Detecting abnormalities of corpus callosum connectivity in autism using magnetic resonance imaging and diffusion tensor tractography

Psychiatry Research: Neuroimaging 194 (2011) 333–339

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Psychiatry Research: Neuroimaging j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / p s yc h r e s n s

Detecting abnormalities of corpus callosum connectivity in autism using magnetic resonance imaging and diffusion tensor tractography Shanshan Hong c, Xiaoyan Ke a,⁎, Tianyu Tang b, Yueyue Hang a, Kangkang Chu a, Haiqing Huang b, Zongcai Ruan b, Zuhong Lu b, Guotai Tao a, Yijun Liu d a

Child Mental Health Research Center of Nanjing Brain Hospital affiliated of Nanjing Medical University, Nanjing, 210029 China Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, 210096 China Department of Neurology, the Affiliated Jiangyin Hospital of Southeast University Medical College, Jiangyin, 214400 China d Department of Psychiatry and McKnight Brain Institue, University of Florida, Gainesville, FL 32601, United States b c

a r t i c l e

i n f o

Article history: Received 16 April 2010 Received in revised form 28 February 2011 Accepted 22 March 2011 Keywords: DTI MRI White matter Fiber tractography Autistic disorder

a b s t r a c t The corpus callosum (CC) has emerged as one of the primary targets of autism research. To detect aberrant CC interhemispheric connectivity in autism, we performed T1-weighted magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI)-based tractography in 18 children with high functioning autism (HFA) and 16 well-matched typically developing (TD) children. We compared global and regional T1 measures (CC volume, and CC density), and the DTI measures [fractional anisotropy (FA), apparent diffusion coefficient (ADC), average fiber length (AFL), and fiber number (FN)] of transcallosal fibers, between the two groups. We also evaluated the relationships between scores on the Childhood Autism Rating Scale (CARS) and CC T1 or DTI measurements. Significantly less white matter density in the anterior third of the CC, and higher ADC and lower FN values of the anterior third transcallosal fiber tracts were found in HFA patients compared to TD children. These results suggested that the anterior third CC density and transcallosal fiber connectivity were affected in HFA children. © 2010 Elsevier Ireland Ltd. All rights reserved.

1. Introduction Autism is a devastating neurodevelopmental disorder characterized by impaired social interaction, communication, and restricted and repetitive behavior (Lord et al., 1994; Rapin and Katzman, 1998). As the largest fiber bundle in the human brain interconnecting the two hemispheres (Hofer and Frahm, 2006), the corpus callosum (CC) plays a critical role in the aberrant connectivity model of autism (Just et al., 2004; Just et al., 2007). Structural magnetic resonance imaging (MRI) is the most frequently used neuroimaging methodology to investigate autism. Changes in magnetic resonance (MR)-visible free water allow for detectable measurements in MRI; increases in free water molecules lead to increases in T1 relaxation time but decreases in signal intensity (Inder and Huppi, 2000; Paus et al., 2001; Diwadkar et al., 2004). Abnormally high MR-visible water in tissue may result from changes in sizes of axons or changes in intra-axonal microtubular density (Baas, 1998). Multiple MRI studies have focused on the exploration of differences in CC mid-sagittal area size, volume, and density, but their findings are inconsistent. Reduced size of the anterior (Manes et al., 1999; Hardan et al., 2000; Just et al., 2007), ⁎ Corresponding author at: Nanjing Brain Hospital affiliated of Nanjing Medical University, Nanjing GuangZhou Road 264#, Nanjing, China 210029. Tel.: + 86 25 83700011; fax: + 86 25 83700011 6194. E-mail address: [email protected] (X. Ke). 0925-4927/$ – see front matter © 2010 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.pscychresns.2011.03.009

body (Piven et al., 1997; Manes et al., 1999) or posterior (Egaas et al., 1995; Piven et al., 1997; Saitoh and Courchesne, 1998; Manes et al., 1999; Just et al., 2007) in the mid-sagittal areas of the CC have been reported. However, some of these studies did not find CC size differences (Gaffney et al., 1987; Elia et al., 2000; Rice et al., 2005). When comparing a classic autism group to a less severely affected autism group, Boger-Megiddo et al. (2006) found that children with classic autism have significantly smaller CC, while those with less severe autism only yield trend differences. Using voxel-based morphometry (VBM), Waiter et al. found reduced white matter volume in the CC and right hemisphere (Waiter et al., 2005). Vidal et al. (2006) first applied three-dimensional surface models of the CC and revealed significant reductions in both the splenium and genu of the CC in autism. Two recent studies also attempted to go beyond the planimetric analyses by using three-dimensional volumetric strategies to include more structural information of the CC and found reductions in the total CC volume and several of its subdivisions in autism (Hardan et al., 2009; Keary et al., 2009). Abnormalities in brain tissue density in autism have been previously documented (Boddaert et al., 2004; Kwon et al., 2004; Spencer et al., 2006; Craig et al., 2007; Ke et al., 2009). Using two-dimensional voxel-based morphometry, Chung et al. (2004) found lower white matter density in the regions of genu, rostrum, and splenium in autism. But systematic analyses of CC density with the region of interest (ROI) method have not been conducted.

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Diffusion tensor imaging (DTI) is a non-invasive in vivo imaging method for mapping the diffusion properties of tissue water (Basser and Jones, 2002). In conjunction with fiber tractography, DTI has opened new possibilities for the investigation of the detailed anatomical structure of white matter. Different quantitative measurements such as the degree of restriction to water diffusion quantified by the parameter “apparent diffusion coefficient” (ADC) and the directionality of water diffusion quantified by the parameter “fractional anisotropy” (FA) can be derived from DTI data (Basser and Jones, 2002). Different subregions of CC fibers link functionally different cortical areas (Doron and Gazzaniga, 2008). Therefore, it is possible that transcallosal fiber deficits could lead to the aberrant connectivity among cortical regions in the two hemispheres. BarneaGoraly et al. (2004) performed the first DTI study to investigate white matter integrity in autism and found reduced FA values in the CC and other brain regions using the voxel-based morphometry (VBM) method. Lower FA values of the CC in autistic patients have also been reported in several DTI studies using the ROI method (Alexander et al., 2007; Keller et al., 2007; Brito et al., 2009). Moreover, the study of Alexander and collaborators suggested that patients with lower IQs than controls had significantly lower size, higher mean diffusivity (MD) and radial diffusivity (RD) in the CC. Two recent investigations employed a DTI-based tractography method and found aberrant fiber connectivity in frontal lobe tracts and the CC in autism (Sundaram et al., 2008; Kumar et al., 2010). In conclusion, it is difficult to compare the previous anatomical studies of the CC because they differ in their study samples (e.g., high functioning vs. low functioning) and analytic methods (e.g., VBM vs. ROI; planimetric vs. volumetric). DTI studies also differ in methodology used, ranging from whole-brain VBM to extraction of diffusion values from ROIs, even to DTI-based fiber tractography. Therefore, it is important to reduce sample heterogeneity and combine different methods for the evaluation of the CC changes before any conclusions about abnormalities can be reached. Moreover, novel methodologies such as three-dimensional volumetric measurement and fiber tractography should be employed since they will provide more quantitative structural information and are more sensitive in detecting structural changes about the ROI (Hardan et al., 2009). In the present study, we combined MRI-based CC morphometry and DTI-based fiber tractography methods in the first set of quantitative studies of the CC's connectivity in high-functioning autistic (HFA) children and well-matched controls. The primary aim of the study was to explore group differences in volume, density and transcallosal fiber tracts of the CC in HFA. In addition, the approach of Witelson (Witelson, 1989) has been widely used to subdivide the CC (Egaas et al., 1995; Piven et al., 1997; Saitoh and Courchesne, 1998; Manes et al., 1999; Hardan et al., 2000; Boger-Megiddo et al., 2006; Hardan et al., 2009). Therefore, we also subdivided the CC by this traditional scheme to be able to compare the results with previous partition studies Witelson, 1989 and to determine which specific subregions of the CC were affected in autism. The second aim was to determine whether the tractography method for DTI data can elucidate the global and regional CC connectivity abnormalities. A further goal of this study was to explore the relationships between the severity of autism symptoms and CC anatomical changes or measures of transcallosal fiber tracts. It was hypothesized that there would be detectable reductions in CC connectivity in autistic children.

(WISC-II) (Chinese version) Full Scale IQ score of 70 or higher. The Autism Diagnostic Interview-Revised (ADI-R) (Lord et al., 1994) has been translated into Chinese by Yanqing Guo et al. The reliability and diagnostic validity for each item of the ADI-R has been tested (Guo et al., 2002). The diagnoses were made using the Autism Diagnostic Interview-Revised (ADI-R) and by clinical observation as based on the Fourth Edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) (American Psychiatric Association, 1994) by child psychiatrists. All patients were medication-naive and met the criteria of the DSM-IV as well as the ADI-R and the Childhood Autism Rating Scale (CARS) (Schopler et al., 1980). The CARS consists of a 15-item behavioral rating scale. Each of the 15 items is given a rating from 1 (within normal limits for that age) to 4 (severely abnormal for that age). Scores below 30 in children were categorized as nonautistic, with 30 and over indicating autism. It is effective to distinguish among the children with the use of the mild, moderate or severe categorization of autism. Reliability and validity findings suggest that the CARS is an effective tool for research and diagnosis of autism (Schopler et al., 1980). Therefore, we applied the CARS total score to assess the severity of autistic symptomatology. Potential HFA subjects were excluded if they had a history of seizure, head injury, birth asphyxia, and genetic or metabolic disorder. Furthermore, potential participants with HFA were also excluded if they had diagnoses of additional disorders such as attention deficit hyperactivity disorder, Tourette syndrome, depression, obsessivecompulsive disorder and anxiety disorder. Sixteen typically developing (TD) control subjects were recruited from the local communities. Potential control subjects were screened by telephone, questionnaire, interview, and clinical observation by two clinicians. None of the control subjects had a history of Axis I or Axis II psychiatric disorders, head trauma, genetic disorder, or medical illness requiring medication. The first degree family status of the control subjects was also explored to exclude a family history of Axis I or Axis II psychiatric disorders. Control subjects were also assessed with the WISC-II, and those with a full scale IQ b 70 were excluded. Comparisons between the HFA and TD groups indicated no significant differences in age, fullscale IQ, weight, height and total cerebral volume (TCV) (Table 1). Each participant's parent or legal guardian provided a written consent form approved by the Institutional Review Board of Nanjing Brain Hospital of Nanjing Medical University. 2.2. Image acquisition Images were acquired on a 1.5 Tesla GE-Signa scanner (NV/i, General Electric Medical System, Milwaukee, Wisconsin, USA) using a standard quadrature head coil housed at Nanjing Brain Hospital. This unit is equipped with a high strength (50 mT/m) and high-speed gradient system (slew rate = 200 T/m/s) capable of conducting diffusion-weighted imaging (DWI). High-resolution images were obtained with a T1-weighted three-dimensional (3D) spoiled gradient (SPGR) sequence. The parameters were as follows: repetition time = 9.9 ms, echo time = 2.0 ms, flip angle = 15°, field of view = 240 × 240 mm2, the acquisition matrix size = 256 × 256, slice thickness = 2.0 mm with −1.0 mm skip for the whole brain coverage, and number of excitations = 1. The image orientation was selected to Table 1 Demographic characteristics between groups.

2. Methods 2.1. Subjects Thirty-four right-handed boys participated in this study. Eighteen children diagnosed with high-functioning autism (HFA) were recruited from the Child Mental Health Research Center of Nanjing Brain Hospital, with a Wechsler Intelligence Scale for Children-II

Age (years) Weight (kg) Height (cm) WISC-II FSIQ TCV (ml)

HFA (N = 18)

TD (N = 16)

t

P

8.69 ± 2.18 29.78 ± 8.77 130.11 ± 12.25 105.22 ± 21.12 1481 ± 72

9.81 ± 1.91 34.38 ± 8.25 137.75 ± 13.32 106.13 ± 20.13 1498 ± 76

− 1.581 − 1.569 − 1.742 − 0.127 − 0.685

0.124 0.126 0.091 0.900 0.498

WISC-II FSIQ, Wechsler Intelligence Scale for Children-II (Chinese version) Full-Scale IQ. TCV, total cerebral volume. All data presented as mean ± S.D.

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be parallel to the anterior commissure–posterior commissure (AC– PC) plane. For DTI acquisitions, a single-shot, spin echo, echo planar sequence (SE-EPI) was used, with diffusion gradients applied in 15 non-collinear directions and b = 1000 s/mm2. The thickness of each slice was 3 mm without gaps, and 39 axial slices were acquired, parallel to the AC–PC line. Other DTI parameters were as follows: echo time= 104.4 ms; repetition time = 8000 ms; field of view = 240 × 240 mm2 and size of the acquisition matrix=128 × 128. The total DTI scanning times were 4.4 min with two signal averages. 2.3. Image processing All T1-weighted and DTI images were transferred to an off-line Microsoft Windows based Intel Pentium system. All images were checked for movement artifacts. For the T1-weighted data, the total cerebral volumes of 34 participants were calculated on an in-house program based on the Matlab version 7.0 software (www.mathworks. com/products/matlab/) as described earlier (Ke et al., 2008). The outline of the entire CC was then manually extracted in the MRIcron software (www.sph.sc.edu/comd/rorden/mricron/) with T1 maps from the midsagittal slice and the six adjacent parasagittal slices on each side. The number of parasagittal slices was determined by the ability to reliably determine the CC contours on each slice. The segmented CC was then automatically sub-divided into five subregions with a self-developed program based on the Matlab version 7.0 software using a method described by Witelson (1989) (Fig. 1). Consequently, the global and regional CC volumes and density were calculated automatically. For the DTI data, tensor calculation and fiber tractography were performed using DTIStudio version 2.40 (www.mristudio.org). An initial tractography of the whole brain was performed individually using the following parameters: fiber tracking started from all the pixels whose FA values are greater than 0.25, and stopped at those pixels with FA values lower than 0.25 or when a tract-turning angle was higher than 70°. The method for fiber-tracking was based on a Fiber Assignment by Continuous Tracking (FACT) approach, by which tracking was performed using a continuous coordinate system rather than a discrete voxel-based matrix grid (Mori et al., 1999). Then, in the mid-sagittal slice of the images, we manually traced the five subregions of the CC out as seed regions as described by Witelson scheme (Witelson, 1989) (Fig. 2). FA, ADC, number of fibers (FN) and average fiber length (AFL) were calculated for all fibers through the mid-sagittal cross-sections of the five CC subregions. All T1 and DTI data were processed by two investigators. Both investigators were blind to the subject's identity and diagnosis. Using a subset of 16 control subjects, the inter-rater reliability was assessed between the observers with correlation coefficients of 0.95, 0.96, 0.90, 0.90, 0.93, and 0.93 for CC volume, CC density, FA, ADC, FN and AFL, respectively (P b 0.01 for all correlations). A P value of b 0.05 was considered statistically significant.

Fig. 2. The set transcallosal fiber tracts of five subregions in the corpus callosum. The coronal plane (a); the mid-sagittal plane (b); and the axial plane (c). From corpus callosum anterior to posterior seed regions: anterior third tracts (blue); anterior midbody tracts (green); posterior midbody tracts (yellow); isthmus tracts (orange); and splenium tracts (red).

2.4. Statistical analysis Statistical analysis was performed using SPSS statistical software version 13.0 (Chicago, IL). Two T1 outcome measures of CC: volume and density and four DTI outcome measures of transcallosal fibers: FA, ADC, FN and AFL, both global and regional, were compared between two groups using analysis of variance (ANOVA) on continuous demographic variables. Age, IQ and TCV did not differ significantly between the two groups (the range of age is 6–14 years, IQ is 77–141 and TCV is 1266–1584 ml). We also covaried T1 and DTI outcome measures for age, IQ and TCV to control potential confounding factors and increase statistical power by analysis of covariance (ANCOVA). The two T1 and four DTI outcome measures of transcallosal fibers (FN, AFL, FA and ADC) were then correlated with the severity of symptoms obtained from CARS using partial Pearson correlations with age and IQ as covariates. For planned correlations, P values were corrected for multiple comparisons using the Bonferroni correction. 3. Results 3.1. Group comparison of outcome measures in the T1-weighted Imaging The HFA group showed significantly lower white matter density in the anterior third of the CC than did the control group (F(1, 32) = 6.096, P = 0.019), even when age, IQ or TCV was used as a covariate (F (1,31) =4.470, P = 0.043; F(1,31) = 6.806, P = 0.014; F(1,31) = 5.491, P = 0.026). No significant differences were observed in other subregions of the CC between the groups. Comparison of the global and regional CC volumes did not reveal significant differences between the groups, and no additional HFA group differences were found when covaried for age, IQ or TCV (Table 2). 3.2. Group comparison of outcome measures in the transcallosal fibers

Fig. 1. Subdivisions of the corpus callosum in five regions: 1) anterior third; 2) anterior midbody; 3) posterior midbody; 4) isthmus; and 5) splenium.

The groups did not differ significantly in terms of FA and AFL values in regional transcallosal tracts. However, for the anterior third region of the CC fibers, the ADC values in the HFA group were significantly higher than those in the TD controls (F(1,32) = 5.739, P = 0.023). The number of fibers in the anterior third region of the CC was significantly lower in

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Table 2 T1 outcome measures of global and regional corpus callosum measures in two groups. HFA (N = 18)

TD (N = 16)

Corpus callosum volumes Anterior third Anterior midbody Posterior midbody Isthmus Splenium Total CC

8.54 ± 1.47 2.52 ± 0.44 2.37 ± 0.54 2.50 ± 0.61 8.50 ± 1.89 24.44 ± 4.40

Corpus callosum density Anterior third Anterior midbody Posterior midbody Isthmus Splenium Total CC

87.66 ± 4.39 86.15 ± 3.39 85.21 ± 2.56 85.77 ± 3.70 92.14 ± 3.42 87.38 ± 2.99

ANCOVAa

ANOVA

ANCOVAb

ANCOVAc

F

P

F

P

F

P

F

P

8.96 ± 1.63 2.43 ± 0.39 2.33 ± 0.38 2.52 ± 0.56 8.57 ± 1.78 24.81 ± 4.49

0.633 0.442 0.074 0.015 0.011 0.059

0.432 0.511 0.788 0.904 0.918 0.809

0.352 0.386 0.249 0.001 0.002 0.014

0.558 0.539 0.621 0.971 0.965 0.907

0.674 0.384 0.052 0.047 0.045 0.103

0.418 0.533 0.821 0.830 0.834 0.751

0.724 0.313 0.094 0.027 0.006 0.069

0.401 0.580 0.762 0.872 0.938 0.794

91.54 ± 4.76 87.39 ± 4.61 84.91 ± 4.57 84.99 ± 4.14 91.70 ± 4.33 88.11 ± 4.23

6.096 0.814 0.057 0.331 0.108 0.335

0.019 0.374 0.812 0.569 0.744 0.567

4.470 0.569 0.077 0.457 0.147 0.166

0.043 0.457 0.783 0.504 0.704 0.686

6.806 0.749 0.110 0.317 0.157 0.275

0.014 0.393 0.743 0.577 0.695 0.604

5.491 0.699 0.030 0.311 0.141 0.286

0.026 0.410 0.864 0.581 0.710 0.596

Note: All data presented as mean ± S.D. ANOVA, analysis of variance; ANCOVA, analysis of covariance. All area measures are given in mm2 and all volume measures are given in cm3; d.f. = 1, 32 for all ANOVA and 1, 31 for all ANCOVA. ANCOVAa completed using age as covariate; ANCOVAb using IQ as covariate; ANCOVAc using total cerebral volume as covariate. Statistically significant differences are shown in bold.

the HFA group than that in the control (F(1,32) = 9.419, P = 0.004). In the other subregions of the CC, no significant differences were observed in ADC and FN between the two groups (Table 3). The effect of FN differences in the anterior third CC of the HFA group remained significant after covariation for age or IQ (F (1,31) = 9.061, P = 0.005; F(1,31) = 9.624, P = 0.004). Similarly, the HFA group continued to have significant effects on ADC values in the anterior third CC when age or IQ was used as a covariate (F(1,31) = 4.193, P = 0.049; F(1,31) = 6.239, P = 0.018). The ADC values of the splenium did not show a significant effect in the ANOVA; however, after covariation for IQ , the ADC values of the splenium were found to be significantly increased in the HFA group (F(1,31) = 4.188, P = 0.049) (Table 3).

3.3. Relationship between the severity of symptoms and T1 measures The relationships between global and regional T1 measures of the CC and scores on the CARS were examined in the HFA group only. No significant relationships were detected between scores on the CARS and T1 measures of the CC. 3.4. Relationship between the severity of symptoms and transcallosal fiber measures An exploratory analysis was performed to investigate the relationship between severity of symptoms and transcallosal fiber measures. This analysis revealed a significant negative correlation

Table 3 Diffusion Tensor Imaging outcome measures of regional transcallosal fiber tracts in two groups. HFA (N = 18)

Fiber number (FN) Anterior third Anterior midbody Posterior midbody Isthmus Splenium

TD (N = 16)

ANCOVAa

ANOVA

ANCOVAb

F

P

F

P

F

P

630 ± 203 223 ± 71 211 ± 92 168 ± 85 677 ± 254

839 ± 193 275 ± 70 230 ± 76 177 ± 80 806 ± 188

9.419 3.185 0.436 0.107 2.813

0.004 0.084 0.514 0.746 0.103

9.061 3.005 0.003 0.178 2.020

0.005 0.093 0.958 0.676 0.165

9.624 3.071 0.429 0.095 2.877

0.004 0.090 0.517 0.760 0.100

Average fiber length (AFL) Anterior third Anterior midbody Posterior midbody Isthmus Splenium

60.27 ± 8.52 58.11 ± 9.36 61.12 ± 15.49 59.99 ± 19.49 79.70 ± 16.74

61.15 ± 9.48 61.23 ± 12.03 60.03 ± 12.63 57.13 ± 14.59 78.97 ± 12.39

0.082 0.718 0.050 0.229 0.020

0.777 0.403 0.825 0.636 0.888

0.003 0.662 0.508 1.096 0.523

0.960 0.422 0.481 0.303 0.475

0.088 0.690 0.044 0.221 0.019

0.769 0.413 0.835 0.642 0.891

Fractional anisotropy (FA) Anterior third Anterior midbody Posterior midbody Isthmus Splenium

0.5906 ± 0.0283 0.5983 ± 0.0412 0.6163 ± 0.0363 0.5930 ± 0.0451 0.6402 ± 0.0309

0.6084 ± 0.0308 0.6083 ± 0.0340 0.6198 ± 0.0511 0.5996 ± 0.0591 0.6361 ± 0.0387

3.086 0.578 0.054 0.134 0.119

0.089 0.453 0.817 0.717 0.733

2.212 0.400 0.067 0.006 0.169

0.147 0.532 0.798 0.940 0.683

2.993 0.546 0.049 0.120 0.151

0.094 0.465 0.827 0.732 0.700

0.88 ± 8.56 0.87 ± 6.02 0.91 ± 7.72 1.00 ± 13.17 0.86 ± 8.06

5.739 0.974 0.473 0.177 4.125

0.023 0.331 0.496 0.677 0.051

4.193 0.861 0.032 0.013 1.889

0.049 0.361 0.859 0.911 0.179

6.239 1.013 0.510 0.169 4.188

0.018 0.322 0.481 0.684 0.049

Apparent diffusion coefficient (ADC) Anterior third 0.94 ± 6.98 Anterior midbody 0.89 ± 8.73 Posterior midbody 0.93 ± 10.18 Isthmus 1.02 ± 9.85 Splenium 0.91 ± 5.83

Note: All data presented as mean ± S.D. ANOVA, analysis of variance; ANCOVA, analysis of covariance. d.f. = 1, 32 for all ANOVA and 1, 31 for all ANCOVA. All average fiber length measures are given in mm. Units of ADC are 10− 3 mm2/s for mean and 10− 5 mm2/s for S.D. ANCOVAa completed using age as covariate; ANCOVAb using IQ as covariate. Statistically significant differences are shown in bold.

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(without corrections for multiple comparisons) between the fiber number of the anterior third CC and scores on the CARS, although the significance did not survive Bonferroni correction (r = − 0.503, P = 0.033). Scores on the CARS were not correlated with the other subregions in the HFA group. 4. Discussion In this study we combined morphometry and fiber tractography methods to detect CC volume, density, and transcallosal fiber connectivity in HFA children. We found both the CC density and transcallosal fibers were affected in HFA, but not all CC subregions were equally affected. The anterior third of the CC was mostly affected in HFA. Less white matter density in the anterior third of the CC was found in HFA patients. Reduced number of fibers and increased ADC values of the anterior third fibers were also observed in HFA compared with TD children, even after controlling for age or IQ. Additionally, when being covaried with IQ , the ADC values of the splenium of the HFA group were found to be significantly higher than in the TD group. Decrease in white matter density can be viewed by using MRI results from decreased axonal myelination (Blakemore and Choudhury, 2006). For DTI data, decreased FA and increased ADC values are the reflection of decreased myelination and axonal density, and of abnormal axonal organization in the brain (Cascio et al., 2007). Fiber length and number of the CC were found to be positively associated with functional development. Increased fiber length or greater number of fibers could be interpreted as an effort to enhance interhemispheric connectivity so that a better functional integration and development could be achieved (Kumar et al., 2010). The terms “number of fibers” and “average fiber length” used in our DTI analysis do not exactly denote the actual fiber count and length but depend on the FA and angle thresholds (Mori et al., 1999). Consistent with the hypothesis of transcallosal fiber underconnectivity in autism, abnormalities in the anterior third of the CC, including the rostrum, genu and rostral body, which connect the prefrontal, premotor, and supplementary motor cortical areas (Witelson, 1989), may be related to well-documented anatomical and functional deficit in the frontal and parietal lobes of autistic patients. Multiple anatomical studies reported that autistic patients had frontal (Chung et al., 2005; Girgis et al., 2007; Schmitz et al., 2007) and parietal (Courchesne et al., 1993; McAlonan et al., 2005) impairments. A previous diffusion tractography study found that short range fibers that predominantly occupy the peripheral white matter areas and long range fibers that are predominantly localized to the central white matter in the frontal lobe were both affected in autism (Sundaram et al., 2008). One recent diffusion tractography study not only reconfirmed the finding of longer frontal fibers, but also found significantly longer CC fiber length with higher fiber density in autism;, however, the investigators did not divide the CC into subregions (Kumar et al., 2009). FMRI abnormalities in the frontal and parietal regions during the performance of theory of mind (Vollm et al., 2006), working memory (Koshino et al., 2005; Kana et al., 2007; Koshino et al., 2008), problem solving (Just et al., 2007), visual sensorimotor (Takarae et al., 2007), perceptual processing (Lee et al., 2007), and spatial attention (Haist et al., 2005) tasks were also well-established. The splenium contains transcallosal fibers from occipital, inferior temporal regions (Witelson, 1989). Increased ADC values were observed in the splenium after controlling for IQ, which supports a possible role for the splenium transcallosal fibers in face discrimination (Schultz et al., 2000) and particularly in the identification of emotion in faces (Dalton et al., 2005). Subtle developmental abnormalities may be responsible for white matter underconnectivity and may be related to functions of the brain regions that they connect (Doron and Gazzaniga, 2008). The anterior third transcallosal fiber tracts are mostly connected to the bilateral frontal lobe (Witelson, 1989). A neuroimaging study using DTI has

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confirmed earlier post-mortem findings of CC topographical organization and has shown that the anterior third of CC connect the frontal regions, while the body and splenium connect parietal, temporal, and occipital regions (Zarei et al., 2006). The dorsolateral prefrontal cortex plays a crucial role in mediating executive control, behavioral inhibition, implementation of control and decision making (Vogt et al., 1992). A previous DTI study suggested that the white matter integrity in prefrontal regions was one mechanism underlying the relation between individual differences in perceptual speed and episodic retrieval (Bucur et al., 2008). It is possible that a transcallosal fibers deficit could be secondary to corticocortical disconnection or could result in underconnectivity among cortical regions in the two hemispheres. Most of the fibers in the anterior third of the CC are small diameter fibers that are thought to be important in maintaining the balance between excitation and inhibition in the cerebral hemispheres (Yazgan et al., 1995). Early neurodevelopment is associated with synaptic pruning and axonal myelination processes, which are linked to the executive function and social cognition (Blakemore and Choudhury, 2006). Hill and Frith (Frith, 2003; Hill and Frith, 2003) proposed that there might be poor connectivity throughout the brain between basic perceptual processes, perhaps due to abnormal connectivity and lack of pruning. Courchesne and Pierce's study found that connectivity within the frontal lobe was excessive, disorganized and inadequately selective, whereas connectivity between the frontal cortex and other systems was poorly synchronized, weakly responsive and information impoverished in autistic disorder. This neurocognitive profile in autistic disorder has been referred to as the frontal cortex unconsciously “talking only to itself” accompanyingby loss of language and impaired social cognition (Courchesne and Pierce, 2005). Correlation analyses of the severity of symptoms as measured by the CARS yielded different results across the transcallosal fiber measures. The decreased fiber number of the anterior third of the CC was related to the increased scores on the CARS in the HFA group. This result indicates that the anterior third of the CC is an important region for the autistic syndrome. Furthermore, the relationship between the CC and autistic manifestations is a reflection of transcallosal fiber projection to cortical structures not only to the CC itself (Nosarti et al., 2004). However, this finding was no longer significant after Bonferroni correction. Thus, this may well have been a chance finding. No significant associations between the severity of symptoms and T1 measures in autism were found. Clearly this result requires replication before further claims for its validity or meaning can be made. Contrary to our hypothesis, average transcallosal fiber length of the anterior third of the CC was reduced in subjects with HFA, but the difference did not reach the level of statistical significance even when controlling for age or IQ. No differences in the FA values of transcallosal fibers and CC volumes were found between the two groups. In the present study, with a view to controlling for the confounders of gender, age, medication status, and severity of symptom, we aimed at studying the CC morphometry and tractography in a sample of male right-handed medication-naive HFA children and well-matched controls. Therefore, it is possible that there would be less significant differences observed. Also, the number of genetic abnormalities attributed to HFA and diverse imaging findings from prior studies strongly suggest that HFA is a heterogeneous disorder (Santangelo and Tsatsanis, 2005; Pardo and Eberhart, 2007; Salmond et al., 2007; Amaral et al., 2008), which may lead to the inconsistency. Moreover, this finding is in accord with those of previous studies that controlled for the severity of symptoms and IQ (Boger-Megiddo et al., 2006; Hardan et al., 2009). Boger-Megiddo et al. compared more severely affected autism to a less severely affected group and found that children with classic autism have significantly smaller CC, while those with less severe autism only yield trend differences (Boger-Megiddo et al., 2006). Hardan et al. employed three-dimensional volumetric

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methods to study the volume of the CC and found reduced global and regional CC volumes in autism; however, these significant effects disappeared after controlling for full-scale IQ , age, and handedness (Hardan et al., 2009). Furthermore, FN, ADC values and CC density may offer more sensitive measures to detect subtle CC abnormalities than AFL, FA values, and CC volume. Further advances in DTI tractography and CC morphometric techniques are still required to resolve this issue. There are some limitations in the present study. First, the sample size in this study was relatively small. Second, the Witelson partition scheme arbitrarily divides the CC, which cannot exactly mirror the texture of the CC at the cellular level. Third, we only traced 13 sagittal slices of the CC because of problems in reliably measuring the lateral slices; future researches should apply novel volumetric methods. Fourth, 15 unique sampling orientations were applied in this study which might have led to high error rates. More unique sampling orientations are required for a robust estimation of DTI measures in future (Jones, 2004). Additionally, it is difficult to track fibers at the regions where fibers cross; development of improved fiber tractography methods may resolve some of these issues. Finally, the clinical data available were not originally intended to test specific hypotheses about brain-behavior correlations but were evaluated to explore possible relationships; future studies should employ more specific neuropsychological tests of interhemispheric connectivity. 4.1. Conclusion In summary, we aimed at combining CC morphometry and fiber tractography methods to detect CC connectivity, and found significantly less transcallosal fiber connectivity in HFA patients. In particular, the anterior third of the CC was most strongly affected, which may be associated with the severity of symptoms in the subjects with HFA. These results suggest a reduction of interhemispheric fiber connectivity in autism, which is consistent with the underconnectivity theory for autism (Just et al., 2004, 2007). Acknowledgments We gratefully acknowledge the patients and their families for their support and participation. The work was supported by the National Natural Science Foundation of China (no: 81071111 and no: 60628101), the Key Program of Medical Development of Nanjing (no: zkx10023), and the Jiangsu Government Visiting Scholarship. References Alexander, A.L., Lee, J.E., Lazar, M., Boudos, R., DuBray, M.B., Oakes, T.R., Miller, J.N., Lu, J., Jeong, E.K., McMahon, W.M., Bigler, E.D., Lainhart, J.E., 2007. Diffusion tensor imaging of the corpus callosum in autism. NeuroImage 34, 61–73. Amaral, D.G., Schumann, C.M., Nordahl, C.W., 2008. Neuroanatomy of autism. Trends in Neurosciences 31, 137–145. American Psychiatric Association, 1994. Diagnostic and Statistical Manual of Mental Disorders. 4th edition (DSM-IV). American Psychiatric AssociationAmerican Psychiatric Press, Washington, DC. Baas, P.W., 1998. The role of motor proteins in establishing the microtubule arrays of axons and dendrites. Journal of Chemical Neuroanatomy 14, 175–180. Barnea-Goraly, N., Kwon, H., Menon, V., Eliez, S., Lotspeich, L., Reiss, A.L., 2004. White matter structure in autism: preliminary evidence from diffusion tensor imaging. Biological Psychiatry 55, 323–326. Basser, P.J., Jones, D.K., 2002. Diffusion-tensor MRI: theory, experimental design and data analysis — a technical review. NMR in Biomedicine 15, 456–467. Blakemore, S.J., Choudhury, S., 2006. Development of the adolescent brain: implications for executive function and social cognition. The Journal of Child Psychology and Psychiatry 47, 296–312. Boddaert, N., Chabane, N., Gervais, H., Good, C.D., Bourgeois, M., Plumet, M.H., Barthelemy, C., Mouren, M.C., Artiges, E., Samson, Y., Brunelle, F., Frackowiak, R.S., Zilbovicius, M., 2004. Superior temporal sulcus anatomical abnormalities in childhood autism: a voxel-based morphometry MRI study. NeuroImage 23, 364–369. Boger-Megiddo, I., Shaw, D.W., Friedman, S.D., Sparks, B.F., Artru, A.A., Giedd, J.N., Dawson, G., Dager, S.R., 2006. Corpus callosum morphometrics in young children

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