An Examination of the Long-Term Neurodevelopmental Impact of Prenatal Zika Virus Infection in a Rat Model Using a High Resolution, Longitudinal MRI Approach.
Rita T PatelBrennan M GallamozaPraveen KulkarniMorgan L ShererNicole A HaasElise LemanskiIbrahim MalikKhan HekmatyarMark S ParcellsJaclyn M SchwarzPublished in: Viruses (2021)
Since Zika virus (ZIKV) first emerged as a public health concern in 2015, our ability to identify and track the long-term neurological sequelae of prenatal Zika virus (ZIKV) infection in humans has been limited. Our lab has developed a rat model of maternal ZIKV infection with associated vertical transmission to the fetus that results in significant brain malformations in the neonatal offspring. Here, we use this model in conjunction with longitudinal magnetic resonance imaging (MRI) to expand our understanding of the long-term neurological consequences of prenatal ZIKV infection in order to identify characteristic neurodevelopmental changes and track them across time. We exploited both manual and automated atlas-based segmentation of MR images in order to identify long-term structural changes within the developing rat brain following inoculation. The paradigm involved scanning three cohorts of male and female rats that were prenatally inoculated with 107 PFU ZIKV, 107 UV-inactivated ZIKV (iZIKV), or diluent medium (mock), at 4 different postnatal day (P) age points: P2, P16, P24, and P60. Analysis of tracked brain structures revealed significantly altered development in both the ZIKV and iZIKV rats. Moreover, we demonstrate that prenatal ZIKV infection alters the growth of brain regions throughout the neonatal and juvenile ages. Our findings also suggest that maternal immune activation caused by inactive viral proteins may play a role in altered brain growth throughout development. For the very first time, we introduce manual and automated atlas-based segmentation of neonatal and juvenile rat brains longitudinally. Experimental results demonstrate the effectiveness of our novel approach for detecting significant changes in neurodevelopment in models of early-life infections.
Keyphrases
- zika virus
- deep learning
- magnetic resonance imaging
- dengue virus
- high resolution
- resting state
- pregnant women
- public health
- white matter
- aedes aegypti
- contrast enhanced
- early life
- convolutional neural network
- functional connectivity
- cerebral ischemia
- single cell
- randomized controlled trial
- machine learning
- computed tomography
- systematic review
- oxidative stress
- high throughput
- cross sectional
- magnetic resonance
- multiple sclerosis
- mass spectrometry
- birth weight
- type diabetes
- preterm infants
- pregnancy outcomes
- diffusion weighted imaging
- body mass index
- brain injury
- congenital heart disease
- physical activity
- skeletal muscle
- subarachnoid hemorrhage
- high speed