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Right hemisphere contribution todevelopmental language disorder

Neuroanatomical and behavioral evidence

Elena Plante a,b, *, Carol Boliek a,b , Nidhi Mahendra a,b ,Jill Story c,1 , Kristen Glaspey a

a Department of Speech and Hearing Sciences, P.O. Box 210071, The University of Arizona, Tucson, AZ 85721-0071, USA

b National Center for Neurogenic Communication Disorders, Tucson, AZ, USAcUniversity of Kansas, Lawrence, KS, USA

Received 9 March 2001; received in revised form 27 May 2001; accepted 13 June 2001

Abstract

Developmental language disorder (DLD) is identified by virtue of the verbal deficitsthat define it. However, numerous studies have also documented nonverbal deficits inthis population. This study attempts to explain the co-occurrence of both verbal andnonverbal deficits in this population from a brain-based perspective. Two samples of adults selected for DLD were compared with subjects without such a history on verbal

and nonverbal skills in exploratory and confirmatory studies. Subjects also received MRIscans, which were used to determine the relation between left- and right-hemisphereregions hypothesized to relate to the behavioral skills tested. Results revealed replicabledifferences between groups on both verbal and nonverbal tasks. In addition, a significant association between performance on tests sensitive to facial affect and spatial rotationwith the gray matter volume within the right supramarginal gyrus was found in bothsamples. These results support the hypothesis of a right hemisphere contribution to the profile of DLD.

0021-9924/01/$ see front matter D 2001 Elsevier Science Inc. All rights reserved.PII: S0 021-99 24(01 )00059 -4

* Corresponding author. Department of Speech and Hearing Sciences, P.O. Box 210071, TheUniversity of Arizona, Tucson, AZ 85721-0071, USA. Tel.: +1-713-520/621-5080; fax: +1-713-520/ 621-9901.

E-mail address : [email protected] (E. Plante).1 Formerly at the University of Arizona.

Journal of Communication Disorders

34 (2001) 415436


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Learning outcomes: As a result of this activity, the participant will be able to describeevidence in support of a role for the right hemisphere in DLD. D 2001 Elsevier ScienceInc. All rights reserved.

Keywords: Language disorder; Specific language impairment; Learning disability; MRI; Brain

1. Introduction

Difficulty comprehending or using language is considered the defining deficit of children with developmental language disorders (DLD). 2 The language deficitsof these children occur in the absence of mental retardation. However, a body of literature now exists demonstrating that these children may have select areas of low nonverbal functioning even when their overall IQ falls within normal limits.The most commonly documented type of nonverbal deficit involves some aspect of anticipatory imagery, including mental rotation (Johnston & Ellis Weismer,1983; Savich, 1984; Swisher, Plante, & Lowell, 1994), haptic recognition(Johnston & Ramstad, 1983; Kamhi, 1981; Kamhi, Catts, Koenig, & Lewis,1984), and other spatial tasks (Johnston & Ramstad, 1983). Likewise, deficits inrecognition of facial affect (Dimitrovosky, Spector, Levy-Shiff, & Vakil, 1998)and comprehension of vocal affect have been reported in several studies (Berk,Doehring, & Bryans, 1983; Courtright & Courtright, 1983; Trauner, Ballantyne,Chase, & Tallal, 1993). Other nonverbal domains show less consistent effectswith deficits found in some studies (Ellis Weismer, 1991; Johnston & Smith,1989; Nelson, Kamhi, & Apel, 1987; Nippold, Ersdkine, & Freed, 1988;Restrepo, Swisher, Plante, & Vance, 1992; Swisher et al., 1994) but not others(Connell & Stone, 1994; Kamhi et al., 1984; Kamhi, Nelson, Freimoth, &Gholson, 1985; Kiernan, Snow, Swisher, & Vance, 1997). More controversial isthe idea that young children with language impairment show delays in thedevelopment of symbolic play (see Casby, 1997 for a review).

Several investigators have invoked the idea of a general symbolic deficit toexplain the co-occurrence of nonverbal and verbal deficits in specific languageimpairment (e.g., Johnston & Ramstad, 1983; Savich, 1984). Others havemodified this general hypothesis to suggest that certain kinds of nonverbal skillsmay better explain certain types of linguistic deficits rather than the broad profileof impaired language (Kamhi et al., 1984; Nelson et al., 1987). However, thereare weaknesses in the symbolic deficit account. Kamhi (1981) noted that nonverbal deficits are more subtle than linguistic deficits, and that the limited

scope of nonlinguistic deficits probably would not account for the variety and

2 The term developmental language disorder is used broadly here to encompass terms such asspecific language impairment, developmental dysphasia, and language/learning disabilities, which allrefer developmental disorders that include impaired language (oral or written) in the absence of frank deficits in other cognitive, sensory, or motor domains.

E. Plante et al. / Journal of Communication Disorders 34 (2001) 415436 416


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extent of linguistic ones. Johnston and Ellis Weismer (1983) pointed out that linguistic and imagery problems could coexist without being functionally related.

This idea has been echoed by others who have suggested that the coincidence of nonverbal and verbal deficits occurs specifically because left hemisphere effects produce language deficits and right hemisphere effects produce presumedindependent deficits including spatial rotation and affect recognition (Restrepoet al., 1992; Trauner et al., 1993).

The idea that both right and left hemisphere deficits might contribute to this behavioral profile is consistent with what is known of the neurobiology of DLDand other related disorders such as learning disability and dyslexia. Disorders that impact some aspect of language development are neurologically characterized by

signs of altered brain development. These signs include the presence of corticaland subcortical neuronal ectopias and developmental dysplasias (Cohen, Camp- bell, & Yaghamai, 1989; Drake, 1968; Galaburda, Sherman, Rosen, Aboitiz, &Geschwind, 1985; Humphreys, Kaufman, & Galaburda, 1990; Landau, Gold-stein, & Kleffner, 1960). These cellular-level features indicate disturbances incellular migration that occur during the prenatal or perinatal period. Furthermore,these cellular-level effects occurred in both hemispheres of the brain.

Likewise, variation in gross anatomy have also been reported and involve both hemispheres of the brain (Clark & Plante, 1998; Duara et al., 1991;

Galaburda et al., 1985; Gauger, Lombardino, & Leonard, 1998; Hynd, Semrud-Clikeman, Lorys, Novey, & Eliopulos, 1990; Jackson & Plante, 1996; Plante,1991; Plante, Swisher, Vance, & Rapcsak, 1991). Although the variations ingross anatomy involve features that can be found in the general population,they occur with significantly greater frequency in the brains of individuals withlanguage-based disorders.

The co-occurrence of both verbal and nonverbal deficits and the presence of bilateral brain involvement suggests that the latter condition may be related tothe former. Investigators who have assessed nonverbal deficits have typically

done so from a behavioral perspective, rather than from a neurobiologicalapproach. Therefore, nonverbal tasks were not selected based solely on their presumed hemispheric or localizing properties. Also, the assumption that allnonverbal tasks necessarily involve right hemisphere processing is false.Deficits associated with some nonverbal tasks can result from either right or left hemisphere damage (e.g., Mulder, Bouma, & Ansink, 1995; Titus, Gall,Yerxa, Roberson, & Mack, 1991). Furthermore, relatively minor changes intasks that typically do rely on the right-hemisphere can result in shifts inhemispheric lateralization (e.g., Adams & Duda, 1986; Cohen & Levy, 1988;

Findlay, Ashton, & McFarland, 1994; Hunt, Edwards, & Quest, 1988). Finally,even truly right hemisphere tasks can sometimes be solved through left-hemisphere processes such as verbal mediation, which is often difficult or impossible to assess from behavioral responses alone.

A stronger test of a right-hemisphere contribution hypothesis would involvethe selection of tasks for which individuals with definitive right-hemisphere



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2. Method

2.1. Study 1

2.1.1. ParticipantsThe participants of Study 1 were a subset of subjects selected from a prior

study on the family aggregation of gyral morphology in families affected byspecific language impairment (Jackson & Plante, 1996). Forty-one subjects inthat study underwent behavioral testing designed to determine their languagestatus for purposes of pedigree analysis (Tomblin, Freese, & Records, 1992). Thetest battery included two norm-referenced measures, the Peabody Picture

Vocabulary Test-Revised (PPVT-R, Dunn & Dunn, 1981) and the Written Spell-ing subtest from the Multilingual Aphasia Examination (Benton & deS Hamsher,1978). The battery also included two standardized measures. The first was amodified version of the Token Test (Modified Token), which was designed todetect subtle language processing difficulties in adults (Morice & McNicol,1985). As such, it contains items that are both longer and more complex thanthose found in the traditional version of the Token Test. The second standardizedmeasure was an index of speaking rate (words-per-minute) from a picture-elicitedspeech sample (Tomblin et al., 1992). The word count for this metric excludes

linguistic dysfluencies, mazes, and fillers, although the interval that theseinvolve, as well as speaker pauses, are included in the total speaking time.Therefore, this metric reflects both articulatory rate and linguistic factors such asthe time assumed to have been spent formulating thoughts into sentences, andtime revising spoken language during the speaking turn. As such, the speakingrate metric appears to encompass aspects of both language formulation andspeech production. In combination, these tests discriminated between adults withand without a history of language therapy during childhood with 97% accuracy(Tomblin et al., 1992).

Of the 20 parents who participated in the Jackson and Plante study, 14 (sixmales, eight females) were identified as having persistent impairment on theTomblin et al. battery. The parents ranged in age from 30 to 51 years of age, witha mean age of 36 years. All but one were right-handed by self report. Six subjectsalso reported a history of left-handedness in a first-degree relative. All subjectshad at least a high school education. However, six also completed some collegeor attended a technical school, two had completed a 4-year college degree, andtwo subjects had received a graduate degree.

Subjects in the comparison group (CG) included seven males and nine

females who were likewise drawn from a larger group of control subjects usedin the Jackson and Plante (1996) study. These individuals lacked both a personal and familial history of speech, language, learning, or other devel-opmental disabilities according to self-report and had normal language skills asmeasured by the Tomblin et al. (1992) battery. They ranged in age from 19 to37 years, with a mean age of 26 years. The discrepancy in ages between this



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group and the parent group reflects, in part, the wider age range of the original pool of control subjects who took part in the Jackson and Plante study.

Selecting a subset based on language status for this study further exaggeratedthis age difference because more older than younger adult subjects were lost toexclusionary criteria for this study. (The age discrepancy in this study wascontrolled for in the confirmatory subject groups selected specifically for thisstudy.) Although only one was left-handed, seven other subjects had first degree relatives who were left-handed. Similar to the experimental group, allsubjects had completed at least a high school education. In addition, 11 alsocompleted some college or attended a technical school and three completed a4-year college degree.

2.1.2. Behavioral measures and proceduresThe language battery used to identify subjects for this study also was used to

test brain-behavior relations. In addition, subjects were tested on a set of nonverbal measures. Two types of tasks were selected for which there wasempirical documentation that individuals with right hemisphere lesions showed poorer performance than individuals with either left hemisphere damage or control subjects without brain damage, with no significant differences recordedin performance between the latter two groups. The first set of tasks that met this

criteria included Subtests 1, 2, and 5 from the Florida Affect Battery (FAB)(Bowers, Blonder, & Heilman, 1991). For these subtests, participants wereshown pictures of various womens faces and asked if pairs of faces were thesame or different (FAB1), whether the facial emotions in a pair of womensfaces were the same or different (FAB2), and to select a face from an array of five womens faces whose emotion matched a target face (FAB5). Each subtest contained 20 items.

The second right hemisphere task involved spatial rotation and replicated a paradigm published by Ratcliff (1979). We will refer to this as the Spatial

Rotation task. In this task, participants were shown a line drawing of a malefigure who is holding balls in each hand, one of which is solid black. The participant was asked to identify which of the figures hand holds the black ball by responding verbally with either right or left. The figure is shown fromthe front and back, and with the ball appearing in either the right or left hand. Thefigure is shown four times in each variation for a total of 32 trials. The order of presentation was randomized for each subject.

Participants were tested individually by trained research assistants. The order of test administration was randomized across subjects. A minimum of 40% of the

testing sessions were observed by a second individual who scored subject responses to assess interrater reliability. The mean interrater reliability ranged between 97.2% and 100% for each behavioral measure. Spoken languagesamples were recorded on video tape or audiotape and transcribed verbatim.Transcription reliability was checked for 20% of the sample with a mean word- by-word reliability of 98.6%.



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2.1.3. MRI procedureScans were obtained on a G.E. 1.5T magnet using a spin-echo sequence. The

scan protocol (TR 400, TE 18; FOV 22 22 Matrix 256 192, 2 NEX) produced aT1 weighted image with contiguous 5-mm sagittal slices that included the full brainvolume. The slice angle was standardized at image acquisition by aligning slices parallel to the middle third of the cerebral midline as seen on an axial scout taken at the level of the basal ganglia. This was done to minimize differences in volumeestimates that can result from differences in slice angle (Plante & Turkstra, 1991).

The images were transferred to a Sun Workstation for computer-assistedanalysis of two regions of interest (ROI). Programs for MRI analysis weredeveloped using Khoros Pro 2.1 (Khoral Research, 1996). Each region was

measured by one of two research assistants who was blind to the participantsidentity and group membership, the behavioral results, and the results of theothers ROI measures. Images were magnified by a factor of 4 via pixelreplication so that details in the images could be clearly seen.

For the purposes of this study, we sought to quantify the gray matter associatedwith two ROI. These are visually depicted in Fig. 1 and details concerning

Fig. 1. Examples of the two ROI are displayed on four sequential (lateral to medial) slices of the left hemisphere a single subjects brain. The supramarginal ROI (SMG) is shaded in gray. The posterior sylvian ROI (PS) is shaded in white. Gray matter within each region was extracted for analysis usingcomputerized segmentation.



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measurement of each ROI are found in Appendix A. The first ROI was thesuperior temporal plane posterior to Heschls gyrus. This ROI included gray

matter along the horizontal and ascending branches of the posterior sylvianfissure. It was selected because the ROI lies in an area for which neuroanatomicaleffects in language and learning disabilities are most frequently documented.Therefore, we hypothesized that there might be a relation between this commonlyaffected neuroanatomical structure and measures previously shown to be sensitiveto long-term signs of DLD. The temporal bank of this region typically has a left-greater-than-right asymmetry whereas the parietal bank tends to have the oppositeasymmetry (Leonard et al., 1993; Witelson & Kigar, 1992). Therefore, an ROI that includes both would not be expected to have a strong asymmetry in either

direction. However, both areas were included in the ROI because lesion datasuggests that language functions are mediated by posterior sylvian regions beyondthe planum temporal.

The second ROI targeted the supramarginal gyrus. Damage to the left supramarginal gyrus region has been associated with language deficits. Damageto this area on the right has been associated with deficits in spatial rotation(Ratcliff, 1979) and affect recognition (Adolphs, Damasio, Tranel, & Damasio,1996; Blonder, Bowers, & Heilman, 1991; Bowers & Heilman, 1984; Voeller,Hanson, & Wendt, 1988).

The volume of both the superior temporal plane and the supramarginalgyrus of each hemisphere was divided by the total area of the cerebrum. Thiswas done to control for overall brain (and body) size differences amongsubjects. The resulting percent volume for each ROI was used in subsequent statistical analyses.

3. Results

3.1. Study 1

3.1.1. Behavioral measuresThe means and standard deviations for each of the behavioral variables are

provided in Table 1. Performance on the FAB1 was virtually error free, with onlyfive of the 30 subjects making any errors at all. On the basis of this ceiling effect,the FAB1 was dropped from further analysis.

Measures for which score distributions permitted parametric analysis wereanalyzed for group differences using a mixed ANOVA. As expected, by virtue of

the subject selection characteristics, the main effect for group was statisticallysignificant ( F = 22.59, df = 1,28, P < .01). The Group Task interaction also wassignificant ( F = 7.01, df = 3,84, P < .01). Post-hoc testing using a Tukeys HSDrevealed significant group differences ( P < .05) on the Modified Token test,Speaking Rate, and Spatial Rotation task, but not on the PPVT-R. Scoredistributions for the remaining tests were skewed. Therefore, group differences



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on these were evaluated using the KruskalWallis test. Significant differences

between groups were obtained for FAB5 ( c2

= 5.21, P < .05). Group differencesfor the FAB2 and Written Spelling were statistically nonsignificant.

3.1.2. Brain-behavior relationsExploratory analyses were conducted to assess the relations between the

behavioral measures and the ROI obtained from the brain images. Becauselanguage measures were assumed to be most closely associated with structures of the left hemisphere, these measures were used to predict the volume of the left sylvian plane (posterior and parietal banks) and left supramarginal gyrus.

Conversely, the nonverbal measures were selected for their sensitivity to right parietal dysfunction, the FAB2, FAB5, and Spatial Rotation task were used to predict the volume of the right supramarginal gyrus. Initial inspection of data plots suggested potentially curvilinear relations among some predictor andcriterion variables. Therefore, both linear and quadratic terms were entered for the Modified Token test, Speaking Rate, FAB2, FAB5, and Spatial Rotation tasksin their respective regressions. In addition, previous research (Restrepo et al.,1992) suggests that correlations among measures can differ between groups whoexperience DLD and normal groups. Therefore, group membership and group x

task interactions were entered as predictor variables. Finally, the possibility of sexdifferences in each of the ROI was considered. However, sex was not asignificant predictor of any ROI ( P > .30 in each of the ROI) and thereforewas not included as a variable in the exploratory regressions.

A relatively liberal criterion for the identification of predictor variables of interest within the regression equation was adopted. Given the exploratory nature

Table 1Results of behavioral testing for Study 1

DLD adults Control subjects

Mean (S.D.) Mean (S.D.)

Verbal tasksToken 80.3 (10.5) 94.3 (5.6)*Spelling 9.8 (1.5) 10.6 (0.6)PPVT-R 99.0 (22.1) 107.5 (11.4)Speaking rate 122.3 (22.7) 158.9 (25.5)*

Nonverbal tasksFAB1 19.8 (0.6) 19.8 (0.6)FAB2 17.4 (2.1) 18.1 (1.0)FAB5 18.1 (1.5) 19.2 (0.8) y

Spatial rotation 26.7 (5.8) 30.6 (2.1)*

FAB = Florida Affect Battery, Token score = percent correct, Spelling = no. of correct out of 11, PPVT-R = standard score, Speaking rate = words per minute, FAB1, FAB2, and FAB5 = no. of correct out of 20 for each, Spatial Rotation= no. of correct out of 32.

* Statistically significant at P < .05; ANOVA followed by Tukeys HSD.y Statistically significant at P < .05; Kruskal Wallis.



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of this analysis, the confirmatory study was intended to include any predictor variables for which the body weight reached a probability level of P < .20. This

reflects both the exploratory nature of the analysis and the fact that although predictor variables can fail to reach conventional levels of significance, theynonetheless can contribute valid variance in combination with other variables in aregression analysis.

Results of the linear regression of the four language measures, group member-ship, and Group Task interactions on the left superior temporal plane, failed toidentify any strong predictor variables. Likewise, these predictor variables were not strongly related to variation in the volume of the left supramarginal gyrus.Probabilities for each of the body weights all exceeded .43, which is beyond even

the most liberal estimates for the inclusion of variables in an exploratory analysis.Results of the linear regression of the three nonverbal measures, groupmembership, and Group Task interactions on the right supramarginal gyrussuggested stronger relations among these variables than those of the previousanalysis. The body weights for the FAB5 and Spatial Rotation task variables (bothlinear and quadratic components) and group membership reached the P


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4. Methods

4.1. Study 2

4.1.1. ParticipantsSubjects in Study 2 included 30 adults who were selected with reference to

their childhood status for speech-language and learning difficulties. Thesesubjects included 15 college-enrolled adults (eight males, seven females) whohad received services for speech-language impairment or learning disabilities(L/LD) during childhood. Their mean age was 22 years, with a range in agefrom 18 to 31 years. Most subjects in the L/LD group were recruited through a

university campus program that served learning-disabled students. Othersreplied to general recruitment fliers and reported a childhood history of speech/language or having had services for a learning disability during their primary or secondary school education. Subjects were screened prior to their participation and those reporting other handicapping conditions (e.g., neuro-logical conditions, attention deficit disorder) or those who received childhoodtherapy limited to single articulation errors (e.g., a lisped /s/, mispronounced /r/)were excluded as subjects. The phone interview also confirmed a childhoodhistory of services for speech-language or learning disabilities. Of the subjects

in the L/LD group, three were left-handed and the remainder were right-handedaccording to self report based on responses to the Edinburgh Handedness Inventory (Oldfield, 1971). Four subjects reported having first degree relativeswho were left-handed. Also, 11 of these subjects were enrolled as under-graduates and four were in graduate programs.

Subjects in the L/LD group were matched with 15 subjects comprising a CG(non-L/LD) on the basis of sex, handedness, and educational achievement (undergraduate vs. graduate enrollment). In addition, selection of non-L/LDsubjects was made so that their ages were similar to members of the L/LD

group. The non-L/LD subjects reported no personal or family history of speech,language, learning, or other developmental disabilities. Their mean age was 22,with an age range of 1829 years. Like the L/LD group, three non-L/LD subjectswere left-handed. Seven of the subjects also reported having first degree relativeswho were left-handed.

4.1.2. Materials and proceduresSubjects in Study 2 were tested with the same behavioral measures as were

used in Study 1. Interrater reliability for scoring of the behavioral measures

ranged between 97% and 100%. Transcription reliability for Speaking Rate wasassessed for 20% of the subject sample, with a word-by-word reliability of 99%.All subjects received MRI scans using the parameters described in Study 1, andthe analysis of those scans replicated the procedures used in that study. Researchassistants measuring ROI were blind to both to subject status and results of Study 1 as well.



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5. Results

5.1. Study 2

The means and standard deviations for each of the behavioral measures are provided in Table 3. Once again, the results for the FAB1 showed perfect or near perfect performance by all subjects and was excluded from further analysis. Inaddition, the score distribution for the Spatial Rotation task was more skewedthan it had been in Study 1. Therefore, it was subjected to nonparametric analysis.Otherwise, all statistical procedures were the same as in Study 1. A mixedANOVA revealed significant group effects ( F = 15.62, df = 1,27, P < .01) and a

significant Group Task effect ( F = 3.21, df = 2,54, P < .05). Post-hoc analysisusing Tukeys HSD indicated significant group differences ( P < .05) on theModified Token test and Speaking Rate tasks, but not for the PPVT-R. Kruskal Wallis tests revealed significant group differences ( P < .05) for Written Spelling,FAB2, FAB5, and the Spatial Rotation task.

A confirmatory regression was conducted for only those variables that hadsignificantly contributed to the prediction of the right supramarginal gyrusvolume in Study 1 (Group, FAB5 and Spatial rotation [linear and quadraticcomponents for each], and the Group Task interactions for these variables). The

results indicated that these variables accounted for 54% of the variance in right supramarginal gyrus volume ( F = 2.58, P < .05). In all cases, probabilities for the body weights associated with each predictor variable were .20 or less. Theseresults are presented in Table 4. As in the exploratory analysis, the results for thespatial rotation task were less strong than for the FAB5 and the strongly skewed

Table 3Results of behavioral testing for Study 2

Adults with LLD Control subjects

Mean (S.D.) Mean (S.D.)Verbal tasksToken 84.4 (9.2) 93.3 (4.0)*Spelling 10.2 (1.1) 10.9 (0.4) y

PPVT-R 106.4 (22.6) 118.5 (11.5)Speaking rate 125.8 (28.0) 151.8 (28.4)*

Nonverbal tasksFace discrimination 19.6 (0.7) 19.5 (0.7)Facial affect discrimination 16.6 (2.2) 18.1 (1.0) y

Facial affect matching 18.3 (1.4) 18.3 (2.3)Spatial rotation 28.4 (2.9) 31.4 (1.3) y

FAB = Florida Affect Battery, Token score = percent correct, Spelling = no. of correct out of 11, PPVT-R = standard score, Speaking rate = words per minute, FAB1, FAB2, and FAB5 = no. of correct out of 20 for each, Spatial Rotation= no. of correct out of 32.

* Statistically significant at P < .05 (ANOVA followed by Tukeys HSD).y Statistically significant at P < .01 (Kruskal Wallis).



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results for the Spatial Rotation task in the control group contributed to the Group by Task interaction effect for this variable.

6. Discussion

The behavioral results obtained from both studies demonstrated that verbal andnonverbal deficits co-occur in this population. Results from Study 1 revealed that parents of SLI children performed less well than the CG on the following tasks: (a)Modified Token test, (b) Speaking Rate, (c) Written Spelling, (d) Spatial Rotationtask, and (e) FAB5. Study 2 analyses revealed that adult L/LD subjects performedless well than the CG on the following tasks: (a) Modified Token test, (b) SpeakingRate, (c) Written Spelling, (d) Spatial Rotation task, and (e) FAB2. In support of our primary hypothesis, both verbal and nonverbal deficits co-occurred in subjectsselected as experiencing language disorder or learning disabilities. However, some

variability occurred between samples, perhaps reflecting the milder and, therefore,less stable nature of the nonverbal deficits in this population.The role of nonverbal deficits in the daily functioning of affected

individuals remains speculative. Some have suggested that verbal deficitsmay have a minimal contribution to nonverbal experiences, but nonverbaldeficits may have a larger impact on verbal processing (Denkla, 1983;Semrud-Clikeman & Hynd, 1991). However, some deficits co-occur, withlittle impact on daily communicative function. For example, spatial rotationdeficits may only impact communication when the affected individual is asked

to provide navigational directions. Other skills, like affect recognition, couldhave a more pervasive impact on daily communication. Poor sensitivity to theemotional reactions of a listener could easily undermine the social and pragmatic competence of the speaker. Because verbal and nonverbal deficitscan co-occur, it is important heuristically and clinically to examine the full behavioral profile of individuals with DLD and to appreciate how both indices

Table 4Confirmatory regression results for Study 2

Variable Parameter estimate S.E. t Probability

Intercept 4.538 2.727 1.66 .11Group 4.490 2.806 1.60 .13FAB5 0.216 0.079 2.72 .01FAB5 2 0.006 0.002 2.73 .01FAB5 Group 1.05 0.059 1.78 .09FAB5 2 Group 0.003 0.002 1.78 .09Spatial rotation 0.482 0.352 1.37 .18Spatial rotation 2 0.008 0.006 1.37 .18Spatial rotation Group 0.239 0.177 1.35 .19Spatial rotation 2 Group 0.004 0.003 1.35 .19

F value for complete model = 2.58, P =.04, R2 =.54.



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of right- and left-hemisphere functioning may contribute to the overall profileof their disorder.

The types of nonverbal deficits documented here for DLD and L/LD adultshave also been reported for children with learning disabilities. Johnson andMyklebust (1971) were among the first to describe a subgroup of children withlearning disabilities as having deficits in select nonverbal skills. Subsequent studies have documented deficits that parallel those seen in SLI, includingdeficits in spatial rotation and in differentiating, evaluating, and interpretingnonverbal affect (Bachara, 1976; Badian, 1983; Creasey & Jarvis, 1987; Love-land, Fletcher, & Bailey, 1990; Ueker, Obrzut, & Nadel, 1994; Wiig & Harris,1974). Some have argued that the presence of nonverbal deficits signals a subtype

of learning disability that is different from language-based learning disabilities.However, most of these studies have not included verbal tasks in addition to thenonverbal assessment (see Semrud-Clikeman & Hynd, 1991 for a review). Otherswho have examined both verbal and nonverbal skills tend to find co-occurringdeficits in both domains to varying degrees (e.g., Denkla, 1978, 1983). Dencklafound deficits in the areas of verbal reasoning, social linguistics, and interpreta-tion of nonverbal gesture. Those studies, and the results of this investigation,demonstrate that verbal and nonverbal deficits can and do coexist.

Evidence that these coexisting verbal and nonverbal deficits are associated

with the neurobiological features of the disorder was the aim of the neuroimaging portion of this study. The results of this endeavor were mixed. Although norelation between selected verbal skills and variation in the left superior temporal plane was found, a significant and replicable relation between performance on theFAB5 and variations in size of the right supramarginal gyrus was documented.The relation between facial affect recognition and the supramarginal gyrus wasmediated by subject status in the current study, as reflected by a significant Group Task interaction. Therefore, the brain-behavior relations in our studiesdid not simply reflect a single severity continuum, but rather suggested that some

variances in this relation arise from the subjects status (DLD or control).The theoretical importance of this relation is that it ties one aspect of the behavioral profile to a biological feature associated with the presence of DLD(i.e., size variation). Furthermore, it is of interest that this link involved a right hemisphere ROI and a nonverbal skill. The brain-behavior relation found here parallels the pattern of facial affect deficits associated with right hemispheredamage seen in acquired disorders in children (Stiles, 1998) and adults (Adolphset al., 1996; Bowers & Heilman, 1984) for this same skill. This suggests that acommonality exists for brain-behavior relations despite the neurobiological

differences for developmental and acquired disorders.The link between right hemisphere structure and nonverbal skills is one pieceof information needed to establish that the former underlies the latter. However,the translation of structural variation to skill deficit requires further informationlinking physiological evidence with poor skill performance. Although this type of evidence awaits further research with the DLD population, the present study



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provides direction in terms of both behavioral tasks and ROI that might revealsuch a link.

Finally, it is important to comment on the lack on relation between thelanguage measures and the left-hemisphere ROI measured in this study. Suchcorrelations have been relatively elusive in the literature. Only a small number of studies have reported relations between anatomical variation and verbal skills(Duara et al., 1991; Kushch et al., 1993; Semrud-Clikeman et al., 1991; Semrud-Clikeman, Hynd, Novey, & Eliopulos, 1991). Further, when brain languagerelations are found, they typically involve only a subset of all verbal skills tested.The relational patterns are also inconsistent across studies. Other studies havefailed to find significant correlations with any of the language measures

employed (Gauger et al., 1998; Haslam, Dalby, Johns, & Rademaker, 1981).Finally, several studies have found relations between neuroanatomical featuresand subject status, but not degree of impairment (Clark & Plante, 1998; Hier,LeMay, Rosenberger, & Perlo, 1978; Larsen, Hoien, Lundberg, & Odegaard,1990; Leonard et al., 1993; Rosenberger & Hier, 1980).

Given the salience of the language deficits in language and learning disabled populations, it is somewhat surprising that more studies have not foundcorrelations between these signs and neuroanatomical effects. Of the many possibilities for this outcome, we would like to speculate on a few. The first is

that posterior language zones are simply not the best fit for the language measuresthat were used in this study. For example, comprehension deficits (e.g., poor Token Test performance) are frequently associated with posterior perisylvianlesions, but damage to additional regions can depress performance as well (e.g.,Aram & Ekelman, 1987; Zaidel, 1977). Another possibility is that language- based skills in individuals with DLD have a different overall pattern of brain language relations, and are simply less lateralized for language than their normalcounterparts. In support of this notion, Boliek, Obrzut, and Shaw (1988) andObrzut et al. (1994) have demonstrated that ear advantages for dichotically

presented speech sounds could be shifted from right to left to a much greater degree in subjects with language-based learning disabilities than in normallydeveloping subjects. Likewise, functional imaging studies have shown reduced or no activity in subjects with dyslexia in areas that activate in response to task demands found in normal subjects (Eden et al., 1996; Rumsey et al., 1992;Shaywitz et al., 1998). For example, Lou, Henriksen, and Bruhn (1984) reporteda lack of activation above baseline during object naming in a rCBF study of children with DLD. Such results would be expected if the functions contributingto task performance were less localized in impaired subjects relative to their

controls. If this interpretation is true, then the lack of a correlation for languagetasks and left hemisphere structures found in this study may simply be reflectiveof a weaker overall relation between language skills and discrete brain regions.

Despite the lack of a significant association between verbal skills and left hemisphere ROI, the behavioral and neuroanatomical findings cast new light on thenature of DLD. The behavioral results document the co-occurrence of verbal and



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nonverbal deficits. The relation between variation in the right supramarginal gyrusand variation in facial affect skills provides preliminary support for the hypothesis

that nonverbal deficits are a consequence of right hemisphere involvement in the behavioral expression of DLD.

Acknowledgments

This work was supported, in part by National Mulitpurpose Research andTraining Center grant DC-01409 and a Clinical Investigator Development AwardDC-00077 from the National Institute on Deafness and Other Communication

Disorders. The authors would like to thank John Obrzut, PhD for his editorialcomments on an earlier version of this manuscript.

Appendix A

A.1. Measurement of the superior temporal plane

To measure this region, a research assistant first identified Heschls gyrus on

the most medial slice on which it appeared. Information from Leonard, Puranik,Kuldau, and Lombardino (1998) was used as a guide to identify this highlyvariable structure. When there were multiple Heschls gyri, measurement began posterior to the first Heschls gyrus on the most medial slice. When Heschls gyruswas no longer visible on lateral slices, measurement started at the imagecoordinates at which the posterior border of Heschls was last identified on prior slices. From that point, the superior temporal and parietal banks of the sylvianfissure, including the posterior ascending ramus, and the surrounding white matter were traced by the research assistant. The measurement extended into the white

matter so that the research assistant did not have to determine the location of thecortical border within the image (this was done through automated tissuesegmentation, as described below). This procedure was completed on subsequent slices moving from the medial to lateral slices. For the exploratory components of this study, each ROI was measured in the right and left hemisphere.

A.2. Measurement of the supramarginal gyrus

The supramarginal gyrus was operationally defined as the gyrus located at the

end of the sylvian fissure such that its posterior sulcus was immediately posterior to the sylvian fissure and its anterior sulcus was the postcentral sulcus. Whenmultiple gyri occurred in this region, all were included in the supramarginal ROI(cf. Leonard et al., 1993; Steinmetz, Ebeling, Huang, & Kahn, 1990). Thesubcentral sulcus (when it occurred) and the superior and posterior ascending portions of the sylvian fissure were not included in this ROI (see Fig. 1 for an



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d. a and b above.e. a and c above.

4. In this study, the results indicated

a. a statistical relation between nonverbal tasks and right supramarginalgyrus volumes.

b. a statistical relation between nonverbal tasks and left supramarginalgyrus volumes.

c. a statistical relation between language tasks and left supramarginal gyrusvolumes.

d. a statistical relation between language tasks and left posterior sylvianvolumes.

e. no group differences on any nonverbal measure.

5. In this study, right hemisphere tasks were selected based on

a. theoretical constructs of right and left hemisphere functioning. b. expert judgement of right and left hemisphere functioning.c. data indicating differential performance by adults with right and left

hemisphere damage.d. a and b above.e. b and c above.

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