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

Friday, 3 May 2013: 18:15
Meeting Room 3 (Kursaal Centre)
L. Uddin1, K. Supekar2, C. Lynch3, A. Khouzam3, J. M. Phillips4, C. Feinstein3, S. Ryali3 and V. Menon1, (1)Stanford University, Stanford, CA, (2)Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, (3)Stanford University, Palo Alto, CA, (4)Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA

Autism spectrum disorders (ASD) affect 1 in 88 children and are characterized by a complex phenotype including social, communicative, and sensorimotor deficits. ASD 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.


We examined connectivity of large-scale brain networks and determined whether specific networks could distinguish children with ASD from typically developing (TD) children and predict symptom severity in children with ASD.


We utilized a case-control design using functional magnetic resonance imaging. Twenty 7-12 year-old children with ASD and twenty age-, gender-, and IQ-matched TD children participated in the study. Our main outcome measures were: (1) Between-group differences in intrinsic functional connectivity of large-scale brain networks, (2) performance of a classifier built to discriminate children with ASD from TD children based on specific brain networks, and (3) 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 hyper-connectivity in ASD encompassed salience, default mode, fronto-temporal, motor, and visual networks. No large-scale networks showed stronger connectivity in TD children compared with children with ASD. This hyper-connectivity 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% (p< 0.03), 75% sensitivity and 80% specificity. The salience network, comprised of anterior cingulate and anterior insular cortices, showed the highest classification accuracy among all networks examined, and BOLD signal in this network predicted restricted and repetitive behaviors.


Salience network hyper-connectivity may be a distinguishing feature in children with ASD. Quantification of brain network connectivity is a step towards the development of biomarkers for objectively identifying children with the disorder.

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