Time to address the problems at the neural interface (original) (raw)
Related papers
Next-generation interfaces for studying neural function
Nature Biotechnology
he human nervous system is composed of a heterogeneous network of cells that communicate with one another through electrical, chemical and physical signals. Diseases of the nervous system, including Alzheimer's disease, Parkinson's disease and epilepsy, affect more than 100 million people and represent an annual burden greater than $800 billion in the United States alone 1. Treatments for these disorders often rely on pharmacotherapy or implanted electrical stimulation devices, but they are generally not specific to neuronal subtypes and thus are accompanied by side effects. This lack of specificity arises as a result of a poor understanding of the underlying mechanisms of action of these interventions, alongside a lack of available tools to interact with the brain at meaningful levels of precision and depth. To fully appreciate this complexity, new probes must be developed to deliver and record signals through multiple modalities while minimizing unwanted side effects 2. In this Review, we discuss principles that should be considered when designing next-generation neural interfaces to communicate bi-directionally with neural circuits through multiple modalities. Advances beyond classic electrical stimulation and recording techniques are likely to contribute to understanding and treating disorders of the nervous system. These efforts should help link the physiological processes associated with neuronal function to normal and pathological behavior and should also enable closed-loop bio-interfaces for therapeutic intervention.
Current Status of Neural Interface
2009
This review focuses on the application of nanomaterials for neural interfacing. The junction between nanotechnology and neural tissues can be particularly worthy of scientific attention for several reasons: (i) Neural cells are electroactive, and the electronic properties of nanostructures can be tailored to match the charge transport requirements of electrical cellular interfacing. (ii) The unique mechanical and chemical properties of nanomaterials are critical for integration with neural tissue as long-term implants. (iii) Solutions to many critical problems in neural biology/medicine are limited by the availability of specialized materials. (iv) Neuronal stimulation is needed for a variety of common and severe health problems. This confluence of need, accumulated expertise, and potential impact on the well-being of people suggests the potential of nanomaterials to revolutionize the field of neural interfacing. In this review, we begin with foundational topics, such as the current status of neural electrode (NE) technology, the key challenges facing the practical utilization of NEs, and the potential advantages of nanostructures as components of chronic implants. After that the detailed account of toxicology and biocompatibility of nanomaterials in respect to neural tissues is given. Next, we cover a variety of specific applications of nanoengineered devices, including drug delivery, imaging, topographic patterning, electrode design, nanoscale transistors for high-resolution neural interfacing, and photoactivated interfaces. We also critically evaluate the specific properties of particular nanomaterials-including nanoparticles, nanowires, and carbon nanotubes-that can be taken advantage of in neuroprosthetic devices. The most promising future areas of research and practical device engineering are discussed as a conclusion to the review.
Brain–Machine Interface Engineering
Synthesis Lectures on Biomedical Engineering, 2007
What has the past decade taught us about mankind's ability to interface with and read information from the brain? Looking back on our experiences, the salient recollection is how ill-prepared the present theories of microelectronic circuit design and signal processing are for building interfaces and interpreting brain's activity. Although there is plenty of room for future improvement, the combination of critical evaluation of current approaches and a vision of nueroengineering are helping us develop an understanding on how to read the intent of motion in brains. The flow of ideas and discovery conveyed in this book is quite chronological, starting back in 2001 with a multi-university research project lead by Dr. Miguel Nicolelis of Duke University to develop the next-generation BMIs. The series of engineering developments explained in this book were made possible by the collaboration with Miguel, his contagious enthusiasm, vision, and brilliant experimentalism, that have led us in a journey of discovery in new theories for interfacing with the brain. Part of the results presented here also utilize data collected in his laboratory at Duke University. It was also a journey of innovation shared with colleagues in ECE. Dr. John Harris was instrumental in designing the chips and proposing new devices and principles to improve the performance of current devices. Dr. Karl Gugel helped develop the DSP hardware and firmware to create the new generation of portable systems. We were fortunate to count with the intelligence, dedication, and hard work of many students. Dr. Justin Sanchez came on board to link his biomedical knowledge with signal processing, and his stay at University of Florida has expanded our ability to conduct research here. Dr. Sung-Phil Kim painstakingly developed and evaluated the BMI algorithms. Drs. Deniz Erdogmus and Yadu Rao helped with the theory and their insights. Scott Morrison, Shalom Darmanjian, and Greg Cieslewski developed and programmed the first portable systems for online learning of neural data. Later on, our colleagues Dr. Toshi Nishida and Dr. Rizwan Bashirullah open up the scope of the work with electrodes and wireless systems. Now, a second generation of students is leading the push forward; Yiwen Wang, Aysegul Gunduz, Jack DiGiovanna, Antonio Paiva, and Il Park are advancing the scope of the work with spike train Foreword v modeling. This current research taking us to yet another unexplored direction, which is perhaps the best indication of the strong foundations of the early collaboration with Duke. This book is only possible because of the collective effort of all these individuals. To acknowledge appropriately their contributions, each chapter will name the most important players. Jose C. Principe and Justin C. Sanchez vi BRaIN-MaChINE INTERFaCE ENgINEERINg 1 The study of repair and regeneration of the central nervous system is quite broad and includes contributions from molecular/cellular neuroscience, tissue engineering, and materials science. For a comprehensive review of the application of each of these to the repair of the nervous system, see References [1-4].
Advanced Neurotechnologies for Chronic Neural Interfaces: New Horizons and Clinical Opportunities
Journal of Neuroscience, 2008
Technological advances in neural interfaces are providing increasingly more powerful "toolkits" of designs, materials, components, and integrated devices for establishing high-fidelity chronic neural interfaces. For a broad class of neuroscience studies, the primary requirements of these interfaces include recording and/or stimulating from a number of discretely sampled volumes at requisite spatial resolutions for specific periods of time. Translational and clinical applications present additional requirements for safety, usability, reliability, patient acceptance, and cost effectiveness. Innovative solutions result from the constructive tension between ever-increasing application requirements and incorporation of technological advances into usable devices. The purpose of this minireview is to present snapshots of the current state-of-the-art in chronic, microscale neural interfaces by highlighting several leading neuroscience applications and discussing their implications for next-generation interface devices.
JOURNAL OF MEDICAL INTERNET RESEARCH, 2019
The first attempts to translate neuronal activity into commands to control external devices were made in monkeys yet in 1960s. After that, during 1960-1970, the biological feedback was realized in monkeys, to provide voluntary control of the firing rate of cortical neurons. The term "brain-computer interface" appeared only in earlier 1970s. The brain-computer interface is usually referred to as a "brain-machine interface" in invasive studies. Nowadays, the brain-computer interface and brain-machine interface research and applications are considered one of the most exciting interdisciplinary areas of science and technology. In particular, brain-computer interfaces are very promising for neurorehabilitation of sensory and motor disabilities, neurocommunication, exoskeletons, cognitive state evaluation, etc. Advanced mathematical methods for extraction and classification of neuronal activity features hold out hope for the future use of brain-computer interfaces in everyday life. At the same time, the lack of effective invasive neuroimaging techniques providing a high-resolution neural activity recording for medical purposes limits the brain-machine interface implementation in clinics. In their paper, Elon Musk and Neuralink have successfully addressed the major issues hampering the next generation of invasive brain-computer interface (or brain-machine interface) development by introducing a novel integrated platform enabling a high-quality registration of thousands of channels. Their device contains arrays of flexible electrode threads with up to 3072 electrodes per array, distributed across 96 threads. To overcome a surgical limitation, the authors have built a neurosurgical robot that inserts 6 threads per minute with a micrometer spatial precision. To increase the biocompatibility, they created a neurosurgical robot, which implants polymer probes much faster and more safely than existing surgical approaches. Using this platform in freely moving rats, the authors report a spiking yield of up to 85.5%. Although the developed system is considered an effective platform for research in rodents, it can serve as an invasive neurointerface prototype for clinical applications. Specifically, multielectrode neurointerfaces may become the basis for new communication systems and advanced assistive technologies for paralyzed people as well as control external devices and interact with the entire environment, eg, by integrating into new fast developed technologies, such as Smart Home and Internet of Things. Moreover, the brain-computer interface applications are very promising for detecting hidden information in the user's brain, which cannot be revealed by conventional communication channels.