Utah Array Research Articles

Enhancing Brain-Computer Interfaces through Kriging-Based Fusion of Sparse Regression Partial Differential Equations to Counter Injection Molding View of Node Displacement Effects.

Injection molding is an efficient and precise manufacturing technology that is widely used in the production of plastic products. In recent years, injection molding technology has made significant progress, especially with the combination of in-mold electronics (IME) technology, which makes it possible to embed electronic components directly into the surface of a product. IME technology improves the integration and performance of a product by embedding conductive materials and functional components in the mold. Brain-computer interfaces (BCIs) are a rapidly growing field of research that aims to capture, analyze, and feedback brain signals by directly connecting the brain to external devices. The Utah array, a high-density microelectrode array, has been widely used for the recording and transmission of brain signals. However, the traditional fabrication method of the Utah array suffers from high cost and low integration, which limits its promotion in practical applications. The lines that receive EEG signals are one of the key parts of a brain-computer interface system. The optimization of injection molding parameters is particularly important in order to effectively embed these lines into thin films and to ensure the precise displacement of the line nodes and the stability of signal transmission during the injection molding process. In this study, a method based on the Kriging prediction model and sparse regression partial differential equations (PDEs) is proposed to optimize the key parameters in the injection molding process. This method can effectively predict and control the displacement of nodes in the film, ensure the stability and reliability of the line during the injection process, and improve the accuracy of EEG signal transmission and system performance. The optimal injection parameters were finally obtained: a holding pressure of 525 MPa, a holding time of 50 s, and a melting temperature of 285 °C. Under this condition, the average node displacement of UA was reduced from the initial 0.19 mm to 0.89 µm, with an optimization rate of 95.32%.

Implantation of Flexible Electrodes for Simultaneous in-vivo Extracellular Recording and Two-Photon Imaging

Introduction: Rigid silicon electrodes like Utah array grids and Neuropixel probes have been used in human and animal brain models to understand the dynamics of neural computation, treat neurodegenerative disorders, and act as brain-machine-interfaces. However, when implanted chronically, glial proliferation can rapidly disrupt the interaction between neurons and electrodes, drastically reducing recording fidelity. The development of flexible electrodes has the potential to minimize tissue damage and inflammation, which allows for long-term recordings over several months. In line with this objective, the Nano-neurotechnology Lab at Purdue University has developed a 6-µm thick, flexible, and biocompatible Parylene probe to facilitate chronic recordings in awake mice. However, flexible electrodes present a unique engineering challenge as the force required to insert into the brain causes the probe to buckle and fail during insertion. 
 Methods and Results: Here, I designed a micropipette shuttle using a glass micropipette and custom insertion system which provided reproducible probe implantation into the cortex. The implantation device was designed in CAD software and 3D-printed for rapid prototyping. The procedure was developed on brain phantoms made of 0.6% agarose with a comparable Young’s modulus to mouse brain tissue. Utilizing 3D-printed pieces and the surface tension of diluted poly-vinyl-acrylate adhesive to align the probe to a micropipette, insertion of the electrode and retraction of the shuttle was accomplished in awake mice. 
 Conclusion: The implications of flexible recording electrodes are extensive. Long-term implantation opens the door for understanding behavioral and learning dynamics over time. Moreover, the flexibility of these probes allows for the combination of 2-photon optical microscopy, thus enabling multi-modal investigation of neuronal physiology. A low-cost, consistent procedure is the first step in the implementation of these flexible probes for further advancements in fundamental neuroscience research and its potential applications in human and animal studies.

Uncovering the organization of neural circuits with Generalized Phase Locking Analysis.

Despite the considerable progress of in vivo neural recording techniques, inferring the biophysical mechanisms underlying large scale coordination of brain activity from neural data remains challenging. One obstacle is the difficulty to link high dimensional functional connectivity measures to mechanistic models of network activity. We address this issue by investigating spike-field coupling (SFC) measurements, which quantify the synchronization between, on the one hand, the action potentials produced by neurons, and on the other hand mesoscopic "field" signals, reflecting subthreshold activities at possibly multiple recording sites. As the number of recording sites gets large, the amount of pairwise SFC measurements becomes overwhelmingly challenging to interpret. We develop Generalized Phase Locking Analysis (GPLA) as an interpretable dimensionality reduction of this multivariate SFC. GPLA describes the dominant coupling between field activity and neural ensembles across space and frequencies. We show that GPLA features are biophysically interpretable when used in conjunction with appropriate network models, such that we can identify the influence of underlying circuit properties on these features. We demonstrate the statistical benefits and interpretability of this approach in various computational models and Utah array recordings. The results suggest that GPLA, used jointly with biophysical modeling, can help uncover the contribution of recurrent microcircuits to the spatio-temporal dynamics observed in multi-channel experimental recordings.

Utah array characterization and histological analysis of a multi-year implant in non-human primate motor and sensory cortices

Objective. The Utah array is widely used in both clinical studies and neuroscience. It has a strong track record of safety. However, it is also known that implanted electrodes promote the formation of scar tissue in the immediate vicinity of the electrodes, which may negatively impact the ability to record neural waveforms. This scarring response has been primarily studied in rodents, which may have a very different response than primate brain. Approach. Here, we present a rare nonhuman primate histological dataset (n = 1 rhesus macaque) obtained 848 and 590 d after implantation in two brain hemispheres. For 2 of 4 arrays that remained within the cortex, NeuN was used to stain for neuron somata at three different depths along the shanks. Images were filtered and denoised, with neurons then counted in the vicinity of the arrays as well as a nearby section of control tissue. Additionally, 3 of 4 arrays were imaged with a scanning electrode microscope to evaluate any materials damage that might be present. Main results. Overall, we found a 63% percent reduction in the number of neurons surrounding the electrode shanks compared to control areas. In terms of materials, the arrays remained largely intact with metal and Parylene C present, though tip breakage and cracks were observed on many electrodes. Significance. Overall, these results suggest that the tissue response in the nonhuman primate brain shows similar neuron loss to previous studies using rodents. Electrode improvements, for example using smaller or softer probes, may therefore substantially improve the tissue response and potentially improve the neuronal recording yield in primate cortex.

Scalable Thousand Channel Penetrating Microneedle Arrays on Flex for Multimodal and Large Area Coverage BrainMachine Interfaces.

The Utah array powers cutting-edge projects for restoration of neurological function, such as BrainGate, but the underlying electrode technology has itself advanced little in the last three decades. Here, advanced dual-side lithographic microfabrication processes is exploited to demonstrate a 1024-channel penetrating silicon microneedle array (SiMNA) that is scalable in its recording capabilities and cortical coverage and is suitable for clinical translation. The SiMNA is the first penetrating microneedle array with a flexible backing that affords compliancy to brain movements. In addition, the SiMNA is optically transparent permitting simultaneous optical and electrophysiological interrogation of neuronal activity. The SiMNA is used to demonstrate reliable recordings of spontaneous and evoked field potentials and of single unit activity in chronically implanted mice for up to 196 days in response to optogenetic and to whisker air-puff stimuli. Significantly, the 1024-channel SiMNA establishes detailed spatiotemporal mapping of broadband brain activity in rats. This novel scalable and biocompatible SiMNA with its multimodal capability and sensitivity to broadband brain activity will accelerate the progress in fundamental neurophysiological investigations and establishes a new milestone for penetrating and large area coverage microelectrode arrays for brain-machine interfaces.

Longevity and reliability of chronic unit recordings using the Utah, intracortical multi-electrode arrays

Objective. Microelectrode arrays are standard tools for conducting chronic electrophysiological experiments, allowing researchers to simultaneously record from large numbers of neurons. Specifically, Utah electrode arrays (UEAs) have been utilized by scientists in many species, including rodents, rhesus macaques, marmosets, and human participants. The field of clinical human brain-computer interfaces currently relies on the UEA as a number of research groups have clearance from the United States Federal Drug Administration (FDA) for this device through the investigational device exemption pathway. Despite its widespread usage in systems neuroscience, few studies have comprehensively evaluated the reliability and signal quality of the Utah array over long periods of time in a large dataset. Approach. We collected and analyzed over 6000 recorded datasets from various cortical areas spanning almost nine years of experiments, totaling 17 rhesus macaques (Macaca mulatta) and 2 human subjects, and 55 separate microelectrode Utah arrays. The scale of this dataset allowed us to evaluate the average life of these arrays, based primarily on the signal-to-noise ratio of each electrode over time. Main results. Using implants in primary motor, premotor, prefrontal, and somatosensory cortices, we found that the average lifespan of available recordings from UEAs was 622 days, although we provide several examples of these UEAs lasting over 1000 days and one up to 9 years; human implants were also shown to last longer than non-human primate implants. We also found that electrode length did not affect longevity and quality, but iridium oxide metallization on the electrode tip exhibited superior yield as compared to platinum metallization. Significance. Understanding longevity and reliability of microelectrode array recordings allows researchers to set expectations and plan experiments accordingly and maximize the amount of high-quality data gathered. Our results suggest that one can expect chronic unit recordings to last at least two years, with the possibility for arrays to last the better part of a decade.

The significance of neural inter-frequency power correlations

It is of great interest in neuroscience to determine what frequency bands in the brain have covarying power. This would help us robustly identify the frequency signatures of neural processes. However to date, to the best of the author’s knowledge, a comprehensive statistical approach to this question that accounts for intra-frequency autocorrelation, frequency-domain oversampling, and multiple testing under dependency has not been undertaken. As such, this work presents a novel statistical significance test for correlated power across frequency bands for a broad class of non-stationary time series. It is validated on synthetic data. It is then used to test all of the inter-frequency power correlations between 0.2 and 8500 Hz in continuous intracortical extracellular neural recordings in Macaque M1, using a very large, publicly available dataset. The recordings were Current Source Density referenced and were recorded with a Utah array. The results support previous results in the literature that show that neural processes in M1 have power signatures across a very broad range of frequency bands. In particular, the power in LFP frequency bands as low as 20 Hz was found to almost always be statistically significantly correlated to the power in kHz frequency ranges. It is proposed that this test can also be used to discover the superimposed frequency domain signatures of all the neural processes in a neural signal, allowing us to identify every interesting neural frequency band.

Long-term intracortical microelectrode array performance in a human: a 5 year retrospective analysis

Objective. Brain-computer interfaces (BCIs) that record neural activity using intracortical microelectrode arrays (MEAs) have shown promise for mitigating disability associated with neurological injuries and disorders. While the chronic performance and failure modes of MEAs have been well studied and systematically described in non-human primates, there is far less reported about long-term MEA performance in humans. Our group has collected one of the largest neural recording datasets from a Utah MEA in a human subject, spanning over 5 years (2014–2019). Here we present both long-term signal quality and BCI performance as well as highlight several acute signal disruption events observed during the clinical study. Approach. Long-term Utah array performance was evaluated by analyzing neural signal metric trends and decoding accuracy for tasks regularly performed across 448 clinical recording sessions. For acute signal disruptions, we identify or hypothesize the root cause of the disruption, show how the disruption manifests in the collected data, and discuss potential identification and mitigation strategies for the disruption. Main results. Neural signal quality metrics deteriorated rapidly within the first year, followed by a slower decline through the remainder of the study. Nevertheless, BCI performance remained high 5 years after implantation, which is encouraging for the translational potential of this technology as an assistive device. We also present examples of unanticipated signal disruptions during chronic MEA use, which are critical to detect as BCI technology progresses toward home usage. Significance. Our work fills a gap in knowledge around long-term MEA performance in humans, providing longevity and efficacy data points to help characterize the performance of implantable neural sensors in a human population. The trial was registered on ClinicalTrials.gov (Identifier NCT01997125) and conformed to institutional requirements for the conduct of human subjects research.

Microscale Physiological Events on the Human Cortical Surface.

Despite ongoing advances in our understanding of local single-cellular and network-level activity of neuronal populations in the human brain, extraordinarily little is known about their "intermediate" microscale local circuit dynamics. Here, we utilized ultra-high-density microelectrode arrays and a rare opportunity to perform intracranial recordings across multiple cortical areas in human participants to discover three distinct classes of cortical activity that are not locked to ongoing natural brain rhythmic activity. The first included fast waveforms similar to extracellular single-unit activity. The other two types were discrete events with slower waveform dynamics and were found preferentially in upper cortical layers. These second and third types were also observed in rodents, nonhuman primates, and semi-chronic recordings from humans via laminar and Utah array microelectrodes. The rates of all three events were selectively modulated by auditory and electrical stimuli, pharmacological manipulation, and cold saline application and had small causal co-occurrences. These results suggest that the proper combination of high-resolution microelectrodes and analytic techniques can capture neuronal dynamics that lay between somatic action potentials and aggregate population activity. Understanding intermediate microscale dynamics in relation to single-cell and network dynamics may reveal important details about activity in the full cortical circuit.

SpikeDeep-classifier: a deep-learning based fully automatic offline spike sorting algorithm

Objective. Advancements in electrode design have resulted in micro-electrode arrays with hundreds of channels for single cell recordings. In the resulting electrophysiological recordings, each implanted electrode can record spike activity (SA) of one or more neurons along with background activity (BA). The aim of this study is to isolate SA of each neural source. This process is called spike sorting or spike classification. Advanced spike sorting algorithms are time consuming because of the human intervention at various stages of the pipeline. Current approaches lack generalization because the values of hyperparameters are not fixed, even for multiple recording sessions of the same subject. In this study, a fully automatic spike sorting algorithm called ‘SpikeDeep-Classifier’ is proposed. The values of hyperparameters remain fixed for all the evaluation data. Approach. The proposed approach is based on our previous study (SpikeDeeptector) and a novel background activity rejector (BAR), which are both supervised learning algorithms and an unsupervised learning algorithm (K-means). SpikeDeeptector and BAR are used to extract meaningful channels and remove BA from the extracted meaningful channels, respectively. The process of clustering becomes straight-forward once the BA is completely removed from the data. Then, K-means with a predefined maximum number of clusters is applied on the remaining data originating from neural sources only. Lastly, a similarity-based criterion and a threshold are used to keep distinct clusters and merge similar looking clusters. The proposed approach is called cluster accept or merge (CAOM) and it has only two hyperparameters (maximum number of clusters and similarity threshold) which are kept fixed for all the evaluation data after tuning. Main results. We compared the results of our algorithm with ground-truth labels. The algorithm is evaluated on data of human patients and publicly available labeled non-human primates (NHPs) datasets. The average accuracy of BAR on datasets of human patients is 92.3% which is further reduced to 88.03% after (K-means + CAOM). In addition, the average accuracy of BAR on a publicly available labeled dataset of NHPs is 95.40% which reduces to 86.95% after (K-mean + CAOM). Lastly, we compared the performance of the SpikeDeep-Classifier with two human experts, where SpikeDeep-Classifier has produced comparable results. Significance. The SpikeDeep-Classifier is evaluated on the datasets of multiple recording sessions of different species, different brain areas and different electrode types without further retraining. The results demonstrate that ‘SpikeDeep-Classifier’ possesses the ability to generalize well on a versatile dataset and henceforth provides a generalized and fully automated solution to offline spike sorting. Clinical trial registration number The clinical trial registration number for patients implanted with the Utah array is NCT 01849822. For the epilepsy patients, approval from the local ethics committee at the Ruhr-University Bochum, Germany, was obtained prior to implantation. The Clinical trial registration number for the epilepsy patients implanted with microwires is 16–5670.

Therapeutic hypothermia reduces cortical inflammation associated with utah array implants

Objective. Neuroprosthetics hold tremendous promise to restore function through brain-computer interfaced devices. However, clinical applications of implantable microelectrodes remain limited given the challenges of maintaining neuronal signals for extended periods of time and with multiple biological mechanisms negatively affecting electrode performance. Acute and chronic inflammation, oxidative stress, and blood brain barrier disruption contribute to inconsistent electrode performance. We hypothesized that therapeutic hypothermia (TH) applied at the microelectrode insertion site will positively modulate both inflammatory and apoptotic pathways, promoting neuroprotection and improved performance in the long-term. Approach. A custom device and thermoelectric system were designed to deliver controlled TH locally to the cortical implant site at the time of microelectrode array insertion and immediately following surgery. The TH paradigm was derived from in vivo cortical temperature measurements and finite element modeling of temperature distribution profiles in the cortex. Male Sprague-Dawley rats were implanted with non-functional Utah microelectrodes arrays (UMEA) consisting of 4 × 4 grid of 1.5 mm long parylene-coated silicon shanks. In one group, TH was applied to the implant site for two hours following the UMEA implantation, while the other group was implanted under normothermic conditions without treatment. At 48 h, 72 h, 7 d and 14 d post-implantation, mRNA expression levels for genes associated with inflammation and apoptosis were compared between normothermic and hypothermia-treated groups. Main results. The custom system delivered controlled TH to the cortical implant site and the numerical models confirmed that the temperature decrease was confined locally. Furthermore, a one-time application of TH post UMEA insertion significantly reduced the acute inflammatory response with a reduction in the expression of inflammatory regulating cytokines and chemokines. Significance. This work provides evidence that acutely applied hypothermia is effective in significantly reducing acute inflammation post intracortical electrode implantation.

SpikeDeeptector: a deep-learning based method for detection of neural spiking activity

Objective. In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordings of some channels contain neural activity, which are often contaminated with noise. Another fraction of channels does not record any neural data, but only noise. By noise, we mean physiological activities unrelated to spiking, including technical artifacts and neural activities of neurons that are too far away from the electrode to be usefully processed. For further analysis, an automatic identification and continuous tracking of channels containing neural data is of great significance for many applications, e.g. automated selection of neural channels during online and offline spike sorting. Automated spike detection and sorting is also critical for online decoding in brain–computer interface (BCI) applications, in which only simple threshold crossing events are often considered for feature extraction. To our knowledge, there is no method that can universally and automatically identify channels containing neural data. In this study, we aim to identify and track channels containing neural data from implanted electrodes, automatically and more importantly universally. By universally, we mean across different recording technologies, different subjects and different brain areas. Approach. We propose a novel algorithm based on a new way of feature vector extraction and a deep learning method, which we call SpikeDeeptector. SpikeDeeptector considers a batch of waveforms to construct a single feature vector and enables contextual learning. The feature vectors are then fed to a deep learning method, which learns contextualized, temporal and spatial patterns, and classifies them as channels containing neural spike data or only noise. Main results. We trained the model of SpikeDeeptector on data recorded from a single tetraplegic patient with two Utah arrays implanted in different areas of the brain. The trained model was then evaluated on data collected from six epileptic patients implanted with depth electrodes, unseen data from the tetraplegic patient and data from another tetraplegic patient implanted with two Utah arrays. The cumulative evaluation accuracy was 97.20% on 1.56 million hand labeled test inputs. Significance. The results demonstrate that SpikeDeeptector generalizes not only to the new data, but also to different brain areas, subjects, and electrode types not used for training. Clinical trial registration number. The clinical trial registration number for patients implanted with the Utah array is NCT 01849822. For the epilepsy patients, approval from the local ethics committee at the Ruhr-University Bochum, Germany, was obtained prior to implantation.

Comparative study of extracellular recording methods for analysis of afferent sensory information: Empirical modeling, data analysis and interpretation

BackgroundPhysiological studies of sensorial systems often require the acquisition and processing of data extracted from their multiple components to evaluate how the neural information changes in relation to the environment changes. In this work, a comparative study about methodological aspects of two electrophysiological approaches is described. New methodExtracellular recordings from deep vibrissal nerves were obtained by using a customized microelectrode Utah array during passive mechanical stimulation of rat´s whiskers. These recordings were compared with those obtained with bipolar electrodes. We also propose here a simplified empirical model of the electrophysiological activity obtained from a bundle of myelinated nerve fibers. ResultsThe peripheral activity of the vibrissal system was characterized through the temporal and spectral features obtained with both recording methods. The empirical model not only allows the correlation between anatomical structures and functional features, but also allows to predict changes in the CAPs morphology when the arrangement and the geometry of the electrodes changes. Comparison with existing method(s)This study compares two extracellular recording methods based on analysis techniques, empirical modeling and data processing of vibrissal sensory information. ConclusionsThis comparative study reveals a close relationship between the electrophysiological techniques and the processing methods necessary to extract sensory information. This relationship is the result of maximizing the extraction of information from recordings of sensory activity.

Advances in Penetrating Multichannel Microelectrodes Based on the Utah Array Platform.

The Utah electrode array (UEA) and its many derivatives have become a gold standard for high-channel count bi-directional neural interfaces, in particular in human subject applications. The chapter provides a brief overview of leading electrode concepts and the context in which the UEA has to be understood. It goes on to discuss the key advances and developments of the UEA platform in the past 15years, as well as novel wireless and system integration technologies that will merge into future generations of fully integrated devices. Aspects covered include novel device architectures that allow scaling of channel count and density of electrode contacts, material improvements to substrate, electrode contacts, and encapsulation. Further subjects are adaptations of the UEA platform to support IR and optogenetic simulation as well as an improved understanding of failure modes and methods to test and accelerate degradation in vitro such as to better predict device failure and lifetime in vivo.

Implantable Neural Probes for Brain-Machine Interfaces - Current Developments and Future Prospects.

A Brain-Machine interface (BMI) allows for direct communication between the brain and machines. Neural probes for recording neural signals are among the essential components of a BMI system. In this report, we review research regarding implantable neural probes and their applications to BMIs. We first discuss conventional neural probes such as the tetrode, Utah array, Michigan probe, and electroencephalography (ECoG), following which we cover advancements in next-generation neural probes. These next-generation probes are associated with improvements in electrical properties, mechanical durability, biocompatibility, and offer a high degree of freedom in practical settings. Specifically, we focus on three key topics: (1) novel implantable neural probes that decrease the level of invasiveness without sacrificing performance, (2) multi-modal neural probes that measure both electrical and optical signals, (3) and neural probes developed using advanced materials. Because safety and precision are critical for practical applications of BMI systems, future studies should aim to enhance these properties when developing next-generation neural probes.

Neuroinflammation, oxidative stress, and blood-brain barrier (BBB) disruption in acute Utah electrode array implants and the effect of deferoxamine as an iron chelator on acute foreign body response

The use of intracortical microelectrode arrays has gained significant attention in being able to help restore function in paralysis patients and study the brain in various neurological disorders. Electrode implantation in the cortex causes vasculature or blood-brain barrier (BBB) disruption and thus elicits a foreign body response (FBR) that results in chronic inflammation and may lead to poor electrode performance. In this study, a comprehensive insight into the acute molecular mechanisms occurring at the Utah electrode array-tissue interface is provided to understand the oxidative stress, neuroinflammation, and neurovascular unit (astrocytes, pericytes, and endothelial cells) disruption that occurs following microelectrode implantation. Quantitative real time polymerase chain reaction (qRT-PCR) was used to quantify the gene expression at acute time-points of 48-hr, 72-hr, and 7-days for factors mediating oxidative stress, inflammation, and BBB disruption in rats implanted with a non-functional 4 × 4 Utah array in the somatosensory cortex. During vascular disruption, free iron released into the brain parenchyma can exacerbate the FBR, leading to oxidative stress and thus further contributing to BBB degradation. To reduce the free iron released into the brain tissue, the effects of an iron chelator, deferoxamine mesylate (DFX), was also evaluated.

Unique electrophysiological and impedance signatures between encapsulation types: An analysis of biological Utah array failure and benefit of a biomimetic coating in a rat model

Intracortical microelectrode arrays, especially the Utah array, remain the most common choice for obtaining high dimensional recordings of spiking neural activity for brain computer interface and basic neuroscience research. Despite the widespread use and established design, mechanical, material and biological challenges persist that contribute to a steady decline in recording performance (as evidenced by both diminished signal amplitude and recorded cell population over time) or outright array failure. Device implantation injury causes acute cell death and activation of inflammatory microglia and astrocytes that leads to a chronic neurodegeneration and inflammatory glial aggregation around the electrode shanks and often times fibrous tissue growth above the pia along the bed of the array within the meninges. This multifaceted deleterious cascade can result in substantial variability in performance even under the same experimental conditions. We track both impedance signatures and electrophysiological performance of 4 × 4 floating microelectrode Utah arrays implanted in the primary monocular visual cortex (V1m) of Long-Evans rats over a 12-week period. We employ a repeatable visual stimulation method to compare signal-to-noise ratio as well as single- and multi-unit yield from weekly recordings. To explain signal variability with biological response, we compare arrays categorized as either Type 1, partial fibrous encapsulation, or Type 2, complete fibrous encapsulation and demonstrate performance and impedance signatures unique to encapsulation type. We additionally assess benefits of a biomolecule coating intended to minimize distance to recordable units and observe a temporary improvement on multi-unit recording yield and single-unit amplitude.

Linear distributed source modeling of local field potentials recorded with intra-cortical electrode arrays.

Planar intra-cortical electrode (Utah) arrays provide a unique window into the spatial organization of cortical activity. Reconstruction of the current source density (CSD) underlying such recordings, however, requires “inverting” Poisson’s equation. For inter-laminar recordings, this is commonly done by the CSD method, which consists in taking the second-order spatial derivative of the recorded local field potentials (LFPs). Although the CSD method has been tremendously successful in mapping the current generators underlying inter-laminar LFPs, its application to planar recordings is more challenging. While for inter-laminar recordings the CSD method seems reasonably robust against violations of its assumptions, is it unclear as to what extent this holds for planar recordings. One of the objectives of this study is to characterize the conditions under which the CSD method can be successfully applied to Utah array data. Using forward modeling, we find that for spatially coherent CSDs, the CSD method yields inaccurate reconstructions due to volume-conducted contamination from currents in deeper cortical layers. An alternative approach is to “invert” a constructed forward model. The advantage of this approach is that any a priori knowledge about the geometrical and electrical properties of the tissue can be taken into account. Although several inverse methods have been proposed for LFP data, the applicability of existing electroencephalographic (EEG) and magnetoencephalographic (MEG) inverse methods to LFP data is largely unexplored. Another objective of our study therefore, is to assess the applicability of the most commonly used EEG/MEG inverse methods to Utah array data. Our main conclusion is that these inverse methods provide more accurate CSD reconstructions than the CSD method. We illustrate the inverse methods using event-related potentials recorded from primary visual cortex of a macaque monkey during a motion discrimination task.

Reusable High Aspect Ratio 3-D Nickel Shadow Mask.

Shadow Mask technology has been used over the years for resistless patterning and to pattern on unconventional surfaces, fragile substrate and biomaterial. In this work, we are presenting a novel method to fabricate high aspect ratio (15:1) three-dimensional (3D) Nickel (Ni) shadow mask with vertical pattern length and width of 1.2 mm and 40 μm respectively. The Ni shadow mask is 1.5 mm tall and 100 μm wide at the base. The aspect ratio of the shadow mask is 15. Ni shadow mask is mechanically robust and hence easy to handle. It is also reusable and used to pattern the sidewalls of unconventional and complex 3D geometries such as microneedles or neural electrodes (such as the Utah array). The standard Utah array has 100 active sites at the tip of the shaft. Using the proposed high aspect ratio Ni shadow mask, the Utah array can accommodate 300 active sites, 200 of which will be along and around the shaft. The robust Ni shadow mask is fabricated using laser patterning and electroplating techniques. The use of Ni 3D shadow mask will lower the fabrication cost, complexity and time for patterning out-of-plane structures.

Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface.

Recent advances in Brain Computer Interfaces (BCIs) have created hope that one day paralyzed patients will be able to regain control of their paralyzed limbs. As part of an ongoing clinical study, we have implanted a 96-electrode Utah array in the motor cortex of a paralyzed human. The array generates almost 3 million data points from the brain every second. This presents several big data challenges towards developing algorithms that should not only process the data in real-time (for the BCI to be responsive) but are also robust to temporal variations and non-stationarities in the sensor data. We demonstrate an algorithmic approach to analyze such data and present a novel method to evaluate such algorithms. We present our methodology with examples of decoding human brain data in real-time to inform a BCI.