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Related Experiment Video

Updated: Jun 24, 2025

A Semantic Priming Event-related Potential ERP Task to Study Lexico-semantic and Visuo-semantic Processing in Autism Spectrum Disorder

A Semantic Priming Event-related Potential ERP Task to Study Lexico-semantic and Visuo-semantic Processing in Autism Spectrum Disorder

Published on: April 12, 2018

Modeling intra-individual inter-trial EEG response variability in autism.

Mingfei Dong¹, Donatello Telesca¹, Michele Guindani¹

¹Department of Biostatistics, University of California, Los Angeles, California.

Statistics in Medicine

|June 1, 2024

Summary

Children with autism exhibit greater trial-to-trial brain signal variability in response to stimuli. This study introduces advanced statistical models to analyze electroencephalography (EEG) data, potentially identifying objective markers for autism spectrum disorder (autism).

Keywords:

autism spectrum disorder (autism)electroencephalography intra‐individual inter‐trial variability minorization‐maximization algorithm nonlinear mixed effects models

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Last Updated: Jun 24, 2025

A Semantic Priming Event-related Potential ERP Task to Study Lexico-semantic and Visuo-semantic Processing in Autism Spectrum Disorder

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Published on: April 12, 2018

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Published on: April 9, 2014

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

Area of Science:

Neuroscience
Biostatistics
Developmental Psychology

Background:

Autism spectrum disorder (autism) is a neurodevelopmental condition marked by social and communication impairments.
Electroencephalography (EEG) is a non-invasive method to study brain function in autism.
Increased trial-to-trial variability in EEG responses is a potential biomarker for autism.

Purpose of the Study:

To introduce nonlinear mixed effects (NLME) models for analyzing trial-level EEG data in autism.
To quantify intra-individual inter-trial variability in EEG responses.
To develop computationally feasible methods for analyzing large EEG datasets.

Main Methods:

Application of multilevel nonlinear (shape-invariant) mixed effects (NLME) models.
Utilizing a novel minorization-maximization (MM) algorithm for scalable estimation.
Analysis of trial-level EEG data, focusing on features like latency and amplitude.

Main Results:

Children with autism showed significantly higher intra-individual inter-trial variability in P1 latency during a visual evoked potential (VEP) task.
The proposed NLME models effectively quantified response variability from noisy trial-level EEG data.
Simulations confirmed the efficacy of the MM algorithm for large datasets.

Conclusions:

NLME models offer a precise method to assess EEG response variability in autism.
Greater inter-trial variability in specific EEG components may serve as an objective marker for autism.
The developed computational methods enable scalable analysis of complex EEG data for autism research.