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Autism Spectrum Disorder

Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.

Traits and States

Traits and States

Personality traits represent consistent patterns in behavior, thoughts, and emotions, reflecting an individual's tendencies across various situations. For example, extraversion, a well-known trait, manifests in individuals as talkative, energetic, and enthusiastic behaviors. These traits are stable over time, offering a reliable framework for predicting how people might act in different contexts. However, they do not define every moment of an individual's life. In contrast to traits,...

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Related Experiment Video

Updated: May 30, 2025

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

Published on: July 31, 2016

Optimizing functional connectivity scanning conditions for predicting autistic traits.

Corey Horien^1,2,3, Francesca Mandino⁴, Abigail S Greene^2,5

¹Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.

Medrxiv : the Preprint Server for Health Sciences

|January 27, 2025

Summary

This summary is machine-generated.

A sustained attention task during functional magnetic resonance imaging (fMRI) scanning improves the prediction of autistic traits. This method shows promise for identifying robust neurobiological markers in autism spectrum disorder.

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Area of Science:

Neuroimaging
Autism Spectrum Disorder Research
Cognitive Neuroscience

Background:

Autism is a complex neurodevelopmental condition with diverse presentations.
Functional magnetic resonance imaging (fMRI) studies have identified neurobiological correlates of autistic features.
Optimal brain states for predicting autism phenotypes and the role of attention remain underexplored.

Purpose of the Study:

To identify optimal brain states for predicting clinically relevant autistic phenotypes using fMRI.
To investigate the role of attentional abilities in mediating autistic features.
To assess the generalizability of predictive models across different datasets and populations.

Main Methods:

Utilized connectome-based predictive modeling (CPM) across three independent datasets.
Compared prediction performance of autistic traits under different fMRI scanning conditions: sustained attention task, social attention task, and resting-state.
Evaluated the generalizability of a predictive network model derived from a sustained attention task.

Main Results:

A sustained attention task (gradual onset continuous performance task) significantly enhanced the prediction of autistic traits compared to other conditions in the first dataset.
The predictive network model for autistic traits generalized to predict attention measures in neurotypical adults (dataset two).
The same model further generalized to predict social responsiveness in data from the Autism Brain Imaging Data Exchange (dataset three).

Conclusions:

In-scanner sustained attention challenges can reveal robust neurobiological markers associated with autistic traits.
These findings support the investigation of specific brain states to optimize phenotype prediction in psychiatric conditions.
Attentional states are crucial for understanding brain-behavior relationships in autism.