Use of resting-state functional MRI to study brain development and injury in neonates

Abstract

Advances in methodology have led to expanded application of resting-state functional MRI (rs-fMRI) to the study of term and prematurely born infants during the first years of life, providing fresh insight into the earliest forms of functional cerebral development. In this review, we detail our evolving understanding of the use of rs-fMRI for studying neonates. We initially focus on the biological processes of cortical development related to resting-state network development. We then review technical issues principally affecting neonatal investigations, including the effects of subject motion during acquisition and image distortions related to magnetic susceptibility effects. We next summarize the literature in which rs-fMRI is used to study normal brain development during the early postnatal period, the effects of prematurity, and the effects of cerebral injury. Finally, we review potential future directions for the field, such as the use of complementary imaging modalities and advanced analysis techniques.

Figure 1. Improvements in rs-fMRI results using rigorous motion correction procedures

Each row shows rs-fMRI correlation maps illustrating the motor network for one of three representative healthy, term control infants. The left column shows data derived using all frames acquired during the course of all single scanning session. The right column shows data derived only from frames remaining following rigorous frame censoring procedures. For motion correction, frames were excluded if the volume-to-volume head displacement was ≥ 0.25 mm or the root mean squared BOLD signal intensity change (DVARS) was ≥ 0.3%. Images depict Fisher z-transformed correlation coefficients obtained using a left motor cortex seed (z(r); color threshold value = 0.5). Identical slices are shown in both columns for each subject. Note the larger local area of correlation near the seed in the uncorrected data (yellow arrow). Note also that longer range connections to the contralateral motor area (white arrow) and supplemental motor area (white arrowhead) are not detected in the uncorrected data.

Figure 2. Multiple methods for correcting BOLD distortions due to magnetic field inhomogeneity

A parasaggital image obtained using echo-planar image acquisition. The cerebellum is indicated with an arrow. Image (A) is uncorrected and shows distortions due to magnetic field inhomogeneity caused by magnetic susceptibility effects. Note the stretching of the occipital and frontal lobes (arrowheads). The other images show the results of correction using (B) a self field map (C) top-down distortion correction, and (D) a mean field map. Note the similarity of improvement across all correction approaches.

Figure 3. Motor network in adult, term and term equivalent subjects

Group mean rs-fcMRI correlation maps illustrating the motor network. The color scale is the Fisher z-transformed correlation coefficients (z(r); color threshold value = 0.12) obtained using a left motor cortex seed overlaid on population-specific, atlas T2-weighted images. Positive correlations are red and yellow, negative correlations are green. The images depict data from (A) an adult, (B) a term control infant and (C) a preterm infant at term equivalent postmenstrual age. Adapted with permission.

Figure 4. Term versus very preterm infant differences

Group mean covariance matrices representing multiple canonical RSNs for (A) term infants and (B) preterm infants at term equivalent postmenstrual age. (C) shows the difference (term minus preterm). Black stars in (C) denote cells with between group difference on two-tailed Mann-Whitney U-test (p<0.05; multiple comparisons uncorrected). (D) Group mean Fisher z-transformed correlation coefficients between homotopic ROIs pairs for term and very preterm infants. Note consistent term > preterm correlation values. Note also that areas that mature relatively early (e.g., motor and visual cortex) have higher correlation coefficients than those that mature more slowly. Adapted with permission.

Figure 5. Support vector machine-multivariate pattern analysis demonstrating differences between term and very preterm infants

Functional connections important for differentiating term versus very preterm infants were determined using SVM. Connections stronger in term infants are shown in green; those stronger in very preterm infants are in orange. The thickness of each connection is weighted by the difference magnitude. Results were generated using 244 regions of interest located throughout the brain. Fifty infants scanned at comparable postmenstrual age with low-motion fcMRI data were included in each group. Findings are displayed on surface rendering of population-specific atlas image. Note that the differences between group are not confined to any particular area or network.