Electrophysiological Signatures for ASD: Putting the “Bio” into Biomarker

Background: Electrophysiological signatures recorded by EEG and MEG are increasingly recognized as distinctive features of ASD, offering neurobiologic insight as well as potential diagnostic/prognostic utility. As such they increasingly attract the label “biomarker”.  However, the mechanistic basis implied in this moniker is often left unspecified. Furthermore, by establishing a biological component to the signature, and justifying the term “biomarker”, an additional potential axis is offered for stratification of the heterogeneous autism spectrum.

Objectives: To evaluate the interpretation of auditory evoked electrophysiologic responses as biomarkers for ASD, using converging evidence form multimodal imaging/spectroscopy studies to provide a biological basis for defining sub-populations for future use as inclusion/enrichment criteria for targeted pharmaceutical trials.

Methods: Approximately 200 school-aged (6-15yrs) children on the autism spectrum, and typically-developing control subjects have been recruited for multimodal imaging studies including magnetoencephalography (MEG) during auditory stimulation, as well as diffusion-magnetic resonance imaging (both DTI and HARDI) and , in a subset, spectrally-edited magnetic resonance spectroscopy (MRS). While MEG measures auditory evoked cortical response component latency (M50 and M100), diffusion measures focus on the microstructure of thalamocortical projections to superior temporal gyrus, and MRS measures address levels of neurotransmitters GABA and glutamate in superior temporal lobe. Clinical assessments include ADOS and ADI-R, as well as dimension scales such as the social responsiveness scale (SRS) and domain-specific measures such as the clinical evaluation of language fundamentals (CELF-4).

Results: Auditory evoked response components M50 and M100 are characterized by significantly delayed latencies in ASD of magnitude ~5ms and ~10ms respectively (p’s <0.05). While the developmental trajectory of these response components shows a maturational slope similar to that observed in typical development, the absolute delay persists. This pattern of atypical maturation is reflected in quantitative diffusion imaging of the thalamocortical white matter in which the mean diffusivity shows a developmental slope similar to neurotypical, but of differing underlying (eigenvalue) basis. As such strong associations between properties of the white matter (such as fractional anisotropy) and cortical response latencies that are significant in typical development (p<0.05) are lost in ASD. Similarly evidence for atypical neurochemistry in ASD is revealed in an approximately 15-20% decrease in levels of inhibitory neurotransmitter GABA in the superior temporal lobe of children with ASD compared to TD (p<0.05). Nonetheless, both diffusion and spectroscopy findings are associated with significant inter-individual variability, perhaps representing a brain level embodiment of autism spectrum heterogeneity, and suggesting their combined use in definition of sub-populations.

Conclusions: Delays in the latency of auditory cortical responses such as the ~100ms M100 component have been consistently observed in ASD (although to varying degrees) and speculatively attributed to both white matter conduction anomalies as well as deficits in synaptic transmission. Support for both of these explanatory neurobiological hypotheses can be found in diffusion imaging of thalamocortical white matter and MRS assays of temporal lobe neurotransmitter balance. Since the potential therapeutic management of these etiologies differs, we propose that such biological measures form the basis of stratification biomarkers, appropriate for tailoring clinical trial inclusion, and subsequently directing patient management.