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Articles, Neurobiology of Disease

Describing the Brain in Autism in Five Dimensions—Magnetic Resonance Imaging-Assisted Diagnosis of Autism Spectrum Disorder Using a Multiparameter Classification Approach

Christine Ecker, Andre Marquand, Janaina Mourão-Miranda, Patrick Johnston, Eileen M. Daly, Michael J. Brammer, Stefanos Maltezos, Clodagh M. Murphy, Dene Robertson, Steven C. Williams and Declan G. M. Murphy
Journal of Neuroscience 11 August 2010, 30 (32) 10612-10623; DOI: https://doi.org/10.1523/JNEUROSCI.5413-09.2010
Christine Ecker
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Andre Marquand
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Janaina Mourão-Miranda
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Patrick Johnston
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Eileen M. Daly
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Michael J. Brammer
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Stefanos Maltezos
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Clodagh M. Murphy
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Dene Robertson
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Steven C. Williams
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Declan G. M. Murphy
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Figures

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  • Figure 1.
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    Figure 1.

    Summary of the five morphometric parameters measured at each cerebral vertex. These included average convexity (A), cortical thickness (B), pial area (C), metric distortion (Jacobian) (D), and mean (radial) curvature (E).

  • Figure 2.
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    Figure 2.

    A, B, ROC graphs for the six discrete classifiers in the left hemisphere (A) and in the right hemisphere (B). Individual points on the graph depict classifiers on the basis of all parameters (A), cortical thickness (B), metric distortion/Jacobian (C), average convexity (D), pial area (E), and mean (radial) curvature (F). C, D, The classification plots for the left (C) and right (D) hemispheres.

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    Figure 3.

    Classification plots showing group allocation of individuals with ADHD (red squares) in the left (A) and right (B) hemispheres using the ASD classifier.

  • Figure 4.
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    Figure 4.

    Discrimination maps for the five different morphometric features in the left (L) and right (R) hemispheres. Color maps represent the weight vector on the basis of the five modality classification for cortical thickness (A), average convexity (B), metric distortion (C), and pial area (D). Weights for the mean (radial) curvature did not exceed the set threshold. Positive weights (i.e., overall excess patters in ASD relative to controls) are displayed in red, and negative weights (i.e., overall deficit patterns in ASD relative to controls) are displayed in blue.

  • Figure 5.
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    Figure 5.

    Visualization of the morphometric abnormalities in the right intraparietal sulcus. Color maps represent the weight vector score (A). B, Outlines of the cortical surface for ASD (red) and control (blue) group. This main discriminating factor in this group was an increase in sulcal depth in ASDs relative to controls. Differences in sulcal depth for this ROI are shown for both groups in C. D, Morphometric profile for this region. Profiles were derived by averaging the weight vector scores across vertices within this region of interest, and for the different morphometric parameters. Weights were identified on the basis of the concatenated SVM model, thus showing the relative contribution of parameters in this ROI.

  • Figure 6.
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    Figure 6.

    A, Visualization of the morphometric abnormalities in the left inferior parietal lobe (BA39). B, Outlines of the cortical surface for ASD and control group. Differences in metric distortion for this ROI are shown for both groups in C. D, Morphometric profile (see Fig. 4 legend).

  • Figure 7.
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    Figure 7.

    A, Morphometric abnormalities in the middle temporal sulcus. B, Visualization of cortical thickness measures for ASD (red straight line) and control (blue straight line) group. In this region cortical thickness exclusively discriminated between groups with individuals with ASD displaying increased thickness relative to controls (C, D).

  • Figure 8.
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    Figure 8.

    A, Morphometric abnormalities in the posterior cingulate gyrus. B, C, Here, a combination of cortical thickness and folding pattern led to a high contribution to the classification in that region.

Tables

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    Table 1.

    Subject demographics

    ASD (n = 20)Controls (n = 20)
    Age, years33 ± 11 (20–68)36 ± 9 (20–49)
    Full-scale IQ103 ± 20 (76–141)110 ± 13 (77–129)
    Verbal IQ102 ± 17 (78–133)106 ± 14 (71–131)
    Performance IQ*98 ± 19 (77–138)110 ± 14 (84–136)
    ADI-R sociala15 ± 4—
    ADI-R communicationa10 ± 3—
    ADI-R repetitive behaviora4 ± 2—
    ADOS totalb10 ± 2—
    • Data are expressed as mean ± SD (range). There were no significant differences between subject groups in age and full-scale IQ on p < 0.05 two-tailed.

    • ↵aInformation was unavailable for 3 out of 20 ASD subjects.

    • ↵bInformation was available for five ASD subjects; two cases had both ADOS and ADI.

    • ↵*Significant on p < 0.05.

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    Table 2.

    Between-group differences in overall brain volume and gray matter volume

    ASDControlt(38)p
    ICV1.67 ± 0.18 × 1061.64 ± 0.21 × 106−0.63<0.60
    Total brain volume9.74 ± 1.14 × 1059.69 ± 0.76 × 105−0.14<0.90
    Total gm volume4.89 ± 0.56 × 1054.81 ± 0.41 × 105−0.58<0.60
    gm volume right2.45 ± 0.28 × 1052.41 ± 0.20 × 105−0.53<0.60
    gm volume left2.44 ± 0.28 × 1052.39 ± 0.21 × 105−0.62<0.60
    • Data are expressed as mean ± SD. ICV, Total intracranial volume; gm, gray matter; all measures are in cubic millimeters.

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    Table 3.

    Results of SVM classification between ASD and control group using different brain morphometric features in the left and right hemispheres

    Morphometric featureCorrectly classified (%)Sensitivity (%)Specificity (%)p
    Left hemisphere
        All parameters8590800*
        Cortical thickness9090900*
        Radial curvature72.56580<0.001
        Average convexity707565<0.004
        Metric distortion8080800*
        Pial area77.570850*
    Right hemisphere
        All parameters656070<0.03
        Cortical thickness606555<0.01
        Radial curvature52.55055<0.30
        Average convexity504060<0.40
        Metric distortion57.54570<0.06
        Pial area454545<0.60
    • Correctly identified ASD cases were considered true positive.

    • ↵*p values of zero indicate that not a single one of the 1000 permutations provided a better classification.

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    Table 4.

    Correlation coefficients between ADI diagnostic criteria and weight vector for the model combining all parameters

    Diagnostic test (n = 17)Left hemisphereRight hemisphere
    rprp
    ADI-R social0.414*<0.04−0.152<0.28
    ADI-R communication0.620**<0.01−0.074<0.38
    ADI-R repetitive behavior0.161<0.26−0.198<0.22
    • ↵* denotes significant correlation on p < 0.05 (1-tailed);

    • ↵** denotes significant correlation on p < 0.01 (1-tailed).

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    Table 5.

    Regions displaying high discrimination weights between ASD and control group for volumetric measures

    ParameterRegionHemixyzw
    Cortical thickness
        ASD > controlsLateral orbitofrontalR2722−55.13
    Supramarginal gyrusL−49−45354.37
    Superior parietalR20−82313.96
    Inferior temporalL−53−28−224.53
    Middle temporalL/R±48−28−76.66
    Superior temporal gyrusL/R±53−20−15.85
    Superior temporal sulcusR51−3474.08
    Parahippocampal gyrusL/R±31−27−163.27
    Fusiform gyrusL/R−36−28−184.64
    Entorhinal cortexL/R±21−17−233.87
    Lateral occipitalL/R±15−9866.92
    Posterior cingulate gyrusL−7−33283.97
    Anterior cingulate gyrusR−628−7−2.71
    PrecuneusL−11−5548−3.82
        ASD < controlsRostral middle frontalL/R±35369−5.60
    Superior frontalL/R±12617−5.39
    Caudal middle frontalL−402036−3.12
    Pars opercularisL
    Inferior parietal lobeR41−6036−3.95
    Superior parietal lobeR17−6450−5.00
    PrecuneusR13−3763−3.18
    Anterior cingulate gyrusR630−5−3.18
    Surface area
        ASD > controlsPrecentralR565153.23
    OrbitofrontalL−18−96−72.60
    Supramarginal gyrusL−55−40363.45
    Inferior parietalR49−50124.57
    Inferior temporal lobeL−45−63−12.69
    Lateral occipitalR17−95142.54
        ASD < controlsSuperior frontalR15397−2.26
    Rostral middle frontalR35368−2.85
    ParacentralR10−646−2.20
    Superior temporalL−51513−3.98
    Pericalcarine fissureL−13−767−4.50
    • Hemi, Hemisphere; L, left hemisphere; R, right hemisphere; w, weight of each cluster centroid.

    • View popup
    Table 6.

    Regions displaying high discriminative weights between ASD and control group for geometric measures

    ParameterRegionHemixyzw
    Metric distortion
        ASD > controlsPrecentral gyrusL−50−5373.23
    Rostral middle frontalL−4424283.27
    Superior frontal gyrusR1440142.51
    Supramarginal gyrusL−51−50255.30
    Postcentral gyrusL−60−8203.67
    Posterior cingulate gyrusL−7−28293.50
    Pericalcarine fissureL−17−67103.46
    Lateral occipital gyrusR28−86162.87
    Inferior parietal lobeR47−45374.90
    Middle temporal gyrusR53−5322.69
    Paracentral gyrusR8−22473.23
    Lingual gyrusR25−5522.50
        ASD < controlsAnterior cingulateL−6340−2.75
    Postcentral gyrusR30−2863−3.98
    Supramarginal gyrusR59−3529−3.75
    Rostral middle frontalR37327−4.27
    Precentral gyrusR42−647−3.59
    Middle temporal gyrusR62−25−12−2.49
    Superior frontal lobeR17756−5.14
    PrecuneusR7−4449−5.13
    Medial orbitofrontal gyrusR732−147−2.36
    Sulcal depth
        ASD > controlsSuperior parietal lobeL/R±26−50443.20
    Rostral middle frontalL−2430283.21
    Inferior parietalL41−5992.57
    Superior frontalL/R2515393.00
        ASD < controlsSupramarginal gyrusL−34−3937−3.94
    Lateral occipital cortexL−45−630−2.85
    Inferior parietalR48−5217−2.76
    Rostral middle frontalR402518−2.92
    Pericalcarine fissureR18−7012−2.83
    • Hemi, Hemisphere; L, left hemisphere; R, right hemisphere; w, weight of each cluster centroid.

    • View popup
    Table 7.

    Mean discrimination weights within regions of interest for individual morphometric features

    RegionMean(w)
    Cortical thicknessMean curvatureSulcal depthMetric distortionSurface area
    Intraparietal sulcus (R)0.970.164.631.512.38
    Inferior parietal lobe (L)1.510.230.994.960.88
    Medial temporal sulcus (L)5.660.070.170.480.62
    Posterior cingulate gyrus (L)3.830.090.783.790.20
    • R, Right hemisphere; L, left hemisphere; w, weight.

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The Journal of Neuroscience: 30 (32)
Journal of Neuroscience
Vol. 30, Issue 32
11 Aug 2010
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Describing the Brain in Autism in Five Dimensions—Magnetic Resonance Imaging-Assisted Diagnosis of Autism Spectrum Disorder Using a Multiparameter Classification Approach
Christine Ecker, Andre Marquand, Janaina Mourão-Miranda, Patrick Johnston, Eileen M. Daly, Michael J. Brammer, Stefanos Maltezos, Clodagh M. Murphy, Dene Robertson, Steven C. Williams, Declan G. M. Murphy
Journal of Neuroscience 11 August 2010, 30 (32) 10612-10623; DOI: 10.1523/JNEUROSCI.5413-09.2010

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Describing the Brain in Autism in Five Dimensions—Magnetic Resonance Imaging-Assisted Diagnosis of Autism Spectrum Disorder Using a Multiparameter Classification Approach
Christine Ecker, Andre Marquand, Janaina Mourão-Miranda, Patrick Johnston, Eileen M. Daly, Michael J. Brammer, Stefanos Maltezos, Clodagh M. Murphy, Dene Robertson, Steven C. Williams, Declan G. M. Murphy
Journal of Neuroscience 11 August 2010, 30 (32) 10612-10623; DOI: 10.1523/JNEUROSCI.5413-09.2010
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