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

Autism spectrum disorder (autism) is a prevalent neurodevelopmental condition characterized by early emerging impairments in social behavior and communication. EEG represents a powerful and non-invasive tool for examining functional brain differences in autism. Recent EEG evidence suggests that greater intra-individual trial-to-trial variability across EEG responses in stimulus-related tasks may characterize brain differences in autism. Traditional analysis of EEG data largely focuses on mean trends of the trial-averaged data, where trial-level analysis is rarely performed due to low neural signal to noise ratio. We propose to use nonlinear (shape-invariant) mixed effects (NLME) models to study intra-individual inter-trial EEG response variability using trial-level EEG data. By providing more precise metrics of response variability, this approach could enrich our understanding of neural disparities in autism and potentially aid the identification of objective markers. The proposed multilevel NLME models quantify variability in the signal's interpretable and widely recognized features (e.g., latency and amplitude) while also regularizing estimation based on noisy trial-level data. Even though NLME models have been studied for more than three decades, existing methods cannot scale up to large data sets. We propose computationally feasible estimation and inference methods via the use of a novel minorization-maximization (MM) algorithm. Extensive simulations are conducted to show the efficacy of the proposed procedures. Applications to data from a large national consortium find that children with autism have larger intra-individual inter-trial variability in P1 latency in a visual evoked potential (VEP) task, compared to their neurotypical peers.

A functional model for studying common trends across trial time in eye tracking experiments.

Eye tracking (ET) experiments commonly record the continuous trajectory of a subject's gaze on a two-dimensional screen throughout repeated presentations of stimuli (referred to as trials). Even though the continuous path of gaze is recorded during each trial, commonly derived outcomes for analysis collapse the data into simple summaries, such as looking times in regions of interest, latency to looking at stimuli, number of stimuli viewed, number of fixations or fixation length. In order to retain information in trial time, we utilize functional data analysis (FDA) for the first time in literature in the analysis of ET data. More specifically, novel functional outcomes for ET data, referred to as viewing profiles, are introduced that capture the common gazing trends across trial time which are lost in traditional data summaries. Mean and variation of the proposed functional outcomes across subjects are then modeled using functional principal components analysis. Applications to data from a visual exploration paradigm conducted by the Autism Biomarkers Consortium for Clinical Trials showcase the novel insights gained from the proposed FDA approach, including significant group differences between children diagnosed with autism and their typically developing peers in their consistency of looking at faces early on in trial time.

Infants' identification of gender in biological motion displays.

Infants' knowledge of social categories, including gender-typed characteristics, is a vital aspect of social cognitive development. In the current study, we examined 9- to 12-month-old infants' understanding of the categories "male" and "female" by testing for gender matching in voices or faces with biological motion depicted in point light displays (PLDs). Infants did not show voice-PLD gender matching spontaneously (Experiment 1) or after "training" with gender-matching voice-PLD pairs (Experiment 2). In Experiment 3, however, infants were trained with gender-matching face-PLD pairs and we found that patterns of visual attention to top regions of PLD stimuli during training predicted gender matching of female faces and PLDs. Prior to the end of the first postnatal year, therefore, infants may begin to identify gender in human walk motions, and perhaps form social categories from biological motion.

Development of infants' representation of female and male faces.

We examined development of 5- and 10.5-month-old infants' face representations, focusing on infants' discrimination and categorization of female and male faces. We tested for gender-based preferences and categorization of female and male faces by presenting infants with pairs of faces and then habituating them to a series of majority female or male face ensembles. We then tested for gender preferences with new face pairs (one female and one male; Study 1) or new face ensembles (majority female and majority male; Study 2). We found that both 5- and 10.5-month-old infants discriminated female from male faces in face pairs, and both age groups looked more at female faces during habituation. Neither age group, however, provided evidence of gender-based categorization. We interpret these findings within a theoretical framework that stresses environmental exposure to different social categories, and infants' ability to detect commonalities of features within categories. We conclude that infants' gender-based categorization of faces is constrained by the set of features available in the input.