Elysia Adams

<h3>Objective:</h3> To quantitatively assess white matter injury (WMI) volume and location in very preterm neonates, and to examine the association of lesion volume and location with 18-month neurodevelopmental outcomes. <h3>Methods:</h3> Volume and location of WMI was quantified on MRI in 216 neonates (median gestational age 27.9 weeks) who had motor, cognitive, and language assessments at 18 months corrected age (CA). Neonates were scanned at 32.1 postmenstrual weeks (median) and 68 (31.5%) had WMI; of 66 survivors, 58 (87.9%) had MRI and 18-month outcomes. WMI was manually segmented and transformed into a common image space, accounting for intersubject anatomical variability. Probability maps describing the likelihood of a lesion predicting adverse 18-month outcomes were developed. <h3>Results:</h3> WMI occurs in a characteristic topology, with most lesions occurring in the periventricular central region, followed by posterior and frontal regions. Irrespective of lesion location, greater WMI volumes predicted poor motor outcomes (<i>p</i> = 0.001). Lobar regional analysis revealed that greater WMI volumes in frontal, parietal, and temporal lobes have adverse motor outcomes (all, <i>p</i> &lt; 0.05), but only frontal WMI volumes predicted adverse cognitive outcomes (<i>p</i> = 0.002). To account for lesion location and volume, voxel-wise odds ratio (OR) maps demonstrate that frontal lobe lesions predict adverse cognitive and language development, with maximum odds ratios (ORs) of 78.9 and 17.5, respectively, while adverse motor outcomes are predicted by widespread injury, with maximum OR of 63.8. <h3>Conclusions:</h3> The predictive value of frontal lobe WMI volume highlights the importance of lesion location when considering the neurodevelopmental significance of WMI. Frontal lobe lesions are of particular concern.

The aim of this prospective cohort study was to assess the association of systematic thoracic ultrasonography findings postweaning on calves' survivability to the first lactation. Three-month-old Jersey heifers (n=250) returning from a custom heifer grower were scanned by thoracic ultrasonography and lungs assessed using a scoring system with a scale from 1 to 4. A score of 1 was attributed to calves with no abnormality. A score of 2 was assigned if multiple comet tails or B-lines (coalescence of multiple comet tails) were observed. A score of 3 was assigned to calves with ≥1 location of lung consolidation ≥1 cm but <6 cm. Calves with extensive consolidation (≥6 cm in one or more locations) or evidence of abscessation or pleural effusion (>1 cm) were assigned a score of 4. Calves with a score of 4 had greater risk of dying or being culled [26% (95% credibility interval: 13-47%)] than calves with a score of 1 [1% (0-6%)], 2 [3% (1-9%)], or 3 [5% (1-17%)]. We found no association between age of first calving in the remaining calves and lung score. Thus, lung lesion severity assessed by thoracic ultrasound is associated with a long-term production outcome.

Preterm births are rising in Canada and worldwide. As clinicians strive to identify preterm neonates at greatest risk of significant developmental or motor problems, accurate predictive tools are required. Infants at highest risk will be able to receive early developmental interventions, and will also enable clinicians to implement and evaluate new methods to improve outcomes. While severe white matter injury (WMI) is associated with adverse developmental outcome, more subtle injuries are difficult to identify and the association with later impairments remains unknown. Thus, our goal was to develop an automated method for detection and visualization of brain abnormalities in MR images acquired in very preterm born neonates. We have developed a technique to detect WMI in T1-weighted images acquired in 177 very preterm born infants (24-32 weeks gestation). Our approach uses a stochastic process that estimates the likelihood of intensity variations in nearby pixels; with small variations being more likely than large variations. We first detect the boundaries between normal and injured regions of the white matter. Following this we use a measure of pixel similarity to identify WMI regions. Our algorithm is able to detect WMI in all of the images in the ground truth dataset with some false positives in situations where the white matter region is not segmented accurately.