overview: A new model of autism development provides insight into how different risk factors lead to ASD symptoms and why there is such great variability between individuals.
sauce: University of Gothenburg
The onset of autism may be easier to understand, thanks to an explanatory model published in a paper from the University of Gothenburg. This model provides new insights into how different risk factors lead to autism and why there is such great inter-individual variability.
Autism, a neurodevelopmental disorder, affects how people perceive the world around them and how they interact and communicate with others. Among individuals with autism, there is great variation in terms of individual characteristics and symptoms.
The new explanatory model is both theoretical and practical. Its various components are measurable through questionnaires, genetic mapping, psychological testing, etc. This model describes a variety of factors and how they combine to drive the diagnosis of autism and lead to other neurodevelopmental disorders.
This model associates three factors. Together, these result in behavioral patterns that meet diagnostic criteria for autism.
- Autistic Personality — Inherited common genetic mutations that cause autistic personality.
- Cognitive Compensation—intellectual and executive functions, such as the ability to learn, understand others, and adapt to social interactions.
- Exposure to risk factors – for example, deleterious genetic mutations, infectious diseases, and other random events during pregnancy and early childhood that adversely affect cognitive performance.
“Autism traits are associated with both cognitive strengths and difficulties, but by themselves do not imply that diagnostic criteria have been met. Exposure to risk factors can affect the ability of individuals diagnosed with autism to cope with difficulties,” said doctor and postdoctoral researcher at the Sahlgrenska Academy of the University of Gothenburg, who wrote his doctoral thesis. One Darko Sarovich said:
The model reveals that it is the combination of various risk factors that drives the large differences between individuals on the spectrum. Various components of the model are supported by results from previous studies.
Higher executive function skills can compensate for deficits in ways that alleviate symptoms and reduce the risk of meeting diagnostic criteria for autism. This may explain why, at the group level, researchers observe lower degrees of intelligence in people diagnosed with autism and other neurodevelopmental conditions.
You can also understand why intellectual disability is more common among these groups. Thus, this model indicates that cognitive deficits are not part of the autistic personality, but rather a risk factor leading to fulfillment of diagnostic criteria.
“Autistic personalities are associated with different strengths. For example, parents of children with autism are overrated among engineers and mathematicians. did not meet the diagnostic criteria for autism.
“The effects of disability were more pronounced in children, for example, due to exposure to risk factors and relatively lower cognitive abilities,” says Sarovich.
difference between girls and boys
Autism is diagnosed more often in boys than girls, and girls are often diagnosed later. After years of diffuse personal hardship, some girls come of age before receiving a diagnosis.
“Girls’ symptoms are often less noticeable to others. It is generally well known that girls have higher social skills. Girls also tend to have fewer features of autism and are less susceptible to risk factors, so the model is better suited to answer questions about gender inequality. It helps,” Sarovich says.
research and diagnostics
The model also proposes a way to estimate and measure three factors: autistic personality, cognitive compensation, and exposure to risk factors. This makes it possible to use the model for designing research studies and interpreting their results.
Diagnostics is another possible area of use. In a pilot study in which 24 participants were diagnosed with autism and 22 controls were undiagnosed, measuring his three factors in the model correctly assigned more than 93% to the correct category. is ready. This model can also be used to explain the development of other neurodevelopmental disorders such as schizophrenia.
About this ASD research news
author: press office
sauce: University of Gothenburg
contact: Press Office – University of Gothenburg
image: Images are credited to the University of Gothenburg
Original research: Paper: “A Multimodal Approach Towards Autism Biological Classification – Development of Theoretical Models, Classification Methods and Biomarkers” by Darko Sarovic.
A Multimodal Approach to Biological Classification of Autism – Development of Theoretical Models, Classification Methods and Biomarkers
Autism spectrum disorder (ASD) is an umbrella term for a group of neurodevelopmental disorders (NDDs), behaviorally defined by the presence of social communication difficulties, and rigid and repetitive behaviors, including sensory deficits. will be
The overarching aim of this paper is to improve the classification of autism through the development of theoretical frameworks and multivariate taxonomies, to aid in the understanding of autism, and to identify biomarkers used for ASD classification. did.
Paper I presents a theoretical framework for the etiology of ASD and other NDDs.
This framework conceptualizes and operationalizes the three-factor model. (2) Cognitive competence as an individual’s ability to compensate for problems that may arise from the ‘prominent’ personality type. (3) neuropathological burden conceptualized as inhibition of neurological and cognitive development resulting from the presence of neurodevelopmental risk factors;
It is concluded that such a framework may contribute to a better understanding of the pathogenesis mechanisms underlying NDD, including ASD.
Papers II–IV are based on structural and functional brain imaging studies of a group of adult males with ASD and a group of age- and IQ-matched neurotypical controls.
Paper II is a morphometric study that presents a multivariable classification method that outperforms machine learning algorithms on the same data set, showing an accuracy of up to 79% for diagnostic status.
In paper III, we investigated source-spatial magnetoencephalography activation in the right fusiform gyrus in response to faces and face-like objects and found only group differences after stimulation. This may be related to differences in top-down cognitive mechanisms.
Paper IV compared changes in gamma-range occipital magnetism to locomotion stimuli and showed a relationship with self-reported sensory sensitivity in both ASD and controls.
In summary, this paper provides a theoretical framework that proposes pathogenesis mechanisms of ASD and other NDDs, a simple taxonomy for multivariate classification using quantitative data, and biomarkers of facial processing and sensory sensitivity. I am presenting.