# Atypical Resting-State and Task-Evoked EEG Signatures in Children with Developmental Language Disorder

## TL;DR

This study finds distinct brain activity patterns in children with Developmental Language Disorder during rest and tasks, suggesting potential biomarkers for diagnosis and treatment.

## Contribution

The study identifies novel EEG-based neurophysiological signatures linking resting-state and task-evoked abnormalities in children with DLD.

## Key findings

- Children with DLD show reduced stability in resting-state brain microstate dynamics and atypical transitions between microstates.
- DLD children exhibit weaker P1/N2 responses and lack a right fronto-central difference wave during a semantic matching task.
- A machine learning model using EEG features can distinguish DLD from typically developing children with moderate accuracy.

## Abstract

Developmental Language Disorder (DLD) is associated with abnormalities in both intrinsic resting-state brain networks and task-evoked neural responses, yet direct electrophysiological evidence linking these levels remains limited. This study examined multi-level EEG markers in 21 typically developing children and 15 children with DLD across resting-state, a semantic matching task, and an auditory oddball task. Resting-state analyses revealed frequency-specific connectivity imbalances, reduced stability of intrinsic microstate dynamics, and atypical transitions between microstates in the DLD group. During the semantic matching task, DLD children showed weaker occipital P1 and N2 responses (100–300 ms) and lacked the right fronto-central difference wave (500–700 ms) observed in TD children. In the auditory oddball task, DLD children exhibited high-theta/low-alpha event-related desynchronization at left frontal electrodes (400–500 ms), in contrast to TD children. A machine learning framework integrating resting-state and task-based features discriminated DLD from TD children (test-set F1 = 70.3–80.0%) but showed limited generalizability, highlighting the constraints of small clinical samples. These findings support a translational neurophysiological signature for DLD, in which atypical intrinsic network organization constrains emergent neural computations, providing a foundation for future biomarker development and targeted intervention strategies.