This grant focuses on research to advance automated neuroimaging techniques that will enhance the potential of human neural stem cell (hNSC) implantation to treat neonatal hypoxic ischemic brain injury (HII), a common condition with serious long-term neurological and developmental disorders such as mental retardation, cerebral palsy or epilepsy. Our goal is to develop magnetic resonance imaging (MRI) and imaging processing methods in a rat pup model of neonatal hypoxic-ischemic brain injury. This model is created by tying off the middle cerebral artery and exposing the pup to hypoxia and is known as the Rice-Vannucci model (RVM). We will use complex MRI methods to automatically determine where areas of brain injury are located as well as where human neural stem cells (hNSCs) are implanted. At the present time, there are no such methods available. Developing such methods are critical if we are to use stem cells for treatment of different diseases. In the first aim (Aim1a) we will implant hNSCs into a special gel (agarose) and image the cells in a MRI scanner. We will determine the optimal number of cells to use for imaging and the strength of the MRI magnet that is most suitable. We will also look at which of four specific type of imaging modalities will give us the most useful information about hNSC activity. In Aim1b, we will compare 3 computational methods using the 10d rat pup RVM at 1, 3, 7 days after implantation. These 3 complex methods include: (i) hierarchical region splitting (HRS), (ii) graph normalized cut (N-Cut) algorithm, and (iii) Bayesian region segmentation (BRS). Testing different computational methods will help us find the one that is best at detecting and monitoring stem cells as they move through brain regions and migrate to the areas of ischemic injury. When stem cells will be used in clinical trials, it will be very important to know if they will migrate to injured tissues, when this happens, how long the cells can survive for and also the degree of increase in their volume (i.e., proliferation). In Aim 2 we will quantify the activities of the stem cells after hypoxic-ischemic injury. In Aim 2a, we will test these computational methods to see how well they describe and detail the evolution and location of hypoxic-ischemic injury as well as the implanted stem cells. In Aim 2b we will correlate the results of the information that we acquire from MRI and the 3 computational methods with examination of the brain by doing tissue staining and microscopic examination (i.e., histology and immunohistochemistry). This will allow us to check the accuracy of the imaging methods. This grant addresses many of the important technical issues that must be considered in order to improve the chance for success of NSC therapy. We strongly believe that automated and quantitative imaging analysis will be one of the most significant technological advances that will increase the likelihood for successful stem cell therapy.
This proposal will benefit children born in California who suffer acute hypoxic brain injury at birth (i.e., perinatal asphyxia or ischemic perinatal stroke) and who develop long term neurological and developmental disorders such as mental retardation, cerebral palsy or epilepsy. Treatment with stem cells should reduce the burden of illness of this very common disorder that occurs in full term newborn infants. It will also benefit the families and caretakers of affected children as it will be easier to provide care for such children as they will have less severe neurological deficits. The long-term outcome of severe perinatal brain injury is a life-time of disability and if this can be reduced, it will also reduce the costs of chronic care, hospitalizations, rehabilitation and care in long-term nursing facilities of affected children. This grant focuses on one critical and important aspect of this endeavor that is related to available automated, reproducible, rapid, and objective methods to quantify and assess hNSC activity both in animal studies but more importantly when stem cells are used in human clinical trials. By developing the imaging methods to help improve candidate selection for treatment, for monitoring stem cell implantation, and for following the effects of treatment on outcome, the studies outlined in this proposal will provide ways to use these automated imaging methods to develop ‘push-button’ automated, fast, unbiased and reproducible software tools that will be used clinically not only for treatment with stem cells but for many other acquired diseases that occur across the pediatric age spectrum.