Evaluation of Mismatch Negativity As Biomarker for Language Impairment in Autism Spectrum Disorder

Saturday, May 16, 2015: 11:30 AM-1:30 PM
Imperial Ballroom (Grand America Hotel)
H. L. Green, L. Goodwin and K. Froud, Biobehavioral Sciences, Columbia University, New York, NY
Background: Currently, autism spectrum disorder (ASD) is diagnosed using the Diagnostic Statistical Manual of Mental Disorders-Fifth Edition (DSM-V) and children often go undiagnosed until around the age of three. Moreover, current language assessments are designed to behaviorally measure language skills, therefore requiring that a child have language or be “of language age” in order to participate. As a result of these diagnostic limitations, speech and language interventions for children with ASD plus language impairment (ASD+LI) are often not initiated until a child is of preschool age. Finding an early and objective way to identify language impairment (LI) in ASD has the potential to lead to earlier speech and language intervention for individuals “at risk” for the disorder. Magnetoencephalography (MEG) studies use the Mismatch Field component (MMF) to investigate how the brain processes speech sounds. Previous MEG studies by Roberts et al. (2011) utilizing the MMF component have shown that increased MMF latency (i.e., longer processing time) is a predictor of LI in children with ASD (sensitivity 82.4%; specificity 71.2%).

Objectives: Since MEG is expensive and not widely used with infants or young children, we attempted to replicate these results using the mismatch negativity (MMN), the electroencephalography (EEG) equivalent of MMF. EEG is inexpensive and can be used with children of all ages making it an appropriate method to identify LI in children on the autism spectrum. We explored increased MMN latency as a potential biomarker for LI in autism.

Methods: EEG was recorded in children ages 6-10 with ASD+LI, ASD-LI and typically developing controls (TD) during a passive auditory oddball experiment presenting pure tones, speech sounds and complex non-speech sounds. During the recording children were instructed to watch a movie and ignore the sounds.

Results: Individuals with ASD+LI demonstrated MMN latency differences in response to all sounds compared to those with ASD-LI and TD controls. We propose that decreased MMN latency is associated with LI in ASD and propose that it is a sensitive and specific predictor of LI in individuals already diagnosed with autism.

Conclusions: It has been proposed in the literature that decreased MMN latency is associated with immature white matter pathways in the brain and inefficiencies in auditory stimulus transmission may play a role in language impairment in autism. If future research reveals that the MMN latency differences predict future ASD+LI diagnosis in infants and young children, MMN could be used as an early biomarker of LI impairment in ASD. Early identification of ASD language impairment (LI) in at risk children is critical for ensuring that these children get access to early intervention.