The emerging field of bioelectronic medicine seeks methods for deciphering and modulating electrophysiological activity in the body to attain therapeutic effects at target organs. Current approaches to interfacing with peripheral nerves and muscles rely heavily on wires, creating problems for chronic use, while emerging wireless approaches lack the size scalability necessary to interrogate small-diameter nerves. Furthermore, conventional electrode-based technologies lack the capability to record from nerves with high spatial resolution or to record independently from many discrete sites within a nerve bundle. Here, we demonstrate neural dust, a wireless and scalable ultrasonic backscatter system for powering and communicating with implanted bioelectronics. We show that ultrasound is effective at delivering power to mm-scale devices in tissue; likewise, passive, battery-less communication using backscatter enables high-fidelity transmission of electromyogram (EMG) and electroneurogram (ENG) signals from anesthetized rats. These results highlight the potential for an ultrasound-based neural interface system for advancing future bioelectronics-based therapies.

During operation, the external transducer alternates between (1) emitting a series of six 540-ns pulses every 100 μs and (2) listening for any reflected pulses. The entire sequence of transmit, receive, and reconstruction events are detailed in Figure 2 ; this sequence (steps A–H) is repeated every 100 μs during operation. Briefly, pulses of ultrasonic energy emitted by the external transducer impinge on the piezocrystal and are, in part, reflected back toward the external transducer. In addition, some of the ultrasonic energy causes the piezocrystal to vibrate; as this occurs, the piezocrystal converts the mechanical power of the ultrasound wave into electrical power, which is supplied to the transistor. Any extracellular voltage change across the two recording electrodes modulates the transistor’s gate, changing the amount of current flowing between the terminals of the crystal. These changes in current, in turn, alter the vibration of the crystal and the intensity of the reflected ultrasonic energy. Thus, the shape of the reflected ultrasonic pulses encodes the electrophysiological voltage signal seen by the implanted electrodes and this electrophysiological signal can be reconstructed externally. The performance specifications of neural dust in comparison to other state-of-the-art systems are summarized in Table 1

(H) Reconstructed waveform is sampled at 10 kHz. Each point of the reconstructed waveform is computed by calculating the area under the curve of the appropriate reflected pulses, received every 100 μs.

(F) Zoomed-in backscatter waveforms, aligned in time with (E). Note the large, saturating signal which overlaps with the transmitted pulses is electrical feedthrough and is ignored. The returning, backscattered pulses can be seen subsequent to the transmission window (green box). A close up of the backscatter pulses is shown in Figure 3 E and discussed in the text.

(C) Upon receiving the trigger from the FPGA, the transceiver board generates a series of transmit pulses. At the end of the transmit cycle, the switch on the ASIC disconnects the transmit module and connects the receive module.

We previously introduced the neural dust ultrasonic backscattering concept to harness the potential advantages of ultrasound and showed that, theoretically, such a system could be scaled well below the mm-scale when used for wireless electrophysiological neural recording (). Here, we present the first experimental validation of a neural dust system in vivo in the rat peripheral nervous system (PNS) and skeletal muscle, reporting both electroneurogram (ENG) recordings from the sciatic nerve and electromyographic (EMG) recordings from the gastrocnemius muscle. The neural dust system consists of an external ultrasonic transceiver board which powers and communicates with a millimeter-scale sensor implanted into either a nerve or muscle ( Figure 1 A). The implanted mote consists of a piezoelectric crystal, a single custom transistor, and a pair of recording electrodes ( Figures 1 B, 1C, and S1 ).

(A) An external transducer powers and communicates with a neural dust mote placed remotely in the body. Driven by a custom transceiver board, the transducer alternates between transmitting a series of pulses that power the device and listening for reflected pulses that are modulated by electrophysiological signals.

In contrast to EM, ultrasound offers an attractive alternative for wirelessly powering and communicating with sub-mm implantable devices (). Ultrasound has two advantages. First, the speed of sound is 10× lower than the speed of light in water, leading to much smaller wavelengths at similar frequencies; this yields excellent spatial resolution at these lower frequencies as compared to radio waves. Second, ultrasonic energy attenuates far less in tissue than EM radiation; this not only results in much higher penetration depths for a given power, but also significantly decreases the amount of unwanted power introduced into tissue due to scattering or absorption. In fact, for most frequencies and power levels, ultrasound is safe in the human body. These limits are well defined, and ultrasound technologies have long been used for diagnostic and therapeutic purposes. As a rough guide, about 72× more power is allowable into the human body when using ultrasound as compared to radio waves ().

Recent technological advances () and fundamental discoveries () have renewed interest in implantable systems for interfacing with the peripheral nervous system. Early clinical successes with peripheral neurostimulation devices, such as those used to treat sleep apnea () or control bladder function in paraplegics () have led clinicians and researchers to propose new disease targets ranging from diabetes to rheumatoid arthritis (). A recently proposed roadmap for the field of bioelectronic medicines highlights the need for new electrode-based recording technologies that can detect abnormalities in physiological signals and be used to update stimulation parameters in real time. Key features of such technologies include high-density, stable recordings of up to 100 channels in single nerves, wireless and implantable modules to enable characterization of functionally specific neural and electromyographic signals, and scalable device platforms that can interface with small nerves of 100 μm diameter or less () as well as specific muscle fibers. Current approaches to recording peripheral nerve activity fall short of this goal; for example, cuff electrodes provide stable chronic performance but are limited to recording compound activity from the entire nerve. Single-lead intrafascicular electrodes can record from multiple sites within a single fascicle but do not enable high-density recording from discrete sites in multiple fascicles (). Similarly, surface EMG arrays allow for very-high-density recording () but do not capture fine details of deep or small muscles. Recently, wireless devices to enable untethered recording in rodents () and nonhuman primates (), as well as mm-scale integrated circuits for neurosensing applications have been developed (). However, most wireless systems use electromagnetic (EM) energy coupling and communication, which becomes extremely inefficient in systems smaller than ∼5 mm due to the inefficiency of coupling radio waves at these scales within tissue (; see also Size Scaling and Electromagnetics in the Supplemental Information ). Further miniaturization of wireless electronics platforms that can effectively interface with small-diameter nerves will require new approaches.

A similar setup was prepared to measure the electroneurogram (ENG) response from the main branch of the sciatic nerve in anesthetized rats. The sciatic nerve was exposed by separating the hamstring muscles and the neural dust mote was placed and sutured to the nerve, with the recording electrodes making contact with the epineurium ( Figure 1 B). We measured a similar graded response on both ground truth and wireless dust backscatter by varying stimulation current amplitude delivered to bipolar stainless steel electrodes placed in the foot ( Figures 6 A and 6B ). The two signals at response-saturating stimulation amplitude (100%) matched with R = 0.886 ( Figure 6 C); the average error was within ±0.2 mV ( Figure 6 D). The peak-to-peak ENG voltage showed a sigmoidal response with the error bars indicating uncertainties from two rats and ten samples each per stimulation amplitude. The minimum signal detected by the neural dust mote was again at 0.25 mV ( Figure 6 E).

We obtained EMG recruitment curves with both ground truth and wireless dust backscatter by varying stimulation amplitude ( Figures 5 A and 5B ). Reconstruction of the EMG signal from the wireless backscatter data was sampled at 10 kHz, while the wired, ground truth measurement was sampled at 100 kHz with a noise floor of 0.02 mV. The two signals at response-saturating stimulation amplitude (100%) matched with R = 0.795 ( Figure 5 C). The difference between the wireless and wired data was within ± 0.4 mV ( Figure 5 D). The salient feature of the neural dust mote EMG response was approximately 1 ms narrower than the ground truth, which caused the largest error in the difference plot ( Figures 5 C and 5D). The responses from skeletal muscle fibers occurred 5 ms post-stimulation and persisted for 5 ms. The peak-to-peak voltage of the EMG shows a sigmoidal response as a function of stimulation intensity ( Figure 5 E) as expected (). The error bars indicate the measurement uncertainties from two rats and ten samples each per stimulation amplitude. The minimum signal detected by the neural dust mote is approximately 0.25 mV, which is in good agreement with the noise floor measurement made in a water tank.

We recorded evoked EMG responses from the gastrocnemius muscle of adult Long-Evans rats under anesthesia using the neural dust system. The mote was placed on the exposed muscle surface, the skin and surrounding connective tissue were then replaced, and the wound was closed with surgical suture ( Figure 4 A). The ultrasonic transducer was positioned 8.9 mm away from the implant (one Rayleigh distance of the external transducer) and commercial ultrasound gel (Aquasonic 100, Parker Labs) was used to enhance coupling. The system was aligned using a manual manipulator by maximizing the harvested voltage on the piezocrystal measured from the flexible leads. Ag/AgCl wire hook electrodes were placed approximately 2 cm distally on the trunk of the sciatic nerve for the bulk stimulation of muscle fiber responses. Stimulation pulses of 200 μs duration were applied every 6 s, and data were recorded for 20 ms around the stimulation window ( Figure 4 B). The power spectral density (PSD) of the reconstructed data with several harmonics due to edges in the waveform is shown in Figure 4 C. This process could be continued indefinitely, within the limit of the anesthesia protocol; a comparison of data taken after 30 min of continuous recording showed no appreciable degradation in recording quality ( Figure 4 D).

(C) Power spectral density (PSD) of the recorded EMG signal showed 4.29e4 μV 2 /Hz and 3.11e4 μV 2 /Hz at 107 Hz for ground truth and the reconstructed dust data, respectively, and several harmonics due to edges in the waveform.

(A) In vivo experimental setup for EMG recording from gastrocnemius muscle in rats; the neural dust mote was placed on the exposed muscle surface, and the wound was closed with surgical suture. The external transducer couples ultrasound to the mote, and the wireless data are recorded and displayed on the laptop.

To interrogate the neural dust mote, six 540-ns pulses every 100 μs were emitted by the external transducer ( Figure 2 ). These emitted pulses reflect off the neural dust mote and produce backscatter pulses back toward the external transducer. Reflected backscatter pulses were recorded by the same transceiver board ( Figures 1 A and 1D). The received backscatter waveform exhibits four regions of interest; these are pulses reflecting from four distinct interfaces ( Figures 3 D and 3E): (1) the water-polymer encapsulation boundary, (2) the top surface of the piezoelectric crystal, (3) the piezo-PCB boundary, and (4) the back of the PCB. As expected, the backscatter amplitude of the signals reflected from the piezoelectric crystal (second region) changed as a function of changes in potential at the recording electrodes. Reflected pulses from other interfaces did not respond to changes in potential at the recording electrodes. Importantly, pulses from the other non-responsive regions were used as a signal level reference, making the system robust to motion or heat-induced artifacts (snce pulses reflected from all interfaces change with physical or thermal disturbances of the neural dust mote but only pulses from the second region change as a function of electrophysiological signals). In a water tank, the system showed a linear response to changes in recording electrode potential and a noise floor of ∼0.18 mV Figure 3 F). The overall dynamic range of the system is limited by the input range of the transistor and is greater than >500 mV (i.e., there is only an incremental change in the current once the transistor is fully on [input exceeds its threshold voltage] or fully off). The noise floor increased with the measured power drop-off of the beam; 0.7 mm of misalignment degraded it by a factor of two (n = 5 devices, Figure 3 H). This lateral misalignment-induced increase in the noise floor constitutes the most significant challenge to neural recordings without a beam-steering system (that is, without the use of an external transducer array that can keep the ultrasonic beam focused on the implanted dust mote and, thus, on axis). On axis, the neural dust mote converted incident acoustic power to electrical power across the load resistance of the piezo with ∼25% efficiency. Figure 3 G plots the off-axis drop-off of voltage and power at one Rayleigh distance for the transducer used in this manuscript. Likewise, Figure 3 I plots the change in effective noise floor as a function of angular misalignment.

The entire system was submerged and characterized in a custom-built water tank with manual 6-degrees of freedom (DOF) linear translational and rotational stages (Thorlabs). Distilled water was used as a propagation medium, which exhibits similar acoustic impedance as tissue, at 1.5 MRayls (). For initial calibration of the system, a current source (2400-LV, Keithley) was used to mimic extracellular signals by forcing electrical current at varying current densities through 0.127-mm-thick platinum wires (773000, A-M Systems) immersed in the tank. The neural dust mote was submerged in the current path between the electrodes. As current was applied between the wires, a potential difference arose across the implant electrodes. This potential difference was used to mimic extracellular electrophysiological signals during tank testing.

An external, ultrasonic transceiver board ( Figure 1 D) interfaces with neural dust motes by both supplying power (transmit [TX] mode) and receiving reflected signals (receive [RX] mode). This system is a low-power, programmable, and portable transceiver board that drives a commercially available external ultrasonic transducer (V323-SU, Olympus). Details of the custom integrated circuit (IC) that drove the external ultrasonic transducer with high-energy efficiency were presented elsewhere (). The transceiver board exhibited a de-rated focus at ∼8.9 mm ( Figure 3 A). The XY cross-sectional beam pattern clearly demonstrated the transition from the near-field to far-field propagation of the beam, with the narrowest beam at the Rayleigh distance ( Figure 3 B). The transducer was driven with a 5-V peak-to-peak voltage signal at 1.85 MHz. The measured de-rated peak rarefaction pressure was 14 kPa, resulting in a mechanical index (MI) of 0.01. De-rated spatial pulse peak average (I) and spatial peak time average (I) of 6.37 mW/cmand 0.21 mW/cmat 10-kHz pulse repetition were 0.0034% and 0.03% of the FDA regulatory limit, respectively (). The transceiver board was capable of outputting up to 32 V peak to peak, and the output pressure increased linearly with the input voltage ( Figure 3 C).

(I) Plot of drop in the effective noise floor as a function of angular misalignment. Angular misalignment results in a skewed beam pattern: ellipsoidal as opposed to circular. This increases the radius of focal spot (spreading energy out over a larger area); the distortion of the focal spot relaxes the constraint on misalignment.

(E) Example backscatter waveform showing different regions of backscatter. The backscatter waveform is found flanked (in time) by regions which correspond to reflections arising from non-responsive regions; these correspond to reflected pulses from other device components shown in (D). The measurement from the non-responsive regions, which do not encode biological data, can be used as a reference. As a result of taking this differential measurement, any movements of the entire structure relative to the external transducer during the experiment can be subtracted out.

The assembly process ( Figure S1 A) shows a neural dust implant mote integrated on a 50-μm-thick polyimide flexible printed circuit board (PCB) where both the piezocrystal (0.75 × 0.75 × 0.75 mm) and the custom transistor (0.5 × 0.45 mm) are attached to the topside of the board with a conductive silver paste. Electrical connections between the components are made using aluminum wirebonds and conductive gold traces. Exposed gold recording pads on the bottom of the board (0.2 × 0.2 mm) are separated by 1.8 mm and make contact on the nerve or muscle to record electrophysiological signals ( Figure 1 C). Recorded signals are sent to the transistor’s input through micro-vias. Additionally, some implants were equipped with 0.35-mm-wide, 25-mm-long, flexible, compliant leads ( Figure S1 B) with test points for simultaneous measurement of both the voltage across the piezocrystal and direct wired measurement of the extracellular potential across the electrode pair used by the mote (we refer to this direct, wired recording of extracellular potential as the ground truth measurement below, which is used as a control for the ultrasonically reconstructed data). The entire implant is encapsulated in a medical grade UV-curable epoxy to protect wirebonds and provide insulation. A single neural dust mote implant measures roughly 0.8 × 3 × 1 mm ( Figures 1 C and S1 ). The size of the implants presented here is limited only by our use of commercial polyimide backplane technology, which is commercially accessible to anyone; relying on more aggressive assembly techniques with in-house polymer patterning would produce implants not much larger than the piezocrystal dimensions (yielding an ∼1-mmimplant).

Discussion

Krook-Magnuson et al., 2015 Krook-Magnuson E.

Gelinas J.N.

Soltesz I.

Buzsáki G. Neuroelectronics and biooptics: closed-loop technologies in neurological disorders. Famm et al., 2013 Famm K.

Litt B.

Tracey K.J.

Boyden E.S.

Slaoui M. Drug discovery: a jump-start for electroceuticals. In recent years, there has been growing interest in the use of neural recording technologies to improve neurostimulation-based treatments as well as to develop new closed-loop neuromodulation therapies for disorders in the central () and peripheral () nervous systems. Because nerves carry both efferent and afferent signals to a variety of target organs, effective recording technologies will need high spatiotemporal resolution to record from multiple discrete sites within a single nerve. In order for these technologies to become clinically viable they will need to be tether-less to avoid potential infections and adverse biological responses due to micro-motion of the implant within the tissue.

To address this looming issue, we designed, built, and implanted a wireless, ultrasonic neural sensor and communication system that enables neural recordings in the peripheral nervous system. In vivo, acute recordings in a stationary, anesthetized rat model were used to collect compound action potentials from the main branch of the sciatic nerve as well as evoked EMG from the gastrocnemius muscle. The performance of the neural dust system was equivalent to conventional electrophysiological recordings employing microelectrodes and cabled electronics.

Seo et al., 2013 Seo, D., Carmena, J.M., Rabaey, J.M., Alon, E., and Maharbiz, M.M. (2013). Neural dust: an ultrasonic, low power solution for chronic brain-machine interface. Published online July 8, 2013. arXiv:1307:2196. http://arxiv.org/abs/1307.2196 Seo et al., 2014 Seo D.

Carmena J.M.

Rabaey J.M.

Maharbiz M.M.

Alon E. Model validation of untethered, ultrasonic neural dust motes for cortical recording. One of the principal strengths of the demonstrated technology is that, unlike conventional radio frequency technology, ultrasound-based systems appear scalable down to <100 μm sizes (see Size Scaling and Electromagnetics in the Supplemental Information ), opening the door to a new technological path in implantable electronics. A complete analysis of this scaling can be found in (). In brief, physics limits how small a good radio frequency receiver can be due to the long wavelengths of radio frequency energy (millimeters to centimeters) and the high degree of absorption of radio frequency energy into tissue (which heats up the tissue and limits the total power than can be sent to an implant). Ultrasonic systems fare much better in both areas, allowing for the design of extremely small receiver devices. In addition, the extreme miniaturization of lower power electronics allows for useful recording electronics to be incorporated into such small packages.

Bertrand et al., 2014 Bertrand A.

Seo D.

Maksimovic F.

Carmena J.M.

Maharbiz M.M.

Alon E.

Rabaey J.M. Beamforming approaches for untethered, ultrasonic neural dust motes for cortical recording: a simulation study. Seo et al., 2015 Seo D.

Tang H.

Carmena J.M.

Rabaey J.M.

Alon E.

Boser B.E.

Maharbiz M.M. Ultrasonic beamforming system for interrogating multiple implantable sensors. A number of technical challenges remain open. The power levels used in this study were limited by the specifications of commercially available transducers; custom transducers will reduce the overall external device footprint, lower the noise floor (by producing higher power densities at the focal spot), and allow for selection of the focal depth to suit specific applications. For example, a flat, low-profile piezo transducer with proper impedance matching would enable a wearable neural dust transceiver board small enough for awake, behaving rodent neurophysiology. Additionally, the development of wearable, battery-powered multi-element arrays would allow for beam steering of the ultrasonic beam, with several advantages: (1) motes could be maintained on axis even in the face of relative motion between mote and external transducer, which is the most significant challenge of the present work; (2) multiple motes could potentially be interrogated by sweeping the focused beam electronically; (3) post-surgical tuning of mote location would be made easier. Additional de-noising of the transceiver drive electronics should also help decrease the noise floor (see Experimental Procedures ). The modifications above are all well-within current state of the art; with others, we have recently shown theoretical and experimental advantages to using beam-forming systems ().

In addition, the calculated scaling predictions suggest that <500-μm scale motes are feasible. To do this, a number of material and microfabrication challenges exist, including the use of microfabricated backplanes, solder microbumping assembly of components (instead of the conventional wirebonding approach used here), and the use of thin film encapsulants (instead of medical grade epoxy) such as parylene. Transitioning away from PZT piezocrystals to biocompatible BaTiO 3 single crystal transducers is also under way; taken together, these developments would open the way for chronic studies of neural dust recording.

Last, as this platform presents a generalized power delivery system, the design and fabrication of neural stimulation systems based on charge delivery through electrodes on the dust motes is also under way.