This article was downloaded by: [Michigan State University], [Jodene Goldenring Fine]On: 13 November 2013, At: 07:23Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Child Neuropsychology: A Journal onNormal and Abnormal Development inChildhood and AdolescencePublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/ncny20

Smaller splenium in children withnonverbal learning disability comparedto controls, high-functioning autismand ADHDJodene Goldenring Finea, Kayla A. Musielaka & Margaret Semrud-Clikemanb

a Department of Counseling, Educational Psychology, and SpecialEducation, Michigan State University, East Lansing, MI 48824 USAb Department of Pediatrics, University of Minnesota MedicalCenter, Minneapolis, MN, USAPublished online: 12 Nov 2013.

To cite this article: Jodene Goldenring Fine, Kayla A. Musielak & Margaret Semrud-Clikeman , ChildNeuropsychology (2013): Smaller splenium in children with nonverbal learning disability comparedto controls, high-functioning autism and ADHD, Child Neuropsychology: A Journal on Normal andAbnormal Development in Childhood and Adolescence, DOI: 10.1080/09297049.2013.854763

To link to this article: http://dx.doi.org/10.1080/09297049.2013.854763

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Smaller splenium in children with nonverbal learning

disability compared to controls, high-functioning autism

and ADHD

Jodene Goldenring Fine1, Kayla A. Musielak1, andMargaret Semrud-Clikeman2

1Department of Counseling, Educational Psychology, and Special Education, MichiganState University, East Lansing, MI 48824 USA2Department of Pediatrics, University of Minnesota Medical Center, Minneapolis, MN, USA

The current study investigated morphological differences in the corpus callosum in children ages 8 to18 years old with nonverbal learning disability (NLD; n = 19), high-functioning autism (HFA;n = 23), predominantly inattentive ADHD (ADHD:PI; n = 23), and combined type ADHD(ADHD:C; n = 25), as well as those demonstrating typical development (n = 57). Midsagittal areaof the corpus callosum and five midsagittal anterior-to-posterior corpus callosum segments wereexamined using magnetic resonance imaging. Controlling for midsagittal brain area and age, no groupdifferences were found for total corpus callosum area. This finding indicates that higher functioningchildren on the autistic spectrum do not have smaller corpus callosi as has been found in previousresearch with heterogeneous samples. Following segmentation of the corpus callosum, the NLDgroup was observed to have significantly smaller splenia compared to all other groups. Smallersplenia in the NLD group was associated with lower WASI PIQ scores but not WASI VIQ scores.Children with HFA were observed to have larger midbody areas than children with NLD andneurotypically developing children. Children with HFA and NLD demonstrated behavioral symptomsof inattention and hyperactivity similar to the ADHD groups indicating that corpus callosumdifferences seen in the NLD and HFA groups are not related to these behaviors.

Keywords: Corpus callosum; High functioning autism; Nonverbal learning disorder; ADHD;Splenium; MRI; Asperger.

The corpus callosum is the largest commissure, or white matter bundle, of the brain. Thisimportant midline structure spans the hemispheres laterally and serves the brain’s abilityto orchestrate regions of the cortex in the service of academic (Fine, Semrud-Clikeman,Keith, Stapleton, & Hynd, 2006), social (Turk, Brown, Symington, & Paul, 2010), andbehavioral (McNally et al., 2010; Paul, 2011) outcomes. The corpus callosum is sensitiveto environmental influences, including in vitro teratogens (Wozniak et al., 2009) andneglect (Teicher et al., 2004). It has also been found to differ in clinical samples when

Address correspondence to Jodene G. Fine, School Psychology, Michigan State University, 620 FarmLane, 440 Erickson Hall, East Lansing, MI 48824, USA. E-mail: finej@msu.edu

Child Neuropsychology, 2013http://dx.doi.org/10.1080/09297049.2013.854763

© 2013 Taylor & Francis

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compared to typical children in a variety of neurodevelopmental conditions (Paul, 2011).Reliably and easily identifiable on magnetic resonance imaging (MRI) scans, the corpuscallosum is a good structure for observing differences among diagnostic groups.

The purpose of this study was to evaluate the corpus callosum in several pediatricclinical groups with emphasis on whether children with Nonverbal Learning Disability(NLD) and those with high-functioning autism (HFA) would show differences in corpuscallosum size. Differentiation of HFA and NLD has been difficult historically (Spreen,2011) causing confusion in both the research and popular literature (Fine, Semrud-Clikeman, Bledsoe, & Musielak, 2013). Because many children with neurodevelopmentalchallenges share common characteristics, comparative studies of pediatric clinical popula-tions that highlight neural differences among groups may help identify the contributionsthat distinct and convergent pathways make to shared behaviors. One observed behaviorthat is shared among children with many different developmental disorders, includingHFA, attention deficit/hyperactivity disorder (ADHD), and NLD, is that of difficulty withsocial interaction.

The central feature of autism is that of social deficit (American PsychiatricAssociation, 2000), and, though children with ADHD also have social problems, themechanisms for their social challenges are seen as different and more related to executivefunctions (Barkley, 1997). The diagnosis of NLD is controversial and officially unrecog-nized in the Diagnostic and Statistical Manual of Mental Disorders, text revision (DSM-IV-TR; American Psychiatric Association, 2000) while ICD-9-CM does recognize it underthe category of symbolic dysfunction unspecified. The core feature of this syndrome isthought to be an initial deficit in visuospatial perception leading to difficulty withmathematics learning (Fine et al., 2013; Mammarella & Cornoldi, 2013; Ris & Nortz,2008; Rourke, 1989). Because many of these children also show difficulty perceiving thenonverbal stream of social communication, some have argued that NLD is related to HFA(Schultz, Romanski, & Tsatsanis, 2000; Volkmar & Klin, 2000). Although these neuro-developmental disorders are often described as distinct, they share inattention and hyper-activity symptoms consistent with ADHD aside from problems with social interaction(Holtmann, Bölte, & Poustka, 2005; Ris & Nortz, 2008). Thus, this study includes notonly children with HFA and NLD but also those with ADHD, with the ADHD sampleserving as clinical controls for the influence of symptoms of inattention and hyperactivity.

Differences in the corpus callosum have been found relative to typically developingchildren and those with ADHD and autism, but findings have been inconsistent. A meta-analytic review by Frazier and Hardan (2009) found reduced area of the corpus callosumin autism particularly in the anterior portion of the structure. Four of the 10 studies in themeta-analysis used high-functioning samples (IQ > 70) and only two of these (Rice et al.,2005; Vidal et al., 2006) were studies of children. Rice et al. compared 12 children withHFA and macrocephaly (head circumference > 97th percentile) with 8 typically develop-ing children with benign macrocephaly. No difference in corpus callosum area was foundbetween the groups. In contrast, Vidal et al. reported significant reductions in the corpuscallosum area in 24 boys with HFA compared to 26 neurotypical boys using a stronger(3.0 T vs. 1.5 T) and, therefore, more precise, magnetic field. Alexander et al. (2007) alsofound reductions in area for all midsaggital regions of the corpus callosum in a sample of43 participants with autism (ages 7–33 years) compared to 34 neurotypical controls,noting in addition that there was wider variability among the autism group compared tocontrols. This study found that the observed group differences were largely driven by anautism subgroup that had lower Performance IQ but did not differ from the other

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participants with autism in social and communication symptoms. At least one well-controlled study of HFA in adults that covaried for whole brain volume found nodifferences in any corpus callosal area (Tepest et al., 2010). The inconsistent literatureregarding brain development in persons with autism have likely been influenced bydifferences in methodology, small sample sizes, and the heterogeneity of characteristicsof persons with autism (Stigler, McDonald, Anand, Saykin, & McDougle, 2011).

Findings regarding the morphology of the corpus callosum in children with ADHDhave been similarly inconsistent. Two studies (Cao et al., 2010; Hill et al., 2003) founddifferences in total corpus callosum area where children with ADHD had smaller areascompared to controls. The genu (Luders et al., 2009), rostrum (Giedd et al., 1994), rostralbody (Giedd et al., 1994; Luders et al., 2009), anterior middle-body (Cao et al., 2010),isthmus (Cao et al., 2010; Luders et al., 2009), anterior splenium (Luders et al., 2009), andsplenium (Hill et al., 2003) have also all been implicated in studies of individuals ofvarious ages, comorbid disorders, and treatment. Alternatively, no significant differenceswere found in two recent studies (McNally et al., 2010; Schnoebelen, Semrud-Clikeman,& Pliszka, 2010) and the effects of observed size differences have disappeared aftercontrolling for comorbid dyslexia (Lyoo, Noam, Lee, & Lee, 1996) and handedness(Baumgardner et al., 1996). No study to date has included discrete samples of bothcombined (ADHD:C) and predominantly inattentive (ADHD:PI) samples.

Although there are no known magnetic resonance imaging (MRI) studies of thecorpus callosum in NLD to date, the theoretical framework explaining NLD character-istics has long been associated with deficits in white matter development (Rourke &Tsatsanis, 2000; Rourke, 1989, 1995). Rourke’s well-established model of NLD proposesthat there is a developmental cascade of assets and deficits arising from white matter thatis rendered dysfunctional (Rourke, 1995). Because white matter connects both the prox-imal and distal areas of the brain, Rourke hypothesized that a reduction in white matterintegrity would cause problems in functioning when extensive integration of neuralsystems for information processing is required (Rourke & Tsatsanis, 2000) in such areasas mathematics learning, adaptation to novel stimuli, higher order cognitive reasoning,and nuanced social engagement. These symptoms share some commonality with agenesisof the corpus callosum (agCC), in which the axons of the corpus callosum are absent inthe brain. Indeed, Rourke proposed that those with agCC are likely to display symptomsconsistent with NLD (Smith & Rourke, 1995). Researchers have found that in agCCgeneral intellectual functioning is largely spared but complex cognitive reasoning andsocial functioning are impaired (Paul et al., 2007). Given that white matter deficits havebeen hypothesized as central to NLD, the corpus callosum would be one place to look fordifferences that might establish external validity for the syndrome.

In addition to the broad supposition that NLD symptoms are related to white matterdisruption, this syndrome has long been assumed to be further associated with deficits invisuospatial functioning. Rourke’s (1995) developmental progression model for NLDnamed tactile perception, visual perception, complex psychomotor, and novel learningdeficits as primary to the cascade of neural shaping that would result in the NLD profile.Diagnostic criteria for NLD have historically included lower visuospatial reasoningcompared to verbal reasoning (e.g., Harnadek & Rourke, 1994; Pelletier, Ahmad, &Rourke, 2001). Recently, a deficit in spatial memory has also been implicated(Mammarella, Lucangeli, & Cornoldi, 2010). Given that at least some of the systemsinvolved in visual and spatial functioning are thought to involve parietal and occipital

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systems in the posterior part of the brain (Knyazeva, 2013), differences in posterior corpuscallosum development might be expected.

The Splenium of the Corpus Callosum

The splenium of the corpus callosum is the most posterior area of the corpuscallosum. It comprises small diameter axons connecting the parietal and temporal areasof the brain as well as thick axons serving the primary and secondary visual areas(Knyazeva, 2013). Postnatal pruning of axons mirrors the shaping that occurs elsewherein the brain but axonal diameters increase as axon loss occurs in this area (Aboitiz,Scheibel, Fisher, & Zaidal, 1992) thus increases in splenial size are seen as childrenmature. The relative development of the splenium compared to anterior portions of thecorpus callosum are currently controversial, with some findings suggesting greaterincreases in the splenium (e.g., Giedd et al., 1999) and others suggesting less drasticdifferences with continuing development through adolescence. Earlier maturation is seenin girls compared to boys (Knyazeva, 2013). Developmental differences in the spleniumhave not been previously examined in NLD, yet this structure might shed light onlongstanding theories regarding abnormal white matter development (Rourke, 1995),visuospatial deficits (Rourke, 1989), and social functioning (Semrud-Clikeman & Glass,2008) in this group.

The Current Study

The current study examined the corpus callosum in children with NLD, HFA,predominantly inattentive type ADHD (ADHD:PI), combined type ADHD (ADHD:C),and neurotypical controls. The purpose of the study was to determine whether there aresignificant differences between HFA and NLD populations given that many researchershave suggested that NLD may be a “mild” form of the autism spectrum (Semrud-Clikeman, 2007). Two ADHD samples were included as clinical control groups in partbecause of the high comorbidity observed of ADHD symptoms in the NLD group.Moreover, we wished to examine the relation of the behavioral symptoms of inattentionand hyperactivity and individual differences in corpus callosum area. We expected to findreduced area of the corpus callosum overall in HFA consistent with previous findings inautism. We further hypothesized that the NLD group would have smaller areas in theposterior regions of the corpus callosum, specifically the splenial region. The ADHDgroups were expected to be similar to neurotypical controls.

METHODS

Children were recruited from across the United States and Canada to participate inneuroimaging studies of NLD and HFA. Controls and children with ADHD were recruitedfrom the Midwestern United States. Children with estimated IQ results less than 85, thosewith reading disabilities, non-native English speakers, and those with significant neuro-logical problems (e.g., seizure disorder, traumatic brain injury) were excluded from thisstudy. Children medicated for symptoms of ADHD were on a 24- to 48-hour wash-outprior to neuropsychological testing.

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Participants

The sample included 147 right-handed children between the ages of 8 and 18 yearsold. Twenty-five children with ADHD:C (3 female), 23 children with ADHD:PI(4 female), 23 children with HFA (5 female), 19 children with NLD (5 female), and57 neurotypical children (21 female) were included. Eighty-three percent of the samplewere self-described as European American, 6.1% African American, 0.7% HispanicAmerican, 0.7% Asian American, 4.7% Mixed Ancestry, and 3.4% declined to state.See Table 1 for demographic information.

Group Assignment

The diagnoses of referred children with HFA were confirmed by two licensedpsychologists (J. F., M. S-C.) using DSM-IV-TR criteria and the Autism Diagnostic

Table 1 Sample Description of Gender, Ethnicity and Age.

Control ADHD-C ADHD-PI HFA NLD Significance1

Total N 57 25 23 23 19 –Female n (%) 21 (36.8) 3 (12%) 4 (17.4) 5 (21.7) 5 (26.3) nsEthnicity ns− White 50 19 14 22 17− Hispanic 1 0 0 0 0− African Am. 2 1 4 1 1− Asian Am. 1 0 0 0 0− Native Am. 0 0 0 0 1− Mixed 1 4 2 0 0− Declined 2 1 3 0 0Age in years 13.75 11.74* 14.88 12.98 12.97 <C− SD 2.94 2.15 2.64 2.48 2.37− Range 9.22–18.8 9.28–18.4 9.7–18.1 8.6–17.6 9.4–18.9FSIQ2 112.56 108.88 103.90 107.26 105.74 ns− SD 10.18 7.35 12.90 17.1 13.60− Range 91–135 97–124 87–136 82–133 80–125VIQ3 112.93 107.64 103.87* 110.96 117.47 <NLD− SD 12.66 10.03 14.50 15.18 13.02− Range 91–145 95–135 80–139 89–140 95–142PIQ3 109.66 108.20 105.22 102.96 97.32* <C− SD 11.19 10.70 11.00 19.11 14.99− Range 86–136 78–134 82–139 77–135 69–119Inattention4 46.89 68.24** 64.75** 62.39** 64.44** >C−SD 10.21 6.55 8.88 9.72 6.45− Range 34–72 52–82 39–75 36–74 54–73Hyperactivity5 47.70 74.72** 64.45** 65.78** 61.11* >C− SD 10.10 11.97 13.21 15.04 11.09 *ADHD:PI < ADHD:C− Range 36–86 54–96 44–93 41–97 43–78 *NLD < ADHD:C

Notes. *p < .05; ** p < .001.1Column shows the group against which the significant comparison (column with *) was found and the

direction (larger or smaller).2FSIQ: WASI Full-Scale IQ Standard score.3VIQ: WASI Verbal IQ T-Score; PIQ: WASI Performance IQ T-Score.4BASC-2 Parent Report Inattention Scale T-score.5BASC-2 Parent Report Hyperactivity Scale T-score.

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Interview-Revised (ADI-R: Rutter, Le Couteur, & Lord, 2003). Children with HFAobtained a total of 20 or more points summed across Scale A (Language/Communication), Scale B (Reciprocal Social Interactions), and Scale C (Restrictive/Repetitive Behaviors). Children who were referred with NLD obtained a score of 4 orless on Scale C of the ADI-R and met additional criteria shown in Table 2. Children withan existing diagnosis of ADHD who also met DSM-IV-TR criteria for combined orinattentive subtype as determined by Section C of the Structured Interview forDiagnostic Assessment of Children (SIDAC) were assigned to one of the two ADHDgroups. All assignments were confirmed by a licensed psychologist.

Comorbidity

Some children in the NLD and HFA groups also displayed symptoms of inattentive and/or combined ADHD. If a child met criteria for NLD or HFA, then they were assigned to thesecells regardless of symptoms that were consistent with an ADHD diagnosis. Based on parentratings on the Inattention scale and Hyperactivity scale of the Behavior Assessment Scale forChildren, Second Edition (BASC-2; Reynolds & Kamphaus, 2004), four children in the NLDgroup were rated as “at-risk” for inattention (T-score = 60–69) and 6 in the range of “clinicalconcern” (T-score = 70+). Seven of the children in the NLD were considered “at-risk” forhyperactivity by their parents and four were in the range of “clinical concern.” Althoughchildren with HFA are not typically diagnosed with ADHD, many of these children alsoshowed symptoms of inattention and hyperactivity. Three children with HFAwere “at-risk” onthe inattention scale and 9 were rated with “clinical concern.”Hyperactivity in the HFA groupwas rated as “at-risk” for 6 children and with “clinical concern” for 7.

Brain Anomalies

As previously found, a high number of children with NLD were shown to havecysts and lesions compared to neurotypical controls (Semrud-Clikeman & Fine, 2011).Children with visible anomalies on their scans included 1 child with HFA (bilateralcerebellar), 2 controls (bilateral cerebellar, left cerebellar), 1 child with ADHD:PI (right

Table 2 Diagnostic Criteria for Nonverbal Learning Disorder.

Required base criteria:

− WASI estimated FSIQ or VIQ = > 85− WJ3 Letter Word = > 85− ADI-R total C < = 4Three of the following six:− WASI Block Design T-score < 40− Rey Z-score less than < –1.0− WJ3 Calculation < 85 or FSIQ-WJ3 Calculation greater than 1.5 SD (22.5)− Purdue Pegboard Z-score, left or right or both < –1.0− CASP nonverbal Z-score < –1.0

– Vineland Social Skills < 85

Note. WASI: Wechsler Abbreviated Scale of Intelligence; WJ3: Woodcock-Johnson Achievement, 3rdRevision; ADI-R: Autism Diagnostic Interview, Revised; Rey: Rey Complex Figure Test; CASP: Child andAdolescent Social Perception; Vineland: Vineland Scales of Adaptive Behavior, 2nd Edition, Parent Rating.

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cerebellar), and 4 children with NLD (1 right occipital, 1 left cerebellar, 1 right temporal,1 left frontal). Note that all scans were read by a neuroradiologist who reported noconcerns. No midline or gray-matter shifts were observed. All scans were visuallyinspected for anomalies at the corpus callosum and in the cortex on the midsagittal slicewhere all of the measurements for this study were obtained. None of these cysts wereobserved at the midsagittal plane in the corpus callosum or cortex, thus measurementstaken within the midsagittal plane are considered valid but should nevertheless beregarded with some caution.

Scanning

Children were scanned using a GE 3T Signa® HDx MR scanner (GE Healthcare,Waukesha, WI) with an eight-channel head coil. During each session, images were firstacquired for the purpose of localization, and first and higher order shimming procedureswere then carried out to improve magnetic field homogeneity (Kim, Adalsteinsson,Glover, & Spielman, 2002). High-resolution volumetric T1-weighted spoiled gradient-recalled (SPGR) images with cerebrospinal fluid suppressed were obtained to cover thewhole brain with 120 1.5 mm sagittal slices, 500 ms time of inversion, 8° flip angle, and24 cm field of view (FOV).

Image Postprocessing

Structural data were analyzed using the Analysis of Functional NeuroImages(AFNI) software (Cox, 1996). AFNI software was used to align the MRI images via theanterior-posterior commissure procedure to obtain uniform density, to identify the area ofthe corpus callosum, and to calculate area of the whole brain at the midsagittal slice. Thescans were normalized at 1 x 1 x 1 mm3 per voxel. Voxels were identified as within thecorpus callosum and were defined as the higher density band of fibers directly above thelateral ventricle and thalamus and below the cingulate gyrus in the midsagittal plane. Allmeasurements were made by a single trained doctoral graduate student and a reliabilityestimate of .977 was obtained based on the remeasurement of 55 randomly selected scans.

Controlling for Brain Size and Age

In order to make the statistical analyses more parsimonious and to control fordifferences in whole brain size, new variables for corpus callosum areas with the effectsof brain size removed were created using the residuals of a regression of corpus callosumareas on whole-brain area at the midsagittal slice. The new variables, referred to as“adjusted” corpus callosum measurements, were used in all subsequent analyses. Afteradjustment for whole brain size at the midsagittal slice, age continued to contributesignificantly to the variance in corpus callosum (R2 = .070, β = 0.264, p = .001), so agewas entered as a covariate into subsequent analyses. With age controlled, neither gendernor full-scale IQ contributed significant influence on corpus callosum size so thesevariables were not used as control variables (Gender: β = 0.025, p = .760; FSIQ:β = 0.122, p = .139).

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Anterior-Posterior Segmentation

Segmentation of the corpus callosum area into five equal areas was accomplisheddigitally with XYZ coordinates from AFNI software that were uploaded into SPSS. Whena five-segment division was not evenly divisible by five, SPSS generated 50 iterationswith random placement of remainders and the average of those 50 iterations was used asthe area variables for analyses (similar to Fine et al., 2006). As for the total corpuscallosum area, anterior-to-posterior segments of the corpus callosum were adjusted forbrain size as described for the whole corpus callosum above. See Figure 1 for a diagramof segmentation.

Behavioral Variables of Interest

An estimate of intellectual functioning was obtained using the WechslerAbbreviated Scale of Intelligence (WASI; Psychological Corporation, 1999), which wasused for inclusion criteria and NLD diagnosis. Full-scale, Verbal, and Perceptual IQstandard scores were calculated. The BASC-2 (Reynolds & Kamphaus, 2004), a broad-spectrum parent rating scale, was used to assess symptoms of inattention due to comor-bidity of this symptom among children with HFA and NLD.

Between-Group Comparisons

Differences between groups were analyzed using analysis-of-variance techniqueswith age as a covariate. Significant multivariate analysis of covariance (MANCOVA)findings were restricted to p = .05. Post hoc between-group comparisons are the focus ofthis study. The FDR (The SAS System) was used to control for Type I error given thesmall sample size and large number of groups (see Shaffer, 1995). Regression modelswere used to examine the relative of influence of corpus callosum areas on the behavioralvariables of interest.

RESULTS

Preliminary analyses of between-group differences in age, IQ, and brain size wereconducted to determine whether covariates should be included in corpus callosum ana-lyses. As discussed above, age was found to contribute approximately 7% of the variancein corpus callosum area, so age was included in between-group analyses of the corpus

Figure 1 Segmentation of the midsagittal corpus callosum slice.

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callosum. Table 1 shows group means, standard deviations, and ranges for the neuropsy-chological variables of interest. Table 3 shows areas in mm2 for whole brain size, corpuscallosum area, and all five segment areas.

Between-Group Age, IQ, and Whole Brain Size

Differences in age among the groups were observed, F(4, 142) = 4.758, p = .001,η2 = .12. Post hoc analyses indicated that the ADHD:PI group was significantly older thanthe ADHD:C (p < .001), HFA (p = .016), and NLD groups (p = .021), but not the controlgroup (p = .083). In addition, Full-Scale IQ scores were higher in the control groupcompared to the ADHD:PI (p = .009) and NLD groups (p = .032) but not the HFA(p = .060) or ADHD:C groups (p = .192), F(4, 142) = 2.465, p = .048, η2 = .065. Therewere no differences between groups in whole brain area at the midsagittal slice,F(4,142) = 0.623, p = .647, η2 = .017.

Corpus Callosum Total Area Between Groups

A 1 (adjusted cc area) by 5 (groups) analysis of covariance (ANCOVA) with agecontrolled showed no significant effect of group on the adjusted total area of the corpuscallosum, F(4, 147) = 2.303, p = .061.

Corpus Callosum Segments Between Groups

A 5 (adjusted segments) by 5 (groups) MANCOVAwith age as the covariate foundno significant overall main effect of group, F(4, 141) = 1.49, Wilks’ Lambda = .810,

Table 3 Whole Brain, Total Corpus Callosum, and Segment Areas in mm2.

Control ADHD-C ADHD-PI HFA NLD

Whole brain 9227.86 8958.88 9265.96 8958.83 8799.92− SD2 784.06 831.10 775.14 782.52 937.73− Range 7671–10,759 7817–11,069 7851–10,700 7983–10,731 7604–11,324CC area2 667.23 650.40 681.13 691.96 619.53− SD 81.59 80.92 85.11 82.19 108.82− Range 535–882 504–796 496–812 584–906 421–886Genu 197.90 191.88 199.24 203.52 185.63− SD 30.23 32.93 27.89 35.56 37.40− Range 147.0–289.7 143.9–251.0 128.3–240.0 145.0–270.1 140–271.67Ant.Body 107.45 101.55 108.25 111.74 100.20− SD 14.46 13.99 17.68 15.25 16.14− Range 81.0–134.1 79.5–137.2 65.2–138.0 79.0–143.0 67.0–127.7Mid Body 87.74 86.12 92.61 95.81 84.93− SD 12.64 10.60 15.37 11.34 14.51− Range 63.0–115.9 65.0–107.1 65.8–120.4 79.1–118.4 52.0–108.2Post.Body 87.03 84.84 91.25 93.47 82.41− SD 17.0 13.41 14.22 16.77 22.97− Range 59.1–148.0 64.0–105.2 70.0–114.5 67.8–134.7 43.0–129.2Splenium 186.08 184.53 188.62 186.29 164.98− SD 23.43 25.05 24.16 28.06 33.07− Range 124.9–238.5 134.1–230.3 139.5–236.1 139.9–259.2 103.2–229.6

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p = .079, η2 = .051. Significant between-group differences in the midbody, F(4,141) = 2.93, p = .023, η2 = .077, and splenium, F(4, 141) = 3.09 p = .018, η2 = .081,were observed with the HFA group showing the largest areas and NLD the smallest areasin both the midbody and splenium. Table 4 shows the univariate analysis of variance(ANOVA) results, while Table 5 shows post hoc between-group raw least significant differ-ence (LSD) p values and False Discovery Rate (FDR) p values for the midbody and splenium.

Figure 2 shows a graph of the age and whole-brain adjusted means by group acrossall five corpus callosum segments. Although the HFA group showed the largest areas thanother groups in all of the segments except the splenium, only the midbody was signifi-cantly larger in relation to the neurotypical group (FDR = .020) and the NLD group(FDR = .020). While the NLD group had the smallest areas in all segments, the splenialarea was the only area that was significantly smaller than other groups (neurotypicalFDR = .020, HFA FDR = .028, ADHD:PI FDR = .033, ADHD:C FDR = .020). Figure 3shows the age-adjusted splenium mean across groups with error bars representing a 95%confidence interval.

Relation of Area to Inattention, Hyperactivity, and IQ

All of the clinical groups showed significantly higher levels of inattention andhyperactivity by parent report compared to neurotypical controls. See Table 3 for meansand standard deviations in T-scores. There were no significant differences between the

Table 4 Univariate results of MANCOVA Analysis Showing the Main Effect of Diagnostic Group for Each ofthe Five Whole-Brain Adjusted Segments of the Corpus Callosum, with Age Controlled.

Segment F p value η2

Genu 1.00 .409 .028Ant-body 1.73 .145 .047Mid-body 2.93 .023 .077Post-body 1.45 .220 .040Splenium 3.10 .018 .081

Table 5 Post Hoc Comparisons Between Groups for Whole-Brain Adjusted Midbody and Splenium, with AgeControlled Showing Raw (LSD) p values and False Discovery Rate (FDR).

Contrast

Midbody Splenium

Raw p FDR Raw p FDR

Neurotypical vs. NLD .544 .777 .003 .020*Neurotypical vs. HFA .003 .020* .822 .913Neurotypical vs. ADHD:PI .215 .430 .899 .936Neurotypical vs. ADHD:C .422 .703 .472 .726NLD vs. HFA .004 .020* .007 .028*NLD vs. ADHD:PI .136 .340 .010 .033*NLD vs. ADHD:C .240 .436 .002 .020*HFA vs. ADHD:PI .156 .347 .936 .936HFA vs ADHD:C .066 .189 .674 .842ADHD:PI vs. ADHD:C .716 .842 .631 .841

Note. * Statistically significant False Discovery Rate.

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HFA and NLD groups on either inattention or hyperactivity (p = .468 and p = .216,respectively). Neither parent report of inattention nor parent report of hyperactivity on theBASC-2 was significantly related to adjusted total corpus callosum size or segment areaswith age controlled in regression models of total sample or within-group samples (allp values > .1). Similar regression models examining the influence of IQ were not found tobe significantly related to adjusted corpus callosum size (all p values > .2).

Splenium Size and Performance IQ

To further explore the finding that splenial area is smaller in children with NLD,regression models were used to relate behavioral symptoms to splenial area. The influenceof splenial area on the WASI Perceptual IQ index (PIQ) was selected because PIQ isthought to be a measure of language-reduced novel reasoning frequently used as an NLD

Figure 2 Areas of the five segments of the corpus callosum by group shown from anterior (left) to posterior(right). Areas are whole-brain size-adjusted means with age controlled. Error bars represent a 95% confidenceinterval.

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marker (Harnadek & Rourke, 1994) and because the WASI PIQ was not used to determinediagnostic group for this study. Group differences on the WASI PIQ subtest weresignificant, F(4, 140) = 3.733, p = .003, η2 = .118, with the NLD and HFA groupsscoring lower than children developing neurotypically (NLD: p < .001, HFA: p = .027)and both ADHD control groups (C: p = .016, PI: p = .024). There was no significantdifference between the NLD and HFA groups (p = .168). All group means were within theaverage range (Standard scores: 97.32–109.66). Initially, a regression comprising allgroups found no significant influence of splenium size on WASI PIQ scores with agecontrolled (R2 = .036, p = .072, βage = 0.049, βsplenium = 0.120). WASI PIQ scores may bearrived at differently based on group due to problems with inattention, impulsivity, andlow reasoning in addition to visuospatial problems. Thus, to examine whether there was adifferential influence based on group, one regression with NLD + neurotypical controlgroups and another with HFA + neurotypical control groups were performed. The resultsof these two regressions are in Table 6 along with results for the total sample. For theNLD group, smaller splenium size was significantly associated with lower WASI PIQscores with age controlled (βsplenium = 0.316, p = .007, ΔR2 = .095), while in the HFAgroup, splenium size was not associated with lower WASI PIQ scores (βsplenium = 0.091,p = .427, ΔR2 = .008). In the NLD and neurotypical control group regression, spleniumarea accounted for 9.5% of the variance in WASI PIQ scores.

To verify whether the splenium differences could explain variance in a verbalreasoning task, the same analyses were run using WASI VIQ scores. Results showedthat there were no significant differences in WASI VIQ between the NLD, HFA, andneurotypical control groups (p values ranging from .109–.476), but that the NLD groupobtained significantly higher WASI VIQ scores compared to both ADHD:C and ADHD:PI (C: p = .01, PI: p = .002), F(4, 140) = 3.499, p = .009, η2 = .090. All group means wereaverage to high average (range in standard scores: 104.5–117.9). With all participantstogether there was no significant effect of splenium size on WASI VIQ with age

Figure 3 Splenium area, adjusted for whole-brain size and age, by group with error bars representing a 95%confidence interval.

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controlled (R2 = .022, p = .188, βage = −0.102, βsplenium = 0.111). Similarly, theNLD + neurotypical control group and HFA + neurotypical control group showed nosignificant effect of splenium size on WASI VIQ scores with age controlled(βsplenium = 0.221, p = .058, ΔR2 = .047, βsplenium = 0.020, p = .864, ΔR2 < .001,respectively).

DISCUSSION

This is the first study to examine corpus callosum comparing groups of childrendiagnosed with HFA, NLD, subtypes of ADHD, or who are neurotypically developing.The results suggest that higher functioning children with autism do not show reducedareas of the corpus callosum as have previous studies with lower functioning children.Rather, our HFA group demonstrated significantly larger areas at the midbody of thecorpus callosum compared to the neurotypical control group in this sample. Prigge et al.(2013) recently reported similar findings. In their high-functioning sample of childrenwith autism, the middle third of the corpus callosum area was larger than expected giventotal brain volume. The current results are also consistent with the Tepest et al. (2010)finding that adults with autism who have average and above intellectual development donot demonstrate the midline white matter decrement seen in samples that include lowerfunctioning individuals.

Herbert et al. (2004) found similar results in a study of autistic children aged 5 to11 years old with IQ scores greater than 80. No differences were found between theautistic group and controls in the corpus callosum in the Herbert et al. study, but a greatervolume of radiate white matter was observed in the autism group. The authors implicatepostnatal processes associated with within-hemisphere myelination rather than neuronalpropagation across the hemispheric midline as an explanation for the increase cerebralvolume seen in autism. Broad impaired functional underconnectivity among neuralregions has been observed in both task-dependent and resting-state conditions in thosewith autism, most often in the frontal lobe regions (Jones et al., 2010). However over-connectivity compared to neruotypical controls has also been seen in subcortical regions(Mizuno, Villalobos, Davies, Dahl, & Müller, 2006) as well as the posterior cingulate,bilateral temporal lobes, and right parahippocampal gyrus (Monk et al., 2009). At thistime there is little doubt that the structural and functional systems of persons with autismdevelop differently from neurotypically developed persons, but consensus and clarity as tothe exact nature of those differences is unknown.

Consistent with our hypothesis that children with NLD might have aberrant devel-opment of the posterior regions of the corpus callosum, the current results demonstrated

Table 6 Results of Regressions Examining the Influence of Splenium Size on Estimated PIQ Scores with AgeControlled in (a) The Total Sample, (b) NLD + Neurotypical Control Group, and (c) HFA + Neurotypical ControlGroup.

Groups Model R2 Model p βage βsplenium ΔR2splenium

a. Total sample .036 .072 −0.167 0.131 .016b. NLD + neurotypical controls .112 .014 −0.189 0.316** .096c. HFA + neurotypical controls .069 .067 −0.268* 0.091 .008

Note. * p < .025; **p < .01; ***p < .001.

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that those with NLD have smaller splenial areas compared to children with HFA, ADHD,and neurotypically developing children. The splenium serves the occipital and parietalcortices, primarily providing connectivity of the bilateral visual systems including boththe striate (primary visual cortex) and association areas (Brodmann areas 18 and 19)(Putnam, Steven, Doron, Riggall, & Gazzaniga, 2009). Moreover, Putnam’s et al. (2009)recent evaluation of spleniel projections in typical adults found a predominance of right-to-left homotopic connections in the splenium that may be related to the “right hemi-sphere’s superiority for higher order perceptual processing” (p. 1668). The syndrome ofNLD has long been hypothesized as a disorder of visuospatial processing (Fine &Semrud-Clikeman, 2011; Fine et al., 2013; Harnadek & Rourke, 1994; Rourke, 1995)and this study offers findings that support the relation of a neuroanatomical differenceassociated with visuospatial differences in NLD.

The smaller posterior areas of the corpus callosum in our NLD group suggests thatthere may be either a divergent neuronal migration prenatally, a postnatal retraction ofneurons, or a failure in the myelination process in this group. The posterior region of thecorpus callosum increases in size more than anterior regions during the childhood yearsthat could be related to the development of the association cortices as children mature(Giedd et al., 1999). Thus, this is an area that may be particularly sensitive to a cascade ofincreasingly abnormal development if there is an early failure in the regional system.

Although the HFA group in this sample performed similarly to the NLD group onthe WASI PIQ, the influence of splenium size on WASI PIQ scores was significant onlyfor NLD + neurotypical control group and not for HFA + neurotypical control group. Thiseffect appears to be stronger for visuospatial compared to verbal tasks; the splenium didnot contribute significantly to WASI VIQ scores in the NLD or HFA + neurotypicalcontrol groups in our sample. The HFA group had splenium sizes similar to neurotypicalcontrols but had WASI PIQ scores similar to the NLD group suggesting that some factornot common in NLD and HFA may explain the lower WASI PIQ scores in HFA.

There may be many mediating variables with varying degrees of shared influencecomprising the relation between the splenium and WASI PIQ; it is unlikely that a simplerelation such as the one explored here can fully explain the differences between the twogroups. However, our findings appear to contradict the idea that NLD is simply a mildsubset of autism. While some who have a diagnosis of HFA may meet criteria for NLD,most persons with HFA do not meet criteria for NLD. Rather, autism is often associatedwith increased ability in abstract spatial reasoning compared to verbal reasoning(Stevenson & Gernsbacher, 2013) and also in spatial working memory (Ozonoff &Strayer, 2001), although not all researchers agree (Erdődi, Lajiness-O’Neill, & Schmitt,2013). A recent focus on the heterogeneity of the autism spectrum has indicated that thehistorical notion of a single genetic pathway to autism is no longer defensible(Georgiades, Szatmari, & Boyle, 2013). Diverse causes of autism are now recognizedincluding both single and multiple gene loci (Mehta & Nurmi, 2013) as well as pre- andpostnatal environmental factors (Lai, Lombardo, Chakrabarti, & Baron-Cohen, 2013). Thecomplex overlap in symptoms among phenotypes such as NLD and HFA are not yet wellunderstood, but the current study is a step in delineating a morphological differencebetween these two groups.

Differences in brain morphology can be deceptively weak in predicting behavior. Infact, there is great variability in behaviors associated with differences in the corpuscallosum in particular. Even when the corpus callosum fails to develop, as in AgCC,behavioral and cognitive symptoms vary widely and can include broadly typical

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development (Badaruddin et al., 2007). In persons with partial AgCC, in which someareas of the corpus callosum are present, heterotropic connections have been found inareas that did not correlate with the position or size of the residual corpus callosum (Wahlet al., 2009) arguing for a very plastic model of transhemispheric connectivity. Thus, asmaller corpus callosum in any particular region does not necessarily mean that additionalhomotopic connections have not been made aberrantly.

Additional research is needed to better describe the relations between splenium sizeand visuospatial functioning. Specifically, diffusion tensor imaging could illuminatewhether the smaller splenium is associated with increased connectivity in other whitematter tracts distal from the midline of the brain in NLD while resting state functionalMRI might show whether white matter differences affect the functional connectivitybetween the posterior and other regions of the brain. Exploring the relation of thiswhite matter difference in conjunction with visuospatial behavioral tasks known to berelated to NLD, such as spatial working memory (Mammarella et al., 2010), wouldprovide evidence for behavioral correlates to the neural differences seen in the currentstudy. Finally, along the lines of increased attention to the wide variety of geneticinfluences related to autism phenotypes (Mehta & Nurmi, 2013), genetic studies mayalso yield insight.

LIMITATIONS OF THE STUDY

Previous research has found more anomalies such as lesions and cysts in the brainsof children with NLD compared to children with HFA and children developing typically(Semrud-Clikeman & Fine, 2011) but the distribution of these brain differences were notrestricted to posterior regions. It is not optimal to include any participants with brainanomalies in a neuroimaging study. However, due to the difficulty in obtaining samples ofchildren with NLD, we decided to include all children with brain anomalies in theanalysis. In the current sample, 4 children in the NLD group had brain anomaliescompared to 2 neurotypical controls, 1 HFA and 1 ADHD:PI. One NLD participant hadan occipital anomaly prompting concern that it might have influenced the findings relatedto splenium. To evaluate the influence of this participant’s measurements on the studyresults, the participant’s data were removed and the analyses for the splenium were rerun.Results remained significant, with the NLD group having smaller areas at the spleniumcompared to all other groups (p values ranging from .007 to .025). Thus, the findings of asmaller splenial area in the NLD group was not strongly influenced by this participant.

Like many neuroimaging studies, the sample size of this one included fewer than 30participants per clinical group. Variability in brain measurements even among typicalpersons has been widely observed, thus results derived from small sample sizes may beinfluenced by nonsystematic individual differences. This study is one of the few in autismspectrum disorders that includes a relatively large sample consisting of children only and acontrol group of over 50 participants.

CONCLUSION

The corpus callosum appears to be typically developing with regards to area at themidsaggital region in ADHD:PI and ADHD:C compared to controls in this sample. In theHFA group, a significantly larger area in the midbody region suggests that a different typeof atypical connectivity may occur in higher compared to lower functioning children with

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autism. In children with NLD, the splenium is significantly smaller, suggesting a neuro-developmental atypicality affecting the systems of the brain related to visuospatialperception and higher level processing. Because the splenium may have more right-to-left hemisphere homotopic connections (Putnam et al., 2009), deficits observed in NLDmay substantiate previous suggestions (Rourke, 1995) that right-hemisphere systems areparticularly involved in deficits observed in children with NLD, although further researchutilizing diffusion tensor imaging, resting state fMRI, and further behavioral correlation isneeded to explore this tenuous hypothesis. Lower WASI PIQ but not WASI VIQ scoresappear to be related to smaller splenia in NLD but not HFA, suggesting that smallersplenium area may be related to visuospatial functioning in NLD. Although much remainsto be done before these findings can be fully validated, clinically they suggest thatstimulation of visual and spatial functioning could be a point of early intervention forchildren with NLD.

Original manuscript received June 26, 2013Revised manuscript accepted October 8, 2013

First published online 12 November 2013

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SMALLER SPLENIUM 19

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NLD

Diagn

ostic

Variables

NMean

Std.Deviatio

nMinim

umMaxim

um

WASIFSIQ

–Stand

ardScore

Control

57112.61

10.180

9113

5NLD

1910

5.74

13.601

8012

5HFA

2310

7.26

17.120

8213

3ADHD_C

2510

8.84

7.352

9712

4ADHD_P

I23

104.78

12.898

8713

6To

tal

147

109.02

12.266

8013

6WASIVIQ

–Stand

ardScore

Control

57112.93

12.659

9114

5NLD

19117.47

13.023

9514

2HFA

23110.96

15.179

8914

0ADHD_C

2510

7.64

10.037

9513

5ADHD_P

I23

103.87

14.502

8013

9To

tal

147

110.89

13.497

8014

5WJ3

Calculatio

n–Stand

ardScore

Control

5610

8.16

12.266

7513

5NLD

1989

.84

11.922

6110

7HFA

2396

.48

17.223

6813

6ADHD_C

2597

.32

11.123

7812

2ADHD_P

I22

96.14

14.456

7513

0To

tal

145

100.21

14.718

6113

6Rey

Com

plex

Figure

Control

29−0

.157

01.0079

5−4.09

1.52

NLD

19−2

.287

81.2400

2−5.89

−0.55

HFA

22−2

.442

02.3349

1−8.59

0.46

ADHD_C

0ADHD_P

I0

Total

70−1

.453

51.9189

4−8.59

1.52

Purdu

ePegsLeftHand

Control

30−0

.597

91.0272

1−2.61

1.18

NLD

19−1

.660

51.5625

5−4.08

1.85

HFA

23−1

.344

81.5885

8−4.21

3.29

ADHD_C

0ADHD_P

I0

Total

72−1

.116

91.4288

5−4.21

3.29

(Con

tinued)

APPENDIX

20 J. G. FINE ET AL.

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(Contin

ued).

NMean

Std.Deviatio

nMinim

umMaxim

um

Purdu

ePegsRight

Hand

Control

30−0

.598

41.0050

4−2.60

1.44

NLD

19−1

.771

81.6336

1−4.89

2.16

HFA

23−1

.358

81.7682

1−6.50

1.39

ADHD_C

0ADHD_P

I0

Total

72−1

.151

01.5179

3−6.50

2.16

Purdu

ePegsBothHands

Control

30−0

.035

12.3246

5−2.76

8.75

NLD

19−0

.844

91.9269

1−4.00

4.06

HFA

23−1

.618

41.5507

9−5.50

2.33

ADHD_C

0ADHD_P

I0

Total

72−0

.754

62.0872

1−5.50

8.75

ADI-RTo

talCIndex–Raw

Score

Control

0NLD

191.58

1.346

04

HFA

237.04

3.052

015

ADHD_C

0ADHD_P

I0

Total

424.57

3.657

015

CASPNon

verbal

–Raw

Score

Control

29−0

.931

01.3609

6−3.00

2.25

NLD

19−1

.881

60.9104

7−3.00

0.25

HFA

23−2

.097

80.9820

5−3.00

1.00

ADHD_C

0ADHD_P

I0

Total

71−1

.563

41.2426

3−3.00

2.25

VinelandSocialSkills

–Stand

ardScore

Control

25110.04

16.655

8014

1NLD

1679

.31

16.386

53116

HFA

2168

.57

9.857

4883

ADHD_C

0ADHD_P

I0

Total

6288

.06

23.576

4814

1

Note.The

currentstudycomprises

data

collected

from

awider

neuroimagingresearch

program

that

included

twodifferentstudies.Thus,notalldata

arepresentforallgrou

ps.

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