Tag
CALM is a framework for learning interpretable associations between brain regions and genetic pathways from completely unpaired datasets, enabling biomarker discovery for neuropsychiatric disorders like autism without requiring paired multimodal data.
This ERP study examines recursive locative processing in Mandarin-speaking children with autism, finding reduced early predictive engagement and increased semantic integration demands in the ASD group.
The article introduces ASD-Bench, a comprehensive benchmark evaluating AI models for Autism Spectrum Disorder screening across four axes: predictive performance, calibration, interpretability, and robustness. It analyzes various models across different age cohorts using AQ-10 data, highlighting the importance of multi-metric evaluation in clinical AI applications.