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Updated: Jan 25, 2026

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

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From Breath to Brain: NICU respiratory interventions and bedside brain signal entropy predict later autism risk

Madelyn G Nance1, Winnie R Chang1, Chad Aldridge1

  • 1Department of Neurology, University of Virginia, Charlottesville, VA, United States.

Developmental Cognitive Neuroscience
|January 23, 2026
PubMed

Insights

This study identifies premature infants at high risk for Autism Spectrum Disorder (ASD) using non-invasive brain activity and respiratory support measures. Early detection through brain signal variability and PRISM scores can guide timely interventions for neurodevelopmental disorders.

Area of Science:

  • Neonatal neuroscience
  • Developmental pediatrics
  • Computational biology

Background:

  • Premature infants face risks like hypoxia and inflammation, increasing susceptibility to neurodevelopmental disorders such as Autism Spectrum Disorder (ASD).
  • Early identification of infants at risk for ASD is crucial for timely intervention and improved outcomes.
  • Existing methods for risk assessment in neonates have limitations.

Purpose of the Study:

  • To develop and validate non-invasive methods for early identification of infants at risk for ASD.
  • To investigate the relationship between respiratory support burden, brain signal variability, and inflammation in premature infants.
  • To determine if these factors can predict ASD risk.

Main Methods:

  • Electroencephalography (EEG) was used to measure brain signal variability in social and nonsocial resting states.
  • Saliva samples were collected to assess inflammatory markers (cytokines).
  • A novel Prognostic Respiratory Intensity Scoring Metric (PRISM) was calculated to quantify respiratory support needs.

Main Results:

  • Higher PRISM scores correlated with increased brain signal entropy in the nonsocial resting state.
  • This association was not significant in the social resting state, especially in male infants.
  • In female infants, the relationship between brain signal entropy and PRISM scores was potentially mediated by cytokines.
  • A model combining nonsocial resting state brain signal entropy, sex, and PRISM scores predicted ASD risk with 88% accuracy.

Conclusions:

  • Non-invasive measures, including brain signal entropy and PRISM scores, can effectively identify premature infants at high risk for ASD.
  • This predictive model, particularly considering sex, offers a promising tool for early ASD risk assessment before hospital discharge.
  • Findings highlight the potential role of inflammation and sex-specific brain activity patterns in ASD development in high-risk infants.

Keywords:
Autism spectrum disorderBrain signal entropyEEGInflammationPrematurityVentilation

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