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Supplementary Information ABIDE repository and users access The ABIDE repository is hosted by the 1000 Functional Connectome Project / International Neuroimaging Data-sharing Initiative (INDI)1, 2 which is designed for unrestricted data-sharing via the Neuroimaging Informatics Tool and Resources Clearinghouse (NITRC) (see http://fcon_1000.projects.nitrc.org for more information and other datasets). Detailed information about the ABIDE initiative, each site and their protocols are available through: http://fcon_1000.projects.nitrc.org/indi/abide/. Data are fully anonymized (i.e., all 18 HIPAA protected health information identifiers were removed, as was face information from structural images) using the INDI anonymization tool (http://www.nitrc.org/frs/?group_id=296; FullAnonymize.sh). Data usage is unrestricted for non-commercial research purposes. To access the fully anonymized behavioral and imaging data, users must register with NITRC and the 1000 Functional Connectomes Project. Users are requested to specify the ABIDE datasets included in their analyses, and to acknowledge their related funding sources in presentations or publications based on ABIDE data. MRI Data Acquisition Given the unfunded nature of this grassroots initiative, preexisting data were shared and data collection was not coordinated. Thus sequence parameters for anatomical and functional data, as well as type of scanner varied across sites, though all data were collected with 3 Tesla scanners. Details regarding data acquisition for each sample have been provided in the ABIDE website (http://fcon_1000.projects.nitrc.org/indi/abide) Overview of Imaging Analyses 1 Following preprocessing (see below), we carried out whole-brain intrinsic functional connectivity (iFC) analyses for both structural and functional parcellation schemes (i.e., structural: Harvard-Oxford Atlas3, functional: Crad-2004), as well as regional voxel-wise measures of intrinsic functional architecture (see below). Echo planar imaging (EPI) restingstate data were preprocessed using an alpha version of the Configurable Pipeline for the Analysis of Connectomes (C-PAC, http://fcp-indi.github.com/C-PAC/). CPAC is a publicly available Nipype-based, automated processing pipeline that uniformly interfaces AFNI (http://afni.nimh.nih.gov/afni/) and the FMRIB software library tool (FSL; www.fmrib.ox.ac.uk) commands for neuroimaging analyses. Group analyses were conducted using the Data Processing Assistant for Resting-State fMRI (DPARSF5; http://www.restfmri.net), a Matlabbased pipeline based on the Resting-State fMRI Data Analysis Toolkit (REST6; http://www.restfmri.net) and Statistical Parametric Mapping (SPM8). Image Preprocessing For each individual participant’s dataset, the first four time points were removed to minimize possible T1 stabilization effects. Then, we applied the following preprocessing steps: 1) Slice timing correction. 3dTshift (AFNI) was used to correct for differences between slices with respect to the true time of acquisition (for each site, correction parameters were specified based on the on site-specific acquisition protocol). 2) Motion realignment. Motion correction was achieved in a two-pass procedure. In the first pass, 3DVolReg (AFNI) was used to carry out three-dimensional (3D) motion correction by aligning each functional volume to the mean image of all volumes using Fourier interpolation. The realigned images were then averaged to form a mean image that was used as the reference for the second round of realignment. At the second realignment round, we also computed the summary movement parameters including mean framewise displacement (FD) per Power et al,7, 8 and the percentage of frames 2 with FD exceeding 0.2mm (See Supplementary Figure 1). Specifically, we computed head realignments across frames to represent instantaneous head motion (six dimensional time series representing instantaneous movements about the three translational and three rotational movements (pitch, yaw and roll)). FD was calculated by summing the absolute differences in motion between adjacent volumes, i.e., absolute differences in 3 translations and rotations (rotations were converted into mm by calculating displacement along the surface of a sphere with a radius of 50 mm). 3) Mean-based intensity normalization. All volumes were scaled by the same factor (10,000). 4) Nuisance signal correction. We regressed out the 6 motion parameters (3 translations and 3 rotations), as well as used component-based noise correction (Compcor9) to remove physiological noise. Compcor entails regression of key principal components obtained from decomposition of a priori specified noise regions-of interest in which the time series temporal changes are not likely to be driven by neuronal signal (i.e., white matter [WM], cerebral spinal fluid [CSF]). Compared to regressing out the averaged signal from WM and CSF, this approach has been shown to better account for voxel-specific phase differences in physiological noise.9, 10 Consistent with prior work,9 the first 5 principal components from a combined WM/CSF mask were included as nuisance parameters within the general linear model. The combined mask was derived by segmenting the anatomical image, applying a 0.98 threshold to the resulting tissue probability maps, resampling masks into 2 x 2 x 2 mm3 space and finally eroding the masks by one voxel. The time series of the mask were extracted from unsmoothed functional data that were registered to anatomical space (see 6 below). Finally, we included linear trend as an additional nuisance regressor. 5) Temporal filtering. 3dBandpass (AFNI) was employed to carry out frequency-based filtering (0.009-0.1Hz) in the frequency domain. Temporal filtering was carried out for all R-fMRI measures except fractional amplitude of low frequency fluctuations (fALFF; see below). 3 6) Registration. After skull removal using 3DSkullStrip (AFNI), registration of each individual’s high-resolution anatomic image to the Montreal Neurological Institute’s 152-brain template (MNI152; 2 mm3 isotropic voxel size) was attained by applying a 12–degrees of freedom linear affine transformation using FLIRT and refined using FNIRT nonlinear registration.11 Then the mean image calculated from the functional data was registered to its corresponding anatomical using a two-step process. First, a 6 degree-of-freedom linear registration (FLIRT) was calculated between the mean functional image and the anatomical image. Next, this transform was refined using boundary based registration (BBR)12 using FSL (FLIRT with cost function of BBR). BBR registers the gradient across the boundaries between WM and gray matter for the mean EPI image, to a WM segmentation calculated based on the anatomical image. The resulting transform is concatenated with the anatomical image registered to the MNI template to create an EPI to MNI transform. These transforms were applied to the functional data before voxel-wise mirrored homotopic connectivity (VMHC) and degree centrality (DC) calculations. Of note, as detailed below, to compute voxel-wise mirrored homotopic connectivity (VMHC) analysis, functional data in MNI space were registered to a symmetric template obtained by averaging the MNI152 template and its left-right flipped version.13 For all other measures, spatial normalization was performed after calculation of the derivatives. 7) Spatial Filtering. For VMHC, preprocessed data were spatially filtered with a 3D Gaussian kernel (FWHM = 6mm) prior to calculation of the derivative. For all other measures, spatial filtering (FWHM = 6mm) was not applied until after calculation of the derivatives and registered to MNI space. R-fMRI Derivative Measures 1) Whole-Brain Intrinsic Functional Connectivity (iFC) Analyses. We carried out whole-brain 4 iFC analyses separately for each of two brain parcellation schemes: structural (HarvardOxford Atlas3) and functional (Crad-2004 atlas). The Harvard Oxford atlas is a validated probabilistic atlas implemented in FSL that divides each hemisphere into regions corresponding to portions of cortical gyri and subcortical gray matter nuclei. Encompassing 112 regions (56 in each hemisphere) the atlas covers the entire cerebrum.3, 14 The Crad-2004 refers to the functional atlas estimated based on constrained spectral clustering of resting state fMRI data. Of the original 200 units encompassing both cortical and subcortical regions, we excluded those located in brainstem and cerebellum, thus yielding 179 units. For each unit included in each parcellation scheme, the mean time course of each region was extracted from the preprocessed 4D time-series in MNI space. We then computed the full-brain connectivity matrix using Pearson correlations and transformed to Z-scores using Fisher’s rto-z transformation to ensure normality. We then employed a general linear model to examine the group differences between individuals with ASD and TC for each connection, while covarying out effects of age, FIQ, site, and mean FD. To correct for multiple comparisons in differences between groups relative to a large number of connections, false discovery rate (FDR; q<0.05) correction was performed.15 To facilitate data characterization and interpretation, we sorted connections based upon lobar (i.e., frontal, temporal, parietal, occipital, subcortical) and functional16 (i.e., heteromodal, unimodal, primary somatosensory, paralimbic, limbic, subcortical) classifications. Additionally, following prior results, 17 we sorted findings based on hemispheric configuration (intrahemispheric, homotopic, heterotopic) using only the HOA parcellation (as the Crad-200 does not provide explicit homotopic regions). Future work may consider alternative brain parcellation schemes or more fine-grained voxelwise analyses; we anticipate other schemes will yield similar findings overall, though differences are to be expected (especially when different scales or spatial resolutions are employed). 5 2) Regional Homogeneity (ReHo).18, 19 For a given voxel within the brain, ReHo is computed as the Kendall's correlation coefficient (KCC) between the time course of that voxel and its 26 neighboring voxels. Edge voxels were excluded from analyses. Subject-level Z-score maps were created by subtracting the mean value for the entire brain from each voxel, and dividing by the corresponding standard deviation.20 3) Voxel-Mirrored Homotopic Connectivity (VMHC).13 To improve the correspondence between the homotopic voxels (left-right correspondence), the spatial normalization for VMHC was performed by registering to a symmetric template (obtained by averaging the MNI152 template and its left-right flipped version). For each voxel, VMHC is calculated as the correlation between any pair of symmetric interhemispheric voxels. Prior to statistical analysis, VMHC values are Z-transformed using the Fisher’s r-to-Z-transform. 4) Fractional Amplitude of Low Frequency Fluctuations (fALFF).20 As detailed elsewhere, no temporal filtering was carried out, because the data were examined in the frequency domain. Briefly, for each time series at each voxel we calculated fALFF as the fraction between the sum of amplitudes of the band ranging from 0.009 to 0.1 Hz and the entire frequency range detectable in a given signal. Subject-level voxel-wise fALFF maps were standardized into subject-level Z-score maps (i.e., by subtracting the mean voxel-wise fALFF obtained for the entire brain, and then dividing by the standard deviation). 5) Degree Centrality (DC).21, 22 As described elsewhere,21 we computed individual DC based on the study-specific functional volume mask (here the connectome) including only voxels (in MNI152 standard space) present in at least 95% of the participants, and further constrained by the MNI152 25% gray-matter probability mask. Prior to graph generation, EPI time-series data were down-sampled to 4mm isotropic voxel-size to decrease computational complexity. 6 Voxel-based graphs were then generated for each individual. Each voxel constitutes a node in the graph, and each significant functional connection (i.e., Pearson correlation) between any pair of voxels is an edge. To obtain each participant’s graph, the correlation between the timeseries of each voxel with every other voxel in the study mask was computed, resulting in a correlation matrix. A binary, undirected adjacency matrix was then obtained by thresholding each correlation at p<0.001. For each subject, based on the graph, DC was calculated by counting the number of significant correlations between each voxel and all other voxels. DC indices were then transformed to z-scores based on each individual mean and standard deviation for DC across all voxels.21, 22 6) iFC Analyses of Default Network (DN) Midline Core.23 For each individual, we derived whole-brain voxelwise correlations associated with the mean time series extracted from two spherical region-of-interest masks (radius=4mm) centered at the anterior medial prefrontal cortex (MNI coordinates x=-6, y=52, z=-2) and posterior cingulate cortex (MNI coordinates x=8, y=-56, z=26 ).23 For each seed region, a Fisher’s r-to-Z-transformed correlation map was generated for each participant and submitted to group analyses. Group-level Analyses For group comparisons of the regional voxel-wise measures (from 2 to 6), we first generated a study-specific functional volume mask including only voxels (in MNI152 standard space) present in at least 95% of the participants. Within this mask, we then examined neuroimaging differences related to diagnosis (ASD vs. TC) while covarying out the effects of age, FIQ, site, and mean FD. Gaussian random field theory correction for multiple comparisons was applied (voxel Z>2.3, cluster-level p<0.05) for the voxel-wise maps of R-fMRI derivatives. Secondary Analyses on R-fMRI data 7 ‘Scrubbed’ Data. Primary analyses accounted for group differences in micromovements by including each subject’s mean FD as a covariate at the group level.24, 25 To verify the effectiveness of this approach, we repeated the above analyses after removing frames with framewise displacement (FD)>0.2mm (‘scrubbing’ 26). Individuals with more than 50% of their time-series removed were excluded from analyses; only sites with at least six individuals per group were included, yielding a sample of 419 individual datasets (184 with ASD and 253 TC; See Supplementary Table 1). As expected, a larger proportion of individuals with ASD than TC were excluded based on this criterion (2=10.6, p=0.001). The length of data included in analyses on average ranged from 3 to 8 1 to 2 minutes across sites (absolute minima range= 2-6 min; maxima range= 3-10 min across sites). fALFF was not calculated with ‘scrubbed’ data as the removal of time points disrupts the temporal structure, precluding standard Fourier transform-based approaches27 barring more complex estimation approaches that would be beyond the scope of this work. We note that all analyses on scrubbed data yielded findings similar to those in our primary analyses. This is consistent with recent work, 25, 28 that carried out a comprehensive examination of the relative effectiveness of scrubbing vs. other correction approaches (e.g., covarying for mean FD at the group level). Comprehensive assessments of the multiple possible configurations of preprocessing or statistical analyses that can determine utility of scrubbing are beyond the scope of the present work, though merit consideration in future studies. Site-Related Variation and Sample Size. As was observed in a prior effort,29 the ability to detect ASD-related findings in the ABIDE sample does not imply a lack of site-related variation. Although not a primary focus of this work, we illustrate site-related variation and demonstrate the impact of sample size on ability to detect group differences for a representative finding. Specifically, we computed the VMHC of a left insula cluster emerging 8 from several regional measures with age, IQ, and mean FD regressed out (See Figure 4). Fitted VMHC Z scores for each group mean (+ SEM) are plotted in Supplementary Figure 5 for each of the 17 samples included in analyses. Given the unfunded nature of this initiative, aggregation occurred without prior coordination across sites, thus variability in results could be related to a number of factors (e.g., scanner sequence, sample characteristics, specific instructions to participants, or variability in participant wakefulness). Fortunately, as demonstrated by our results, inter-site sources of variation did not preclude us from being able to effectively pool data and carry out discovery-based analyses. Further, again focusing on VMHC of the left insula cluster, we ran a parametric analysis to quantify the effect of group size in detecting diagnostic differences. Specifically, using VMHC of the same left insula cluster with the same covariates, the Z values of group differences ± 2 standard deviations (SD) were computed with 1000 permutations as a function of sample size (with samples increasing by 1 from 5 to 360 individuals per group). The random pairing of individuals per group was conducted in two ways. In the first (Supplementary Figure 6A), ASD and TC participants were randomly selected and paired within each site. In the other (Supplementary Figure 6B), ASD and TC participants were randomly selected and paired without regard to site of provenance. Both approaches revealed that power to detect group effects increases with increasing sample size. Particularly, mean Z values < -2.3 are evident once group sizes reached about 100. Confidence intervals narrowed with increasing group size, particularly for the first approach. These analyses demonstrate the value of obtaining comparable numbers of participants for both diagnostic groups in future large-scale data aggregation initiatives. Normality. Given the multisite nature of the ABIDE repository and its relative large size, we attempted to test for normality of each functional measure examined here. We computed 9 voxel-wise normality maps using Lilliefors' normality test using the REST Normality Test Module30 on each of the 4D residual files generated by the group analysis model for each derivative (fALFF, VMHC, DC, ReHo, and iFC for posterior cingulate cortex and anterior medial prefrontal cortex) across the 763 functional datasets employed in the current study. The Lilliefors test challenges the null hypothesis that data are normally distributed. As illustrated in Supplementary Figure 7, results showed regional variation in the acceptance of the null hypothesis (p>0.05). VMHC was particularly impressive with respect to widespread findings of acceptance of normality throughout the brain, while ReHo was the least so. Visual exploration of the distribution of residuals across subjects in regions for which normality was rejected, revealed that the distributions tended to be super-gaussian in nature; extreme outliers and skewness did not appear to be primary causes for rejection of the normality assumption. To address any potential concerns that our primary findings obtained with traditional parametric testing could be driven by violations of the normality assumption, we carried out confirmatory analyses using non-parametric permutation testing at the cluster level. Specifically, on the 7 clusters identified to be showing differences overlapping in R-fMRI measures (Figure 4), we repeated group comparisons with 10000 permutations on the mean value within each cluster for each of the regional measures (i.e., VMHC, DC, ReHo, fALFF). Bonferroni correction for multiple comparisons was employed (7 × 4 = 28 conditions). As summarized in Supplementary Table 4 the findings of permutation testing were nearly identical to those emerging from primary analyses using parametric testing (Figure 4). This similarity in results is not surprising, as in practice, parametric tests are generally robust to violations of normality with respect to false positives and have been noted to be more conservative than non-parametric in imaging data under some circumstances.31 To explore 10 whether use of parametric analyses led to false negative results, we performed permutationbased voxel-wise non-parametric analyses using the randomise program available in FSL. Given the computational time for a sample of this size, we performed 500 permutations. To correct for multiple comparisons at the cluster level we employed Gaussian random field theory (voxel Z>2.3, cluster-level p<0.05; note: the t-statistic generated by Randomise was used as an estimate of Z given their equivalence in large sample sizes). As shown in Supplementary Figure 8 group comparison results are highly similar to those emerging from primary analyses for all regional measures except for fALFF whose finding did not survive statistical threshold. Future studies would benefit from exploration of the relative merits of the non-parametric testing in neuroimaging analyses31, and comparison of the multitude of existing nonparametric approaches. Structural Volumetric Analyses Although beyond the scope of this study, to demonstrate the potential to utilize ABIDE repository’s structural images, we computed global volumes for gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) as well as total intracranial volume (ICV). Specifically, we quantified each subject structural image (T1-weighted) in native space following skull stripping using FSL’s BET. We segmented each image into gray matter, white mater and CSF volumes by using the FMRIB’s Automated Segmentation Tool (FAST), which has been integrated in the C-PAC's pipeline. The gray matter volume is computed by summing the gray matter density across all voxels and multiplying it by the voxel size, as with white matter and CSF volume. Total intracranial volume is calculated by summing the volume of the three tissues. The same group analysis model (ANCOVA) was employed to compare the two groups (ASD vs. TC) relative to each of these structural measures covarying for age, 11 IQ, mean FD and site. No significant differences were found between the two groups’ means (ICV ml=1476 143 ml and 1474 132 GM=591 63 and 591 66 ml; Wm= 498 for ASD and TC respectively). Supplementary figure captions Supplementary Figure 1. Movement artifacts. (A) Mean framewise displacement and (B) percentage of scans with framewise displacement > .2mm are plotted for each subject’s functional dataset at each site (labeled alphabetically, see Supplementary Table 2 for label key) for the data of individuals with autism spectrum disorder (ASD; violet right side plots) and typical control (TC; green left side plots). The red dashed line in panel A represents the cutoff used for inclusion in neuroimaging analyses (=0.8 mm which corresponds to two standard deviations above the mean of the whole sample). The dashed line in panel B represents the cutoff (50%) for inclusion in secondary analyses conducted on scrubbed data (only sites with a minimum of 6 datasets per group with at least 50% frames < .2 mm were included in these analyses). Of note, 1111 functional data are plotted here as one functional dataset with four time points (site f) was discarded from any analyses. It is important to note that the ABIDE consortium, after substantial consideration, decided that data should be shared regardless of movement artifacts. As highlighted by Milham (2012)2, this decision was motivated by the lack of consensus regarding data quality standards, and potential differences in quality requirements based on individual user’s needs. Additionally, poor quality data may be useful for efforts to develop algorithms capable of correcting movement artifacts, and may one day prove useful for data analysis if more effective correction approaches emerge. As Supplementary Figure 1 shows, most shared data had limited micromovements. With regard to variations among sites, some sites only shared data that had been quality-controlled. To make users aware of such variations, we draw their attention to the ABIDE website (http://fcon_1000.projects.nitrc.org/indi/abide/), which states whether quality control was 12 applied to select data to be shared, and provides information on the protocols utilized to prepare participants for scan sessions at each site. Supplementary Figure 2. Volumetric Measures. The Tukey box-whiskers plots depict the distribution of total intracranial volume, total gray matter and total white matter, and cerebrospinal fluid (CSF) volumes for the datasets of individuals with autism spectrum disorder (ASD, violet) and typical controls (TC, green). For illustration purposes, we plotted fitted values by regressing out the covariates used for ANCOVAs group comparisons (i.e., age, FIQ, mean frame wise displacement, and site). The results of ANCOVA group comparisons showed that the two groups did not significantly differ in regard to these global measures. See Supplementary Information for details on volumetric analyses. Supplementary Figure 3. Whole-Brain Intrinsic Functional Connectivity (iFC) Analyses on ‘Scrubbed’ data. Whole-brain analyses were repeated using individual data censored for frames with movement >0.2mm (‘scrubbing’): only sites with at least six subjects per group remaining with at least 50% frames retained were included (184 with Autism Spectrum Disorders [ASD] and 253 Typical Controls [TC]). The glass brains display the pattern of significant group differences (i.e., ASD vs. TC) for iFC between each of the 112 parcellation units (56 per hemisphere) included in the structural Harvard-Oxford Atlas (HOA). Parcellations are represented with their centers of mass overlaid as spheres on glass brains. The upper panel shows the intrinsic functional connections (blue lines) that were significantly weaker in ASD vs. TC. The lower panel shows the intrinsic functional connections that were significantly stronger in ASD relative to TC (red lines). Each HOA unit is colored based on its membership in the six functional divisions per Mesulam et al.32 [yellow=primary sensorimotor (SM); green=unimodal association; blue=heteromodal association; orange=paralimbic; red=limbic; pink=subcortical]. Interhemispheric iFC is noted on dorsal and coronal views. Glass brains 13 (left lateral, dorsal, and coronal views, shown from left to right) were generated using BrainNet Viewer (http://www.nitrc.org/projects/bnv/). Displayed results are corrected for multiple comparisons using false discovery rate (FDR) at p<0.05. Supplementary Figure 4. Intrinsic Functional Architecture Following Data “Scrubbing.” We repeated analyses on regional measures of intrinsic functional architecture as well as seed-based correlation of the core midline seeds of the default network (DN) with individual data censoring frames with movement >0.2mm (‘scrubbing’). Only datasets from sites remaining with at least six subjects per each diagnostic group (Autism Spectrum Disorders [ASD] and Typical Controls [TC]) presenting with at least 50% frames retained were included (184 with ASD and 253 TC). (A) Z maps of the grand means (i.e., across all 437 scrubbed individual data) and (B) significant group differences between individuals with ASD and TC for regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC), degree centrality (DC), as well as seed-based correlation of posterior cingulate cortex (PCC) and anterior medial prefrontal cortex (aMPFC). Seeds were centered at Montreal Neurological Institute stereotaxic coordinates x=-8, y=-56, z=26 for PCC and x=-6, y=52, z=-2 for aMPFC, and are depicted as white dots on the surface maps. Fractional amplitude of low frequency fluctuations (fALFF) were not calculated with ‘scrubbed’ data as the removal of time points disrupts the temporal structure and thus limits standard Fourier transform-based approaches.33 We employed Gaussian random field theory to carry out cluster-level corrections for multiple comparisons (Z> 2.3, p<0.05). Significant clusters are overlaid on inflated surface maps generated using BrainNet Viewer (http://www.nitrc.org/projects/bnv/), as well as on axial images generated with REST Slice Viewer (http://www.restfmri.net). L= Left hemisphere; R= Right hemisphere 14 Supplementary Figure 5 Effect of sample size on group (Autism Spectrum Disorders [ASD] and Typical Controls [TC]) differences for voxel mirrored homotopic connectivity (VMHC) of the left insula cluster identified based on convergent abnormalities across three regional measures in primary analyses (see Figure 4). Shown in both plots is the Z value of the group differences ± 2 standard deviations (SD) computed with 1000 permutations as a function of sample size (increasing by 1 from 5 to 360 individuals per group). A dashed line is set at Z= -2.3. In panel a) each between-group calculation was conducted by randomly pairing ASD and TC selected within each site. In panel b) each between-group calculation was conducted by randomly pairing ASD and TC selected regardless of their site of provenance. Supplementary Figure 6 Site-related variation in group differences for the voxel mirrored homotopic connectivity (VMHC) of the left insula cluster identified based on convergent abnormalities across three regional measures in primary analyses (See Figure 4). Age, FIQ, and mean framewise displacement were regressed out from VMHC across subjects, thus the group means (± SEM) represent adjusted VMHC Z values. See Supplementary Information. Supplementary Figure 7. Regional Normality. Maps of the voxel-wise statistical significance (indexed as a negative base 10 logarithm) of the Lilliefors normality test conducted on the 763 functional datasets included in analyses for the regional measures: examined in the study. These included fractional amplitude of low frequency fluctuations (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC), degree centrality (DC), and intrinsic functional connectivity of the posterior cingulate (PCC) and of the anterior medial prefrontal cortex (aMPFC). The Lilliefors normality test was performed on the residuals acquired in the ANCOVA group analysis model, i.e., testing the normality of the error terms. Orange to dark red colors indicate rejection of the null hypothesis (p < 0.05). Statistical maps 15 are overlaid on inflated surface maps generated using BrainNet Viewer (http://www.nitrc.org/projects/bnv/). L= Left hemisphere; R= Right hemisphere. Supplementary Figure 8. Non-parametric voxel-wise group comparisons. Z maps of the significant group differences between individuals with Autism Spectrum Disorders (ASD) and Typical Controls (TC) resulting from parametric testing used in primary analyses (ANCOVA; left columns per panel) and from non-parametric testing (FSL’s Randomize program; Right columns per panel). These tests were performed for each of the following regional measures: fractional amplitude of low frequency fluctuations (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC), degree centrality (DC), and intrinsic functional connectivity (iFC) of the posterior cingulate cortex (PCC) and anterior medial prefrontal cortex (aMPFC). We employed Gaussian random field theory to carry out cluster-level corrections for multiple comparisons (voxel-level Z>2.3; cluster significance: p<0.05, corrected). Significant clusters are overlaid on inflated surface maps generated using BrainNet Viewer (http://www.nitrc.org/projects/bnv/). 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