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Vandermosten, M. 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. doi:10.1016/j.cobeha.2016.06.007

Summary by Dr. Jenifer Juranek


A meta-analysis of functional and structural neuroimaging studies in young prereaders (Vandermosten, Hoeft, & Norton, 2016) shows evidence of differences that look like those observed in older children and adults. A study like this is important because it can help differentiate between brain areas implicated in poor reading skills due to an underlying functional or structural anomaly versus areas altered due to years of struggling with learning to read.


The development of reading skills in school-aged children is fundamental to achieving academic performance goals. It is sometimes easy to forget that reading is a learned skill—a skill that is not hardwired into our brains, but is acquired by tapping resources our brains have available to accomplish the task. Learning to read is a complex process formally taught to children in their earliest years of school. Though most children successfully navigate the process of learning to read, some struggle with the core components of reading, including automatic word identification, fluency, and text comprehension.

Developmental dyslexia is the most common learning disability identified in young children, yet the etiology (i.e., the underlying cause) of dyslexia remains poorly understood. Because dyslexia is more common in related rather than unrelated individuals, genetic influences substantially affect measures of reading ability. Similarly, properties of brain structure and function are also highly heritable, suggesting that altered features of brain development underlie problems in learning to read for children with dyslexia. However, the role of environmental factors, particularly the availability and extent of reading experience, can also have a significant impact on measures of reading ability. Thus, a key issue is whether atypical brain structure and function described in neuroimaging studies of developmental dyslexia emerge from a lack of quality reading experience or interfere with skill development in reading ability. Although challenging, resolving this issue has implications for maximizing individual response to reading intervention strategies.

Only recently have MRI protocols for evaluating brain structure and function become child-friendly (i.e., quick to acquire and not too loud) and feasible for young prereaders. Given the strong genetic component contributing to developmental dyslexia, prereaders can be identified as being at risk for developing dyslexia if there is a family history of reading difficulties. Imaging the at-risk prereaders and following them until their reading abilities can be characterized as typical or dyslexic would be the ideal study design. However, very few studies have been able to take this approach. Yet several research groups have conducted imaging studies comparing at-risk and not-at-risk prereaders for developing dyslexia. Consistently, across these studies, differences between at-risk and not-at-risk prereaders have resembled findings in older children and adults with and without dyslexia. Thus, altered brain development typically observed in older children and adults with dyslexia is already present in children at risk for developing dyslexia before formally learning to read.

Etiological Theories of Dyslexia and Supporting Evidence From Brain Imaging Studies

  1. Phonological deficit: Difficulties processing and manipulating sound structure of words have been linked to developmental dyslexia across a wide number of behavioral studies. Furthermore, phonological training has led to improvements in reading skill (Duff, Hayiou-Thomas, & Hulme, 2012). In neuroimaging studies of adults and school-aged children, the part of the brain responsible for receptive language, the left temporal-parietal network, consistently demonstrates abnormal structure and function in dyslexics. To address whether these brain differences are evident before learning to read, a few MRI studies have evaluated prereaders at risk for dyslexia. Interestingly, structural and functional differences in the left temporal-parietal network of prereaders have been not only observed, but also found to be related to phonological processing performance (Raschle, Chang, & Gaab, 2011; Raschle, Zuk, & Gaab, 2012).
  2. Orthographic deficit: Difficulties processing whole words are evident in dyslexics' poor scores on fluency measures. The part of the brain responsible for recognizing whole words, the visual word form area in the left occipito-temporal region (Dehaene & Cohen, 2011), is less active in dyslexic relative to skilled readers. Even in prereaders, brain structure and function in this region are reduced in children at risk for developing dyslexia (Im, Raschle, Smith, Ellen Grant, & Gaab, 2016; Raschle et al., 2011).
  3. Perceptual and other deficits: Difficulties in sensory perception of auditory and visual information have been proposed as potential precursors of dyslexia, yet few neuroimaging studies have investigated this theory in prereaders. Although deficits in visual-spatial attention abilities (Franceschini, Gori, Ruffino, Pedrolli, & Facoetti, 2012) and processing of nonlinguistic auditory stimuli (White-Schwoch & Kraus, 2013) have been reported in prereaders, no evidence supports a direct link between perceptual deficits and dyslexia.


  1. Brain areas found in prereaders (based on family history) are not significantly discrepant from the areas implicated for later readers who have struggled for many years, suggesting that there may be brain markers of their at-risk status even before learning to read.
  2. Although intriguing, we cannot yet identify dyslexic prereaders with neuroimaging. Such results should be considered in conjunction with other findings that show malleability following intervention (Simos et al., 2006).
  3. At present, behavioral methods and family history data remain crucial for identifying high-risk prereaders and providing reading intervention as early as feasible.
  4. Whether brain differences evident in at-risk prereaders selectively remain in those who do develop dyslexia (and disappear in those at risk who do not develop dyslexia) remains to be investigated.


Dehaene, S., & Cohen, L. (2011). The unique role of the visual word form area in reading. Trends in Cognitive Sciences, 15(6), 254–262. doi:10.1016/j.tics.2011.04.003

Duff, F. J., Hayiou-Thomas, M. E., & Hulme, C. (2012). Evaluating the effectiveness of a phonologically based reading intervention for struggling readers with varying language profiles. Reading and Writing, 25(3), 621–640. doi:10.1007/s11145-010-9291-6

Franceschini, S., Gori, S., Ruffino, M., Pedrolli, K., & Facoetti, A. (2012). A causal link between visual spatial attention and reading acquisition. Current Biology, 22(9), 814–819. doi:10.1016/j.cub.2012.03.013

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. doi:10.1093/cercor/bhu305

Raschle, N. M., Chang, M., & Gaab, N. (2011). Structural brain alterations associated with dyslexia predate reading onset. NeuroImage, 57(3), 742–749. doi:10.1016/j.neuroimage.2010.09.055

Raschle, N. M., Zuk, J., & Gaab, N. (2012). Functional characteristics of developmental dyslexia in left-hemispheric posterior brain regions predate reading onset. Proceedings of the National Academy of Sciences, 109(6), 2156–2161. doi:10.1073/pnas.1107721109

Simos, P. G., Fletcher, J. M., Denton, C., Sarkari, S., Billingsley-Marshall, R., & Papanicolaou, A. C. (2006). Magnetic source imaging studies of dyslexia interventions. Developmental Neuropsychology, 30(1), 591–611. doi:10.1207/s15326942dn3001_4

White-Schwoch, T., & Kraus, N. (2013). Physiologic discrimination of stop consonants relates to phonological skills in pre-readers: A biomarker for subsequent reading ability? Frontiers in Human Neuroscience, 7(899), 1–9. doi:10.3389/fnhum.2013.00899

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