UCSF

Background and purpose

Diffusion and fMRI has been providing insights to brain development in addition to anatomic imaging. This study aimed to evaluate the microstructure of white matter tracts underlying the default mode network in premature infants by using resting-state functional MR imaging in conjunction with diffusion tensor imaging-based tractography.

Materials and methods

A cohort of 44 preterm infants underwent structural T1-weighted imaging, resting-state fMRI, and DTI at 3T, including 21 infants with brain injuries and 23 infants with normal-appearing structural imaging as controls. Neurodevelopment was evaluated with the Bayley Scales of Infant Development at 12 months' adjusted age. Probabilistic independent component analysis was applied to resting-state fMRI data to explore resting-state networks. The localized clusters of the default mode network were used as seeding for probabilistic tractography. The DTI metrics (fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity) of the reconstructed primary tracts within the default mode network-cingula were measured.

Results

Results revealed decreased fractional anisotropy (0.20 ± 0.03) and elevated radial diffusivity values (1.24 ± 0.16) of the cingula in the preterm infants with brain injuries compared with controls (fractional anisotropy, 0.25 ± 0.03; P < .001; radial diffusivity, 1.06 ± 0.16; P = .001). The Bayley Scales of Infant Development cognitive scores were significantly associated with cingulate fractional anisotropy (P = .004) and radial diffusivity (P = .021); this association suggests that the microstructural properties of interconnecting axonal pathways within the default mode network are of critical importance in the early neurocognitive development of infants.

Conclusions

This study of combined resting-state fMRI and DTI at rest suggests that such studies may allow the investigation of key functional brain circuits in premature infants, which could function not only as diagnostic tools but also as biomarkers for long-term neurodevelopmental outcomes.