2 resultados para Crayfish Neuromuscular-junction

em Duke University


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Brain-computer interfaces (BCI) have the potential to restore communication or control abilities in individuals with severe neuromuscular limitations, such as those with amyotrophic lateral sclerosis (ALS). The role of a BCI is to extract and decode relevant information that conveys a user's intent directly from brain electro-physiological signals and translate this information into executable commands to control external devices. However, the BCI decision-making process is error-prone due to noisy electro-physiological data, representing the classic problem of efficiently transmitting and receiving information via a noisy communication channel.

This research focuses on P300-based BCIs which rely predominantly on event-related potentials (ERP) that are elicited as a function of a user's uncertainty regarding stimulus events, in either an acoustic or a visual oddball recognition task. The P300-based BCI system enables users to communicate messages from a set of choices by selecting a target character or icon that conveys a desired intent or action. P300-based BCIs have been widely researched as a communication alternative, especially in individuals with ALS who represent a target BCI user population. For the P300-based BCI, repeated data measurements are required to enhance the low signal-to-noise ratio of the elicited ERPs embedded in electroencephalography (EEG) data, in order to improve the accuracy of the target character estimation process. As a result, BCIs have relatively slower speeds when compared to other commercial assistive communication devices, and this limits BCI adoption by their target user population. The goal of this research is to develop algorithms that take into account the physical limitations of the target BCI population to improve the efficiency of ERP-based spellers for real-world communication.

In this work, it is hypothesised that building adaptive capabilities into the BCI framework can potentially give the BCI system the flexibility to improve performance by adjusting system parameters in response to changing user inputs. The research in this work addresses three potential areas for improvement within the P300 speller framework: information optimisation, target character estimation and error correction. The visual interface and its operation control the method by which the ERPs are elicited through the presentation of stimulus events. The parameters of the stimulus presentation paradigm can be modified to modulate and enhance the elicited ERPs. A new stimulus presentation paradigm is developed in order to maximise the information content that is presented to the user by tuning stimulus paradigm parameters to positively affect performance. Internally, the BCI system determines the amount of data to collect and the method by which these data are processed to estimate the user's target character. Algorithms that exploit language information are developed to enhance the target character estimation process and to correct erroneous BCI selections. In addition, a new model-based method to predict BCI performance is developed, an approach which is independent of stimulus presentation paradigm and accounts for dynamic data collection. The studies presented in this work provide evidence that the proposed methods for incorporating adaptive strategies in the three areas have the potential to significantly improve BCI communication rates, and the proposed method for predicting BCI performance provides a reliable means to pre-assess BCI performance without extensive online testing.

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The six-layered neuron structure in the cerebral cortex is the foundation for human mental abilities. In the developing cerebral cortex, neural stem cells undergo proliferation and differentiate into intermediate progenitors and neurons, a process known as embryonic neurogenesis. Disrupted embryonic neurogenesis is the root cause of a wide range of neurodevelopmental disorders, including microcephaly and intellectual disabilities. Multiple layers of regulatory networks have been identified and extensively studied over the past decades to understand this complex but extremely crucial process of brain development. In recent years, post-transcriptional RNA regulation through RNA binding proteins has emerged as a critical regulatory nexus in embryonic neurogenesis. The exon junction complex (EJC) is a highly conserved RNA binding complex composed of four core proteins, Magoh, Rbm8a, Eif4a3, and Casc3. The EJC plays a major role in regulating RNA splicing, nuclear export, subcellular localization, translation, and nonsense mediated RNA decay. Human genetic studies have associated individual EJC components with various developmental disorders. We showed previously that haploinsufficiency of Magoh causes microcephaly and disrupted neural stem cell differentiation in mouse. However, it is unclear if other EJC core components are also required for embryonic neurogenesis. More importantly, the molecular mechanism through which the EJC regulates embryonic neurogenesis remains largely unknown. Here, we demonstrated with genetically modified mouse models that both Rbm8a and Eif4a3 are required for proper embryonic neurogenesis and the formation of a normal brain. Using transcriptome and proteomic analysis, we showed that the EJC posttranscriptionally regulates genes involved in the p53 pathway, splicing and translation regulation, as well as ribosomal biogenesis. This is the first in vivo evidence suggesting that the etiology of EJC associated neurodevelopmental diseases can be ribosomopathies. We also showed that, different from other EJC core components, depletion of Casc3 only led to mild neurogenesis defects in the mouse model. However, our data suggested that Casc3 is required for embryo viability, development progression, and is potentially a regulator of cardiac development. Together, data presented in this thesis suggests that the EJC is crucial for embryonic neurogenesis and that the EJC and its peripheral factors may regulate development in a tissue-specific manner.