Biomarkers for autism spectrum disorder: opportunities for magnetoencephalography (MEG)

Abstract

This paper reviews a candidate biomarker for ASD, the M50 auditory evoked response component, detected by magnetoencephalography (MEG) and presents a position on the roles and opportunities for such a biomarker, as well as converging evidence from allied imaging techniques (magnetic resonance imaging, MRI and spectroscopy, MRS). Data is presented on prolonged M50 latencies in ASD as well as extension to include children with ASD with significant language and cognitive impairments in whom M50 latency delays are exacerbated. Modeling of the M50 latency by consideration of the properties of auditory pathway white matter is shown to be successful in typical development but challenged by heterogeneity in ASD; this, however, is capitalized upon to identify a distinct subpopulation of children with ASD whose M50 latencies lie well outside the range of values predictable from the typically developing model. Interestingly, this subpopulation is characterized by low levels of the inhibitory neurotransmitter GABA. Following from this, we discuss a potential use of the M50 latency in indicating "target engagement" acutely with administration of a GABA-B agonist, potentially distinguishing "responders" from "non-responders" with the implication of optimizing inclusion for clinical trials of such agents. Implications for future application, including potential evaluation of infants with genetic risk factors, are discussed. As such, the broad scope of potential of a representative candidate biological marker, the M50 latency, is introduced along with potential future applications.This paper outlines a strategy for understanding brain dysfunction in individuals with intellectual and developmental disabilities (IDD). It is proposed that a multimodal approach (collection of brain structure, chemistry, and neuronal functional data) will identify IDD subpopulations who share a common disease pathway, and thus identify individuals with IDD who might ultimately benefit from specific treatments. After briefly demonstrating the need and potential for scope, examples from studies examining brain function and structure in children with autism spectrum disorder (ASD) illustrate how measures of brain neuronal function (from magnetoencephalography, MEG), brain structure (from magnetic resonance imaging, MRI, especially diffusion MRI), and brain chemistry (MR spectroscopy) can help us better understand the heterogeneity in ASD and form the basis of multivariate biological markers (biomarkers) useable to define clinical subpopulations. Similar approaches can be applied to understand brain dysfunction in neurodevelopmental disorders (NDD) in general. In large part, this paper represents our endeavors as part of the CHOP/Penn NICHD-funded intellectual and developmental disabilities research center (IDDRC) over the past decade.

Fig. 1

A Auditory evoked responses depicted in sensor and source space identify the M50 (red vertical line), B confirms the magnetic field topography (i.e., left hemisphere M50 magnetic field topography) and C the two-dipole source model used to extract the source waveforms in A and D shows group level M50 latency differences (mean ± SEM), significant bilaterally and with ~ 5 ms M50 latency delay in ASD compared to TDC

Fig. 2

Left and right M50 response latencies are delayed in verbal children with ASD (ASD-V) and further delayed in minimally verbal/non-verbal children with ASD (ASD-MVNV)

Fig. 3

Multimodal approaches to mechanism and statistical definition of ASD subpopulations: a white-matter fiber tracking of the thalamocortical auditory radiations, defined using high angular resolution diffusion imaging (HARDI). b Sagittal and axial depiction of the voxel placement for spectrally edited MEGAPRESS MRS, yielding GABA estimates (c). Modeling M50 latency (from Fig. 1) using the acoustic radiations FA along with age in d allowed prediction of M50 latency in TD children (accounting for 52% of the variance) with e white-matter FA a significant predictor of M50 latency (p < 0.0001). The model, although still significant (but with different coefficients), did not perform as well in the ASD cohort, likely due to the heterogeneity of the ASD cohort. However, and in fact addressing the heterogeneity of ASD (f), a subpopulation of extreme M50 latency outliers was identified, as depicted in the histograms of residual latency (deviation from the TD model). This subpopulation was characterized by having significantly lower GABA than the ASD children that conformed to the TD model [1]

Fig. 4

Schematic of the MEG Protocol for Low-Language/Cognitive Ability Neuroimaging (MEG-PLAN)