Robust functional clustering of ERP data with application to a study of implicit learning in autism

Abstract

SUMMARYMotivated by a study on visual implicit learning in young children with Autism Spectrum Disorder (ASD), we propose a robust functional clustering (RFC) algorithm to identify subgroups within electroencephalography (EEG) data. The proposed RFC is an iterative algorithm based on functional principal component analysis, where cluster membership is updated via predictions of the functional trajectories obtained through a non-parametric random effects model. We consider functional data resulting from event-related potential (ERP) waveforms representing EEG time-locked to stimuli over the course of an implicit learning experiment, after applying a previously proposed meta-preprocessing step. This meta-preprocessing is designed to increase the low signal-to-noise ratio in the raw data and to mitigate the longitudinal changes in the ERP waveforms which characterize the nature and speed of learning. The resulting functional ERP components (peak amplitudes and latencies) inherently exhibit covariance heterogeneity due to low data quality over some stimuli inducing the averaging of different numbers of waveforms in sliding windows of the meta-preprocessing step. The proposed RFC algorithm incorporates this known covariance heterogeneity into the clustering algorithm, improving cluster quality, as illustrated in the data application and extensive simulation studies. ASD is a heterogeneous syndrome and identifying subgroups within ASD children is of interest for understanding the diverse nature of this complex disorder. Applications to the implicit learning paradigm identify subgroups within ASD and typically developing children with diverse learning patterns over the course of the experiment, which may inform clinical stratification of ASD.

A Multi-Dimensional Functional Principal Components Analysis of EEG Data

“…Since the mean surfaces represent condition differentiation, the opposing mean trends between diagnostic groups imply that children with ASD have higher EEG values in the expected condition while those in the TD group have higher values in the unexpected condition. This is consistent with our previous findings in Hasenstab et al (2015) and Hasenstab et al (2016). Another difference between diagnostic groups is in the timing of maximal condition differentiation (trial 35 for ASD and trial 25 in TD).…”

“…There is exploratory evidence that the TD group starts differentiating between the two conditions of the experiment earlier (trial 25) than the ASD group (trial 35). Modeling the P3 waveform instead of just the P3 peak amplitude (see our previous work (Hasenstab et al, 2015, 2016)) allowed us to compare the variation in the longitudinal and functional dimensions. Variations/changes over longitudinal time (trials) explain more of the total variation in the data than variation in the functional dimension.…”

Inferring Brain Signals Synchronicity From a Sample of EEG Readings

“…A similar conclusion is noted in the higher entropy associated with ASD consensus estimates (1.b and 2.b). This observation echoes some of our previous findings in EEG studies of implicit-learning in ASD and TD children (Hasenstab et al 2015;Hasenstab et al 2016a;Hasenstab et al 2016b).…”