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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).
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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
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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).
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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.
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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
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‘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
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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/). L= Left hemisphere; R= Right hemisphere.
References
1.
Mennes M, Biswal B, Castellanos FX, Milham MP. Making data sharing work: The
FCP/INDI experience. Neuroimage 2012: [Epub ahead of print].
2.
Milham MP. Open neuroscience solutions for the connectome-wide association era.
Neuron 2012; 73(2): 214-218.
3.
Kennedy DN, Lange N, Makris N, Bates J, Meyer J, Caviness VS, Jr. Gyri of the
human neocortex: an MRI-based analysis of volume and variance. Cereb Cortex 1998;
8(4): 372-384.
4.
Craddock RC, James GA, Holtzheimer PE, 3rd, Hu XP, Mayberg HS. A whole brain
fMRI atlas generated via spatially constrained spectral clustering. Hum Brain Mapp
2012; 33(8): 1914-1928.
5.
Yan C, Z Y. DPARSF: A MATLAB Toolbox for "Pipeline" Data Analysis of RestingState fMRI. Front Syst Neurosci 2010; 4: 13.
16
6.
Song XW, Dong ZY, Long XY, Li SF, Zuo XN, Zhu CZ et al. REST: a toolkit for restingstate functional magnetic resonance imaging data processing. PLoS One 2011; 6(9):
e25031.
7.
Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but
systematic correlations in functional connectivity MRI networks arise from subject
motion. Neuroimage 2012; 59(3): 2142-2154.
8.
Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Steps toward
optimizing motion artifact removal in functional connectivity MRI; a reply to Carp.
Neuroimage 2012.
9.
Behzadi Y, Restom K, Liau J, Liu TT. A component based noise correction method
(CompCor) for BOLD and perfusion based fMRI. Neuroimage 2007; 37(1): 90-101.
10.
Chai XJ, Castanon AN, Ongur D, Whitfield-Gabrieli S. Anticorrelations in resting state
networks without global signal regression. Neuroimage 2012; 59(2): 1420-1428.
11.
Non-linear registration, aka Spatial normalisation. FMRIB Analysis Group Technical
Reports. 2007, acesed on March 20 2013.
12.
Greve DN, Fischl B. Accurate and robust brain image alignment using boundary-based
registration. Neuroimage 2009; 48(1): 63-72.
13.
Zuo XN, Kelly C, Di Martino A, Mennes M, Margulies DS, Bangaru S et al. Growing
together and growing apart: regional and sex differences in the lifespan developmental
trajectories of functional homotopy. J Neurosci 2010; 30(45): 15034-15043.
14.
Meyer JW, Makris N, Bates JF, Caviness VS, Kennedy DN. MRI-Based topographic
parcellation of human cerebral white matter. Neuroimage 1999; 9(1): 1-17.
15.
Genovese CR, Lazar NA, Nichols T. Thresholding of statistical maps in functional
neuroimaging using the false discovery rate. Neuroimage 2002; 15(4): 870-878.
16.
Mesulam MM. From sensation to cognition. Brain 1998; 121 ( Pt 6): 1013-1052.
17.
Stark DE, Margulies DS, Shehzad ZE, Reiss P, Kelly AM, Uddin LQ et al. Regional
variation in interhemispheric coordination of intrinsic hemodynamic fluctuations. The
Journal of Neuroscience 2008; 28(51): 13754-13764.
18.
Zang Y, Jiang T, Lu Y, He Y, Tian L. Regional homogeneity approach to fMRI data
analysis. Neuroimage 2004; 22(1): 394-400.
19.
Paakki JJ, Rahko J, Long X, Moilanen I, Tervonen O, Nikkinen J et al. Alterations in
regional homogeneity of resting-state brain activity in autism spectrum disorders. Brain
Res 2010; 1321: 169-179.
20.
Zuo XN, Di Martino A, Kelly C, Shehzad ZE, Gee DG, Klein DF et al. The oscillating
brain: complex and reliable. Neuroimage 2010; 49(2): 1432-1445.
17
21.
Zuo XN, Ehmke R, Mennes M, Imperati D, Castellanos FX, Sporns O et al. Network
centrality in the human functional connectome. Cereb Cortex 2012; 22(8): 1862-1875.
22.
Buckner RL, Sepulcre J, Talukdar T, Krienen FM, Liu H, Hedden T et al. Cortical hubs
revealed by intrinsic functional connectivity: mapping, assessment of stability, and
relation to Alzheimer's disease. J Neurosci 2009; 29(6): 1860-1873.
23.
Andrews-Hanna JR, Reidler JS, Sepulcre J, Poulin R, Buckner RL. Functionalanatomic fractionation of the brain's default network. Neuron 2010; 65(4): 550-562.
24.
Van Dijk KR, Sabuncu MR, Buckner RL. The influence of head motion on intrinsic
functional connectivity MRI. Neuroimage 2012; 59(1): 431-438.
25.
Yan C, Cheung B, Kelly C, Colcombe S, Craddock C, Di Martino A et al.
Comprehensive Assessment of Regional Variation in the Impact of Head
Micromovements on Functional Connectomics. Neuroimage 2013; 76: 183-201..
26.
Power JD, Kelly AB, Snyder AZ, Schlaggar BL, Petersen SE. Steps toward optimizing
motion artifact removal in functional connectivity MRI; a reply to Carp. Neuroimage
2013; 76: 439-441..
27.
Kelly C, Biswal BB, Craddock RC, Castellanos FX, Milham MP. Characterizing
variation in the functional connectome: promise and pitfalls. Trends Cogn Sci 2012;
16(3): 181-188.
28.
Fair DA, Nigg JT, Iyer S, Bathula D, Mills KL, Dosenbach NU et al. Distinct neural
signatures detected for ADHD subtypes after controlling for micro-movements in
resting state functional connectivity MRI data. Front Syst Neurosci 2012; 6: 80.
29.
Biswal BB, Mennes M, Zuo XN, Gohel S, Kelly C, Smith SM et al. Toward discovery
science of human brain function. Proc Natl Acad Sci U S A 2010; 107(10): 4734-4739.
30.
Zang ZX, Yan CG, Dong ZY, Huang J, Zang YF. Granger causality analysis
implementation on MATLAB: a graphic user interface toolkit for fMRI data processing.
J Neurosci Methods 2012; 203(2): 418-426.
31.
Nichols TE, Holmes AP. Nonparametric permutation tests for functional neuroimaging:
a primer with examples. Hum Brain Mapp 2002; 15(1): 1-25.
32
Mesulam MM. From sensation to cognition. Brain 1998; 121 ( Pt 6): 1013-1052
33
Kelly C, Biswal BB, Craddock RC, Castellanos FX, Milham MP. Characterizing
variation in the functional connectome: promise and pitfalls. Trends Cogn Sci 2012;
16(3): 181-188.
18