Brain, Vol. 126, No. 2, 482-494,
February 2003
© 2003 Guarantors of Brain
doi: 10.1093/brain/awg026
Anatomical correlates of dyslexia: frontal and cerebellar findings
1 Department of Neuroscience, McKnight Brain Institute of the University of Florida, Gainesville, FL, 2 Departments of Radiology and 3 Education, University of Washington, Seattle, WA, USA
Correspondence to: Mark Eckert, PO Box 100244, Department of Neuroscience, University of Florida McKnight Brain Institute, Gainesville, FL 32610, USA E-mail: eckert{at}ufl.edu
Received August 7, 2002. Accepted August 22, 2002.
 |
Summary |
|---|
 Top Summary IntroductionMethodsResultsDiscussionReferences |
|---|
In this study, we examined the neuroanatomy of dyslexic (14 males, four females) and control (19 males, 13 females) children in grades 4–6 from a family genetics study. The dyslexics had specific deficits in word reading relative to the population mean and verbal IQ, but did not have primary language or motor deficits. Measurements of the posterior temporal lobe, inferior frontal gyrus, cerebellum and whole brain were collected from MRI scans. The dyslexics exhibited significantly smaller right anterior lobes of the cerebellum, pars triangularis bilaterally, and brain volume. Measures of the right cerebellar anterior lobe and the left and right pars triangularis correctly classified 72% of the dyslexic subjects (94% of whom had a rapid automatic naming deficit) and 88% of the controls. The cerebellar anterior lobe and pars triangularis made significant contributions to the classification of subjects after controlling for brain volume. Correlational analyses showed that these neuroanatomical measurements were also significantly correlated with reading, spelling and language measures related to dyslexia. Age was not related to any anatomical variable. Results for the dyslexic children from the family genetics study are discussed with reference to dyslexic adults from a prior study, who were ascertained on the basis of a discrepancy between phonological coding and reading comprehension. The volume of the right anterior lobe of the cerebellum distinguished dyslexic from control participants in both studies. The cerebellum is one of the most consistent locations for structural differences between dyslexic and control participants in imaging studies. This study may be the first to show that anomalies in a cerebellar-frontal circuit are associated with rapid automatic naming and the double-deficit subtype of dyslexia.
Keywords: dyslexia; MRI; cerebellum; frontal lobe; rapid automatic naming
Abbreviations: ANT = anterior lobe; PTR = pars triangularis; RAN = Rapid Naming of Letters; RAS = Alternating/Switching Letters and Numbers test
|
Introduction |
|---|
|
TopSummaryIntroductionMethodsResultsDiscussionReferences |
|---|
Structural MRI studies of dyslexic adolescents and adults have demonstrated differences from controls in a variety of brain regions, including the inferior frontal gyrus, cerebellum, insula, caudate, corpus callosum, left temporal lobe and thalamus (Pennington et al., 1999
; Eliez et al., 2000; Brown et al., 2001; Leonard et al., 2001; Rae et al., 2002; Robichon et al., 2000a, b). The cerebellar and inferior frontal gyrus findings have been most consistent, but few studies have measured a broad range of structures and no study has tried to replicate findings prospectively in a new sample.
The widespread neuroanatomical differences between adult dyslexic and control brains support a multivariate approach to characterizing the dyslexic brain. Leonard and colleagues (Leonard et al., 2001) used such an analysis in a study examining the morphology of language-related areas in a group of dyslexic college students who were characterized by a discrepancy between their poor phonological decoding ability and reading comprehension. Compared with controls, dyslexic subjects were more likely to exhibit a duplication of the left Heschl’s gyrus, extreme leftward asymmetry of the planum temporale and parietale, small right cerebellar anterior lobes and leftward cerebral asymmetry (Leonard et al., 2001). The probability of a dyslexia diagnosis increased with the additional presence of each of these anatomical measures (Leonard et al., 2001). Frontal lobe measurements were not collected in the study of Leonard and colleagues.
Although multiple structural differences have been identified in dyslexics compared with controls, it is not clear whether these findings map onto the behavioural deficits exhibited by dyslexics. These include problems in phonological coding (Shaywitz et al., 1998; Wagner et al., 2000) and rapid naming of letters or switching letters and numbers (Wolf, 1986). Wolf and colleagues (Wolf et al., 2002) examined 144 severely reading-impaired dyslexics from grades 2 and 3 who exhibited a discrepancy between their reading achievement and cognitive ability. Sixty per cent of these children demonstrated a double deficit in phonological and rapid naming skills. The inferior frontal gyrus and cerebellar findings (Brown et al., 2001; Leonard et al., 2001) could explain the deficits in phonological accuracy, rate or both.
This study was designed to replicate the study of Leonard and colleagues (Leonard et al., 2001) in a group of dyslexic children characterized by poor reading skills but average to superior verbal intelligence. A second goal of this study was to determine the probability of a dyslexia diagnosis for each of the anatomical measures, including a measure of the inferior frontal gyrus [pars triangularis (PTR)]. Finally, this study examined the relation between anatomical measures and measures of reading, spelling, verbal intelligence and selected language skills related to reading.
|
Methods |
|---|
|
TopSummaryIntroductionMethodsResultsDiscussionReferences |
|---|
Participants
The University of Washington Institutional Review Board approved this project and participant consent was obtained according to the Declaration of Helsinki. Participants and parents provided written informed consent. The 18 dyslexic participants were recruited from probands in a family genetics study. To qualify as a proband, children had to meet these inclusion criteria for dyslexia: a difference of at least one standard deviation between pro-rated verbal IQ (verbal comprehension factor) and at least one reading or spelling measure. Although this was the only formal requirement for inclusion, the dyslexic probands in the participating families were actually discrepant on multiple reading and writing measures, had deficits on language markers associated with dyslexia (phonological, orthographic and rapid automatic naming tasks), and a history of difficulty in learning to read despite special help in reading (Table 4). For further information on subject ascertainment see the study by Berninger and colleagues (Berninger et al., 2001
). All dyslexic participants had pro-rated verbal IQs (verbal comprehension factor) in the average to superior range (mean 110.8, range 92–129).
|
Dyslexia was defined as underachievement relative to verbal IQ rather than performance IQ because prior research showed that verbal IQ is a better predictor than performance IQ of reading achievement in referred (Greenblatt et al., 1990
) and unreferred (Wechsler, 1991; Vellutino et al., 2001) populations. The pro-rated verbal IQ, which is based on all the same subtests (except Arithmetic) as the verbal IQ, was used so that we would have a pure measure of the Verbal Comprehension Factor (Wechsler, 1991) not confounded with number reasoning. This approach allowed us to document that word reading was underdeveloped compared with verbal reasoning in the functional reading system (Berninger, 2001b) of these children with reading disability.
Additional clinical measures confirmed that this sample had specific reading disability (dyslexia) rather than reading problems related to primary language disability. The sample was impaired in both accuracy [see Table 4 for WRMT-R (Woodcock Reading Mastery Test—Revised) Word Identification and Word Attack] and rate of oral reading of words [mean 82.8, SD 10.4, sight word efficiency; mean 82.4, SD 8.2, real word efficiency (TOWRE, Test of Word Reading Efficiency; Torgesen et al., 2001)] and text [mean 3.9, SD 3.2, rate; mean 4.4, SD 2.0 (GORT-3, Gray Oral Reading Test; Wiederholt and Bryant, 1992)] and in spelling [mean 80.8, SD 7.8 (WRAT3, Wide Range Achievement Tests—Revised; Wilkinson, 1993); mean 78.7, SD 8.5 (WIAT II, Wechsler Individual Achievement Test, 2nd edition; Psychological Corporation, 2001)]. The sample was not impaired (at or near the mean) in handwriting (three measures used in our research programme for over a decade), expressive language [mean 10.3, SD 2.5 (CELF-3, Clinical Evaluation of Language Fundamentals—Third Edition, Formulated Sentences; Seml et al., 1995], oral-motor planning (measure of repetition of alternating consonant–vowel syllables) or fine-motor planning [sequential finger movements (Finger Succession; Berninger, 2001b)]. None had a history of language delay or disability in the preschool years. Yet they were impaired (at or near a standard deviation below the mean) in phonological coding (Table 4), orthographic coding [see Table 4 for expressive coding and 28th percentile (Word Choice; Berninger, 2001b)] and rapid automatic naming (see Table 4), the three markers associated with the language phenotype for dyslexia (Berninger et al., 2001).
The 32 control subjects were recruited with advertisements. Parental interviews helped to screen for control children who learned to read with ease. Standardized tests of reading and the Wechsler Intelligence Scales for Children—III (WISC III) vocabulary subtest, as an estimate of the verbal comprehension factor, were then used to determine that controls read well for their age and ability. Six controls and one dyslexic did not receive a full behavioural testing battery. In addition, poor image quality precluded anatomical data collection for one male dyslexic. Age of the control and dyslexic children was not significantly different (dyslexic males, 135.4 months; dyslexic females, 138.3 months; control males, 134.9 months; control females, 139.8 months). One dyslexic child had a diagnosis of attention deficit disorder.
Psychometric measures
The psychometric battery included measures of phonological, rapid naming, orthographic and verbal ability. Accuracy for single-word (Word Identification) and pseudoword (Word Attack) reading was assessed with the WRMT-R (Woodcock, 1987). Spelling ability was determined with the WRAT-3 (Wilkinson, 1993). Phonological awareness was assessed using a prepublication version of Elision from the Comprehensive Test of Phonological Processing (CTOPP) (Wagner et al., 2000). Orthographic processing was tested using the Process Assessment of the Learner (PAL-RW) (Berninger, 2001a). Rapid automatic naming was assessed using the Rapid Naming of Letters (RAN) and Alternating/Switching Letters and Numbers test (RAS) (Wolf, 1986). Pro-rated verbal IQ (all Verbal Scale subtests except Arithmetic) was obtained using the Wechsler Intelligence Scales for Children—III (WISC-III) (Wechsler, 1991). Dyslexic hand preference was established using the Edinburgh handedness questionnaire (Oldfield, 1971). All but one dyslexic were strongly right-handed. Writing hand was used as an index of handedness for the controls, and all were right-handed.
MRI data acquisition
Sagittal images (1.2 mm) were acquired using a GE Signa 1.5 tesla scanner (version 5.8) (General Electric, Milwaukee, WI, USA) using a 3D fast spoiled gradient echo pulse sequence. Imaging parameters were as follows: TR (repetition time) 11.1 ms, TE (echo time) 2.2 ms, flip angle 25°, field of view 24 cm. The entire acquisition time was 4 min 36 s.
The images were assigned a random blind number to hide identifying information from anatomists at the University of Florida. Programs written in PV-Wave (Visual Numerics, Boulder, CO, USA) processed the electronically transferred images and placed the images into a single file. The Talairach proportional grid method was used to examine comparable brain regions across subjects (Talairach and Tornoux, 1988). The Talairach system standardizes positions by relating them to an atlas brain where the horizontal plane intersects the anterior and posterior commissure. The scans were reformatted into a final set of 1 mm thick sagittal images to adjust for head tip in each plane of section.
Surface area and volume measurements
Cerebral and cerebellar hemispheres
The volume of each hemisphere was estimated by tracing around the circumference of every sagittal 1 mm section starting at the midline. This measure includes cortical and subcortical structures but excludes the brainstem and cerebellum. Intraclass correlation demonstrated that both intrarater and inter-rater reliability were 0.99 for the brain volume measure. The volume of the anterior lobe of the cerebellum was estimated by measuring from the mid-sagittal section to the lateral boundary where the posterior cerebral artery disappeared. The primary sulcus served as the posterior boundary for the measurement. The right anterior lobe volume was used as a predictor variable, as opposed to the asymmetry measure (Leonard et al., 2001), because the right anterior lobe contributed to all of the predictive cerebellar asymmetry variance in the study of Leonard and colleagues. Intraclass correlation demonstrated that both intrarater and inter-rater reliability were 0.95 for the cerebellar measurement.
Planum temporale and planum parietale
The anterior border of the planum temporale (PT) was defined as the depth of the sulcus that is the posterior border of Heschl’s gyrus (Heschl’s sulcus). The posterior boundary was defined as the origin of the posterior ascending ramus or the termination of the sylvian fissure. The planum parietale rises from the termination of the PT into the parietal cortex. The surface areas of the PT and planum parietale were measured sequentially on every section between x = 46 mm and x = 56 mm. These measurement boundaries were chosen to maximize planar asymmetry (Best and Demb, 1999), ensure reliability and replicate previous MRI studies showing cognitive associations with PT measurements (Foundas et al., 1994; Leonard et al., 1996; Eckert et al., 2001). Intraclass correlation demonstrated that intrarater and inter-rater reliability was 0.97 and 0.89, respectively, for the PT measurement.
Heschl’s gyri. Many individuals exhibit two Heschl’s gyri. A study of normal variation for the presence of Heschl duplications (H2) in 105 scans found that the frequency of a left H2 increased continuously from medial to lateral cortex (Leonard et al., 1998). The incidence was 17% at x = 34 (Talairach coordinate for the mediolateral axis) but increased to 56% by x = 48. The surface areas of H1 and H2 were traced between their limiting sulci on consecutive sagittal images between x = 34 and x = 48 because this region exhibited the greatest individual variability in Heschl’s gyrus morphology. Intraclass correlation demonstrated that intrarater and inter-rater reliability was 0.97 and 0.98, respectively, for the Heschl’s gyrus measurement.
Pars triangularis (PTR)
The PTR was measured from 39 to 49 Talairach mm by tracing the surface formed by the anterior ascending ramus (AAR) and the anterior horizontal ramus (AHR) of the sylvian fissure. These are two major branches of the sylvian fissure and thus easily identified. The surface was traced from the tip of the AAR, down to the sylvian fissure and then, following the AHR, to the end. Intraclass correlation demonstrated that intrarater and inter-rater reliability was 0.95 and 0.95, respectively, for the PTR measurement.
Statistics
Multivariate analysis of variance (MANOVA) was used to compare anatomical measurements between the control and dyslexic subjects. Significant anatomical measures from the MANOVA were then used in binary logistic regression analysis to determine the probability of a dyslexia diagnosis on the basis of these anatomical measures. Multiple regression was used to examine quantitative relations between behavioural measures predictive of dyslexia and the anatomical variables.
|
Results |
|---|
|
TopSummaryIntroductionMethodsResultsDiscussionReferences |
|---|
Pearson correlation and t tests were performed to determine if age and gender were related to the anatomical measures. Age was not related to any anatomical variable. Males, however, had larger brain volumes than females [t(1,48) = 3.09, P < 0.005].
Anatomical differences
Table 1 presents descriptive statistics for the brain structure measures by group and gender. The table also includes ANOVA comparisons between the male dyslexic and control subjects. Male dyslexics exhibited a significantly smaller right cerebellar anterior lobe (ANT), right PTR and brain volume compared with male controls. There were too few female dyslexics to compare the female subjects.
|
MANOVA, controlling for gender, was used to test the prediction that dyslexic brain structures differed from controls. Table 2 presents the results for tests of between-subjects effects [multivariate test: F(9,48) = 3.76, P < 0.005]. Dyslexic subjects had a smaller right ANT, smaller right and left PTR and a smaller brain volume. Figure 1 presents images of control and dyslexic brains with the ANT and PTR outlined. The significant interaction between gender and group for the left H2 was due to female controls with large left H2 surface areas. There were no differences between groups for measures of the PT or Heschl’s gyrus.
|
|
Anatomical probability of dyslexia
Hierarchical binary logistic regression was used to determine the odds of group membership on the basis of the anatomical variables that were significantly different between the two groups. The PTR and cerebellar measures were entered in the first level of the regression. Eighty-eight per cent of the control (28 of 32) and 77% of dyslexic (13 of 17) children were classified correctly in step 1 (–2 log likelihood = 34.8; Cox and Snell R2 = 0.441; Nagelkerke R2 = 0.608). Right ANT, left PTR and right PTR each made a unique contribution in predicting group membership (Table 3). Only one additional control participant was correctly classified when cerebral volume and cerebral asymmetry were added to the regression (–2 log likelihood = 33.8; Cox and Snell R2 = 0.452; Nagelkerke R2 = 0.623). The unique contributions of right ANT, left PTR and right PTR to the prediction of group membership were reduced, however, with this addition. The addition of gender did not unmask any anatomical relation to group membership (–2 log likelihood = 29.1; Cox and Snell R2 = 0.502; Nagelkerke R2 = 0.693). Figure 2 shows the probability of group membership based on the contribution of right ANT, left PTR, right PTR, cerebral volume and cerebral asymmetry.
|
|
Anatomical relation to behavioural language measures
The anatomical variables that distinguish dyslexic from control participants were predicted to relate to behavioural measures that characterize dyslexia. Table 4 presents means and standard deviations for the behavioural language measures. Table 5 reports intercorrelations among the behavioural language measures for the entire sample. All of the measures were significantly correlated, except verbal IQ with orthographic coding and naming speed.
|
Table 6 presents correlations for the behavioural variables with the anatomical measures that distinguished dyslexic from control children. The anatomical variables that differentiate dyslexic from control subjects were consistently significantly correlated with real word reading, pseudoword reading and spelling, the three language skills on which the dyslexic children were reliably different from the controls (Table 4). However, of the four reading-related language processes (phoneme elision, orthographic coding, RAN and RAS) only RAN was consistently significantly correlated with the anatomical measures that distinguished dyslexic from control participants.
|
Elision, orthographic coding and RAN were selected as examples of phonological, orthographic and rapid naming skills to determine if the right ANT, left PTR and right PTR accounted for different sources of variance in these language measures. Three linear multiple regressions were performed for each of the three reading-related language measures. The results of these analyses are presented in Table 7. Figures 3, 4 and 5 demonstrate these results graphically. To summarize, 20, 20 and 41% of the variance in elision, orthographic coding and RAN, respectively, was explained by the three anatomical measures. The right ANT, right PTR and left PTR predicted the same variance in elision performance. The right ANT uniquely predicted orthographic coding. Although the right cerebellar anterior lobe and brain volume shared some variance in RAN, the right ANT, right PTR and left PTR all uniquely predicted RAN after controlling for brain volume.
|
|
Double-deficit dyslexics
A dyslexic was classified as having a double deficit if performance was more than 1 standard deviation below their pro-rated verbal IQ on both the elision and the RAN or RAS (Berninger et al., 2001
). Sixty-five per cent of dyslexic children in this study had both phonological awareness and naming speed deficits. Ninety-four per cent of dyslexics had a naming speed deficit. One dyslexic subject had only a phonological awareness deficit. The small number of dyslexic children with only naming speed or phonological awareness deficits prevented statistical comparisons with the double-deficit group. Visual inspection of the anatomical data did not reveal any trends that might distinguish the single-deficit from the double-deficit group. There were two control subjects with below average naming speed. Anecdotally, the left and right PTRs of these two controls were within the dyslexic distribution for these measures. Their right cerebellar anterior lobe and brain volume were within the normal range, however. |
Discussion |
|---|
|
TopSummaryIntroductionMethodsResultsDiscussionReferences |
|---|
This study shows that measures of the right cerebellar anterior lobe and inferior frontal gyrus distinguished dyslexic children from controls with a high probability. The frontal and cerebellar measures each contributed to classifying a subset of dyslexic participants by differentiating them as a group from controls and by predicted reading skill performance across the sample. Although unexamined brain regions may contribute to the risk of developing dyslexia, these findings suggest that deficits in a frontal-cerebellar system could lead to symptoms of dyslexia.
Measures of the temporal lobe, including the PT, did not differentiate dyslexic from control participants. This is at least the eighth study demonstrating that individuals with dyslexia do not exhibit reversed asymmetry of the PT (Leonard et al., 1993; Rumsey et al., 1997; Best and Demb, 1999; Heiervang et al., 2000; Robichon et al., 2000a). Planar asymmetry predicts a wide range of verbal ability (Eckert et al., 2001) but does not predict individual variability in phonology when phonology is discrepant from verbal ability (Eckert and Leonard, 2000).
Multivariate approach
A single anatomical marker that perfectly differentiates dyslexic from control participants has not been reported and is unlikely to be found, given the complex nature of the reading process. Multiple neuroanatomical measures were necessary to predict a diagnosis of dyslexia in children. Each measure accounted for overlapping subsets of dyslexic subjects. These findings may help explain why the structural neuroimaging literature has been plagued by failed replications (Eckert and Leonard, 2000).
Genetic findings for dyslexia support this perspective. Careful phenotyping in genetic studies has resulted in positive linkage between dyslexia and measures of phonological processing with genetic markers on chromosomes 1, 2, 3, 6, 15 and 18 (Cardon et al., 1994; Grigorenko et al., 1997, 2000, 2001; Schulte-Korne et al., 1998; Fagerheim et al., 1999; Fisher et al., 1999, 2002; Gayan et al., 1999; Nothen et al., 1999; Nopola-Hemmi et al., 2000, 2001; Petryshen et al., 2001). One gene or a combination of genes within these marker regions could have developmental effects on a variety of neural reading systems, leading to a common behavioural outcome but heterogeneous neurobiological findings.
Comparison with dyslexic college students
In contrast to a previous study of college-educated dyslexic adults (Leonard et al., 2001), the dyslexic children in this study had smaller brain volumes than controls and an absence of duplicated left Heschl’s gyrus or extreme leftward asymmetry of the PT and planum parietale. The dyslexic college students were defined as having phonological decoding discrepant from their passage comprehension, in contrast to the dyslexic children, who were characterized as having phonological decoding deficits that were discrepant from their verbal IQ. In general, the dyslexic college students had superior passage comprehension and rapid naming performance compared with the dyslexic children in this study. Failure to replicate the duplicated Heschl’s gyrus and exaggerated leftward planar asymmetry findings from the study of Leonard and colleagues (Leonard et al., 2001) could be due to these cognitive differences between the samples. Alternatively, the passage comprehension and rapid naming differences could disappear with age as the dyslexic children learn to compensate for their reading problems. In this case, failure to replicate could be attributable to the post hoc nature of the study of Leonard and colleagues (Leonard et al., 2001).
Age is a possible, but unlikely, explanation for some of the differences between studies. The gyral and sulcal measures are unlikely to exhibit dramatic changes with age. In contrast, a longitudinal study of the dyslexic children might demonstrate protracted increases in brain volume during development.
The one anatomical measure that was comparable in the child and adult dyslexics was that of the right cerebellar anterior lobe (Leonard et al., 2001). This effect may have been due to decreased grey matter rather than white matter. Rae and colleagues (Rae et al., 2002) reported that dyslexic adults failed to exhibit rightward whole cerebellum grey matter asymmetry, in part as a result of reduced volume of right cerebellar grey matter. There was no difference between control and dyslexic subjects for whole cerebellum white matter measures.
The results for cerebellum involvement in reading are consistent with an emerging research literature that shows that the cerebellum activates during learning and not just motor function—the circuits of the cerebellum that are activated during the learning phase differ from those activated during the automatic phase following practice and learning (Nicolson et al., 1999; Poldrack and Gabrielli, 2001). The study by Nicolson and colleagues (Nicolson et al., 1999) showed that dyslexic participants demonstrated less PET activation in the right cerebellar anterior lobe when they performed learned finger movement sequences. The reduced activation volume could be related to reduced anterior lobe size. These findings are consistent with Fawcett and Nicolson’s cerebellar deficit hypothesis, which attributes the cognitive and motor problems exhibited by individuals with dyslexia to abnormal cerebellar development (Nicolson and Fawcett, 1990; Nicolson et al., 2001).
Studies of children with cerebellar tumours demonstrate a critical role for the right cerebellar hemisphere in linguistic performance (Riva and Giorgi, 2000; Scott et al., 2001). For example, small samples of children with right cerebellar tumours exhibited poor verbal and literacy performance, in contrast to children with left cerebellar tumours and spatial deficits (Scott et al., 2001). Akshoomoff and colleagues (Akshoomoff et al., 1992) reported poor naming performance in a child with a right cerebellar hemisphere tumour.
The results are also consistent with the proposal that the cerebellum is affected indirectly in dyslexic adults and children (Ivry and Justus, 2001; Zeffiro and Eden, 2001; Bishop, 2003). The lateral prefrontal cortex, superior temporal sulcus and posterior parietal association cortices project to the cerebellum via pontine nuclei in macaque monkeys (Levin, 1936; Schmahmann, 1997; Schmahmann and Pandya, 1997). The cerebellum also projects to these association cortices through the thalamus (Middleton and Strick, 1997). Cerebellar or frontal anomalies could produce downstream effects on the architecture and function of either region. The dyslexics in this study had problems in learning to read but not in the motor skills associated with learning to read: oral–motor for mouth movements during oral reading and graphomotor for hand movements during spelling. They did have problems in the rate of reading single words and text, which is consistent with the role of the cerebellum in precise timing.
The left and right PTR were smaller in dyslexic than in control children. This finding is consistent with previous research identifying bilateral area and/or volume differences in the inferior frontal gyri of dyslexic and control adults (Robichon et al., 2000a; Brown et al., 2001). These findings support the idea that early frontal lobe dysfunction may play a role in the development of dyslexia. The findings may not be specific to dyslexia, however. Gauger and colleagues (Gauger et al., 1997) reported smaller PTR measurements in children with specific language impairment.
Frontal lobe dysfunction is the centrepiece of the motor-articulatory feedback hypothesis for dyslexia (Heilman et al., 1996). This hypothesis maintains that an inability to associate the position of articulators with speech sounds is due to a lack of awareness regarding the position of articulators during speech. Heilman suggests that left inferior frontal lobe dysfunction could explain dyslexic symptoms related to motor feedback. This argument is supported by evidence showing that (i) patients with left anterior perisylvian lesions are not capable of making grapheme-to-phoneme conversions (Adair et al., 1999), (ii) normal participants exhibit activation of the inferior frontal gyrus during grapheme-to-phoneme conversion (Newman and Twieg, 2001; Fiebach et al., 2002), and (iii) this region is abnormally activated in dyslexic subjects (Shaywitz et al., 1998; Georgiewa et al., 2002).
The anatomical findings suggest that impairments in a frontal-cerebellar network may play a role in delayed reading development in dyslexia. Functional imaging studies show activation in the left inferior frontal and right cerebellar hemisphere during fluency tasks (Schlosser et al., 1998), passive listening to clicks (Ackermann et al., 2001), linguistic working memory tasks (Desmond et al., 1997) and rapid production of consonant–vowel stimuli (Wildgruber et al., 2001). Interestingly, a PET study of aphasic patients with left inferior frontal damage found hypoactivation of the right cerebellar hemisphere (Metter et al., 1987). This network may be important for temporal processing of verbal utterances or more generally for error correction operations. Considering the high percentage of dyslexic children in this sample with the double deficit in rapid automatic naming and phonological awareness (Wolf and Bowers, 1999), this frontal-cerebellar network may be critical to the precise timing mechanism that Wolf and Bowers (1999) hypothesize to underlie the double deficit. Both the orthographic coding and rapid naming tasks, which were predicted by the cerebellar measure, have time constraints: the orthographic task presents words for a brief moment (1 s), during which they must be encoded into short-term memory, and the rapid naming task records time for naming highly familiar alphanumeric symbols.
Dyslexic subtypes could be produced by processing deficits anywhere in the frontal-cerebellar phonological system. The unique contribution of each frontal and cerebellar measure to the classification of dyslexic participants and the prediction of phonological and naming performance supports this view. Each region may play a distinct, but related, role in reading that is responsive to instructional intervention.
Future directions
A twin study of reading-disabled children reported that orthographic coding and phoneme awareness have unique genetic foundations (Gayan and Olson, 2001). This finding suggests that multiple neurobiological pathways for dyslexia could be the consequence of having unique genetic beginnings for orthography and phonology. Anomalous development of a left posterior cerebral-cerebellar network may explain the orthographic deficits. Area V5/MT and the fusiform gyrus process motion (Salzman et al., 1990) and visual word stimuli (Cohen et al., 2000), respectively, and each has been implicated in dyslexia (Eden et al., 1996; Demb et al., 1997; Brunswick et al., 1999; Shaywitz et al., 2002). Future studies will examine whether these brain regions constitute a separate structural network that accounts for the orthographic deficits in dyslexia or whether a common structural network accounts for both orthographic and phonological deficits. Future work will also examine whether functional activation for phonological and orthographic tasks map onto posterior cerebral-cerebellar and frontal-cerebellar structural network(s) for orthographic and phonological processing.
|
Acknowledgements |
|---|
We wish to thank the children and families who participated in this study and the NICHD (National Institute of Child Health and Human Development) for its support (P50 38812–06).
|
|
|
References |
|---|
|
TopSummaryIntroductionMethodsResultsDiscussionReferences |
|---|
Ackermann H, Riecker A, Mathiak K, Erb M, Grodd W, Wildgruber D. Rate-dependent activation of a prefrontal-insular-cerebellar network during passive listening to trains of click stimuli: an fMRI study. Neuroreport 2001; 12: 4087–92.[CrossRef][ISI][Medline]
Adair JC, Schwartz RL, Williamson DJ, Raymer AM, Heilman KM. Articulatory processes and phonologic dyslexia. Neuropsychiatry Neuropsychol Behav Neurol 1999; 12: 121–7.[ISI][Medline]
Akshoomoff NA, Courchesne E, Press GA, Iragui V. Contribution of the cerebellum to neuropsychological functioning: evidence from a case of cerebellar degenerative disorder. Neuropsychologia 1992; 30: 315–28.[CrossRef][ISI][Medline]
Berninger V. Process Assessment of the Learner (PAL) Test Battery for Reading and Writing. San Antonio (TX): Psychological Corporation; 2001a.
Berninger V. Understanding the lexia in dyslexia. Ann Dyslexia 2001b; 51: 23–48.
Berninger V, Abbot R, Thomson J, Raskin W. Language phenotype for reading and writing disability: a family approach. Scient Stud Read 2001; 15: 59–106.
Best M, Demb JB. Normal planum temporale asymmetry in dyslexics with a magnocellular pathway deficit. Neuroreport 1999; 10: 607–12.[ISI][Medline]
Bishop D. Commentary: cerebellar abnormalities in developmental dyslexia: cause, correlate or consequence? Cortex. In press 2003.
Brown WE, Eliez S, Menon V, Rumsey JM, White CD, Reiss AL. Preliminary evidence of widespread morphological variations of the brain in dyslexia. Neurology 2001; 56: 781–3.
Brunswick N, McCrory E, Price CJ, Frith CD, Frith U. Explicit and implicit processing of words and pseudowords by adult developmental dyslexics: a search for Wernicke’s Wortschatz? Brain 1999; 122: 1901–17.
Cardon LR, Smith SD, Fulker DW, Kimberling WJ, Pennington BF, DeFries JC. Quantitative trait locus for reading disability on chromosome 6. Science 1994; 266: 276–9.
Cohen L, Dehaene S, Naccache L, Lehericy S, Dehaene-Lambertz G, Henaff MA, et al. The visual word form area: spatial and temporal characterization of an initial stage of reading in normal subjects and posterior split-brain patients. Brain 2000; 123: 291–307.
Demb JB, Boynton GM, Heeger DJ. Brain activity in visual cortex predicts individual differences in reading performance. Proc Natl Acad Sci USA 1997; 94: 13363–6.
Desmond JE, Gabrieli JD, Wagner AD, Ginier BL, Glover GH. Lobular patterns of cerebellar activation in verbal working-memory and finger-tapping tasks as revealed by functional MRI. J Neurosci 1997; 17: 9675–85.
Desmond JE, Gabrieli JDE, Glover GH. Dissociation of frontal and cerebellar activity in a cognitive task: evidence for a distinction between selection and search. Neuroimage 1998; 7: 368–76.[CrossRef][ISI][Medline]
Eckert MA, Leonard CM. Structural imaging in dyslexia: the planum temporale. [Review]. Ment Retard Dev Disabil Res Rev 2000; 6: 198–206.[CrossRef][ISI][Medline]
Eckert MA, Lombardino LJ, Leonard CM. Planar asymmetry tips the phonological playground and environment raises the bar. Child Dev 2001; 72: 988–1002.[CrossRef][ISI][Medline]
Eckert MA, Leonard CM, Molloy EA, Blumenthal J, Zijdenbos A, Giedd JN. The epigenesis of planum temporale asymmetry in twins. Cerebral Cortex 2002; 12: 749–55.
Eden GF, VanMeter JW, Rumsey JM, Maisog JM, Woods RP, Zeffiro TA. Abnormal processing of visual motion in dyslexia revealed by functional brain imaging. Nature 1996; 382: 66–9.[CrossRef][Medline]
Eliez S, Rumsey JM, Giedd JN, Schmitt JE, Patwardhan AJ, Reiss AL. Morphological alteration of temporal lobe gray matter in dyslexia: an MRI study. J Child Psychol Psychiatry 2000; 41: 637–44.[CrossRef][ISI][Medline]
Fagerheim T, Raeymaekers P, Tonnessen FE, Pedersen M, Tranebjaerg L, Lubs HA. A new gene (DYX3) for dyslexia is located on chromosome 2. J Med Genet 1999; 36: 664–9.
Fiebach CJ, Friederici AD, Muller K, von Cramon DY. fMRI Evidence for dual routes to the mental lexicon in visual word recognition. J Cogn Neurosci 2002; 14: 11–23.
Fisher SE, Marlow AJ, Lamb J, Maestrini E, Williams DF, Richardson AJ, et al. A quantitative-trait locus on chromosome 6p influences different aspects of developmental dyslexia. Am J Hum Genet 1999; 64: 146–56.[CrossRef][ISI][Medline]
Fisher SE, Francks C, Marlow AJ, MacPhie IL, Newbury DF, Cardon LR, et al. Independent genome-wide scans identify a chromosome 18 quantitative-trait locus influencing dyslexia. Nature Genet 2002; 30: 86–91.[CrossRef][ISI][Medline]
Foundas AL, Leonard CM, Gilmore R, Fennell E, Heilman KM. Planum temporale asymmetry and language dominance. Neuropsychologia 1994; 32: 1225–31.[CrossRef][ISI][Medline]
Gauger LM, Lombardino LJ, Leonard CM. Brain morphology in children with specific language impairment. J Speech Lang Hear Res 1997; 40: 1272–84.
Gayan J, Olson R K. Genetic and environmental influences on orthographic and phonological skills in children with reading disabilities. Dev Neuropsychol 2001; 20: 483–507.[CrossRef][ISI][Medline]
Gayan J, Smith SD, Cherny SS, Cardon LR, Fulker DW, Brower AM, et al. Quantitative-trait locus for specific language and reading deficits on chromosome 6p. Am J Hum Genet 1999; 64: 157–64.[CrossRef][ISI][Medline]
Georgiewa P, Rzanny R, Gaser C, Gerhard UJ, Vieweg U, Freesmeyer D, et al. Phonological processing in dyslexic children: a study combining functional imaging and event related potentials. Neurosci Lett 2002; 318: 5–8.[CrossRef][ISI][Medline]
Greenblatt E, Mattis S, Trad P. Nature and prevalence of learning disabilities in a child psychiatric population. Dev Neuropsychol 1990; 6: 71–83.
Grigorenko EL, Wood FB, Meyer MS, Hart LA, Speed WC, Shuster A, et al. Susceptibility loci for distinct components of developmental dyslexia on chromosomes 6 and 15. Am J Hum Genet 1997; 60: 27–39.[CrossRef][ISI][Medline]
Grigorenko EL, Wood FB, Meyer MS, Pauls DL. Chromosome 6p influences on different dyslexia-related cognitive processes: further confirmation. Am J Hum Genet 2000; 66: 715–23.[CrossRef][ISI][Medline]
Grigorenko EL, Wood FB, Meyer MS, Pauls JE, Hart LA, Pauls DL. Linkage studies suggest a possible locus for developmental dyslexia on chromosome 1p. Am J Med Genet 2001; 105: 120–9.[CrossRef][ISI][Medline]
Heiervang E, Hugdahl K, Steinmetz H, Inge Smievoll A, Stevenson J, Lund A, et al. Planum temporale, planum parietale and dichotic listening in dyslexia. Neuropsychologia 2000; 38: 1704–13.[CrossRef][ISI][Medline]
Heilman KM, Voeller K, Alexander AW. Developmental dyslexia: a motor-articulatory feedback hypothesis. [Review]. Ann Neurol 1996; 39: 407–12.[CrossRef][ISI][Medline]
Ivry RB, Justus TC. A neural instantiation of the motor theory of speech perception. [Review]. Trends Neurosci 2001; 24: 513–5.[CrossRef][ISI][Medline]
Leonard CM, Voeller KK, Lombardino LJ, Morris MK, Hynd GW, Alexander AW, et al. Anomalous cerebral structure in dyslexia revealed with magnetic resonance imaging. Arch Neurol 1993; 50: 461–9.[Abstract]
Leonard CM, Lombardino LJ, Mercado LR, Browd SR, Breier JI, Agee OF. Cerebral asymmetry and cognitive development in children: a magnetic resonance imaging study. Psychol Sci 1996; 7: 79–85.
Leonard CM, Puranik C, Kuldau JM, Lombardino LJ. Normal variation in the frequency and location of human auditory cortex landmarks. Heschl’s gyrus: where is it? Cereb Cortex 1998; 8: 397–406.
Leonard CM, Eckert MA, Lombardino LJ, Oakland T, Kranzler J, Mohr CM, et al. Anatomical risk factors for phonological dyslexia. Cereb Cortex 2001; 11: 148–57.
Levin PM. The efferent fibers of the frontal lobe of the monkey, Macaca mulatta. J Comp Neurol 1936; 63: 369–419.[CrossRef]
Metter EJ, Kempler D, Jackson CA, Hanson WR, Riege WH, Camras LR, et al. Cerebellar glucose metabolism in chronic aphasia. [Review]. Neurology 1987; 37: 1599–606.
Middleton FA, Strick PL. Cerebellar output channels. Int Rev Neurobiol 1997; 41: 61–82.[ISI][Medline]
Newman SD, Twieg D. Differences in auditory processing of words and pseudowords: an fMRI study. Hum Brain Mapp 2001; 14: 39–47.[CrossRef][ISI][Medline]
Nicolson RI, Fawcett AJ. Automaticity: a new framework for dyslexia research? Cognition 1990; 35: 159–82.[CrossRef][ISI][Medline]
Nicolson RI, Fawcett AJ, Berry EL, Jenkins IH, Dean P, Brooks DJ. Association of abnormal cerebellar activation with motor learning difficulties in dyslexic adults. Lancet 1999; 353: 1662–7.[CrossRef][ISI][Medline]
Nicolson RI, Fawcett AJ, Dean P. Developmental dyslexia: the cerebellar deficit hypothesis. [Review]. Trends Neurosci 2001; 24: 508–11.[CrossRef][ISI][Medline]
Nopola-Hemmi J, Taipale M, Haltia T, Lehesjoki AE, Voutilainen A, Kere J. Two translocations of chromosome 15q associated with dyslexia. J Med Genet 2000; 37: 771–5.
Nopola-Hemmi J, Myllyluoma B, Haltia T, Taipale M, Ollikainen V, Ahonen T, et al. A dominant gene for developmental dyslexia on chromosome 3. J Med Genet 2001; 38: 658–64.
Nothen MM, Schulte-Korne G, Grimm T, Cichon S, Vogt IR, Muller-Myhsok B, et al. Genetic linkage analysis with dyslexia: evidence for linkage of spelling disability to chromosome 15. Eur Child Adolesc Psychiatry 1999; 8 Suppl 3: 56–9.
Oldfield RC. The assessment of handedness: the Edinburgh inventory. Neuropsychologia 1971; 9: 97–113.[CrossRef][ISI][Medline]
O’Leary DD, Nakagawa Y. Patterning centers, regulatory genes and extrinsic mechanisms controlling arealization of the neocortex. [Review]. Curr Opin Neurobiol 2002; 12: 14–25.[CrossRef][ISI][Medline]
Pennington BF, Filipek PA, Lefly D, Churchwell J, Kennedy DN, Simon JH, et al. Brain morphometry in reading-disabled twins. Neurology 1999; 53: 723–9.
Petryshen TL, Kaplan BJ, Fu Liu M, de French NS, Tobias R, Hughes ML, et al. Evidence for a susceptibility locus on chromosome 6q influencing phonological coding dyslexia. Am J Med Genet 2001; 105: 507–17. [CrossRef][ISI][Medline]
Poldrack RA, Gabrieli JD. Characterizing the neural mechanisms of skill learning and repetition priming. Brain 2001; 124: 67–82.
Psychological Corporation. Wechsler Individual Achievement Test. 2nd ed. San Antonio (TX); Psychological Corporation; 2001.
Rae C, Harasty JA, Dzendrowskyj TE, Talcott JB, Simpson JM, Blamire AM, et al. Cerebellar morphology in developmental dyslexia. Neuropsychologia 2002; 40: 1285–92.[CrossRef][ISI][Medline]
Riva D, Giorgi C. The cerebellum contributes to higher functions during development: evidence from a series of children surgically treated for posterior fossa tumours. Brain 2000; 123: 1051–61.
Robichon F, Levrier O, Farnarier P, Habib M. Developmental dyslexia: atypical cortical asymmetries and functional significance. Eur J Neurol 2000a; 7: 35–46.[CrossRef][ISI][Medline]
Robichon F, Bouchard P, Demonet J, Habib M. Developmental dyslexia: re-evaluation of the corpus callosum in male adults. Eur Neurol 2000b; 43: 233–7.[CrossRef][ISI][Medline]
Rumsey JM, Donohue BC, Brady DR, Nace K, Giedd JN, Andreason P. A magnetic resonance imaging study of planum temporale asymmetry in men with developmental dyslexia. Arch Neurol 1997; 54: 1481–9.[Abstract]
Salzman CD, Britten KH, Newsome WT. Cortical microstimulation influences perceptual judgements of motion direction. Nature 1990; 346: 174–7.[CrossRef][Medline]
Schlosser R, Hutchinson M, Joseffer S, Rusinek H, Saarimaki A, Stevenson J, et al. Functional magnetic resonance imaging of human brain activity in a verbal fluency task. J Neurol Neurosurg Psychiatry 1998; 64: 492–8.
Schmahmann JD, Pandya DN. The cerebrocerebellar system. [Review]. Int Rev Neurobiol 1997a; 41: 31–60.[ISI][Medline]
Schmahmann JP, Pandya D. Anatomic organization of the basilar pontine projections from prefrontal cortices in rhesus monkey. [Review]. J Neurosci 1997b; 17: 438–58.
Schulte-Korne G, Grimm T, Nothen MM, Muller-Myhsok B, Cichon S, Vogt IR, et al. Evidence for linkage of spelling disability to chromosome 15. Am J Hum Genet 1998; 63: 279–82.[CrossRef][ISI][Medline]
Scott RB, Stoodley CJ, Anslow P, Paul C, Stein JF, Sugden EM, et al. Lateralized cognitive deficits in children following cerebellar lesions. Dev Med Child Neurol 2001; 43: 685–91.[CrossRef][ISI][Medline]
Seml E, Wiig E, Secord W. Clinical Evaluation of Language Fundamentals—Third Edition (CELF-3). San Antonio (TX): Psychological Corporation; 1995.
Shaywitz SE, Shaywitz BA, Pugh KR, Fulbright RK, Constable RT, Mencl WE, et al. Functional disruption in the organization of the brain for reading in dyslexia. Proc Natl Acad Sci USA 1998; 95: 2636–41.
Shaywitz BA, Shaywitz SE, Pugh KR, Mencl WE, Fulbright RK, Skudlarski P, et al. Disruption of posterior brain systems for reading in children with developmental dyslexia. Biol Psychiatry 2002; 52: 101–10.[CrossRef][ISI][Medline]
Talairach J, Tornoux P. Co-planar stereotaxic atlas of the human brain: 3-dimensional proportional system: an approach to cerebral imaging. New York: Thieme Medical Publishers; 1988.
Torgesen J, Wagner R, Rashotte C. Test of Word Reading Efficiency (TOWRE). Austin (TX): Pro-Ed; 2001.
Vellutino F, Scanlon D, Tanzman M. Bridging the gap between cognitive and neuropsychological conceptualizations of reading disability. Learn Individ Diff 2001; 3: 181–203.
Wagner R, Torgesen J, Rashotte C. Comprehensive Test of Phonological Processes. Austin (TX): Pro-Ed; 2000.
Wechsler D. Wechsler Intelligence Scale for Children. 3rd ed. San Antonio (TX): Psychological Corporation; 1991.
Wiederholt J, Bryant B. Gray Oral Reading Test (GORT-3). 3rd ed. Odessa (FL): Psychological Assessment Resources; 1992.
Wildgruber D, Ackermann H, Grodd W. Differential contributions of motor cortex, basal ganglia, and cerebellum to speech motor control: effects of syllable repetition rate evaluated by fMRI. Neuroimage 2001; 13: 101–9.[ISI][Medline]
Wilkinson G. Wide Range Achievement Tests—Revised. Wilmington (DE): Wide Range; 1993.
Wolf M. Rapid alternating stimulus naming in the developmental dyslexias. [Review]. Brain Lang 1986; 27: 360–79.[CrossRef][ISI][Medline]
Wolf M, Bowers P. The double-deficit hypothesis for the developmental dyslexias. J Educ Psychol 1999; 91: 415–38.
Wolf M, O’Rourke AG, Gidney C, Lovett M, Cirino P, Morris R. The second deficit: an investigation of the independence of phonological and naming-speed deficits in developmental dyslexia. Read Writ Interdisciplin J 2002; 15: 43–72.
Woodcock R. Woodcock Reading Master Tests—Revised. Circle Pines (MN): American Guidance Service; 1987.
Zeffiro T, Eden G. The cerebellum and dyslexia: perpetrator or innocent bystander? [Review]. Trends Neurosci 2001; 24: 512–3.[CrossRef][ISI][Medline]