A Subject-Specific Multiscale Model of Transcranial Magnetic Stimulation

Transcranial magnetic stimulation (TMS) is a neuromodulation technique used to treat a variety of neurological disorders. While many types of neuromodulation therapy are invasive, TMS is an attractive alternative because it is noninvasive and has a very strong safety record. However, clinical use of TMS has preceded a thorough scientific understanding: its mechanisms of action remain elusive, and the spatial extent of modulation is not well understood. We created a subject-specific, multiscale computational model to gain insights into the physiological response during motor cortex TMS. Specifically, we developed an approach that integrates three main components: 1) a high-resolution anatomical MR image of the whole head with diffusion weighted MRI data; 2) a subject-specific, electromagnetic, non-homogeneous, anisotropic, finite element model of the whole head with a novel time-dependent solver; 3) a population of multicompartmental pyramidal cell neuron models. We validated the model predictions by comparing them to motor evoked potentials (MEPs) immediately following single-pulse TMS of the human motor cortex. This modeling approach contains several novel components, which in turn allowed us to gain greater insights into the interactions of TMS with the brain. Using this approach we found that electric field magnitudes within gray matter and white matter vary substantially with coil orientation. Our results suggest that 1) without a time-dependent, subject-specific, non-homogeneous, anisotropic model, loci of stimulation cannot be accurately predicted; 2) loci of stimulation depend upon biophysical properties and morphologies of pyramidal cells in both gray and white matter relative to the induced electric field. These results indicate that the extent of neuromodulation is more widespread than originally thought. Through medical imaging and computational modeling, we provide insights into the effects of TMS at a multiscale level, which would be unachievable by either method alone. Finally, our approach is amenable to clinical implementation. As a result, it could provide the means by which TMS parameters can be prescribed for treatment and a foundation for improving coil design

Neurostimulator with Waveforms Inspired by Nature for Wearable Electro-Acupuncture

The work presented here has 3 goals: establish the need for novel neurostimulation waveform solutions through a literature review, develop a neurostimulation pulse generator, and verify the operation of the device for neurostimulation applications. The literature review discusses the importance of stimulation waveforms on the outcomes of neurostimulation, and proposes new directions for neurostimulation research that would help in improving the reproducibility and comparability between studies. The pulse generator circuit is then described that generates signals inspired by the shape of excitatory or inhibitory post-synaptic potentials (EPSP, IPSP). The circuit analytical equations are presented, and the effects of the circuit design components are discussed. The circuit is also analyzed with a capacitive load using a simplified Randles model to represent the electrode-electrolyte interface, and the output is measured in phosphate-buffered saline (PBS) solution as the load with acupuncture needles as electrodes. The circuit is designed to be used in different types of neurostimulators depending on the needs of the application, and to study the effects of varying neurostimulation waveforms. The circuit is used to develop a remote-controlled wearable veterinary electro-acupuncture machine. The device has a small form-factor and 3D printed enclosure, and has a weight of 75 g with leads attached. The device is powered by a 500 mAh lithium polymer battery, and was tested to last 6 hours. The device is tested in an electro-acupuncture animal study on cats performed at the Louisiana State University School of Veterinary Medicine, where it showed expected electro-acupuncture effects. Then, a 2-channel implementation of the device is presented, and tested to show independent output amplitude, frequency, and stimulation duration per channel. Finally, the software and hardware requirements for control of the wearable veterinary electro-acupuncture machine are detailed. The number of output channels is limited to the number of hardware PWM timers available for use. The Arduino software implements PWM control for the output amplitude and frequency. The stimulation duration control is provided using software timers. The communications protocol between the microcontroller board and Android App are described, and communications are performed via Bluetooth

SpikeSEE: An Energy-Efficient Dynamic Scenes Processing Framework for Retinal Prostheses

Intelligent and low-power retinal prostheses are highly demanded in this era, where wearable and implantable devices are used for numerous healthcare applications. In this paper, we propose an energy-efficient dynamic scenes processing framework (SpikeSEE) that combines a spike representation encoding technique and a bio-inspired spiking recurrent neural network (SRNN) model to achieve intelligent processing and extreme low-power computation for retinal prostheses. The spike representation encoding technique could interpret dynamic scenes with sparse spike trains, decreasing the data volume. The SRNN model, inspired by the human retina special structure and spike processing method, is adopted to predict the response of ganglion cells to dynamic scenes. Experimental results show that the Pearson correlation coefficient of the proposed SRNN model achieves 0.93, which outperforms the state of the art processing framework for retinal prostheses. Thanks to the spike representation and SRNN processing, the model can extract visual features in a multiplication-free fashion. The framework achieves 12 times power reduction compared with the convolutional recurrent neural network (CRNN) processing-based framework. Our proposed SpikeSEE predicts the response of ganglion cells more accurately with lower energy consumption, which alleviates the precision and power issues of retinal prostheses and provides a potential solution for wearable or implantable prostheses

Proceedings of the Conference on Progress in Electrically Active Implants - Tissue and Functional Regeneration (ELAINE 2020)

The conference on Progress in Electrically Active Implants - Tissue and Functional Regeneration (ELAINE 2020) focused on novel methods in the electric stimulation of bio-material compounds of living cells and implantable electric stimulation devices. ELAINE 2020 provided international scientists a virtual platform to discuss the latest achievements in the form of invited presentations, selected talks from abstract submissions, and virtual poster sessions. In addition, we particularly invited critical reviews and contributions with negative results or unsuccessful replications to foster the scientific discussion and explicitly encourage young scientists to contribute and submit their work

The neural basis of perceived intensity in natural and artificial touch

Electrical stimulation of sensory nerves is a powerful tool for studying neural coding because it can activate neural populations in ways that natural stimulation cannot. Electrical stimulation of the nerve has also been used to restore sensation to patients who have suffered the loss of a limb. We have used long-term implanted electrical interfaces to elucidate the neural basis of perceived intensity in the sense of touch. To this end, we assessed the sensory correlates of neural firing rate and neuronal population recruitment independently by varying two parameters of nerve stimulation: pulse frequency and pulse width. Specifically, two amputees, chronically implanted with peripheral nerve electrodes, performed each of three psychophysical tasks-intensity discrimination, magnitude scaling, and intensity matching-in response to electrical stimulation of their somatosensory nerves. We found that stimulation pulse width and pulse frequency had systematic, cooperative effects on perceived tactile intensity and that the artificial tactile sensations could be reliably matched to skin indentations on the intact limb. We identified a quantity we termed the activation charge rate (ACR), derived from stimulation parameters, that predicted the magnitude of artificial tactile percepts across all testing conditions. On the basis of principles of nerve fiber recruitment, the ACR represents the total population spike count in the activated neural population. Our findings support the hypothesis that population spike count drives the magnitude of tactile percepts and indicate that sensory magnitude can be manipulated systematically by varying a single stimulation quantity

Neuromorphic hardware for somatosensory neuroprostheses

In individuals with sensory-motor impairments, missing limb functions can be restored using neuroprosthetic devices that directly interface with the nervous system. However, restoring the natural tactile experience through electrical neural stimulation requires complex encoding strategies. Indeed, they are presently limited in effectively conveying or restoring tactile sensations by bandwidth constraints. Neuromorphic technology, which mimics the natural behavior of neurons and synapses, holds promise for replicating the encoding of natural touch, potentially informing neurostimulation design. In this perspective, we propose that incorporating neuromorphic technologies into neuroprostheses could be an effective approach for developing more natural human-machine interfaces, potentially leading to advancements in device performance, acceptability, and embeddability. We also highlight ongoing challenges and the required actions to facilitate the future integration of these advanced technologies

A gaussian process emulator for estimating the volume of tissue activated during deep brain stimulation

The volume of tissue activated (VTA) is a well-established approach to model the direct effects of deep brain stimulation (DBS) on neural tissue and previous studies have pointed to its potential clinical applications. However, the elevated computational time required to estimate the VTA with standard techniques used in biological neural modeling limits its suitability for practical use. The goal of this project was to develop a novel methodology to reduce the computation time of VTA estimation. To that end, we built a Gaussian process emulator. It combines a field of multi-compartment axon models coupled to the stimulating electric field with a Gaussian process classifier (GPC); following the premise that computing the VTA from a field of axons is in essence a binary classification problem. We achieved a considerable reduction in the average time required to estimate the VTA, under both ideal isotropic and realistic anisotropic brain tissue conductive conditions, limiting the loss of accuracy and overcoming other drawbacks entailed by alternative methods

Closed-loop approaches for innovative neuroprostheses

The goal of this thesis is to study new ways to interact with the nervous system in case of damage or pathology. In particular, I focused my effort towards the development of innovative, closed-loop stimulation protocols in various scenarios: in vitro, ex vivo, in vivo

Wireless tools for neuromodulation

Epilepsy is a spectrum of diseases characterized by recurrent seizures. It is estimated that 50 million individuals worldwide are affected and 30% of cases are medically refractory or drug resistant. Vagus nerve stimulation (VNS) and deep brain stimulation (DBS) are the only FDA approved device based therapies. Neither therapy offers complete seizure freedom in a majority of users. Novel methodologies are needed to better understand mechanisms and chronic nature of epilepsy. Most tools for neuromodulation in rodents are tethered. The few wireless devices use batteries or are inductively powered. The tether restricts movement, limits behavioral tests, and increases the risk of infection. Batteries are large and heavy with a limited lifetime. Inductive powering suffers from rapid efficiency drops due to alignment mismatches and increased distances. Miniature wireless tools that offer behavioral freedom, data acquisition, and stimulation are needed. This dissertation presents a platform of electrical, optical and radiofrequency (RF) technologies for device based neuromodulation. The platform can be configured with features including: two channels differential recording, one channel electrical stimulation, and one channel optical stimulation. Typical device operation consumes less than 4 mW. The analog front end has a bandwidth of 0.7 Hz - 1 kHz and a gain of 60 dB, and the constant current driver provides biphasic electrical stimulation. For use with optogenetics, the deep brain optical stimulation module provides 27 mW/mm2 of blue light (473 nm) with 21.01 mA. Pairing of stimulating and recording technologies allows closed-loop operation. A wireless powering cage is designed using the resonantly coupled filter energy transfer (RCFET) methodology. RF energy is coupled through magnetic resonance. The cage has a PTE ranging from 1.8-6.28% for a volume of 11 x 11 x 11 in3. This is sufficient to chronically house subjects. The technologies are validated through various in vivo preparations. The tools are designed to study epilepsy, SUDEP, and urinary incontinence but can be configured for other studies. The broad application of these technologies can enable the scientific community to better study chronic diseases and closed-loop therapies

MEART: The Semi-Living Artist

Here, we and others describe an unusual neurorobotic project, a merging of art and science called MEART, the semi-living artist. We built a pneumatically actuated robotic arm to create drawings, as controlled by a living network of neurons from rat cortex grown on a multi-electrode array (MEA). Such embodied cultured networks formed a real-time closed-loop system which could now behave and receive electrical stimulation as feedback on its behavior. We used MEART and simulated embodiments, or animats, to study the network mechanisms that produce adaptive, goal-directed behavior. This approach to neural interfacing will help instruct the design of other hybrid neural-robotic systems we call hybrots. The interfacing technologies and algorithms developed have potential applications in responsive deep brain stimulation systems and for motor prosthetics using sensory components. In a broader context, MEART educates the public about neuroscience, neural interfaces, and robotics. It has paved the way for critical discussions on the future of bio-art and of biotechnology