Resting-state functional connectivity predicts longitudinal change in autistic traits and adaptive functioning in...

Area of Science:

• Neuroscience
• Developmental Psychology
• Medical Imaging

Background:

• Autism spectrum disorder (ASD) symptoms persist lifelong, with significant outcome variability even in individuals without intellectual disability.
• Previous outcome predictors (IQ, language, baseline behaviors) explain limited functional variance in ASD.
• Identifying biomarkers for predicting ASD trajectory is crucial for personalized interventions.

Purpose of the Study:

• To determine if resting-state functional connectivity MRI (rs-fcMRI) can predict unexplained variance in long-term adaptive behaviors and autistic traits in ASD.
• To investigate the predictive power of specific brain network connectivity for ASD outcomes.

Main Methods:

• Utilized rs-fcMRI data from individuals with ASD.
• Correlated functional connectivity measures with behavioral outcomes (adaptive behaviors, autistic traits) at least one year post-scan.
• Analyzed connectivity within the salience network (SN), default-mode network (DMN), and frontoparietal task control network (FPTCN).

Main Results:

• Connectivity in the SN, DMN, and FPTCN significantly predicted future autistic traits and changes in autistic traits and adaptive behaviors.
• SN connectivity, particularly involving the anterior insula and dorsal anterior cingulate, showed high sensitivity (100%) and precision (70.59%) in predicting adaptive behavior improvement.
• rs-fcMRI measures accounted for outcome variance not explained by traditional behavioral metrics.

Conclusions:

• Functional brain connectivity, especially within specific networks, serves as a powerful predictor of long-term outcomes in ASD.
• rs-fcMRI offers a novel approach to understanding and predicting heterogeneity in ASD symptom trajectories.
• These findings highlight the salience network's critical role in adaptive behavior changes in ASD.