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- All Subjects: Neuroscience
- Creators: Harrington Bioengineering Program
commands designed to streamline post-processing of MRI images. Using this partnership, the Applied Neuroscience and Technology Lab at PCH has been able to complete production of a post-processing pipeline which integrates locally sourced smoothing techniques to help identify lesions in patients with evidence of Focal Cortical Dysplasia. The end result is a system in which a patient with epilepsy may experience more successful post-surgical results due to the
combination of a lesion detection mechanism and the radiologist using their trained eye in the presurgical stages. As one of the main points of this work is the global aspect of it, Barrett thesis funding was dedicated for a trip to London in order to network with other MELD project collaborators. This was a successful trip for the project as a whole in addition to this particular thesis. The ability to troubleshoot problems with one another in a room full of subject matter
experts allowed for a high level of discussion and learning. Future work includes implementing machine learning approaches which consider all morphometry parameters simultaneously.
2D fetal echocardiography (ECHO) can be used for monitoring heart development in utero. This study’s purpose is to empirically model normal fetal heart growth and function changes during development by ECHO and compare these to fetuses diagnosed with and without cardiomyopathy with diabetic mothers. There are existing mathematical models describing fetal heart development but they warrant revalidation and adjustment. 377 normal fetuses with healthy mothers, 98 normal fetuses with diabetic mothers, and 37 fetuses with cardiomyopathy and diabetic mothers had their cardiac structural dimensions, cardiothoracic ratio, valve flow velocities, and heart rates measured by fetal ECHO in a retrospective chart review. Cardiac features were fitted to linear functions, with respect to gestational age, femur length, head circumference, and biparietal diameter and z-scores were created to model normal fetal growth for all parameters. These z-scores were used to assess what metrics had no difference in means between the normal fetuses of both healthy and diabetic mothers but differed from those diagnosed with cardiomyopathy. It was found that functional metrics like mitral and tricuspid E wave and pulmonary velocity could be important predictors for cardiomyopathy when fitted by gestational age, femur length, head circumference, and biparietal diameter. Additionally, aortic and tricuspid annulus diameters when fitted to estimated gestational age showed potential to be predictors for fetal cardiomyopathy. While the metrics overlapped over their full range, combining them together may have the potential for predicting cardiomyopathy in utero. Future directions of this study will explore creating a classifier model that can predict cardiomyopathy using the metrics assessed in this study.
2D fetal echocardiography (ECHO) can be used for monitoring heart development in utero. This study’s purpose is to empirically model normal fetal heart growth and function changes during development by ECHO and compare these to fetuses diagnosed with and without cardiomyopathy with diabetic mothers. There are existing mathematical models describing fetal heart development but they warrant revalidation and adjustment. 377 normal fetuses with healthy mothers, 98 normal fetuses with diabetic mothers, and 37 fetuses with cardiomyopathy and diabetic mothers had their cardiac structural dimensions, cardiothoracic ratio, valve flow velocities, and heart rates measured by fetal ECHO in a retrospective chart review. Cardiac features were fitted to linear functions, with respect to gestational age, femur length, head circumference, and biparietal diameter and z-scores were created to model normal fetal growth for all parameters. These z-scores were used to assess what metrics had no difference in means between the normal fetuses of both healthy and diabetic mothers, but differed from those diagnosed with cardiomyopathy. It was found that functional metrics like mitral and tricuspid E wave and pulmonary velocity could be important predictors for cardiomyopathy when fitted by gestational age, femur length, head circumference, and biparietal diameter. Additionally, aortic and tricuspid annulus diameters when fitted to estimated gestational age showed potential to be predictors for fetal cardiomyopathy. While the metrics overlapped over their full range, combining them together may have the potential for predicting cardiomyopathy in utero. Future directions of this study will explore creating a classifier model that can predict cardiomyopathy using the metrics assessed in this study.
Advancing the understanding and treatment of many neurological disorders can be achieved by improving methods of neuronal detection at increased depth in the mammalian brain. Different cell subtypes cannot be detected using non-invasive techniques beyond 1 mm from cortical surface, in the context of targeting particular cell types in vivo (Wang, 2012). These limitations in the depth of imaging and targeting are due to optical scattering (Ntziachristos, 2010). In order to overcome these restrictions, longer wavelength fluorescent proteins have been utilized by researchers to see tagged cells at depth. Optical techniques such as two-photon and confocal microscopy have been used in combination with fluorescent proteins to expand depth, but are still limited by the penetration depth of light due to optical scattering (Lee, 2015). This research aims to build on other detection methods, such as the photoacoustic effect and automated fluorescence-guided electrophysiology, to overcome this limitation.
With millions of people living with a disease as restraining as migraines, there are no ways to diagnose them before they occur. In this study, a migraine model using nitroglycerin is used in rats to study the awake brain activity during the migraine state. In an attempt to search for a biomarker for the migraine state, we found multiple deviations in EEG brain activity across different bands. Firstly, there was a clear decrease in power in the delta, beta, alpha, and theta bands. A slight increase in power in the gamma and high frequency bands was also found, which is consistent with other pain-related studies12. Additionally, we searched for a decreased pain threshold in this deviation, in which we concluded that more data analysis is needed to eliminate the multiple potential noise influxes throughout each dataset. However, with this study we did find a clear change in brain activity, but a more detailed analysis will narrow down what this change could mean and how it impacts the migraine state.