebro-cerebellar network at 8–13 Hz during reading. Cereb. Cortex. 2007;17:1476–1485. doi: 10.1093/cercor/bhl059.
    1. Paulesu E, et al. A cultural effect on brain function. Nat. Neurosci. 2000;3:91–96. doi: 10.1038/71163.
    1. Ramus F. Neuroimaging sheds new light on the phonological deficit in dyslexia. Trends Cogn. Sci. 2014;18:274–275. doi: 10.1016/j.tics.2014.01.009.
    1. Krishnan S, Watkins KE, Bishop DVM. Neurobiological basis of language learning difficulties. Trends Cogn. Sci. 2016;20:701–714. doi: 10.1016/j.tics.2016.06.012.
    1. Beneventi H, Tønnessen FE, Ersland L, Hugdahl K. Working memory deficit in dyslexia: Behavioral and FMRI evidence. Int. J. Neurosci. 2010;120:51–59. doi: 10.3109/00207450903275129.
    1. Drake WE. Clinical and pathological findings in a child with a developmental learning disability. J. Learn. Disabil. 1968;1:486–502. doi: 10.1177/002221946800100901.
    1. Ramus F, Altarelli I, Jednoróg K, Zhao J, Scotto di Covella L. Neuroanatomy of developmental dyslexia: Pitfalls and promise. Neurosci. Biobehav. Rev. 2018;84:434–452. doi: 10.1016/j.neubiorev.2017.08.001.
    1. Linkersdörfer J, Lonnemann J, Lindberg S, Hasselhorn M, Fiebach CJ. Grey matter alterations co-localize with functional abnormalities in developmental dyslexia: An ALE meta-analysis. PLoS ONE. 2012;7:e43122. doi: 10.1371/journal.pone.0043122.
    1. Richlan F. Structural abnormalities in the dyslexic brain: A meta-analysis of voxel-based morphometry studies. Hum. Brain Mapp. 2012;34:3055–3065. doi: 10.1002/hbm.22127.
    1. Eckert MA, Berninger VW, Vaden KI, Gebregziabher M, Tsu L. Gray matter features of reading disability: A combined meta-analytic and direct analysis approach. ENeuro. 2016;3:11296–11301. doi: 10.1523/ENEURO.0103-15.2015.
    1. Panizzon MS, et al. Distinct genetic influences on cortical surface area and cortical thickness. Cereb. Cortex. 2009;19:2728–2735. doi: 10.1093/cercor/bhp026.
    1. Frye RE, et al. Surface area accounts for the relation of gray matter volume to reading-related skills and history of dyslexia. Cereb. Cortex. 2010;20:2625–2635. doi: 10.1093/cercor/bhq010.
    1. Altarelli I, et al. A functionally guided approach to the morphometry of occipitotemporal regions in developmental dyslexia: Evidence for differential effects in boys and girls. J. Neurosci. 2013;33:11296–11301. doi: 10.1523/JNEUROSCI.5854-12.2013.
    1. Ma Y, et al. Cortical thickness abnormalities associated with dyslexia, independent of remediation status. Neuroimage Clin. 2014;7:177–186. doi: 10.1016/j.nicl.2014.11.005.
    1. Ashburner J, Friston KJ. Voxel-based morphometry–the methods. Neuroimage. 2000;11:805–821. doi: 10.1006/nimg.2000.0582.
    1. Dahnke R, Yotter RA, Gaser C. Cortical thickness and central surface estimation. Neuroimage. 2013;65:336–348. doi: 10.1016/j.neuroimage.2012.09.050.
    1. Fischl B, et al. Cortical folding patterns and predicting cytoarchitecture. Cereb. Cortex. 2008;18:1973–1980. doi: 10.1093/cercor/bhm225.
    1. Lefly DL, Pennington BF. Reliability and validity of the adult reading history questionnaire. J. Learn. Disabil. 2000;33:286–296. doi: 10.1177/002221940003300306.
    1. Laasonen M, Lehtinen M, Leppämäki S, Tani P, Hokkanen L. Project DyAdd: Phonological processing, reading, spelling, and arithmetic in adults with dyslexia or ADHD. J. Learn. Disabil. 2010;43:3–14. doi: 10.1177/0022219409335216.
    1. Kessler RC, et al. The World Health Organization adult ADHD self-report scale (ASRS): A short screening scale for use in the general population. Psychol. Med. 2005;35:245–256. doi: 10.1017/S0033291704002892.
    1. Nevala, J., Kairaluoma, L., Ahonen, T., Aro, M. & Holopainen, L., editors. Lukemis-Ja Kirjoittamistaitojen Yksilötestistö Nuorille Ja Aikuisille. [Individual Dyslexia Test For Youth And Adults]. (Jyväskylä, Finland: Niilo Mäki Institute, 2006).
    1. Laasonen M, Service ME, Virsu V. Crossmodal temporal order and processing acuity in developmentally dyslexic young adults. Brain Lang. 2002;80:340–354. doi: 10.1006/brln.2001.2593.
    1. Wolf M. Rapid alternating stimulus naming in the developmental dyslexias. Brain Lang. 1986;27:360–379. doi: 10.1016/0093-934X(86)90025-8.
    1. Torgesen JK, Wagner RK, Rashotte CA. Longitudinal studies of phonological processing and reading. J. Learn. Disabil. 1994;27:276–286. doi: 10.1177/002221949402700503.
    1. Wechsler D. WMS-III Manual. Helsinki: Psykologien kustannus Oy; 2008.
    1. Luders E, et al. A curvature-based approach to estimate local gyrification on the cortical surface. Neuroimage. 2006;29:1224–1230. doi: 10.1016/j.neuroimage.2005.08.049.
    1. Yotter RA, Nenadic I, Ziegler G, Thompson PM, Gaser C. Local cortical surface complexity maps from spherical harmonic reconstructions. Neuroimage. 2011;56:961–973. doi: 10.1016/j.neuroimage.2011.02.007.
    1. Hayasaka S, Phan KL, Liberzon I, Worsley KJ, Nichols TE. Nonstationary cluster-size inference with random field and permutation methods. Neuroimage. 2004;22:676–687. doi: 10.1016/j.neuroimage.2004.01.041.
    1. Barnes J, et al. Head size, age and gender adjustment in MRI studies: a necessary nuisance? Neuroimage. 2010;53:1244–1255. doi: 10.1016/j.neuroimage.2010.06.025.
    1. Tzourio-Mazoyer N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002;15:273–289. doi: 10.1006/nimg.2001.0978.
    1. Paulesu E, et al. Dyslexia: Cultural diversity and biological unity. Science. 2001;291:2165–2167. doi: 10.1126/science.1057179.
    1. Paulesu E, Danelli L, Berlingeri M. Reading the dyslexic brain: Multiple dysfunctional routes revealed by a new meta-analysis of PET and fMRI activation studies. Front. Hum. Neurosci. 2014;8:830. doi: 10.3389/fnhum.2014.00830.
    1. Richlan F, Kronbichler M, Wimmer H. Functional abnormalities in the dyslexic brain: A quantitative meta-analysis of neuroimaging studies. Hum. Brain Mapp. 2009;30:3299–3308. doi: 10.1002/hbm.20752.
    1. Brown WE, et al. Preliminary evidence of widespread morphological variations of the brain in dyslexia. Neurology. 2001;56:781–783. doi: 10.1212/WNL.56.6.781.
    1. Pernet C, Andersson J, Paulesu E, Demonet JF. When all hypotheses are right: A multifocal account of dyslexia. Hum. Brain Mapp. 2009;30:2278–2292. doi: 10.1002/hbm.20670.
    1. Silani G, et al. Brain abnormalities underlying altered activation in dyslexia: A voxel based morphometry study. Brain. 2005;128:2453–2461. doi: 10.1093/brain/awh579.
    1. Morken F, Helland T, Hugdahl K, Specht K. Children with dyslexia show cortical hyperactivation in response to increasing literacy processing demands. Front. Psychol. 2014;5:1491. doi: 10.3389/fpsyg.2014.01491.
    1. Lebel C, et al. Developmental trajectories of white matter structure in children with and without reading impairments. Dev. Cogn. Neurosci. 2019;36:100633. doi: 10.1016/j.dcn.2019.100633.
    1. Boets B, et al. Intact but less accessible phonetic representations in adults with dyslexia. Science. 2013;342:1251–1254. doi: 10.1126/science.1244333.
    1. Hoeft F, et al. Functional and morphometric brain dissociation between dyslexia and reading ability. Proc. Natl. Acad. Sci. USA. 2007;104:4234–4239. doi: 10.1073/pnas.0609399104.
    1. Bauernfeind AL, et al. A volumetric comparison of the insular cortex and its subregions in primates. J. Hum. Evol. 2013;64:263–279. doi: 10.1016/j.jhevol.2012.12.003.
    1. Caldiroli A, et al. The relationship of IQ and emotional processing with insula volume in schizophrenia. Schizophr. Res. 2018;202:141–148. doi: 10.1016/j.schres.2018.06.048.
    1. Bailey SK, Aboud KS, Nguyen TQ, Cutting LE. Applying a network framework to the neurobiology of reading and dyslexia. J. Neurodev. Disord. 2018;10:1–9. doi: 10.1186/s11689-018-9251-z.
    1. Deen B, Pitskel NB, Pelphrey KA. Three systems of insular functional connectivity identified with cluster analysis. Cereb. Cortex. 2011;21:1498–1506. doi: 10.1093/cercor/bhq186.
    1. Ghaziri J, et al. Subcortical structural connectivity of insular subregions. Sci. Rep. 2018;8:8596. doi: 10.1038/s41598-018-26995-0.
    1. Oh A, Duerden EG, Pang EW. The role of the insula in speech and language processing. Brain Lang. 2014;135:96–103. doi: 10.1016/j.bandl.2014.06.003.
    1. Borowsky R, et al. FMRI of ventral and dorsal processing streams in basic reading processes: Insular sensitivity to phonology. Brain Topogr. 2006;18:233–239. doi: 10.1007/s10548-006-0001-2.
    1. Wang Z, et al. Structural and functional abnormality of the putamen in children with developmental dyslexia. Neuropsychologia. 2019;130:26–37. doi: 10.1016/j.neuropsychologia.2018.07.014.
    1. Woolnough O, Forseth KJ, Rollo PS, Tandon N. Uncovering the functional anatomy of the human insula during speech. Elife. 2019;8:e53086. doi: 10.7554/eLife.53086.
    1. Paulesu E, et al. Is developmental dyslexia a disconnection syndrome? Evidence from PET scanning. Brain. 1996;119:143–157. doi: 10.1093/brain/119.1.143.
    1. Steinbrink C, Ackermann H, Lachmann T, Riecker A. Contribution of the anterior insula to temporal auditory processing deficits in developmental dyslexia. Hum. Brain Mapp. 2009;30:2401–2411. doi: 10.1002/hbm.20674.
    1. Lou C, et al. White matter network connectivity deficits in developmental dyslexia. Hum. Brain Mapp. 2019;40:505–516. doi: 10.1002/hbm.24390.
    1. Steinbrink C, et al. The contribution of white and gray matter differences to developmental dyslexia: Insights from DTI and VBM at 3.0 T. Neuropsychologia. 2008;46:3170–3178. doi: 10.1016/j.neuropsychologia.2008.07.015.
    1. Dickens JV, et al. Localization of phonological and semantic contributions to reading. J. Neurosci. 2019;39:5361–5368. doi: 10.1523/JNEUROSCI.2707-18.2019.
    1. Ullman MT, Earle FS, Walenski M, Janacsek K. The neurocognition of developmental disorders of language. Annu. Rev. Psychol. 2020;71:389–417. doi: 10.1146/annurev-psych-122216-011555.
    1. Liu X, et al. Differences between child and adult large-scale functional brain networks for reading tasks. Hum. Brain Mapp. 2018;39:662–679. doi: 10.1002/hbm.23871.
    1. Scarpazza C, Tognin S, Frisciata S, Sartori G, Mechelli A. False positive rates in voxel-based morphometry studies of the human brain: Should we be worried? Neurosci. Biobehav. Rev. 2015;52:49–55. doi: 10.1016/j.neubiorev.2015.02.008.

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