μECoG Arrays Research Articles

Recording of single-unit activities with flexible micro-electrocorticographic array in rats for decoding of whole-body navigation

Objective.Micro-electrocorticographic (μECoG) arrays are able to record neural activities from the cortical surface, without the need to penetrate the brain parenchyma. Owing in part to small electrode sizes, previous studies have demonstrated that single-unit spikes could be detected from the cortical surface, and likely from Layer I neurons of the neocortex. Here we tested the ability to useμECoG arrays to decode, in rats, body position during open field navigation, through isolated single-unit activities.Approach. μECoG arrays were chronically implanted onto primary motor cortex (M1) of Wistar rats, and neural recording was performed in awake, behaving rats in an open-field enclosure. The signals were band-pass filtered between 300-3000 Hz. Threshold-crossing spikes were identified and sorted into distinct units based on defined criteria including waveform morphology and refractory period. Body positions were derived from video recordings. We used gradient-boosting machine to predict body position based on previous 100 ms of spike data, and correlation analyses to elucidate the relationship between position and spike patterns.Main results.Single-unit spikes could be extracted during chronic recording fromμECoG, and spatial position could be decoded from these spikes with a mean absolute error of prediction of 0.135 and 0.090 in the x- and y- dimensions (of a normalized range from 0 to 1), and Pearson's r of 0.607 and 0.571, respectively.Significance. μECoG can detect single-unit activities that likely arise from superficial neurons in the cortex and is a promising alternative to intracortical arrays, with the added benefit of scalability to cover large cortical surface with minimal incremental risks. More studies should be performed in human related to its use as brain-machine interface.

Predicting lever press in a vibrotactile yes/no detection task from S1 cortex of freely behaving rats by µECoG arrays

Aim of the Study Brain-computer interfaces (BCIs) may help patients with severe neurological deficits communicate with the external world. Based on microelectrocorticography (µECoG) data recorded from the primary somatosensory cortex (S1) of unrestrained behaving rats, this study attempts to decode lever presses in a psychophysical detection task by using machine learning algorithms. Materials and Methods 16-channel Pt-Ir microelectrode arrays were implanted on the S1 of two rats, and µECoG was recorded during a vibrotactile yes/no detection task. For this task, the rats were trained to press the right lever when they detected the vibrotactile stimulus and the left lever when they did not. The multichannel µECoG data was analysed offline by time-frequency methods and its features were used for binary classification of the lever press at each trial. Several machine learning algorithms were tested as such. Results The psychophysical sensitivities (A') were similar and low for both rats (0.58). Rat 2 (B'': −0.11) had higher bias for the right lever than Rat 1 (B'': − 0.01). The lever presses could be predicted with accuracies over 66% with all the tested algorithms, and the highest average accuracy (78%) was with the support vector machine. Conclusion According to the recent studies, sensory feedback increases the benefit of the BCIs. The current proof-of-concept study shows that lever presses can be decoded from the S1; therefore, this area may be utilised for a bidirectional BCI in the future.

High-density cortical µECoG arrays concurrently track spreading depolarizations and long-term evolution of stroke in awake rats

Spreading depolarizations (SDs) are widely recognized as a major contributor to the progression of tissue damage from ischemic stroke even if blood flow can be restored. They are characterized by negative intracortical waveforms of up to -20 mV, propagation velocities of 3 - 6 mm/min, and massive disturbance of membrane ion homeostasis. High-density, micro-electrocorticographic (μECoG) epidural electrodes and custom, DC-coupled, multiplexed amplifiers, were used to continuously characterize and monitor SD and µECoG cortical signal evolution in awake, moving rats over days. This highly innovative approach can define these events over a large brain surface area (~ 3.4 × 3.4 mm), extending across the boundaries of the stroke, and offers sufficient electrode density (60 contacts total per array for a density of 5.7 electrodes / mm2) to measure and determine the origin of SDs in relation to the infarct boundaries. In addition, spontaneous ECoG activity can simultaneously be detected to further define cortical infarct regions. This technology allows us to understand dynamic stroke evolution and provides immediate cortical functional activity over days. Further translational development of this approach may facilitate improved treatment options for acute stroke patients.

Functional hierarchies in brain dynamics characterized by signal reversibility in ferret cortex.

Brain signal irreversibility has been shown to be a promising approach to study neural dynamics. Nevertheless, the relation with cortical hierarchy and the influence of different electrophysiological features is not completely understood. In this study, we recorded local field potentials (LFPs) during spontaneous behavior, including awake and sleep periods, using custom micro-electrocorticographic (μECoG) arrays implanted in ferrets. In contrast to humans, ferrets remain less time in each state across the sleep-wake cycle. We deployed a diverse set of metrics in order to measure the levels of complexity of the different behavioral states. In particular, brain irreversibility, which is a signature of non-equilibrium dynamics, captured by the arrow of time of the signal, revealed the hierarchical organization of the ferret's cortex. We found different signatures of irreversibility and functional hierarchy of large-scale dynamics in three different brain states (active awake, quiet awake, and deep sleep), showing a lower level of irreversibility in the deep sleep stage, compared to the other. Irreversibility also allowed us to disentangle the influence of different cortical areas and frequency bands in this process, showing a predominance of the parietal cortex and the theta band. Furthermore, when inspecting the embedded dynamic through a Hidden Markov Model, the deep sleep stage was revealed to have a lower switching rate and lower entropy production. These results suggest functional hierarchies in organization that can be revealed through thermodynamic features and information theory metrics.

Multiplexed Surface Electrode Arrays Based on Metal Oxide Thin-Film Electronics for High-Resolution Cortical Mapping.

Electrode grids are used in neuroscience research and clinical practice to record electrical activity from the surface of the brain. However, existing passive electrocorticography (ECoG) technologies are unable to offer both high spatial resolution and wide cortical coverage, while ensuring a compact acquisition system. The electrode count and density are restricted by the fact that each electrode must be individually wired. This work presents an active micro-electrocorticography (µECoG) implant that tackles this limitation by incorporating metal oxide thin-film transistors (TFTs) into a flexible electrode array, allowing to address multiple electrodes through a single shared readout line. By combining the array with an incremental-ΔΣ readout integrated circuit (ROIC), the system is capable of recording from up to 256 electrodes virtually simultaneously, thanks to the implemented 16:1 time-division multiplexing scheme, offering lower noise levels than existing active µECoG arrays. Invivo validation is demonstrated acutely in mice by recording spontaneous activity and somatosensory evoked potentials over a cortical surface of ≈8×8mm2 . The proposed neural interface overcomes the wiring bottleneck limiting ECoG arrays, holding promise as a powerful tool for improved mapping of the cerebral cortex and as an enabling technology for future brain-machine interfaces.

Flexible, high-resolution cortical arrays with large coverage capture microscale high-frequency oscillations in patients with epilepsy.

Effective surgical treatment of drug-resistant epilepsy depends on accurate localization of the epileptogenic zone (EZ). High-frequency oscillations (HFOs) are potential biomarkers of the EZ. Previous research has shown that HFOs often occur within submillimeter areas of brain tissue and that the coarse spatial sampling of clinical intracranial electrode arrays may limit the accurate capture of HFO activity. In this study, we sought to characterize microscale HFO activity captured on thin, flexible microelectrocorticographic (μECoG) arrays, which provide high spatial resolution over large cortical surface areas. We used novel liquid crystal polymer thin-film μECoG arrays (.76-1.72-mm intercontact spacing) to capture HFOs in eight intraoperative recordings from seven patients with epilepsy. We identified ripple (80-250 Hz) and fast ripple (250-600 Hz) HFOs using a common energy thresholding detection algorithm along with two stages of artifact rejection. We visualized microscale subregions of HFO activity using spatial maps of HFO rate, signal-to-noise ratio, and mean peak frequency. We quantified the spatial extent of HFO events by measuring covariance between detected HFOs and surrounding activity. We also compared HFO detection rates on microcontacts to simulated macrocontacts by spatially averaging data. We found visually delineable subregions of elevated HFO activity within each μECoG recording. Forty-seven percent of HFOs occurred on single 200-μm-diameter recording contacts, with minimal high-frequency activity on surrounding contacts. Other HFO events occurred across multiple contacts simultaneously, with covarying activity most often limited to a .95-mm radius. Through spatial averaging, we estimated that macrocontacts with 2-3-mm diameter would only capture 44% of the HFOs detected in our μECoG recordings. These results demonstrate that thin-film microcontact surface arrays with both highresolution and large coverage accurately capture microscale HFO activity and may improve the utility of HFOs to localize the EZ for treatment of drug-resistant epilepsy.

NeuralTree: A 256-Channel 0.227-μJ/Class Versatile Neural Activity Classification and Closed-Loop Neuromodulation SoC.

Closed-loop neural interfaces with on-chip machine learning can detect and suppress disease symptoms in neurological disorders or restore lost functions in paralyzed patients. While high-density neural recording can provide rich neural activity information for accurate disease-state detection, existing systems have low channel counts and poor scalability, which could limit their therapeutic efficacy. This work presents a highly scalable and versatile closed-loop neural interface SoC that can overcome these limitations. A 256-channel time-division multiplexed (TDM) front-end with a two-step fast-settling mixed-signal DC servo loop (DSL) is proposed to record high-spatial-resolution neural activity and perform channel-selective brain-state inference. A tree-structured neural network (NeuralTree) classification processor extracts a rich set of neural biomarkers in a patient- and disease-specific manner. Trained with an energy-aware learning algorithm, the NeuralTree classifier detects the symptoms of underlying disorders (e.g., epilepsy and movement disorders) at an optimal energy-accuracy trade-off. A 16-channel high-voltage (HV) compliant neurostimulator closes the therapeutic loop by delivering charge-balanced biphasic current pulses to the brain. The proposed SoC was fabricated in 65nm CMOS and achieved a 0.227μJ/class energy efficiency in a compact area of 0.014mm2/channel. The SoC was extensively verified on human electroencephalography (EEG) and intracranial EEG (iEEG) epilepsy datasets, obtaining 95.6%/94% sensitivity and 96.8%/96.9% specificity, respectively. In-vivo neural recordings using soft μECoG arrays and multi-domain biomarker extraction were further performed on a rat model of epilepsy. In addition, for the first time in literature, on-chip classification of rest-state tremor in Parkinson's disease (PD) from human local field potentials (LFPs) was demonstrated.

Thin-film microfabrication and intraoperative testing of µECoG and iEEG depth arrays for sense and stimulation

Objective. Intracranial neural recordings and electrical stimulation are tools used in an increasing range of applications, including intraoperative clinical mapping and monitoring, therapeutic neuromodulation, and brain computer interface control and feedback. However, many of these applications suffer from a lack of spatial specificity and localization, both in terms of sensed neural signal and applied stimulation. This stems from limited manufacturing processes of commercial-off-the-shelf (COTS) arrays unable to accommodate increased channel density, higher channel count, and smaller contact size. Approach. Here, we describe a manufacturing and assembly approach using thin-film microfabrication for 32-channel high density subdural micro-electrocorticography (µECoG) surface arrays (contacts 1.2 mm diameter, 2 mm pitch) and intracranial electroencephalography (iEEG) depth arrays (contacts 0.5 mm × 1.5 mm, pitch 0.8 mm × 2.5 mm). Crucially, we tackle the translational hurdle and test these arrays during intraoperative studies conducted in four humans under regulatory approval. Main results. We demonstrate that the higher-density contacts provide additional unique information across the recording span compared to the density of COTS arrays which typically have electrode pitch of 8 mm or greater; 4 mm in case of specially ordered arrays. Our intracranial stimulation study results reveal that refined spatial targeting of stimulation elicits evoked potentials with differing spatial spread. Significance. Thin-film, μECoG and iEEG depth arrays offer a promising substrate for advancing a number of clinical and research applications reliant on high-resolution neural sensing and intracranial stimulation.

Sufficient sampling for kriging prediction of cortical potential in rat, monkey, and human µECoG

Objective. Large channel count surface-based electrophysiology arrays (e.g. µECoG) are high-throughput neural interfaces with good chronic stability. Electrode spacing remains ad hoc due to redundancy and nonstationarity of field dynamics. Here, we establish a criterion for electrode spacing based on the expected accuracy of predicting unsampled field potential from sampled sites. Approach. We applied spatial covariance modeling and field prediction techniques based on geospatial kriging to quantify sufficient sampling for thousands of 500 ms µECoG snapshots in human, monkey, and rat. We calculated a probably approximately correct (PAC) spacing based on kriging that would be required to predict µECoG fields at 10% error for most cases (95% of observations). Main results. Kriging theory accurately explained the competing effects of electrode density and noise on predicting field potential. Across five frequency bands from 4–7 to 75–300 Hz, PAC spacing was sub-millimeter for auditory cortex in anesthetized and awake rats, and posterior superior temporal gyrus in anesthetized human. At 75–300 Hz, sub-millimeter PAC spacing was required in all species and cortical areas. Significance. PAC spacing accounted for the effect of signal-to-noise on prediction quality and was sensitive to the full distribution of non-stationary covariance states. Our results show that µECoG arrays should sample at sub-millimeter resolution for applications in diverse cortical areas and for noise resilience.

A modular high-density μECoG system on macaque vlPFC for auditory cognitive decoding

Objective. A fundamental goal of the auditory system is to parse the auditory environment into distinct perceptual representations. Auditory perception is mediated by the ventral auditory pathway, which includes the ventrolateral prefrontal cortex (vlPFC). Because large-scale recordings of auditory signals are quite rare, the spatiotemporal resolution of the neuronal code that underlies vlPFC’s contribution to auditory perception has not been fully elucidated. Therefore, we developed a modular, chronic, high-resolution, multi-electrode array system with long-term viability in order to identify the information that could be decoded from μECoG vlPFC signals. Approach. We molded three separate μECoG arrays into one and implanted this system in a non-human primate. A custom 3D-printed titanium chamber was mounted on the left hemisphere. The molded 294-contact μECoG array was implanted subdurally over the vlPFC. μECoG activity was recorded while the monkey participated in a ‘hearing-in-noise’ task in which they reported hearing a ‘target’ vocalization from a background ‘chorus’ of vocalizations. We titrated task difficulty by varying the sound level of the target vocalization, relative to the chorus (target-to-chorus ratio, TCr). Main results. We decoded the TCr and the monkey’s behavioral choices from the μECoG signal. We analyzed decoding accuracy as a function of number of electrodes, spatial resolution, and time from implantation. Over a one-year period, we found significant decoding with individual electrodes that increased significantly as we decoded simultaneously more electrodes. Further, we found that the decoding for behavioral choice was better than the decoding of TCr. Finally, because the decoding accuracy of individual electrodes varied on a day-by-day basis, electrode arrays with high channel counts ensure robust decoding in the long term. Significance. Our results demonstrate the utility of high-resolution and high-channel-count, chronic µECoG recording. We developed a surface electrode array that can be scaled to cover larger cortical areas without increasing the chamber footprint.

Multiplexed neural sensor array of graphene solution-gated field-effect transistors

Electrocorticography (ECoG) is a well-established technique to monitor electrophysiological activity from the surface of the brain and has proved crucial for the current generation of neural prostheses and brain–computer interfaces. However, existing ECoG technologies still fail to provide the resolution necessary to accurately map highly localized activity across large brain areas, due to the rapidly increasing size of connector footprint with sensor count. This work demonstrates the use of a flexible array of graphene solution-gated field-effect transistors (gSGFET), exploring the concept of multiplexed readout using an external switching matrix. This approach does not only allow for an increased sensor count, but due to the use of active sensing devices (i.e. transistors) over microelectrodes it makes additional buffer transistors redundant, which drastically eases the complexity of device fabrication on flexible substrates. The presented results pave the way for upscaling the gSGFET technology towards large-scale, high-density μECoG–arrays, eventually capable of resolving neural activity down to a single neuron level, while simultaneously mapping large brain regions.

Improved metal-graphene contacts for low-noise, high-density microtransistor arrays for neural sensing

Poor metal contact interfaces are one of the main limitations preventing unhampered access to the full potential of two-dimensional materials in electronics. Here we present graphene solution-gated field-effect-transistors (gSGFETs) with strongly improved linearity, homogeneity and sensitivity for small sensor sizes, resulting from ultraviolet ozone (UVO) contact treatment. The contribution of channel and contact region to the total device conductivity and flicker noise is explored experimentally and explained with a theoretical model. Finally, in-vitro recordings of flexible microelectrocorticography (μ-ECoG) probes were performed to validate the superior sensitivity of the UVO-treated gSGFET to brain-like activity. These results connote an important step towards the fabrication of high-density gSGFET μ-ECoG arrays with state-of-the-art sensitivity and homogeneity, thus demonstrating the potential of this technology as a versatile platform for the new generation of neural interfaces.

μECoG Recordings Through a Thinned Skull.

The studies described in this paper for the first time characterize the acute and chronic performance of optically transparent thin-film micro-electrocorticography (μECoG) grids implanted on a thinned skull as both an electrophysiological complement to existing thinned skull preparation for optical recordings/manipulations, and a less invasive alternative to epidural or subdurally placed μECoG arrays. In a longitudinal chronic study, μECoG grids placed on top of a thinned skull maintain impedances comparable to epidurally placed μECoG grids that are stable for periods of at least 1 month. Optogenetic activation of cortex is also reliably demonstrated through the optically transparent μECoG grids acutely placed on the thinned skull. Finally, spatially distinct electrophysiological recordings were evident on μECoG electrodes placed on a thinned skull separated by 500–750 μm, as assessed by stimulation evoked responses using optogenetic activation of cortex as well as invasive and epidermal stimulation of the sciatic and median nerve at chronic time points. Neural signals were collected through a thinned skull in mice and rats, demonstrating potential utility in neuroscience research applications such as in vivo imaging and optogenetics.

Progress in the Field of Micro-Electrocorticography.

Since the 1940s electrocorticography (ECoG) devices and, more recently, in the last decade, micro-electrocorticography (µECoG) cortical electrode arrays were used for a wide set of experimental and clinical applications, such as epilepsy localization and brain–computer interface (BCI) technologies. Miniaturized implantable µECoG devices have the advantage of providing greater-density neural signal acquisition and stimulation capabilities in a minimally invasive fashion. An increased spatial resolution of the µECoG array will be useful for greater specificity diagnosis and treatment of neuronal diseases and the advancement of basic neuroscience and BCI research. In this review, recent achievements of ECoG and µECoG are discussed. The electrode configurations and varying material choices used to design µECoG arrays are discussed, including advantages and disadvantages of µECoG technology compared to electroencephalography (EEG), ECoG, and intracortical electrode arrays. Electrode materials that are the primary focus include platinum, iridium oxide, poly(3,4-ethylenedioxythiophene) (PEDOT), indium tin oxide (ITO), and graphene. We discuss the biological immune response to µECoG devices compared to other electrode array types, the role of µECoG in clinical pathology, and brain–computer interface technology. The information presented in this review will be helpful to understand the current status, organize available knowledge, and guide future clinical and research applications of µECoG technologies.

Long-term recording reliability of liquid crystal polymer µECoG arrays

Objective. The clinical use of microsignals recorded over broad cortical regions is largely limited by the chronic reliability of the implanted interfaces. Approach. We evaluated the chronic reliability of novel 61-channel micro-electrocorticographic (µECoG) arrays in rats chronically implanted for over one year and using accelerated aging. Devices were encapsulated with polyimide (PI) or liquid crystal polymer (LCP), and fabricated using commercial manufacturing processes. In vitro failure modes and predicted lifetimes were determined from accelerated soak testing. Successful designs were implanted epidurally over the rodent auditory cortex. Trends in baseline signal level, evoked responses and decoding performance were reported for over one year of implantation. Main results. Devices fabricated with LCP consistently had longer in vitro lifetimes than PI encapsulation. Our accelerated aging results predicted device integrity beyond 3.4 years. Five implanted arrays showed stable performance over the entire implantation period (247–435 d). Our regression analysis showed that impedance predicted signal quality and information content only in the first 31 d of recordings and had little predictive value in the chronic phase (>31 d). In the chronic phase, site impedances slightly decreased yet decoding performance became statistically uncorrelated with impedance. We also employed an improved statistical model of spatial variation to measure sensitivity to locally varying fields, which is typically concealed in standard signal power calculations. Significance. These findings show that µECoG arrays can reliably perform in chronic applications in vivo for over one year, which facilitates the development of a high-density, clinically viable interface.

In vitro and in vivo stability of black-platinum coatings on flexible, polymer microECoG arrays

Objective. Intracranial EEG (iEEG) or micro-electrocorticography (µECoG) microelectrodes offer high spatial resolution in recordings of neuronal activity from the exposed brain surface. Reliability of dielectric substrates and conductive materials of these devices are under intensive research in terms of functional stability in biological environments. Approach. The aim of our study is to investigate the stability of electroplated platinum recording sites on 16-channel, 8 micron thick, polyimide based, flexible µECoG arrays implanted underneath the skull of rats. Scanning electron microscopy and electrochemical impedance spectroscopy was used to reveal changes in either surface morphology or interfacial characteristics. The effect of improved surface area (roughness factor = 23 ± 0.12) on in vivo recording capability was characterized in both acute and chronic experiments. Main results. Besides the expected reduction in thermal noise and enhancement in signal-to-noise ratio (up to 39.8), a slight increase in the electrical impedance of individual sites was observed, as a result of changes in the measured interfacial capacitance. In this paper, we also present technology processes and protocols in detail to use such implants without crack formation of the porous platinum surfaces. Significance. Our findings imply that black-platinum coating deposited on the recording sites of flexible microelectrodes (20 microns in diameter) provides a stable interface between tissue and device.

Quantitative simulation of extracellular single unit recording from the surface of cortex

Objective. Neural recording is important for a wide variety of clinical applications. Until recently, recording from the surface of the brain, even when using micro-electrocorticography (μECoG) arrays, was not thought to enable recording from individual neurons. Recent results suggest that when the surface electrode contact size is sufficiently small, it may be possible to record single neurons from the brain’s surface. In this study, we use computational techniques to investigate the ability of surface electrodes to record the activity of single neurons. Approach. The computational model included the rat head, μECoG electrode, two existing multi-compartmental neuron models, and a novel multi-compartmental neuron model derived from patch clamp experiments in layer 1 of the cortex. Main results. Using these models, we reproduced single neuron recordings from μECoG arrays, and elucidated their possible source. The model resembles the experimental data when spikes originate from layer 1 neurons that are less than 60 μm from the cortical surface. We further used the model to explore the design space for surface electrodes. Although this model does not include biological or thermal noise, the results indicate the electrode contact area should be 100 μm2 or smaller to maintain a detectable waveform amplitude. Furthermore, the model shows the width of lateral insulation could be reduced, which may reduce scar formation, while retaining 95% of signal amplitude. Significance. Overall, the model suggests single-unit surface recording is limited to neurons in layer 1 and further improvement in electrode design is needed.

Achieving Ultra-Conformability With Polyimide-Based ECoG Arrays.

Micro-electrode arrays for electrocorticography (ECoG) represent the best compromise between invasiveness and signal quality, as they are surface devices that still allow high sensitivity recordings. In this work, an assessment of different technical aspects determining the ultimate performance of ultra-conformable polyimide-based μECoG arrays is conducted via a finite element model, impedance spectroscopy measurements and recordings of sensorimotor evoked potentials (SEPs) in rats. The finite element model proves that conformability of thin-film arrays can be achieved with polyimide, a non-stretchable material, by adjusting its thickness according to the curvature of the targeted anatomical area. From the electrochemical characterization of the devices, intrinsic thermal noise of platinum and gold electrodes is estimated to be 3-5 μV. Results show that electrode size and in vitro impedance do not influence the amplitude of the recorded SEPs. However, the use of a reference on-skull (a metal screw), as compared to reference on-array (a metal electrode surrounding the recording area), provides higher-amplitude SEPs. Additionally, the incorporation of a grounded metal shield in the thin-film devices limits crosstalk between tracks and does not compromise the recording capabilities of the arrays.

Glassy carbon MEMS for novel origami-styled 3D integrated intracortical and epicortical neural probes

We report on a novel technology for microfabricating 3D origami-styled micro electro-mechanical systems (MEMS) structures with glassy carbon (GC) features and a supporting polymer substrate. GC MEMS devices that open to form 3D microstructures are microfabricated from GC patterns that are made through pyrolysis of polymer precursors on high-temperature resisting substrates like silicon or quartz and then transferring the patterned devices to a flexible substrate like polyimide followed by deposition of an insulation layer. The devices on flexible substrate are then folded into 3D form in an origami-fashion.These 3D MEMS devices have tunable mechanical properties that are achieved by selectively varying the thickness of the polymeric substrate and insulation layers at any desired location. This technology opens new possibilities by enabling microfabrication of a variety of 3D GC MEMS structures suited to applications ranging from biochemical sensing to implantable microelectrode arrays. As a demonstration of the technology, a neural signal recording microelectrode array platform that integrates both surface (cortical) and depth (intracortical) GC microelectrodes onto a single flexible thin-film device is introduced.When the device is unfurled, a pre-shaped shank of polyimide automatically comes off the substrate and forms the penetrating part of the device in a 3D fashion. With the advantage of being highly reproducible and batch-fabricated, the device introduced here allows for simultaneous recording of electrophysiological signals from both the brain surface (electrocorticography—ECoG) and depth (single neuron). Our device, therefore, has the potential to elucidate the roles of underlying neurons on the different components of µECoG signals. For in vivo validation of the design capabilities, the recording sites are coated with a poly(3,4-ethylenedioxythiophene)—polystyrene sulfonate—carbon nanotube composite, to improve the electrical conductivity of the electrodes and consequently the quality of the recorded signals. Results show that both µECoG and intracortical arrays were able to acquire neural signals with high-sensitivity that increased with depth, thereby verifying the device functionality.

Estimating cortical column sensory networks in rodents from micro-electrocorticograph (μECoG) recordings

Micro-electrocorticograph (μECoG) arrays offer the flexibility to record local field potentials (LFPs) from the surface of the cortex, using high density electrodes that are sub-mm in diameter. Research to date has not provided conclusive evidence for the underlying signal generation of μECoG recorded LFPs, or if μECoG arrays can capture network activity from the cortex. We studied the pervading view of the LFP signal by exploring the spatial scale at which the LFP can be considered elemental. We investigated the underlying signal generation and ability to capture functional networks by implanting, μECoG arrays to record sensory-evoked potentials in four rats. The organization of the sensory cortex was studied by analyzing the sensory-evoked potentials with two distinct modeling techniques: (1) The volume conduction model, that models the electrode LFPs with an electrostatic representation, generated by a single cortical generator, and (2) the dynamic causal model (DCM), that models the electrode LFPs with a network model, whose activity is generated by multiple interacting cortical sources. The volume conduction approach modeled activity from electrodes separated < 1000 μm, with reasonable accuracy but a network model like DCM was required to accurately capture activity > 1500 μm. The extrinsic network component in DCM was determined to be essential for accurate modeling of observed potentials. These results all point to the presence of a sensory network, and that μECoG arrays are able to capture network activity in the neocortex. The estimated DCM network models the functional organization of the cortex, as signal generators for the μECoG recorded LFPs, and provides hypothesis-testing tools to explore the brain.