Quantitative analysis of the shape of the corpus callosum in patients with autism and comparison...

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published online 1 March 2011AutismL. Williams, Nancy J. Minshew and Thomas E. Conturo

Manuel F. Casanova, Ayman El-Baz, Ahmed Elnakib, Andrew E. Switala, Emily L. Williams, Dianeand comparison individuals

Quantitative analysis of the shape of the corpus callosum in patients with autism

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autism © 2010SAGE Publicationsand The National

Autistic SocietyVol 15(2) 1–16; 386506

1362-3613(2010)

Quantitative analysis of theshape of the corpus callosumin patients with autism andcomparison individuals

M A N U E L F . C A S A N O VA University of Louisville, Kentucky, USA

A Y M A N E L - B A Z University of Louisville, Kentucky, USA

A H M E D E L N A K I B University of Louisville, Kentucky, USA

A N D R E W E . S W I TA L A University of Louisville, Kentucky, USA

E M I L Y L . W I L L I A M S University of Louisville, Kentucky, USA

D I A N E L . W I L L I A M S Duquesne University, Pittsburgh,Pennsylvania, USA

N A N C Y J . M I N S H E W University of Pittsburgh, Pittsburgh,Pennsylvania, USA

T H O M A S E . C O N T U R O Washington University, St. Louis,Missouri, USA

A B S T R A C T Multiple studies suggest that the corpus callosum inpatients with autism is reduced in size. This study attempts to elucidatethe nature of this morphometric abnormality by analyzing the shapeof this structure in 17 high-functioning patients with autism and anequal number of comparison participants matched for age, sex, IQ,and handedness.The corpus callosum was segmented from T1 weightedimages acquired with a Siemens 1.5 T scanner. Transformed coordi-nates of the curvilinear axis were aggregated into a parametric map andcompared across series to derive regions of statistical significance. Ourresults indicate that a reduction in size of the corpus callosum occursover all of its subdivisions (genu, body, splenium) in patients withautism. Since the commissural fibers that traverse the different anatom-ical compartments of the corpus callosum originate in disparate brainregions our results suggest the presence of widely distributed corticalabnormalities in people with autism.

A D D R E S S Correspondence should be addressed to: M A N U E L F. C A S A N OVA,Department of Psychiatry and Behavioral Sciences, University of Louisville, 500 S Preston StBldg 55A Rm 217,Louisville, Kentucky 40202,USA. e-mail:m0casa02@ louisville.edu

K E Y W O R D S

autisticdisorder;

cerebral cortexgrowth and

development;corpus

callosum;magneticresonanceimaging

1Copyright © The Author(s), 2010. Reprints and permissions:http://www.sagepub.co.uk/journalspermissions.navDOI: 10.1177/1362361310386506



There is at present a convergence of findings stemming from structuralneuroimaging studies indicating a reduction in size of the corpus callosumin patients with autism (Frazier and Hardan, 2009; Lainhart et al., 2005).This applies both to areal measurements (e.g. using one medial sagittalslice) and to volumetric assessments (Freitag et al., 2009; Hardan et al.,2009; Keary et al., 2009). Modeling of the corpus callosum using meta-analyses has shown medium effect size with area magnitude reductionsdecreasing along its caudal extent (i.e. genu → splenium) (Frazier andHardan, 2009). The fact that the corpus callosum is smaller in patients withautism is all the more striking when considering that, in this patientpopulation, total brain size apparently exhibits an age-dependent volu-metric increase (Hrdlicka, 2008).

The present study aims at elucidating the nature of the corpus callosumabnormality in patients with autism. It complements previous morpho-metric studies by quantifying the shape of the corpus callosum outlineacross series of individuals with autism and comparisons. Since commis-sural fibers bear a strict topographic relationship to originating/projectingcortical areas, focal shape abnormalities of the corpus callosum could helppinpoint which cortical region, or regions, are involved in the pathophys-iology of autism. In addition, focal abnormalities of the corpus callosumcould provide clues as to the timing and nature of a particular pathologi-cal change.

In this study we examine the outline of the corpus callosum in a seriesof patients with autism and age-matched comparisons. The morphometricmethod employed in our study provides a point by point descriptor ofcorpora callosa contours while being invariant to translation, rotation, andscaling. Inverse construction allowed for multidimensional representationsof the contours and anatomical depiction of the changes. The basic hypoth-esis of the study was that individuals with autism would exhibit a general-ized reduction in size of the corpus callosum. In other words, we expectedfor the corpus callosum of patients with autism to be conservative of shapebut to be otherwise reduced in size when compared to neurotypicals.

Method

The two groups of participants consisted of 17 high-functioning adoles-cents/adults diagnosed with autism (14 males, 3 females) and 17 matchedcomparisons (see Table 1). Participants in our comparison group werepaired with persons with autism on the basis of sex, age, ethnicity, hand-edness, and full scale IQ (Wechsler, 1999). All participants diagnosed withautism and most of the comparison participants were recruited from theUniversity of Pittsburgh’s Collaborative Program of Excellence in Autism

A U T I S M 15(2)

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(CPEA). Written informed consent was obtained from all subjects followingexplanation of the procedures.

Participants diagnosed with autism were given the ADOS (Lord et al.,1989) and a parent/guardian or family member answered the ADI-R (Lordet al., 1994). Cutoff scores for ADOS were Communication: 3, Social: 6, andSocial/Communication Total: 10. Standard diagnostic cutoffs were utilizedwith the ADI-R. Participants diagnosed with autism did not have comorbidor causative conditions (chromosomal, neurologic, or infectious), had nohistory of seizures, perinatal brain injury, or head trauma, and were in goodgeneral medical health. The group of individuals with autism had bothverbal and full-scale IQ ≥ 80 to ensure adequate cooperation during proce-dures and testing, as well as to ensure good match to comparisons.

Candidates within the comparison group were screened for neurologic,psychiatric, and medical disorders, and their family history was screenedfor the same. Inclusion criteria for comparison individuals included: goodgeneral physical health, no regular use of psychotropic medications, goodperformance at school or on the job, and good peer relationships basedon self-report or staff observations during testing. Exclusion criteria forcomparison participants included: first-, second-, or third-degree relativediagnosed with ASD, personal and/or family history of psychiatric dis-orders, learning disability, perinatal brain injury, and head trauma. Bothgroups were screened for metal, claustrophobia, and body mass ≥ 114 kg,any of which would interfere with MRI scanning.

T1-weighted images were acquired with Siemens MAGNETOM Vision1.5 T scanners using an MPRAGE acquisition sequence. The two deviceswere located at University of Pittsburgh Medical Center (Shadyside) andWashington University School of Medicine. A single scanner operator em-ployed identical scanning protocols at both locations. Previously reportedDTT data from this equipment (Conturo et al., 2008) found that the twoscanners produce equivalent results. Each volume included 128 sagittalslices, comprising the entire head with a pixel size 1 mm2 and slice spacing1.25 mm.

Corpus callosum segmentation and mapping

Probabilistic model of the corpus callosum’s shapeMost of the recent literature on image segmentation employs level set-basedrepresentations of shape, in which a region’s boundary is the set of points{x: F(x) = k} for x = (x, y, z) a point in space, F a scalar function, and k aconstant. Without loss of generality one may always take k = 0. (Just letG(x) = F(x) – k; then the level set is {x: G(x) = 0}.) With k = 0, useful

I N T E R E S T- B A S E D L E A R N I N G

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A U T I S M 15(2)

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choices for F are signed metrics of displacement from the boundary; i.e.F < 0 inside the region, F > 0 outside the region, and |F| increases withdistance from the boundary. Then the region’s shape is approximated bythe closest linear combination of training shapes.The main drawback of thisrepresentation is that the space of signed distance functions is not closedwith respect to linear operations. Linear combinations of the distance func-tions may result in level sets with invalid or even physically impossibletopology, e.g. the approximation may be self-intersecting.

To circumvent this limitation, we represent the shape of the corpuscallosum having been learned from a training set of co-registered MRI witha ‘soft’ template, or probabilistic 3D model, s: R → [0,1], where s(x, y, z)is the empirical probability that the voxel (x, y, z) in anatomical space Rbelongs to the corpus callosum.The soft template is constructed as follows:

Co-align the training set of MRI using a rigid 3D registration with mutualinformation as a similarity measure (Viola and Wells, 1995).

Manually segment the corpus callosum from each MRI in the aligned set.

Estimate the voxel-wise probabilities s(x, y, z) as the fraction of aligned imageswhere voxel (x, y, z) was labeled as belonging to the corpus callosum.

Once constructed, s is used to facilitate segmentation of the corpuscallosum from an MRI. Let g denote a given grayscale MRI, and let mdenote the desired binary region map (i.e. m(x, y, z) = 1 for (x, y, z) insidethe corpus callosum, and m(x, y, z) = 0 otherwise). Proceed as follows:

Align g to a prototype selected from the training set for s, again using mutualinformation as a similarity measure.

Estimate the probability distribution of voxel values within the corpus callo-sum, P(g|m), by fitting a bimodal linear combination of discrete Gaussians.

From P(g|m) and the learned probabilistic shape model s, construct an initialregion map m.

Use the initial region map to update the conditional intensity P(g|m), anditerate. For details see El-Baz and Gimel’farb (2009).

MappingComparison of the corpus callosum between different subjects and groupsis facilitated by mapping their respective boundaries into a standardizedcoordinate system.We employed a pseudocylindrical system (ρ, ϕ, z) basedupon the medial axis of the corpus callosum extending from splenium togenu (Figure 1); i.e. the centerline. The mapping is defined as follows(Casanova et al., 2010): a given point on the boundary of the corpuscallosum lies in some plane intersecting the centerline at a right angle. The

A U T I S M 15(2)

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arc length along the centerline from the splenium to the point of intersec-tion becomes the z coordinate. Then ϕ is the polar angle in the orthogonalplane measured clockwise from anatomical right, and ρ is the straight-line(Euclidean) distance from the boundary point to the point of intersection.

The construction is analogous to the cylindrical equal-area projectionsometimes used for maps of the Earth or other bodies. Of course themapping is not equal-area in the case of the corpus callosum, which is farfrom spherical in shape. It must not be presumed that each pixel in Figure2 represents a similarly sized patch of the CC boundary.

StatisticsThe boundary of each corpus callosum was treated as a two-dimensionalmap ρ(ϕ, z) providing the effective radius of the corpus callosum in a givendirection from a given point along the centerline. Each matched pair oftransformed surfaces was differenced pointwise, ∆ρ(ϕ, z) = ρautism(ϕ, z) –ρcomparisons(ϕ, z). The pairwise differences were aggregated pointwise toyield a statistical parametric map of t statistics with 16 degrees of freedom.Regions where ∆ρ was statistically significant – i.e. where we reject the nullhypothesis ∆ρ(ϕ, z) = 0 – were identified by thresholding the raw two-sided p values using the method of Benjamini and Hochberg (1995) witha false discovery rate q* = 0.05.


7

Figure 1 Mapping the surface of the corpus callosum. In the pseudocylindricalcoordinate system, the z coordinate is the arc length along the centerline fromthe splenium (left). Radius and polar angle are then measured within the planeorthogonal to the centerline at z.



Results

Mean age of the participants with autism was 26.1 years (s.d. 10.8 years)at the time of MRI acquisition, while mean age of the comparison clientswas 26.5 years (s.d. 10.9 years). There was no significant difference be-tween groups in age (paired t = .78; 16 df.; p = .45), and no paired subjectsdiffered in age by more than three years. Mean IQ scores of those withautism were full-scale 105 (s.d. 9.4), verbal 103 (s.d. 10.3), and perfor-mance 108 (s.d. 9.2). Among the comparison clients mean IQ scores werefull-scale 104 (s.d. 8.3), verbal 101 (s.d. 10.3), and performance 107 (s.d.12.6). Again there was no significant difference between groups (paired t= –.48, –.97, –.26, respectively; 16 df.; p = .64, .35, .80, respectively).Paired subjects did not differ by more than 14 IQ points (full-scale), orby more than 15 and 16 points on the verbal and performance scales,respectively.

White matter in autism was reduced bilaterally along the body of thecorpus callosum, where ∆ρ took values as low as –4.3 mm (s.d. 2.3 mm)on the left side and –2.8 mm (s.d. 1.6 mm) on the right. There was apartfrom this an increase of up to ∆ρ = 2.3 mm (s.d. 2.4 mm) in the regionproximate to the right cingulate gyrus. There was also a reduction in thesplenium in autism, of up to ∆ρ = –5.1 mm, although it was not statisti-cally significant. There was otherwise no significant difference around themedial sagittal plane (Figure 2, Figure 3).

Where ∆ρ was significantly negative along the left side, there was aslight positive trend with increasing z. That is, ∆ρ was more negative onthe left posterior body of the CC than on the left anterior body (Figure 4).There was no significant evidence for a similar trend on the right CC body.

Discussion

The results of our study, in agreement with the previous literature, showeda smaller corpus callosum in patients with autism when examined againsta comparison group (Frazier and Hardan, 2009; Lainhart et al., 2005). Thecorpus callosum was significantly reduced in size throughout its extent(lengthwise), with the largest differences noted at the level of the splenium.Although we did not attempt to parcellate the corpus callosum, statisticalanalysis suggested a gradient of severity spanning the rostro-caudal axis ofthe corpus callosum. The significance of the finding is hard to interpret,being present on the left but absent on the right side (Figure 4). Thefindings could be due to the smaller left hemisphere planum temporalereported in autism (Rojas et al., 2002). A gradient of severity for the corpuscallosum in autism was also described in a meta-analysis (Frazier and

A U T I S M 15(2)

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Hardan, 2009). The latter study reported results as effect size (estimatedparameter relative to the standard error of the estimate) for studies usingsingle midsagittal cross sections. The gradient we report is in the estimated∆ρ, unnormalized by standard error. The differences in measurements be-tween our study and that of Frazier and Hardan (2009) preclude furthercomparison.

Morphometric abnormalities of the corpus callosum provide an inter-esting clinicopathological correlate to autism due to this structure’s role indevelopmental integration and learning processes (Mihrshahi, 2006). Theauthors speculate that the distributed nature of the corpus callosum abnor-mality suggests a disturbance of both heterotopic and homotopic callosalconnections. This would provide for a global callosal deficit manifested byabnormalities in the ability to establish those temporal relations essential tothe integrative representation of sensory features (heterotopic connections),while simultaneously altering the topographical correspondence between


9

Figure 2 The map of �ρρ = ρρautism(ϕϕ, z) – ρρcomparison(ϕϕ, z), i.e. the mean deficit orexcess in ρρ(ϕϕ, z) in patients with autism and comparisons. Differences arestatistically significant (for q* = 0.05) in the outlined areas.



right and left maps contributing to the generation of feature-selective recep-tive fields (homotopic connections) (Houzel and Milleret, 1999). Giventhe role of heterotopic and homotopic connections it is not surprising thatpatients with agenesis of the corpus callosum exhibit social and behavioralproblems associated with autism (Badaruddin et al., 2007).

According to the homotopic callosal inhibitory theory of Cook (1984),the process of activation of cortical columns in the left hemisphere duringverbal processing provides for surround inhibition, which in turn preventsactivation of neighboring columns with their contiguous cognitive content.The genesis of this surround inhibition rests within the peripheral neuropilspace wherein different anatomical elements provide a shower curtain of

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Figure 3 Regions where the corpus callosum of individuals with autism andcomparisons appear to differ, as in Figure 2, mapped onto a reference surface(the average corpus callosum of the comparison group) and viewed from aboveand from below.The corpus callosum of an individual with autism would tend toenvelop the reference surface in the highlighted areas (�ρρ > 0). It would tendto lie entirely inside the reference surface in the darkened areas (�ρρ < 0). In themedium gray labeled area (�ρρ ≈≈ 0), it would not differ significantly from thereference.



inhibition surrounding pyramidal cells within the core of the cell mini-column. In essence, gamma-aminobutyric acid (GABA) interneurons con-tribute to a circumferential zone of inhibitory and disinhibitory activitygating communication of the central minicolumnar core with surroundingareas (Casanova et al., 2003). Both anatomical and functional studies suggestabnormalities of surround inhibition in autism (Casanova, Buxhoevedenand Brown, 2002; Tommerdahl et al., 2007), which may be due, in turn,to faulty homotopic callosal inhibition (Cook, 1984).

The callosal findings reported in this study appear related to neuro-pathological findings of a minicolumnopathy in patients with autism


11

Figure 4 Mean �ρρ as a function of z, averaging over ϕϕ, within sections of thecorpus callosum body encompassing those regions where �ρρ was significantlyless than 0.These sections are boxed in the inset, upper left (compare Figure 2),and they fall within the regions labeled dark gray on the left and right side of thecorpus callosum in Figure 3. Along the left corpus callosum body (L), as onemoves from the posterior aspect toward the anterior, mean �ρρ increases (95%confidence interval on the slope of the least squares-fit line: [0.004, 0.045]).There is no evidence of such a trend on the right (R), where the slope isconsistent with zero (95% confidence interval [–0.017, 0.025]). Confidenceintervals were estimated via bootstrap resampling of matched case/comparisonclient pairs.



(Casanova, Buxhoeveden, Switala, et al., 2002; Casanova et al., 2006). Thecerebral cortex is composed of elementary processing units arranged incolumnar fashion (Hofman, 1985). Rakic has proposed that the size of theneocortex (i.e. the number of columnar arrangements) is the product ofsupernumerary mitotic activity within the germinal cells of the periventri-cular zone and their longevity during individual embryonic development(Rakic and Kornack, 2001). A series of asymmetrical divisions involvingthese cells provides for clones which migrate to the cortex under theguidance of a glial scaffolding (Casanova, El-Baz, Vanbogaert, et al., 2009).With each generated minicolumn there is an increased need for creatingsupporting interconnections. It has been calculated that each minicolumnis associated with approximately 103 other minicolumns (Casanova, 2004).The overall increase in minicolumns seen during corticalization thereforeprovides for bigger brains and a biased gray/white matter ratio (Casanova,Buxhoeveden and Brown, 2002). In essence, the bigger brains within anencephalization plot have more white matter than expected when consid-ering size alone (Hofman, 2001).

Since mean diameter of the longer projection fibers is essentially constantacross species (Olivares et al., 2001) average transmission times across com-misural pathways increase directly with size of the brain. This temporaldelay for interhemispheric communication is significant enough that thehuman brain has found it more efficient to emphasize an ‘intrahemisphericmodus operandi’ (Doty, 2007). In effect the white matter increase observedwith increasing encephalization is made primarily of short arcuate pro-jections at the expense of longer corticocortical connections including thecommisural tracks (Casanova, 2004). Across species, those with biggerbrains have smaller corpora callosa (Olivares et al., 2000; Rilling and Insel,1999). This finding is the putative mechanism underlying both cerebraldominance and the emergence of higher cognitive functions such aslanguage (Casanova and Tillquist, 2008). Many of the reported morpho-metric abnormalities in autism (e.g., bigger brains, overabundance ofarcuate fibers, smaller corpus callosum) may therefore be interdependent.In essence, abnormalities of both corpus callosum size and cerebral domi-nance have been implicated in psychiatric conditions, such as autism, thatare characterized by increased brain size and the presence of languagedisorder (Herbert et al., 2005; Johnson and Casanova, 2009; Williams andCasanova, 2010).

The findings of our study acquire relevance from the recent literaturein autism espousing a bias in connectivity favoring short connections (e.g.arcuate fibers) at the expense of longer corticocortical projections (e.g.commisural fibers). Hypotheses to this effect were postulated by Belmonteet al. (2004) and Mottron et al. (2006). Belmonte’s hypothesis was based

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on clinical observations suggesting that patients with autism experience alack of ‘central coherence’ (Happé and Frith, 2006) and gamma frequencyabnormalities (Brock et al., 2002; Sokhadze et al., 2009). Mottron’s model,referred to as enhanced perceptual functioning, was based on enhancedlow-level operations. The latter author proposed atypical inhibition andfunctional autonomy of perceptual operations from top-down processinginfluences. Anatomical evidence in support of these hypotheses came fromparcellation studies of the white matter (Herbert et al., 2004), indicatingan enlarged outer radiate compartment (i.e. space occupied by arcuatefibers), smaller neurons (Casanova et al., 2006) favoring transmission alongshorter pathways, and structural imaging studies showing a reduction ofthe gyral window (Casanova, El-Baz, Mott, et al., 2009), which would serveto constrain the size of longer projection fibers in patients with autism.Functional neuroimaging studies have similarly shown a number of measuressuggesting underconnectivity among distant brain regions (Just et al., 2007).

Results from our study indicate a generalized reduction of the corporacallosa in those participants diagnosed with autism. Generalized findings,involving the rostro-caudal extent of the corpus callosum, is consistent witha mitotic event affecting periventricular germinal cells and the total numberof cortical minicolumns (i.e. the putative mechanism for encephalization).This suggests the presence of either a time window of vulnerability duringearly development as symmetrical divisions of periventricular germinal cellstake place (i.e. first forty days of gestation in primates) (Rakic and Kornack,2001), or a later acting exogenous influence capable of propitiating thismitotic process at an inopportune moment. Furthermore, since the com-misural fibers that transverse the different anatomical compartments of thecorpus callosum originate in disparate brain regions, our results suggestthe presence of widely distributed cortical abnormalities in people diag-nosed with autism.

It is worthwhile noticing that completely opposite morphometricfindings have now been reported in developmental dyslexia. Both dyslexiaand autism appear to fall at opposite ends of a minicolumnar spectrumwhich is manifested by divergent findings in regards to measurements fortotal brain volume, gyral window, white matter parcellation, and size ofthe corpus callosum (Williams and Casanova, 2010). Although a pervasivedisorder may affect, by definition, learning abilities, our findings and thosepreviously reported (e.g. white matter parcellation) suggest that the under-lying substratum for this deficit is different for both autism and develop-mental dyslexia.


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FundingThis work was supported primarily by the Nancy Lurie Marks Family Foun-dation; also by the National Institutes of Health (grant numbers ACE P50HD55748, CPEA U19 HD35469, K23 DC06691, R01 HD65279, R01MH86784, R01 NS39538, R01 NS52470, R21 MH90494); and AutismSpeaks (grant number AS03799).

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