Optimizing functional connectivity scanning conditions for predicting autistic traits

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.