Salience Network–Based Classification and Prediction of Symptom Severity in Children With Autism
Open accessAbstract
IMPORTANCE: Autism spectrum disorder (ASD) affects 1 in 88 children and is characterized by a complex phenotype, including social, communicative, and sensorimotor deficits. Autism spectrum disorder has been linked with atypical connectivity across multiple brain systems, yet the nature of these differences in young children with the disorder is not well understood. OBJECTIVES: To examine connectivity of large-scale brain networks and determine whether specific networks can distinguish children with ASD from typically developing (TD) children and predict symptom severity in children with ASD. DESIGN, SETTING, AND PARTICIPANTS: Case-control study performed at Stanford University School of Medicine of 20 children 7 to 12 years old with ASD and 20 age-, sex-, and IQ-matched TD children. MAIN OUTCOMES AND MEASURES: Between-group differences in intrinsic functional connectivity of large-scale brain networks, performance of a classifier built to discriminate children with ASD from TD children based on specific brain networks, and correlations between brain networks and core symptoms of ASD. RESULTS: We observed stronger functional connectivity within several large-scale brain networks in children with ASD compared with TD children. This hyperconnectivity in ASD encompassed salience, default mode, frontotemporal, motor, and visual networks. This hyperconnectivity result was replicated in an independent cohort obtained from publicly available databases. Using maps of each individual's salience network, children with ASD could be discriminated from TD children with a classification accuracy of 78%, with 75% sensitivity and 80% specificity. The salience network showed the highest classification accuracy among all networks examined, and the blood oxygen-level dependent signal in this network predicted restricted and repetitive behavior scores. The classifier discriminated ASD from TD in the independent sample with 83% accuracy, 67% sensitivity, and 100% specificity. CONCLUSIONS AND RELEVANCE: Salience network hyperconnectivity may be a distinguishing feature in children with ASD. Quantification of brain network connectivity is a step toward developing biomarkers for objectively identifying children with ASD.
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Another study examining a younger cohort (age 7-12) using a logistic regression classifier found that the salience network, including the anterior insular cortices, contained information that could be used to discriminate children with ASD from TD children with 78% accuracy.
“Another study examining a younger cohort (age 7–12) using a logistic regression classifier also found that the salience network, including the anterior insular cortices, contained information that could be used to discriminate children with ASD from TD children with 78% accuracy.30”
From Progress and roadblocks in the search for brain-based biomarkers of autism and attention-deficit/hyperactivity disorder (2017) · cites this paper as [30]
Data collected at another institution could be classified with 80% accuracy based on the classifier built by the authors.
“Data collected at another institution could be classified with 80% accuracy based on the classifier built by the authors.”
From Progress and roadblocks in the search for brain-based biomarkers of autism and attention-deficit/hyperactivity disorder (2017) · cites this paper as [30]
Both hyperconnectivity and hypoconnectivity have been reported in children with ADHD and ASD.
“Both hyperconnectivity (Uddin et al., 2013; de Celis et al., 2014) and hypoconnectivity (Bos et al., 2014; Yoo et al., 2017) have been reported in children with ADHD and ASD.”
From Investigating functional brain network integrity using a traditional and novel categorical scheme for neurodevelopmental disorders (2019) · cites this paper as Uddin et al., 2013
Past studies using ICA dual regression to compare children with ASD or ADHD to TD children used small sample sizes (n = 20-26 per group).
“Past studies using ICA dual regression to compare children with ASD or ADHD to TD children used small sample sizes (n = 20–26 per group) (Uddin et al., 2013; Yoo et al., 2017; Nomi and Uddin, 2015; Washington et al., 2014; Choi et al., 2013).”
From Investigating functional brain network integrity using a traditional and novel categorical scheme for neurodevelopmental disorders (2019) · cites this paper as Uddin et al., 2013
There have been reports of both within-network hyperconnectivity and hypoconnectivity of the FPN and DMN in ASD.
“For example, there have been reports of both within-network hyperconnectivity (Uddin et al., 2013; Nomi and Uddin, 2015) and hypoconnectivity of the FPN (Bos et al., 2014) and DMN (Bos et al., 2014; Washington et al., 2014).”
From Investigating functional brain network integrity using a traditional and novel categorical scheme for neurodevelopmental disorders (2019) · cites this paper as Uddin et al., 2013
Significance was determined using a threshold-free cluster enhancement of p < .05 (FWE-corrected) for both within- and between-network analyses.
“Significance was determined using a threshold-free cluster enhancement of p < .05 (FWE-corrected) for both within- and between-network analyses, as in previous studies (Uddin et al., 2013; Nomi and Uddin, 2015).”
From Investigating functional brain network integrity using a traditional and novel categorical scheme for neurodevelopmental disorders (2019) · cites this paper as Uddin et al., 2013
The ASD literature is similarly inconsistent, even when limited to studies using dual regression ICA.
“The ASD literature is similarly inconsistent, even when limited to studies using dual regression ICA.”
From Investigating functional brain network integrity using a traditional and novel categorical scheme for neurodevelopmental disorders (2019) · cites this paper as Uddin et al., 2013
The right anterior insula has been posited as a locus of dysfunction in ASD, and functional and effective connectivity of this region and the broader midcingulo-insular network is associated with symptom severity in the domain of restricted and repetitive behaviors.
“The rAI has been posited to be a locus of dysfunction in ASD (86), and functional and effective connectivity of this region and the broader M-CIN is associated with symptom severity in the domain of RRBs (87,88).”
From Brain Mechanisms Supporting Flexible Cognition and Behavior in Adolescents With Autism Spectrum Disorder (2021) · cites this paper as [88]
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Autism: The first firm finding = underconnectivity?
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Atypical diffusion tensor hemispheric asymmetry in autism
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An fMRI investigation of responses to peer rejection in adolescents with autism spectrum disorders
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The promise and the pitfalls of autism research: An introductory note for new autism researchers
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Salience processing and insular cortical function and dysfunction
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“with ASD would exhibit hyperconnectivity within FPN, DMN, and SN (Uddin et al., 2013; Nomi and Uddin, 2015) compared with TD children.”
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- Authors
- Uddin, L. Q., Supekar, K., Lynch, C. J., Khouzam, A., Phillips, J., Feinstein, C., Ryali, S., & Menon, V.
- Affiliations
- Stanford Medicine (US)
- Corresponding author
- Uddin, Lucina Q.
- Menon, Vinod
- ORCID
- Venue
- JAMA Psychiatry
- Publisher
- American Medical Association (AMA)
- Published
- 2013-08-01
- Volume
- 70
- Issue
- 8
- Pages
- 869
- ISSN
- 2168-622X (ISSN-L)
- PMC
- PMC3951904
- OpenAlex
- W1973140577
- Cited by
- 621
- References
- 92
- Citations by year
- 2026: 40 · 2025: 41 · 2024: 32 · 2023: 43 · 2022: 57 · 2021: 57 · 2020: 58 · 2019: 47 · 2018: 61 · 2017: 46 · 2016: 47 · 2015: 47 · 2014: 30 · 2013: 14
- FWCI
- 24.78
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- JAMA Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (MH084164, K01MH092288, R01MH084164)
- National Institute on Deafness and Other Communication Disorders (R21DC011095, DC011095)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Neurodevelopmental Disorders
- Keywords
- autism spectrum disorder, salience network, functional connectivity, hyperconnectivity, large-scale brain networks, symptom severity prediction, restricted and repetitive behaviors, ASD classification, biomarkers, children, default mode network, fronto-temporal network, motor network, visual network
- MeSH terms
- Brain, Child, Child Development Disorders, Pervasive, Female, Humans, Magnetic Resonance Imaging, Male, Nerve Net, Predictive Value of Tests, Sensitivity and Specificity, Severity of Illness Index, Cohort Studies, Case-Control Studies, Principal Component Analysis, Connectome
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