Mapping Brain Abnormalities in Boys with Autism

Caroline Bruna, Rob Nicolson, MDb, Natasha Leporé, PhDa, Yi-Yu Chou, MSa, Christine N.Vidal, PhDa, Timothy J. DeVito, PhDc, Dick J. Drost, PhDc, Peter C. Williamson, MDb,Nagalingam Rajakumar, MDb, Arthur W. Toga, PhDa, and Paul M. Thompson, PhDa

a Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, LosAngeles, CA, USAb Dept. of Psychiatry, University of Western Ontario, London, Ontario, Canadac Dept. of Medical Biophysics, University of Western Ontario, London, Ontario, Canada

AbstractChildren with autism spectrum disorder (ASD) exhibit characteristic cognitive and behavioraldifferences, but no systematic pattern of neuroanatomical differences has been consistently found.Recent neurodevelopmental models posit an abnormal early surge in subcortical white mattergrowth in at least some autistic children, perhaps normalizing by adulthood, but other studiesreport subcortical white matter deficits. To investigate the profile of these alterations in 3D, wemapped brain volumetric differences using a relatively new method, tensor-based morphometry(TBM). 3D T1-weighted brain MRIs of 24 male children with ASD (age: 9.5 years ± 3.2 SD) and26 age-matched healthy controls (age: 10.3 ± 2.4 SD) were fluidly registered to match a commonanatomical template. Autistic children had significantly enlarged frontal lobes (by 3.6% on the leftand 5.1% on the right), and all other lobes of the brain were enlarged significantly, or at trendlevel. By analyzing the applied deformations statistically point-by-point, we detected significantgray matter volume deficits in bilateral parietal, left temporal and left occipital lobes (p=0.038,corrected), trend-level cerebral white matter volume excesses, and volume deficits in thecerebellar vermis, adjacent to volume excesses in other cerebellar regions. This profile of excessesand deficits in adjacent regions may (1) indicate impaired neuronal connectivity, resulting fromaberrant myelination and/or an inflammatory process, and (2) help to understand inconsistentfindings of regional brain tissue excesses and deficits in autism.

KeywordsAutism; TBM; white matter; gray matter; cerebellum; morphometry

IntroductionAutism is a developmental disorder characterized by social deficits, impairedcommunication, and restricted and repetitive behavior patterns (American PsychiatricAssociation, 2000). Postmortem and structural magnetic resonance imaging studies havehighlighted the frontal lobes, amygdala and cerebellum as pathological in autism (Amaral etal., 2008), but there has yet to be agreement on the anatomical extent, timing, andconsistency across subjects of the biological abnormalities (Williams and Minshew, 2007).

Please address correspondence to: Dr. Paul Thompson, Professor of Neurology, Laboratory of Neuro Imaging, Dept. of Neurology,UCLA School of Medicine, 635 Charles E. Young Drive South, Suite 225E, Los Angeles, CA 90095-7332, USA, Phone: (310)206-2101 Fax: (310) 206-5518 E-mail: thompson@loni.ucla.edu.

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Published in final edited form as:Hum Brain Mapp. 2009 December ; 30(12): 3887–3900. doi:10.1002/hbm.20814.

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Brain imaging studies of these developmental abnormalities often report an increased totalbrain volume (Hazlett et al., 2005) and early acceleration in brain growth in autism, but it isnot agreed whether this enlargement is restricted to childhood or continues into adulthood(Nicolson and Szatmari, 2003).

Studies examining the differential contributions of gray and white matter to this abnormalgrowth in autistic patients have not had entirely consistent results, some detecting anincrease in only gray matter or only white matter, but others finding it in both tissue types(Nicolson and Szatmari, 2003). The localization of this brain volume increase is alsodebated (Bonilha et al., 2008): frontal areas may contribute disproportionately to the volumeincrease (Carper et al., 2002), but some suggest that more posterior brain regions aredisproportionately affected (Hazlett et al., 2006). A recent meta-analysis also found anoverall increase in cerebellar volume (which may be proportional to the increase in totalbrain volume) and in the caudate nucleus, but found consistent reductions in the cross-sectional area of the corpus callosum (Stanfield et al., 2007).

Most traditional volumetric analysis have used region of interest analyses, using manualtracing of structures or automated segmentation (Yushkevich et al., 2006). Measures ofoverall structure volumes may fail to detect subtle or highly localized anatomical differencesbetween groups, and may overlook consistent regional differences in anatomical shape.Recently, computational mapping methods have been used increasingly to examine brainstructure. Unlike traditional volumetric methods, statistical maps can detect highly localizedgroup differences in brain morphology without the need for manual tracing or priorspecification of regions of interest (Thompson et al., 2004a; 2004b). These methods havedetected regional thinning of the corpus callosum (Vidal et al., 2006), subtle hippocampalvolume reductions (Nicolson et al., 2006), and ventricular volume reductions (Vidal et al.,2008) in autism, even when significant volume reductions in the brain as a whole were notdetectable. In Vidal et al. (2008), surface-based statistical maps of group differencesrevealed subtle, localized reductions in ventricular size in patients with autism in the leftfrontal and occipital horns, which may reflect exaggerated brain growth early in life.Ventricular volumes measured using traditional methods did not differ significantly betweengroups. Other voxel-based anatomical mapping techniques, such as voxel-basedmorphometry (Ashburner and Friston, 2000), have been used to detect subtle alterations inthe corpus callosum in autism (Chung et al., 2004). One study suggested that increases intemporal and parietal cortical thickness (Hardan et al., 2006) may contribute to thevolumetric increases in autism and may also relate to anomalies in cortical connectivity.Even so, another voxel-based mapping study had apparently conflicting findings (McAlonanet al., 2005): children with autism had a significant reduction in total gray matter volumeand significant increase in CSF volume. They had significant localized gray matterreductions within fronto-striatal and parietal gray matter and additional decreases in ventraland superior temporal gray matter.

To better understand the distribution and direction of these effects, further voxel-basedstudies are urgently needed.

Tensor-based morphometry (TBM) is a related structural image analysis technique that canreveal profiles of volumetric gains and deficits in patients versus control populations. TBMhas not, to our knowledge, been applied to study autism. In TBM, a fluid image warpingapproach reshapes a set of brain images to match a common anatomical template. Fromthese fluid deformation mappings, relative volume differences are computed between eachindividual and the anatomical template, and displayed voxel-by-voxel as a map. These mapsmay be compared across groups to identify regions with systematic volumetric differences.TBM has been used previously to characterize brain differences in various neurological

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disorders such as Alzheimer’s disease, semantic dementia, HIV/AIDS (Chiang et al., 2005,2007; Leow et al., 2006; Hua et al., 2008, Leporé, 2008a), and neurodevelopmentaldisorders such as Fragile X syndrome (Lee et al., 2007) and Williams syndrome (Chiang etal., 2007). A similar approach has been applied to longitudinal scans to study brain changesover time (Thompson et al., 2000; Chung et al., 2001; Aljabar et al., 2008). TBM may alsobe used to study statistical associations between regional brain volumes and relevantpredictors, such as age, sex, or IQ (Chiang et al., 2007).

This study had two goals. First we examined the three dimensional (3D) profile ofsystematic morphometric differences between patients with autism and controls using TBM.While TBM can reveal differences throughout the brain in 3D without a priori specificationof regions of interest, we hypothesized that patients with autism would have diffusevolumetric excesses throughout the brain, particularly in the white matter, based on reportsof white matter overgrowth in infancy. In line with prior reports, we anticipated localizedgray matter abnormalities (either reductions or excesses, as the direction of the effects is notconsistent in the literature) in temporal and parietal regions that include classical languageprocessing systems. We also hypothesized that we would detect volume increases in thecerebellum, a region frequently reported as abnormal in autism.

In the original version of TBM, the determinants of the Jacobian matrices are derived fromthe local deformation field obtained after the nonlinear registration. These encodecompressions and expansions, and can be used to map regional volume differences betweenpatients and controls. In this study, we used the more general method described in Pennec(2004) and Leporé et al. (2008a) (summarized in Figure 1), in which the local deformationtensor field is analyzed statistically to detect local volume and local shape differences intissue.

2. Materials and Methods2.1 Subjects

Study participants included 24 males with autism (age: 9.5 ± 3.2 years; range: 6 to 16 years)diagnosed using the Autism Diagnostic Interview-Revised (ADI-R) (Lord et al., 1994), theAutism Diagnostic Observation Schedule (ADOS-R) (Lord et al., 2000), and by clinicalobservation. All patients met DSM-IV-TR criteria for autism (American PsychiatricAssociation, 2000) as well as ADI-R and ADOS algorithm criteria. Patients were alsoassessed using the Wechsler Intelligence Scale for Children, 3rd Edition (WISC-III) or theLeiter International Performance Scale. Socioeconomic status was determined for eachpatient (Hollingshead, 1975). Patients with a non-verbal IQ below 70 were excluded. Allpatients had a physical examination prior to the study; subjects with a seizure disorder orother neurological condition or a cytogenetic abnormality or genetic syndrome (such asFragile X syndrome) were excluded. At scan time, eight patients were medication naïve;four others had discontinued their previous medications prior to the scan. Among theremainder, five were being treated with dopamine antagonists, eight were taking stimulants,four were receiving SSRIs (selective serotonin re-uptake inhibitors), and one was beingtreated with a cholinesterase inhibitor.

Twenty-six healthy males (age: 10.3 ± 2.4 years; range: 6 to 16 years), drawn from the localcommunity through advertisement and word of mouth, participated as control subjects. Theywere assessed with the Schedule for Affective Disorders and Schizophrenia-ChildhoodVersion (K-SADS) (Kaufman et al., 1997) to ensure that none had a major psychiatricdisorder. None had a personal history of neurological disorders or learning disorders or afamily history of autism, mental retardation, language disorders, or learning disorders.Controls were also assessed with the WISC-III or the Wechsler Abbreviated Scale of

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Intelligence; a full-scale IQ of less than 70 was exclusionary. Age, race, handedness, height,and intelligence were compared between the two groups using t-tests or chi-squaredanalyses (Table 1).

This study was approved by the Health Sciences Research Ethics Board at the University ofWestern Ontario. The parents or legal guardians of all subjects provided written consent forparticipation in this study, while the subjects provided written assent.

2.2 Scanning procedureAll subjects were scanned on a 3-Tesla scanner (IMRIs, Winnipeg, Canada). Sixteen of thesubjects with autism required sedation with oral midazolam, to complete their scans.Standard T1-weighted localizer images were acquired initially. Images used for volumetricanalysis were then acquired using a T1-weighted 3-D MP-RAGE (Magnetization PreparedRapid Gradient Echo) sequence (TI=200ms, TR=11ms, TE=5 ms, flip-angle=12 degrees,total scan time: 8 minutes) with 1.2mm isotropic voxels.

2.3 PreprocessingImage distortions due to radiofrequency field inhomogeneities were corrected using anonparametric method (Sled et al., 1998). Extra-cerebral tissues were removed, assisted bymanual editing, in the BrainSuite software package (Shattuck and Leahy, 2002). MRI brainscans were first globally aligned to the International Consortium of Brain Mapping braintemplate (ICBM-53; Mazziotta et al., 2001) using a 9-parameter registration (3 translations,3 rotations and 3 orthogonal scales) with the ANIMAL software (Collins et al., 1994). Thecerebellum was manually traced in each subject using the program Multitracer (Woods,2003; available at http://www.loni.ucla.edu/Software/Software_Detail.jsp?software_id=10)and registered using a 9-parameter registration (with the ANIMAL software). It wasdelineated from the most posterior section in the coronal view, where the fissure separatingthe cerebellum from the cerebrum becomes visible. The brain stem was carefully excludedfrom the mask in the regions where it begins to merge with the cerebellum (which is morevisible in sagittal slices; triaxial views were used to make this easier to identify). The middlecerebellar peduncle and the brachium conjunctivum were excluded at the point where itcompletely fuses with the middle cerebellar peduncle. Lobar regions of interest weredelineated on the control subject used as a target (see paragraph 2.4) according to the criteriaused to define lobar boundaries in the ICBM-53 atlas. Gray and white matter segmentationswere also created for this same volume, using the BrainSuite software package (Shattuckand Leahy, 2002).

2.4 Fluid image registrationSome TBM studies generate a minimal deformation target (MDT) from the scans, with amathematically-defined mean geometry for a population (Christensen et al, 1996; Good etal., 2002; Joshi et al., 2004; Kochunov et al., 2001, 2002; Leporé et al., 2007b; Lorenzen etal., 2004; Hua et al., 2007). As in other TBM studies (e.g., Davatzikos et al, 2001), wepreferred using registration to a single control subject’s image versus a multi-subjectaverage intensity atlas as it had higher contrast, better spatial resolution and sharper features.Template optimization for TBM is the subject of further on-going study by us and others(Kochunov et al., 2002; Leporé et al., 2007b). All scans were non-linearly registered to thissubject’s scan using a fluid image registration algorithm (Bro-Nielsen and Gramkow, 1996;Leporé et al., 2008b; Brun et al., 2007), which was accelerated using a fast filter (describedin Gramkow et al., 1996). Details of the method are described in Leporé et al. (2008b).

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2.5 Statistical analysisNon-rigid registration of each individual brain image to the common anatomical templategave a 3D displacement vector field from which we computed Jacobian matrices J of thedeformation. Determinants of these Jacobian matrices, det(J), are commonly used in TBMstudies and interpreted as “local expansion factors” (Leporé et al., 2008a). They quantifylocal expansions (where det(J) > 1) or local contractions (where det(J) < 1), and reflectregional volumetric differences between each subject and the corresponding anatomicalregions in the template. Another recently developed approach is to retain the fullinformation in the transformation by computing symmetric definite-positive matrices S=JTJ,at each point, called deformation matrices. Multivariate statistics are then computed on thesematrices (Leporé et al., 2008a) using the log-Euclidean framework to account for thecurvature of the space of positive-definite symmetric matrices to which S belongs (Arsignyet al, 2005). The intuitive meaning of this approach is to detect anatomical regions wherestructures may be locally enlarged or compressed along certain directions (as explained inFigure 1). As brain growth is anisotropic, i.e., not uniform in all directions (Thompson et al.,2000), some structures may become enlarged in disease, relative to the control average,along certain directions. Multivariate TBM is designed to pick up on these anisotropicchanges. Past studies found that multivariate TBM can identify regional abnormalities thatare overlooked by the analysis of the determinant (local volume difference) only (Leporé etal., 2008a).

Before statistical analysis, we also covaried the computed deformation matrices anddeterminants at each voxel with age, to adjust for possible age effects. We computed Scov,ijwith ij one component of the matrix and (det J)cov, according to the regression equation

with regression coefficients βi, diagnosis coded using a dummy binary variable, and Scov,ij =Sij − Sij,predicted the resulting adjusted measure. Once adjusted for age effects, thedeterminants of these Jacobian matrices were used to compute the lobar volumes for eachsubject after delineating each lobe in the registration target image (as explained in paragraph2.3). Lobe volumes were averaged within the two groups and compared. The data was alsoanalyzed with two types of statistics: (1) a univariate Student’s t-test on the age-adjustedvolumes, after logarithmic transformation, i.e., log10((det J)cov and (2) a multivariateHotelling’s T2-test on log(Scov). To avoid assuming that our random variables are normallydistributed, we used voxelwise permutation tests to establish a null distribution at each voxel(Nichols and Holmes, 2002), using the suprathreshold volume. We permuted theassignments of subjects to groups 5000 times. This number of permutations N wasdetermined according to Edgington (1995), to control the standard error SEp, of the omnibusprobability p, which follows a binomial distribution B(N,p) with SEp = p(p−1)/N. Theoverall significance of the observed pattern of effects in the statistical maps is assessed bycomputing this omnibus probability, p (that we will call corrected pcorrected) which is a wayof correcting for the multiple spatial comparisons implicit in computing maps of statistics.The general method is further detailed in Nichols and Holmes (2002). pcorrected values werecomputed for the overall gray and white matter in each lobe, using the lobar volumes and theclassified gray and white matter segmentations (see paragraph 2.3) to the Jacobiandeterminant maps obtained in each subject.

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3 Results3.1 Subjects

The groups did not differ significantly in age, socioeconomic status, race, or height (seeTable 1), although there were proportionally more left-handed subjects in the patient group.While there was no significant difference in non-verbal IQ between the two groups, patientsdid have a significantly lower verbal and full-scale IQ.

3.2 Analysis of the cerebrumVolumetric summaries are shown in Figure 2.A. and 2.B. In the raw (unscaled) data, theautistic group had significantly higher frontal lobe volumes in the left (+3.6%; p=0.049) andright hemispheres (+5.1%; p=0.011). The occipital, temporal, and limbic regions were alsoabnormally enlarged in the autism group on the left, and at trend-level on the right (seeTable for significance levels). After adjusting these data for overall differences in brain scaleacross subjects, none of the lobes showed evidence for reduction or excess in patients withautism compared to controls; even so, group differences in gray matter and trend-leveldifferences in white matter were still detected in the group difference maps (Figure 3).

We expected univariate and multivariate maps to offer more power than lobar averages todetect relative regional volume differences throughout the brain. Figure 3 shows volumetricexcesses in the white matter (Figure 3.A) and excesses and losses in the gray matter (Figure3.B) in the autism group in terms of the volume ratio (i.e., mean autism volume divided bymean control volume). Statistical maps are also shown, based on the univariate (DET) andmultivariate tests (LOG). The volume ratio (RATIO) and the univariate ((det J)cov) analyses(DET) show a complex pattern of volume differences (mostly reductions) in the gray matterand excesses in the white matter. Although the differences seem quite prominent in thewhite matter in the ratio maps, their significance is only at trend level after multiplecomparisons correction (pcorrected = 0.1 for DET and pcorrected = 0.09 for LOG); the graymatter volume reductions, however are significant overall when the gray matter alone isassessed (pcorrected = 0.038 for DET and pcorrected = 0.05 for LOG). When using the unscaleddata, the difference is not significant either in the white matter, or in the gray matter, whichis expected, given the wide variations in brain volume across subjects.

As differences were hypothesized independently in the white matter (based on earlier reportsin independent samples Herbert et al., 2004), and in the gray matter (McAlonan et al., 2005)we also conducted analysis restricted to these two regions.

We performed two-tailed t-tests at each voxel to assess the hypothesis of local gray matterabnormalities. Given the significance of these tests (pcorrected = 0.038 for DET and pcorrected=0.05 for LOG, see previous paragraph), we conducted additional one-tailed t-tests in thesame region to determine the direction of the changes; as these were post-hoc tests, thesignificance of the gray matter deficits described should be based on the 2-tailed test only.Two one-tailed t-tests were similarly performed at each voxel in the white matter to assessthe alternative hypotheses of white matter excess or deficits in the patient group.

For completeness, as a post hoc test, we also subsequently examined the results of theopposite contrasts (designed to detect white matter loss), and confirmed that there were noeffects in those directions.

In Figure 3.A, regions with white matter excesses in the RATIO maps (shown in red,equivalent to +15–20%) correspond to regions of trend-level volume differences in the DETmaps, whereas volume deficits in the RATIO maps (light blue, -5 %) do not correspond toany signal in the DET maps (dark blue corresponding to a p-value = 1). These maps indicate

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that there was only weak evidence for distributed white matter volume excesses in thepatients, and no evidence for deficits. When computing one-tailed t-tests over the entireimage, there was there was no significant overall white matter excess in autism compared tocontrols; it was only a trend (this was also the case when using the unscaled data). Althoughthere were excesses in some white matter regions, it is not logically implied that the whitematter is enlarged overall, as there may also be subtle but nonsignificant reductions in otherwhite matter regions, i.e., a redistribution, with no net overall gain. Tests were alsoperformed in the different lobes in the gray matter (Figure 3.B), where we found an overallsignificant omnibus probability pcorrected = 0.038, a finding that was not replicated in theunscaled case. Whereas the RATIO maps exhibit a complex pattern of gray matter excessesand losses, one-tailed t-tests showed evidence for overall gray matter losses, especially inthe left and right parietal lobes, the left temporal lobe and the left occipital lobe (Table 2). Itmay therefore be too simplistic to expect a generalized gray matter deficit in patients; it maybe that, as in Williams syndrome (Thompson et al., 2005) some areas show deficits whileothers show excesses or no systematic difference.

These results are consistent with the localization of significant p-values in the DET maps inFigure 3.B.

Anisotropic changes, as assessed with the multivariate analysis, were also significant, withpcorrected =0.05 for the Hotelling’s T2 statistic computed in the gray matter and pcorrected =0.09 (i.e., a trend level effect) in the white matter. The multivariate method is sensitive toboth anisotropic and uniform volumetric differences. Gray matter regions with significantgroup differences at the voxel level were mostly found in the left hemisphere including thesupramarginal and superior temporal gyri, and around the anterior part of the central sulcus(see Figure 3.B). Generally, these morphometric differences are consistent with a complexpattern of local volume deficits and excesses in cortical areas (including both gray and whitematter).

3.3 Analysis of the cerebellumFigure 4 shows horizontal sections through the cerebellum in the rostral to caudal direction.The first column shows the volume ratio (i.e., mean autism divided by mean controlvolume). Both univariate and multivariate analysis implicate the same regions at the voxellevel. Cerebellar volume excesses were found to be significant after multiple comparisonscorrection in the univariate analysis (pcorrected = 0.006). These were detected only at trendlevel by the multivariate analysis, with pcorrected =0.07. The spatial distribution of effectswas similar, suggesting that the univariate test has greater signal-to-noise ratio for detectingdifferences in this case. Significant cerebellar volume excesses in autism were observedprimarily in the vermis; systematic structural differences were also found in lobes III andVIII and in the corpus medullare (volume reduction) and in lobes Vc, Vd, VIIa, VIIb, andIX (volume excess). Corrected p-values for gain (pcorrected = 0.03) and deficits (pcorrected=0.02) were computed from two one-tailed t-tests and were both significant in thecerebellum, suggesting that volume loss and volume gain may occur in different lobules ofthe cerebellum as shown in the maps. Even so, to take into account the multiple testing foreffects of gain and loss, it would be conventional to use a Bonferroni correction to doublethese significance values. Therefore, we should regard the gain as a trend (pcorrected = 0.06)but the loss as statistically significant (pcorrected = 0.04). This is equivalent to performing atwo-tailed test followed by inspection of the gain and loss effects separately.

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4 DiscussionNeurobiological findings

In this study, autistic children had significantly enlarged frontal lobes (by 3.6% on the leftand 5.1% on the right), and all other lobes of the brain were enlarged significantly, or attrend level. By analyzing the applied deformations statistically point-by-point, we detectedsignificant gray matter volume deficits in bilateral parietal, left temporal and left occipitallobes (p=0.038, corrected), trend-level cerebral white matter volume excesses, and volumedeficits in the cerebellar vermis, adjacent to volume excesses in other cerebellar regions. Ourmaps also suggested trend-level excesses in central white matter volume among thesesubjects and gray matter losses mainly in the parietal and left temporal and occipital lobes.These results were found using both univariate and multivariate mapping methods andrelated volumetric regions of interest. Patients with autism also had regional excesses inwhite matter volume as well as deficits in the volume of lobes III and VIII of the cerebellarvermis and an increase in volume of vermal lobes Vc, Vd, VIIa, VIIb, and IX.

The cerebellum has been studied extensively in autism, since the early work of Courchesneet al. (1988). That paper suggested that for non-adjusted data (i.e., data at its original scale),cerebellar vermal lobules VI–VII were smaller in patients than in controls, suggesting adevelopmental hypoplasia, while the lobules I–V were normal. Those findings led to acontroversy as they were not replicated by some other investigators; Filipek (1995b) statedthat a definitive conclusion on the vermis pathology was premature. Indeed, differences inthe non-scaled cerebellar vermis volume have often been found (Haas et al., 1996), but notin all studies. Courchesne (1999) found an increased volume in the vermis while Levitt et al.(1999) found that lobules VIII–X were smaller. In our data, which was scaled to adjust fordifferences in overall brain volume across subjects, we found both volume excesses anddeficits in the cerebellum between autistic patients and controls.

We found significant lobar enlargement in autism (in the raw, unscaled data), consistentwith prior reports. The autistic group had significantly enlarged frontal lobes (by 3.6% onthe left and 5.1% on the right; p<0.049, p<0.011), and all other lobes of the brain wereenlarged either significantly, or at trend level, with average enlargements in different lobesranging from +3.2 to +6.7%. Prior studies of young children (Courchesne et al., 2001;Codyet al., 2006) as well as older children and adolescents (Herbert et al., 2003) have accordinglyreported enlargement of white matter volumes in autism. Here we also found gray matterdeficits in the bilateral parietal lobes, as well as the left temporal and left occipital lobes.These differences are also relatively subtle and may not be universally found; in otherstudies of the gray matter in autism, some groups have found increased or decreased volumeand others detected no difference.

White matter excesses in autism have been interpreted in functional neuroanatomical termsas suggesting that brain connectivity may be impaired in regions showing volume excesses.The abnormally rapid growth over the brain overall, observed during early infancy, may bethe result of an abnormal myelination process during childhood. We recently found excesssubcortical gray matter in Fragile X syndrome, a neurodevelopmental disorder whosemechanism is thought to be a genetically mediated impairment in dendritic pruning (Lee etal., 2007). Although the mechanism is different, we also found an excess in corticalthickness in Williams syndrome, another genetically-mediated neurodevelopmental disorder,and this thickening may reflect a failure in cortical neuronal packing, due to deficiencies inthe elastin gene (Thompson et al., 2005). As such, it is plausible that excesses in whitematter, observed here, may be attributable to an over-production of myelin in infancy, anabnormality in myelin packing, or anomalies in the production or anatomical distribution ofoligodendrocytes that produce myelin throughout the white matter.

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The presence of neuroinflammation is another factor implicated in white matter volumeenlargement. In patients with autism, an active neuroinflammatory process has been shownto exist in the white matter, cortex and cerebellum (Vargas et al., 2005), a finding that maycontribute to the volumetric increase in white matter in this and other studies. Although it isnot clear what the cause of neuroinflammation might be in autism, there are other situationsin which an inflammatory hypothesis has been invoked to explain white matter excesses. Intwo independent studies of methamphetamine abusers, we (Thompson et al., 2004) andothers (Jernigan et al., 2005) found white matter excess in methamphetamine abusers versuscontrols, in conjunction with gray matter deficits, and we suggested that, in line with theanimal literature, inflammatory processes may contribute to the white matter hypertrophy.

In autism there is no drug-induced change (or known pathogen) to trigger an inflammatoryprocess, but it remains a candidate explanation for the enlarged white matter.

If these white matter alterations indicate impaired axonal conduction velocity or impairedneuronal connectivity, this may also lead to a delayed or incomplete development of corticalgray matter structures, in line with the gray matter deficits seen here. Studies with diffusiontensor and functional imaging are required to better evaluate this possibility. We also foundgray matter abnormalities in regions that include the left posterior temporal lobes in autisticpatients compared to controls, a deficit that may be implicated in the characteristicdifficulties in vocabulary and language processing in autism. The left occipital lobe andparietal lobes showed regional gray matter excesses and reductions, which may relate torecent findings demonstrating an abnormal magnocellular pathway in children with autism,which may affect visual processing and sensory integration (Milne et al., 2002).

AnisotropyNo well-replicated pattern of characteristic brain abnormalities has yet been found in autism,although some review papers suggest evidence for white matter hypertrophy in at least asubset of autistic patients (Herbert, 2005). In this paper, multivariate tests - minor variants ofthe standard TBM - were used in addition to the commonly used univariate methods. Inprinciple, they include a larger amount of information on brain morphology, as they analyzethe Jacobian matrix J which is derived from the vector fields after fluid registration and notjust the determinant of this matrix. For this reason, one might expect these statistics to beconsistently more powerful, as they are sensitive to both volume and anisotropic volumedifferences. However, in our study, multivariate tests did not give greater effect sizes. Theoverall corrected significance values were not substantially different using the methods thatassess volume difference alone (p=0.038, corrected for gray matter, p=0.1 corrected in whitematter) versus those that assess potential stretching or compression along a given direction(where p=0.05, corrected, for gray matter, p=0.09, corrected, in white matter), even so theanalyses support each other to some extent. The noise in each of the multivariate parametersmust be taken into account, and it may generally require a larger sample to estimate themreliably. In other analyses with the same method (Leporé et al., 2008a) we found that theanisotropy statistics detected brain atrophy in HIV-AIDS with genuinely better power thanstandard volumetric assessments, but in the current autism study, the anisotropy statisticsessentially agreed with the volumetric assessments. It is therefore plausible that thedifference in autism is better represented as a simple volume difference (with no directionalpreference), whereas the neurodegeneration in HIV/AIDS may occur preferentially in acertain direction (e.g., radially along corticothalamic tracts in the brain).

TBM/VBMAs noted earlier, prior MRI studies of regional gray and white matter volumes in autism,using traditional analysis methods, have not always been consistent in their results. TBM

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may be beneficial in this population as it can reveal systematic differences in brain structureeven in situations where overall lobar volume measures cannot. In particular, this situation ispossible when one selective subregion belonging to a structure shows systematic gain and asecond one shows deficit, as in the present study. In this case, the power to detect the effectis depleted when using overall volumes to summarize differences over lobar regions, whichmotivated our use of TBM. In other types of voxel-based studies, such as voxel-basedmorphometry (VBM; Ashburner and Friston, 2000), a question sometimes arises as towhether the findings may be attributable to imperfect registration. This question arisesbecause in VBM, smoothed maps of classified gray matter, derived from an explicit tissueclassification of the image into gray and white matter and CSF, are automatically alignedacross subjects and smoothed, and then statistical inferences are made regarding groupdifferences, by voxel-by-voxel subtraction of the group-averaged images. As such it ispossible that a difference detected at any one location is due to imperfect registration(Thacker, 2005).

In TBM, however, the signals analyzed are based only on the registrations of the images andnot the aligned gray matter classifications, so it is not required that the gray matter beperfectly registered across subjects as the gray matter density is not analyzed at eachstereotaxic location. As such, false positive findings due to systematic group differences inregistration errors are less likely. Even so, there may be false negative findings, because thepower to detect morphometric differences depends on the scale at which anatomic data canbe matched by the warping algorithm.

When using voxel-based methods such as VBM or TBM, the difficulty in matching corticalregions across subjects may mean that subtle regional differences in cortical structure maygo undetected. In TBM, all morphometric differences are inferred from deformation fieldsbased on automated matching of intensities in the images, and the spatial smoothness ofthese fields makes it difficult to register the entire cortical mantle across subjects, as wouldbe required to gauge the level of systematic atrophy in cortical gray matter. Alternativeapproaches can compute cortical thickness at each point, but these are typically more time-consuming as they generally extract explicit models of the cortical surface as geometricmeshes, prior to computing the cortical thickness directly from the meshes (Lerch andEvans, 2005), or by tissue classification of the images and voxel-coding (Thompson et al.,2004b; Aganj et al., 2008). Even so, there are at least two possible solutions to bettersensitize our TBM approach for detecting cortical gray matter loss. The first is to use voxel-based morphometry (VBM; Ashburner and Friston, 2000) or a related approach termedRAVENS (Davatzikos et al., 2001). A second method to identify cortical gray matteratrophy with TBM was developed by Studholme et al. (2003), in which deformation-basedcompression signals at each point are smoothed adaptively depending on the amount of graymatter lying under the filter kernel. This is a way to avoid incorrect assignment of graymatter differences to the white matter, when both tissues are partial volumed within a voxel.A third solution is to run the deformation maps at a very high spatial resolution and with lessspatial regularization, or with a regularizer (smoothness term) that enforces continuity butnot smoothness. We plan to investigate these methods in the future, to quantify the graymatter reductions more precisely with independent but more time-consuming methods.

Limitation and future workOur results should be interpreted taking into account certain specific limitations. As thereare known gender differences in the prevalence and severity of autism (Fombonne, 2003),and to some extent in normal brain development (Lenroot et al., 2007), the inclusion ofmales only in this study may have highlighted group differences by removing gendervariables affecting neurodevelopment, and prevents the applicability of the conclusions togirls with autism. The lack of girls in this sample limits what can be said about females with

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autism, but this is not necessarily a limitation in terms of what can be said about groupdifferences in males.

Furthermore, some patients in the present study were taking psychotropic medications,which may potentially have influenced the results, as we have previously shown for sometypes of drug treatment in psychiatric cohorts (e.g., in bipolar patients taking lithium:Bearden et al., 2007). In particular, it has been shown that dopamine antagonists, such asrisperidone (and other atypical antipsychotics), may influence the extrapyramidal system(e.g., medulla, pons, cerebellum), which plays a role in motor coordination (Chevreuil et al.,2008; Baghdadli et al., 2002).

None of the other medications have been reported to affect brain structures, although there isa lack of studies examining this. There is no conclusive evidence of a possible adverse effectof stimulants, such as ritalin commonly used in ASD, on brain function and development(Ghanizadeh, 2009). SSRIs, such as citalopram, have been shown to affect braindevelopment during prenatal exposure (van der Veere et al., 2007). Even so, to ourknowledge, use of SSRIs and cholinesterase inhibitors, has not been shown to be associatedwith detectable differences in brain structure in children. However, future studies in a largercohort are required to assess modulatory effects of psychotropic medications. In principle,differences in handedness in the population (Table 1) may also lead to confounding effects(Sun et al., 2006). Even so, some very large studies of normal subjects with voxel-basedmorphometry (VBM) (N=465; Good et al., 2001) have failed to detect effects ofhandedness, suggesting that effects of handedness on brain structure might be relativelyminimal.

Further studies are required to confirm the differences found here in larger samples. Asemphasized by Thompson et al. (2005a) and Shaw et al. (2006, 2008), cortical developmentis associated with an increase and then a decrease in gray matter. In psychiatric populations(e.g., bipolar illness; Gogtay et al., 2007), or in normal children with above-averageintellectual ability (IQ; Shaw et al., 2006), cortical maturation may be accelerated or delayedversus the normal time-course, leading to time-points in which excesses in certain tissuetypes are detected and other time-points in which deficits are detected, even in the samebrain regions (Gogtay et al., 2004). As such, longitudinal data is needed to determinewhether these gray matter deficits and lobar volume excesses persist into adulthood, or whenthey are first detectable. One limitation of this study is cross-sectional design, which dealswith age by covarying it out prior to other analysis. Given that the previously demonstratedability of this method to capture longitudinal changes (Thompson et al., 2000; Hua et al.,2007; Gogtay et al., 2008), we hope to apply this method, in the future, in a longitudinalstudy design. In the future, TBM may be used within such a design to better understandapparently conflicting voxel-based studies of tissue deficits and excesses (Hardan et al.,2006, McAlonan et al., 2005). Once the developmental trajectory of these structural brainchanges is better established, the anatomy of autism and its developmental time-course willbe better understood.

AcknowledgmentsThis work was funded by grants from the National Institute of Aging, the National Institute for Biomedical Imagingand Bioengineering, and the National Center for Research Resources (AG016570, EB01651, RR019771 to PT).Other financial contributions came from the Child and Parent Resource Institute, the London Health ScienceFoundation, the Ontario Mental Health Foundation, the Hospital for Sick Children Foundation, and the HumanBrain Mapping Project, funded by NIMH and NIDA (MH/DA52176), RR13642, MH655166 to AWT).

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Figure 1.

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Figure 2.

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Figure 3.

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Figure 4.

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Tabl

e 1

Dem

ogra

phic

and

clin

ical

cha

ract

eris

tics o

f pat

ient

s with

aut

ism

and

con

trol s

ubje

cts.

Dem

ogra

phic

mea

sure

Aut

ism

(n=2

4)C

ontr

ol (n

=26)

Tes

t Sta

tistic

dfp-

valu

e

Age

(yea

rs)

10.0

±3.3

11.0

±2.5

t=1.

448

0

Rac

e (#

Cau

casi

an)

23/2

426

/26

χ2=

1.1

10.

3

Hei

ght (

cm)

143.

9±19

.514

7.4±

14.9

t=0.

744

0.5

Hea

d ci

rcum

fere

nce

(cm

)55

.4±2

.654

.9±1

.7t=

0.9

480.

4

Han

dedn

ess (

Rig

ht:L

eft)

18:6

26:0

χ2=7

.41

0.00

7

Ver

bal I

Q92

.9±1

3.3

105.

4±11

.2t=

3.3

410.

002

Non

-ver

bal I

Q99

.1±1

4.0

104.

1±14

.0t=

1.2

460.

2

Full-

scal

e IQ

scor

e95

.9±1

1.5

104.

8±11

.7t=

2.5

410.

02

All

cont

inuo

us d

ata

pres

ente

d as

mea

n ±

SD.

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Table 2

Significance of the gray matter excesses and losses computed using one-tailed t-tests. Note that there was noclear directional hypothesis regarding these differences, as the literature is inconsistent and different studieshave reported losses and excesses. These p-values are from post-hoc one-tailed tests, performed to verify thedirection of the effect. Their significance levels should be doubled when assessing the significance of theabnormalities, as there was no a priori hypothesis as to whether excess or deficits were expected.

Pcorrected* (volume autism < volume control) Pcorrected

* (volume autism > volume control)

Left parietal 0.008 0.93

Right parietal 0.017 0.90

Left temporal 0.011 0.71

Left occipital 0.044 0.13

*values computed according to the method described in Nichols and Holmes (2002)

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