Method for cycle detection in sparse, irregularly sampled, long-term neuro-behavioral timeseries: Basis pursuit denoising with polynomial detrending of long-term, inter-ictal epileptiform activity.

Numerous physiological processes are cyclical, but sampling these processes densely enough to perform frequency decomposition and subsequent analyses can be challenging. Mathematical approaches for decomposition and reconstruction of sparsely and irregularly sampled signals are well established but have been under-utilized in physiological applications. We developed a basis pursuit denoising with polynomial detrending (BPWP) model that recovers oscillations and trends from sparse and irregularly sampled timeseries. We validated this model on a unique dataset of long-term inter-ictal epileptiform discharge (IED) rates from human hippocampus recorded with a novel investigational device with continuous local field potential sensing. IED rates have well established circadian and multiday cycles related to sleep, wakefulness, and seizure clusters. Given sparse and irregular samples of IED rates from multi-month intracranial EEG recordings from ambulatory humans, we used BPWP to compute narrowband spectral power and polynomial trend coefficients and identify IED rate cycles in three subjects. In select cases, we propose that random and irregular sampling may be leveraged for frequency decomposition of physiological signals. Trial Registration: NCT03946618.

Acute to long-term characteristics of impedance recordings during neurostimulation in humans.

Objective.This study aims to characterize the time course of impedance, a crucial electrophysiological property of brain tissue, in the human thalamus (THL), amygdala-hippocampus, and posterior hippocampus over an extended period.Approach.Impedance was periodically sampled every 5-15 min over several months in five subjects with drug-resistant epilepsy using an investigational neuromodulation device. Initially, we employed descriptive piecewise and continuous mathematical models to characterize the impedance response for approximately three weeks post-electrode implantation. We then explored the temporal dynamics of impedance during periods when electrical stimulation was temporarily halted, observing a monotonic increase (rebound) in impedance before it stabilized at a higher value. Lastly, we assessed the stability of amplitude and phase over the 24 h impedance cycle throughout the multi-month recording.Main results.Immediately post-implantation, the impedance decreased, reaching a minimum value in all brain regions within approximately two days, and then increased monotonically over about 14 d to a stable value. The models accounted for the variance in short-term impedance changes. Notably, the minimum impedance of the THL in the most epileptogenic hemisphere was significantly lower than in other regions. During the gaps in electrical stimulation, the impedance rebound decreased over time and stabilized around 200 days post-implant, likely indicative of the foreign body response and fibrous tissue encapsulation around the electrodes. The amplitude and phase of the 24 h impedance oscillation remained stable throughout the multi-month recording, with circadian variation in impedance dominating the long-term measures.Significance.Our findings illustrate the complex temporal dynamics of impedance in implanted electrodes and the impact of electrical stimulation. We discuss these dynamics in the context of the known biological foreign body response of the brain to implanted electrodes. The data suggest that the temporal dynamics of impedance are dependent on the anatomical location and tissue epileptogenicity. These insights may offer additional guidance for the delivery of therapeutic stimulation at various time points post-implantation for neuromodulation therapy.

Acute to long-term characteristics of impedance recordings during neurostimulation in humans.

Objective: This study aims to characterize the time course of impedance, a crucial electrophysiological property of brain tissue, in the human thalamus (THL), amygdala-hippocampus (AMG-HPC), and posterior hippocampus (post-HPC) over an extended period. Approach: Impedance was periodically sampled every 5-15 minutes over several months in five subjects with drug-resistant epilepsy using an experimental neuromodulation device. Initially, we employed descriptive piecewise and continuous mathematical models to characterize the impedance response for approximately three weeks post-electrode implantation. We then explored the temporal dynamics of impedance during periods when electrical stimulation was temporarily halted, observing a monotonic increase (rebound) in impedance before it stabilized at a higher value. Lastly, we assessed the stability of amplitude and phase over the 24-hour impedance cycle throughout the multi-month recording. Main results: Immediately post-implantation, the impedance decreased, reaching a minimum value in all brain regions within approximately two days, and then increased monotonically over about 14 days to a stable value. The models accounted for the variance in short-term impedance changes. Notably, the minimum impedance of the THL in the most epileptogenic hemisphere was significantly lower than in other regions. During the gaps in electrical stimulation, the impedance rebound decreased over time and stabilized around 200 days post-implant, likely indicative of the foreign body response and fibrous tissue encapsulation around the electrodes. The amplitude and phase of the 24-hour impedance oscillation remained stable throughout the multi-month recording, with circadian variation in impedance dominating the long-term measures. Significance: Our findings illustrate the complex temporal dynamics of impedance in implanted electrodes and the impact of electrical stimulation. We discuss these dynamics in the context of the known biological foreign body response of the brain to implanted electrodes. The data suggest that the temporal dynamics of impedance are dependent on the anatomical location and tissue epileptogenicity. These insights may offer additional guidance for the delivery of therapeutic stimulation at various time points post-implantation for neuromodulation therapy.

High and low frequency anterior nucleus of thalamus deep brain stimulation: Impact on memory and mood in five patients with treatment resistant temporal lobe epilepsy.

High frequency anterior nucleus of the thalamus deep brain stimulation (ANT DBS) is an established therapy for treatment resistant focal epilepsies. Although high frequency-ANT DBS is well tolerated, patients are rarely seizure free and the efficacy of other DBS parameters and their impact on comorbidities of epilepsy such as depression and memory dysfunction remain unclear. The purpose of this study was to assess the impact of low vs high frequency ANT DBS on verbal memory and self-reported anxiety and depression symptoms. Five patients with treatment resistant temporal lobe epilepsy were implanted with an investigational brain stimulation and sensing device capable of ANT DBS and ambulatory intracranial electroencephalographic (iEEG) monitoring, enabling long-term detection of electrographic seizures. While patients received therapeutic high frequency (100 and 145 Hz continuous and cycling) and low frequency (2 and 7 Hz continuous) stimulation, they completed weekly free recall verbal memory tasks and thrice weekly self-reports of anxiety and depression symptom severity. Mixed effects models were then used to evaluate associations between memory scores, anxiety and depression self-reports, seizure counts, and stimulation frequency. Memory score was significantly associated with stimulation frequency, with higher free recall verbal memory scores during low frequency ANT DBS. Self-reported anxiety and depression symptom severity was not significantly associated with stimulation frequency. These findings suggest the choice of ANT DBS stimulation parameter may impact patients' cognitive function, independently of its impact on seizure rates.

Correction: Neural adaptation and fractional dynamics as a window to underlying neural excitability.

[This corrects the article DOI: 10.1371/journal.pcbi.1010527.].

Seizure occurrence is linked to multiday cycles in diverse physiological signals.

OBJECTIVE: The factors that influence seizure timing are poorly understood, and seizure unpredictability remains a major cause of disability. Work in chronobiology has shown that cyclical physiological phenomena are ubiquitous, with daily and multiday cycles evident in immune, endocrine, metabolic, neurological, and cardiovascular function. Additionally, work with chronic brain recordings has identified that seizure risk is linked to daily and multiday cycles in brain activity. Here, we provide the first characterization of the relationships between the cyclical modulation of a diverse set of physiological signals, brain activity, and seizure timing. METHODS: In this cohort study, 14 subjects underwent chronic ambulatory monitoring with a multimodal wrist-worn sensor (recording heart rate, accelerometry, electrodermal activity, and temperature) and an implanted responsive neurostimulation system (recording interictal epileptiform abnormalities and electrographic seizures). Wavelet and filter-Hilbert spectral analyses characterized circadian and multiday cycles in brain and wearable recordings. Circular statistics assessed electrographic seizure timing and cycles in physiology. RESULTS: Ten subjects met inclusion criteria. The mean recording duration was 232 days. Seven subjects had reliable electroencephalographic seizure detections (mean = 76 seizures). Multiday cycles were present in all wearable device signals across all subjects. Seizure timing was phase locked to multiday cycles in five (temperature), four (heart rate, phasic electrodermal activity), and three (accelerometry, heart rate variability, tonic electrodermal activity) subjects. Notably, after regression of behavioral covariates from heart rate, six of seven subjects had seizure phase locking to the residual heart rate signal. SIGNIFICANCE: Seizure timing is associated with daily and multiday cycles in multiple physiological processes. Chronic multimodal wearable device recordings can situate rare paroxysmal events, like seizures, within a broader chronobiology context of the individual. Wearable devices may advance the understanding of factors that influence seizure risk and enable personalized time-varying approaches to epilepsy care.

Neural adaptation and fractional dynamics as a window to underlying neural excitability.

The relationship between macroscale electrophysiological recordings and the dynamics of underlying neural activity remains unclear. We have previously shown that low frequency EEG activity (<1 Hz) is decreased at the seizure onset zone (SOZ), while higher frequency activity (1-50 Hz) is increased. These changes result in power spectral densities (PSDs) with flattened slopes near the SOZ, which are assumed to be areas of increased excitability. We wanted to understand possible mechanisms underlying PSD changes in brain regions of increased excitability. We hypothesized that these observations are consistent with changes in adaptation within the neural circuit. We developed a theoretical framework and tested the effect of adaptation mechanisms, such as spike frequency adaptation and synaptic depression, on excitability and PSDs using filter-based neural mass models and conductance-based models. We compared the contribution of single timescale adaptation and multiple timescale adaptation. We found that adaptation with multiple timescales alters the PSDs. Multiple timescales of adaptation can approximate fractional dynamics, a form of calculus related to power laws, history dependence, and non-integer order derivatives. Coupled with input changes, these dynamics changed circuit responses in unexpected ways. Increased input without synaptic depression increases broadband power. However, increased input with synaptic depression may decrease power. The effects of adaptation were most pronounced for low frequency activity (< 1Hz). Increased input combined with a loss of adaptation yielded reduced low frequency activity and increased higher frequency activity, consistent with clinical EEG observations from SOZs. Spike frequency adaptation and synaptic depression, two forms of multiple timescale adaptation, affect low frequency EEG and the slope of PSDs. These neural mechanisms may underlie changes in EEG activity near the SOZ and relate to neural hyperexcitability. Neural adaptation may be evident in macroscale electrophysiological recordings and provide a window to understanding neural circuit excitability.

Optimization of closed-loop electrical stimulation enables robust cerebellar-directed seizure control.

Additional treatment options for temporal lobe epilepsy are needed, and potential interventions targeting the cerebellum are of interest. Previous animal work has shown strong inhibition of hippocampal seizures through on-demand optogenetic manipulation of the cerebellum. However, decades of work examining electrical stimulation-a more immediately translatable approach-targeting the cerebellum has produced very mixed results. We were therefore interested in exploring the impact that stimulation parameters may have on seizure outcomes. Using a mouse model of temporal lobe epilepsy, we conducted on-demand electrical stimulation of the cerebellar cortex, and varied stimulation charge, frequency and pulse width, resulting in over 1000 different potential combinations of settings. To explore this parameter space in an efficient, data-driven, manner, we utilized Bayesian optimization with Gaussian process regression, implemented in MATLAB with an Expected Improvement Plus acquisition function. We examined three different fitting conditions and two different electrode orientations. Following the optimization process, we conducted additional on-demand experiments to test the effectiveness of selected settings. Regardless of experimental setup, we found that Bayesian optimization allowed identification of effective intervention settings. Additionally, generally similar optimal settings were identified across animals, suggesting that personalized optimization may not always be necessary. While optimal settings were effective, stimulation with settings predicted from the Gaussian process regression to be ineffective failed to provide seizure control. Taken together, our results provide a blueprint for exploration of a large parameter space for seizure control and illustrate that robust inhibition of seizures can be achieved with electrical stimulation of the cerebellum, but only if the correct stimulation parameters are used.

Functional states shape the spatiotemporal representation of local and cortex-wide neural activity in mouse sensory cortex.

The spatiotemporal representation of neural activity during rest and upon sensory stimulation in cortical areas is highly dynamic and may be predominantly governed by cortical state. On the mesoscale level, intrinsic neuronal activity ranges from a persistent state, generally associated with a sustained depolarization of neurons, to a bimodal, slow wave-like state with bursts of neuronal activation alternating with silent periods. These different activity states are prevalent under certain types of sedatives or are associated with specific behavioral or vigilance conditions. Neurophysiological experiments assessing circuit activity usually assume a constant underlying state, yet reports of variability of neuronal responses under seemingly constant conditions are common in the field. Even when a certain type of neural activity or cortical state can be stably maintained over time, the associated response properties are highly relevant for explaining experimental outcomes. Here we describe the spatiotemporal characteristics of ongoing activity and sensory-evoked responses under two predominant functional states in the sensory cortices of mice: persistent activity (PA) and slow wave activity (SWA). Using electrophysiological recordings and local and wide-field calcium recordings, we examine whether spontaneous and sensory-evoked neuronal activity propagate throughout the cortex in a state-dependent manner. We find that PA and SWA differ in their spatiotemporal characteristics, which determine the cortical network's response to a sensory stimulus. During PA state, sensory stimulation elicits gamma-based short-latency responses that precisely follow each stimulation pulse and are prone to adaptation upon higher stimulation frequencies. Sensory responses during SWA are more variable, dependent on refractory periods following spontaneous slow waves. Although spontaneous slow waves propagated in anterior-posterior direction in a majority of observations, the direction of propagation of stimulus-elicited wave depends on the sensory modality. These findings suggest that cortical state explains variance and should be considered when investigating multiscale correlates of functional neurocircuit activity.NEW & NOTEWORTHY Here we dissect the cortical representation of brain states based on local photometry recordings and on mesoscale cortical calcium imaging, complemented by electrophysiological recordings in mice. We identify two distinct functional states in the sensory cortices, which differ in their spatiotemporal characteristics on the local and global cortical scales. We examine how intrinsic and stimulus-evoked neuronal activity propagates throughout the cortex in a state-dependent manner, supporting the notion that cortical state is a relevant variable to consider for a wide range of neurophysiological experiments.

Typical somatomotor physiology of the hand is preserved in a patient with an amputated arm: An ECoG case study.

Electrophysiological signals in the human motor system may change in different ways after deafferentation, with some studies emphasizing reorganization while others propose retained physiology. Understanding whether motor electrophysiology is retained over longer periods of time can be invaluable for patients with paralysis (e.g. ALS or brainstem stroke) when signals from sensorimotor areas may be used for communication or control over neural prosthetic devices. In addition, a maintained electrophysiology can potentially benefit the treatment of phantom limb pains through prolonged use of these signals in a brain-machine interface (BCI). Here, we were presented with the unique opportunity to investigate the physiology of the sensorimotor cortex in a patient with an amputated arm using electrocorticographic (ECoG) measurements. While implanted with an ECoG grid for clinical evaluation of electrical stimulation for phantom limb pain, the patient performed attempted finger movements with the contralateral (lost) hand and executed finger movements with the ipsilateral (healthy) hand. The electrophysiology of the sensorimotor cortex contralateral to the amputated hand remained very similar to that of hand movement in healthy people, with a spatially focused increase of high-frequency band (65-175 Hz; HFB) power over the hand region and a distributed decrease in low-frequency band (15-28 Hz; LFB) power. The representation of the three different fingers (thumb, index and little) remained intact and HFB patterns could be decoded using support vector learning at single-trial classification accuracies of >90%, based on the first 1-3 s of the HFB response. These results indicate that hand representations are largely retained in the motor cortex. The intact physiological response of the amputated hand, the high distinguishability of the fingers and fast temporal peak are encouraging for neural prosthetic devices that target the sensorimotor cortex.

An in-plane, mirror-symmetric visualization tool for deep brain stimulation electrodes.

Deep brain stimulation (DBS) is used to treat a range of neurologic conditions. Determining the anatomic location of the DBS lead and inferring the microelectrode recording track from co-registered pre-operative and post-operative scans is important for stereotactic surgery and neurophysiology research. Reslicing images with the DBS lead in-plane while maintaining mirror symmetry is not possible with current clinical navigation software. Therefore, we developed an open source software tool in Matlab for visualizing DBS lead placement and anatomic segmentation with computed tomography and magnetic resonance images. The code and graphical user interface are available at: github.com/camplaboratory/DBS_reslice.

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.

Reversible neuroinhibition by focused ultrasound is mediated by a thermal mechanism.

BACKGROUND: Transcranial focused ultrasound (tFUS) at low intensities has been reported to directly evoke responses and reversibly inhibit function in the central nervous system. While some doubt has been cast on the ability of ultrasound to directly evoke neuronal responses, spatially-restricted transcranial ultrasound has demonstrated consistent, inhibitory effects, but the underlying mechanism of reversible suppression in the central nervous system is not well understood. OBJECTIVE/HYPOTHESIS: In this study, we sought to characterize the effect of transcranial, low-intensity, focused ultrasound on the thalamus during somatosensory evoked potentials (SSEP) and investigate the mechanism by modulating the parameters of ultrasound. METHODS: TFUS was applied to the ventral posterolateral nucleus of the thalamus of a rodent while electrically stimulating the tibial nerve to induce an SSEP. Thermal changes were also induced through an optical fiber that was image-guided to the same target. RESULTS: Focused ultrasound reversibly suppressed SSEPs in a spatially and intensity-dependent manner while remaining independent of duty cycle, peak pressure, or modulation frequency. Suppression was highly correlated and temporally consistent with in vivo temperature changes while producing no pathological changes on histology. Furthermore, stereotactically-guided delivery of thermal energy through an optical fiber produced similar thermal effects and suppression. CONCLUSION: We confirm that tFUS predominantly causes neuroinhibition and conclude that the most primary biophysical mechanism is the thermal effect of focused ultrasound.

Phase relationship between micro-electrocorticography and cortical neurons.

OBJECTIVE: Electrocorticography (ECoG) is commonly used to map epileptic foci and to implement brain-computer interfaces. Understanding the spatiotemporal correspondence between potentials recorded from the brain's surface and the firing patterns of neurons within the cortex would inform the interpretation of ECoG signals and the design of (microfabricated) micro-ECoG electrode arrays. Based on the theory that synaptic potentials generated by neurons firing in synchrony superimpose to generate local field potentials (LFPs), we hypothesized that neurons in the cortex would fire at preferential phases of the micro-ECoG signal in a spatially dependent way. APPROACH: We custom fabricated micro-ECoG electrode arrays with a small opening for silicon arrays (NeuroNexus) to be inserted into the cortex. MAIN RESULTS: We found that the spectral coherence between micro-ECoG signals and intracortical LFPs decreased with distance and frequency, but the coherence with spiking units did not simply decrease over distance, likely due to the structure of the cortex. The majority of sorted units spiked during a preferred phase (usually downward) and frequency (usually below 20 Hz) of the micro-ECoG signal. Their preferred frequency decreased with administration of dexmeditomidine, a sedative commonly used for cortical mapping in patients with epilepsy prior to surgical resection. Dexmedetomidine concomitantly shifted the micro-ECoG spectral density towards lower frequencies. Therefore, the phase relationship between micro-ECoG signals and cortical spiking depends on the state of the brain, and spectrum shifts towards lower frequencies in the electrocorticography signal are a signature of increased spike-phase coupling. However, spike-phase coupling is not a static property since visual stimuli were found to modulate the magnitude of phase coupling at gamma frequency ranges (30-80 Hz), providing empirical evidence that neurons transiently phase-lock. SIGNIFICANCE: The phase relationship between intracortical spikes and micro-ECoG signals depends on brain state, site separation, cortical structure, and external stimuli.

Neuroma Implantation into Long Bones: Clinical Foundation for a Novel Osseointegrated Peripheral Nerve Interface.

Symptomatic neuroma after major extremity amputation is a challenging clinical problem for which there are many described treatment options. Neuroma excision and implantation into the medullary canal of long bones offers durability and insulation, and minimizes chronic pain. Another challenge in amputees is impaired function and an ongoing need for accessible and functional prostheses that are "bidirectional," in that they provide both fine motor control and sensory feedback. Drawing on clinical experience with neuroma implantation into the medullary canal of long bones, the authors propose a novel neural interface whereby a terminal nerve end is redirected into the medullary canal of a nearby long bone and interfaced with an electrode array. The osseointegrated neural interface aims to exploit electrical signals from peripheral nerves to control advanced prosthetic devices for amputees. The purpose of this article is to present 2 clinical cases of nerve translocation into bone that serve as the clinical foundation of the osseointegrated neural interface as an innovative interface for prosthetic control.

Flexible and stretchable nanowire-coated fibers for optoelectronic probing of spinal cord circuits.

Studies of neural pathways that contribute to loss and recovery of function following paralyzing spinal cord injury require devices for modulating and recording electrophysiological activity in specific neurons. These devices must be sufficiently flexible to match the low elastic modulus of neural tissue and to withstand repeated strains experienced by the spinal cord during normal movement. We report flexible, stretchable probes consisting of thermally drawn polymer fibers coated with micrometer-thick conductive meshes of silver nanowires. These hybrid probes maintain low optical transmission losses in the visible range and impedance suitable for extracellular recording under strains exceeding those occurring in mammalian spinal cords. Evaluation in freely moving mice confirms the ability of these probes to record endogenous electrophysiological activity in the spinal cord. Simultaneous stimulation and recording is demonstrated in transgenic mice expressing channelrhodopsin 2, where optical excitation evokes electromyographic activity and hindlimb movement correlated to local field potentials measured in the spinal cord.

Fabrication and utility of a transparent graphene neural electrode array for electrophysiology, in vivo imaging, and optogenetics.

Transparent graphene-based neural electrode arrays provide unique opportunities for simultaneous investigation of electrophysiology, various neural imaging modalities, and optogenetics. Graphene electrodes have previously demonstrated greater broad-wavelength transmittance (∼90%) than other transparent materials such as indium tin oxide (∼80%) and ultrathin metals (∼60%). This protocol describes how to fabricate and implant a graphene-based microelectrocorticography (µECoG) electrode array and subsequently use this alongside electrophysiology, fluorescence microscopy, optical coherence tomography (OCT), and optogenetics. Further applications, such as transparent penetrating electrode arrays, multi-electrode electroretinography, and electromyography, are also viable with this technology. The procedures described herein, from the material characterization methods to the optogenetic experiments, can be completed within 3-4 weeks by an experienced graduate student. These protocols should help to expand the boundaries of neurophysiological experimentation, enabling analytical methods that were previously unachievable using opaque metal-based electrode arrays.

Optical Feedback Control and Electrical-Optical Costimulation of Peripheral Nerves.

BACKGROUND: Optogenetics is an emerging technology that enables the expression of light-activated ion channels in mammalian cells. Neurons expressing light-activated ion channels can be depolarized using the appropriate wavelength of light. Optical stimulation of neurons could have important implications for further understanding and managing peripheral nerve deficits leading to paresis or paralysis. This study examines the utility of this technology in a feedback-controlled system and the advantages of coupling this technology with conventional electrical stimulation. METHODS: The sciatic nerves of transgenic mice expressing blue light-activated ion channels (channelrhodopsin-2) were optically manipulated to generate electromyographic responses in the gastrocnemius muscle and to develop two potential applications of this technology: feedback-controlled optical stimulation using a proportional-integral controller, and simultaneous electrical-optical stimulation. RESULTS: The authors observed repeatable and predictable behavior of the optical controller in over 200 trials and a statistically significant decreased error when using optical feedback control as opposed to non-feedback controlled stimulation (n = 6 limbs). A second application of this technology was the amplification of electrically generated peripheral nerve signals using an optical source. Amplification of electrical activity was observed even when subthreshold electrical stimulation was used. CONCLUSIONS: Optical feedback control and optical amplification of subthreshold activity extend the versatility of optogenetics in peripheral nerve applications. Optical feedback control is a new application of an approach originally developed for functional electrical stimulation. Optical amplification of subthreshold electrical stimulation motivates future investigations into the optical amplification of endogenous subthreshold peripheral nerve activity (e.g., following spinal cord injury).

Closed-Loop Optogenetic Brain Interface.

This paper presents a new approach for implementation of closed-loop brain-machine interface algorithms by combining optogenetic neural stimulation with electrocorticography and fluorescence microscopy. We used a new generation of microfabricated electrocorticography (micro-ECoG) devices in which electrode arrays are embedded within an optically transparent biocompatible substrate that provides optical access to the brain tissue during electrophysiology recording. An optical setup was designed capable of projecting arbitrary patterns of light for optogenetic stimulation and performing fluorescence microscopy through the implant. For realization of a closed-loop system using this platform, the feedback can be taken from electrophysiology data or fluorescence imaging. In the closed-loop systems discussed in this paper, the feedback signal was taken from the micro-ECoG. In these algorithms, the electrophysiology data are continuously transferred to a computer and compared with some predefined spatial-temporal patterns of neural activity. The computer which processes the data also readjusts the duration and distribution of optogenetic stimulating pulses to minimize the difference between the recorded activity and the predefined set points so that after a limited period of transient response the recorded activity follows the set points. Details of the system design and implementation of typical closed-loop paradigms are discussed in this paper.