Objective This study examined variability in autism symptom trajectories in toddlers referred for possible autism spectrum disorder (ASD) who had frequent observations from 14 to 36 months of age. Method In total, 912 observations of the Autism Diagnostic Observation Schedule (ADOS) were obtained from 149 children (103 with ASD) followed from 14 to 36 months of age. As a follow-up to a previous analysis of ADOS algorithm scores, a different analytic approach (Proc Traj) was implemented to identify several courses of symptom trajectories using ADOS Calibrated Severity Scores in a larger sample. Proc Traj is a statistical method that clusters individuals into separate groups based on different growth trajectories. Changes in symptom severity based on individual ADOS items also were examined. Results Trajectory analysis of overall symptom severity identified 4 clusters (non-spectrum ~25%; worsening ~27%; moderately-improving ~25%; severe-persistent ~23%). Trajectory clusters varied significantly in the proportions of confirmatory ASD diagnosis, level of baseline and final verbal and nonverbal abilities, and symptom severity. For the moderately-improving group, social communication improved, whereas restricted and repetitive behaviors were stable over time. Language and verbal and nonverbal communication improved for many children, but several social affect and restricted and repetitive behavior symptoms remained stable or worsened. Conclusion Significant variability in symptom trajectories was observed among toddlers referred for possible ASD. Changes in social and restricted and repetitive behavior domain scores did not always co-occur. Similarly, item-level trajectories did not always align with trajectories of overall severity scores. These findings highlight the importance of monitoring individual symptoms within broader symptom domains when conducting repeated assessments for young children with suspected ASD.

Purpose Heating of gradient coils and passive shim components is a common cause of instability in the B 0 field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-time field-frequency locking on MRI scanners by comparing field drift data from a large number of sites. Method A standardized protocol was developed for 80 participating sites using 99 3T MR scanners from 3 major vendors. Phantom water signals were acquired before and after an EPI sequence. The protocol consisted of: minimal preparatory imaging; a short pre-fMRI PRESS; a ten-minute fMRI acquisition; and a long post-fMRI PRESS acquisition. Both pre- and post-fMRI PRESS were non-water suppressed. Real-time frequency stabilization/adjustment was switched off when appropriate. Sixty scanners repeated the protocol for a second dataset. In addition, a three-hour post-fMRI MRS acquisition was performed at one site to observe change of gradient temperature and drift rate. Spectral analysis was performed using MATLAB. Frequency drift in pre-fMRI PRESS data were compared with the first 5:20 minutes and the full 30:00 minutes of data after fMRI. Median (interquartile range) drifts were measured and showed in violin plot. Paired t-tests were performed to compare frequency drift pre- and post-fMRI. A simulated in vivo spectrum was generated using FID-A to visualize the effect of the observed frequency drifts. The simulated spectrum was convolved with the frequency trace for the most extreme cases. Impacts of frequency drifts on NAA and GABA were also simulated as a function of linear drift. Data from the repeated protocol were compared with the corresponding first dataset using Pearson's and intraclass correlation coefficients (ICC). Results Of the data collected from 99 scanners, 4 were excluded due to various reasons. Thus, data from 95 scanners were ultimately analyzed. For the first 5:20 min (64 transients), median (interquartile range) drift was 0.44 (1.29) Hz before fMRI and 0.83 (1.29) Hz after. This increased to 3.15 (4.02) Hz for the full 30 min (360 transients) run. Average drift rates were 0.29 Hz/min before fMRI and 0.43 Hz/min after. Paired t-tests indicated that drift increased after fMRI, as expected (p < 0.05). Simulated spectra convolved with the frequency drift showed that the intensity of the NAA singlet was reduced by up to 26%, 44 % and 18% for GE, Philips and Siemens scanners after fMRI, respectively. ICCs indicated good agreement between datasets acquired on separate days. The single site long acquisition showed drift rate was reduced to 0.03 Hz/min approximately three hours after fMRI. Discussion This study analyzed frequency drift data from 95 3T MRI scanners. Median levels of drift were relatively low (5-min average under 1 Hz), but the most extreme cases suffered from higher ...

Although electrophysiological (electroencephalography) measures of executive functions (e.g. error monitoring) have been used to predict academic achievement in typically developing children, work investigating a link between error monitoring and academic skills in children with autism spectrum disorder is limited. In this study, we employed traditional electrophysiological and advanced time–frequency methods, combined with principal component analyses, to extract neural activity related to error monitoring and tested their relations to academic achievement in cognitively able kindergarteners with autism spectrum disorder. In total, 35 cognitively able kindergarteners with autism spectrum disorder completed academic assessments and the child-friendly “Zoo Game” Go/No-go task at school entry. The Go/No-go task successfully elicited an error-related negativity and error positivity in children with autism spectrum disorder as young as 5 years at fronto-central and posterior electrode sites, respectively. We also observed increased response-related theta power during errors relative to correct trials at fronto-central sites. Both larger error positivity and theta power significantly predicted concurrent academic achievement after controlling for behavioral performance on the Zoo Game and intelligence quotient. These results suggest that the use of time–frequency electroencephalography analyses, combined with traditional event-related potential measures, may provide new opportunities to investigate neurobiological mechanisms of executive function and academic achievement in young children with autism spectrum disorder.

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