Unification of optimal targeting methods in transcranial electrical stimulation

One of the major questions in high-density transcranial electrical stimulation (TES) is: given a region of interest (ROI) and electric current limits for safety, how much current should be delivered by each electrode for optimal targeting of the ROI? Several solutions, apparently unrelated, have been independently proposed depending on how ?optimality? is defined and on how this optimization problem is stated mathematically. The least squares (LS), weighted LS (WLS), or reciprocity-based approaches are the simplest ones and have closed-form solutions. An extended optimization problem can be stated as follows: maximize the directional intensity at the ROI, limit the electric fields at the non-ROI, and constrain total injected current and current per electrode for safety. This problem requires iterative convex or linear optimization solvers. We theoretically prove in this work that the LS, WLS and reciprocity-based closed-form solutions are specific solutions to the extended directional maximization optimization problem. Moreover, the LS/WLS and reciprocity-based solutions are the two extreme cases of the intensity-focality trade-off, emerging under variation of a unique parameter of the extended directional maximization problem, the imposed constraint to the electric fields at the non-ROI. We validate and illustrate these findings with simulations on an atlas head model. The unified approach we present here allows a better understanding of the nature of the TES optimization problem and helps in the development of advanced and more effective targeting strategies.Fil: Fernandez Corazza, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaFil: Turovets, Sergei. University of Oregon; Estados UnidosFil: Muravchik, Carlos Horacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; Argentin

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

Restoring Fine Motor Skills through Neural Interface Technology.

Loss of motor function in the upper-limb, whether through paralysis or through loss of the limb itself, is a profound disability which affects a large population worldwide. Lifelike, fully-articulated prosthetic hands exist and are commercially available; however, there is currently no satisfactory method of controlling all of the available degrees of freedom. In order to generate better control signals for this technology, and help restore normal movement, it is necessary to interface directly with the nervous system. This thesis is intended to address several of the limitations of current neural interfaces and enable the long-term extraction of control signals for fine movements of the hand and fingers. The first study addresses the problems of low signal amplitudes and short implant lifetimes in peripheral nerve interfaces. In two rhesus macaques, we demonstrate the successful implantation of regenerative peripheral nerve interfaces (RPNI), which allowed us to record high amplitude, functionally-selective signals from peripheral nerves up to 20 months post-implantation. These signals could be accurately decoded into intended movement, and used to enable monkeys to control a virtual hand prosthesis. The second study presents a novel experimental paradigm for intracortical neural interfaces, which enables detailed investigation of fine motor information contained in primary motor cortex. We used this paradigm to demonstrate accurate decoding of continuous fingertip position and enable a monkey to control a virtual hand in closed-loop. This is the first demonstration of volitional control of fine motor skill enabled by a cortical neural interface. The final study presents the design and testing of a wireless implantable neural recording system. By extracting signal power in a single, configurable frequency band onboard the device, this system achieves low power consumption while maintaining decode performance, and is applicable to cortical, peripheral, and myoelectric signals. Taken together, these results represent a significant step towards clinical reality for neural interfaces, and towards restoration of full and dexterous movement for people with severe disabilities.PhDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120648/1/irwinz_1.pd

Neuromorphic Neuromodulation: Towards the next generation of on-device AI-revolution in electroceuticals

Neuromodulation techniques have emerged as promising approaches for treating a wide range of neurological disorders, precisely delivering electrical stimulation to modulate abnormal neuronal activity. While leveraging the unique capabilities of artificial intelligence (AI) holds immense potential for responsive neurostimulation, it appears as an extremely challenging proposition where real-time (low-latency) processing, low power consumption, and heat constraints are limiting factors. The use of sophisticated AI-driven models for personalized neurostimulation depends on back-telemetry of data to external systems (e.g. cloud-based medical mesosystems and ecosystems). While this can be a solution, integrating continuous learning within implantable neuromodulation devices for several applications, such as seizure prediction in epilepsy, is an open question. We believe neuromorphic architectures hold an outstanding potential to open new avenues for sophisticated on-chip analysis of neural signals and AI-driven personalized treatments. With more than three orders of magnitude reduction in the total data required for data processing and feature extraction, the high power- and memory-efficiency of neuromorphic computing to hardware-firmware co-design can be considered as the solution-in-the-making to resource-constraint implantable neuromodulation systems. This could lead to a new breed of closed-loop responsive and personalised feedback, which we describe as Neuromorphic Neuromodulation. This can empower precise and adaptive modulation strategies by integrating neuromorphic AI as tightly as possible to the site of the sensors and stimulators. This paper presents a perspective on the potential of Neuromorphic Neuromodulation, emphasizing its capacity to revolutionize implantable brain-machine microsystems and significantly improve patient-specificity.Comment: 17 page

Resource efficient on-node spike sorting

Current implantable brain-machine interfaces are recording multi-neuron activity by utilising multi-channel, multi-electrode micro-electrodes. With the rapid increase in recording capability has come more stringent constraints on implantable system power consumption and size. This is even more so with the increasing demand for wireless systems to increase the number of channels being monitored whilst overcoming the communication bottleneck (in transmitting raw data) via transcutaneous bio-telemetries. For systems observing unit activity, real-time spike sorting within an implantable device offers a unique solution to this problem. However, achieving such data compression prior to transmission via an on-node spike sorting system has several challenges. The inherent complexity of the spike sorting problem arising from various factors (such as signal variability, local field potentials, background and multi-unit activity) have required computationally intensive algorithms (e.g. PCA, wavelet transform, superparamagnetic clustering). Hence spike sorting systems have traditionally been implemented off-line, usually run on work-stations. Owing to their complexity and not-so-well scalability, these algorithms cannot be simply transformed into a resource efficient hardware. On the contrary, although there have been several attempts in implantable hardware, an implementation to match comparable accuracy to off-line within the required power and area requirements for future BMIs have yet to be proposed. Within this context, this research aims to fill in the gaps in the design towards a resource efficient implantable real-time spike sorter which achieves performance comparable to off-line methods. The research covered in this thesis target: 1) Identifying and quantifying the trade-offs on subsequent signal processing performance and hardware resource utilisation of the parameters associated with analogue-front-end. Following the development of a behavioural model of the analogue-front-end and an optimisation tool, the sensitivity of the spike sorting accuracy to different front-end parameters are quantified. 2) Identifying and quantifying the trade-offs associated with a two-stage hybrid solution to realising real-time on-node spike sorting. Initial part of the work focuses from the perspective of template matching only, while the second part of the work considers these parameters from the point of whole system including detection, sorting, and off-line training (template building). A set of minimum requirements are established which ensure robust, accurate and resource efficient operation. 3) Developing new feature extraction and spike sorting algorithms towards highly scalable systems. Based on waveform dynamics of the observed action potentials, a derivative based feature extraction and a spike sorting algorithm are proposed. These are compared with most commonly used methods of spike sorting under varying noise levels using realistic datasets to confirm their merits. The latter is implemented and demonstrated in real-time through an MCU based platform.Open Acces

Network Dynamics of Visual Object Recognition

Visual object recognition is the principal mechanism by which humans and many animals interpret their surroundings. Despite the complexity of neural computation required, object recognition is achieved with such rapidity and accuracy that it appears to us almost effortless. Extensive human and non-human primate research has identified putative category-selective regions within higher-level visual cortex, which are thought to mediate object recognition. Despite decades of study, however, the functional organization and network dynamics within these regions remain poorly understood, due to a lack of appropriate animal models as well as the spatiotemporal limitations of current non-invasive human neuroimaging techniques (e.g. fMRI, scalp EEG). To better understand these issues, we leveraged the high spatiotemporal resolution of intracranial EEG (icEEG) recordings to study rapid, transient interactions between the disseminated cortical substrates within category-specific networks. Employing novel techniques for the topologically accurate and statistically robust analysis of grouped icEEG, we found that category-selective regions were spatially arranged with respect to cortical folding patterns, and relative to each other, to generate a hierarchical information structuring of visual information within higher-level visual cortex. This may facilitate rapid visual categorization by enabling the extraction of different levels of object detail across multiple spatial scales. To characterize network interactions between distributed regions sharing the same category-selectivity, we evaluated feed-forward, hierarchal and parallel, distributed models of information flow during face perception via measurements of cortical activation, functional and structural connectivity, and transient disruption through electrical stimulation. We found that input from early visual cortex (EVC) to two face-selective regions – the occipital and fusiform face areas (OFA and FFA, respectively) – occurred in a parallelized, distributed fashion: Functional connectivity between EVC and FFA began prior to the onset of subsequent re-entrant connectivity between the OFA and FFA. Furthermore, electrophysiological measures of structural connectivity revealed independent cortico- cortical connections between the EVC and both the OFA and FFA. Finally, direct disruption of the FFA, but not OFA, impaired face-perception. Given that the FFA is downstream of the OFA, these findings are incompatible with the feed-forward, hierarchical models of visual processing, and argue instead for the existence of parallel, distributed network interactions

Classification of Frequency and Phase Encoded Steady State Visual Evoked Potentials for Brain Computer Interface Speller Applications using Convolutional Neural Networks

Over the past decade there have been substantial improvements in vision based Brain-Computer Interface (BCI) spellers for quadriplegic patient populations. This thesis contains a review of the numerous bio-signals available to BCI researchers, as well as a brief chronology of foremost decoding methodologies used to date. Recent advances in classification accuracy and information transfer rate can be primarily attributed to time consuming patient specific parameter optimization procedures. The aim of the current study was to develop analysis software with potential ‘plug-in-and-play’ functionality. To this end, convolutional neural networks, presently established as state of the art analytical techniques for image processing, were utilized. The thesis herein defines deep convolutional neural network architecture for the offline classification of phase and frequency encoded SSVEP bio-signals. Networks were trained using an extensive 35 participant open source Electroencephalographic (EEG) benchmark dataset (Department of Bio-medical Engineering, Tsinghua University, Beijing). Average classification accuracies of 82.24% and information transfer rates of 22.22 bpm were achieved on a BCI naïve participant dataset for a 40 target alphanumeric display, in absence of any patient specific parameter optimization

Building And Validating Next-Generation Neurodevices Using Novel Materials, Fabrication, And Analytic Strategies

Technologies that enable scientists to record and modulate neural activity across spatial scales are advancing the way that neurological disorders are diagnosed and treated, and fueling breakthroughs in our fundamental understanding of brain function. Despite the rapid pace of technology development, significant challenges remain in realizing safe, stable, and functional interfaces between manmade electronics and soft biological tissues. Additionally, technologies that employ multimodal methods to interrogate brain function across temporal and spatial scales, from single cells to large networks, offer insights beyond what is possible with electrical monitoring alone. However, the tools and methodologies to enable these studies are still in their infancy. Recently, carbon nanomaterials have shown great promise to improve performance and multimodal capabilities of bioelectronic interfaces through their unique optical and electronic properties, flexibility, biocompatibility, and nanoscale topology. Unfortunately, their translation beyond the lab has lagged due to a lack of scalable assembly methods for incorporating such nanomaterials into functional devices. In this thesis, I leverage carbon nanomaterials to address several key limitations in the field of bioelectronic interfaces and establish scalable fabrication methods to enable their translation beyond the lab. First, I demonstrate the value of transparent, flexible electronics by analyzing simultaneous optical and electrical recordings of brain activity at the microscale using custom-fabricated graphene electronics. Second, I leverage a recently discovered 2D nanomaterial, Ti3C2 MXene, to improve the capabilities and performance of neural microelectronic devices. Third, I fabricate and validate human-scale Ti3C2 MXene epidermal electrode arrays in clinical applications. Leveraging the unique solution-processability of Ti3C2 MXene, I establish novel fabrication methods for both high-resolution microelectrode arrays and macroscale epidermal electrode arrays that are scalable and sufficiently cost-effective to allow translation of MXene bioelectronics beyond the lab and into clinical use. Thetechnologies and methodologies developed in this thesis advance bioelectronic technology for both research and clinical applications, with the goal of improving patient quality of life and illuminating complex brain dynamics across spatial scales

Computationally efficient algorithms and implementations of adaptive deep brain stimulation systems for Parkinson's disease

Clinical deep brain stimulation (DBS) is a tool used to mitigate pharmacologically intractable neurodegenerative diseases such as Parkinson's disease (PD), tremor and dystonia. Present implementations of DBS use continuous, high frequency voltage or current pulses so as to mitigate PD. This results in some limitations, among which there is stimulation induced side effects and shortening of pacemaker battery life. Adaptive DBS (aDBS) can be used to overcome a number of these limitations. Adaptive DBS is intended to deliver stimulation precisely only when needed. This thesis presents work undertaken to investigate, propose and develop novel algorithms and implementations of systems for adapting DBS. This thesis proposes four system implementations that could facilitate DBS adaptation either in the form of closed-loop DBS or spatial adaptation. The first method involved the use of dynamic detection to track changes in local field potentials (LFP) which can be indicative of PD symptoms. The work on dynamic detection included the synthesis of validation dataset using mainly autoregressive moving average (ARMA) models to enable the evaluation of a subset of PD detection algorithms for accuracy and complexity trade-offs. The subset of algorithms consisted of feature extraction (FE), dimensionality reduction (DR) and dynamic pattern classification stages. The combination with the best trade-off in terms of accuracy and complexity consisted of discrete wavelet transform (DWT) for FE, maximum ratio method (MRM) for DR and k-nearest neighbours (k-NN) for classification. The MRM is a novel DR method inspired by Fisher's separability criterion. The best combination achieved accuracy measures: F1-score of 97.9%, choice probability of 99.86% and classification accuracy of 99.29%. Regarding complexity, it had an estimated microchip area of 0.84 mm² for estimates in 90 nm CMOS process. The second implementation developed the first known PD detection and monitoring processor. This was achieved using complementary detection, which presents a hardware-efficient method of implementing a PD detection processor for monitoring PD progression in Parkinsonian patients. Complementary detection is achieved by using a combination of weak classifiers to produce a classifier with a higher consistency and confidence level than the individual classifiers in the configuration. The PD detection processor using the same processing stages as the first implementation was validated on an FPGA platform. By mapping the implemented design on a 45 nm CMOS process, the most optimal implementation achieved a dynamic power per channel of 2.26 μW and an area per channel of 0.2384 mm². It also achieved mean accuracy measures: Mathews correlation coefficient (MCC) of 0.6162, an F1-score of 91.38%, and mean classification accuracy of 91.91%. The third implementation proposed a framework for adapting DBS based on a critic-actor control approach. This models the relationship between a trained clinician (critic) and a neuro-modulation system (actor) for modulating DBS. The critic was implemented and validated using machine learning models, and the actor was implemented using a fuzzy controller. Therapy is modulated based on state estimates obtained through the machine learning models. PD suppression was achieved in seven out of nine test cases. The final implementation introduces spatial adaptation for aDBS. Spatial adaptation adjusts to variation in lead position and/or stimulation focus, as poor stimulation focus has been reported to affect therapeutic benefits of DBS. The implementation proposes dynamic current steering systems as a power-efficient implementation for multi-polar multisite current steering, with a particular focus on the output stage of the dynamic current steering system. The output stage uses dynamic current sources in implementing push-pull current sources that are interfaced to 16 electrodes so as to enable current steering. The performance of the output stage was demonstrated using a supply of 3.3 V to drive biphasic current pulses of up to 0.5 mA through its electrodes. The preliminary design of the circuit was implemented in 0.18 μm CMOS technology