Facial expression recognition as a candidate marker for autism spectrum disorder: how frequent and severe are deficits?

Abstract

BackgroundImpairments in social communication are a core feature of Autism Spectrum Disorder (ASD). Because the ability to infer other people’s emotions from their facial expressions is critical for many aspects of social communication, deficits in expression recognition are a plausible candidate marker for ASD. However, previous studies on facial expression recognition produced mixed results, which may be due to differences in the sensitivity of the many tests used and/or the heterogeneity among individuals with ASD. To ascertain whether expression recognition may serve as a diagnostic marker (which distinguishes people with ASD from a comparison group) or a stratification marker (which helps to divide ASD into more homogeneous subgroups), a crucial first step is to move beyond identification of mean group differences and to better understand the frequency and severity of impairments.MethodsThis study tested 46 individuals with ASD and 52 age- and IQ-matched typically developing (TD) participants on the Films Expression Task, which combines three key features of real-life expression recognition: naturalistic facial expressions, a broad range of simple and complex emotions, and short presentation time. Test-retest reliability was assessed in 28 individuals who did not participate in the main study and revealed acceptable reliability (ICC r = .74).ResultsCase-control comparisons showed highly significant mean group differences for accuracy (p = 1.1 × 10− 10), with an effect size (Cohen’s d = 1.6), more than twice as large as the mean effect size reported in a previous meta-analysis (Uljarevic and Hamilton, 2012, J Autism Dev Disord). The ASD group also had significantly increased mean reaction times overall (p = .00015, d = .83) and on correct trials (p = .0002, d = .78). However, whereas 63% of people with ASD showed severe deficits (they performed below two standard deviations of the TD mean, a small subgroup (15.3%) performed normally (within one standard deviation of the mean).ConclusionThese findings indicate that the majority of people with ASD have severe expression recognition deficits and that the Films Expression Test is a sensitive task for biomarker research in ASD. Future work is needed to establish whether ASD subgroups with and without expression recognition deficits differ from one another in terms of their symptom profile or neurobiological underpinnings.Electronic supplementary materialThe online version of this article (10.1186/s13229-018-0187-7) contains supplementary material, which is available to authorized users.

Visual scanning during emotion recognition in long‐term recovered anorexia nervosa: An eye‐tracking study

“…Third, a subgroup of individuals with (recovered) AN meets full criteria for autism spectrum disorder (ASD) and significant FER deficits have repeatedly been shown in ASD ( Baron-Cohen, Wheelwright, Hill, Raste, & Plumb, 2001;Loth et al, 2018;Uljarevic & Hamilton, 2013). In order to investigate whether FER deficits are specific for (recovered) AN, it is important to control for the presence of an ASD diagnosis.…”

Converting tests of fundamental social, cognitive, and affective processes into clinically useful bio‐behavioral markers for neurodevelopmental conditions

“…A first step consists of going beyond investigations of mean‐group differences and to ascertain the frequency and severity of impairments or differences among a certain group (e.g., autistic individuals). For example, in a recent study on facial expression recognition we reported that strong mean group differences with large effect sizes ( p = 1.1 x 10 −10 , effect size Cohen's d = 1.6, Loth, Garrido et al, ; Loth, Moessnang et al, ) translated into 63% of individuals with ASD with “severe deficits” (they performed below two SD s of the control mean), 22% with “milder deficits” (between 1 and 2 SD s below the control mean) and 15% who performed normally (within one SD of the mean or above). Lombardo et al () applied a sophisticated clustering approach from systems biology to identify “subgroups” based on item‐level performance on the Reading the Mind in the Eyes Task; a widely used test that requires recognizing complex emotion and mental states from the eye region.…”

A Facial Affect Analysis System for Autism Spectrum Disorder

“…Our network is a standard CNN that learns spatial representations by stacking convolution and down-sampling units, as shown in Table 1. During training, we minimize the following multi-task loss function 2 We use a HoG-based face detector for its good trade-off between speed and accuracy on an iPad. However, any other face detector can be used.…”

“…Since there are no publicly available datasets for autism with all of these different attributes, we learn representations for these attributes by leveraging two large-scale facial datasets of natural images that are collected in a wide variety of settings, including age, gender, race, pose, and lighting variations. The contributions of this work are: (1) present an ASD classification system based on facial attributes, (2) show the importance of these facial attributes in improving the performance of our system through statistical analysis, and (3) analysis of single vs. multi-task learning for facial attribute recognition.…”