Frequency of Spontaneous BOLD Signal Differences between Moderate and Late Preterm Newborns and Term Newborns

Neurotox Res. 2016 Oct;30(3):539-51. doi: 10.1007/s12640-016-9642-4. Epub 2016 Jun 17.

Abstract

Little is known about the frequency features of spontaneous neural activity in the brains of moderate and late preterm (MLPT) newborns. We used resting-state functional magnetic resonance imaging (rs-fMRI) and the amplitude of low-frequency fluctuation (ALFF) method to investigate the frequency properties of spontaneous blood oxygen level-dependent (BOLD) signals in 26 MLPT and 35 term newborns. Two frequency bands, slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz), were analyzed. Our results showed widespread differences in ALFF between the two bands; differences occurred mainly in the primary sensory and motor cortices and to a lesser extent in association cortices and subcortical areas. Compared with term newborns, MLPT newborns showed significantly altered neural activity predominantly in the primary sensory and motor cortices and in the posterior cingulate gyrus/precuneus. In addition, a significant interaction between frequency bands and groups was observed in the primary somatosensory cortex. Intriguingly, these primary sensory and motor regions have been proven to be the major cortical hubs during the neonatal period. Our results revealed the frequency of spontaneous BOLD signal differences between MLPT and term newborns, which contribute to the understanding of regional development of spontaneous brain rhythms of MLPT newborns.

Keywords: Amplitude of low-frequency fluctuations; Moderate and late preterm; Primary somatosensory cortex; Resting-state functional magnetic resonance imaging.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Analysis of Variance
  • Brain / diagnostic imaging*
  • Brain / growth & development
  • Brain / physiology*
  • Brain Mapping
  • Female
  • Humans
  • Infant, Newborn
  • Infant, Premature*
  • Magnetic Resonance Imaging*
  • Male
  • Rest
  • Signal Processing, Computer-Assisted