2021-2022 Senior Honors Theses
Permanent URI for this collectionhttps://hdl.handle.net/10657/10473
This collection contains theses produced by Class of 2022 Honors students
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Browsing 2021-2022 Senior Honors Theses by Department "Biomedical Engineering, Department of"
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Item Prenatal Nicotine Exposure Alters Genetic Profiles of Neurons in the Sub-regions of the VTA During Early Postnatal Development(2022-05-11) McGill, Lindsey D.Brain growth occurs during the first two weeks of postnatal development in rats. This developmental period is equivalent to the third trimester of human gestation. Dendritic arborization, axonal growth, and gliogenesis are observed along with a significant maturation of neurotransmission during this critical development period. Furthermore, nicotine exposure during early development causes deficiencies in sensory and cognitive processing in adults. In this study, we further investigated the neuron populations and the influence of perinatal nicotine exposure on gene expressions of neurons within the sub-regions of the ventral tegmental area (VTA) in one week (P7), two week (P14) and three week (P21) old rat pups. We exposed pregnant rats to nicotine perinatally to investigate its effect in rat pups during early neuronal development (P7, P14, and P21). Real time PCR (RT-qPCR) was used to determine the relative expressions of GABA, dopamine (DA), and glutamate neuron markers within the sub-regions of the VTA including the paranigral nucleus (PN), parainterfascicular (PIF), and parabrachial pigmented nucleus (PBP). Our results indicated that during early maturation, the dopamine marker TH was not significantly expressed within the sub-regions of the VTA in the nicotine exposed P7 group. However, TH was significantly expressed within the PN sub-region compared to the PBP sub-region of the VTA in both the P14 and P21 groups and within the PN sub-region compared to the PIF sub-region in the P21 group. These results suggest that following perinatal nicotine exposure, VTA DA neurons, especially within the PN sub-region, are significantly excited after two weeks of maturation.Item Towards a Gamified Therapeutic Brain-Computer Interface for Children with Gait Impairment(2022-06-13) Desabhotla, Krishna SarvaniCentral nervous system (CNS) disorders cause over 1 billion people to live with a life-altering handicap. Some CNS disorders, such as cerebral palsy and spina bifida, affect one-four per 1000 and one per 2758 children respectively, according to the Centers for Disease Control. These pediatric CNS disorders leave patients with many years of living with partial or complete motor impairment. Brain-computer interfaces (BCIs) have been researched as tools for rehabilitation for adults with disabilities due to neurological disease, brain injury or amputation; however, research on the design of BCI systems for children has not received the same level of attention by the scientific community. This is unfortunate as the developing brain is very plastic, thus, children may be the best candidates for BCIs for neurorehabilitation. The primary aim of this project was to adapt a system, developed in the Laboratory for Non-Invasive Brain-Machine Interface Systems at the University of Houston, that can be used for BCI system development for children. Such a system will provide real-time data capture from two types of sensors (scalp electroencephalography or EEG, and joint angle data from the lower limbs) during treadmill walking while providing real-time visual feedback of the child’s gait pattern via a digital avatar. To achieve this aim, a system was created in the MATLAB programming environment that initializes, acquires and synchronizes EEG and joint angles, and then, filters and sends joint angles to control the digital avatar and in parallel, stores time-locked unprocessed EEG and joint angle data for offline processing - the first step in designing a BCI system. Applications of the system include, but are not limited to, investigating the neural representations for motor control in children, and extracting neural and kinematic features for diagnostic purposes and for the design of closed-loop BCI systems for children.