Dehkhoda F, Soltan A, Ramezani R, Zhao H, Liu Y, Constandinou TG, Degenaar P, 2015, Smart Optrode for Neural Stimulation and Sensing, 2015 IEEE SENSORS, Publisher: IEEE, Pages: 1965-1968, ISSN: 1930-0395
Implantable neuro-prosthetics considerable clinical benefit to a range of neurological conditions. Optogenetics is a particular recent interest which utilizes high radiance light for photo-activation of genetic cells. This can provide improved biocompatibility and neural targeting over electrical stimuli. To date the primary optical delivery method in tissue for optogenetics has been via optic fibre which makes large scale multiplexing difficult. An alternative approach is to incorporate optical micro-emitters directly on implantable probes but this still requires electrical multiplexing. In this work, we demonstrate a fully active optoelectronic probe utilizing industry standard 0.35μm CMOS technology, capable of both light delivery and electrical recording. The incorporation of electronic circuits onto the device further allows us to incorporate smart sensors to determine diagnostic state to explore long term viability during chronic implantation.
Dehkhoda F, Soltan A, Ponon N, Jackson A, O'Neill AG and Degenaar P, 2018. Journal of Neural Engineering. Published 25 January 2018.
Abstract: This work presents a method to determine the surface temperature of microphotonic medical implants like LEDs. Our inventive step is to use the photonic emitter (LED) employed in an implantable device as its own sensor and develop readout circuitry to accurately determine the surface temperature of the device. There are two primary classes of applications where microphotonics could be used in implantable devices; opto-electrophysiology, and fluorescence sensing. In such scenarios, intense light needs to be delivered to the target. As blue wavelengths are scattered strongly in tissue, such delivery needs to be either via optic fibres, two-photon approaches, or through local emitters. In the latter case, as light emitters generate heat, there is a the potential for probe surfaces to exceed the 2oC regulatory. However, currently there are no convenient mechanisms to monitor this in-situ. Main results. We present the electronic control circuit and calibration method to monitor the surface temperature change of implantable optrode. The efficacy is demonstrated in air, saline, and brain. Significance. This paper, therefore, presents a method to utilize the light emitting diode as its own temperature sensor.
Dehkhoda F, Soltan A, Ramezani R and Degenaar P, 2016. Biomedical Circuits and Systems Conference (BioCAS)
Optogenetics is a gene-therapy technique which utilizes high radiance light to stimulate the genetically modified nerve cells. An optical delivery method in brain tissue for optogenetics can be performed using incorporated micro-LEDs. Optrode lifespan can be affected by the electrodes degradation caused by the generated charges over the tissue after long term stimulation. Here, we propose a biphasic LED driving method to balance the charge over the brain tissue. This is done by sending bidirectional pulses and providing different modes of operation for micro-LEDs using the designed H-tree driver circuit. The proposed structure and included functionalities enable a full range of control on micro-LEDs biasing where a tissue ground can provide the needed shielding for the device. The proposed driver is designed using standard 0.35μm CMOS technology.
Gao C, Ghoreishizadeh S, Liu Y, Constandinou TG, 2017 (accepted), On-chip ID Generation for Multi-node Implantable Devices using SA-PUF, IEEE International Symposium on Circuits & Systems (ISCAS)
Abstract: This paper presents a 64-bit on-chip identification system featuring low power consumption and randomness compensation for multi-node bio-implantable devices. A sense amplifier based bit-cell is proposed to realize the silicon physical unclonable function, providing a unique value whose probability has a uniform distribution and minimized influence from the temperature and supply variation. The entire system is designed and implemented in a typical 0.35 m CMOS technology, including an array of 64 bit-cells, readout circuits, and digital controllers for data interfaces. Simulated results show that the proposed bit-cell design achieved a uniformity of 50.24% and a uniqueness of 50.03% for generated IDs. The system achieved an energy consumption of 6.0 pJ per bit with parallel outputs and 17.3 pJ per bit with serial outputs.
Ghoreishizadeh S, Haci D, Liu Y, Constandinou TG, 2017, A 4-wire interface SoC for shared multi-implant power transfer and full-duplex communication, IEEE Latin American symposium on Circuits and Systems (LASCAS), Pages: 49-52, 2017
Abstract: This paper describes a novel system for recovering power and providing full-duplex communication over an AC-coupled 4-wire lead between active implantable devices. The target application requires a single Chest Device be connected to a Brain Implant consisting of multiple identical optrodes that record neural activity and provide closed loop optical stimulation. The interface is integrated within each optrode So Callowing full-duplex and fully-differential communication based on Manchester encoding. The system features a head-to-chest uplink data rate (1.6 Mbps) that is higher than that of the chest-to-head downlink (100 kbps) superimposed on a power carrier. On-chip power management provides an unregulated 5V DC supply with up to 2.5 mA output current for stimulation, and a regulated 3.3 V with 60 dB PSRR for recording and logic circuits. The circuit has been implemented in a 0.35 μm CMOS technology, occupying 1.4 mm2 silicon area, and requiring a 62.2 μA average current consumption.
Ghoreishizadeh S, Haci D, Liu Y, Donaldson N & Constandinou TG, 2017, Four-Wire Interface ASIC for a Multi-Implant Link, IEEE Transactions on Circuits and Systems 1: Regular Papers. PP(99): 1-12.
This paper describes an on-chip interface for recovering power and providing full-duplex communication over an AC-coupled 4-wire lead between active implantable devices. The target application requires two modules to be implanted in the brain (cortex) and upper chest; connected via a subcutaneous lead. The brain implant consists of multiple identical ''optrodes'' that facilitate a bidirectional neural interface (electrical recording and optical stimulation), and the chest implant contains the power source (battery) and processor module. The proposed interface is integrated within each optrode ASIC allowing full-duplex and fully-differential communication based on Manchester encoding. The system features a head-to-chest uplink data rate (up to 1.6 Mbps) that is higher than that of the chest-to-head downlink (100 kbps), which is superimposed on a power carrier. On-chip power management provides an unregulated 5-V dc supply with up to 2.5-mA output current for stimulation, and two regulated voltages (3.3 and 3 V) with 60-dB power supply rejection ratio for recording and logic circuits. The 4-wire ASIC has been implemented in a 0.35-μm CMOS technology, occupying a 1.5-mm 2 silicon area, and consumes a quiescent current of 91.2 μA. The system allows power transmission with measured efficiency of up to 66% from the chest to the brain implant. The downlink and uplink communication are successfully tested in a system with two optrodes and through a 4-wire implantable lead.
Haci D, Liu Y, Constandinou TG, 2017. 32-Channel Ultra-Low-Noise Arbitrary Signal Generation Platform for Biopotential Emulation, IEEE International Symposium on Circuits & Systems (ISCAS)
Abstract: This paper presents a multichannel, ultra-low-noise arbitrary signal generation platform for emulating a wide range of different biopotential signals (e.g. ECG, EEG, etc). This is intended for use in the test, measurement and demonstration of bioinstrumentation and medical devices that interface to electrode inputs. The system is organized in 3 key blocks for generating, processing and converting the digital data into a parallel high performance analogue output. These blocks consist of: (1) a Raspberry Pi 3 (RPi3) board; (2) a custom Field Programmable Gate Array (FPGA) board with low-power IGLOO Nano device; and (3) analogue board including the Digital-to-Analogue Converters (DACs) and output circuits. By implementing the system this way, good isolation can be achieved between the different power and signal domains. This mixed-signal architecture takes in a high bitrate SDIO (Secure Digital Input Output) stream, recodes and packetizes this to drive two multichannel DACs, with parallel analogue outputs that are then attenuated and filtered. The system achieves 32-parallel output channels each sampled at 48kS/s, with a 10kHz bandwidth, 110dB dynamic range and uV-level output noise.
Luan S, Liu Y, Williams I, Constandinou TG, 2016, An Event-Driven SoC for Neural Recording, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 404-407
Abstract: This paper presents a novel 64-channel ultra-low power/low noise neural recording System-on-Chip (SoC) featuring a highly reconfigurable Analogue Front-End (AFE) and block-selectable data-driven output. This allows a tunable bandwidth/sampling rate for extracting Local Field Potentials (LFPs)and/or Extracellular Action Potentials (EAPs). Realtime spike detection utilises a dual polarity simple threshold to enable an event driven output for neural spikes (16-sample window). The 64-channels are organised into 16 sets of 4-channel recording blocks, with each block having a dedicated 10-bit SAR ADC that is time division multiplexed among the 4 channels. Each channel can be individually powered down and configured for bandwidth, gain and detection threshold. The output can thus combine continuous-streaming and event-driven data packets with the system configured as SPI slave. The SoC is implemented in a commercially-available 0.35u m CMOS technology occupying a silicon area of 19.1mm^2 (0.3mm^2 gross per channel) and requiring 32uW/channel power consumption (AFE only).
Mifsud A, Haci D, Ghoreishizadeh SS, Liu Y, Constandinou TG, 2017. Biomedical Circuits and Systems Conference (BioCAS), 2017 IEEE. Publisher: IEEE
Emerging applications for implantable devices are requiring multi-unit systems with intrabody transmission of power and data through wireline interfaces. This paper proposes a novel method for power delivery within such a configuration that makes use of closed loop dynamic regulation. This is implemented for an implantable application requiring a single master and multiple identical slave devices utilising a parallel-connected 4-wire interface. The power regulation is achieved within the master unit through closed loop monitoring of the current consumption to the wired link. Simultaneous power transfer and full-duplex data communication is achieved by superimposing the power carrier and downlink data over two wires and uplink data over a second pair of wires. Measured results using a fully isolated (AC coupled) 4-wire lead, demonstrate this implementation can transmit up to 120 mW of power at 6 V (at the slave device, after eliminating any losses). The master device has a maximum efficiency of 80 % including a dominant dynamic power loss. A 6 V constant supply at the slave device is recovered 1.5 ms after a step of 22 mA.
Ramezani R, Dehkhoda F, Soltan A, Degenaar P, Liu Y, Constandinou TG, 2016, An optrode with built-in self-diagnostic and fracture sensor for cortical brain stimulation, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 392-395
Abstract: This paper proposes a self-diagnostic subsystem for a new generation of brain implants with active electronics. The primary objective of such probes is to deliver optical pulses to optogenetic tissue and record the subsequent activity, but lifetime is currently unknown. Our proposed circuits aim to increase the safety of implanting active electronic probes into human brain tissue. Therefore, prolonging the lifetime of the implant and reducing the risks to the patient. The self-diagnostic circuit will examine the optical emitter against any abnormality or malfunctioning. The fracture sensor examines the optrode against any rapture or insertion breakage. The optrode including our diagnostic subsystem and fracture sensor has been designed and successfully simulated at 350nm AMS technology node and sent for manufacture.
Ramezani R, Liu Y, Dekhoda F, Soltan A, Haci D, Zhao, H, Firfilionis D, Hazra A, Cunningham M, Jackson A, Constandinou TG, Degenaar P. IEEE Transaction on Biomedical Circuits and Systems Conference 2018, Turin.
Neuromodulation technologies are progressing from pacemaking and sensory operations to full closed loop control. In particular, optogenetics – the genetic modification of light sensitivity into neural tissue allows for simultaneous optical stimulation and electronic recording. This paper presents a neural interface ASIC for intelligent optoelectronic probes. The architecture is designed to enable simultaneous optical neural stimulation and electronic recording. It provides four low noise (2.08 µVrms) recording channels optimized for recording local field potentials (0.1 – 300 Hz bandwidth, ±5 mV range, sampled 10-bit@4 kHz), which are more stable for chronic applications. For stimulation it provides six independently addressable optical driver circuits, which can provide both intensity (8-bit resolution across a 1.1 mA range) and pulse-width modulation for high radiance LEDs. The system includes a fully-digital interface using a Serial Peripheral Interface (SPI) protocol to allow for use with embedded controllers. The SPI interface is embedded within a Finite State Machine (FSM) which implements a command interpreter that can send out LFP data whilst receiving instructions to control LED emission. The circuit has been implemented in a commercially-available 0.35 µm CMOS technology occupying a 1.95 mm×1.10 mm footprint for mounting onto the head of a silicon probe. Measured results are given for a variety of bench-top, in vitro and in vivo experiments, quantifying system performance and also demonstrating concurrent recording and stimulation within relevant experimental models.
Conference paper accessible here.
Zhao H, Dehkhoda F, Ramezani R, Sokolov D, Constandinou TG, Liu Y, Degenaar P, 2016, A CMOS-Based Neural Implantable Optrode for Optogenetic Stimulation and Electrical Recording, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 286-289
Abstract: This paper presents a novel integrated optrode for simultaneous optical stimulation and electrical recording for closed -loop optogenetic neuro-prosthetic applications. The design has been implemented in a commercially available 0.35μm CMOS process. The system includes circuits for controlling the optical stimulations; recording local field potentials (LFPs); and onboard diagnostics. The neural interface has two clusters of stimulation and recording sites. Each stimulation site has a bonding point for connecting a micro light emitting diode (μLED) to deliver light to the targeted area of brain tissue. Each recording site is designed to be post-processed with electrode materials to provide monitoring o fneural activity. On-chip diagnostic sensing has been included to provide real-time diagnostics for post-implantation and during normal operation.
Zhao H, Soltan A, Maaskant P, Dong N, Sun X and Degenaar P, 2017, A Scalable Optoelectronic Neural Probe Architecture with Self-Diagnostic Capability, IEEE Transactions on Circuits and Systems-I: Regular Papers
Abstract: There is a growing demand for the development of new types of implantable optoelectronics to support both basic neuroscience and optogenetic treatments for neurological disorders. Target specification requirements include multisite optical stimulation, programmable radiance profile, safe operation, and miniaturization. It is also preferable to have a simple serial interface rather than large numbers of control lines. This paper demonstrates an optrode structure comprising of a standard complementary metal-oxide-semiconductor process with 18 optical stimulation drivers. Furthermore, diagnostic sensing circuitry is incorporated to determine the long-term functionality of the photonic elements. A digital control system is incorporated to allow independent multisite control and serial communication with external control units.
Alfonsa, H, Lakey JH, Lightowlers RN, & Trevelyan AJ. Cl-out is a novel cooperative optogenetic tool for extruding chloride from neurons. Nat. Commun. 7, 13495
Abstract: Chloride regulation affects brain function in many ways, for instance, by dictating the GABAergic reversal potential, and thereby influencing neuronal excitability and spike timing. Consistent with this, there is increasing evidence implicating chloride in a range of neurological conditions. Investigations about these conditions, though, are made difficult by the limited range of tools available to manipulate chloride levels. In particular, there has been no way to actively remove chloride from neurons; we now describe an optogenetic strategy, ‘Cl-out', to do exactly this. Cl-out achieves its effect by the cooperative action of two different component opsins: the proton pump, Archaerhodopsin and a chloride channel opsin. The removal of chloride happens when both are activated together, using Archaerhodopsin as an optical voltage clamp to provide the driving force for chloride removal through the concurrently opened, chloride channels. We further show that this novel optogenetic strategy can reverse an in vitro epileptogenic phenotype.
Sinha, N, Dauwels J, Kaiser M, Cash SS, Westover MB, Wang Y, Taylor PN, 2017. Predicting neurosurgical outcomes in focal epilepsy patients using computational modelling. Brain. 140(2): 319-332
Abstract: Surgery can be a last resort for patients with intractable, medically refractory epilepsy. For many of these patients, however, there is substantial risk that the surgery will be ineffective. The prediction of who is likely to benefit from a surgical approach is crucial for being able to inform patients better, conduct principled prospective clinical trials, and ultimately tailor therapeutic approaches to these patients more effectively. Dynamical computational models, informed with patient data, can be used to make predictions and give mechanistic insight. In this study, we develop patient-specific dynamical network models of epileptogenic cortex. We infer the network connectivity matrix from non-seizure electrographic recordings of patients and use these connectivity matrices as the network structure in our model. The model simulates the dynamics of a bi-stable switch at every node in this network, meaning that every node starts in a background state, but has the ability to transit to a co-existing seizure state. Whether a transition happens in a node is partly determined by the stochastic nature of the input to the node, but also by the input the node receives from other connected nodes in the network. By conducting simulations with such a model, we can detect the average transition time for nodes in a given network, and therefore define nodes with a short transition time as highly epileptogenic. In a retrospective study, we found that in some patients the regions with high epileptogenicity in the model overlap with those identified clinically as the seizure onset zone. Moreover, it was found that the resection of these regions in the model reduces the overall likelihood of a seizure. Following removal of these regions in the model, we predicted surgical outcomes and compared these to actual patient outcomes. Our predictions were found to be 81.3% accurate on a dataset of 16 patients with intractable epilepsy. Intriguingly, in patients with unsuccessful outcomes, the proposed computational approach is able to suggest alternative resection sites. The model presented here gives mechanistic insight as to why surgery may be unsuccessful in some patients. This may aid clinicians in presurgical evaluation by providing a tool to explore various surgical options, offering complementary information to existing clinical techniques.
Wang, Y, Hutchings, F, Kaiser, M, 2015. Chapter 9 – Computation modelling of neurostimulation in brain diseases. Progress in Brain Research. Volume 222, Pages 191-228.
Abstract: Neurostimulation as a therapeutic tool has been developed and used for a range of different diseases such as Parkinson's disease, epilepsy, and migraine. However, it is not known why the efficacy of the stimulation varies dramatically across patients or why some patients suffer from severe side effects. This is largely due to the lack of mechanistic understanding of neurostimulation. Hence, theoretical computational approaches to address this issue are in demand.
This chapter provides a review of mechanistic computational modeling of brain stimulation. In particular, we will focus on brain diseases, where mechanistic models (e.g., neural population models or detailed neuronal models) have been used to bridge the gap between cellular-level processes of affected neural circuits and the symptomatic expression of disease dynamics. We show how such models have been, and can be, used to investigate the effects of neurostimulation in the diseased brain. We argue that these models are crucial for the mechanistic understanding of the effect of stimulation, allowing for a rational design of stimulation protocols. Based on mechanistic models, we argue that the development of closed-loop stimulation is essential in order to avoid inference with healthy ongoing brain activity. Furthermore, patient-specific data, such as neuroanatomic information and connectivity profiles obtainable from neuroimaging, can be readily incorporated to address the clinical issue of variability in efficacy between subjects.
We conclude that mechanistic computational models can and should play a key role in the rational design of effective, fully integrated, patient-specific therapeutic brain stimulation.
Wang Y, Trevelyan AJ, Valentin A, Alarcon G, Taylor PN, Kaiser M (2017) Mechanisms underlying different onset patterns of focal seizures. PLoS Comput Biol 13(5): e1005475
Focal seizures are episodes of pathological brain activity that appear to arise from a localised area of the brain. The onset patterns of focal seizure activity have been studied intensively, and they have largely been distinguished into two types—low amplitude fast oscillations (LAF), or high amplitude spikes (HAS). Here we explore whether these two patterns arise from fundamentally different mechanisms. Here, we use a previously established computational model of neocortical tissue, and validate it as an adequate model using clinical recordings of focal seizures. We then reproduce the two onset patterns in their most defining properties and investigate the possible mechanisms underlying the different focal seizure onset patterns in the model. We show that the two patterns are associated with different mechanisms at the spatial scale of a single ECoG electrode. The LAF onset is initiated by independent patches of localised activity, which slowly invade the surrounding tissue and coalesce over time. In contrast, the HAS onset is a global, systemic transition to a coexisting seizure state triggered by a local event. We find that such a global transition is enabled by an increase in the excitability of the “healthy” surrounding tissue, which by itself does not generate seizures, but can support seizure activity when incited. In our simulations, the difference in surrounding tissue excitability also offers a simple explanation of the clinically reported difference in surgical outcomes. Finally, we demonstrate in the model how changes in tissue excitability could be elucidated, in principle, using active stimulation. Taken together, our modelling results suggest that the excitability of the tissue surrounding the seizure core may play a determining role in the seizure onset pattern, as well as in the surgical outcome.
Mackay, M, Mahlaba, H, Gavillet, E, Whittaker, RG. Seizure. Volume 51, October 2017, Pages 180-185.
Purpose: Many patients report being able to predict their own seizures, and yet most seizures appear to strike out of the blue. This inherent contradiction makes the topic of seizure self-prediction controversial as well as difficult to study. Here we review the evidence for whether this ability exists, how many patients are capable of self-prediction and the nature of this capability, and whether this could provide a target for intervention.
Methods: Systematic searches of bibliographic databases including MEDLINE, EMBASE and PsycINFO through OVID were performed to identify relevant papers which were then screened by the study authors for inclusion in the study. 18 papers were selected for inclusion as the focus of this review.
Results: On the basis of two studies, between 17% and 41% of patients demonstrate a significantly greater than chance ability to predict an upcoming seizure in the following 12-h time window. This risk is correlated with self-reported anxiety, stress, sleep deprivation, mood and certain prodromal symptoms. However, there is no evidence for any subjective experience which directly heralds an imminent seizure. Thus, while patients may be aware of seizure risk, and have some ability to predict seizure occurrence over a wide time window, they are unable to subjectively recognise seizure onset in advance.
Conclusion: Utilising subjectively acquired knowledge of seizure risk may provide a widely implementable tool for targeted intervention. The risk fluctuates over a time course appropriate for pharmacotherapy which may improve seizure control and the side-effect profile of anti-epileptic medication.
Gándara C, Afflect V and Stoll EA, 2018. Human Gene Therapy Methods. 29(1): 1-5.
Lentiviral vectors are increasingly the gene transfer tool of choice for gene or cell therapies, with multiple clinical investigations showing promise for this viral vector in terms of both safety and efficacy. The third-generation vector system is well characterized, effectively delivers genetic material and maintains long-term stable expression in target cells, delivers larger amounts of genetic material than other methods, is nonpathogenic, and does not cause an inflammatory response in the recipient. This report aims to help academic scientists and regulatory managers negotiate the governance framework to achieve successful translation of a lentiviral vector-based gene therapy. The focus is on European regulations and how they are administered in the United Kingdom, although many of the principles will be similar for other regions, including the United States. The report justifies the rationale for using third-generation lentiviral vectors to achieve gene delivery for in vivo and ex vivo applications; briefly summarizes the extant regulatory guidance for gene therapies, categorized as advanced therapeutic medicinal products (ATMPs); provides guidance on specific regulatory issues regarding gene therapies; presents an overview of the key stakeholders to be approached when pursuing clinical trials authorization for an ATMP; and includes a brief catalogue of the documentation required to submit an application for regulatory approval of a new gene therapy.