Abstract

Although motor deficits are common in autism, the neural correlates underlying the disruption of even basic motor execution are unknown. Motor deficits may be some of the earliest identifiable signs of abnormal development and increased understanding of their neural underpinnings may provide insight into autism-associated differences in parallel systems critical for control of more complex behaviour necessary for social and communicative development. Functional magnetic resonance imaging was used to examine neural activation and connectivity during sequential, appositional finger tapping in 13 children, ages 8–12 years, with high-functioning autism (HFA) and 13 typically developing (TD), age- and sex-matched peers. Both groups showed expected primary activations in cortical and subcortical regions associated with motor execution [contralateral primary sensorimotor cortex, contralateral thalamus, ipsilateral cerebellum, supplementary motor area (SMA)]; however, the TD group showed greater activation in the ipsilateral anterior cerebellum, while the HFA group showed greater activation in the SMA. Although activation differences were limited to a subset of regions, children with HFA demonstrated diffusely decreased connectivity across the motor execution network relative to control children. The between-group dissociation of cerebral and cerebellar motor activation represents the first neuroimaging data of motor dysfunction in children with autism, providing insight into potentially abnormal circuits impacting development. Decreased cerebellar activation in the HFA group may reflect difficulty shifting motor execution from cortical regions associated with effortful control to regions associated with habitual execution. Additionally, diffusely decreased connectivity may reflect poor coordination within the circuit necessary for automating patterned motor behaviour. The findings might explain impairments in motor development in autism, as well as abnormal and delayed acquisition of gestures important for socialization and communication.

Introduction

Autism is characterized by deficits in social cognition, disordered communication and restricted interests and repetitive behaviours (American Psychiatric Association, 2000). Subsumed within each of these, motor impairments are a common finding in autism spectrum disorders (ASD), noted in even the earliest descriptions (Kanner, 1943; Frith, 1991). Since then a number of studies have revealed impairments in basic motor control (Vilensky et al., 1981; Ghaziuddin and Butler, 1998; Teitelbaum et al., 1998, 2004; Noterdaeme et al., 2002; Nayate et al., 2005; Jansiewicz et al., 2006) and skilled motor gestures (DeMyer et al., 1972; Jones and Prior, 1985; Ohta, 1987; Smith and Bryson, 1994; Rogers et al., 1996; Williams et al., 2001; Mostofsky et al., 2006) as well as impairments in motor learning (Hughes, 1996; Mostofsky et al., 2000; Rinehart et al., 2001).

Greater insight into motor functioning in autism may prove beneficial to understanding its neurological basis and to early identification of children in the autism spectrum. Though autism is most often diagnosed during toddler or preschool years, retrospective studies using video analysis suggest motor signs may be apparent in children with ASD as early as the first year of life (Teitelbaum et al., 1998, 2004). Furthermore, recently reported findings suggest that gross and fine motor delays are among the earliest identifiable signs distinguishing infants with autism from their typically developing (TD) peers (Landa and Garrett-Mayer, 2006). Moreover, motor signs are more quantifiable and reproducible than complex communicative or social behaviours, making them a more tenable process for investigation in studies of the brain basis of autism. Because motor, social and communication deficits may be linked traits that result from dysfunction in parallel neural circuits, careful consideration of motor signs may yield insights into the underlying mechanisms of autism and its defining characteristics (Gidley Larson and Mostofsky, 2006).

Despite the number of behavioural studies of motor dysfunction in ASD (DeMyer et al., 1972; Vilensky et al., 1981; Jones and Prior, 1985; Ohta, 1987; Smith and Bryson, 1994; Hughes, 1996; Rogers et al., 1996; Ghaziuddin and Butler, 1998; Teitelbaum et al., 1998, 2004; Mostofsky et al., 2000, 2006; Rinehart et al., 2001; Williams et al., 2001; Noterdaeme et al., 2002; Nayate et al., 2005; Jansiewicz et al., 2006), the neural circuitry underlying even basic motor skill deficits remains relatively unclear. While the mechanisms for abnormal neural development are unknown, it is of note that some of the most consistently reported structural brain abnormalities in autism occur within regions of the brain involved in movement, including the frontal lobe (Carper and Courchesne, 2000; Carper et al., 2002; Herbert et al., 2002; Levitt et al., 2003; Salmond et al., 2003; Schmitz et al., 2007) and, more notably, the cerebellum (Courchesne et al., 1987, 1988, 1994,a–c, 2001; Murakami et al., 1989; Kleiman et al., 1992; Hashimoto et al., 1995; Saitoh et al., 1995; Ciesielski et al., 1997; Levitt et al., 1999; Carper and Courchesne, 2000; Sparks et al., 2002; Kaufmann et al., 2003).

Recently, functional neuroimaging has been used to explore the neural correlates of motor execution in young adults with ASD. Findings have included abnormal activational scatter beyond regions typically involved in basic motor execution, as well as greater individual variability compared with controls (Muller et al., 2001, 2003; Allen et al., 2004). Further, studies of motor learning have revealed atypical involvement of motor regions along different stages of learning, with comparatively greater involvement of primary motor regions in late-stage learning (Muller et al., 2004). Finally, many of these studies have reported abnormal cerebellar activation during motor execution, either increased or decreased from controls (Allen and Courchesne, 2003; Allen et al., 2004).

While these adult studies suggest abnormalities in multiple motor systems, it is unclear that findings would be the same in children and adolescents. To our knowledge, there have been no neuroimaging studies of basic motor execution in children with ASD. This is a critical area of investigation not only because motor deficits are often the earliest observable signs (Teitelbaum et al., 1998, 2004; Landa and Garrett-Mayer, 2006; Bryson et al., 2007; Chawarska et al., 2007), but also because there continues to be debate regarding at what stage of development the most consistently reported structural brain abnormalities, the cerebellar dysmorphologies, occur.

In the current study, we looked at fMRI activity during self-paced sequential finger tapping in children with high-functioning autism (HFA), as compared with their TD age- and sex-matched peers. We additionally examined functional connectivity within the motor systems critical for motor coordination and execution. There has been increasing speculation that autism may be associated with abnormalities in structural and functional connectivity, with deficits in ‘weak central coherence’ (Shah and Frith, 1993) and ‘complex information processing’ (Minshew et al., 1997) thought to be related to abnormal connectivity between distant brain regions. Consistent with this proposed model, post-mortem analysis has revealed an abundance of short relative to long connective fibres in frontal and temporal regions (Casanova et al., 2002). Similarly, MRI analysis of white matter in autism has revealed increased volume localized to outer zone (‘radiate’) regions principally comprised of localized U-fibre connections (Herbert et al., 2004), with contrasting decreased size of the corpus callosum, comprised of long-range interhemispheric white matter tracts (Chung et al., 2004; Piven et al., 1997). Functional relevance of these findings was recently established using measures of motor function, with increased local white matter volume in primary motor cortex predicting the degree of motor impairment in children with autism (Mostofsky et al., 2007).

These findings of white matter abnormalities in autism have further led to investigation into functional connectivity, or how distant brain regions are ‘connected’ based on similarities in their profiles of functional activity. Decreased functional connectivity has been observed in autism during performance of cognitive tasks (Just et al., 2004, 2007; Kana et al., 2006, 2007); however, functional connectivity during simple motor coordination and execution has not yet been examined.

Acquisition and execution of motor skills is dependent on coordinated activity across a network of cortical and subcortical regions (Doyon et al., 2002). Given that autism is a developmental disorder with onset of skill deficits (motor, social and communicative) very early in childhood, we hypothesized that, when compared with TD children, children with autism would show decreased activity in regions important for achieving automaticity of motor skills, in particular, the cerebellum, with associated increased activity in cerebral cortical regions, necessary for continued effortful control of movement. We further hypothesized that children with autism would show decreased connectivity between these regions.

Materials and Methods

Participant selection

Twenty-six children, aged 8–12 years, participated in the study: 13 meeting study criteria for HFA(mean age = 10.9 years, SD = 1.5) and thirteen age- and sex-matched, TD peers (mean age = 10.5 years, SD = 1.4). Each group consisted of eleven boys and two girls. All subjects were right-handed based upon the Physical and Neurological Exam for Subtle Signs (PANESS) (Denckla, 1985).

Subjects were recruited from several sources, including out-patient clinics at Kennedy Krieger Institute and through advertisements placed within community-wide service groups, schools, and hospitals. Autism diagnoses were based on DSM-IV criteria (American Psychiatric Association, 2000) and were confirmed using the Autism Diagnostic Interview-Revised (ADI-R) (Lord et al., 1994) and Autism Diagnostic Observation Schedule-Generic (ADOS-G) (Lord et al., 2000).

The Diagnostic Interview for Children and Adolescents, Fourth Edition (DICA-IV) (Reich, 2000) was used to determine the presence of additional psychiatric diagnoses. TD children with any diagnosis on the DICA-IV or with any immediate family members with autism were excluded. All TD subjects had no history of seizures or evidence of any other neurological disorder. Within the high-functioning autism group, the following DICA-IV diagnoses were also met: Attention-Deficit/Hyperactivity Disorder (two children), Obsessive-Compulsive Disorder (two), Specific Phobia (three), Generalized Anxiety Disorder (one) and Oppositional Defiant Disorder (five); DICA-IV results were not available for two children in the HFA group. Eight children with HFA were reported to be taking psychoactive medications, including stimulants (six children), selective norepinephrine reuptake inhibitor (two), selective serotonin reuptake inhibitors (two), clonidine (one) and buspirone (one). Stimulant medications were discontinued the day prior to testing; all other medications were taken as prescribed. No participants in the TD group were taking any psychoactive medications.

Intelligence was assessed at the time of study using the Wechsler Intelligence Scale for Children, Third or Fourth Edition (WISC-III or IV) (Wechsler, 1991, 2003), except one child with HFA who received the Differential Abilities Scale (DAS) (Elliott, 1993). All HFA and TD subjects obtained full-scale IQ (FSIQ) standard scores of 75 or above. Children with HFA had significantly lower FSIQ scores than TD controls (HFA mean = 103 ± 18, TD mean = 118 ± 14, P = 0.02); there was a near-significant difference between groups on non-verbal indexes from the WISC batteries (WISC-III Perceptual Organization Index and WISC-IV Perceptual Reasoning Index) (HFA mean =106 ± 18, TD mean = 120 ± 19; P = 0.08).

This study was approved by the Johns Hopkins Medical Institutional Review Board. For all subjects, written consent was obtained from a parent or legal guardian and assent was obtained from the participating child.

Motor assessment

The PANESS was used to assess motor function outside of the scanner. As part of this examination, time to complete 20 finger taps (five complete sequences) for each hand was recorded using a stopwatch. The PANESS has been found to have adequate test–retest reliability (Holden et al., 1982), inter-rater reliability and internal consistency (Vitiello et al., 1989). Furthermore, it has been found to be valid in assessing effects of age and gender in TD children (Gidley Larson et al., 2007) and for distinguishing children with a range of developmental disabilities, including autism (Jansiewicz et al., 2006).

fMRI finger-sequencing paradigm

For both right-handed finger sequencing (RHFS) and left-handed finger sequencing (LHFS), subjects were asked to successively tap each finger to the thumb in a fixed sequence (index–middle–ring–little) until they received their next visual instruction. Periods of RHFS and LHFS alternated with periods of rest, during which subjects were instructed not to move their hands. For all subjects, the hands were comfortably positioned on the torso.

Immediately prior to entering the scanner, participants were taught the proper sequence of movements and asked to demonstrate understanding by performing it with each hand. Care was taken to instruct participants not to count taps or name the fingers aloud during training or testing. Just before the acquisition of images, subjects engaged in a short practice session in which they were shown the same computer screens presented during the actual task (‘Tap your Right Hand’, ‘Tap your Left Hand’ and ‘Rest’). During the practice session, they received verbal feedback about their performance.

The fMRI task consisted of a 30 s block of rest, followed by four cycles of 30 s blocks of RHFS, LHFS and rest for a total scan time of 390 s; starting hand was counterbalanced across subjects. Paradigm programming and display were done using E-Prime (Psychology Software Tools Inc., 2002) on a Windows operating system. Subjects were prompted with visual instructions that remained on the screen throughout each 30s time period. During scanning, finger movements were video recorded; videotapes were later reviewed by an examiner, blind to diagnosis, to determine the total number of finger taps for each hand.

Scanning procedure

Scanning was carried out in a 1.5 T ACS-NT Powertrack 6000 MRI scanner (Philips Medical Systems, Inc.) using body coil transmission and quadrature end-capped head coil reception. T1-weighted high resolution fast-field echo (FFE) anatomical images were acquired coronally [flip angle 45°, repetition time (TR) 35 ms, TE 6 ms, matrix size 256 × 256, field-of-view 240 mm, pixel spacing 0.9375 × 0.9375, slice thickness 1.5 mm]; these were used to create a study-specific template in standardized space used for normalization. For the functional images, axially oriented volumes were acquired every 3.0 s using single-shot echo planar imaging (EPI) 64 × 64 voxel matrix, 3.59 × 3.59 × 5.5 mm voxels, TE 64 ms and flip angle 70°. Each volume was composed of 26 5 mm slices (0.5 mm gap).

Post-acquisition processing

All post-acquisition image processing was carried out using MATLAB version 6.1 (The Mathworks, Inc.) and SPM2 (Wellcome Department of Imaging Neuroscience http://www.fil.ion.ucl.ac.uk/spm/software/spm2/). Region of interest (ROI) analyses were run using MarsBar (http://marsbar.sourceforge.net/).

fMRI data preprocessing

Paediatric brains differ from adult brains in both regional and global dimensions; spatial normalization of paediatric brains to a standard adult template is therefore problematic (Casey et al., 2000; Courchesne et al., 2000). In order to achieve the best possible spatial normalization, a study specific template was created from all 26 participating children. To create the template, each subject's high resolution anatomical Digital Imaging and Communications in Medicine (DICOM) images were converted to Analyze format and segmented in SPM2. The grey-matter images were then normalized to the Montreal Neurological Institute (MNI) grey-matter template using a 12-parameter affine transformation and averaged to create the study-specific template. As anatomical images have superior geometrical fidelity with respect to echo planar images, the parameters of the transformation into standardized space was not determined from functional images alone. Instead, each individual's grey-matter image was co-registered to the first volume of the functional scan using a six-parameter affine transformation. The functional volumes were time-corrected to adjust for within-volume time of acquisition differences (Calhoun et al., 2000) and spatially realigned to the location of the first image in the time series. Following this, the co-registered grey-matter images were normalized to the study-specific template using a 12-parameter linear transformation and 16 non-linear transformation iterations and then resampled in voxels of (2 mm)3; these parameters were applied to the functional images. The functional images were then smoothed (Friston et al., 1996) using a Gaussian kernel that was half the resolution of the acquisition matrix (7 × 7 × 11 mm3).

fMRI activation data analysis

SPM2 was used to construct and test the fit of the image data to a general linear model (Friston et al., 1995) corresponding to the time-course of RHFS in contrast to rest and LHFS in contrast to rest. Motion regressors were included as regressors of no interest to account for variance associated with head movement during scanning. Voxel-wise t-maps were constructed for each subject as a first level analysis; the amplitude maps were then carried to a second level to test for significant group effects using Gaussian random field theory. The two level strategy described is equivalent to a random effects analysis, in that it provides a representative activation for a given population that is dominated by inter-subject variance rather than inter-scan variance (Holmes and Friston, 1998).

Single-group, whole-brain random effects analyses for both the HFA and TD groups were accomplished in SPM2 by executing one sample t-tests on the individual subject's right- and left-rest contrast images. These maps were thresholded at P = 0.0001 and 32 voxels, in order to achieve a corrected statistical threshold of P = 0.05, as determined by the program AlphaSim (B. D. Ward; http://afni.nimh.nih.gov/afni/docpdf/ALPHASim.pdf), used to run 1000 Monte Carlo simulations. The location of voxels significantly associated with RHFS (right-rest contrast) and with LHFS (left-rest contrast) were determined for the single group's contrasts. They were summarized by their local maxima separated by at least 8 mm, and the maxima were converted from MNI to Talairach coordinate space using formulas provided by Matthew Brett (Medical Research Council-Cognition and Brain Sciences Unit, http://www.mrc-cbu.cam.ac.uk/Imaging/Common/mnispace.shtml). These coordinates were assigned neuroanatomic and cytoarchitectonic labels using the Talairach Daemon (Research Imaging Center, University of Texas Health Science Center at San Antonio http://ric.uthscsa.edu/resources/body.html) and were reviewed by a neurologist (SHM).

Region of interest (ROI) analyses were run to determine if there were significant differences in activation between the HFA and TD groups within regions seen in the single-group, whole-brain analyses. This approach has precedence in the literature (Durston et al., 2006; Suskauer et al., 2008), as it restricts between-group analyses to task-relevant regions and allows for more robust statistical comparisons within regions relative to whole-brain analyses. ROI were defined as the union (‘OR’) between the HFA and TD single-group maps; separate sets of ROIs were created for right- and left-rest contrasts. Within ROIs, two-sample t-tests were run comparing activation between groups for RHFS and LHFS. Results were adjusted for multiple comparisons using a Bonferroni correction based upon the number of ROIs in each analysis (RHFS: 10 ROIs, LHFS: eight ROIs), and significant group differences are reported at a Bonferroni-corrected level of P < 0.05, with trends reported at a Bonferroni-corrected level of P < 0.1.

Additionally, between-group whole-brain analyses were run via a two-sample t-test. These results are shown in uncorrected maps, thresholded at P = 0.05, in order to further explore regions that differ between groups.

fMRI connectivity data analysis

Functional connectivity analyses were run using approaches similar to those previously employed in autism (Just et al., 2004, 2007; Kana et al., 2006, 2007). Motor network ROIs were selected based on one-sample t-tests of RHFS- and LHFS-rest contrasts across the entire sample (HFA and TD, n = 26), for a total of seven regions [bilateral primary motor, bilateral anterior cerebellum, bilateral thalamus and supplementary motor area (SMA)]. These ROIs were then used as a mask on each individual subject and only voxels active at a threshold of P = 0.001 in each subject were considered for further analysis. If a subject had less than thirteen active voxels in a ROI, they were excluded from further analyses of that ROI (Kana et al., 2006; Kana et al., 2007). Additionally, control ROIs in the right primary auditory cortex and the brainstem were used to verify the specificity of differences in connectivity.

Time courses for each ROI were extracted using MarsBar, were subsequently high-pass filtered (128 s) and extreme outlier volumes (variance >5 SDs from mean) were excluded (Mazaika et al., 2007). Partial correlations were then run between each ROI pair (21 motor pairs and 15 control pairs), covarying for motion (six regressors, derived from spatial realignment during preprocessing). Of the 21 motor pairs, six were classified as right hand circuits (both ROIs derived from RHFS map), six were classified as left hand circuits (both ROIs derived from LHFS map) and nine were classified as neutral circuits (one ROI derived from each map). Further analyses of these ROIs covaried for activity in the control ROIs (two time course regressors); results of these analyses were qualitatively the same as those that did not covary for activity in control ROIs, and hence only the analyses covarying for both motion and control ROI activity are reported.

In addition to correlations across the entire time course, connectivity within each condition (rest, RHFS, LHFS) was examined by dividing the time course into the separate conditions. For each 30 s block, composed of 10 3 s TR's, the first three volumes (6 s) were discarded and one volume (3 s) of the next block was included so as to minimize the effects of hemodynamic delay from previous conditions on within-condition connectivity analyses; this resulted in eight time points per block. Connectivity analyses were run in two ways: (i) by calculating connectivity within each block and averaging correlation coefficients across blocks in the same condition; and (ii) by concatenating blocks in the same condition and correlating the resulting time courses. Partial correlations covarying for motion and control activation could not be carried out due to a lack of degrees of freedom (8 time points versus 10 regressors); however, correlations without covariates demonstrated that the results of the two methods were qualitatively the same, and hence the concatenation method was used for further analyses. After concatenation, partial correlation analyses were run as described above.

After correlations were calculated, these values were normalized using a Fisher's z transformation. Repeated measures MANOVA analyses were then run examining for effects of diagnosis and condition. For analyses examining individual region-pairs (i.e. correlation between two specific regions), Bonferroni-corrections for multiple comparisons were made separately for each circuit (P = 0.0083 for right- and left-hand circuits, P = 0.0056 for neutral circuits).

Results

Task performance

On the motor assessment prior to scanning, children with HFA had significantly higher total PANESS scores than TD children, indicating poorer performance on this composite measure of subtle motor abnormalities [HFA mean = 40.1 ± 12.8, TD mean =18.8 ± 8.7; t(23) = 4.93, P= 0.0001; data missing for 1 HFA child]. However, there were no significant between-group differences in time to complete five finger-tapping sequences during either RHFS [HFA mean = 10.5 s ± 3.0, TD mean = 9.5 s ± 3.7; t(23) = 0.72, P = 0.48] or LHFS [HFA mean = 11.0 s ± 2.3, TD mean = 9.5 s ± 3.3; t(23) = 1.29, P = 0.21], consistent with published PANESS findings in children with HFA (Jansiewicz et al., 2006).

For task performance during scanning, videotape recordings for 24 (12 TD and 12 HFA) subjects were available for visual count of individual taps. Finger-sequencing speed measured outside the scanner correlated with that inside the scanner for both RHFS [r2 = 0.26; t(21) = 2.70, P= 0.01] and LHFS [r2 = 0.34; t(21) = 3.31, P= 0.003]. There was no significant difference in individual taps per 30s block between groups during RHFS [HFA mean = 58 ± 9, TD mean = 65 ± 15; t(22) = 1.36, P = 0.19], although there was during LHFS [HFA mean = 56 ± 9, TD mean = 65 ± 11; t(22) = 2.38, P = 0.03].

Within-group whole brain activation analyses

First, separate random effects analyses were computed with right-rest (RHFS) and left-rest (LHFS) contrasts for the HFA and TD groups. During RHFS and LHFS (Fig. 1), both groups demonstrated the expected finding of predominant activation in the contralateral pre/postcentral gyrus (BA3/4) and ipsilateral anterior cerebellum (lobules IV/V). Additionally, both groups showed bilateral activation (left > right) in the superior medial wall (BA6) and contralateral activation in the thalamus. Full details of within-group results can be seen in Table 1.

Table 1

Results of individual group analyses for RHFS and LHFS

    TD
 
HFA
 
Cond Hem Region BA x y z Vol t x y z Vol t 
RHFS Primary sensorimotor cortex 3/4 −38 −27 47 1587 14.49 −51 −19 47 974 11.72 
 Anterior cerebellum (Lobules IV/V)  16 −51 −19 1041 15.62 22 −53 −12 206 7.59 
 Thalamusa  −18 −27 12 417 8.42 −14 −23 358 12.90 
 Putamena       −30 −4 −1 58 7.30 
 Medial frontal wall −10 10 42 231 9.58 −8 48 44 6.48 
 Posterior/Inferior cerebellum (Lobule VIII A/B)  20 −62 −39 228 16.66      
 Anterior cerebellum (Lobules IV–V)  −22 −56 −26 188 9.48      
 Lingual/Fusiform gyrusb 18/19 −2 −94 −5 148 14.49 10 −82 −1 83 8.86 
 Postcentral gyrus 50 −18 38 101 6.84      
 Fusiform gyrusb 19 −20 −82 −11 80 9.67      
LHFS Primary sensorimotor cortex 3/4 46 −17 52 2189 17.29 38 −19 56 678 9.81 
 Anterior cerebellum  −16 −51 −16 760 19.01 −63 −7 264 10.76 
  (Lobules IV/V)            
 Medial frontal wall −6 42 266 8.43 −2 −1 59 161 8.24 
 Precentral gyrus −34 −9 61 174 8.35      
 Inferior parietal lobule 40 −40 −33 42 163 7.79      
 Posterior cerebellum (Lobule VI)  24 −59 −16 94 8.49      
 Thalamus  12 −13 12 87 11.31 −17 98 9.09 
 Postcentral gyrus −59 −21 38 55 7.61      
    TD
 
HFA
 
Cond Hem Region BA x y z Vol t x y z Vol t 
RHFS Primary sensorimotor cortex 3/4 −38 −27 47 1587 14.49 −51 −19 47 974 11.72 
 Anterior cerebellum (Lobules IV/V)  16 −51 −19 1041 15.62 22 −53 −12 206 7.59 
 Thalamusa  −18 −27 12 417 8.42 −14 −23 358 12.90 
 Putamena       −30 −4 −1 58 7.30 
 Medial frontal wall −10 10 42 231 9.58 −8 48 44 6.48 
 Posterior/Inferior cerebellum (Lobule VIII A/B)  20 −62 −39 228 16.66      
 Anterior cerebellum (Lobules IV–V)  −22 −56 −26 188 9.48      
 Lingual/Fusiform gyrusb 18/19 −2 −94 −5 148 14.49 10 −82 −1 83 8.86 
 Postcentral gyrus 50 −18 38 101 6.84      
 Fusiform gyrusb 19 −20 −82 −11 80 9.67      
LHFS Primary sensorimotor cortex 3/4 46 −17 52 2189 17.29 38 −19 56 678 9.81 
 Anterior cerebellum  −16 −51 −16 760 19.01 −63 −7 264 10.76 
  (Lobules IV/V)            
 Medial frontal wall −6 42 266 8.43 −2 −1 59 161 8.24 
 Precentral gyrus −34 −9 61 174 8.35      
 Inferior parietal lobule 40 −40 −33 42 163 7.79      
 Posterior cerebellum (Lobule VI)  24 −59 −16 94 8.49      
 Thalamus  12 −13 12 87 11.31 −17 98 9.09 
 Postcentral gyrus −59 −21 38 55 7.61      

a In the TD RHFS map, the left thalamus and left putamen were part of a single cluster. This cluster was divided into two separate clusters for the ROI analyses.

b The occipital regions were combined and divided into left and right portions for the ROI analyses.

Cond = Condition; Hem = Hemisphere; BA = Brodmann Area; Vol = Volume (in 8 mm3 voxels).

Figure 1

Glass brain and sectional maps showing regions where fMRI activation was significantly associated with LHFS (left images) and RHFS (right images), each contrasted with rest, for TD children (upper images) and children with autism (lower images). All maps were thresholded at P = 0.05 corrected for multiple comparisons. Neurologic convention is used (i.e. right = right hemisphere; projections looking rightward or into the page).

Figure 1

Glass brain and sectional maps showing regions where fMRI activation was significantly associated with LHFS (left images) and RHFS (right images), each contrasted with rest, for TD children (upper images) and children with autism (lower images). All maps were thresholded at P = 0.05 corrected for multiple comparisons. Neurologic convention is used (i.e. right = right hemisphere; projections looking rightward or into the page).

Between-group activation analyses

The results of between-group ROI analyses can be seen in Table 2 and Fig. 2. For both RHFS and LHFS, the TD group showed greater activation in the ipsilateral anterior cerebellum (lobules IV/V). Additionally, for RHFS, the TD group showed greater activation in the left (contralateral) anterior cerebellum (lobules IV/V), right (ipsilateral) posterior/inferior cerebellum (lobule VIII A/B) and the left lingual/fusiform gyrus (BA18/19).

Table 2

ROI results for between-group analyses for RHFS and LHFS

Hem Region BA tUncorrected P Uncorrected P covarying for FS speed Uncorrected P covarying for FS speed and FSIQ 
RHFS       
Primary sensorimotor cortex 3/4 0.08 0.467 0.400 0.451 
Anterior cerebellum (Lobules IV/V)  2.55 0.009 0.008 0.007 
Thalamus  −0.72 0.238 0.809 0.775 
Putamen  0.46 0.324 0.438 0.639 
Medial frontal wall 1.03 0.157 0.154 0.401 
Posterior/Inferior cerebellum (Lobule VIII A/B)  3.90 < 0.001 < 0.001 < 0.001 
Anterior cerebellum (Lobules IV/V)  3.39 0.001 < 0.001 < 0.001 
Lingual/Fusiform gyrus 18/19 0.91 0.186 0.211 0.505 
Postcentral gyrus 0.23 0.409 0.424 0.655 
Lingual/Fusiform gyrus 18/19 2.49 0.010 0.004 0.029 
LHFS       
Primary sensorimotor cortex 3/4 0.33 0.374 0.364 0.602 
Anterior cerebellum (Lobules IV/VI)  3.33 0.001 0.001 0.002 
Medial frontal wall 0.50 0.311 0.317 0.441 
Precentral gyrus 1.02 0.160 0.135 0.174 
Inferior parietal lobule 40 1.35 0.096 0.125 0.212 
Posterior cerebellum (Lobule VI)  1.41 0.085 0.012 0.020 
Thalamus  0.71 0.243 0.402 0.411 
Postcentral gyrus 1.81 0.041 0.063 0.063 
Hem Region BA tUncorrected P Uncorrected P covarying for FS speed Uncorrected P covarying for FS speed and FSIQ 
RHFS       
Primary sensorimotor cortex 3/4 0.08 0.467 0.400 0.451 
Anterior cerebellum (Lobules IV/V)  2.55 0.009 0.008 0.007 
Thalamus  −0.72 0.238 0.809 0.775 
Putamen  0.46 0.324 0.438 0.639 
Medial frontal wall 1.03 0.157 0.154 0.401 
Posterior/Inferior cerebellum (Lobule VIII A/B)  3.90 < 0.001 < 0.001 < 0.001 
Anterior cerebellum (Lobules IV/V)  3.39 0.001 < 0.001 < 0.001 
Lingual/Fusiform gyrus 18/19 0.91 0.186 0.211 0.505 
Postcentral gyrus 0.23 0.409 0.424 0.655 
Lingual/Fusiform gyrus 18/19 2.49 0.010 0.004 0.029 
LHFS       
Primary sensorimotor cortex 3/4 0.33 0.374 0.364 0.602 
Anterior cerebellum (Lobules IV/VI)  3.33 0.001 0.001 0.002 
Medial frontal wall 0.50 0.311 0.317 0.441 
Precentral gyrus 1.02 0.160 0.135 0.174 
Inferior parietal lobule 40 1.35 0.096 0.125 0.212 
Posterior cerebellum (Lobule VI)  1.41 0.085 0.012 0.020 
Thalamus  0.71 0.243 0.402 0.411 
Postcentral gyrus 1.81 0.041 0.063 0.063 

*Positive t-values show regions where TD > HFA, negative t-values show regions where HFA > TD.

P < 0.1, Bonferroni-corrected for multiple comparisons.

P < 0.05, Bonferroni-corrected for multiple comparisons.

Cond = Condition, Hem = Hemisphere, BA = Brodmann Area

Figure 2

Charts showing results of RHFS (upper chart) and LHFS (lower chart) with bar graphs representing mean percent signal change (± SEM) compared with rest for children with autism (blue) and TD controls (red) in ROIs derived from the individual group maps (Fig. 1). **P < 0.05, Bonferroni-corrected for multiple comparisons; *P < 0.1, Bonferroni-corrected for multiple comparisons.

Figure 2

Charts showing results of RHFS (upper chart) and LHFS (lower chart) with bar graphs representing mean percent signal change (± SEM) compared with rest for children with autism (blue) and TD controls (red) in ROIs derived from the individual group maps (Fig. 1). **P < 0.05, Bonferroni-corrected for multiple comparisons; *P < 0.1, Bonferroni-corrected for multiple comparisons.

As group differences in finger-sequencing speed were noted (significant only for LHFS), ROI analyses were recalculated covarying for sequencing speed in the scanner. This data was not available for two children (1 TD, 1 HFA); however, because finger-sequencing speed inside and outside the scanner were significantly correlated, the regression between the two was calculated and values were imputed for the two children with missing data. After covarying for finger-sequencing speed, all ROIs with greater TD than HFA activation remained significant. Additionally, for LHFS, the right posterior cerebellum (lobule VI) ROI reached significance with Bonferroni correction (Table 2).

Because there was a significant difference in FSIQ, an additional analysis was run covarying for both finger-sequencing speed and FSIQ. Again, results were qualitatively identical, except for two regions that no longer met Bonferroni correction: left lingual gyrus (BA18/19) for RHFS and right posterior cerebellum (lobule VI) for LHFS (Table 2).

Whole brain analyses were also run to determine regions differing between groups, although not at a statistically corrected threshold (Fig. 3). The findings, which should be viewed as exploratory, largely confirmed ROI analyses, highlighting greater activation for both RHFS and LHFS in the TD group in several regions spanning both hemispheres of the cerebellum, as well as several left-lateralized posterior regions, including the left inferior parietal lobule (BA40) and left lingual/fusiform gyrus (BA18/19). In contrast, greater activation for both RHFS and LHFS in the HFA group was seen bilaterally in the posterior portion of the SMA (BA6).

Figure 3

Sectional maps showing localization of differences in fMRI activation between children with autism and TD children during RHFS (red), LHFS (blue), and the overlap between RHFS and LHFS (pink). The upper maps show regions where TD children showed greater activation than did those with autism; the lower maps show regions where children with autism showed greater activation than did TD children. The results are based on a Gaussian random effects analysis of each group of 13 participants; all maps were thresholded at P = 0.05 uncorrected for multiple comparisons. Representative slices are shown in the sagittal (left hemisphere), coronal and axial planes. Neurologic convention is used (i.e. right = right hemisphere; projections looking rightward or into the page).

Figure 3

Sectional maps showing localization of differences in fMRI activation between children with autism and TD children during RHFS (red), LHFS (blue), and the overlap between RHFS and LHFS (pink). The upper maps show regions where TD children showed greater activation than did those with autism; the lower maps show regions where children with autism showed greater activation than did TD children. The results are based on a Gaussian random effects analysis of each group of 13 participants; all maps were thresholded at P = 0.05 uncorrected for multiple comparisons. Representative slices are shown in the sagittal (left hemisphere), coronal and axial planes. Neurologic convention is used (i.e. right = right hemisphere; projections looking rightward or into the page).

Functional connectivity analyses

Within both groups, connectivity was seen within motor circuits (HFA: mean z = 9.4, P < 0.0001; TD: mean z = 14.9, P < 0.0001) that was not seen within control circuits (HFA: mean z = 1.3, P = 0.19; TD: mean z = 0.4, P = 0.72), and between-group analyses revealed no significant main effects of diagnosis or condition on connectivity within control circuits and no significant interactions. However, within motor circuits, connectivity in the HFA group was significantly less than in the TD group across the entire time-course [F(1,18) = 13.6, P = 0.0017]; when examined separately by condition, robust differences were seen for RHFS [mean difference = 0.47, F(1,18) = 26.9, P < 0.0001] and LHFS [mean difference = 0.37, F(1,18) = 27.0, P < 0.0001], whereas only marginally significant differences were seen for rest [mean difference = 0.19, F(1,18) = 3.1, P = 0.096] (for details, see Figs 4 and 5). In examining region-pairs individually, a main effect of diagnosis was seen for all the region-pairs (all P < 0.0056) except for the right and left cerebellum (P = 0.081). Significant interaction between diagnosis and condition was seen between the left and right thalamus (P = 0.004), left thalamus and SMA (P = 0.0004) and right thalamus and SMA (P = 0.0006).

Figure 4

Bar graphs demonstrating differences in functional connectivity between HFA and TD children. The legend in the grey box explains how information is presented. Within the triangle as a whole, the six boxes in the top-left portion represent connectivity between region-pairs in left-handed (L) motor circuits (e.g. right motor cortex, left cerebellum), the six boxes in the bottom-right portion represent connectivity between region-pairs in right-handed (R) motor circuits (e.g. left motor cortex, right cerebellum), and the nine boxes in the top-right represent connectivity between region-pairs in hand-neutral (N) motor circuits (e.g. right motor cortex, left motor cortex). Within each box, the three plots, from left to right, represent connectivity between the region pair during rest (left), RHFS (middle) and LHFS (right), respectively; children with HFA are shown in red and TD children are show in blue. Also within each box, the y-axis represents normalized r-values, and the axis ranges from 0 to 2; standard deviation bars are shown. Labels at the top and left of the figure demonstrated the region-pairs examined in each box (R = right; L = left; M1 = primary motor cortex; cer = anterior cerebellum; thal = thalamus).

Figure 4

Bar graphs demonstrating differences in functional connectivity between HFA and TD children. The legend in the grey box explains how information is presented. Within the triangle as a whole, the six boxes in the top-left portion represent connectivity between region-pairs in left-handed (L) motor circuits (e.g. right motor cortex, left cerebellum), the six boxes in the bottom-right portion represent connectivity between region-pairs in right-handed (R) motor circuits (e.g. left motor cortex, right cerebellum), and the nine boxes in the top-right represent connectivity between region-pairs in hand-neutral (N) motor circuits (e.g. right motor cortex, left motor cortex). Within each box, the three plots, from left to right, represent connectivity between the region pair during rest (left), RHFS (middle) and LHFS (right), respectively; children with HFA are shown in red and TD children are show in blue. Also within each box, the y-axis represents normalized r-values, and the axis ranges from 0 to 2; standard deviation bars are shown. Labels at the top and left of the figure demonstrated the region-pairs examined in each box (R = right; L = left; M1 = primary motor cortex; cer = anterior cerebellum; thal = thalamus).

Figure 5

Sectional maps and illustration demonstrating differences in functional connectivity between HFA and TD children. The thickness of the lines represents the magnitude of the difference in standard errors (differences of < 1 SEM are not shown).

Figure 5

Sectional maps and illustration demonstrating differences in functional connectivity between HFA and TD children. The thickness of the lines represents the magnitude of the difference in standard errors (differences of < 1 SEM are not shown).

Discussion

To our knowledge, this is the first fMRI study to explore the neural activation and connectivity associated with simple motor execution in children with autism. The results are remarkable for a relative dissociation of cerebral and cerebellar motor regions between children with HFA and their TD peers. While both groups displayed the expected predominant activations in cortical and subcortical regions critical to motor execution (e.g. contralateral pericentral gyrus and ipsilateral cerebellum), children in the TD group showed greater activation in the ipsilateral anterior cerebellum, as well as additional activation in the anterior lobe of the contralateral cerebellum (lobules IV/V) that was absent in the HFA group. The TD findings are consistent with prior imaging studies of basic finger movements in normal adults (Rijntjes et al., 1999; Grodd et al., 2001; Nitschke et al., 2003; Thickbroom et al., 2003; Habas et al., 2004), including the finding of significant, though lesser, activation in the posterior lobule. In contrast, exploratory whole brain analyses revealed greater cerebral activation in the HFA group, located in the SMA proper, which is consistent with findings of increased premotor activation during cued finger sequencing in adults with autism (Muller et al., 2003). This observed pattern of decreased cerebellar activation and increased premotor activation in the HFA group is both distinctive and robust, with interesting implications for further exploration.

There are several potential explanations for the observed cerebral/cerebellar dissociation. First, it is possible that the HFA group's increased frontal activation and failure to recruit cerebellar regions results directly from anatomical or functional abnormalities in those regions. Consistent with this, adults with ASD were found to show increased frontal activation compared with controls during executive tasks, with structural analyses revealing associated decreases in grey-matter density in those areas (Schmitz et al., 2006). Moreover, cerebellar abnormalities are a common finding in post-mortem studies (Williams et al., 1980; Ritvo et al., 1986; Bailey et al., 1998; Kemper and Bauman, 2002), with several studies demonstrating direct associations between behavioural functioning and cerebellar integrity (Pierce and Courchesne, 2001; Akshoomoff et al., 2004; Kates et al., 2004).

The lesser anterior cerebellar activation in our HFA group may also reflect those subjects’ relative inability to shift responsibility of continued motor execution from premotor regions associated with effortful movement to those associated with over-learned or habitual movement. Several studies in normal adults have demonstrated stage-dependent activation in various motor regions, leading to the suggestion that the cerebellum may be preferentially involved in automatic or ‘learned’ motor execution (Seitz et al., 1990; Burnod and Duffose, 1991; Doyon et al., 1996; Shadmehr and Holcomb, 1997; Krebs et al., 1998; Muller et al., 2002). In fact, for simple, repeated single digit tapping that requires much less in-scanner learning, adults with autism show the opposite pattern of fMRI activation with decreased premotor (Muller et al., 2001) and increased ipsilateral cerebellar (Allen et al., 2004) activation.

Efficient neuro-functioning is predicated upon the automatization of learned or habitual outputs. From a developmental perspective, deficits in automatization and motor sequence learning might explain impairments in motor coordination commonly reported in autism (Jansiewicz et al., 2006), as well as abnormal and delayed acquisition of motor gestures important for social communication (Gidley Larson and Mostofsky, 2006).

Learning-dependent shifts in motor control depend on the integrity of connections between cortical and subcortical regions. Inefficient, or less organized, neural activation (Muller et al., 2001, 2003; Turner et al., 2006) has proven to be a relatively consistent finding in neuroimaging studies of adults with autism, with motor studies revealing greater variability, with scattered activation extending beyond sites typically dedicated to basic movement in both frontal regions and the cerebellum (Muller et al., 2001, 2003; Turner et al., 2006). As such, aberrant neural organization may be a broad neurofunctional or neuroanatomic characteristic of the disorder. This functional disorganization has been attributed to a ‘local overconnectivity’ and ‘long-distance underconnectivity’ of neural circuits in individuals with autism, leading to lesser integration of remote cortical areas (Minshew et al., 1997; Herbert et al., 2004; Happe and Frith, 2006). Anatomic imaging studies reveal increased volume of outer ‘radiate’ white matter volumes comprising localized connections to be the primary contributor to overall brain volume increases in boys with autism (Herbert et al., 2003) and recent findings revealed increased volume of primary motor cortex white matter to be a highly robust predictor of impaired motor function in children with HFA (Mostofsky et al., 2007).

Consistent with this, we found that children with HFA show decreased functional connectivity across nearly the entire network of regions activated during both RHFS and LHFS. The failure of our HFA group to recruit cerebellar regions and their greater reliance instead on premotor cortical regions during finger sequencing, combined with the observed decreased functional connectivity within motor networks that include cerebellar and premotor regions, suggests that autism-associated deficits in motor execution may result from anomalous long-tract connections within the fronto–cerebello–thalamo–frontal network. Further, HFA-associated reductions in functional connectivity within this motor control network were more robust during finger sequencing than during rest, suggesting that decreased connectivity is particularly evident while the network is active (during motor execution). Indeed, it is worth noting that, as shown in Fig. 4, motor task performance often drives inter-regional correlations in autism below what they are during rest. This might suggest that, given the relative underconnectivity between these distant brain regions, with increasing task demand, it may be more efficient for children with autism to utilize these regions as independent processors, rather than to have them work in concert.

A limitation of the current study is the potential for the behavioural differences between groups to have driven the observed neural activation. While the pre-scanning motor exam suggested no difference in rate of finger apposition, the TD group did have a higher number of taps during imaging of RHFS and LHFS (significant only for LHFS). To address this, the data were reanalysed covarying for number of finger taps; the results did not change, suggesting that the fMRI findings could not be accounted for by differences in finger-sequencing speed.

Some of the children with HFA were taking psychoactive medications, and the potential impact of this cannot be discounted. Future investigations might benefit from exclusion of children taking medications, though this would have a detrimental impact on recruitment of numbers sufficient to examine group differences using BOLD fMRI. With sufficient numbers, comparisons of subjects with autism on/off medications could be applied to future studies.

One particular strength of this study is the use of a study-specific template to normalize the functional data into standardized space. Spatial normalization of paediatric brains to a standard adult template is problematic, since paediatric brains differ from adult brains in both regional and global size and composition (Casey et al., 2000; Courchesne et al., 2000). Additionally, the use of a standard template is especially problematic in disorders such as autism, which have been associated with differences in cerebral volume and composition (Courchesne et al., 2001). As it has been shown that utilizing custom paediatric templates for normalization in paediatric populations improves the quality of normalization (Wilke et al., 2002), our use of a customized study-specific template allowed us to minimize artefacts due to poor normalization.

Though the aetiology of autism is yet unknown, the pervasiveness of symptoms across modalities suggests that impairments are likely not limited to a single system and that neurological onset is likely quite early. As such, careful examination of the neurologic underpinnings of motor dysfunction in autism may provide insight into mechanisms within parallel systems important for cognitive and behavioural control (Gidley Larson and Mostofsky, 2006). Further, as one of the earliest identifiable traits, motor impairment may serve a principal role in the behavioural phenotype of the disorder, with broad downstream effects across other domains; i.e. early deficits in basic motor abilities may impede the development of compound motor skills and social gestures, contributing to the defining behavioural features of the disorder. Receptive language far outpaces expressive language in many children with autism (Gernsbacher et al., 2008) and motor dysfunction might also contribute to delays in productive speech. Indeed, neural systems important for procedural acquisition of motor skills appear to also be critical for language and social development. It follows that abnormalities in these systems may contribute not only to impaired motor skill acquisition in children with autism, but also to impaired communicative and social development (Mostofsky et al., 2000; Walenski et al., 2006). As evidence, recent findings reveal the clearest predictor of optimal outcome in toddlers diagnosed with an autism spectrum disorder is motor skills at age 2 years (Sutera et al., 2007). As such, continued investigation of the neural mechanisms underlying motor development in children with autism is critical to our ongoing understanding of the disorder, as well as the design of effective early interventions. The current study reflects the initial attempts to do so, beginning with a targeted exploration of a simple form of motor execution.

Funding

National Alliance for Autism Research/Autism Speaks and from the National Institutes of Health (K02 NS 044850 to S.H.M.); (RO1 NS048527 to SHM); (K01 MH01824 to MCG); the Mental Retardation and Developmental Disabilities Research Center (HD-24061); the National Center for Resources (P41 RR15241); and the Johns Hopkins, University School of Medicine Institute for Clinical and Translational Research, an NIH/NCRR CTSA Program, UL1-RR025005. The National Center for Research Resources (NCRR) is a component of the National Institutes of Health (NIH). The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official view of NCRR or NIH.

*Present address: St. Louis Children's Hospital & Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA

References

Akshoomoff
N
Lord
C
Lincoln
AJ
Courchesne
RY
Carper
RA
Townsend
J
, et al.  . 
Outcome classification of preschool children with autism spectrum disorders using MRI brain measures
J Am Acad Child Adolesc Psychiatry
 , 
2004
, vol. 
43
 (pg. 
349
-
57
)
Allen
G
Courchesne
E
Differential effects of developmental cerebellar abnormality on cognitive and motor functions in the cerebellum: an fMRI study of autism
Am J Psychiatry
 , 
2003
, vol. 
160
 (pg. 
262
-
73
)
Allen
G
Muller
RA
Courchesne
E
Cerebellar function in autism: functional magnetic resonance image activation during a simple motor task
Biol Psychiatry
 , 
2004
, vol. 
56
 (pg. 
269
-
78
)
American Psychiatric Association
Diagnostic and statistical manual of mental disorders (4th ed., Text Revision)
2000
Washington DC
Bailey
A
Luthert
P
Dean
A
Harding
B
Janota
I
Montgomery
M
, et al.  . 
A clinicopathological study of autism
Brain
 , 
1998
, vol. 
121
 (pg. 
889
-
905
)
Bryson
SE
Zwaigenbaum
L
Brian
J
Roberts
W
Szatmari
P
Rombough
V
, et al.  . 
A prospective case series of high-risk infants who developed autism
J Autism Dev Disord
 , 
2007
, vol. 
37
 (pg. 
12
-
24
)
Burnod
Y
Duffose
M
Paillard
J
A model for the co-operation between cerebral cortex and cerebellar cortex in movement learning
Brain and space
 , 
1991
Oxford
Oxford Press
(pg. 
446
-
58
)
Calhoun
V
Adali
T
Kraut
M
Pearlson
G
A weighted least-squares algorithm for estimation and visualization of relative latencies in event-related functional MRI
Magn Reson Med
 , 
2000
, vol. 
44
 (pg. 
947
-
54
)
Carper
RA
Courchesne
E
Inverse correlation between frontal lobe and cerebellum sizes in children with autism
Brain
 , 
2000
, vol. 
123
 (pg. 
836
-
44
)
Carper
RA
Moses
P
Tigue
ZD
Courchesne
E
Cerebral lobes in autism: early hyperplasia and abnormal age effects
Neuroimage
 , 
2002
, vol. 
16
 (pg. 
1038
-
51
)
Casanova
MF
Buxhoeveden
DP
Switala
AE
Roy
E
Minicolumnar pathology in autism
Neurology
 , 
2002
, vol. 
58
 (pg. 
428
-
32
)
Casey
BJ
Giedd
JN
Thomas
KM
Structural and functional brain development and its relation to cognitive development
Biol Psychol
 , 
2000
, vol. 
54
 (pg. 
241
-
57
)
Chawarska
K
Paul
R
Klin
A
Hannigen
S
Dichtel
LE
Volkmar
F
Parental recognition of developmental problems in toddlers with autism spectrum disorders
J Autism Dev Disord
 , 
2007
, vol. 
37
 (pg. 
62
-
72
)
Chung
MK
Dalton
KM
Alexander
AL
Davidson
RJ
Less white matter concentration in autism: 2D voxel-based morphometry
Neuroimage
 , 
2004
, vol. 
23
 (pg. 
242
-
51
)
Ciesielski
K
Harris
R
Hart
B
Pabst
H
Cerebellar hypoplasia and frontal lobe cognitive deficits in disorders of early childhood
Neuropsychologia
 , 
1997
, vol. 
35
 (pg. 
643
-
55
)
Courchesne
E
Chisum
HJ
Townsend
J
Cowles
A
Covington
J
Egaas
B
, et al.  . 
Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers
Radiology
 , 
2000
, vol. 
216
 (pg. 
672
-
82
)
Courchesne
E
Hesselink
JR
Jernigan
TL
Yeung-Courchesne
R
Abnormal neuroanatomy in a nonretarded person with autism. Unusual findings with magnetic resonance imaging
Arch Neurol
 , 
1987
, vol. 
44
 (pg. 
335
-
41
)
Courchesne
E
Karns
CM
Davis
HR
Ziccardi
R
Carper
RA
Tigue
ZD
, et al.  . 
Unusual brain growth patterns in early life in patients with autistic disorder: an MRI study
Neurology
 , 
2001
, vol. 
57
 (pg. 
245
-
54
)
Courchesne
E
Saitoh
O
Townsend
JP
Yeung-Courchesne
R
Press
GA
Lincoln
AJ
, et al.  . 
Cerebellar hypoplasia and hyperplasia in infantile autism
Lancet
 , 
1994
, vol. 
343
 (pg. 
63
-
4
)
Courchesne
E
Saitoh
O
Yeung-Courchesne
R
Press
GA
Lincoln
AJ
Haas
RH
, et al.  . 
Abnormality of cerebellar vermian lobules VI and VII in patients with infantile autism: identification of hypoplastic and hyperplastic subgroups with MR imaging
AJR Am J Roentgenol
 , 
1994
, vol. 
162
 (pg. 
123
-
30
)
Courchesne
E
Townsend
J
Saitoh
O
The brain in infantile autism: Posterior fossa structures are abnormal
Neurology
 , 
1994
, vol. 
44
 (pg. 
214
-
23
)
Courchesne
E
Yeung-Courchesne
R
Press
GA
Hesselink
JR
Jernigan
TL
Hypoplasia of cerebellar lobules VI and VII in infantile autism
N Engl J Med
 , 
1988
, vol. 
318
 (pg. 
1349
-
54
)
DeMyer
M
Alpern
G
Barton
S
DeMyer
W
Churchill
D
Hingtgen
J
, et al.  . 
Imitation in autistic, early schizophrenic, and non-psychotic subnormal children
J Autism Child Schiz
 , 
1972
, vol. 
2
 (pg. 
264
-
87
)
Denckla
MB
Revised neurological examination for subtle signs
Psychopharmacol Bull
 , 
1985
, vol. 
21
 (pg. 
773
-
9
)
Doyon
J
Owen
AM
Petrides
M
Sziklas
V
Evans
A
Functional anatomy of visuomotor skill learning in human subjects examined with positron emission tomography
Eur J Neurosci
 , 
1996
, vol. 
8
 (pg. 
637
-
48
)
Doyon
J
Song
AW
Karni
A
Lalonde
F
Adams
MM
Ungerleider
LG
Experience-dependent changes in cerebellar contributions to motor sequence learning
Proc Natl Acad Sci USA
 , 
2002
, vol. 
99
 (pg. 
1017
-
22
)
Durston
S
Mulder
M
Casey
BJ
Ziermans
T
van Engeland
H
Activation in ventral prefrontal cortex is sensitive to genetic vulnerability for attention-deficit hyperactivity disorder
Biol Psychiatry
 , 
2006
, vol. 
60
 (pg. 
1062
-
70
)
Elliott
CD
Differential ability scale (DAS)
Child Assess News
 , 
1993
, vol. 
3
 (pg. 
1
-
10
)
Friston
KJ
Holmes
A
Poline
JB
Price
CJ
Frith
CD
Detecting activations in PET and fMRI: levels of inference and power
Neuroimage
 , 
1996
, vol. 
4
 (pg. 
223
-
35
)
Friston
KJ
Holmes
AP
Worsley
KJ
Poline
JP
Frith
CD
Frackowiak
RSJ
Statistical parametric maps in functional imaging: a general linear approach
Hum Brain Mapp
 , 
1995
, vol. 
2
 (pg. 
189
-
210
)
Frith
U
Autism and Asperger syndrome
 , 
1991
Cambridge, New York
Cambridge University Press
Gernsbacher
MA
Sauer
EA
Geye
HM
Schweigert
EK
Hill Goldsmith
H
Infant and toddler oral- and manual-motor skills predict later speech fluency in autism
J Child Psychol Psychiatry
 , 
2008
, vol. 
49
 (pg. 
43
-
50
)
Ghaziuddin
M
Butler
E
Clumsiness in autism and Asperger syndrome: a further report
J Intellect Disabil Res
 , 
1998
, vol. 
42
 (pg. 
43
-
8
)
Gidley Larson
JC
Mostofsky
SH
Tuchman
R
Rapin
I
Motor deficits in autism
Autism: a neurological disorder of early brain development
 , 
2006
London
Mac Keith Press for the International Review of Child Neurology Series
Gidley Larson
JC
Mostofsky
SH
Goldberg
MC
Cutting
LE
Denckla
MB
Mahone
EM
Effects of gender and age on motor exam in typically developing children
Dev Neuropsychol
 , 
2007
, vol. 
32
 (pg. 
543
-
62
)
Grodd
W
Hulsmann
E
Lotze
M
Wildgruber
D
Erb
M
Sensorimotor mapping of the human cerebellum: fMRI evidence of somatotopic organization
Hum Brain Mapp
 , 
2001
, vol. 
13
 (pg. 
55
-
73
)
Habas
C
Axelrad
H
Nguyen
TH
Cabanis
EA
Specific neocerebellar activation during out-of-phase bimanual movements
Neuroreport
 , 
2004
, vol. 
15
 (pg. 
595
-
9
)
Happe
F
Frith
U
The weak coherence account: detail-focused cognitive style in autism spectrum disorders
J Autism Dev Disord
 , 
2006
, vol. 
36
 (pg. 
5
-
25
)
Hashimoto
T
Tayama
M
Murakawa
K
Yoshimoto
T
Miyazaki
M
Harada
M
, et al.  . 
Development of brainstem and cerebellum in autistic patients
J Autism Dev Disord
 , 
1995
, vol. 
25
 (pg. 
1
-
18
)
Herbert
MR
Harris
GJ
Adrien
KT
Ziegler
DA
Makris
N
Kennedy
DN
, et al.  . 
Abnormal asymmetry in language association cortex in autism
Ann Neurol
 , 
2002
, vol. 
52
 (pg. 
588
-
96
)
Herbert
MR
Ziegler
DA
Deutsch
CK
O’Brien
LM
Lange
N
Bakardjiev
A
, et al.  . 
Dissociations of cerebral cortex, subcortical and cerebral white matter volumes in autistic boys
Brain
 , 
2003
, vol. 
126
 (pg. 
1182
-
92
)
Herbert
MR
Ziegler
DA
Makris
N
Filipek
PA
Kemper
TL
Normandin
JJ
, et al.  . 
Localization of white matter volume increase in autism and developmental language disorder
Ann Neurol
 , 
2004
, vol. 
55
 (pg. 
530
-
40
)
Holden
EW
Tarnowski
KJ
Prinz
RJ
Reliability of neurological soft signs in children: reevaluation of the PANESS
J Abnorm Child Psychol
 , 
1982
, vol. 
10
 (pg. 
163
-
72
)
Holmes
AP
Friston
KJ
Generalisability, random effects & population inference
Neuroimage
 , 
1998
, vol. 
7
 pg. 
S754
 
Hughes
C
Brief report: planning problems in autism at the level of motor control
J Autism Dev Disord
 , 
1996
, vol. 
26
 (pg. 
99
-
107
)
Jansiewicz
E
Goldberg
MC
Newschaffer
CJ
Denckla
MB
Landa
RJ
Mostofsky
SH
Motor signs distinguish children with high functioning autism and Asperger's syndrome from controls
J Autism Dev Disord
 , 
2006
Jones
V
Prior
M
Motor imitation ability and neurological signs in autistic children
J Autism Dev Disord
 , 
1985
, vol. 
15
 (pg. 
37
-
46
)
Just
MA
Cherkassky
VL
Keller
TA
Kana
RK
Minshew
NJ
Functional and anatomical cortical underconnectivity in autism: evidence from an FMRI study of an executive function task and corpus callosum morphometry
Cereb Cortex
 , 
2007
, vol. 
17
 (pg. 
951
-
61
)
Just
MA
Cherkassky
VL
Keller
TA
Minshew
NJ
Cortical activation and synchronization during sentence comprehension in high-functioning autism: evidence of underconnectivity
Brain
 , 
2004
, vol. 
127
 (pg. 
1811
-
21
)
Kana
RK
Keller
TA
Cherkassky
VL
Minshew
NJ
Just
MA
Sentence comprehension in autism: thinking in pictures with decreased functional connectivity
Brain
 , 
2006
, vol. 
129
 (pg. 
2484
-
93
)
Kana
RK
Keller
TA
Minshew
NJ
Just
MA
Inhibitory control in high-functioning autism: decreased activation and underconnectivity in inhibition networks
Biol Psychiatry
 , 
2007
, vol. 
62
 (pg. 
198
-
206
)
Kanner
L
Autistic disturbances of affective contact
Nerv Child
 , 
1943
, vol. 
2
 (pg. 
217
-
50
)
Kates
WR
Burnette
CP
Eliez
S
Strunge
LA
Kaplan
D
Landa
R
, et al.  . 
Neuroanatomic variation in monozygotic twin pairs discordant for the narrow phenotype for autism
Am J Psychiatry
 , 
2004
, vol. 
161
 (pg. 
539
-
46
)
Kaufmann
WE
Cooper
KL
Mostofsky
SH
Capone
GT
Kates
WR
Newschaffer
CJ
, et al.  . 
Specificity of cerebellar vermian abnormalities in autism: a quantitative magnetic resonance imaging study
J Child Neurol
 , 
2003
, vol. 
18
 (pg. 
463
-
70
)
Kemper
TL
Bauman
ML
Neuropathology of infantile autism
Mol Psychiatry
 , 
2002
, vol. 
7
 
Suppl 2
(pg. 
S12
-
3
)
Kleiman
MD
Neff
S
Rosman
NP
The brain in infantile autism: are posterior fossa structures abnormal?
Neurology
 , 
1992
, vol. 
42
 (pg. 
753
-
60
)
Krebs
H
Brashers-Krug
T
Rauch
S
Savage
C
Hogan
N
Rubin
R
, et al.  . 
Robot-aided functional imaging: application to a motor learning study
Hum Brain Mapp
 , 
1998
, vol. 
6
 (pg. 
59
-
72
)
Landa
R
Garrett-Mayer
E
Development in infants with autism spectrum disorders: a prospective study
J Child Psychol Psychiatry
 , 
2006
, vol. 
47
 (pg. 
629
-
38
)
Levitt
JG
Blanton
R
Capetillo-Cunliffe
L
Guthrie
D
Toga
A
McCracken
JT
Cerebellar vermis lobules VIII-X in autism
Prog Neuropsychopharmacol Biol Psychiatry
 , 
1999
, vol. 
23
 (pg. 
625
-
33
)
Levitt
JG
Blanton
RE
Smalley
S
Thompson
PM
Guthrie
D
McCracken
JT
, et al.  . 
Cortical sulcal maps in autism
Cereb Cortex
 , 
2003
, vol. 
13
 (pg. 
728
-
35
)
Lord
C
Risi
S
Lambrecht
L
Cook
EHJ
Leventhal
BL
DiLavore
PC
, et al.  . 
The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism
J Autism Dev Disord
 , 
2000
, vol. 
30
 (pg. 
205
-
23
)
Lord
C
Rutter
M
Le Couteur
A
Autism diagnostic interview-revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders
J Autism Dev Disord
 , 
1994
, vol. 
24
 (pg. 
659
-
85
)
Mazaika
PK
Whitfield-Gabrieli
S
Reiss
A
Glover
G
Artifact repair of FMRI data from high motion clinical subjects
In: Organization of Human Brain Mapping International Conference Chicago, IL
 , 
2007
Minshew
NJ
Goldstein
G
Siegel
DJ
Neuropsychologic functioning in autism: profile of a complex information processing disorder
J Int Neuropsychol Soc
 , 
1997
, vol. 
3
 (pg. 
303
-
16
)
Mostofsky
SH
Burgess
MP
Gidley Larson
JC
Increased motor cortex white matter volume predicts motor impairment in autism
Brain
 , 
2007
, vol. 
130
 (pg. 
2117
-
22
)
Mostofsky
SH
Dubey
P
Jerath
VK
Jansiewicz
EM
Goldberg
MC
Denckla
MB
Developmental dyspraxia is not limited to imitation in children with autism spectrum disorders
J Int Neuropsychol Soc
 , 
2006
, vol. 
12
 (pg. 
314
-
26
)
Mostofsky
SH
Goldberg
MC
Landa
RJ
Denckla
MB
Evidence for a deficit in procedural learning in children and adolescents with autism: Implications for cerebellar contribution
J Int Neuropsychol Soc
 , 
2000
, vol. 
6
 (pg. 
752
-
9
)
Muller
RA
Cauich
C
Rubio
MA
Mizuno
A
Courchesne
E
Abnormal activity patterns in premotor cortex during sequence learning in autistic patients
Biol Psychiatry
 , 
2004
, vol. 
56
 (pg. 
323
-
32
)
Muller
RA
Kleinhans
N
Kemmotsu
N
Pierce
K
Courchesne
E
Abnormal variability and distribution of functional maps in autism: an FMRI study of visuomotor learning
Am J Psychiatry
 , 
2003
, vol. 
160
 (pg. 
1847
-
62
)
Muller
RA
Kleinhans
N
Pierce
K
Kemmotsu
N
Courchesne
E
Functional MRI of motor sequence acquisition: effects of learning stage and performance
Brain Res Cogn Brain Res
 , 
2002
, vol. 
14
 (pg. 
277
-
93
)
Muller
RA
Pierce
K
Ambrose
JB
Allen
G
Courchesne
E
Atypical patterns of cerebral motor activation in autism: a functional magnetic resonance study
Biol Psychiatry
 , 
2001
, vol. 
49
 (pg. 
665
-
76
)
Murakami
JW
Courchesne
E
Press
G
Yeung-Courchesne
R
Hesselink
JR
Reduced cerebellar hemisphere size and its relationship to vermal hypoplasia in autism
Arch Neurol
 , 
1989
, vol. 
46
 (pg. 
689
-
94
)
Nayate
A
Bradshaw
JL
Rinehart
NJ
Autism and Asperger's disorder: are they movement disorders involving the cerebellum and/or basal ganglia?
Brain Res Bull
 , 
2005
, vol. 
67
 (pg. 
327
-
34
)
Nitschke
MF
Stavrou
G
Melchert
UH
Erdmann
C
Petersen
D
Wessel
K
, et al.  . 
Modulation of cerebellar activation by predictive and non-predictive sequential finger movements
Cerebellum
 , 
2003
, vol. 
2
 (pg. 
233
-
40
)
Noterdaeme
M
Mildenberger
K
Minow
F
Amorosa
H
Evaluation of neuromotor deficits in children with autism and children with a specific speech and language disorder
Eur Child Adolesc Psychiatry
 , 
2002
, vol. 
11
 (pg. 
219
-
25
)
Ohta
M
Cognitive disorders of infantile autism: a study employing the WISC, spatial relationships, conceptualization, and gesture imitations
J Autism Dev Disord
 , 
1987
, vol. 
17
 (pg. 
45
-
62
)
Pierce
K
Courchesne
E
Evidence for a cerebellar role in reduced exploration and stereotyped behaviour in autism
Biol Psychiatry
 , 
2001
, vol. 
49
 (pg. 
655
-
64
)
Piven
J
Saliba
K
Bailey
J
Arndt
S
An MRI study of autism: the cerebellum revisited
Neurology
 , 
1997
, vol. 
49
 (pg. 
546
-
51
)
Reich
W
Diagnostic interview for children and adolescents (DICA)
J Am Acad Child Adolesc Psychiatry
 , 
2000
, vol. 
39
 (pg. 
59
-
66
)
Rijntjes
M
Buechel
C
Kiebel
S
Weiller
C
Multiple somatotopic representations in the human cerebellum
Neuroreport
 , 
1999
, vol. 
10
 (pg. 
3653
-
8
)
Rinehart
NJ
Bradshaw
JL
Moss
SA
Brereton
AV
Tonge
BJ
A deficit in shifting attention present in high-functioning autism but not Asperger's disorder
Autism
 , 
2001
, vol. 
5
 (pg. 
67
-
80
)
Ritvo
ER
Freeman
BJ
Scheibel
AB
Duong
T
Robinson
H
Guthrie
D
, et al.  . 
Lower Purkinje cell counts in the cerebella of four autistic subjects: initial findings of the UCLA-NSAC autopsy research report
Am J Psychiatry
 , 
1986
, vol. 
143
 (pg. 
862
-
6
)
Rogers
S
Bennetto
L
McEvoy
R
Pennington
B
Imitation and pantomime in high-functioning adolescents with autism spectrum disorders
Child Dev
 , 
1996
, vol. 
67
 (pg. 
2060
-
73
)
Roid
GH
Stanford-Binet Intelligence Scales
 , 
2003
5th
Itasca, IL
Riverside Publishing
Saitoh
O
Courchesne
E
Egaas
B
Lincoln
AJ
Schreibman
L
Cross-sectional area of the posterior hippocampus in autistic patients with cerebellar and corpus callosum abnormalities
Neurology
 , 
1995
, vol. 
45
 (pg. 
317
-
24
)
Salmond
CH
de Haan
M
Friston
KJ
Gadian
DG
Vargha-Khadem
F
Investigating individual differences in brain abnormalities in autism
Philos Trans R Soc Lond B Biol Sci
 , 
2003
, vol. 
358
 (pg. 
405
-
13
)
Schmitz
N
Daly
E
Murphy
D
Frontal anatomy and reaction time in Autism
Neurosci Lett
 , 
2007
, vol. 
412
 (pg. 
12
-
7
)
Schmitz
N
Rubia
K
Daly
E
Smith
A
Williams
S
Murphy
DG
Neural correlates of executive function in autistic spectrum disorders
Biol Psychiatry
 , 
2006
, vol. 
59
 (pg. 
7
-
16
)
Seitz
R
Roland
P
Bohm
C
Torgny
G
Stone-Elander
S
Motor learning in man: a positron emission study
NeuroReport
 , 
1990
, vol. 
1
 (pg. 
57
-
66
)
Shadmehr
R
Holcomb
HH
Neural correlates of motor memory consolidation
Science
 , 
1997
, vol. 
277
 (pg. 
821
-
5
)
Shah
A
Frith
U
Why do autistic individuals show superior performance on the block design task? J Child Psychol Psychiatry
 , 
1993
, vol. 
34
 (pg. 
1351
-
64
)
Smith
IM
Bryson
SE
Imitation and action in Autism: a critical review
Psychol Bull
 , 
1994
, vol. 
116
 (pg. 
259
-
73
)
Sparks
BF
Friedman
SD
Shaw
DW
Aylward
EH
Echelard
D
Artru
AA
, et al.  . 
Brain structural abnormalities in young children with autism spectrum disorder
Neurology
 , 
2002
, vol. 
59
 (pg. 
184
-
92
)
Suskauer
SJ
Simmonds
DJ
Fotedar
SG
Blankner
JG
Pekar
JJ
Denckla
MB
, et al.  . 
FMRI evidence for abnormalities in response selection in ADHD: differences in activation associated with response inhibition but not habitual motor response
J Cogn Neurosci
 , 
2008
, vol. 
20
 (pg. 
478
-
93
)
Sutera
S
Pandey
J
Esser
EL
Rosenthal
MA
Wilson
LB
Barton
M
, et al.  . 
Predictors of optimal outcome in toddlers diagnosed with autism spectrum disorders
J Autism Dev Disord
 , 
2007
, vol. 
37
 (pg. 
98
-
107
)
Teitelbaum
O
Benton
T
Shah
PK
Prince
A
Kelly
JL
Teitelbaum
P
Eshkol-Wachman movement notation in diagnosis: the early detection of Asperger's syndrome
Proc Natl Acad Sci USA
 , 
2004
, vol. 
101
 (pg. 
11909
-
14
)
Teitelbaum
P
Teitelbaum
O
Nye
J
Fryman
J
Maurer
RG
Movement analysis in infancy may be useful for early diagnosis of autism
Proc Natl Acad Sci USA
 , 
1998
, vol. 
95
 (pg. 
13982
-
7
)
Thickbroom
GW
Byrnes
ML
Mastaglia
FL
Dual representation of the hand in the cerebellum: activation with voluntary and passive finger movement
Neuroimage
 , 
2003
, vol. 
18
 (pg. 
670
-
4
)
Turner
KC
Frost
L
Linsenbardt
D
McIlroy
JR
Muller
RA
Atypically diffuse functional connectivity between caudate nuclei and cerebral cortex in autism
Behav Brain Funct
 , 
2006
, vol. 
2
 pg. 
34
 
Vilensky
JA
Damasio
AR
Maurer
RG
Gait disturbance in patients with autistic behaviour
Arch Neurol
 , 
1981
, vol. 
38
 (pg. 
646
-
9
)
Vitiello
B
Ricciuti
AJ
Stoff
DM
Behar
D
Denckla
MB
Reliability of subtle (soft) neurological signs in children
J Am Acad Child Adolesc Psychiatry
 , 
1989
, vol. 
28
 (pg. 
749
-
53
)
Walenski
M
Tager-Flusberg
H
Ullman
MT
Moldin
SO
Rubenstein
JLR
Language in Autism
Understanding autism: from basic neuroscience to treatment
 , 
2006
Boca Raton, FL
CRC Press
(pg. 
175
-
203
)
Wechsler
D
Wechsler Intelligence Scale for Children-III
 , 
1991
San Antonio, TX
The Psychological Corporation
Wechsler
DL
Wechsler intelligence scale for children
 , 
2003
Fourth
San Antonio, TX
The Psychological Corporation
Wilke
M
Schmithorst
VJ
Holland
SK
Assessment of spatial normalization of whole-brain magnetic resonance images in children. Hum Brain Mapp 2002; 17: 48–60.Williams JH, Whiten A, Suddendorf T, Perrett DI. Imitation, mirror nurons and autism
Neurosci Biobehav Rev
 , 
2001
, vol. 
25
 (pg. 
287
-
95
)
Williams
RS
Hauser
SL
Purpura
DP
DeLong
GR
Swisher
CN
Autism and mental retardation: neuropathologic studies performed in four retarded persons with autistic behaviour
Arch Neurol
 , 
1980
, vol. 
37
 (pg. 
749
-
53
)

Abbreviations

    Abbreviations
  • ASD

    autism spectrum disorders

  • FSIQ

    full-scale IQ

  • HFA

    high-functioning autism

  • LHFS

    left-handed finger sequencing

  • RHFS

    right-handed finger sequencing

  • SMA

    supplementary motor area

  • TD

    typically developing