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. 2012 Feb 22;34(7):1685–1695. doi: 10.1002/hbm.22018

Callosal fiber length and interhemispheric connectivity in adults with autism: Brain overgrowth and underconnectivity

John D Lewis 1,✉, Rebecca J Theilmann 2, Vladimir Fonov 1, Pierre Bellec 1, Alan Lincoln 3,4, Alan C Evans 1, Jeanne Townsend 5,6
PMCID: PMC6870247  PMID: 22359385

Abstract

Typical adults show an inverse relation between callosal fiber length and degree of interhemispheric connectivity. This has been hypothesized to be a consequence of the influence of conduction delays and cellular costs during development on axonal pruning, both of which increase with fiber length. Autism spectrum disorder (ASD) provides a test of this hypothesis: Children with ASD are known to have enlarged brains; thus, adults with ASD should show reductions in interhemispheric connectivity proportional to their degree of brain overgrowth during development. This prediction was tested by assessing the relation between both the size and structure of the corpus callosum and callosal fiber length, adjusting for intracranial volume, which is thought to reflect maximum brain size achieved during development. Using tractography to estimate the length of callosal fibers emanating from all areas of cortex, and through which region of the corpus callosum they pass, we show that adults with ASD show an inverse relation between callosal fiber length, adjusted for intracranial volume, and callosum size, and a positive relation between adjusted callosal fiber length and radial diffusivity. The results provide support for the hypothesized impact of fiber length during development. Hum Brain Mapp, 2013. © 2011 Wiley Periodicals, Inc.

Keywords: brain size, lateralization, optimal wiring, underconnectivity, corpus callosum

INTRODUCTION

In cross‐species comparisons, greater brain volume is associated with larger white‐ than gray‐matter increases [Frahm et al., 1982; Rilling and Insel, 1999b; Schlenska, 1974; Zhang and Sejnowski, 2000], but with gray‐matter increases outpacing increases in the size of the corpus callosum [Rilling and Insel, 1999a]. This has been hypothesized to be due to the greater conduction delays and cellular costs associated with callosal fibers in larger brains [Ringo, 1991; Ringo et al., 1994], which tend to be longer [Braitenberg, 2001]. The conduction delay associated with an axon is an increasing function of its length and a decreasing function of its diameter [Waxman, 1977]; and cellular costs increase with both increases in fiber length and fiber diameter [Karbowski, 2007]. For the vast majority of callosal fibers, axon diameter does not scale sufficiently with brain size to compensate for the increased length [Aboitiz et al., 1992; Jerison, 1991; Olivares et al., 2001; Schüz and Preissl, 1996]. Instead, evolution appears to have increased lateralization in larger brains to reduce conduction delays, thereby also reducing cellular costs [Changizi, 2001; Chklovskii et al., 2002; Kaas, 2000; Karbowski, 2003; Ringo, 1991; Ringo et al., 1994].

This hyposcaling of the corpus callosum (CC) is also seen in individual differences amongst human adults [Jäncke et al., 1997; Lewis et al., 2009], but less so in children [cf. Jäncke et al., 1999; Lewis et al., Submitted‐b]. Lewis et al. [2009; Submitted‐b] used diffusion tensor imaging to estimate callosal fiber length and patterns of interhemispheric connectivity, and magnetic resonance imaging to estimate degree of interhemispheric connectivity. In adults, an inverse relation between callosal fiber‐tract length and degree of interhemispheric connectivity accounted for much of the variance in degree of interhemispheric connectivity [Lewis et al., 2009]. Males aged 7–11 years, however, show this inverse relation only in the anterior‐ and posterior‐most subregions of the CC [Lewis et al., Submitted‐b]. In the rostral‐ and mid‐body of the callosum, there is no relation in children, whereas in adults there is; and the child versus adult difference is significant in both subregions. The inverse relation between the length of the callosal fibers and degree of interhemispheric connectivity thus appears to develop [cf., Jäncke et al., 1999].

Large numbers of transient projections are produced during cortical development [LaMantia and Rakic, 1990; Rakic et al., 1986], and which connections are retained is likely influenced by differences in length. Axonal pruning is driven by competition for neurotrophins [Van Ooyen and Willshaw, 1999]. The increased conduction delays and cellular costs associated with longer fibers puts them at a disadvantage in this competition. The greater the conduction delay associated with a fiber, the less effective it will be in tasks which require rapid processing—e.g., speech processing [Lewis and Elman, 2008; Ringo, 1991; Ringo et al., 1994]. The less effective a connection is, the less neurotrophins it receives [Van Ooyen and Willshaw, 1999]. Because of the lesser degree of myelination and the smaller diameter fibers in the developing brain than in the mature brain, the differences in conduction delays associated with differences in fiber length will be substantially greater [Eyre et al., 1991; Kaiser, 2008; Paus et al., 1999, 2001; Thatcher et al., 2008]. Also, the differences in cellular costs associated with differences in connection length will be substantially greater [Chugani, 1998; Chugani et al., 1987; Paus et al., 1999, 2001]. Thus, the impact of the increased conduction delays and cellular costs on normal developmental processes may underlie the inverse relation between callosal fiber length and degree of interhemispheric connectivity [Lewis and Elman, 2008; Lewis et al., Submitted‐b].

Autism provides a natural test of this hypothesis. Children with Autism Spectrum Disorder (ASD) have abnormally large brains by the second year of life [Aylward et al., 2002; Bailey et al., 1993, 1998; Bauman and Kemper, 1985; Courchesne et al., 2001, 2003; Davidovitch et al., 1996; Dementieva et al., 2005; Fombonne et al., 1999; Hazlett et al., 2005; Kemper and Bauman, 1998; Lainhart et al., 1997; Miles et al., 2000; Piven et al., 1995; Sparks et al., 2002; Woodhouse et al., 1996], and for several years thereafter this size difference can be multiple standard deviations above the norm [Courchesne et al., 2001, 2003; Dementieva et al., 2005; Hazlett et al., 2005; Redcay and Courchesne, 2005; Sparks et al., 2002]. This early brain overgrowth should result in a greater‐than‐normal amount of pruning of long‐distance connectivity, with greater overgrowth resulting in greater pruning. An inverse relation should thus be seen between degree of interhemispheric connectivity and maximum brain size achieved during development.

By adulthood, brain size differences between individuals with autism and typical individuals diminish [Aylward et al., 2002; Rapoport et al., 2009; Redcay and Courchesne, 2005]. But, head circumference and intracranial volume remain significantly greater, and it has been suggested that these head size measures reflect maximum brain size achieved during development [Aylward et al., 2002; Buckner et al., 2004; Whitwell et al., 2001].

Using diffusion tensor imaging and tractography to estimate the length of the callosal fibers emanating from all areas of cortex, and through which region of the corpus callosum those fibers pass, and using intracranial volume to estimate maximum brain volume achieved during development, we test the prediction that individuals with ASD should show an inverse relation between interhemispheric connectivity and callosal fiber tract length adjusted for maximum brain overgrowth.

METHODS

Subjects

A total of 42 adult males participated in the study: 20 with autism spectrum disorder (ASD) ranging between 19 and 45 years of age (mean 31.74; stddev 9.5), and 22 typical adult males ranging between 20 and 45 years of age (mean 32.25; stddev 9.98). All ASD participants met diagnostic criteria for ASD on the DSM‐IV as confirmed by a licensed clinician. Sixteen of the 20 participants met the DSM diagnosis for autistic disorder (classic autism) and, based on absence of early language delay and no significant abnormality in communication, 4 of the 20 subjects additionally met diagnostic criteria for Asperger's disorder. ADI‐R scores were available for 14 participants and ADOS and CARS scores were available for 11 participants. Table 1 summarizes these data. In all cases the ASD diagnosis was confirmed by these additional assessments. Individuals with a history of significant medical or neurological disorders including seizures or with Fragile X syndrome were excluded from the sample. General intellectual ability was evaluated by the Wechsler Adult Intelligence Scale‐Revised (WAIS‐R) or the Wechsler Abbreviated Scale of Intelligence (WASI). Mean scores were: Verbal IQ, 87.87 ± 20.1; Non‐verbal IQ, 96.93 ± 16.9. The control subjects were the subjects from Lewis et al. (2009). Those subjects who were capable gave informed consent; a parent gave informed consent for the others. The study was approved by the Human Research Protections Program at the University of California, San Diego.

Imaging and Image Processing

All subjects were scanned at the UCSD Center for fMRI on a GE Signa EXCITE 3.0T short bore scanner with an eight‐channel array head coil. Three types of images were acquired from each subject: (i) one set of 3D T 1‐weighted images (Fast Gradient Echo, Spoiled Gradient Recalled; Echo Time [TE] = 3.1 ms; flip angle = 12°; Number of Excitations [NEX] = 1; Field of View [FOV] = 25 cm; matrix = 256 × 256); (ii) two sets of diffusion weighted images isotropically distributed along 15 directions (dual spin‐echo, EPI; TR = 15 s; TE = 89 ms; 45 axial slices; NEX = 2; FOV = 24 cm; matrix = 128 × 128; resolution = 1.875 × 1.875 × 3 mm3; 3 mm interleaved contiguous slices; b value = 1,400 s mm−2); and (iii) fieldmaps matched to the diffusion‐weighted images.

The two sets of diffusion weighted images were both acquired with a NEX of 2—so each image was acquired four times. Fieldmaps were acquired together with each set of diffusion images. Where motion made the use of a scan questionable, the scan sequence was aborted and reinitiated.

The T 1‐volumes were processed with CIVET, a fully automated structural image analysis pipeline developed at the Montreal Neurological Institute [MacDonald et al., 2000], to obtain white‐ and gray‐matter surfaces for each subject, measures of brain volume and cortical surface area, and linear and nonlinear transformations to the MNI‐152 template. The CIVET nonlinear transformation was then refined using minctrac, and the result used to warp a high‐resolution parcellation of the CC, defined on the MNI‐152 template, to overlay each subject's T 1‐volume. This procedure ensures that subregions of the CC are comparable across subjects. The procedure is illustrated in Figure 1, and described in detail in Ganjavi et al. [2011].

Four‐dimensional volumes were created from the two sets of diffusion‐weighted images; epidewarp.ucsd, a script developed by the UCSD Center for fMRI was used to correct the diffusion‐weighted images, using the fieldmaps, of distortions caused by inhomogeneities in the magnetic field, and to correct for within‐scan motion. The b0‐volumes of both sets of diffusion‐weighted images were then coregistered, and the resultant transform used to align the two 4D volumes. The two volumes were then merged with FSL's fslmerge. The merged diffusion‐weighted volumes were then processed with FSL's bedpostx [Behrens et al., 2003] to produce the inputs for probabilistic tractography. The default parameters for bedpostx were used: a maximum of two fibers per voxel, a multiplicative factor of 1 on the prior for the second fiber, and a burn‐in of 1,000. The merged diffusion‐weighted volumes were also processed with FSL's dtifit to determine the corresponding tensor parameters at each voxel, from which a radial diffusivity volume was created.

The T 1‐volumes were then registered to the b0‐volumes with FSL's flirt to provide a mapping from the seed space to the diffusion data for each subject. The inverse transform was applied to the radial diffusivity volume, and the mean radial diffusivity within each of the 25 subregions of the CC was computed (Figure 1).

Probabilistic tractography was then used to estimate the length of the callosal fibers originating from all points of cortex, and through which region of the CC they pass. The procedure is described in detail in Lewis et al. [Submitted‐a]. Briefly, probabilistic tractography was done using FSL's probtrackx [Behrens et al., 2003], seeding from a surface 3‐mm inside the white‐matter, with the CC as a termination mask, and the 25 subregions of the CC as classification masks. A step‐length of 0.5 mm was used; a curvature threshold of 0.2, and the maximum number of steps was 1,000. A filter constructed on the AAL atlas [Tzourio‐Mazoyer et al., 2002] was used to further restrict tracts to eliminate spurious results arising from kissing and crossing fibers. Probabilistic tractography was seeded from each surface vertex, counting the number of connections to each subregion of the callosum, and the total length of the connections. Seeding was iterated until callosal tracts were determined for 85% of the surface vertices.

FSL's find‐the‐biggest was then used to establish a mode connectivity map for each subject, i.e., a map of the CC subregion in which tracts from each vertex most often terminate. A group mode connectivity map was then constructed by taking the most common value across subjects at each vertex (Fig. 2). This provided both a way to standardize termination points for callosal tract length measures across subjects, and to parcellate the cortical surface for the calculations of relative CC size. For all subjects, two measures were computed based on this group mode connectivity map: (i) the mean length of the tracts from each vertex to the subregion of the CC with which it was associated in the group mode connectivity map; and (ii) the area of CC subregion i divided by the cortical surface area of the region of cortex associated with CC subregion i in the group mode connectivity map. The group mean of these mean length maps was then calculated (excluding missing data for each individual), and the group mean for the control group was used to interpolate the missing data for individual subjects. The individual mean length maps were then multiplied by the ratio of the subject's intracranial volume to the mean intracranial volume of the control group, to estimate maximum callosal tract length during development.

The relation between callosal tract‐length, adjusted for intracranial volume, and both the radial diffusivity in, and the relative size of, each subregion of the CC was assessed in linear models, controlling for age, group, and age × group interactions. Random field theory (RFT) was used to adjust for multiple comparisons in the assessment of significance [Adler, 2009; Adler and Taylor, 2007; Hayasaka et al., 2004; Taylor and Worsley, 2007; Worsley et al., 2009].

RESULTS

The t‐statistic and the random‐field‐theory‐corrected significance for the relation between adjusted callosal tract‐length and relative CC size are shown in Figure 3. The t‐statistic and the random‐field‐theory‐corrected significance for the relation between adjusted callosal tract‐length and radial diffusivity in the CC are shown in Figure 4. The interaction of group and adjusted callosal tract length was nonsignificant over the entire cortex for both relative CC size and radial diffusivity in the CC. The mean and standard deviation of absolute CC size and cortical surface area for each of the 25 subregions, for the control and ASD groups, are listed in Table II. The mean and standard deviation of radial diffusivity in each CC subregion, for the control and ASD groups, are listed in Table III. Intra‐cranial volume for the ASD group (1707.1 ± 180.4 ml) did not differ significantly from the control group (1684.2 ± 192.7 ml).

The relation between adjusted callosal tract‐length and relative CC size is significant over bilateral temporal and occipital lobes, the precunei and posterior cingulate cortices, and scattered regions of the frontal and parietal lobes. The relation between adjusted callosal tract‐length and radial diffusivity in the CC is significant over bilateral temporal lobes, the posterior cingulate cortices, the right precuneus and paracentral lobule, and scattered regions of the frontal and parietal lobes. Note that where there is a relation between adjusted callosal tract‐length and relative CC size, the relation is negative; conversely, where there is a relation between adjusted callosal tract‐length and radial diffusivity in the CC, the relation is positive. Increased radial diffusivity in the CC implies either decreased fiber density, decreased myelin, or an increase in mean axonal diameter; but an increase in mean axonal diameter without a decrease in fiber density or myelin would demand an increase in CC size. Thus, together, the results suggest that greater callosal fiber length during development results in greater pruning of callosal fibers. Moreover, the lack of a significant interaction of group and adjusted callosal tract length suggests that individual differences in callosal tract length impact individuals with ASD similarly to typically developing individuals.

DISCUSSION

Brain size is increased in autism by the second year of life, and long‐distance connections are elongated throughout childhood [Aylward et al., 2002; Bailey et al., 1993, 1998; Bauman and Kemper, 1985; Courchesne et al., 2001, 2003; Davidovitch et al., 1996; Dementieva et al., 2005; Fombonne et al., 1999; Hazlett et al., 2005; Kemper and Bauman, 1998; Kumar et al., 2010; Lainhart et al., 1997; Miles et al., 2000; Piven et al., 1995; Sparks et al., 2002; Woodhouse et al., 1996]. Particularly before myelination is complete, these longer fibers will have longer conduction delays and will be more metabolically expensive. It has been hypothesized that brain organization will be impacted by these greater conduction delays and cellular costs, such that long‐distance connectivity should be reduced in proportion to the degree of brain overgrowth [Lewis and Elman, 2008]. The results here support that hypothesis. Utilizing tractography to estimate callosal tract length, and through which area of the callosum each tract passes, and intracranial volume to estimate maximum callosal tract length during development [Aylward et al., 2002; Buckner et al., 2004; Whitwell et al., 2001], we have shown here that the relative size of the corpus callosum is inversely related to the length of the callosal connections, and that radial diffusivity is positively correlated with the length of the callosal connections. At the midsagittal slice, callosal fibers are all approximately parallel, with no crossing fibers; therefore, radial diffusivity reflects fiber density, myelination, and the distribution of fiber diameters. A positive relation between adjusted callosal fiber tract length and radial diffusivity in the CC indicates that longer callosal fibers result in a reduction in the number of fibers, a reduction in the amount of myelin, or an increase in mean fiber diameter. A negative relation between adjusted callosal fiber tract length and the relative size of the CC indicates that longer callosal fibers result in a reduction in the number of fibers, a reduction in the amount of myelin, or a decrease in mean fiber diameter. Thus, the two results together indicate that greater callosal tract length during development results in greater structural reductions.

Underconnectivity in autism is well documented. Previous research has identified long‐range functional and anatomical under‐connectivity [Alexander et al., 2007; Frazier and Hardan, 2009; Horwitz et al., 1988; Just et al., 2004, 2007b; Koshino et al., 2005] in children, adolescents, and adults with ASD. This aberrant connectivity has also been linked to various aspects of the behavioral phenotype [Alexander et al., 2007; Just et al., 2004, 2007a; Koshino et al., 2005; Lewis and Elman, 2008]. The results here suggest that these reductions in structural and functional connectivity may be, at least in part, attributable to the early brain overgrowth.

That the impact of adjusted callosal tract length was predominately over posterior cortical areas is also noteworthy. The callosal fibers that emanate from temporal cortex are the longest of the inter‐hemispheric connections, followed by those of the occipital lobe, and then the parietal lobe; the callosal fibers that connect the frontal lobes are the shortest [Caminiti et al., 2009; Lewis et al., 2009]. Further, a large proportion of these posterior callosal fibers are small‐diameter fibers, which translate small increases in length into large increases in conduction delay [Aboitiz et al., 1992, 2003]. These longer, thinner fibers yield substantially larger conduction delays, e.g., the mean conduction delay between left and right visual cortices is approximately three times greater than between left and right premotor cortices [Caminiti et al., 2009]. This may underlie abnormalities in, for example, visual‐perceptual processing, which have been argued to stem from disconnection of the left and right visual cortices [Caron et al., 2006; Delis et al., 1986; Evans et al., 2000; Mottron et al., 2006; Schulte and Müller‐Oehring, 2010; Van Kleeck, 1989; Yamaguchi et al., 2000], and abnormalities in lateralization of temporal lobe language functions [Dawson, 1983; Flagg et al., 2005; Lange et al., 2010], which may be part of the explanation for language abnormalities in ASD. Social and communication deficits in ASD have also been associated with the posterior cingulate and precuneus [Groen et al., 2010; Oblak et al., 2011; Rojas et al., 2006]. The hemispheric asymmetries in our results suggest that these differences in lateralization may, in part, stem from asymmetric brain growth.

The CC is unique both in that the axons which comprise it are predominantly long‐distance connections, and that, at the midsagittal slice, all of the fibers are aligned, and the boundary of the bundle is well circumscribed. The former provided for a relatively pure test of the hypothesis that greater fiber length should result in greater fiber pruning over development; the latter allowed for a relatively unambiguous interpretation of the diffusion data. But, it should be kept in mind that the hypothesis applies to all long‐distance connections, e.g., also to anterior–posterior connections. Moreover, that there were no significant interactions between group and callosal tract length for both relative CC size and radial diffusivity in the CC suggests that fiber length impacts connectivity to a similar extent in individuals with ASD and typically developing individuals. Thus, this is also potentially part of an answer to such things as gender differences in lateralization, and to individual variation in connectivity, more generally. 1, 2, 3, 4, I, II, III

Figure 1.

Figure 1

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(a–e) The definition of the CC on the MNI‐152 template: (a) the boundary is determined via an active contour; (b) lines are radiated from the centroid, and the midpoints of those that intersect the CC are calculated; (c) the shortest lines that cross the CC passing through the midpoints in b are found, and their midpoints are calculated; (d) the curve passing through the midpoints in c and extending to the CC boundary is divided into 25 equal length segments; the shortest lines crossing the CC at the ends of a segment define the subregion boundaries; (e) in color. The right part of the figure illustrates how the CC template (shown in the inset with the red border) is fit to individual subjects. The template T 1 is registered to the subject's T 1, and the transform applied to the template CC. The subject's b0 is registered to the subject's T 1; the b0 to T 1 transform is applied to the subject's radial diffusivity volume, thus overlaying the CC parcellation on the radial diffusivity volume as shown in the inset with the yellow border. The inverse of the b0 to T 1 transform provides the mapping between the seed/target space and the diffusion space required by probtrackx.

Figure 2.

Figure 2

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The most common pattern of connectivity from cortex to CC across the ASD subjects.

Figure 3.

Figure 3

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The relation between callosal tract length at each vertex, adjusted for intracranial volume, and the relative size of the CC subregion to which that vertex is associated in the mode connectivity map. The top portion of the figure shows the t‐statistic. The bottom part of the figure shows the regions which are significant after correction for multiple comparisons. The relation is significant over bilateral temporal and occipital lobes, the precunei and posterior cingulate cortices, and scattered regions of the frontal and parietal lobes. Note that where there is a relation, the relation is negative.

Figure 4.

Figure 4

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The relation between callosal tract length at each vertex, adjusted for intracranial volume, and radial diffusivity in the CC subregion to which that vertex is associated in the mode connectivity map. The top portion of the figure shows the t‐statistic. The bottom part of the figure shows the regions which are significant after correction for multiple comparisons. The relation is significant over bilateral temporal lobes, the posterior cingulate cortices, the right precuneus and paracentral lobule, and scattered regions of the frontal and parietal lobes. Note that where there is a relation, the relation is positive.

Table I.

The behavioral data for the individuals with autism spectrum disorder

Cutoff Range Mean St. Dev.
ADI‐R social 10 13–54 27.57 8.9
ADI‐R communication (Verbal) 8 6–22 18.50 4.4
ADI‐R repetitive behaviors 3 3–14 8.28 3.0
ADOS social 4 5–20 11.45 4.1
ADOS communication 2 2–9 6.45 2.1
ADOS stereotyped behavior 0–13 2.78 3.9
CARS 30 23.5–51.5 37.05 7.5
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Cutoff scores for ADI‐R and CARS are for autism; Cutoff scores for ADOS are for Autism Spectrum.

Table II.

The mean and standard deviation for the regional absolute CC size and cortical surface area for the control and ASD groups

Control Autism
CC area CSA CC area CSA
μ σ μ σ μ σ μ σ
CC1 15.19 3.39 4039.64 351.80 14.57 2.90 4139.82 574.83
CC2 26.15 4.90 3918.63 370.55 24.65 5.88 4173.18 564.76
CC3 34.19 5.82 3464.91 348.05 32.40 6.74 3680.87 660.19
CC4 37.35 4.95 5987.41 667.48 37.72 8.03 6163.42 1163.33
CC5 29.24 3.26 4964.47 608.46 28.13 4.38 5021.73 768.03
CC6 26.48 3.52 5951.48 925.32 26.24 4.71 6049.72 849.82
CC7 23.26 3.39 3729.66 616.52 22.58 4.40 3818.54 701.54
CC8 24.39 3.39 8809.12 1249.13 23.70 4.66 8986.05 1499.20
CC9 20.84 2.56 4508.12 531.39 20.47 3.53 4579.88 749.78
CC10 23.79 2.67 4370.84 486.42 22.60 4.03 4495.45 864.48
CC11 23.86 2.81 3781.26 480.00 23.06 3.86 3871.31 617.87
CC12 22.94 3.21 1406.07 238.19 22.67 4.26 1401.27 268.17
CC13 23.83 3.56 1521.03 286.58 22.57 3.78 1565.11 246.30
CC14 22.51 3.88 1502.85 300.21 21.42 3.24 1583.37 266.03
CC15 22.47 2.86 1172.26 294.57 21.09 3.19 1180.07 229.28
CC16 19.09 3.03 9326.95 988.22 17.75 2.99 9740.19 1022.06
CC17 15.34 2.12 1822.18 280.11 15.10 1.97 1835.66 238.31
CC18 19.32 2.68 3581.53 392.44 20.03 2.48 3740.97 432.96
CC19 19.47 2.63 10619.68 1238.24 19.71 2.89 10920.94 1217.58
CC20 24.24 4.03 3032.71 463.78 23.76 3.02 3202.13 432.78
CC21 35.16 5.36 40288.34 4080.12 34.23 5.44 42348.95 4791.26
CC22 34.29 5.95 43117.69 3835.50 33.58 7.99 44427.68 4947.09
CC23 33.46 8.19 5779.76 600.39 32.18 6.71 5973.97 633.11
CC24 45.32 7.64 27345.30 2442.65 43.56 8.33 28287.03 3044.38
CC25 22.41 3.12 14618.26 1095.21 20.97 2.85 14616.01 1201.48
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The units are mm2.

Table III.

The mean and standard deviation of radial diffusivity in each CC subregion for the control and ASD groups

Control Autism
μ σ μ σ
CC1 0.79E‐3 0.10E‐3 0.91E‐3 0.19E‐3
CC2 0.59E‐3 0.06E‐3 0.70E‐3 0.15E‐3
CC3 0.50E‐3 0.11E‐3 0.59E‐3 0.17E‐3
CC4 0.48E‐3 0.10E‐3 0.56E‐3 0.15E‐3
CC5 0.54E‐3 0.09E‐3 0.62E‐3 0.08E‐3
CC6 0.59E‐3 0.08E‐3 0.67E‐3 0.09E‐3
CC7 0.61E‐3 0.06E‐3 0.74E‐3 0.11E‐3
CC8 0.65E‐3 0.07E‐3 0.78E‐3 0.14E‐3
CC9 0.69E‐3 0.08E‐3 0.79E‐3 0.14E‐3
CC10 0.64E‐3 0.06E‐3 0.77E‐3 0.13E‐3
CC11 0.65E‐3 0.09E‐3 0.75E‐3 0.12E‐3
CC12 0.62E‐3 0.09E‐3 0.72E‐3 0.11E‐3
CC13 0.58E‐3 0.08E‐3 0.68E‐3 0.12E‐3
CC14 0.57E‐3 0.08E‐3 0.67E‐3 0.17E‐3
CC15 0.57E‐3 0.08E‐3 0.67E‐3 0.18E‐3
CC16 0.58E‐3 0.09E‐3 0.70E‐3 0.21E‐3
CC17 0.63E‐3 0.10E‐3 0.75E‐3 0.22E‐3
CC18 0.70E‐3 0.17E‐3 0.79E‐3 0.22E‐3
CC19 0.69E‐3 0.14E‐3 0.74E‐3 0.18E‐3
CC20 0.57E‐3 0.10E‐3 0.65E‐3 0.13E‐3
CC21 0.48E‐3 0.06E‐3 0.55E‐3 0.11E‐3
CC22 0.40E‐3 0.05E‐3 0.48E‐3 0.08E‐3
CC23 0.35E‐3 0.06E‐3 0.42E‐3 0.08E‐3
CC24 0.35E‐3 0.06E‐3 0.46E‐3 0.11E‐3
CC25 0.74E‐3 0.13E‐3 0.82E‐3 0.17E‐3
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The units are mm2 s−1.

Acknowledgements

Computations were performed on the Colosse supercomputer at the CLUMEQ HPC Consortium (http://www.clumeq.ca).

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