Optimizing functional connectivity scanning conditions for predicting autistic traits

Area of Science:

• Neuroscience
• Developmental Neuroscience
• Psychiatric Neuroscience

Background:

• Autism spectrum disorder (ASD) 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 revealing brain-phenotype relationships in ASD remain underexplored.

Purpose of the Study:

• To identify optimal brain states for predicting clinically relevant phenotypes in autism using fMRI.
• To assess the generalizability of predictive network models across different datasets and populations.
• To determine if a sustained attention task enhances the prediction of autistic traits.

Main Methods:

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

Main Results:

• A sustained attention task significantly improved the prediction of autistic traits compared to resting-state and social attention tasks in youth with autism.
• The predictive network model generated from the sustained attention task generalized to predict attention measures in neurotypical adults.
• The same model further generalized to predict social responsiveness in large independent ASD and neurotypical cohorts (Autism Brain Imaging Data Exchange and Healthy Brain Network).

Conclusions:

• An in-scanner sustained attention challenge can optimize fMRI scanning for delineating robust neurobiological markers of autistic traits.
• This approach enhances the predictive power of brain-phenotype relationships in ASD.
• Findings suggest a promising method for identifying reliable biomarkers for autism spectrum disorder.