Detecting Biomarkers for Autism

Biomarker page image 

We are striving to detect biomarkers, i.e., objectively measurable and quantifiable indicators of autism spectrum disorder (ASD) that can quantify responses to behavioral and pharmaceutical interventions.

We are pursuing a multimodal, interdisciplinary approach to identify biomarkers of ASD in early developmental stages, before clinical diagnosis can be made, and investigating the potential of these measures to predict developmental and intervention outcomes in adolescence and adulthood. Our research combines various neuroimaging techniques, such as structural and functional magnetic resonance imaging (fMRI) (Kaiser et al., 2010), electroencephalography (EEG) (Pitskel et al., 2014; Cardinaux et al., 2015), physiological measures (Bjornsdotter et al., 2016), computational modeling of behavior (Rosenblau et al., 2017), and brain networks (Venkataraman et al., 2015).

Potential biomarkers:

  • Differences in the structure and/or volume of the brain (Hazlett et al., 2017), such as an abnormal extension of the brain’s surface area (Shen et al., 2017), are potential biomarkers. Studies have found that the extent of these differences is linked to the severity of ASD symptoms (Yang et al., 2016). FMRI scans can identify such differences before ASD symptoms become apparent; while biomarkers can be detected in infancy (Hazlett et al., 2017), they remain present throughout life (van Rooij et al., 2017).
  • Brain activity is a sensitive measure of social information processing differences in individuals with ASD (Bjornsdotter et al., 2016). Observations of how brain activity changes in reaction to treatments such as intranasal oxytocin (Gordon et al., 2016) can be used to inform and validate the effectiveness of therapeutic interventions. 
  • Brain activity signaling differences in sensory processing of young children with ASD can become an early marker of ASD before behavioral symptoms become apparent (Small and Pelphrey, 2015). As electroencephalogram (EEG) recordings do not require language or overt attention from the subject, they have potential to become a routine screening measure even in newborns. 
  • We are currently testing whether neural and physiological differences in habituation to audiovisual stimuli are potential biomarkers for autism in young children.

Literature:

Bjornsdotter M, Wang N, Pelphrey K, Kaiser MD (2016) Evaluation of quantified social perception circuit activity as a neurobiological marker of autism spectrum disorder. JAMA Psychiatry.

Cardinaux A, Nejati H, K Rogers C, Tsourides K, Gandhi T, Kjelgaard M, Sinha P (2015) Decreased Habituation to Naturalistic Stimuli in Autism.

Gordon I, Jack A, Pretzsch CM, Vander Wyk BC, Leckman JF, Feldman R, Pelphrey KA (2016) Intranasal oxytocin enhances connectivity in the neural circuitry supporting social motivation and social perception in children with autism. Sci Rep.

Hazlett HC et al. (2017) Early brain development in infants at high risk for autism spectrum disorder. Nat Publ Gr.

Kaiser MD, Hudac CM, Shultz S, Lee SM, Cheung C, Berken AM, Deen B, Pitskel NB, Sugrue DR, Voos AC, Saulnier CA, Ventola P, Wolf JM, Klin A, Vander Wyk BC, Pelphrey KA (2010) Neural signatures of autism. Proc Natl Acad Sci.

Pitskel NB, Bolling DZ, Kaiser MD, Pelphrey KA, Crowley MJ (2014) Neural systems for cognitive reappraisal in children and adolescents with autism spectrum disorder. Dev Cogn Neurosci.

Rosenblau G, Korn CW, Pelphrey KA (2017) A computational account of optimizing social predictions reveals that adolescents are conservative learners in social contexts. J Neurosci.

Shen MD et al. (2017) Increased Extra-axial Cerebrospinal Fluid in High-Risk Infants Who Later Develop Autism. Biol Psychiatry.

Small DM, Pelphrey KA (2015) Autism Spectrum Disorder: Sniffing Out a New Biomarker. Curr Biol.

van Rooij D et al. (2017) Cortical and Subcortical Brain Morphometry Differences Between Patients With Autism Spectrum Disorder and Healthy Individuals Across the Lifespan: Results From the ENIGMA ASD Working Group. Am J Psychiatry 175:359–369 Available at: https://doi.org/10.1176/appi.ajp.2017.17010100.

Venkataraman A, Duncan JS, Yang DYJ, Pelphrey KA (2015) An unbiased Bayesian approach to functional connectomics implicates social-communication networks in autism. NeuroImage Clin.

Yang DYJ, Beam D, Pelphrey KA, Abdullahi S, Jou RJ (2016) Cortical morphological markers in children with autism: A structural magnetic resonance imaging study of thickness, area, volume, and gyrification. Mol Autism.