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Volume 7, Issue 2
April 2018
By Fleming Peck, Alicia Leong, Leo Zekelman, Fumiko Hoeft M.D. Ph.D.
INTRODUCTION
Consensus among researchers is that dyslexia is neurobiological in its origin and evidence-based reading interventions are currently the most effective treatments. Use of effective reading interventions are likely to result in either recovery, compensation, or both.
Recovery is usually defined as the normalization of weak component processes of reading (e.g., phonological processing) and reading-related brain networks. Compensation, different from what is typically considered to be a process of recovery, refers to the use of alternative strategies or brain mechanisms that lead to improvement in reading. This article presents current findings related to the neural mechanisms underlying the compensatory strategies used by individuals with dyslexia.
Research studies report that individuals with dyslexia use a variety of compensatory strategies. Linguistic strategies include the use of lexical context (Bernstein, 2009; Frith & Snowling, 1983; Nation & Snowling, 1998), semantic knowledge (Snowling, Bishop, & Stothard, 2000), morphological awareness (Casalis, Colé, & Sopo, 2004; Cavalli, Duncan, Elbro, El Ahmadi, & Colé, 2017; Elbro & Arnbak, 1996; Law, Wouters, & Ghesquière, 2015), language comprehension, and articulation (Cowan et al., 2017; Hancock, Richlan, & Hoeft, 2017; Pugh et al., 2000; Richlan, Kronbichler, & Wimmer, 2011). Nonlinguistic strategies may also support compensation (i.e., the use of visual memory, declarative memory [Ullman & Pullman, 2015], and conscious effort [Nicolson & Fawcett, 1994]).
Many of these strategies that are related to reading are also used by readers without dyslexia. Should these then be called compensatory strategies?
It appears that individuals with dyslexia use these strategies to a greater extent, or until a later age, than those without dyslexia. For example, (sub)vocalization is a common strategy children with and without dyslexia use (Besner, 1987), but adults with dyslexia may continue to utilize this strategy, unlike typical adult readers (Cowan et al., 2017; Hancock et al., 2017; Pugh et al., 2000; Richlan et al., 2011). Using this justification then, these strategies may fall under the category of compensatory strategies. Comparison of the underlying neurobiological mechanisms of individuals with dyslexia who use compensatory strategies that result in improved reading, individuals with dyslexia who have persistently poor reading skills and do not employ compensatory strategies, and typical readers may shed light on this controversy.
The results of research studies that examine the neural mechanisms underlying the compensatory strategies used by individuals with dyslexia have thus far been equivocal. This is partially due to vague definitions used in the literature. There is a great need for more rigorous and systematic research in this area. An increased understanding of these neural processes may help in the following ways:
- Increase the understanding of the mechanisms individuals with dyslexia use when their reading is improving,
- Explain why specific interventions are more effective with some individuals with dyslexia than others, and
- Reveal how to make currently available interventions more effective.
At least four types of neuroscientific research are relevant to understanding the effects of using compensatory strategies during reading for individuals with dyslexia:
- Increased neural activation and connectivity in those with dyslexia compared to those without Changes in neural patterns in response to effective intervention
- Changes in neural patterns and development of compensatory mechanisms over time
- Neural patterns that predict positive outcomes (Figure 1).
BRAIN-RELATED CHARACTERISTICS OF COMPENSATION IN DYSLEXIA
Increased Neural Activation and Connectivity
Increased neural activity in individuals with dyslexia, compared to those without dyslexia, is known as hyperactivation. In individuals with dyslexia, this hyperactivation generally occurs in regions typically not involved in reading. Increases in connectivity patterns within and between deficient reading networks may also be found in those with dyslexia. These hyperactivation patterns have been proposed as indicators of the use of compensatory mechanisms (Richlan, Kronbichler, & Wimmer, 2009). Increased hyperactivation in individuals with dyslexia has been observed in the following neural areas:
- Frontal regions typically not involved in dyslexia that may support rote memory for recognition of real words (S. E. Shaywitz et al., 2003)—whether these are effective strategies or simply reflect effort is unknown.
- Frontal-subcortical networks (often in the premotor cortex and striatum deep under the cortex) that may reflect articulatory strategies (sub- or overt vocalization) used during reading—which is a known and successful strategy used in younger children without dyslexia (Hancock et al., 2017).
- Right hemisphere regions, particularly on the opposite side of the primary left temporo-parietal region and related to poor phonological processing—which is generally more involved in the process of beginning reading (Démonet, Taylor, & Chaix, 2004; Pugh et al., 2000; S. E. Shaywitz & Shaywitz, 2005)—and may reflect a developmental delay or the need for increased effort.
Currently, there is a lack of consensus among researchers regarding how these overactive (hyperactive) neural patterns relate to use of the cognitive strategies that produce them. Further, there has been little research done to connect these hyperactivation patterns to behavioral improvement. Considered together, however, these studies show multiple neural pathways that may be recruited by people with dyslexia to compensate. These findings are in line with the idea that dyslexia is not a single phenotype with weakness in one neural pathway, but rather involves many interacting cognitive and neural processes (Pennington, 2006). This is likely to apply to compensatory strategies as well.
Together, these multiple interacting processes and compensatory mechanisms may jointly predict an individual’s ability to read.
Change in Neural Patterns as Response to Effective Intervention
Compensation may also be considered within the context of response to intervention. Following reading intervention, changes in neural patterns typically occur (Barquero, Davis, & Cutting, 2014). This may show up as normalization (or reversal/recovery) in the activity of pathways deficient in people with dyslexia. These pathways include the left temporo-parietal regions that are important for phonological processing and the occipito-temporal regions important for orthographic processing. For example, poor readers who improve with intervention show a normalization of typical brain activity and connectivity in regions such as the left temporo-parietal region (Simos et al., 2007).
Response to intervention may also involve increases in the activation of pathways not involved in reading to a greater extent than in typical readers. If the latter, and this is associated with reading improvement, these neural patterns may be considered compensatory networks (Barquero et al., 2014; Xia, Hancock, & Hoeft, 2017).
For example, when compensated readers with dyslexia were compared to individuals with dyslexia who did not compensate and remained poor readers (i.e., persistent dyslexia), there was increased activation in the right superior frontal and middle temporal and in the anterior cingulate regions (S. E. Shaywitz et al., 2003)—but the functions of these regions are not necessarily clear. Furthermore, the study cited above was not an intervention study; hence, it is unknown whether the increase in activation was due to improved reading or whether it was present before improvement was observed. However, intervention studies of at-risk children (Yamada et al., 2011) and adults with dyslexia (Eden et al., 2004) have shown increased activation in the right temporo-parietal region (in addition to normalization of the left temporo-parietal region in adults).
Together, the findings tentatively suggest that the right hemisphere may play a key role in compensation.
On the other hand, some researchers have shown that after intervention, particularly in those who responded positively to the intervention (Odegard, Ring, Smith, Biggan, & Black, 2008), right temporo-parietal and middle temporal gyrus activation actually decreased, rather than increased (B. A. Shaywitz et al., 2002). Further, those with dyslexia who did not respond to interventions showed ineffective patterns of activation (e.g., in regions such as the frontal regions of both hemispheres and the right temporo-parietal region (Simos et al., 2007). Researchers have also found atypical neural signatures in those who do not end up compensating. For example, compared to typical readers who never had dyslexia, individuals with persistent dyslexia had greater reliance on the left occipital region and left occipital to right inferior frontal connectivity.
If we only compare those with and without dyslexia, … some of the hyperactivation reported may reflect maladaptive mechanisms rather than successful compensation. Or it could simply be … due to increased attention and effort required …
Importantly, while many studies have shown increased activation in the right temporo-parietal region after intervention (along with normalization of the deficient left temporo-parietal region), surprisingly, these regions have not surfaced in the metanalysis of reading intervention (Barquero et al., 2014). Recovery of deficient dyslexia-specific regions has also not been observed in the metanalaysis of reading intervention (Xia et al., 2017).
These rather puzzling results suggest that either there are large individual differences in the networks that change with interventions and/or much more work is needed to determine the specific neural mechanisms involved in the response to specific interventions. Perhaps an analysis of the components (language content, principles of instructional delivery, degree of integration, emphasis on skills versus functional use, etc.) of various interventions will shed light on these enduring questions.
Neural Patterns and Development of Compensatory Mechanisms Over Time
Compensatory mechanisms in dyslexia are believed to develop over time. Support for this evidence comes from studies showing that hyperactivation in the left and right inferior frontal gyri in individuals with dyslexia correlated with age (B. A. Shaywitz et al., 2002). Interestingly, a later meta-analysis showed hyperactivation in both the ventral and dorsal aspects of the left precentral gyrus in children. In adults, hyperactivation in different regions of the dorsal aspect of the left precentral gyrus and in the bilateral subcortical regions were observed. The findings in the subcortical regions in the basal ganglia, including the caudate, showed a statistically significant increase in hyperactivation in adults compared to children with dyslexia.
This suggests an increased reliance on using articulatory processes as compensatory strategies as these individuals become older ( (Richlan et al., 2011). However, such changes need to be interpreted with caution as this observation could reflect many factors (e.g., general developmental changes in the brain, use of different cognitive compensatory strategies at different stages of development).
Neural Pathways That Predict Positive Outcomes
Finally, identifying neural pathways that predict positive outcomes in individuals with dyslexia may be another way to identify compensatory mechanisms. For example, one two-and-a-half-year study found that a combination of greater right inferior frontal gyrus activity and stronger white matter integrity in the right superior longitudinal fasciculus (a fiber that connects the temporo-parietal and frontal regions) predicted greater improvement in word identification for children with dyslexia, but not for typical readers (Hoeft et al., 2011). Further, right temporo-parietal and middle temporal activation (Rezaie et al., 2011), along with bilateral (right and left) inferior frontal connectivity (Farris et al., 2011), have all predicted a positive outcome in dyslexia.
Several studies have begun to examine the neural patterns of pre-readers as potential markers for predicting reading outcomes inside and outside of the neural regions typically related to reading. For example, greater left hemisphere grey matter in lower-level sensory areas, responsible for auditory and visual processing and core executive functions, have predicted the development of dyslexia. These areas have not typically been considered to be within the reading network (Clark et al., 2014).
These studies also hold potential, from the perspective of the use of neural markers, for earlier diagnosis of dyslexia and outcome prediction. Nonetheless, the patterns are quite divergent, and it is currently unclear how these relate to the neural patterns identified through other studies.
CONCLUSION
Many remediation approaches currently recommended for students with dyslexia include evidence-based, phonics-based, and more comprehensive structured literacy interventions. Consensus among researchers is that these interventions are by far the most effective treatment. While the study of compensatory neural mechanisms in dyslexia is just emerging, findings to date suggest that students with dyslexia may use a variety of neural pathways and strategies to achieve successful reading skills—and these may include development of compensatory strategies.
With further research, we may be able to identify a scientific foundation strong enough to design appropriate and personalized interventions for all individuals with dyslexia—interventions that take advantage of both characteristics within individual learners (e.g., subtype [phenotype], personal learning strategies, and optimal [positive] compensatory mechanisms) and components of the interventions themselves (e.g., language content, principles of instructional delivery, degree of integration, emphasis on skills versus functional use, etc.). Results of these studies may be particularly useful to individuals with dyslexia for whom current intervention programs have been inadequate. Such research may also reveal clearer links between current research on effectiveness of reading interventions and, for example, the brain mechanisms underlying the beneficial effects of assistive technology and accommodation.
REFERENCES
Bach, S., Richardson, U., Brandeis, D., Martin, E., & Brem, S. (2013). Print-specific multimodal brain activation in kindergarten improves prediction of reading skills in second grade. Neuroimage, 82, 605–615. doi:10.1016/j.neuroimage.2013.05.062
Barquero, L. A., Davis, N., & Cutting, L. E. (2014). Neuroimaging of reading intervention: a systematic review and activation likelihood estimate meta-analysis. Plos One, 9(1), e83668. doi:10.1371/journal.pone.0083668
Bernstein, S. E. (2009). Phonology, decoding, and lexical compensation in vowel spelling errors made by children with dyslexia. Reading and Writing, 22(3), 307–331. doi:10.1007/s11145-008-9116-z
Besner, D. (1987). Phonology, lexical access in reading, and articulatory suppression: A critical review. The Quarterly Journal of Experimental Psychology Section A, 39(3), 467–478. doi:10.1080/14640748708401799
Casalis, S., Colé, P., & Sopo, D. (2004). Morphological awareness in developmental dyslexia. Annals of Dyslexia, 54(1), 114–138.
Cavalli, E., Duncan, L. G., Elbro, C., El Ahmadi, A., & Colé, P. (2017). Phonemic-morphemic dissociation in university students with dyslexia: An index of reading compensation? Annals of Dyslexia, 67(1), 63–84. doi:10.1007/s11881-016-0138-y
Clark, K. A., Helland, T., Specht, K., Narr, K. L., Manis, F. R., Toga, A. W., & Hugdahl, K. (2014). Neuroanatomical precursors of dyslexia identified from pre-reading through to age 11. Brain: A Journal of Neurology, 137(Pt 12), 3136–3141. doi:10.1093/brain/awu229
Cowan, N., Hogan, T. P., Alt, M., Green, S., Cabbage, K. L., Brinkley, S., & Gray, S. (2017). Short-term memory in childhood dyslexia: Deficient serial order in multiple modalities. Dyslexia, 23(3), 209–233. doi:10.1002/dys.1557
Démonet, J.-F., Taylor, M. J., & Chaix, Y. (2004). Developmental dyslexia. Lancet, 363(9419), 1451–1460. doi:10.1016/S0140-6736(04)16106-0
Eden, G. F., Jones, K. M., Cappell, K., Gareau, L., Wood, F. B., Zeffiro, T. A., … Flowers, D. L. (2004). Neural changes following remediation in adult developmental dyslexia. Neuron, 44(3), 411–422. doi:10.1016/j.neuron.2004.10.019
Elbro, C., & Arnbak, E. (1996). The role of morpheme recognition and morphological awareness in dyslexia. Annals of Dyslexia, 46(1), 209–240. doi:10.1007/BF02648177
Farris, E. A., Odegard, T. N., Miller, H. L., Ring, J., Allen, G., & Black, J. (2011). Functional connectivity between the left and right inferior frontal lobes in a small sample of children with and without reading difficulties. Neurocase: Case Studies in Neuropsychology, Neuropsychiatry, and Behavioural Neurology, 17(5), 425–439. doi:10.1080/13554794.2010.532141
Frith, U., & Snowling, M. (1983). Reading for meaning and reading for sound in autistic and dyslexic children. British Journal of Developmental Psychology, 1(4), 329–342. doi:10.1111/j.2044-835X.1983.tb00906.x
Hancock, R., Richlan, F., & Hoeft, F. (2017). Possible roles for fronto-striatal circuits in reading disorder. Neuroscience and Biobehavioral Reviews, 72, 243–260. doi:10.1016/j.neubiorev.2016.10.025
Hoeft, F., McCandliss, B. D., Black, J. M., Gantman, A., Zakerani, N., Hulme, C., … Gabrieli, J. D. E. (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. doi:10.1073/pnas.1008950108
Law, J. M., Wouters, J., & Ghesquière, P. (2015). Morphological awareness and its role in compensation in adults with dyslexia. Dyslexia, 21(3), 254–272. doi:10.1002/dys.1495
Myers, C. A., Vandermosten, M., Farris, E. A., Hancock, R., Gimenez, P., Black, J. M., … Hoeft, F. (2014). White matter morphometric changes uniquely predict children’s reading acquisition. Psychological Science, 25(10), 1870–1883. doi:10.1177/0956797614544511
Nation, K., & Snowling, M. J. (1998). Individual differences in contextual facilitation: Evidence from dyslexia and poor reading comprehension. Child Development, 69(4), 996–1011. doi:10.1111/j.1467-8624.1998.tb06157.x
Nicolson, R. I., & Fawcett, A. J. (1994). Comparison of deficits in cognitive and motor skills among children with dyslexia. Annals of Dyslexia, 44(1), 147–164. doi:10.1007/BF02648159
Odegard, T. N., Ring, J., Smith, S., Biggan, J., & Black, J. (2008). Differentiating the neural response to intervention in children with developmental dyslexia. Annals of Dyslexia, 58(1), 1–14. doi:10.1007/s11881-008-0014-5
Pennington, B. F. (2006). From single to multiple deficit models of developmental disorders. Cognition, 101(2), 385–413. doi:10.1016/j.cognition.2006.04.008
Pugh, K. R., Mencl, W. E., Jenner, A. R., Katz, L., Frost, S. J., Lee, J. R., … Shaywitz, B. A. (2000). Functional neuroimaging studies of reading and reading disability (developmental dyslexia). Mental Retardation and Developmental Disabilities Research Reviews, 6(3), 207–213. doi:10.1002/1098-2779(2000)6:3<207::AID-MRDD8>3.0.CO;2-P
Rezaie, R., Simos, P. G., Fletcher, J. M., Cirino, P. T., Vaughn, S., & Papanicolaou, A. C. (2011). Temporo-parietal brain activity as a longitudinal predictor of response to educational interventions among middle school struggling readers. Journal of the International Neuropsychological Society, 17(5), 875–885. doi:10.1017/S1355617711000890
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. doi:10.1002/hbm.20752
Richlan, F., Kronbichler, M., & Wimmer, H. (2011). Meta-analyzing brain dysfunctions in dyslexic children and adults. Neuroimage, 56(3), 1735–1742. doi:10.1016/j.neuroimage.2011.02.040
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.
Shaywitz, S. E., & Shaywitz, B. A. (2005). Dyslexia (specific reading disability). Biological Psychiatry, 57(11), 1301–1309. doi:10.1016/j.biopsych.2005.01.043
Shaywitz, S. E., Shaywitz, B. A., Fulbright, R. K., Skudlarski, P., Mencl, W. E., Constable, R. T., … Gore, J. C. (2003). Neural systems for compensation and persistence: Young adult outcome of childhood reading disability. Biological Psychiatry, 54(1), 25–33. doi:10.1016/S0006-3223(02)01836-X
Simos, P. G., Fletcher, J. M., Sarkari, S., Billingsley, R. L., Denton, C., & Papanicolaou, A. C. (2007). Altering the brain circuits for reading through intervention: A magnetic source imaging study. Neuropsychology, 21(4), 485–496. doi:10.1037/0894-4105.21.4.485
Snowling, M., Bishop, D. V., & Stothard, S. E. (2000). Is preschool language impairment a risk factor for dyslexia in adolescence? Journal of Child Psychology and Psychiatry, and Allied Disciplines, 41(5), 587–600.
Ullman, M. T., & Pullman, M. Y. (2015). A compensatory role for declarative memory in neurodevelopmental disorders. Neuroscience and Biobehavioral Reviews, 51, 205–222. doi:10.1016/j.neubiorev.2015.01.008
Xia, Z., Hancock, R., & Hoeft, F. (2017). Neurobiological bases of reading disorder Part I: Etiological investigations. Language and Linguistics Compass, 11(4), e12239. doi:10.1111/lnc3.12239
Yamada, Y., Stevens, C., Dow, M., Harn, B. A., Chard, D. J., & Neville, H. J. (2011). Emergence of the neural network for reading in five-year-old beginning readers of different levels of pre-literacy abilities: An fMRI study. Neuroimage, 57(3), 704–713. doi:10.1016/j.neuroimage.2010.10.057
BIOS
Fleming Peck is an intern at UCSF brainLENS and a sophomore at Princeton University. She plans to major in neuroscience and complete a minor in cognitive science to combine study in neuroscience, psychology, and philosophy. She hopes to focus on her interests in cognitive neuroscience and fMRI following her undergraduate graduation with the intention of studying the cognitive development of infants and children at-risk for and diagnosed with mental illnesses and neurodevelopmental disorders.
Alicia Leong is an intern at UCSF brainLENS and a second-year undergraduate student at Rice University pursuing a cognitive sciences major and a poverty, justice, and human capabilities minor. She is interested in understanding how chronic childhood adversity can affect development and amplify lifelong physical and mental health disparities. By combining knowledge from neuroscience, psychology, medicine, and education, she hopes to contribute to efforts seeking to counteract the effects of toxic stress, particularly neurodevelopmental disruption.
Leo Zekelman received a B.A. in cognitive science with a minor in Spanish from the University of Michigan, Ann Arbor. Currently, Leo works at UCSF brainLENS as the bilingual neuroscience research program manager. Leo wants to learn how the bilingual neural network of young children interacts with the development of their reading neural network. Leo is particularly interested in the impact bilingualism may have on the expression of neurobiological disorders.
Fumiko Hoeft M.D., Ph.D. is a professor of child and adolescent psychiatry at Weill Institute for Neurosciences, director of the UCSF Laboratory for Educational Neuroscience (brainLENS.org), deputy director of the UCSF Dyslexia Center (dyslexia.ucsf.edu), executive director of the UC-Stanford Multi-University Precision Learning Center (PrecL.org), and co-director of Haskins Global L2 (Language & Literacy) Innovation Hub. She is also a board member/scientific advisor for the International Dyslexia Association (IDA) and the National Center for Learning Disabilities (NCLD) and a research scientist at Haskins Laboratories. Her team is interested in how cognitive science can inform educational and clinical practices, with specific interests in understanding the neurobiological cause of dyslexia, early identification and prediction, and the emotional resiliency necessary to succeed. Honors include the 2014 Norman Geschwind Award from IDA, 2015 Transforming Education through Neuroscience Award from Learning & the Brain Foundation, 2017 Multicampus Research Program Award from the University of CA Office of the President, and the 2018 Honor for Significant Contribution from the Northern CA Branch of the IDA (NCBIDA). Her work has been widely covered in the media, including the New York Times, NPR, CNN, the New Yorker, and Scientific American.
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