Neonatal brain injury and aberrant connectivity

Abstract

Brain injury sustained during the neonatal period may disrupt development of critical structural and functional connectivity networks leading to subsequent neurodevelopmental impairment in affected children. These networks can be characterized using structural (via diffusion MRI) and functional (via resting state-functional MRI) neuroimaging techniques. Advances in neuroimaging have led to expanded application of these approaches to study term- and prematurely-born infants, providing improved understanding of cerebral development and the deleterious effects of early brain injury. Across both modalities, neuroimaging data are conducive to analyses ranging from characterization of individual white matter tracts and/or resting state networks through advanced 'connectome-style' approaches capable of identifying highly connected network hubs and investigating metrics of network topology such as modularity and small-worldness. We begin this review by summarizing the literature detailing structural and functional connectivity findings in healthy term and preterm infants without brain injury during the postnatal period, including discussion of early connectome development. We then detail common forms of brain injury in term- and prematurely-born infants. In this context, we next review the emerging body of literature detailing studies employing diffusion MRI, resting state-functional MRI and other complementary neuroimaging modalities to characterize structural and functional connectivity development in infants with brain injury. We conclude by reviewing technical challenges associated with neonatal neuroimaging, highlighting those most relevant to studying infants with brain injury and emphasizing the need for further targeted study in this high-risk population.

Keywords: Brain injury; Functional connectivity; Infant; Magnetic resonance imaging; Prematurity; Structural connectivity.

Figure 1.

Brain connectomes can be analyzed using graph theoretical methods. A) Functional connectivity can be assessed by correlating the BOLD signal between regions of the brain. B) A matrix depicting BOLD correlations between all pairs of brain regions forms the functional connectome. C) Measures of network segregation include modularity and clustering coefficients, which can be used to delineate regions of the brain into functionally distinct brain networks. Metrics of network integration, including node connectedness (orange lines) and measures based on shortest path length between regions (red lines), can be used to characterize communication within and between networks of the brain. Nodes that play a central function in communicating information across networks can be characterized as hubs (black circles) and may belong to a ‘rich club’ (dashed line). Due to heirt importance in network communication, injury to hub regions may result in poorer clinical outcomes than that in brain regions less central to communication across regions (Rubinov & Sporns, 2010).

Figure 2.. Brain injury in term and preterm infants.

Representative coronal T₂-weighted and transverse diffusion-weighted MR images illustrating A) cystic periventricular leukomalacia, B) grade IV intraventricular hemorrhage and C) post-hemorrhagic hydrocephalus in prematurely-born infants at term equivalent postmenstrual age. Areas of hemorrhage appear dark and cerebrospinal fluid appears bright on these T₂-weighted images. Also demonstrated are D) basal ganglia and E) watershed injury patterns on diffusion-weighted images in term-born infants with hypoxic-ischemic brain injury.

Figure 3.. Effect of brain injury on structural connectivity in prematurely-born infants.

Anterior and lateral views of the bilateral corticospinal tracts identified using diffusion tensor tractography in representative individual very preterm infants scanned at term equivalent postmenstrual age with A) grade IV intraventricular hemorrhage (note the asymmetry in tract volume between injured and uninjured hemispheres), B) post-hemorrhagic hydrocephalus and cystic periventricular leukomalacia. Results from D) an uninjured very preterm infant and E) a healthy, term-born infant are provided for comparison. Note the differences in effects on tract development across different forms of brain injury common in this clinical population.

Figure 4.. Effect of brain injury on functional connectivity in prematurely-born infants.

Transverse views of the motor resting state network identified using resting state-functional MRI in representative, individual very preterm infants scanned at term equivalent postmenstrual age with A) grade IV intraventricular hemorrhage (note the asymmetric effect dependent upon hemisphere of injury), B) post-hemorrhagic hydrocephalus and C) cystic periventricular leukomalacia. Results from D) an uninjured very preterm infant and E) a healthy, term-born infant are provided for comparison. Note the differences in effects on resting state network development across each form of brain injury common in this clinical population.

Figure 5.. Effect of neurosurgical intervention on structural and functional connectivity in an infant with post-hemorrhagic hydrocephalus.

A) Axial T₂-weighted images demonstrating post-hemorrhagic hydrocephalus in the same prematurely-born infant during the week before and after ventriculoperitoneal shunt placement. Cerebrospinal fluid appears bright in these images. B) Motor resting state networks before and after shunt placement. Note the increase in interhemispheric connectivity in post-operative compared to pre-operative results. C) Diffusion tensor tractography showing corticospinal tracts for the same subject. Note the asymmetry between the more injured and less injured hemispheres.

Figure 6.. Resting state network architecture characterized using a community detection algorithm in an infant with brain injury.

A) T₂-weighted image demonstrating the location and severity of grade IV intraventricular hemorrhage in an individual very preterm infant scanned at term equivalent age. B) Brain networks generated using the Infomap community detection algorithm to cluster rs-fMRI data in the same infant; 200 cortical and subcortical gray matter regions of interest were used for analysis. Displayed are color-coded individual resting state network results on an individual-specific cortical surface identified using a consensus algorithm across an edge density range of 2.5–3.1%. C) The corresponding correlation matrix demonstrating rs-fMRI relationships within and across networks. Note the strong relationships within and across networks based upon anatomic location. Warm colors denote positive correlations and cool colors denote negative correlations.

Figure 7.. Methodological considerations for connectivity analyses in infants with brain injury.

A) Accurate registration of images and transformation to standard (711–2N) atlas space is feasible for infants with brain injury using age appropriate atlas targets and registration procedures. B) Regions of interest in the bilateral motor cortex identified using standard atlas coordinates with the corresponding correlation map generated using a left motor cortex seed in an infant with cystic periventricular leukomalacia. Note the anterior location of regions of interest in relation to the actual motor cortex bilaterally. C) Individual-specific bilateral motor cortex regions of interest in the same subject accounting for effects of cerebral injury with results from identical correlation analysis. Note the marked qualitative improvement in the motor network result using individual-specific regions of interest. This is also noted on quantitative analysis, with an interhemispheric correlation between regions of interest of 0.35 for the result in B and 0.64 for the result in C.