20653
Longitudinal Changes in M100 Latency in Children with ASD and Neurotypical Controls
One of the more replicated electrophysiological signatures of ASD is the auditory M100 latency delay of ~10ms in children with ASD, as compared to typically developing age matched controls. This electrophysiological observation has great potential as a biological marker (biomarker), exhibiting a plausible putative biological underpinning in the correlation between thalamocortical white matter microstructure and M100 latencies. Children with ASD do not demonstrate the typical maturation of white matter microstructure and relation of this white matter microstructure to function. As such, M100 latency delays may be due to altered maturation or decoupling of structure to function relationships. Previous studies utilizing cross-sectional design have suggested a similar maturation rate of 3 ms/year for children with ASD and typically developing controls, although inter-individual variability is high (perhaps reflecting phenotypic heterogeneity).
Objectives:
The purpose of this study was to obtain longitudinal (3yr follow-up) M100 latency data to assess cortical response maturation within-subjects in contrast to previous cross-sectional data.
Methods:
Utilizing a longitudinal design with an inter-scan interval of 3-5 years (aged 6-9 yrs old at first scan), both typically developing and children with ASD passively listen to simple sinusoidal tones (200, 300, 500 & 1000 Hz,130 tones/frequency; 45dB, 300ms, 10 ms ramps, binaurally) while ensemble neuronal responses were recorded with whole head MEG. Artifact rejection and source modeling was performed using BESA. A linear mixed model with condition as a fixed effect was used to predict a stimulus independent M100 was generated for left and right hemisphere. The rate of maturation was derived in two separate ways: 1) using group level regression of data, and comparing the slope of the TD and ASD cohorts fits; 2) determination of individual subject maturation rates, and comparing these at the group level.
Results:
Maturation derived either through group level regression or via individual maturation rates appear consistent with prior cross-sectional findings, with maturation rates being ~ 2ms/year for both TD and ASD. However; right hemisphere responses differed depending on which method of calculation was used. For maturation rates derived from individual subjects rates, ASD demonstrated a greater rate of maturation (5.3ms/year) than TD (1.8 ms/year) (p<0.01). Group-level regression failed to replicate such findings, with only qualitatively greater maturation rates in ASD (4.29ms/year) than TD (1.4 ms/year) (p=0.16).
Conclusions:
Aberrant M100 maturation in the right hemisphere of children with ASD may account for latency alterations observed across ages, though results derived from individual subject maturation rates suggest that a convergence of latencies may occur in adulthood. The relationship of this within-subject maturation to within-subject white matter maturation has yet to be analyzed, however; previous studies suggest this is also perturbed in ASD. To determine if the discordance derived findings from individual based maturation rates and previous studies is actual more subjects are needed.