Neural Engineering Student Projects

Permanent URI for this collection

https://hdl.handle.net/1920/11428

These projects were created as part of the graduate level neural engineering course which focuses on current topics in neural engineering. Examples include brain-machine interfaces, neurophysiological methods, instrumentation for interfacing electronics to the nervous system (in vivo and in vitro), optogenetics, ultrasound interfaces (for stimulation and recording in the nervous system), neural modeling, and prosthesis design.

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Optogenetics: Using Light to Investigate and Potentially Treat Neurological and Psychological Disorders
(2019) Kub, Michael
Optogenetics is an innovative neuromodulation technique involving the use of light and light-sensitive proteins to control molecular events within a genetically modified cell. The fundamental mechanism behind optogenetics is the deliberate shining of light at light-sensitive cellular membrane proteins which causes some sort of change within a cell. These proteins, called opsins, come in many forms including ion channels, pumps, and Gprotein-coupled receptors and they are found in a wide range of organisms from vertebrates to prokaryotes. When utilizing optogenetics, researchers must make several considerations including the light source to be used to control the cellular event, the type of cell to be activated by the light,and the tools to be utilized for measuring such cellular activity. We reviewed in detail the mechanism behind optogenetics and the considerations researchers make in employing this technique. We also reviewed outcomes from several studies centered around it and its current limitations. In conducting this review, we utilized web-based archives such as PubMed, Nature, and ScienceDirect. The studies that we specifically reviewed include the application of optogenetics for analyzing the effect that grafted cells have on relieving Parkinson’s Disease symptoms in animal models, the capability of optogenetics in instantly controlling depression-like states in mice, and the capability of optogenetics in regulating epilepsy in cultured animal brain models. In each of these studies, the type of cell that was sought to be controlled was the neuron, which all studies had substantial success in doing so. One area which was not addressed in these studies and which should be in future studies, is the plausibility that optogenetics could someday be used on humans. Based on the outcomes of these studies and the overall indication that optogenetics is an effective and precise technique in evoking cellular events, we conclude that optogenetics will likely have an enormous impact on research for years to come. Furthermore, given concerns over safety and use on humans, which we get into later in this paper, we also conclude that optogenetics has an uncertain future for clinical application.
Spiking Dynamics Observed in Three Neurons
(2019) Perera, Kevin
A leaky integrate and fire (LIF) model is a basic mathematical model designed to simulate a neuron. The simplicity and relative ease with which network architectures can be constructed using patterns of LIF neurons make it a common choice for modeling. The RC circuit is used as the basis of the model. By way of algebraic manipulation, we can solve I(t) = (u(t)–u(rest)/R + C*du/dt, where u-u(rest) is the voltage across the resistor. The product of C*du/dt is the capacitive current. Through integration and Ohm’s Law, we can derive membrane potential in a linear differential equation[1]. Delgado et al. aims to model the membrane potential of the LIF neuron through a random process known as Ornstein-Uhlenbeck. Through the random variable Tf, we can predict the time between neuron firings. We can find an estimator of the average firing rate of the neuron[2]. The Nengo framework is an open-source neural modeling architecture leveraged across Python and the TensorFlow libraries, which are used for developing artificial intelligence models. Using LIF neurons, it is possible to create a high-level model of the brain[3]. More attention is spent by the modeler to conceptualize neural architecture; however, the drawback to using the software is that we are unable to manipulate dynamics on the neuron-to-neuron scale, making Nengo more appropriate for large scale modeling.
Literature Review on Methods of Modeling the Cerebral Network and the Circle of Willis
(2019-05) Perera, Kevin
The Circle of Willis is a network of internal carotid arteries, basilar arteries, and vertebral arteries all coming together to perfuse the brain with blood. In the event that stenosis occludes part of the circle, the communicating arteries located at the posterior and anterior of the circle can compensate for the blood flow. However, if the communicating arteries themselves are occluded or entirely missing, the risk of ischemic stroke and subsequent death is possible. The literature surrounding the Circle of Willis is robust; the following review establishes historical context surrounding the development of mathematical models around the cerebral network and those specific to the circle of Willis, while also highlighting the applications and future for modelling.
Stimulation and Recording of Compound Action Potentials in Lumbricus terrestris, Homarus Americanus and Cambarus bartonii nerves
(2019-05) Selmer, Kaitlyn
When an individual suffers nerve damage as a result of a stroke or severe trauma, their quality of life suffers greatly. In many cases, this can lead to paralysis of limbs due to peripheral nerve damage. To improve mobility for these patients, it is necessary to understand the extent to which they are impaired and to remedy it through specialized therapies, such as neurostimulation. Current research in the field of neurostimulation of model organisms, such as earthworms, lobster and crayfish, have demonstrated that it is possible to stimulate nerve bundles to propagate compound action potentials [12,15]. Once these action potentials are sent along nerves, it is possible to stimulate muscle contraction and with enough activation a patient could experience improved limb mobility over time. Ultrasound is a technique that has been widely used to stimulate and record neural activity [15]. The goal of this study is to investigate the feasibility of ultrasound as a method of producing consistent activation of nerves. This project will make use of ultrasound to observe changes that occur in the nerves of crayfish when a signal is being sent through but will also be coupled with electrical stimulation as a proof of nerve viability. Cross-sectional ultrasound images of the nerve will be taken at specific points to observe change in diameter as the action potential passes along the nerve. The diameter is expected to increase and decrease due to the flux of sodium ions [9]. Due to challenges difficulties on obtaining crayfish specimens and technical issues with the electrophysiological equipment, the intended goal of this study was not met. The protocols for an effective study were investigated however and will be incorporated into the experimental framework going forward.
Comparing Muscle Spindle Afferent Models
(Teknos, 2020) Hachem, Stephanie
Proprioception, the internal sense of where your body parts are relative to each other, is essential for many, particularly bimanual, daily activities. Unfortunately, because modern prosthetics lacks this sense it is often difficult or impossible to perform hand-eye coordination tasks with them, and thus upper extremity prosthetics can become a nuisance or burden amputees frequently abandon at home (Biddiss & Chau, 2007). Towards creating a naturally-functional prosthetic able to provide proprioception, this project aims to compare computational models of muscle spindle afferents, using experimental data, in hopes that the best computational model could later be used to predict what voltages should be provided to which afferent nerves in a residual limb.Two muscle spindle models were compared using experimentally-measured afferent and muscle length data digitally extracted from figures in 10 articles. Data was from cats and humans. Both models implement the same formulas in either MATLAB or Python, take muscle length as an input, and can provide primary and/or secondary afferent output. Comparing the experimentally-measured and the predicted afferents, the more recent, Python model was found to provide more accurate afferent output.
Biophysical Neuromodulation: An Integrative Approach
(2020) Bush, Joshua
The brain remains the key to our experiential reality. From sensory perception to an internal expanse the brain remains the hardware and processing unit. The age in which we can precisely alter the hardware with which we process and create experience is dawning. From the advent of optogenetics to the developing use of transcranial magnetic stimulation (TMS), we are rapidly expanding our control over our brains. It is essential that the evolution of our understanding of the brain maintains a reasonable pace with the technological breakthroughs and human urgency. In this pursuit, we explore the emerging methods for biophysical neuromodulation and the materials through which these methods can be enhanced and/or used to develop translational in vitro models. The methods involved encompass thermal, electrical, magnetic, optical, and ultrasonic modulation of neurons and neuronal networks. A variety of materials such as conductive polymers, graphene, and optoelectronic semi-conductors have been utilized to harness these physical stimuli in the study of neuromodulation. Materials continue to emerge with greater precision and control over the matrix mechanical properties, conductivity, ligand densities, and nano-architecture. With environmental control, real-time physical neuromodulation, and evolving multiplexed sensing capabilities, the rate at which we can further our comprehensive understanding of neuronal information encoding expands rapidly. This review aims to provide a cohesive overview of the maturing coupling between biomaterials and biophysical neuromodulation.
Neuroscience Research in Spatial Navigation Using Robotic Animals
(2020) Sutton, Nate
The focus of this review is neuroscience research using robots programmed to navigate spatial environments using methods based on animal cognition. The use of cognition-based methods in robot navigation has multiple translational applications such as self-driving cars, and robots that handle hostile, dull, dangerous, and dirty activities. The use of robots for testing spatial cognition neural theories can provide advantages in accumulating real-world environment data, obtaining a diversity of test environments, and being able to directly reuse test environments where animal neural recordings were captured. Multiple types of neurons encode separate properties of space that are used in navigation activities, e.g., using information around one’s body to determine position while comparing that to a goal path (path integration). Research producing better knowledge of brain spatial processing mechanisms with those cell types helps inform the design of algorithms for navigation in robots. A type of advance that has been made in neural algorithm design was the inclusion of conjunctive cells modeling. The cells combine and compress sensory information in cells, e.g., visual landmarks, odors, sounds, touch, for neural computational efficiency gains. A type of challenge in navigation is simultaneous localization and mapping (SLAM), which is a problem that has attracted widespread attention in the field of computer vision. An innovative approach toward SLAM that is based on rodent cognition is named RatSLAM. It uses an attractor network algorithm to process spatial information cues to understand environments, allowing the navigation to be robust to ambiguous spatial landmark information and path integration errors. RatSLAM has been expanded on with features such as merging information between robot rats toward their common objective of understanding spatial environments. Integration of advanced forms of neuromorphic hardware, for instance, International Business Machines’ (IBM) Neurosynaptic System, into robots designed for navigation enables computational efficiency gains. Research with robots designed with navigation algorithms based on neural cognition has been advanced by studies investigating multiple factors involved in the activity of navigation. Several research projects have used discoveries learned from the fields of robotic navigation and neuroscience to create new investigations that improved the state of spatial navigation science.
Graphene-based nanocomposites used as a suitable biomaterial for neural growth and networking
(2020) O'Shea, Bailey
A novelty to counter neurodegenerative diseases such as Alzheimer’s or peripheral nerve injury (PNI) has been in high demand for the past century. Alzheimer’s disease is a well-known disorder that causes nerve tissue to progressively degrade with time. Additionally, PNI is refers to an array of conditions that cause peripheral nerve damage; for example, brachial plexus injury and Wallerian degeneration are considered to be PNIs. Current treatments, such as pharmaceutical drugs and surgery, are reported to help manage symptoms caused by these injuries and disorders. Microsurgeries are being utilized to alleviate pain from PNI, however, because human biology is so complex which hinders neuronal regeneration, any damage caused may be permanent. While these procedures are effective, they are reported to lack post-neurorepair recoveries and growth [1]. Thus, the need for a commodity to rejuvenate the damaged neuronal tissue has seen a great deal of attention.
Interpreting Speech and Sounds from Neural Activity, a Brief Overview
(2020) Ryan, Andrew
For people who are mute, or are completely paralyzed, one of the primary problems they have to deal with is being able to communicate. One potential solution to compensate for decreased communication functions is by using a brain-computer interface (BCI). The idea would be to quantify neural activation in the brain that correlates to imagined speech from the patient, and decode that into legible text that can be interpreted by the receiver. Due to the intricacy of speech interpretation, direct access to regions of the brain and individual neurons isrequired. As a result, many tests done on BCI speech interpretation involve using ECoG sensors on epilepsy patients when they are available. Some approaches used to analyze these signals for feature extraction include word based classification, and phoneme based classification. One approach mentioned less in the literature, is if there is a method to pull a sound signal directly from the activated regions of the brain. Advancement of the technology has potential use as a speech replacement for people suffering from paralysis, as well as in prosthetics.
Study in Novel Peripheral Nerve Interface and Application
(2020) Samra, Amrtpal
Bi-directional exchange of information between the peripheral nervous system (PNS) and a computer interface can be achieved through the use of a peripheral nerve interface (PNI). Sophisticated PNI’s can be used to augment a PNS compromised by injury or disease. In addition to supplementing the PNS, PNI’s enable researchers to study the form and function of the PNS. Deploying effective PNI’s come with a set of challenges not unlike other neural engineering solutions; namely: low signal-to-noise ratio (SNR) from sensitivity limitations current equipment provide, functional resolution deficiencies and managing inadvertent stimulation as the whole field is still in its infancy, and the stability of the interface itself that tends to degrade over time from the body’s inherent nature of attacking foreign objects. There are many prominent figures in the field that are currently developing novel approaches and technologies to interface with the PNS. For example, imagine a system that can track internal biological markers that classify a disease state and automatically stimulate a peripheral nerve to provide non-pharmacological treatment of the medical condition; bringing the body and mind back to a ‘normal’ state, [6]. Imagine applying imaging techniques used in a different field of study in a novel way to increase the received SNR on electrodes, and consequentially the precision and accuracy of stimulation. Or, the ability to re-learn an ‘every-day’ function that has been affected by an injury or disease; for example, learning how to walk again after having a stroke that severs those neural pathways. Of course there is no one-size-fits-all solution and there are many trade-offs between resolution, form, and longevity that vary greatly across applications. However, a successful implementation of a PNI should allow information to be both introduced to, and extracted from the PNS. In this paper, we will briefly introduce select techniques and their applications; and then explore how using the principles of engineering, one might use some easy-to-gather data to quickly derive a meaningful way to control the gait of a Parkinson’s Disease (PD) patient.
Utilizing EEG to detect covert command-following in vegetative traumatic brain injury patients: A review
(2020) Leonard, Julia
1.7 million people in the US suffer from traumatic brain injury (TBI) each year, typically resulting from car accidents, contact sports, military operations, and falls [1]. Currently, healthcare personnel rely on the Glasgow Coma Scale (GCS) and other behavioral assessments for TBI diagnosis, which are rather subjective and poor diagnostic tools [1]. Other commonplace methods include medical resonance imaging (MRI) and computed tomography (CT) scans, which lack portability and are a financial burden on patients and hospitals. Because of this, both MRI and CT lack easy repeatability. Current techniques result in 36% of TBI patients receiving a misdiagnosis, and proper diagnosis is only seen after 5 assessments over 2 weeks [2]. Diagnosis is important for outcome, as patients that show covert command-following have a better chance of survival [3]. This review will highlight current new methods for diagnosing TBI patients with little or no physical movement and response to environment. Studies utilizing methods such as electroencephalography (EEG) and P300 display how patients in varying disorders of consciousness have covert responses to commands, although behavioral assessments would diagnose them as nonresponsive. 17-20% of vegetative patients completing lacking in physical movements show brain activity response to imagery tasks [4,5]. EEG and P300 prove to be a promising tool moving forward for TBI diagnoses, as it is more portable and a fraction of the cost of MRIs, allowing for multiple assessments over time. Limitations include high heterogeneity in EEG data, which can lead to false positives and negatives, as well as P300 methods need greater control before they can be fully adopted [5,6]. Ultimately, EEG techniques show clinical applicability for TBI diagnosis, especially as the methods continue to improve.
Prosthetic Retina Literature Review
(2020) Holland, William
Retinal implants have become a more feasible eye-corrective tool in recent years. Examples of modern retinal implants include: the Argus II electronic epiretinal device, and the IMS electronic subretinal device (Jalil, Mills, & Stanga, 2017). Some more examples include, the Intelligent Retinal Implant system II (IRIS II) which has gained a CE mark1, and the EPI-RET3 Retinal Implant System which is an entirely intraocular implant. (Bloch, 2019). In addition, the use of retinal implants has now become both economically advantageous as well as approved for public use as in 2011 the Argus II Retinal Prothesis system received a CE mark (Jalil, Mills, & Stanga, 2017). In 2013 it was approved by the FDA (Jalil, Mills, & Stanga, 2017). According to an economic evaluation on the cost effectiveness of the Argus II retinal prothesis on patients with retinitis pigmentosa, patients are now willing to pay for the cost of the implants within countries in the Eurozone (Borgonovi et al.,2014). Unfortunately, while the retinal devices do improve vision, they do not provide a “high enough resolution or acuity for a patient to regain a fully functional life” (Jalil, Mills, & Stanga, 2017). Some studies use a sub-retinal approach using a semi-conductor-based prosthetic to stimulate the retina electrically (Chow et al., 2001). However, this study employed the use of felines as test subjects, not human subjects. Other studies that have used near infrared radiation to stimulate retinal cells as visible light is not powerful enough to create adequate stimulation (Hierzenberger et al., 1999). Additionally, there have been methods which attempt to resolve blindness employing electrical stimulation of the retina using an epiretinal electrode array and conclude that microelectrode arrays can stimulate human retinas invoking visual perceptions in blind patients (Bornfeld et al., 2012). Overall, there has been a plethora of different methods over the past few decades used to stimulate the retinas in order to improve or enable vision in patients with poor or no eyesight. In more recent years, it has been demonstrated that not only are retinal implants safe for use in humans, but they are also economically viable for most patients.
K-nearest neighbors algorithm (KNN) and artificial neural networks (ANN) accurately predicting malignancy of breast cancer (BC)
(2020) O'Shea, Bailey
With the reoccurrence of unnecessary open surgeries on potential malignant tissue, there is a need for additional non-invasive tools oncologists and radiologists can utilize to help argue the reason behind performing surgical biopsies. Thus, machine learning algorithms (MLAs) have seen a great deal of attention to the classification of tissue malignancy. One major benefit rises in having the ability to utilize past accessible datasets to accurately predict/classify new data/patients with similar features. The purpose of this paper was to apply and assess two MLAs—k-nearest neighbor (KNN) and artificial neural network (ANN)—on classification accuracy of breast cancer (BC) malignancy. Importantly, features used for the MLAs are acquired from imaging modalities, solely. For this particular dataset, features seen to be extracted from medical images include clump thickness, uniformity of cell size, uniformity of cell shape and marginal adhesion. The optimal k-nearest neighbor and ANN hidden layer will be reported. After implementing and testing the two MLAs, the accuracy for the KNN and ANN were 100% at 132-nearest neighbors and 95.24% ± 0.224 respectively. Considering the performance across both MLAs, the optimal classification algorithm for this dataset is the KNN algorithm. Thus, allowing for the possibility of clinical use as an additional consultation tool.
Using Neuron Activation as Way of Powering Artificial Muscle Fibers
(2020) Lombardo, Anthony
Developing technologies and mechanisms that perform like our biological muscles have great potential of being useful for prosthetics research. Developing these technologies can be thought of as the easy part of the design, but the difficult part is having them work in synchronicity with all the accompanying muscle fibers within the body. This is important because biological muscles have high power to weight ratios, compliance, damping, and fast actuation. However, to date the robotic models are not able to perform up to the standards of biological muscles due to the complexity of the biological systems (Mirvakili et al., 2014). This is especially true in attempting to create prosthetics mechanisms that are as thin and small as the biological muscles. Novel technology, first developed by Disney, is the use of super coiled polymer actuators that perform extremely like biological muscle fibers when subjected to electrical current. These super coiled polymer actuators are super thin because they are constructed using nylon fishing line. The fishing line is coiled upon itself and then heat treated to set the fishing line in the coiled shape. In order to activate these super coiled polymer actuators two additional aspects, need to be added. First, the nylon fishing line needs to be coated in silver so that they can conduct current and furthermore be heated and cooled. When heated and cooled the fishing line coils will expand and contract, and in turn will act very similarly to biological muscle fibers (Yip & Niemeyer, 2015). Second, electrodes and computer coding need to be utilized so that the artificial muscle fibers are operating at the same time as the biological muscle. This technology has been proven in laboratory settings to be capable of lifting cars when the super coiled polymer actuators are coupled together. Combining these technologies can have a major impact on the prosthetics industry by providing an efficient and cost-effective method for creating powerful novel prosthetic devices (Fishing Line Makes for Superhuman Artificial Muscles—IEEE Spectrum, n.d.).
Measurements to Detect Mental Fatigue
(2020-05) Asif, Zara
Mental fatigue is defined as a “state of reduced mental alertness that impairs performance” It affects nearly everyone in society at some point and has become one of the leading causes of workplace accidents [2]. Common symptoms of the condition include discomfort, tiredness, and reduced motivation [1]. Recently, there has been a significant push for the development of technology to detect mental fatigue and prevent related accidents. Through this project, I will review current detection methods and their results. I will then explore whether such a detection system is possible to create with current technology and whether it could feasibly be implemented in real world scenarios. Several signals have been analyzed in their reliability of detecting the onset of mental fatigue. These are blink rate, heart rate variability, respiration, and brain activity. Since blink rate increases with fatigue, it can be analyzed using continuous recording and facial recognition techniques. Similarly, heart rate variability increases and respiration rate becomes more erratic. Both can be measured with physical sensors or physical sensors. Out of these, brain activity is the most accurate indicator of mental fatigue and has to be monitored with an EEG sensor. Yamada and Kobayashi collected eye tracking data from subjects who watched video clips before to induce mental fatigue. They found that participants showed significant increases in blink rate and duration as they became more fatigued and a fatigue detection model was developed which was able to achieve 91% accuracy [6]. To investigate the effects of mental fatigue on heart rate variability and respiration, Huang and his team gave 35 participants a wearable ECG device known as “LaPatch,” which was capable of recording ECG and respiration states. Four classifiers were created and out of these KNN was the most accurate with a 75% chance of correctly identifying mental fatigue [5]. Tanaka and others conducted a study to understand how mental fatigue affects cognitive performance. Magnetoencephalography data was recorded and analyzed. They found that these tasks caused the alpha frequency band power, 8-13 Hz, to decrease, which suggests that mental fatigue causes over activation of the visual cortex [1]. Another approach by Shen and others used Electroencephalography data, which was then classified by a support vector machine algorithm. The results indicated a 90% accuracy in detecting lapses in cognitive performance [2]. The main deterrent to developing a mental fatigue detection system with all four of these signals is that it would not necessarily be contactless. Including an EEG reading would significantly increase the accuracy of the system overall, but it is the only signal that requires a physical sensor. The most beneficial detection system would have to be entirely contactless in order to be applicable in multiple situations and this is not possible with the current technology. As such, currently there is no detection system for mental fatigue on the market.
A machine learning approach to predict rtms therapy response in major depressive disorder
(2020-05) Shams, Mohammad
Machine learning techniques have been utilized to predict the outcome of repetitive transcranial magnetic stimulation (rTMS) treatment in depression, e.g., through classifying the responders (R) and non-responders (NR) to rTMS treatment for major depression disorder (MDD) patients. MDD is among the leading causes of disability in the world with affecting more than 260 million people, and a major contributor to the overall global burden of disease. In this study, the outputs of the Local Subset Feature Selection (LSFS) method were used by an SVM classifier to evaluate the capability of the proposed method in the prediction of rTMS treatment response in depression cases. A Leave-One-Out cross-validation method is applied to the input data to evaluate the performance of the response classification. The achieved accuracy, sensitivity, and specificity were 89.5%, 90%, and 87%, respectively. The main restriction of this study that would limit its usage in clinical applications is the small sample size.
DNA nanotechnology in neural engineering: A perspective
(2020-05) Vargas, Merlyn
DNA nanotechnology has great potential as a platform to enhance neural engineering approaches. DNA based nanoparticles are biocompatible and easy to functionalize [1]. Peptides or proteins can be conjugated to DNA nanoparticles to target specific cells and tissues [2], [3], as well as imaging agents to help diagnosis and monitoring purposes [4], [5]. In addition, recent studies have shown the capacity of some DNA based nanoparticles to cross the blood-brain barrier to target brain tumors in rats [4]. Others have shown DNA-peptide nanoparticles that enhanced differentiation of neural stem cell proliferation and neural differentiation [6], as well as new technologies to construct DNA-based molecular circuitry [7]. Besides all of these promising features offered by DNA nanotechnology, DNA has also shown to be a great scaffold for the production of nanoelectronics, giving a realistic perspective of the creation of nano devices that can target a desired tissue or cell and perform as nanochips for diagnosis, sensing or modulatory functions. Therefore, this work reviews some of the characteristics of nanotechnology and DNA-based nanoelectronics that are favorable for the development of nanodevices as neural system probes, as well as some perspectives for this type of technology in the field of neural engineering.
The Future of Personalized Brain Stimulation for the Treatment of Neuropsychiatric Disorders
(2021) Fredriksz, Madeline
Over a billion individuals worldwide suffer from neuropsychiatric disorders, many of which are resistant to traditional medications and therapy. Both invasive and non-invasive electrical stimulation have emerged as promising treatment options in recent years since they have been shown to attenuate neuropsychiatric symptoms by modulating the underlying abnormal brain network activity. However, neurological disorders manifest differently across individuals and the way brain activity and patient symptoms respond to stimulation is highly individualized. In addition, brain activity is not constant over an individual’s lifetime. Changes due to development, aging, and the progression of chronic conditions need to be taken into account. All of this reveals the need for personalized treatments that can adapt to brain activity dynamics.
Investigating Zolpidem for Treatment of Various Neurological Conditions
(2021) Stewart, Carson
While Neural Engineering has had breakthrough technologies including cochlear implants for hearing restoration and deep brain stimulation for a range of diseases including Parkinson’s, engineering approaches to several neurological disorders following traumatic brain injury (TBI) have been largely unsuccessful [1]. The Brain is extraordinarily complex, and despite decades of research, remains poorly understood [1][3]. The pharmaceutical Zolpidem originally prescribed for sleep disorders, has gained attention due to its ability to paradoxically increase alertness in patients with TBI, temporarily reduce the symptoms of Parkinson’s disease, and even transiently induce brief periods of consciousness in comatose patients. Zolpidem has been shown to manifest dramatic differences in mechanistic activity between healthy patients and patients with disorders of consciousness (DOC) or TBI. Only small subsets of patients with TBI’s respond to this form of pharmaceutical intervention. It is a lack of understanding of this phenomenon that is causing schisms between researching its causal mechanisms of neural activity and leveraging neural engineering to treat targeted patients [2][3]. Due to EEG, MEG, and MRI comparisons, advances for neuromodulation using Zolpidem have been made to target specific disorders and TBI’s. This review hopes to examine how Zolpidem is being investigated using neuromodulation to explain the vast differences in patient recovery following severe brain injuries and diseases.
Neural Control of Cardiac Function, Rhythm, and Contraction
(2021) O'Connell, Rachel
This literature review discusses how researchers can control cardiac cells with neural networks. The cardiac system is controlled by the autonomic nervous system and coordinates with the respiratory system. Numerous approaches evaluate the efficiency, malfunction, and abnormalities of this connection between brain and heart, through computational analysis, with pharmacological influences, and with physiological analysis. In Roger A. L. Dampney’s paper, he discusses how the connection between the nervous system and cardiovascular system is achieved through feedforward and feedback regulation to maintain homeostasis, while Y. Zhong et al. takes a more dynamic approach and manipulates the patients into supine and upright positions while administering the drug, atropine, which involuntarily blocks the nervous system from working. Lastly, X. Chen et al. derived new PNS and SNS indexes by multisignal analysis of cardiorespiratory variability using transfer functions and a combination of physiological and pharmacological experiments. He selected autonomic nervous system blockers to analyze the beta-sympathetic nervous system and its overlap with the parasympathetic nervous system. He sought to find where these systems overlap in low frequency bands and offer specific measurements of the cardiac autonomic nervous system due to inputs from heart rate. Overall, the purpose of the literature review is to study the techniques that are being used in research to evaluate the relationship between the nervous system and cardiovascular system. The literature review will range from computational analysis to physiological experiments and include the analysis of pharmacological influences on mediating the ANS. In the Introduction, I will explain some of the terminology that is necessary to understand the experiments that follow

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