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Original Investigation |

Salience Network–Based Classification and Prediction of Symptom Severity in Children With Autism

Lucina Q. Uddin, PhD1; Kaustubh Supekar, PhD1; Charles J. Lynch, BA1; Amirah Khouzam, MA1; Jennifer Phillips, PhD1; Carl Feinstein, MD1; Srikanth Ryali, PhD1; Vinod Menon, PhD1,2,3,4
[+] Author Affiliations
1Departments of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
2Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California
3Program in Neuroscience, Stanford University School of Medicine, Stanford, California
4Stanford Institute for Neuro-Innovation & Translational Neurosciences, Stanford University School of Medicine, Stanford, California
JAMA Psychiatry. 2013;70(8):869-879. doi:10.1001/jamapsychiatry.2013.104.
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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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Figures

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Figure 1.
Large-scale Brain Networks Identified Using Independent Component Analysis (ICA)

Data from 40 children (20 children with autism spectrum disorder [ASD] and 20 typically developing [TD] children) were combined in a group ICA to identify 25 independent components (networks) across all participants in a data-driven manner. Ten of these components correspond to previously identified functional networks: salience (A), central executive (B), posterior default mode (C), ventral default mode (D), anterior default mode (E), dorsal attention (F), motor (G), visual association (H), primary visual (I), and frontotemporal (J). Maps are displayed at z > 2.3 (P < .01).

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Figure 2.
Brain Network Hyperconnectivity in Children With Autism Spectrum Disorder (ASD) Compared With Typically Developing (TD) Children

Autism spectrum disorder greater than TD functional connectivity was observed in 6 of the 10 networks examined: salience (A), posterior default mode (B), motor (C), visual association (D), primary visual (E), and frontotemporal (F). Group difference maps were thresholded using threshold-free cluster enhancement (P < .05).

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Figure 3.
Classification Analysis and Accuracy

A, Classification analysis flowchart. The 10 components identified from each participant served as features to be input into classification analyses. A linear classifier built using logistic regression was used to classify children with autism spectrum disorder (ASD) from typically developing (TD) children. B, Classification accuracy for brain networks. The salience network produced the highest classification accuracy at 78% (P = .02). DMN indicates default mode network.

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