Neural Noise

fNIRS connectivity dynamics linked to symptom severity and cognitive abilities in children with ASD

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  • An fNIRS study links connectivity dynamics to symptom severity and cognitive abilities in children with autism spectrum disorder (Journal of Neuroscience).1
  • Useful for non-invasive neuroimaging and physiological time-series in populations; tier-1.1 1

Weekly enrichment (2026-07-20)

  • The study analyzed fNIRS data from 44 children aged 2.08–6.67 years (30 boys; 21 with ASD, 23 typically developing) recorded while they watched a silent cartoon; it was published in the Journal of Neuroscience (2025).23
  • Dynamic connectivity was computed via sliding-window correlation and k-means clustering; children with ASD showed reduced dwell time in a specific brain state and fewer state transitions than typically developing peers.24
  • The atypical brain-state metrics correlated negatively with autism symptom severity and positively with adaptive behavior and cognitive performance across participants.23
  • Mediation analysis found that adaptive behavior fully mediated the relationship between brain dynamics and cognitive performance.24
  • Dynamic connectivity features classified ASD versus typically developing children with 74.4% accuracy, and the brain-dynamics/cognition link replicated in an independent typically developing sample.23
  • Complementary multimodal EEG–fNIRS work (58 ASD, 63 typically developing) reported atypical beta- and gamma-band dynamic-connectivity states and suppressed right superior temporal–left Broca covariance, with early neurovascular features predicting adaptive-function trajectory.5
  • An earlier fNIRS network study of young children found weak global network efficiency in ASD and up to 83.3% classification accuracy using k-means on network-efficiency features, implicating the right prefrontal cortex.6
  • Clinical/BCI implication: passive, motion-tolerant fNIRS during naturalistic viewing offers a scalable early-screening biomarker for young or preverbal children in whom EEG or fMRI are harder to deploy.26

Footnotes

  1. https://news.google.com/rss/articles/CBMiYEFVX3lxTE5XQW1RYnBBZUxuTTNjT3ZmQmVVeUViZmR6SV9NRTRmS1hIQ0tPWDAzUlNwZmFCWWY5ZDQzVWZmUkpIRlRuUHAzSnV6aDBKMkhPdTlIMjNETVhYMnluSXA5Zg?oc=5 ↩ ↩2 ↩3

  2. https://www.jneurosci.org/content/45/44/e0161252025 ↩ ↩2 ↩3 ↩4 ↩5 ↩6

  3. https://pubmed.ncbi.nlm.nih.gov/41006060/ ↩ ↩2 ↩3

  4. https://doi.org/10.1523/jneurosci.0161-25.2025 ↩ ↩2

  5. https://doi.org/10.1016/j.neuroimage.2024.120895 ↩

  6. https://www.sciencedirect.com/science/article/abs/pii/S0278262616301531 ↩ ↩2

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