RESEARCH - IA de Investigación especializada en el MIT

INVESTIGACIÓN

Coactivation pattern analysis reveals altered
salience network dynamics in children with
autism spectrum disorder

Emily Marshall

1∗

, Jason S. Nomi

1∗

, Bryce Dirks1, Celia Romero1,

Lauren Kupis1, Catie Chang2,3,4, and Lucina Q. Uddin1,5

1Department of Psychology, University of Miami, Coral Gables, Florida, EE.UU
2Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, EE.UU
3Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, EE.UU
4Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, EE.UU
5Neuroscience Program, University of Miami Miller School of Medicine, Miami, Florida, EE.UU
These authors contributed equally.

∗

Palabras clave: Anterior insula, Dynamic coactivation patterns, Conectividad funcional, Lateral
fronto-parietal network, Medial fronto-parietal network, Midcingulo-insular network

ABSTRACTO

Brain connectivity studies of autism spectrum disorder (ASD) have historically relied on static
measures of functional connectivity. Recent work has focused on identifying transient
conﬁgurations of brain activity, yet several open questions remain regarding the nature of
speciﬁc brain network dynamics in ASD. We used a dynamic coactivation pattern (CAP)
approach to investigate the salience/midcingulo-insular (M-CIN) network, a locus of
dysfunction in ASD, in a large multisite resting-state fMRI dataset collected from 172 niños
(ages 6–13 years; norte = 75 ASD; norte = 138 masculino). Following brain parcellation by using
independent component analysis, dynamic CAP analyses were conducted and k-means
clustering was used to determine transient activation patterns of the M-CIN. The frequency of
occurrence of different dynamic CAP brain states was then compared between children with
ASD and typically developing (TD) niños. Dynamic brain conﬁgurations characterized by
coactivation of the M-CIN with central executive/lateral fronto-parietal and default
mode/medial fronto-parietal networks appeared less frequently in children with ASD
compared with TD children. This study highlights the utility of time-varying approaches for
studying altered M-CIN function in prevalent neurodevelopmental disorders. We speculate
that altered M-CIN dynamics in ASD may underlie the inﬂexible behaviors commonly
observed in children with the disorder.

RESUMEN DEL AUTOR

Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with
altered patterns of functional brain connectivity. Little is currently known about how
moment-to-moment brain dynamics differ in children with ASD and typically developing
(TD) niños. Altered functional integrity of the midcingulo-insular network (M-CIN) tiene
been implicated in the neurobiology of ASD. Here we use a novel coactivation analysis
approach applied to a large sample of resting-state fMRI data collected from children with
ASD and TD children to demonstrate altered patterns of M-CIN dynamics in children with
the disorder. We speculate that these atypical patterns of brain dynamics may underlie
behavioral inﬂexibility in ASD.

un acceso abierto

diario

Citación: marshall, MI., Nomi, j. S.,
Dirks, B., Romero, C., Kupis, l., Chang,
C., & Uddin, l. q. (2020). Coactivation
pattern analysis reveals altered
salience network dynamics in children
with autism spectrum disorder.
Neurociencia en red, 4(4), 1219–1234.
https://doi.org/10.1162/netn_a_00163

DOI:
https://doi.org/10.1162/netn_a_00163

Supporting Information:
https://doi.org/10.1162/netn_a_00163

Recibió: 27 Abril 2020
Aceptado: 29 Julio 2020

Conflicto de intereses: Los autores tienen
declaró que no hay intereses en competencia
existir.

Autor correspondiente:
Lucina Q. Uddin
l.uddin@miami.edu

Editor de manejo:
Caterina Gratton

Derechos de autor: © 2020
Instituto de Tecnología de Massachusetts
Publicado bajo Creative Commons
Atribución 4.0 Internacional
(CC POR 4.0) licencia

La prensa del MIT

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Salience network dynamics in autism

INTRODUCCIÓN

Autism spectrum disorders (ASDs) are neurodevelopmental disorders with central features of
atypical social communication and restricted and repetitive behavioral patterns (Americano
Psychiatric Association, 2013). These disorders have a high prevalence among school-aged
children and adolescents in the United States, and have recently been estimated to affect
alrededor 1 en 40 individuals between the ages of 3 y 17 (Kogan et al., 2018). Because of
the broad range and individual variability of the associated symptoms and etiologies of these
disorders, they are difﬁcult to understand and to treat. Sin embargo, neuroimaging research in
the past decade has identiﬁed brain network functional connectivity (FC) as a metric with
which to potentially develop diagnostic indicators for ASD (Uddin et al., 2017). These stud-
ies have leveraged advances in machine learning to provide initial evidence that FC of the
salience/midcingulo-insular (M-CIN) network (Uddin, yo, & Spreng, 2019), comprising the
bilateral anterior insula and anterior cingulate cortices (Uddin, 2015), can discriminate ASD
from typical development (Anderson et al., 2011; Uddin et al., 2013).

Previous FC and activation studies of ASD have indicated that atypical functioning of the
M-CIN, default mode/medial fronto-parietal (M-FPN), and central executive/lateral fronto-
parietal (L-FPN) is associated with the disorder (Abbott et al., 2016; Padmanabhan et al., 2017;
Di Martino et al., 2009; Green et al., 2016). Because these brain networks support social
and emotional behavior and executive functions, it is possible that altered relationships be-
tween them may underlie the social communication deﬁcits and inﬂexible behaviors associ-
ated with ASD. Within the broader M-CIN, the anterior insula is a key node that orchestrates
switching between the M-FPN and L-FPN (menón & Uddin, 2010) and is posited to be a spe-
ciﬁc locus of dysfunction in ASD (Uddin & menón, 2009; Nomi, Molnar-Szakacs, & Uddin,
2019). In studies of neurotypical individuals, the anterior insula has been shown to exhibit
patterns of dynamic FC that link it transiently with the M-FPN (Nomi et al., 2016; Chang &
guantero, 2010). Connectivity studies focusing on the anterior insula and the broader M-CIN in
ASD have produced mixed ﬁndings, with some indicating that this network may be hypercon-
nected in children with the disorder (Uddin et al., 2013) while others report hypoconnectivity
of this network (Abbott et al., 2016; Ebisch et al., 2011).

Traditional FC approaches operate under the assumption that the brain’s functional archi-
tecture remains relatively static throughout an entire fMRI scan. Although these static FC meth-
ods have provided informative estimates of the functional architecture of the brain, a growing
body of evidence suggests that time-varying analysis of functional networks may reveal addi-
tional, dynamic aspects of brain function that have been previously overlooked (Calhoun et al.,
2014; Hutchison et al., 2013). Because of this, dynamic or time-varying approaches have re-
cently become an area of interest for characterizing brain function and dysfunction (Uddin &
Karlsgodt, 2018; Lurie et al., 2020). In contrast to the single, average FC estimate produced by
static approaches, dynamic analyses can identify multiple transient brain “states” or coactiva-
tion patterns (CAPs) that recur throughout an fMRI scan (Liu et al., 2018; Chen et al., 2015).

The current study uses a dynamic CAP approach based on previous research demonstrat-
ing that whole-brain patterns, captured at the peaks of a brain region’s signal, can resolve a
traditional region of interest (ROI)–based FC map into multiple transient patterns occurring at
different points in time. Liu and Duyn ﬁrst showed that applying k-means clustering to spatial
patterns derived from averaging across all brain voxels related to the highest ∼15% of activa-
tion time frames from the BOLD signal of a posterior cingulate cortex (PCC) ROI permits the
identiﬁcation of multiple different coactivation patterns, breaking down traditional static FC
ﬁndings into multiple contributing patterns (Liu & duyn, 2013). Chen and colleagues showed

Midcingulo-insular network:
Core cortical brain regions within the
midcingulo-insular network (M-CIN)
include bilateral anterior insula and
anterior midcingulate cortex. Este
network encapsulates the previously
characterized “salience,” “ventral
atención,” and “cingulo- opercular”
redes.

Medial fronto-parietal network:
Core cortical brain regions within
the medial fronto-parietal network
(M-FPN) include medial prefrontal
and posterior cingulate cortex. Este
network is also referred to as the
“default mode” network.

Lateral fronto-parietal network:
Core cortical brain regions within
the lateral fronto-parietal network
(L-FPN) include lateral prefrontal and
anterior inferior parietal cortex. Este
network is also referred to as the
“control” or “central executive”
network.

Coactivation pattern analysis:
Coactivation pattern (CAP) análisis
tracks brain state alterations at the
individual time frame level, and is
one approach for quantifying brain
dinámica.

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Salience network dynamics in autism

that a dynamic CAP analysis of a PCC node that employs k-means clustering across the highest
activation time frames (instead of averaging across such time frames) produces dynamic CAPs
that are less variable (es decir., more stable) than dynamic brain states identiﬁed using a sliding win-
dow correlation analysis (Chen et al., 2015). Además, unlike sliding window correlations,
dynamic CAP analyses do not require the arbitrary selection of a window size over which to
average FC measures (Allen et al., 2014). De este modo, dynamic CAP analyses offer some advantages
over sliding window dynamic FC approaches.

Compared with static FC methods, dynamic time-varying approaches may exhibit superior
accuracy in characterizing the behavior of typically developing (TD) individuals, así como
in discriminating clinical from nonclinical populations including autism, schizophrenia, de-
presion, and bipolar disorder (Chen et al., 2017; Rashid et al., 2018; Damaraju et al., 2014;
Demirtas et al., 2016; Kaiser et al., 2019). Recent studies have used dynamic FC approaches to
investigate ASD, revealing abnormal patterns of whole-brain network conﬁgurations (Uddin,
2020b). De Lacy et al. (2017) and Watanabe and Rees (2017) both reported a reduction in the
number of transitions between brain state conﬁgurations in ASD, suggesting decreased func-
tional ﬂexibility or overly stable dynamic properties of the autistic brain. In both global and
local brain networks, ASD has also been associated with reductions in other measures of dy-
namism, including functional ﬂuidity and dynamic range (Fu et al., 2019). In a large sample of
niños, Rashid et al. (2018) found that those with autistic traits spent more time in a “globally
disconnected” state. Similarmente, Chen et al. (2017) found that individuals with ASD had gener-
ally weaker dynamic functional connections within whole-brain states. Individuals with ASD
have also been shown to have longer mean dwell times in these whole-brain conﬁgurations
with weaker connectivity (Yao et al., 2016). These studies provide evidence for consistent ab-
normalities in dynamic brain conﬁgurations in ASD, but have focused mostly on whole-brain
network organization. Además, no previous study has used a CAP analysis to probe brain
dynamics in ASD. Further investigation is needed to determine whether the ﬁndings reported
to date are related to speciﬁc brain networks implicated in ASD pathology such as the M-CIN
(Uddin & menón, 2009).

En el presente estudio, we applied independent component analysis (ICA) to resting-state fMRI
data collected from three cohorts of age- and IQ-matched children with ASD and TD children
and conducted a dynamic CAP analysis focusing on the M-CIN. The goals of the study were
a (a) identify patterns of dynamic CAPs related to the M-CIN, y (b) compare these dynamic
M-CIN coactivation patterns between TD and ASD groups. We hypothesized that children
with ASD would show altered M-CIN dynamics compared with TD children.

METHODS AND MATERIALS

Participantes

Participant data were obtained from the Autism Brain Imaging Data Exchange (ABIDE I and II)
public databases (Di Martino et al., 2014, 2017), combined with data collected by the Brain
Connectivity and Cognition Laboratory at the University of Miami (UM). Institutional review
board approval was provided by UM and each data contributor in the ABIDE databases. El
sample selected from the databases met the following inclusion criteria: (a) subjects were be-
tween ages 6 y 13; (b) subjects had more than 160 volumes (5 minutes and 20 artículos de segunda clase)
of resting-state fMRI data acquisition; (C) subjects had fMRI data collected using a repetition
tiempo (TR) = 2 artículos de segunda clase; y (d) subjects had their eyes closed during the resting-state scan
(Nair et al., 2018). Un total de 70 children scanned at UM met these criteria. Data for an addi-
tional 136 subjects were downloaded from the ABIDE databases. We selected the following

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Salience network dynamics in autism

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Cifra 1. Participant selection process. Neuroimaging and phenotypic data from three sites
(University of Miami, Erasmus University Medical Center Rotterdam, and Stanford University) eran
incluido.

sites that met these criteria: Universidad Stanford (ABIDE I and II) and Erasmus University Medi-
cal Center Rotterdam (ABIDE II). After quality control and removal of subjects with high levels
of head motion (mean framewise displacement (FD) > 0.5 mm and/or more than 35 marcos
with FD > 0.5 mm), the ﬁnal sample consisted of 172 subjects (138 machos) (Cifra 1). El
ASD (norte = 75) and TD (norte = 97) groups did not differ in terms of age (pag = 0.285), full-scale
IQ (pag = 0.127), or mean head motion (pag = 0.917). For participants from UM and Stanford
Universidad, ASD diagnosis was conﬁrmed by a licensed psychologist using the Autism Diag-
nostic Observation Schedule (ADOS-2) (Lord et al., 2012). For the Erasmus Medical Center
Participantes, an ASD diagnosis was conﬁrmed from central medical records of the children’s
general practitioners in the Netherlands. Additional demographic information for participants
can be found in Table 1.

MRI Data Acquisition

At all three data collection sites, participants underwent a mock scanning session to familiarize
them with the MRI session procedure and offer them the opportunity to opt out before under-
going the MRI scan. For the resting-state fMRI scan, participants were instructed to keep their

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Salience network dynamics in autism

Number of participants

Age (años)
Sex

Handedness

Full IQ
Verbal IQ

Performance IQ

Mesa 1. Participant demographics

ASD

9.79 ± 1.74 (6.3−13.2)
74 machos, 23 hembras

10.07 ± 1.70 (6.5−13.0)
64 machos, 11 hembras

88 bien, 4 izquierda, 5 ambi
112.9 ± 13.9 (79−151)
113.1 ± 15.7 (67−153)
107.5 ± 16.0 (71−152)
0.157 ± 0.088 (0.046−0.453)

66 bien, 6 izquierda, 3 ambi
108.8 ± 17.1 (67−141)
108.3 ± 18.4 (72−149)
106.3 ± 15.5 (66−133)
0.155 ± 0.071 (0.055−0.430)

P value
−

0.285
−
−

0.127
0.100

0.632

Mean head motion (mm)
Nota. Mean ± standard deviation (minimum-maximum). IQ was measured based on Wechsler abbrevi-
ated scale of intelligence (WASI, WASI-II) or Snijders–Oomen Nonverbal Intelligence Test (SON-R). TD,
typically developing; ASD, autism spectrum disorder; IQ, intelligence quotient.

0.917

eyes closed while remaining awake. The scanner information and data acquisition parameters
for each site are detailed in Supporting Information Table S1.

Preprocessing of Resting-State fMRI Data

As the three sites utilized different scan lengths, each scan was trimmed to 155 timepoints,
which was the length of the data collected at the Erasmus site. We removed the ﬁrst ﬁve
volumes from the beginning of the scans to remove any scanner initiation artifacts, and re-
moved additional volumes from the end of the scans, as needed, to make each scan 5 minutos
y 10 seconds in duration. The scans were then subjected to quality control procedures as
follows. All neuroimaging data underwent a visual quality control procedure conducted by
trained research assistants to identify scanner- or motion-induced artifacts. Artifacts included,
but were not limited to, signal loss, head coverage, motion slicing, ringing, blurring, ghosting,
and wrapping. Data were rated as either pass, qualiﬁed pass, or fail. All images included
in the analysis received either a pass or a qualiﬁed pass rating. The resting-state scans were
then preprocessed using DPABI version 3.1 (http://rfmri.org/dpabi) (Yan et al., 2016) and SPM
versión 12 (https://www.ﬁl.ion.ucl.ac.uk/spm/software/spm12/). Preprocessing steps included
realignment, normalization to the 3-mm MNI template, y alisado (6 mm at full width at
half maximum).

We did not apply global signal regression (GSR) in the current analysis following previous
CAP studies (Chen et al., 2015; Kaiser et al., 2019). It is worth noting that in CAP analysis,
functional networks are deﬁned on the bases of instantaneous regional synchrony at each
volume of data, and network deﬁnition does not rely on FC and thus may be less sensi-
tive to the motion concerns inherent to FC analysis (Liu et al., 2018). The global signal has
been demonstrated to contain both nuisance signals and neural signals (Murphy & Fox, 2017;
Liu, Nalci, & Falahpour, 2017). Although removal of the global signal as a preprocessing step
signiﬁcantly mitigates artifacts from a variety of sources (Power et al., 2017; Ciric et al., 2018),
it also results in removal of neuronal signal to a degree that is unknown in any given dataset
(Uddin, 2017). Evidence from electrophysiological recordings in macaques (Scholvinck et al.,
2010) and magnetic resonance spectroscopy studies in rodents (Hyder & Rothman, 2010)
clearly demonstrates that the global signal also includes neural signals. The global signal
has been further shown to have a direct neuronal source in studies of macaques (Turchi et al.,
En el estudio actual, we were concerned that GSR might
2018) and rats (Ma et al., 2020).

Global signal regression:
Global signal regression (GSR) refers
to the removal of the average value of
all whole-brain or gray matter signals
via regression as a resting state fMRI
data denoising strategy.

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Salience network dynamics in autism

differentially affect the ASD and TD groups under investigation. As previously noted in sim-
ulation studies (Saad et al., 2012) and empirical studies of ASD, GSR can lead to a reversal
in the direction of group correlation differences relative to other preprocessing approaches
(Gotts et al., 2013). It is for these multiple reasons that we did not apply GSR in the current
análisis, though we acknowledge that the costs and beneﬁts associated with doing so depend
on the research question and thus may differ from study to study (Uddin, 2020a).

Group Independent Component Analysis

The resting-state fMRI data from all 172 participants were subjected to a high model order
ICA by using the Group ICA of fMRI Toolbox (GIFT) (https://trendscenter.org/software/gift/).
We used a model order of 100 independent components (ICs) as in previous similar work
(Allen et al., 2014; Nomi et al., 2016), as individual brain networks are not always effectively
separated when using lower model orders such as 25 o 50 (Ray et al., 2013), pero puede ser
too ﬁnely parcellated at higher model orders above 100 (Kiviniemi et al., 2009). The info-
max algorithm was utilized for the ICA (http://mialab.mrn.org/software/gift) (Calhoun et al.,
2001). To ensure the stability of this estimation, the ICA algorithm was repeated 20 times us-
ing ICASSO (http://www.cis.hut.ﬁ/projects/ica/icasso). Todo 100 ICs were visually inspected and
classiﬁed as noise or non-noise by two of the authors (JN & EM). The ICA components related
to movement, white matter, or cerebrospinal ﬂuid were removed from further analysis. El
remaining 69 components were grouped into 12 functional domains to facilitate CAP interpre-
tation: salience/M-CIN, default mode/M-FPN, central executive/L-FPN, sensorimotor, frontal,
parietal, temporal, occipital, subcortical, atención, cerebellum, and brainstem (Cifra 2).

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Cifra 2.
(A) Organization of ICA components into 12 functional networks. (B) Sagittal, coronal,
and axial views of the salience/midcingulo-insular (M-CIN) component of interest in the analyses.

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Salience network dynamics in autism

The network of interest in this study was the salience/M-CIN, with key nodes in the bi-
lateral anterior insula and anterior cingulate cortices (Uddin, 2015), chosen because of its
previous designation as a major locus of dysfunction in ASD (Uddin & menón, 2009; Nomi,
Molnar-Szakacs, & Uddin, 2019).

Post-ICA Processing of fMRI Data
The time series were analyzed as a 155 (volumes) × 100 (ICs) matrix for each of the 172
Participantes, and were postprocessed using Matlab code from the GIFT toolbox. Postprocessing
included linear detrending, despiking using the AFNI 3D despike command, and bandpass
ﬁltering (0.01–0.1 Hz) Allen et al. (2014).

Statistical Analyses

A k-means clustering algorithm was applied to a concatenated matrix of all subjects’ time-
points of non-noise components to extract dynamic CAPs corresponding to different resting-
redes estatales (Liu et al., 2018). For the anterior insula/anterior cingulate component we
identiﬁed as the salience/M-CIN for each participant, timepoints with signal intensity among
the top 20% (31 timepoints) y 30% (47 timepoints) of activation strength were extracted for
análisis (Chen et al., 2015). This resulted in thresholding at the subject level. The optimal
number of clusters was determined to be k = 5 by running k-means clustering with k-values 2
a través de 20 on a concatenated data matrix of all non-noise components for the selected time-
points across all participants. This optimal k was identiﬁed using the cluster validity index,
deﬁned as the elbow point (Cifra 3) of a least-squares ﬁt line plotted across the cluster validity

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Cifra 3.
(A) Sample time course depicting extraction of top 20% of timepoints. Blue represents
arriba 20% activation timepoints for salience/M-CIN component. (B) Elbow criteria graph to identify
optimal k as 5.

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índice (the ratio between the within-cluster distance and the between-cluster distance) across
all values of k (Allen et al., 2014). Following k-means clustering, the frequency of occur-
rence of each brain conﬁguration was calculated for each participant and compared between
grupos.

Participants were divided into TD and ASD groups. For the top 20% and top 30% analiza,
the mean frequencies of occurrence of CAPs were determined for the two groups. Frecuencia
was computed as the proportion of occurrence for a speciﬁc brain state out of all possible
brain states. Multiple linear regression models were used to compare the frequency of dynamic
CAP occurrence between groups while controlling for data collection site (UM, Erasmus, o
stanford), mean head motion, edad, and handedness.

Additional follow-up analyses were run to further explore patterns of activity when each
group was clustered separately, to examine the relationship between head motion and CAPs,
and to compare the current results against surrogate phase-randomized data. To determine
if each group could be characterized by distinct CAPs rather than differences in frequency of
the same CAPs, the ASD and TD groups were clustered separately using the optimal value
of k as determined for all data. To determine if there was a relationship between speciﬁc
CAPs, head motion and frequency of occurrence, average FD was computed for all TRs as-
signed to each CAP. Finalmente, to ensure that the identiﬁed CAPs were not driven by random
differences in the data rather than brain activity, k-means clustering was conducted on phase-
randomized surrogate data according to the procedure in (Lancaster et al., 2018). Los datos
were phase randomized producing surrogate data with the same mean, variance, and autocor-
relation of the original time series. K-means clustering was then conducted using the optimal
k value previously identiﬁed for the top 20% and top 30% of time points as in the previous
analiza.

RESULTADOS

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For the optimal k of ﬁve clusters, the activation patterns for the top 20% of time points are
como se muestra en la figura 4. For each of the ﬁve clusters, el 10 components with the highest coacti-
vation with the M-CIN component of interest were identiﬁed and are listed according to their
brain network (Cifra 5). The most notable brain conﬁguration patterns were States 1 y 2.
Estado 1 was characterized by strong coactivation with other components of the M-CIN. En
both the top 20% and top 30% analiza, children with ASD entered this brain state more fre-
quently than their TD counterparts, although this difference was not statistically signiﬁcant (pag =
0.1698, pag = 0.1856) (Cifra 6). Estado 2 was characterized by coactivation with the posterior
cingulate cortex (PCC) and medial prefrontal cortex (mPFC) of the M-FPN, as well as bilateral
regions of the L-FPN. This pattern of coactivation was signiﬁcantly more frequently observed in
the TD children compared with ASD in both analyses (pag = 0.0198, k = 0.0232) (Cifra 6). Sig-
niﬁcant group differences were not observed for frequency of occurrence of states 3–5. Resultados
for the top 30% of time points are presented in Supporting Information Figures S1 and S2.

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To ensure that our k-means clustering algorithm produced similar clusters in the ASD and TD
Participantes, the above steps were run on the two groups, separately. There was no noticeable
difference between the CAPs when the groups were clustered separately. The results of these
analyses are detailed in Supporting Information Figures S3 and S4, and Supporting Information
Table S2.

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Cifra 4. Activation of components within each brain network for Top 20% análisis. COI, component of interest; SN, salience network;
DMN, default mode network; cen, central executive network; Front., frontal; Par., parietal; SM, sensorimotor; Sub., subcortical; Temp.,
temporal; Occ., occipital; Att., atención; CB, cerebellum.

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There was no identiﬁable systematic relationship between head motion, the frequency of
occurrence, or amount of TRs in each CAP (Supporting Information Figure S5). The most
frequently occurring CAP (p.ej., the most TRs) did not have the highest average motion (CAP 4).
The CAP with the lowest frequency of occurrence (CAP 1) did not have the average lowest
movimiento (CAP 2). The distribution of TR FD for each CAP also did not appear to drive the
clustering results (p.ej., some CAPs with all TRs being high motion while other TRs were all
low motion) while the average FD for each CAP was low (average FD < 0.2 mm). Finally, phase-randomized surrogate data produced no noticeable CAPs of brain activity (Supporting Information Figure S6). The CAPs from the primary analysis show strong patterns of activation related to distinct brain networks. The CAPs from the phase-randomized surrogate data show weak activation across network nodes that does not appear to be related to distinct brain networks. Network Neuroscience 1227 Salience network dynamics in autism l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / t / e d u n e n a r t i c e - p d l f / Figure 5. Top 10 most strongly activated components of each brain state in the top 20% analysis. / / / 4 4 1 2 1 9 1 8 6 7 0 8 2 n e n _ a _ 0 0 1 6 3 p d . t / f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Figure 6. Observed frequency of occurrence of coactivation patterns 1–5 for typically developing (TD) and autism spectrum disorder (ASD) groups in the top 20% and top 30% analyses; *p < .05 in t test; **p < .05 in t test and multiple regression analyses. Network Neuroscience 1228 Salience network dynamics in autism DISCUSSION ASDs are widely thought to be associated with atypical patterns of functional brain connectivity (Uddin, Supekar, & Menon, 2013; Vissers, Cohen, & Geurts, 2012; Müller et al., 2011) within and between large-scale brain networks important for high-level cognitive and emotional processes (Padmanabhan et al., 2017; Nomi, Molnar-Szakacs, & Uddin, 2019). Analysis of brain dynamics is beginning to reveal insights into neurodevelopmental disorders affecting brain connectivity (Uddin & Karlsgodt, 2018; White & Calhoun, 2019). This emerging lit- erature provides initial evidence for alterations in ASD related to the number of transitions between brain states (Uddin, 2020a). Most of these studies have focused on characterizing whole-brain dynamic patterns and utilized sliding window dynamic FC approaches that have known potential pitfalls such as arbitrary window sizes (Preti, Bolton, & Van De Ville, 2017; Lurie et al., 2020). Here we focus speciﬁcally on dynamics of a brain network known as the salience network, or M-CIN, with key nodes in the bilateral anterior insula and anterior cin- gulate cortices (Uddin, 2015), thought to be a locus of dysfunction in ASD (Uddin & Menon, 2009; Nomi, Molnar-Szakacs, & Uddin, 2019). We use dynamic coactivation pattern (CAP) analysis, which relies on fewer assumptions than the sliding window approach, and permits the examination of brain state alterations closer to the temporal resolution of individual time frames (Chen et al., 2015). Using a combined ICA and CAP analytic approach, we investi- gated the dynamic nature of M-CIN organization in children with ASD. By utilizing only time points with the highest overall activation in the M-CIN in our analyses, we were able to focus on this key network and note differences in the frequency of its coactivation with other major large-scale networks of the brain. In three of the ﬁve dynamic states or CAPs identiﬁed across TD and ASD participants, the component of interest was consistently coactivated with other components of the M-CIN, in- cluding the anterior cingulate and the posterior and ventral regions of the insular cortex. This coactivation was particularly strong in State 1 and was also observed to some extent in States 3 and 4, which showed similar coactivation of the M-CIN components. Previous static FC work has demonstrated altered M-CIN network properties in children and adolescents with ASD (Ebisch et al., 2011; Abbott et al., 2016), and FC of the M-CIN can be used to discriminate ASD from typical development (Uddin et al., 2013; Anderson et al., 2011). The relative frequency of occurrence of State 2 differed signiﬁcantly between the TD and ASD groups. In this state, the component of interest was not strongly coactivated with any other components of the M-CIN, and its coactivation with components of other networks was relatively weak, except a single component in the central executive/lateral fronto-parietal network (L-FPN) and two components in the default mode/medial fronto-parietal network (M-FPN). Children with ASD exhibited this CAP signiﬁcantly less frequently than TD children. The lower frequency of occurrence of this state for children with ASD indicates less coactiva- tion of the M-CIN with components of the M-FPN and L-FPN, especially the medial prefrontal cortex (mPFC) and PCC. Previous work has demonstrated that effective connectivity among nodes of these three networks can be used to discriminate task-evoked and resting states in TD children to a greater extent than in children with ASD. The same study found that this brain state discriminability was related to symptom severity in the domain of restricted and repeti- tive behaviors in children with ASD (Uddin et al., 2015). The reduced communication among these three networks observed in our ASD samples could be related to symptoms of cognitive and behavioral inﬂexibility commonly observed in children with the disorder. With regard to the L-FPN and M-FPN, two other major networks that have been impli- cated in autism and other neurodevelopmental disorders, components of these networks were Network Neuroscience 1229 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / / t e d u n e n a r t i c e - p d l f / / / / 4 4 1 2 1 9 1 8 6 7 0 8 2 n e n _ a _ 0 0 1 6 3 p d / . t f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Salience network dynamics in autism consistently among the 10 with strongest coactivation with the M-CIN in all 5 observed CAPs. Within the M-FPN, the components with high activation included the mPFC in States 2, 3, and 4 as well as the PCC in State 2. For the L-FPN, the supramarginal gyrus (SMG) was a top 10 component in States 1 and 3, and bilateral regions of the L-FPN were strongly activated in States 2 and 5. As activation of the L-FPN and M-FPN has been shown to be inﬂuenced and controlled by the M-CIN (Uddin, 2015), this commonly observed CAP is consistent with previous research examining interactions among these three networks (Goulden et al., 2014). Only one previous study has speciﬁcally examined anterior insula dynamics in ASD. Us- ing a large ABIDE sample, Guo and colleagues identiﬁed an anterior insula ROI using Neu- rosynth (Yarkoni et al., 2011); they then used a sliding window analytic approach to show that this ROI exhibited decreased FC with the mPFC and PCC in certain brain states compared with TD individuals (Guo et al., 2019). Our current ﬁndings of reduced coactivation between M-CIN and M-FPN in ASD is in line with this observation, and employs a method that over- comes some of the limitations of sliding window correlation analyses (Lurie et al., 2020; Preti, Bolton, & Van De Ville, 2017). Limitations A few limitations of the current work should be noted. First, our sample includes data collected at three different sites. While this increases the sample size and statistical power, the use of data across multiple sites presents its own limitations in that intersite variability may affect the analyses. Though we selected data from a limited age range by using similar acquisition parameters and participant eye status (eyes closed), and attempted to control for acquisition site across all analyses, we cannot be sure that inherent between-site effects are completely accounted for. In addition, we cannot ascertain whether diagnostic procedures and scanner sessions were conducted in the same way for all participants at the three sites. There were also some discrepancies in the information that was reported to the ABIDE database for the different sites, eliminating the possibility of using behavioral measures like the Repetitive Behavior Scale or the Social Communication Questionnaire to explore relationships between dynamic FC parameters and symptom severity in the ASD group. Furthermore, similarly to most studies of ASD, our sample consisted mostly of males, with only 34 female participants included in the analyses. Although sex was included as a variable in our statistical analyses and had minimal effect on the signiﬁcant results reported, this imbalance of males to females may fail to account for differences in the brain activity of the two sexes (Lai et al., 2017). Despite these limitations, this study identiﬁes the M-CIN, including the bilateral anterior insula and anterior cingulate, as a network of particular interest for further investigation with dynamic FC approaches in children with ASD. Further research could use tasks known to elicit activation in this network to investigate the differential activation in children and ado- lescents with ASD (Odriozola et al., 2016), as well as adults and older individuals, as patterns of dynamic FC may change signiﬁcantly with age (Hutchison & Morton, 2016, 2015). The relationship between the M-CIN, M-FPN, and L-FPN in ASD also merits further investigation, as the identiﬁcation of connections or dynamic coactivations (or lack thereof) between these networks may be utilized in the future as potential diagnostic identiﬁers of ASD and other neurodevelopmental disorders (Uddin et al., 2017). Conclusion The ﬁndings of this study build on the growing body of literature on the use of dynamic connec- tivity approaches in the investigation of neurodevelopmental disorders, and ASD in particular. Network Neuroscience 1230 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / / t e d u n e n a r t i c e - p d l f / / / / 4 4 1 2 1 9 1 8 6 7 0 8 2 n e n _ a _ 0 0 1 6 3 p d t / . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Salience network dynamics in autism Our combination of ICA and CAP allowed for the identiﬁcation of highly speciﬁc networks of interest, particularly those centered on the bilateral insular and anterior cingulate cortices. The results of this study provide further insight into the dynamic FC abnormalities that may underlie the clinical presentation of ASD, but future studies are needed to identify the neural mechanisms by which these brain abnormalities are related to ASD symptoms, particularly those related to inﬂexible behaviors. ACKNOWLEDGMENTS The authors gratefully acknowledge Willa Voorhies, Paola Odriozola, Kristafor Farrant, Dina Dajani, Casey Burrows, Taylor Bolt and Shruti Vij for assistance with MRI data collection, as well as Amy Beaumont, Sandra Cardona, Meaghan Parlade, and Michael Alessandri for assistance with clinical assessments. SUPPORTING INFORMATION Supporting information for this article is available at https://doi.org/10.1162/netn_a_00163. AUTHOR CONTRIBUTIONS Emily Marshall: Data curation; Formal analysis; Visualization; Writing - Original Draft. Jason Nomi: Conceptualization; Formal analysis; Funding acquisition; Investigation; Supervision; Visualization; Writing - Original Draft; Writing - Review & Editing. Bryce Dirks: Data curation; Supervision; Writing - Review & Editing. Celia Romero: Conceptualization; Data curation; Supervision; Writing - Review & Editing. Lauren Kupis: Formal analysis; Supervision; Writing - Review & Editing. Catie Chang: Formal analysis; Methodology; Resources; Writing - Review & Editing. Lucina Q. Uddin: Conceptualization; Funding acquisition; Project administration; Resources; Supervision; Writing - Original Draft; Writing - Review & Editing. FUNDING INFORMATION Lucina Q. Uddin, National Institute of Mental Health (http://dx.doi.org/10.13039/100000025), Award ID: R01MH107549. Lucina Q. Uddin, Canadian Institute for Advanced Research. Lucina Q. Uddin, University of Miami Gabelli Senior Scholar Award. Jason Nomi, National In- stitute of Mental Health (http://dx.doi.org/10.13039/100000025), Award ID: R03MH121668. Jason Nomi, Brain & Behavior Research Foundation. REFERENCES Abbott, A. E., Nair, A., Keown, C. L., Datko, M., Jahedi, A., Fishman, I., & Muller, R. A. (2016). Patterns of atypical functional connectivity and behavioral links in autism differ between de- fault, salience, and executive networks. Cerebral Cortex, 26(10), 4034–4045. 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