A two-step temporal data augmentation and supervised learning framework for predicting autism diagnosis at 36 months...

Area of Science:

• Neuroscience
• Developmental Pediatrics
• Machine Learning

Background:

• Autism spectrum disorder (ASD) affects a significant portion of children with tuberous sclerosis complex (TSC).
• Early ASD identification in this high-risk group is challenging due to complex factors.
• Timely intervention is crucial for improving outcomes in children with TSC and ASD.

Purpose of the Study:

• To integrate longitudinal diffusion tensor imaging (DTI) metrics with early behavioral features.
• To predict autism spectrum disorder (ASD) outcomes at 36 months using supervised learning.
• To identify key early biomarkers for ASD in children with TSC.

Main Methods:

• Utilized DTI metrics (axial diffusivity, fractional anisotropy, mean diffusivity, radial diffusivity) from 27 white matter tracts.
• Developed a data augmentation algorithm to standardize DTI data to ages 12, 24, and 36 months.
• Included 9 behavioral features from ADOS-2 and ADI-R assessments at 24 months; trained supervised learning models.

Main Results:

• Regularized logistic regression models (LASSO, Elastic Net) showed balanced performance.
• Compared two input settings: DTI at 24 months + behavior vs. DTI at 12 & 24 months + behavior.
• Setting 1 (24-month DTI + behavior) performed comparably or better in predicting ASD diagnosis.

Conclusions:

• Integrating early neuroimaging (DTI) and behavioral data shows promise for predicting ASD outcomes in children with TSC.
• A multimodal machine learning approach highlights 24-month DTI and behavioral measures as key early biomarkers.
• Regularized regression techniques are effective for analyzing small, heterogeneous clinical datasets.