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Does prematurity effect functional connectivity in the developing brain? A resting functional magnetic resonance imaging study

D KIM BA 1, A CAPRIHAN 1, D RUHL 1, C GASPAROVIC 1,2,
S DUVALL2, L SILVA 2, P MACLEAN 2, J LOWE 2, R OHLS 2,
J PHILLIPS2,1

1. Mind Research Network, Albuquerque, NM;
2. University of New Mexico, Albuquerque, NM, USA

Background/Objectives: Using functional magnetic resonance imaging (fMRI), a distinct set of temporally coherent functional brain networks can be identified that are representative of the brain’s resting state. These networks, referred to as resting-state networks (RSNs), may represent functional connectivity within the brain and have been largely studied in adult populations. The purpose of our study is to investigate RSNs in the brains of young children and to determine how these networks differ from those identified in adult populations. Furthermore, we sought to characterize the effect of prematurity on development of RSNs in children.

Design: Cross-Sectional Study.

Participants and Setting: Children between 18-22 months and 3-4 years of age were recruited through the Newborn Intensive Care Unit at the University of New Mexico Hospital. Preterm children had birth weights (BW) below 1500 g and gestational ages (GA) below 32 weeks. Fourteen preterm children and 10 term controls were used for this analysis.

Materials/Methods: An echo-planar imaging sequence (number of slices: 27, slice thickness=4 mm w/1 mm gap, TR=2000 ms, TE=30 ms, FOV=22 cm, matrix 64 x 64, voxel dimensions=3.4 x 3.4 x 4 mm, time-points: 158) was used for the fMRI resting scan. fMRI datasets were pre-processed using the software package SPM (http://www.fil.ion.ucl.ac.uk/spm/). Independent component analysis (ICA) was performed on the preprocessed fMRI datasets using the GIFT toolbox (http://icatb.sourceforge.net). ICA split the fMRI datasets into a final set of 41 spatially independent components that represent brain regions which share a temporally coherent signal. These signals were converted into a power spectrum using a fast Fourier transformation and subsequently analyzed using a two sample t-test to determine between-group differences in preterm infants and matched controls.

Results: A set of 12 RSNs were found in the infant brain that matched RSNs seen in previous adult studies using a similar analytical approach. These RSNs included regions such as the posterior cingulated, anterior cingulated, medial prefrontal cortex, precuneus, and parahippocampus. Many of the RSNs were lateralized to the right hemisphere, including the dorsal lateral prefrontal cortex as well as the anterior cingulated and insula. Between-group comparisons of VLBW subjects and matched term controls showed a strong statistically significant differences (P<0.005, FDR corrected) in the power spectrum for several RSNs. These differences were most apparent in the lower frequency spectrum (0.01 – 0.05 Hz), where preterm infants showed reduced power compared to normal term-controls.

Conclusions/Significance: RSNs identified in these young children were strongly correlated with RSNs found in adult studies. This suggests that these networks are emerging as early as 18 months of age in children born either preterm or term. Furthermore, significant differences between the power spectrums of RSNs in children born preterm compared to term controls suggests that they may be a sensitive marker of brain injury related to prematurity.

 
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