High-precision machine learning identifies a reproducible functional connectivity signature of autism spectrum diagnosis in a subset of individuals.

ABSTRACT:

Background

Discovery of predictive biomarkers is essential for understanding the neurobiological underpinnings of autism spectrum diagnosis (ASD) and improving identification. Resting-state functional connectivity analyses of individuals with ASD have established sensitivity of brain connectivity at the group level. However, the extensive heterogeneity in ASD limits the translation of these findings into reliable individual-level biomarkers. We analyzed the Autism Brain Imaging Data Exchange 1 and 2 datasets, calculating Pearson's correlation-based functional connectivity across 18 brain networks. Using transductive conformal prediction, a machine learning approach that assigns confidence scores to predictions based on conformality to known classes, we classified individuals with A

SUBMITTER: Clarke N 

PROVIDER: S-EPMC12406215 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature