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

Aimin Liang1,2, Zhijun Cui2, Yang Shi2

  • 1School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China.

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|January 28, 2026
PubMed
Summary
This summary is machine-generated.

This study reveals electrophysiological differences in brain activity between children with Developmental Language Disorder (DLD) and typically developing children during rest and cognitive tasks. These findings suggest a neurophysiological signature for DLD, aiding future biomarker development.

Keywords:
developmental language disorder (DLD)electroencephalography (EEG)event-related potentials (ERPs)neural biomarkersresting-state networks

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Area of Science:

  • Neuroscience
  • Developmental Psychology
  • Speech-Language Pathology

Background:

  • Developmental Language Disorder (DLD) is linked to atypical brain network function, but direct electrophysiological evidence connecting intrinsic and task-based neural activity is scarce.
  • Understanding these multi-level brain abnormalities is crucial for identifying biomarkers and developing targeted interventions for DLD.

Purpose of the Study:

  • To investigate multi-level electroencephalography (EEG) markers in children with DLD and typically developing (TD) children.
  • To explore differences in resting-state brain networks and task-evoked neural responses between the two groups.
  • To assess the potential of integrating these EEG markers for DLD classification.

Main Methods:

  • Examined resting-state EEG, semantic matching task responses, and auditory oddball task responses in 21 TD children and 15 children with DLD.
  • Analyzed frequency-specific connectivity, microstate dynamics, event-related potentials (ERPs), and event-related desynchronization (ERD).
  • Utilized a machine learning framework to integrate multi-level EEG features for DLD classification.

Main Results:

  • Children with DLD exhibited altered resting-state connectivity, reduced microstate stability, and atypical microstate transitions.
  • DLD group showed diminished occipital P1/N2 responses and lacked a specific difference wave during semantic matching.
  • DLD children displayed distinct frontal theta/alpha desynchronization during the auditory oddball task.
  • Machine learning models integrating resting-state and task features achieved moderate classification accuracy (F1 = 70.3-80.0%) but showed limited generalizability.

Conclusions:

  • A translational neurophysiological signature for DLD is supported, linking intrinsic network organization to emergent neural computations.
  • Atypical intrinsic brain organization in DLD may constrain neural processing during cognitive tasks.
  • Findings provide a foundation for developing DLD biomarkers and informing intervention strategies, despite limitations from small sample sizes.