Quantification of brain-wide vascular resistivity via ultrafast Doppler in human neonates helps early detection of white matter injury.

Preterm birth is associated with cerebrovascular development disruption and can induce white matter injuries (WMI). Transfontanellar ultrasound Doppler is the most widely used clinical imaging technique to monitor neonatal cerebral vascularisation and haemodynamics based on vascular indexes such as the resistivity index (RI); however, it has poor predictive value for brain damage. Indeed, these RI measurements are currently limited to large vessels, leading to a very limited probing of the brain's vascularisation, which may hinder prognosis. Here we show that ultrafast Doppler imaging (UfD) enables simultaneous quantification, in the whole field of view, of the local RI and vessel diameter, even in small vessels. Combining both pieces of information, we defined two new comprehensive resistivity parameters of the vascular trees. First, we showed that our technique is more sensitive in the early characterisation of the RI modifications between term and preterm neonates and for the first time we could show that the RI depends both on the vessel diameter and vascular territory. We then showed that our parameters can be used for early prediction of WMI. Our results demonstrate the potential of UfD to provide new biomarkers and pave the way for continuous monitoring of neonatal brain resistivity.

Transfontanellar shear wave elastography of the neonatal brain for quantitative evaluation of white matter damage.

Cerebral white matter damage (WMD) is the most frequent brain lesion observed in infants surviving premature birth. Qualitative B-mode cranial ultrasound (cUS) is widely used to assess brain integrity at bedside. Its limitations include lower discriminatory power to predict long-term outcomes compared to magnetic resonance imaging (MRI). Shear wave elastography (SWE), a promising ultrasound imaging modality, might improve this limitation by detecting quantitative differences in tissue stiffness. The study enrolled 90 neonates (52% female, mean gestational age = 30.1 ± 4.5 weeks), including 78 preterm and 12 term controls. Preterm neonates underwent B-mode and SWE assessments in frontal white matter (WM), parietal WM, and thalami on day of life (DOL) 3, DOL8, DOL21, 40 weeks, and MRI at term equivalent age (TEA). Term infants were assessed on DOL3 only. Our data revealed that brain stiffness increased with gestational age in preterm infants but remained lower at TEA compared to the control group. In the frontal WM, elasticity values were lower in preterm infants with WMD detected on B-mode or MRI at TEA and show a good predictive value at DOL3. Thus, brain stiffness measurement using SWE could be a useful screening method for early identification of preterm infants at high WMD risk.Registration numbers: EudraCT number ID-RCB: 2012-A01530-43, ClinicalTrial.gov number NCT02042716.

Bedside functional monitoring of the dynamic brain connectivity in human neonates.

Clinicians have long been interested in functional brain monitoring, as reversible functional losses often precedes observable irreversible structural insults. By characterizing neonatal functional cerebral networks, resting-state functional connectivity is envisioned to provide early markers of cognitive impairments. Here we present a pioneering bedside deep brain resting-state functional connectivity imaging at 250-µm resolution on human neonates using functional ultrasound. Signal correlations between cerebral regions unveil interhemispheric connectivity in very preterm newborns. Furthermore, fine-grain correlations between homologous pixels are consistent with white/grey matter organization. Finally, dynamic resting-state connectivity reveals a significant occurrence decrease of thalamo-cortical networks for very preterm neonates as compared to control term newborns. The same method also shows abnormal patterns in a congenital seizure disorder case compared with the control group. These results pave the way to infants' brain continuous monitoring and may enable the identification of abnormal brain development at the bedside.