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

OBJECTIVE: 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. METHODS: 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. RESULTS: 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. SIGNIFICANCE: 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.

Intraoperative microseizure detection using a high-density micro-electrocorticography electrode array.

One-third of epilepsy patients suffer from medication-resistant seizures. While surgery to remove epileptogenic tissue helps some patients, 30-70% of patients continue to experience seizures following resection. Surgical outcomes may be improved with more accurate localization of epileptogenic tissue. We have previously developed novel thin-film, subdural electrode arrays with hundreds of microelectrodes over a 100-1000 mm2 area to enable high-resolution mapping of neural activity. Here, we used these high-density arrays to study microscale properties of human epileptiform activity. We performed intraoperative micro-electrocorticographic recordings in nine patients with epilepsy. In addition, we recorded from four patients with movement disorders undergoing deep brain stimulator implantation as non-epileptic controls. A board-certified epileptologist identified microseizures, which resembled electrographic seizures normally observed with clinical macroelectrodes. Recordings in epileptic patients had a significantly higher microseizure rate (2.01 events/min) than recordings in non-epileptic subjects (0.01 events/min; permutation test, P = 0.0068). Using spatial averaging to simulate recordings from larger electrode contacts, we found that the number of detected microseizures decreased rapidly with increasing contact diameter and decreasing contact density. In cases in which microseizures were spatially distributed across multiple channels, the approximate onset region was identified. Our results suggest that micro-electrocorticographic electrode arrays with a high density of contacts and large coverage are essential for capturing microseizures in epilepsy patients and may be beneficial for localizing epileptogenic tissue to plan surgery or target brain stimulation.

Multiregional communication and the channel modulation hypothesis.

Multiregional communication is important to understanding the brain mechanisms supporting complex behaviors. Work in animals and human subjects shows that multiregional communication plays significant roles in cognitive function and is associated with neurological and neuropsychiatric disorders of brain function. Recent experimental advances enable empirical tests of the mechanisms of multiregional communication. Recent mechanistic insights into brain network function also suggest new therapies to treat disordered brain networks. Here, we discuss how to use the concept of communication channel modulation can help define and constrain what we mean by multiregional communication. We discuss behavioral and neurophysiological evidence for multiregional channels modulation. We then consider the role of causal manipulations and their implications for developing novel therapies based on multiregional communication.

Modelling and prediction of the dynamic responses of large-scale brain networks during direct electrical stimulation.

Direct electrical stimulation can modulate the activity of brain networks for the treatment of several neurological and neuropsychiatric disorders and for restoring lost function. However, precise neuromodulation in an individual requires the accurate modelling and prediction of the effects of stimulation on the activity of their large-scale brain networks. Here, we report the development of dynamic input-output models that predict multiregional dynamics of brain networks in response to temporally varying patterns of ongoing microstimulation. In experiments with two awake rhesus macaques, we show that the activities of brain networks are modulated by changes in both stimulation amplitude and frequency, that they exhibit damping and oscillatory response dynamics, and that variabilities in prediction accuracy and in estimated response strength across brain regions can be explained by an at-rest functional connectivity measure computed without stimulation. Input-output models of brain dynamics may enable precise neuromodulation for the treatment of disease and facilitate the investigation of the functional organization of large-scale brain networks.

A Causal Network Analysis of Neuromodulation in the Mood Processing Network.

Neural decoding and neuromodulation technologies hold great promise for treating mood and other brain disorders in next-generation therapies that manipulate functional brain networks. Here we perform a novel causal network analysis to decode multiregional communication in the primate mood processing network and determine how neuromodulation, short-burst tetanic microstimulation (sbTetMS), alters multiregional network communication. The causal network analysis revealed a mechanism of network excitability that regulates when a sender stimulation site communicates with receiver sites. Decoding network excitability from neural activity at modulator sites predicted sender-receiver communication, whereas sbTetMS neuromodulation temporarily disrupted sender-receiver communication. These results reveal specific network mechanisms of multiregional communication and suggest a new generation of brain therapies that combine neural decoding to predict multiregional communication with neuromodulation to disrupt multiregional communication.

A Modular Implant System for Multimodal Recording and Manipulation of the Primate Brain.

Neural circuitry can be investigated and manipulated using a variety of techniques, including electrical and optical recording and stimulation. At present, most neural interfaces are designed to accommodate a single mode of neural recording and/or manipulation, which limits the amount of data that can be extracted from a single population of neurons. To overcome these technical limitations, we developed a chronic, multi-scale, multi-modal chamber-based neural implant for use in non-human primates that accommodates electrophysiological recording and stimulation, optical manipulation, and wide-field imaging. We present key design features of the system and mechanical validation. We also present sample data from two non-human primate subjects to validate the efficacy of the design in vivo.

Estimation of the Electrode-Fiber Bioelectrical Coupling From Extracellularly Recorded Single Fiber Action Potentials.

Selective peripheral neural interfaces are currently capable of detecting minute electrical signals from nearby nerve fibers as single fiber action potential (SFAP) waveforms. Each detected single unit has a distinct shape originating from the unique bioelectrical coupling that exists between the neuroprosthetic electrode, the nerve fiber and the extracellular milieu. The bioelectrical coupling manifests itself as a series of low-pass Bessel filters acting on the action currents along the nerve fiber. Here, we present a method to estimate the electrode-fiber bioelectrical coupling through a quantitative analysis of the spectral distribution of the single units extracellularly recorded with the thin-film longitudinal intrafascicular electrode (tfLIFE) in an in vivo mammalian peripheral nerve animal model. The bioelectrical coupling estimate is an estimate of the electrode sensitivity function traversed by the nerve fiber, suggesting that it is as a means to directly measure the spatial relationship between the nerve fiber and electrode. It not only reflects a shape change to the SFAP, but has implications for in situ nerve fiber location tracking, in situ diagnostics of nerves and neuroproshetic electrodes, and assessment of the biocompatibility of neural interfaces and the health of the reporting nerve fibers.

Bioelectric interfaces for the peripheral nervous system.

The peripheral nervous system (PNS) is an attractive target for those developing neural interfaces as an access point to the information flow coursing within our bodies. A successful neural interface could not only offer the means to understand basic neurophysiological mechanisms, such as how the body accomplishes complex coordinated control of multi degree of freedom body segments, but also could serve as the means of delivering treatment or therapies to restore physiological functions lost due to injury or disease. Our work in the development of such a neural interface focuses upon multi-microelectrode devices that are placed within the body of the nerve fascicle; mulit-channel intra-fascicular devices called the thin-film Longitudinal Intra-Fascicular Electrode (tfLIFE) and the Transversely Implanted Multi-Electrode (TIME). These structures provide high resolution access to the PNS and have demonstrated promise in animal work as well as in preliminary sub-acute work in human volunteers. However, work remains to improve upon their longevity and biocompatibility before full translation to clinical work can occur.

Influence of unit distance and conduction velocity on the spectra of extracellular action potentials recorded with intrafascicular electrodes.

The use of highly selective penetrating electrodes yields multi-unit extracellular action potential (AP) recordings of the nerve fibers in the vicinity of the electrode. Accessing the information carried within the neural data stream further requires discrimination and separation of the multi-unit recording into their constituent multiple single unit spike trains. Shape differences in the single fiber action potentials (SFAPs) are typically used as the criteria for unit separation. The present paper explores the origins of the shape differences through analysis of the SFAP in the frequency domain. We present the derivation and computational model predictions of a method to quantitatively analyse changes in the spectral components of SFAPs with an axially located intrafascicular electrode with non-radially symmetrical sensitivity function. A spatial tissue filter relationship was derived using reciprocity equations in the spatial frequency domain and transformed to time frequency. A three dimensional bioelectrical volume conductor finite element model of a recording electrode residing in a nerve fascicle was developed to explore the potential distribution in the nerve fascicle and further derive the electrode-fiber coupling function in the time-frequency domain. It was found that the spectral distribution of the SFAP was multimodal in nature, similar to empirical reported earlier, and could be predicted by taking the single fiber action currents (SFACs) filtered by the electrode-fiber coupling function. This function manifested itself as a low-pass filter of the SFAC, dependent upon the fiber's location relative to the electrode and conduction velocity. Analysis of the spectral distribution revealed that changes in the landmarks of the distribution could be related to the fiber location and conduction velocity. Moreover, a consistent relationship was found when exploring the distribution of fibers located off the one axis of symmetry.

Determination of electrode to nerve fiber distance and nerve conduction velocity through spectral analysis of the extracellular action potentials recorded from earthworm giant fibers.

Microneurography and the use of selective microelectrodes that can resolve single-unit nerve activity have become a tool to understand the coding within the nervous system and a clinical diagnostic tool to assess peripheral neural pathologies. Central to these techniques is the use of the differences in the shape of the extracellular action potential (AP) waveform to identify and discriminate units from one another. Theoretical modeling of the origins of these shape differences has shown that the position of the nerve fiber relative to the electrode and the conduction velocity of the unit contribute to these differences giving rise to the hypothesis that more information about the fiber and its relationship to the electrode could be extracted given further analysis of the AP waveform. This paper addresses this question by exploring the electrical coupling between the electrode and nerve fiber. Idealized models and the literature indicate that two parameters, the electrode-fiber distance and the unit conduction velocity, contribute to the amplitude of the extracellular AP detected by the electrode, which confounds the quantification of coupling using the spike amplitude alone. To resolve this, we develop a method that enables differential quantification of these two parameters using spectral analysis of the single-unit AP waveform and demonstrate that the two parameters could be effectively decoupled in an in vitro earthworm model. The method could open the way forward toward micro-scale in situ monitoring of the interaction of nerve fiber and neural interface.

Stationary wavelet transform and higher order statistical analyses of intrafascicular nerve recordings.

Nerve signals were recorded from the sciatic nerve of the rabbits in the acute experiments with multi-channel thin-film longitudinal intrafascicular electrodes. 5.5 s sequences of quiescent and high-level nerve activity were spectrally decomposed by applying a ten-level stationary wavelet transform with the Daubechies 10 (Db10) mother wavelet. Then, the statistical distributions of the raw and subband-decomposed sequences were estimated and used to fit a fourth-order Pearson distribution as well as check for normality. The results indicated that the raw and decomposed background and high-level nerve activity distributions were nearly zero-mean and non-skew. All distributions with the frequency content above 187.5 Hz were leptokurtic except for the first-level decomposition representing frequencies in the subband between 12 and 24 kHz, which was Gaussian. This suggests that nerve activity acts to change the statistical distribution of the recording. The results further demonstrated that quiescent recording contained a mixture of an underlying pink noise and low-level nerve activity that could not be silenced. The signal-to-noise ratios based upon the standard deviation (SD) and kurtosis were estimated, and the latter was found as an effective measure for monitoring the nerve activity residing in different frequency subbands. The nerve activity modulated kurtosis along with SD, suggesting that the joint use of SD and kurtosis could improve the stability and detection accuracy of spike-detection algorithms. Finally, synthesizing the reconstructed subband signals following denoising based upon the higher order statistics of the subband-decomposed coefficient sequences allowed us to effectively purify the signal without distorting spike shape.