Brain-Computer Interfaces (BCI) are a class of devices that record the analog signals generated by the neural activity of the brain and convert them to digital data that can be read by external devices. Interest in capturing and reading the signals generated by the brain for the purpose of exporting digitized conscious will to command has been of great interest in the world of neuroscience and medical application. Traditionally, these signals have been captured using non-invasive electroencephalography (EEG) or MRI. However, due to their nature and design, these devices lack the necessary channels and bandwidth to perfectly capture the thoughts and actions of an individual for the use of controlling complex devices or machinery. A fully realized BCI can revolutionize how communicate, collaborate and educate. In my presentation, I will be discussing developments in this field related to current breakthroughs, research, and applications.

Epilepsy is the fourth most common neurological disorder in the US impacting ~1% of the population, and 30% of all epileptic patients are resistant to traditional drug treatments [1].The ketogenic diet (KD) is one of oldest treatments of drug resistant epilepsy. Generally, the KD is suggested to function through a metabolic switch from carbohydrates to fatty acids as the main fuel source [2]. Interestingly, dynamic changes in glucose utilization and TCA cycle function have been implicated during various stages of epileptogenesis, suggesting epilepsy as a potential metabolic disease [3,4]. The purpose of this research is to characterize potential metabolic interaction between the brain and liver and how this interaction may contribute to the development of seizures. To study this, a kainic acid-induced mouse model of epilepsy was used to induce seizures. Brain and liver tissue was collected for later extraction and metabolic/proteomic analysis. Metabolic profiling of polar and nonpolar metabolites was performed via GC-MS and LC-MS analysis, respectively. Shotgun proteomic analysis was used to correlate changes in protein levels with metabolites. From this research we find that interestingly, the liver is heavily affected by seizure presence (namely amino acid and central carbon pathways) along with the brain. The findings of this study further implicate the importance of considering liver metabolism as the metabolic dynamics of epilepsy continue to be studied and may uncover metabolic targets which could be used to develop more effective drugs for the treatment of refractory epilepsy. References: [1] Institute of Medicine (U.S.). Committee on the Public Health Dimensions of the Epilepsies., M.J. England, Epilepsy across the spectrum: promoting health and understanding, National Academies Press, 2012. [2] A.A.M. Morris, Cerebral ketone body metabolism, Journal of Inherited Metabolic Disease. 28 (2005) 109–121. https://doi.org/10.1007/s10545-005-5518-0. [3] M. Patel, A Metabolic Paradigm for Epilepsy, Epilepsy Current. 18 (2018) 318–322. [4] F. Murgia, A. Muroni, M. Puligheddu, L. Polizzi, L. Barberini, G. Orofino, P. Solla, S. Poddighe, F. del Carratore, J.L. Griffin, L. Atzori, F. Marrosu, Metabolomics as a Tool for the characterization of Drug-resistant epilepsy, Frontiers in Neurology. 8 (2017). https://doi.org/10.3389/fneur.2017.00459.

Functional Magnetic Resonance Imaging (fMRI) measures brain activity by detecting changes associated with blood flow. After applying a radio-frequency pulse, echo time (TE) is the actual time when the signal of the T2 decay curve is measured. The optimal TE for a particular tissue is represented by T2*. As such, fMRI data of infants' brain activity can be challenging to interpret because of their underdeveloped neuronal structure and their small size. The objective of this study is to understand which are the optimal echo times for infants. It is hypothesized that optimal echo time for fMRI in infants would be greater than that of toddlers and adults. Study subjects include two newborn infants, two toddlers ranging from 26-29 months, and two adults. 3T multi-band, multi-echo MRI scans are analyzed. Anatomical images of the brain are segmented using deep learning algorithms and overlaid with T2* maps for different brain areas to be analyzed. The T2* values of different brain areas are calculated, and their distributions are compared across the three groups. Results and conclusions are pending and will be available by July/20

Polycystic kidney disease (PKD), both autosomal dominant (AD) and autosomal recessive (AR) forms, remains the leading cause of inherited renal (i.e. kidney) disease in adults and children, respectively. PKD is defined principally by the gradual growth of fluid-filled cysts in the kidney that eventually leads to decrease renal filtration function and inevitably renal failure. There is no cure beyond a renal transplantation. Current treatment options are extremely limited and associated with a poor quality of life. One major contribution to cyst growth is vasopressin (AVP) signaling through the vasopressin type-2 receptor (V2R). One understudied mediator of AVP activity is through renal afferent (i.e. sensory) nerves. We have recently reported afferent renal nerve activity (ARNA) is elevated in a preclinical model of PKD, and surgical ablation of afferent nerves stunts the cystogenesis. It remains unclear how ARNA directly mediates cyst progression. We hypothesize that renal cystogenesis is mediated by elevated ARNA and its direct activation of AVP release and AVP-V2R-mediated renal cell proliferation. To test this hypothesis, we will use an animal model of PKD (PCK rat). We will perform afferent targeted renal denervation (ARDNx) in adult PCK rats, and assess the circulating AVP by ELISA and renal cell proliferation by 24-hour urinary cyclic adenosine monophosphate (cAMP) excretion before and two weeks after surgical intervention. We expect circulating AVP and urinary cAMP to decrease following ARDNx, supporting a direct role for ARNA on AVP regulation and renal cystogenic/proliferative signaling. These studies are underway and will be completed by Spring 2023.