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13 May 2018

Advanced Neural Implants and Control :(


Advanced Neural Implants and Control 


mind control chips, 
and subdermal interfaces already in use,
please view blue link above for images

Daryl R. Kipke Associate Professor Department of Bioengineering Arizona State University Tempe, AZ 85287 kipke@asu.edu 

for full details and images see blue link above
Approved for Public Release, Distribution Unlimited: 01-S-1097 The Underlying Premise… The ability to engineer reliable, high-capacity direct interfaces to the brain and then integrate these into a host of new technologies will cause the world of tomorrow to be much different than that of today. However… � There are some serious scientific barriers between where we stand today and where we can stand in the future. • How do we establish permanent and reliable interfaces to selected areas of the central nervous system? • How do we use these interfaces to directly and reliably communicate at high rates with the brain? Applied Neural Implants and Control Project Director Kipke (BME) Advisory Committee Raupp, Hoppensteadt, Farin Visualization & Modeling Farin (CSE) Nelson (CSE) Razdan (CSE) Smith (Math) Systems Science & Signal Processing He (BME) Hoppensteadt (Math & EE) Kipke (BME) Si (EE) Neural & Tissue Engineering Kipke (BME) Massia (BME) Panitch (BME) Rousche (BME) Tissue Culture & Analysis Capco (Bio) Massia (BME) Pauken (Bio) Materials Synthesis & Bioactive Coatings Ehestraimi (BME) Massia (BME) Panitch (BME) Raupp (ChemE) MEMS Shen (EE) Pivin (EE) Li (EE) INFO BIO MICRO Primary Goals of the BIO:INFO:MICRO Project � Develop new neural implant technologies to establish reliable, high-capacity, and long-term information channels between the brain and external world. � Develop real-time signal processors and system controllers to optimize information transmission between the brain and the external world. SysSci VizMod NeuEng MEMS TisClt Mat'lSyn Systems-level Approach… Feedback control signals Subject Neural system (global) Controlled neural plasticity local Neural Implant Adaptive Controller External World Objective 2: Optimize Adaptive Controller Objective 1: Optimize neural interface Topics � Project overview � Towards the Development of Next-generation Neural Implants (BIO, MICRO, and INFO) � Bioactive Coatings to Control the Tissue Responses to Implanted Microdevices � Modeling the Device-Tissue Interface � Direct Cortical Control of an Actuator � Neural Control of Auditory Perception � Wrap-up Focus on Next-Generation Neural Implants Feedback signals: local Subject Neural system (global) Controlled neural plasticity local Neural Implant Neural Controller External World host response Info. Signals: electrical & chemical Objective 2: Optimize Adaptive Controller Objective 1: Optimize neural interface to achieve reliable, two-way, high-capacity information channels. …and “self-diagnostic” Fundamental Problem of Implantable Microelectrode Arrays � Brain often encapsulates the device with scar tissue � Normal brain movement may cause micro-motion at the tissueelectrode interface � Proteins adsorb onto device surface � Useful neural recordings are eventually lost Electrode 1 Electrode N Implant Failure Month 1 Implant Month N 3rd-Generation Neural Implants Technology Spectrum 1st-generation Microwires 2nd-generation Silicon arrays 3rd-generation Neural Implants Desired Properties • Very high channel count (<1000) • Bioactive coatings • Flexible • Engineered surfaces • Controlled biological response • Integrated electronics “Brain-centered” Design of Neural Implants Initial conceptual designs B B A A A A B B through hole connecting channel recording site bioactive gel Standard Perforated Probe Simple Bioactive Probe Differential Bioactive Probe recording site through hole bioactive gel flexible polyimide substrate bond pads e.g. corticosteroid NGF e.g. GABA cross-section (A-A) cross-section (B-B) Polymer-substrate Neural Implants • 2-D planar devices can be bent into 3-D structures • Increases insertion complexity Holes to promote integration with neuropil 90 degree angles Recordings From Polymer-substrate Neural Implants Chan. 9 Chan. 10 One Day Post-op Lost most unit activity after 7 days – Most likely due to failure to properly close dural opening. Flexible Neural Implants Present Surgical Challenges � While the “micro-motion” hypothesis suggests that flexible neural implants should be more stable, the same flexibility presents significant new surgical challenges. “Difficult” insertion “Easy” insertion Rdr2, 9-00 Rdr3, 9-00 Using Dissolvable Coatings to Stiffen the Neural Implant � Dip-coat microdevice with polyethylene glycol (PEG) • Provides mechanical stiffening prior to implant • Quickly dissolves when in contact with tissue First insertion of coated microdevice into Second insertion of coated microdevice gelatin -- Device easily penetrates into gelatin – The device is too flexible to material penetrate material because the PEG has dissolved. Micromachined Surgical Devices Vacuum nozzle Flexible probe Insertion aid Vacuum Actuated Knife/Inserter PEG Silicon Knife/Inserter Exploratory Functionality Bioactive Component Storage Structures Passive Surface Engineering Active FET Devices, ChemFETs Electrical Recording/Stimulating Surfaces Other Active Devices (Thermal, Magnetic, Strain, etc.) Fluid Microchannels Polymer Substrate • Magnetic/thermal stimulation • Drug delivery channels • Active micromanipulation of probes Currently... Internal Review Feasibility Studies Insertion Aids Mechanical Transfer Structures Signal Processing Termination Multiple Dimensions and Forms Implant Coatings and Surface Modifications Parylene-N,C Photo-crosslinked Cl Polyimides Cl O O O n C C C C smooth porous N N O O Surface Plasma Treatments NH2 NH2 NH2 NH2 (NH3 - Amination) Advanced Neuro-Device Interfaces Passive Chemical/Electronic NH2 NH2 NH2 NH2 Amplification ion beam metal modified region site or interdigits release layer polymer (PI/P-C) or substrate Active Silicon FETs? Topics � Project overview � Towards the Development of 3rd-Generation Neural Implants (BIO, MICRO, and INFO) � Bioactive Coatings for Controlled Biological Response (BIO, MICRO, and INFO) � Modeling the Device-Tissue Interface � Direct Cortical Control of an Actuator � Neural Control of Auditory Perception � Wrap-up Approach Advanced biomaterials and micro-devices for long-term implants (BIO, MICRO, INFO) Cellular and biochemical response characterization (BIO, MICRO) Models and 3-D visualization of device-tissue dynamics (BIO, INFO) Engineer the neural implant surface in order to control both the material response and the host response. Factors Limiting Chronic Soft Tissue Implants � Inability to control cellular interactions at biomaterial-tissue interface � Initial adsorption of biological proteins • Non-selective cellular adhesion � Unavoidable “generic” foreign body reactions • Inflammation • Fibrous capsule formation Potential Solution � Engineer surface for minimal protein adsorption and selective cell adhesion • Cell-resistant polymer coatings • Synthetic: Polyethylene Glycol, Polyvinyl Alcohol • Natural: Polysaccharides, Phospholipids • Surface immobilization of biologically active molecules • Mimic biochemical signals of extracellular matrix • Cell binding domains for integrin receptors Biomimetic Surface Modification NH2 NH2 OH O HO N O O OH OH O O OH OH O HO HO HO O OH OH O O HO HO N OH O HO O NTF NTF Material Surface Recombinant NGF Fusion Protein Factor IIIa Active or inactive plasmindegradable substrate Degraded plasminsubstrate substrate Human b-NGF plasmin Fibrin Plasmin cleavage Human b-NGF Fibrin Bioactive Functionality Methods 6-hour diffusion in rat cortex Fluorescence Intensity Profile 250 NeuroTrace� DiI tissue-labeling paste, inverted fluorescent microscope with FITC/rhodamine filter cube 200 150 Pixel Value 100 5 0 0 0 20 40 60 80 100 120 140 160 Distance (microns) Topics � Project overview � Towards the Development of 3rd-Generation � Bioactive Coatings to Control the Tissue Neural Implants (BIO, MICRO, and INFO) Responses to Implanted Microdevices (BIO, MICRO, and INFO) � Modeling the Device-Tissue Interface (BIO, MICRO, and INFO) � Direct Cortical Control of a Motor Prosthesis � Neural Control of Auditory Perception � Wrap-up The Device-Tissue Interface Neural Interface: Micro-device, Neurons, Glia, Extracellular Space The Goal is to Characterize, Predict, and Control the Device-Tissue Interface Tissue State (e.g., encapsulation, excitability) Biophysical Model of the Device-Tissue Interface Device Function (e.g., impedance spectrum) • Integrate bioelectrical, histological and biochemical data • Optimize electrode specifications Visualization of the Chronic Device-Tissue Interface With Confocal Microscopy A B C D In vivo Visualization of the Chronic Device-Tissue Interface Multi-Domain Continuum Model ( ) ( ) ( ) / / / At each "point" in space: volume fraction potential , conductivity tensor membrane parameters , , , etc. ei ei ei L r f rt G a C g F r r r r • Tissue is two (or more) coupled volume-conducting media • Electrode is boundary condition r r Equations for a Multi-Domain Continuum Model Volume conductor equations (conservation of current) - fe��(Ge�Fe ) = +�Imemi + Iapp i - fi ��(Gi �Fi ) = -Imemi i = index over intracellular domains Membrane potential(s) and membrane current(s) � ¶V � Vi = F i -F e I memi = ai � Ł Ci ¶t i + Iioni � ł -1 F= potential (mV) ai = surface to volume ratio (cm) Vi = membrane potential (mV) ei / 3 2 Gei = conductivity (mS/cm) Imemi = membrane current (mA/cm) Ci = membrane capacitance (mF/cm ) / 3 2 fei = volume fraction Iapp = applied current (m A/cm) Iioni = membrane current (mA/cm ) / Levels of Modeling Numerical Multiple intracellular domains Voltage-dependent conductances ioni = � g ij � qijk (Vi - E j ) j k ¥ ¶qijk qijk -q V ijk ( i ) = - ¶t tijk (Vi ) Complex electrode geometry Tissue inhomogeneous and anisotropic under construction Analytical A single intracellular domain Passive membrane conductance Iion = g L (V E - L ) Simple electrode geometry Tissue assumed homogenous and isotropic much progress I Bi-domain Model for the Microcapillary Bioreactor Calculate profiles F1 ei / (x;w) in bioreactor ...and impedance... Z(w) = F1 e ( L;w) -F1 e (0;w) j 1 100 Hz 1 Write BCs and assume: 1 / (, ) ( ; ) i t i t e i ei j j e x t x w = � F = F w Z w ( ) / ...and predict as tissue parameters , , , , , are experimentally manipulated e i e i L Z f G C g E w a V F e F i E L / ew / L Recap � Focused & integrated effort • BioMEMS…Neural Engineering…Materials… Computational Neuroscience…Cellular Biology…Visualization � Why are we so excited? • We have the very real potential of characterizing the biological responses to neural implants and then engineering new classes of microdevices to provide a permanent high-capacity interface to the brain BIO INFO MICRO Why the BIO, INFO, and MICRO Program? � Wide-open Challenges • Characterizing and modeling the biological (cellular and chemical) responses around a neural implant • Controlling the dynamic biological responses around a neural implant. • Designing, fabricating, and using “advanced” neural implants � Collaboration Possibilities • Additional functionalities for implantable microdevices of the class that we are working on. • Exploring fundamentally new types of tissue-device interfaces. • Complementary studies of the neural interface (experimental and analytical) • Confocal microscopy of the neural interface • Sharing technologies, procedures, insights, etc… • New emergent ideas… Systems-level Analysis of Advanced Neuroprosthetic Systems Feedback control signals Subject Neural system (global) Controlled neural plasticity local Neural Implant Adaptive Controller External World Objective 2: Optimize Adaptive Controller Objective 1: Optimize neural interface Systems-level Approach for Advanced Neuroprosthetic Systems Subject Neural system (global) Controlled neural plasticity local Neural Implant Adaptive Controller External World Feedback control signals Objective 2: Develop Objective 1: Optimize neural adaptive controller to interface optimize system performance. Advanced Neuroprosthetic Systems High-Level Neural Computation Sensory Transduction & Pre-processing Motor Commands Movement Perception, Decision, Detection Sensory Integration External World Neuroprosthetic System � Underlying System Principles •Two-way communication with targeted neural systems •Harness neural plasticity to our advantage •Appropriately balanced “wet-side” and “dry-side” computation Approach � Four Project Areas �Direct neural control of actuators �Detection of novel sensory stimuli through monitoring neural activity �Neural control of behavior �Investigate signal transformations from ensembles of single neurons to local field potentials to EEG. Topics � � � � Project overview Towards the Development of 3rd-Generation Neural Implants (BIO, MICRO, and INFO) Bioactive Coatings to Control the Tissue Responses to Implanted Microdevices (BIO, MICRO, and INFO) Modeling the Device-Tissue Interface (BIO, MICRO, and INFO) � Direct Cortical Control of a Motor Prosthesis (BIO, MICRO, and INFO) � Neural Control of Auditory Perception � Wrap-up Direct Cortical Control of Actuators High-Level Neural Computation Sensory Transduction & Pre-processing Motor Commands Movement Perception, Decision, Detection Sensory Integration External World Neuroprosthetic System Goal: Control arm-related actuator External Actuator Robotic Arm or Virtual Reality Fundamental Questions � What are “optimal” real-time signal processing strategies for precise 3-D control of external, armrelated actuators in the presence of sensory distractions and/or physical perturbations to the arm? � To what extent can we use composite neural signals [neuronal (unit) recordings, local field potentials, and brain-surface recordings] for control signals? � How do we take advantage of inherent or controlled neural plasticity in order to optimize system performance? Experimental Preparation • Train monkeys to perform tracking and/or reaching tasks. • Record cortical responses with multichannel neural implants. • Measure arm movement in 3-D space. Chronic Neural Recordings � Multi-channel neural implants in motor and sensorimotor cortical areas. � Eventually: Sub-dural electrodes for local potentials -0.2 0 0.2 0.4 0.6 0 10 dsp009b -0.2 0 0.2 0.4 0.6 0 20 40 dsp012a -0.2 0 0.2 0.4 0.6 0 10 20 dsp018a -0.2 0 0.2 0.4 0.6 0 20 40 dsp024a -0.2 0 0.2 0.4 0.6 0 20 40 dsp025a -0.2 0 0.2 0.4 0.6 Time (sec) 0 10 dsp030a -0.2 0 0.2 0.4 0.6 0 100 dsp034a -0.2 0 0.2 0.4 0.6 0 50 100 150 dsp037a -0.2 0 0.2 0.4 0.6 0 5 10 15 dsp040a -0.2 0 0.2 0.4 0.6 0 10 20 dsp042a -0.2 0 0.2 0.4 0.6 0 20 dsp042b -0.2 0 0.2 0.4 0.6 Time (sec) 0 10 20 30 dsp045a -0.2 0 0.2 0.4 0.6 0 20 40 dsp046a -0.2 0 0.2 0.4 0.6 0 5 10 15 dsp051a -0.2 0 0.2 0.4 0.6 0 20 40 dsp057a -0.2 0 0.2 0.4 0.6 0 40 80 dsp058a Perievent Histograms Target 1, reference = C_rel, bin = 20 ms Neural Recording System Offline Analysis Real-time Signal Processing Actuator Control Extracellular recordings Direct Cortical Control of Movement Green ball: Target Yellow ball: Actual hand position, or hand position estimated from cortical responses m0602pa Topics � � � � � Project overview Towards the Development of 3rd-Generation Neural Implants (BIO, MICRO, and INFO) Bioactive Coatings to Control the Tissue Responses to Implanted Microdevices (BIO, MICRO, and INFO) Modeling the Device-Tissue Interface (BIO, MICRO, and INFO) Direct Cortical Control of a Motor Prosthesis (BIO, MICRO, and INFO) � Neural Control of Auditory Perception(BIO, MICRO, and INFO) � Wrap-up Neural Control of Auditory Perception High-Level Neural Computation Sensory Transduction & Pre-processing Motor Commands Movement Perception, Decision, Detection Sensory Integration External World Neuroprosthetic System Goal: Control auditory perception Fundamental Questions � To what extent can we control auditory-mediated behavior using intra-cortical microstimulation (ICMS) through the neural interface? Transmitter Channel Receiver Source Signal Received Signal Stimulator Neural Interface Auditory Cortex � What are the information transmission characteristics of the multichannel neural implant in high-level cortical areas using ICMS? � Channel capacity (bits per second) � Channel reliability � Channel resolution � How can we optimize information transmission � Implant designs, Neural implant locations, Signal encoding strategies, Controlled neural plasticity Chronic Neural Recordings � Multi-channel neural implants in primary auditory cortex Extracellular recordings in auditory cortex Estimation of Neural Recording System Offline Analysis Neuronal Response Properties Algorithm Selection Signal Encoder Sounds Electrical Stimulation to Aud. Ctx. Behavioral performance to both sounds and cortical electrical stimulation Auditory Behavior • Lever-press sound or ICMS discrimination task • Center paddle hit starts trial, 2-tone pair presented • Reward obtained by signaling the correct stimulus sequence center left right rat Frequency response areas Frequency Selectivity in Auditory Cortex 1 2 5 10 20 30 40 60 80 dsp024b 28. 56. 1 2 5 10 20 30 40 60 80 dsp018d 11. 22. 1 2 5 10 20 30 40 60 80 dsp020a 10. 20. 1 2 5 10 20 30 40 60 80 dsp024a 10. 20. 1 2 5 10 20 30 40 60 80 dsp012a 21. 42. 1 2 5 10 20 30 40 60 80 dsp018b 10.5 21. 1 2 5 10 20 30 40 60 80 dsp018c 22. 44. 1 2 5 10 20 30 40 60 80 dsp002a 3. 6. 1 2 5 10 20 30 40 60 80 dsp002b 5.5 11. 1 2 5 10 20 30 40 60 80 dsp010b 12. 24. Freq. Sound Level Signal Encoding Algorithm: Frequency Selectivity ICMS pattern is based solely on frequency selectivity of neurons recorded on an electrode dB 80 60 40 u5b 8 6 Spikes 4 2 0 1 5 10 30 kHz u32a 0 2 4 6 8 kHz 1 5 10 30 Spikes 80 60 dB 40 Behavioral Performance Ricms6 Rat Behavioral Performance RICMS 6 100 09/06/00 09/16/00 09/26/00 10/06/00 10/16/00 10/26/00 Training day Implanted 90 80 Percent Correct 70 60 50 40 30 20 10 0 Cortical Electrodes D Expected Results to ICMS Stimuli Begin ICMS 100 % D% due to ICMS Trial # Auditory trial = ICMS Algorithm1 = ICMS Algorithm2 = Behavioral Curve RICMS 6 10/25 (Only Session) 100 80 Percentage audPercent, icmsPercent, 60 40 20 0 0 100 200 Trial Alternative Signal Encoding Algorithm: Cortical Activation Pattern For a given electrode, the unit firing pattern is used as a template for ICMS delivery Auditory Stimulus Sound on Response Raster Matching ICMS ‘pattern’ ***Procedure is simultaneously duplicated on each active electrode Recap � Focused & integrated effort • Neural Engineering…Signal Processing…Systems Neurophysiology…Visualization � Why are we so excited? • We have the very real potential of developing new classes of neuroprosthetic systems to explore our ability to interact directly with the brain. BIO INFO MICRO BIO, INFO, and MICRO… � Wide-open Challenges • Appropriate mathematical constructs for describing neural encoding and decoding. • Advanced data visualization techniques for understanding this new class of neural data. • Understanding signal transformations as a function of the spatial and temporal scale of the neural data. � Collaboration Possibilities • Exploring new signal encoding and decoding strategies for particular neuroprosthetic applications. • Sharing technologies, procedures, insights, etc… • New emergent ideas… Topics � � � � � � Project overview Towards the Development of 3rd-Generation Neural Implants (BIO, MICRO, and INFO) Bioactive Coatings to Control the Tissue Responses to Implanted Microdevices (BIO, MICRO, and INFO) Modeling the Device-Tissue Interface (BIO, MICRO, and INFO) Direct Cortical Control of a Motor Prosthesis (BIO, MICRO, and INFO) Neural Control of Auditory Perception(BIO, MICRO, and INFO) � Wrap-up Project Challenges � Scientific • Overcoming engineering and scientific hurdles. • Identifying and fostering strategic alliances with appropriate external groups. • Crossing disciplines � Management • Strategic planning • Resource allocation • Open and effective communication among the diverse project team • Team-building: Maintaining enthusiasm, energy, and focus after the initial “honeymoon” period “Insanely Intense Interdisciplinary” Research “pieces of a puzzle” “easy synergism” BIO INFO MICRO MICRO BIO INFO Breakthrough Science •Hard work •Open minds •Honesty •Top-notch research -- What Does the Future Hold? “Perhaps within 25 years there will be some new ways to put information directly into our brains. With the implant technology that will be available by about 2025, doctors will be able to put something like a chip in your brain to prevent a stroke, stop a blood clot, detect an aneurysm, help your memory or treat a mental condition. You may be able to stream (digital) information through your eyes to the brain. New drugs may enhance your memory and fire up your neurons.” Dr. Arthur Caplan, Director of the Center of Bioethics, University of Pennsylvania Arizona Republic, Dec 27, 1998. Acknowledgments � ASU Colleagues • 13 co-PI’s, 5 research faculty, numerous graduate and undergraduate students. � Arizona State University administration • Seed funding from Department, College, and University • Significant cost-share on this project � DARPA Program Managers • Eric Eisenstadt, Abe Lee, and Gary Strong 
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