Autism Brain & Behavior

Marie Schaer, MD PhD

Assistant Professor

Campus Biotech

9, chemin de Mines

1202 Geneva

Switzerland

Our group is interested in understanding the development of young children with autism, with a particular interest in measuring brain changes associated with early intervention in autism, and in developing tools for early detection of this disorder.

To this end, we are following a cohort of young children with autism spectrum disorder (ASD) as well as typically developing children of the same age. We use these data to obtain a fine-grained analysis of the clinical phenotype of children when they are diagnosed with autism, and to follow the trajectory of their symptoms over the years following diagnosis. We are involved in the development and validation of neuroscience-based biomarkers to measure the severity of autism and the evolution of these children, using eye-tracking and brain imaging (EEG and MRI).

We seek to identify subtypes within the very heterogeneous spectrum of autism, in particular subtypes that would predict the evolution or the response to specific types of treatments. Finally, we are measuring changes associated with therapeutic intervention, clinically, but also with specific markers such as eye-tracking or brain imaging. Ultimately, this research aims to provide concrete elements to improve care and intervention for children diagnosed with autism.

news

Feb 24, 2022	We have a PhD position opening in machine learning to further advance the digital phenotyping of autism spectrum disorder. Check it out or download the .pdf
Jun 14, 2021	Thanks to the tremendous implication of many people, our third MOOC (and last for now!) on autism is now out. A French-speaking course on intervention, designed for parents and professionals.
Mar 22, 2021	Very glad to share that our second second MOOC is now online on Coursera, providing an overview of latest research on the biology and neuroscience of autism (in French).
Nov 20, 2019	Our MOOC about the diagnosis of autism was just released. The first one of a tryptic dedicated to summarize latest advances in autism for French-speaking parents and profesionals.

selected publications

Attention to Face as a Predictor of Developmental Change and Treatment Outcome in Young Children with Autism Spectrum Disorder

Latrèche, K., Kojovic, N., Franchini, M., and Schaer, M.

Biomedicines 2021

Abstract URL PDF

The beneficial effect of early intervention is well described for children with autism spectrum disorder (ASD). Response to early intervention is, however, highly heterogeneous in affected children, and there is currently only scarce information about predictors of response to intervention. Based on the hypothesis that impaired social orienting hinders the subsequent development of social communication and interactions in children with ASD, we sought to examine whether the level of social orienting modulates treatment outcome in young children with ASD. We used eye-tracking technology to measure social orienting in a group of 111 preschoolers, comprising 95 young children with ASD and 16 children with typical development, as they watched a 29 s video of a woman engaging in child-directed speech. In line with previous studies, we report that attention to face is robustly correlated with autistic symptoms and cognitive and adaptive skills at baseline. We further leverage longitudinal data in a subgroup of 81 children with ASD and show that the level of social orienting at baseline is a significant predictor of developmental gains and treatment outcome. These results pave the way for identifying subgroups of children who show a better response to early and intensive intervention, a first step toward precision medicine for children with autism.
Using 2D Video-based Pose Estimation for Automated Prediction of Autism Spectrum Disorders in Preschoolers

Kojovic, N.*, Natraj, S.*, Mohanty, S. P., Maillart, T., and Schaer, M.

Scientific Reports 2021

Abstract URL PDF

Clinical research in autism has recently witnessed promising digital phenotyping results, mainly focused on single feature extraction, such as gaze, head turn on name-calling or visual tracking of the moving object. The main drawback of these studies is the focus on relatively isolated behaviors elicited by largely controlled prompts. We recognize that while the diagnosis process understands the indexing of the specific behaviors, ASD also comes with broad impairments that often transcend single behavioral acts. For instance, the atypical nonverbal behaviors manifest through global patterns of atypical postures and movements, fewer gestures used and often decoupled from visual contact, facial affect, speech. Here, we tested the hypothesis that a deep neural network trained on the non-verbal aspects of social interaction can effectively differentiate between children with ASD and their typically developing peers. Our model achieves an accuracy of 80.9% (F1 score: 0.818; precision: 0.784; recall: 0.854) with the prediction probability positively correlated to the overall level of symptoms of autism in social affect and repetitive and restricted behaviors domain. Provided the non-invasive and affordable nature of computer vision, our approach carries reasonable promises that a reliable machine-learning-based ASD screening may become a reality not too far in the future.
Early alterations of large-scale brain networks temporal dynamics in young children with autism

Bochet, A.*, Sperdin, H. F.*, Rihs, T. A., Kojovic, N., Franchini, M., Jan, R. K., Michel, C. M., and Schaer, M.

Communications Biology 2021

Abstract URL PDF

Autism spectrum disorders (ASD) are associated with disruption of large-scale brain net- work. Recently, we found that directed functional connectivity alterations of social brain networks are a core component of atypical brain development at early developmental stages in ASD. Here, we investigated the spatio-temporal dynamics of whole-brain neuronal net- works at a subsecond scale in 113 toddlers and preschoolers (66 with ASD) using an EEG microstate approach. We first determined the predominant microstates using established clustering methods. We identified five predominant microstate (labeled as microstate classes A–E) with significant differences in the temporal dynamics of microstate class B between the groups in terms of increased appearance and prolonged duration. Using Markov chains, we found differences in the dynamic syntax between several maps in toddlers and preschoolers with ASD compared to their TD peers. Finally, exploratory analysis of brain–behavioral relationships within the ASD group suggested that the temporal dynamics of some maps were related to conditions comorbid to ASD during early developmental stages.
Measuring the Emergence of Specific Abilities in Young Children with Autism Spectrum Disorders: The Example of Early Hyperlexic Traits

Solazzo, S., Kojovic, N., Robain, F., and Schaer, M.

Brain Sciences 2021

Abstract URL PDF

The presence of a restricted interest in written materials, including an early ability to name and recognize letters and numbers, is regularly reported in preschoolers with autism spectrum disorders (ASDs). There is, however, scarce information on this early ability akin to emerging hyperlexic traits in preschoolers with ASD younger than 3 years old. Here, we defined a measure of early naming and recognition of letters and numbers in 155 preschoolers with ASD using a sliding window approach combined with a 90th percentile threshold criterion, and subsequently compared the profiles of children with ASD with and without early hyperlexic traits. Using this measure, we found that 9% of children with ASD showed early hyperlexic traits. The early ability to name and recognize letters and numbers was associated with a higher level of restricted and repetitive behaviors yet more social-oriented behaviors at baseline and with better expressive and written communication at baseline and one year later. This study contributes to a better definition of the profile of children with ASD with an early ability in letters and numbers akin to emerging hyperlexic traits, a skill that is associated with promising social strengths and language abilities in this subgroup of children.