Abstract
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 hyperconnectivitymay 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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 869-879 |
| Number of pages | 11 |
| Journal | JAMA Psychiatry |
| Volume | 70 |
| Issue number | 8 |
| DOIs | |
| State | Published - Aug 2013 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Arts and Humanities (miscellaneous)
- Psychiatry and Mental health
Cite this
Salience network-based classification and prediction of symptom severity in children with autism. / Uddin, Lucina Q; Supekar, Kaustubh; Lynch, Charles J.; Khouzam, Amirah; Phillips, Jennifer; Feinstein, Carl; Ryali, Srikanth; Menon, Vinod.
In: JAMA Psychiatry, Vol. 70, No. 8, 08.2013, p. 869-879.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Salience network-based classification and prediction of symptom severity in children with autism
AU - Uddin, Lucina Q
AU - Supekar, Kaustubh
AU - Lynch, Charles J.
AU - Khouzam, Amirah
AU - Phillips, Jennifer
AU - Feinstein, Carl
AU - Ryali, Srikanth
AU - Menon, Vinod
PY - 2013/8
Y1 - 2013/8
N2 - 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 hyperconnectivitymay 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.
AB - 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 hyperconnectivitymay 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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UR - http://www.scopus.com/inward/citedby.url?scp=84880346529&partnerID=8YFLogxK
U2 - 10.1001/jamapsychiatry.2013.104
DO - 10.1001/jamapsychiatry.2013.104
M3 - Article
C2 - 23803651
AN - SCOPUS:84880346529
VL - 70
SP - 869
EP - 879
JO - JAMA Psychiatry
JF - JAMA Psychiatry
SN - 2168-622X
IS - 8
ER -