Dataset Information

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

ABSTRACT: Motivated 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.

SUBMITTER: Hasenstab K

PROVIDER: S-EPMC4915609 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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

Hasenstab Kyle K Sugar Catherine C Telesca Donatello D Jeste Shafali S Şentürk Damla D

Biostatistics (Oxford, England) 20160204 3

Motivated 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 ...[more]

PMID: 26846337

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Project description:Autism spectrum disorder (ASD) and specific language impairment (SLI) are two neurodevelopmental disorders characterized by deficits in verbal and nonverbal communication skills. These skills are thought to develop largely through implicit-or automatic-learning mechanisms. The aim of the current paper was to investigate the role of implicit learning abilities in the atypical development of communication skills in ASD and SLI. In the current study, we investigated Response Times (RTs) and Event Related Potentials (ERPs) during implicit learning on a Serial Reaction Time (SRT) task in a group of typically developing (TD) children (n?=?17), a group of autistic children (n?=?16), and a group of children with SLI (n?=?13). Findings suggest that learning in both ASD and SLI are similar to that in TD. However, electrophysiological findings suggest that autistic children seem to rely mainly on more automatic processes (as reflected by an N2b component), whereas the children with SLI seem to rely on more controlled processes (as reflected by a P3 component). The TD children appear to use a combination of both learning mechanisms. These findings suggest that clinical interventions should aim at compensating for an implicit learning deficit in children with SLI, but not in children with ASD. Future research should focus on developmental differences in implicit learning and related neural correlates in TD, ASD, and SLI. Autism Res 2018, 11: 1050-1061. © 2018 The Authors Autism Research published by International Society for Autism Research and Wiley Periodicals, Inc. LAY SUMMARY:Autism and Specific Language Impairment (SLI) are two disorders characterized by problems in social communication and language. Social communication and language are believed to be learned in an automatic way. This is called "implicit learning." We have found that implicit learning is intact in autism. However, in SLI there seems different brain activity during implicit learning. Maybe children with SLI learn differently, and maybe this different learning makes it more difficult for them to learn language.

Dataset Information

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

Publications

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

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