Cortical Local Field Potentials Research Articles

Dissociative and prioritized modeling of behaviorally relevant neural dynamics using recurrent neural networks

Understanding the dynamical transformation of neural activity to behavior requires new capabilities to nonlinearly model, dissociate and prioritize behaviorally relevant neural dynamics and test hypotheses about the origin of nonlinearity. We present dissociative prioritized analysis of dynamics (DPAD), a nonlinear dynamical modeling approach that enables these capabilities with a multisection neural network architecture and training approach. Analyzing cortical spiking and local field potential activity across four movement tasks, we demonstrate five use-cases. DPAD enabled more accurate neural–behavioral prediction. It identified nonlinear dynamical transformations of local field potentials that were more behavior predictive than traditional power features. Further, DPAD achieved behavior-predictive nonlinear neural dimensionality reduction. It enabled hypothesis testing regarding nonlinearities in neural–behavioral transformation, revealing that, in our datasets, nonlinearities could largely be isolated to the mapping from latent cortical dynamics to behavior. Finally, DPAD extended across continuous, intermittently sampled and categorical behaviors. DPAD provides a powerful tool for nonlinear dynamical modeling and investigation of neural–behavioral data.

The primate cortical LFP exhibits multiple spectral and temporal gradients and widespread task dependence during visual short-term memory.

Although cognitive functions are hypothesized to be mediated by synchronous neuronal interactions in multiple frequency bands among widely distributed cortical areas, we still lack a basic understanding of the distribution and task dependence of oscillatory activity across the cortical map. Here, we ask how the spectral and temporal properties of the local field potential (LFP) vary across the primate cerebral cortex, and how they are modulated during visual short-term memory. We measured the LFP from 55 cortical areas in two macaque monkeys while they performed a visual delayed match to sample task. Analysis of peak frequencies in the LFP power spectra reveals multiple discrete frequency bands between 3 and 80 Hz that differ between the two monkeys. The LFP power in each band, as well as the sample entropy, a measure of signal complexity, display distinct spatial gradients across the cortex, some of which correlate with reported spine counts in cortical pyramidal neurons. Cortical areas can be robustly decoded using a small number of spectral and temporal parameters, and significant task-dependent increases and decreases in spectral power occur in all cortical areas. These findings reveal pronounced, widespread, and spatially organized gradients in the spectral and temporal activity of cortical areas. Task-dependent changes in cortical activity are globally distributed, even for a simple cognitive task.NEW & NOTEWORTHY We recorded extracellular electrophysiological signals from roughly the breadth and depth of a cortical hemisphere in nonhuman primates (NHPs) performing a visual memory task. Analyses of the band-limited local field potential (LFP) power displayed widespread, frequency-dependent cortical gradients in spectral power. Using a machine learning classifier, these features allowed robust cortical area decoding. Further task dependence in LFP power were found to be widespread, indicating large-scale gradients of LFP activity, and task-related activity.

Aperiodic components of local field potentials reflect inherent differences between cortical and subcortical activity.

The aperiodic component of local field potentials can be modeled to produce useful and reproducible indices of neural activity. Here we refined a widely used phenomenological model for extracting aperiodic parameters (namely the exponent, offset and knee), with which we fit cortical, basal ganglia, and thalamic intracranial local field potentials, recorded from unique cohorts of movement disorders and epilepsy patients. We found that the aperiodic exponent in motor cortex is higher in Parkinson's disease patients with more severe motor symptoms, suggesting that aperiodic features may have potential as electrophysiological biomarkers for movement disorders symptoms. Remarkably, we found conspicuous differences in the aperiodic parameters of basal ganglia and thalamic signals compared to those from neocortex.

Effects of nitrous oxide and ketamine on electrophysiological and molecular responses in the prefrontal cortex of mice: A comparative study

Nitrous oxide (N2O; laughing gas) has recently reported to produce rapid antidepressant effects, but little is known about the underlying mechanisms. We performed transcriptomics, in situ hybridization, and electrophysiological studies to examine the potential shared signatures induced by 1 h inhalation of 50% N2O and a single subanesthetic dose of ketamine (10 mg/kg, i.p.) in the medial prefrontal cortex (mPFC) in adult mice. Both treatments similarly affected the transcription of several negative regulators of mitogen-activated protein kinases (MAPKs), namely, dual specificity phosphatases (DUSPs). The effects were primarily located in the pyramidal cells. Notably, the overall effects of N2O on mRNA expression were much more prominent and widespread compared to ketamine. Ketamine caused an elevation of the spiking frequency of putative pyramidal neurons and increased gamma activity (30–100 Hz) of cortical local field potentials. However, N2O produced no such effects. Spiking amplitudes and spike-to-local field potential phase locking of putative pyramidal neurons and interneurons in this brain area showed no uniform changes across treatments. Our findings suggest that N2O and subanesthetic-dose ketamine target MAPK pathway in the mPFC but produce varying acute electrophysiological responses.

Evoked responses to single pulse electrical stimulation reveal impaired striatal excitability in a rat model of Parkinson's disease

BackgroundSensorimotor beta oscillations are increased in Parkinson's disease (PD) due to the alteration of dopaminergic transmission. This electrophysiological read-out is reported both in patients and in animal models such as the 6-OHDA rat model obtained with unilateral nigral injection of 6-hydroxydopamine (6-OHDA). Current treatments, based on dopaminergic replacement, transiently normalize this pathological beta activity and improve patients' quality of life. ObjectivesWe wanted to assess in vivo whether the abnormal beta oscillations can be correlated with impaired striatal or cortical excitability of the sensorimotor system and modulated by the pharmacological manipulation of the dopaminergic system. MethodsIn the unilateral 6-OHDA rat model and control animals, we used intra-striatal and intra-cortical single-pulse electrical stimulation (SPES) and concurrent local field potentials (LFP) recordings. In the two groups, we quantified basal cortico-striatal excitability from time-resolved spectral analyses of LFP evoked responses induced remotely by intracerebral stimulations. The temporal dependance of cortico-striatal excitability to dopaminergic transmission was further tested using electrophysiological recordings combined with levodopa injection. ResultsLFP evoked responses after striatal stimulation showed a transient reduction of power in a large time-frequency domain in the 6-OHDA group compared to the sham group. This result was specific to the striatum, as no significant difference was observed in cortical LFP evoked responses between the two groups. This impaired striatal excitability in the 6-OHDA group was observed in the striatum at least during the first 3 months after the initial lesion. In addition, the striatum responses to SPES during a levodopa challenge showed a transient potentiation of the decrease of responsiveness in frequencies below 40 Hz. ConclusionThe spectral properties of striatal responses to SPES show high sensitivity to dopaminergic transmission in the unilateral 6-OHDA rat model. We thus propose that this approach could be used in preclinical models as a time-resolved biomarker of impaired dopaminergic transmission capable of monitoring progressive neurodegeneration and/or challenges to drug intake.

Increased noise relates to abnormal excitation-inhibition balance in schizophrenia: a combined empirical and computational study.

Electroencephalography studies link sensory processing issues in schizophrenia to increased noise level-noise here is background spontaneous activity-as measured by the signal-to-noise ratio. The mechanism, however, of such increased noise is unknown. We investigate if this relates to changes in cortical excitation-inhibition balance, which has been observed to be atypical in schizophrenia, by combining electroencephalography and computational modeling. Our electroencephalography task results, for which the local field potentials can be used as a proxy, show lower signal-to-noise ratio due to higher noise in schizophrenia. Both electroencephalography rest and task states exhibit higher levels of excitation in the functional excitation-inhibition (as a proxy of excitation-inhibition balance). This suggests a relationship between increased noise and atypical excitation in schizophrenia, which was addressed by using computational modeling. A Leaky Integrate-and-Fire model was used to simulate the effects of varying degrees of noise on excitation-inhibition balance, local field potential, NMDA current, and . Results show a noise-related increase in the local field potential, excitation in excitation-inhibition balance, pyramidal NMDA current, and spike rate. Mutual information and mediation analysis were used to explore a cross-level relationship, showing that the cortical local field potential plays a key role in transferring the effect of noise to the cellular population level of NMDA.

Toward a cerebello-thalamo-cortical computational model of spinocerebellar ataxia

Computational neural network modelling is an emerging approach for optimization of drug treatment of neurological disorders and fine-tuning of rehabilitation strategies. In the current study, we constructed a cerebello-thalamo-cortical computational neural network model to simulate a mouse model of cerebellar ataxia (pcd5J mice) by manipulating cerebellar bursts through reduction of GABAergic inhibitory input. Cerebellar output neurons were projected to the thalamus and bidirectionally connected with the cortical network. Our results showed that reduction of inhibitory input in the cerebellum orchestrated the cortical local field potential (LFP) dynamics to generate specific motor outputs of oscillations of the theta, alpha, and beta bands in the computational model as well as in mouse motor cortical neurons. The therapeutic potential of deep brain stimulation (DBS) was tested in the computational model by increasing the sensory input to restore cortical output. Ataxia mice showed normalization of the motor cortex LFP after cerebellum DBS. We provide a novel approach to computational modelling to investigate the effect of DBS by mimicking cerebellar ataxia involving degeneration of Purkinje cells. Simulated neural activity coincides with findings from neural recordings of ataxia mice. Our computational model could thus represent cerebellar pathologies and provide insight into how to improve disease symptoms by restoring neuronal electrophysiological properties using DBS.

Early Detection of Alzheimer's Disease from Cortical and Hippocampal Local Field Potentials using an Ensembled Machine Learning Model.

Early diagnosis of Alzheimer's disease (AD) is a very challenging problem and has been attempted through data-driven methods in recent years. However, considering the inherent complexity in decoding higher cognitive functions from spontaneous neuronal signals, these data-driven methods benefit from the incorporation of multimodal data. This work proposes an ensembled machine learning model with explainability (EXML) to detect subtle patterns in cortical and hippocampal local field potential signals (LFPs) that can be considered as a potential marker for AD in the early stage of the disease. The LFPs acquired from healthy and two types of AD animal models (n=10 each) using linear multielectrode probes were endorsed by electrocardiogram and respiration signals for their veracity. Feature sets were generated from LFPs in temporal, spatial and spectral domains and were fed into selected machine-learning models for each domain. Using late fusion, the EXML model achieved an overall accuracy of 99.4%. This provided insights into the amyloid plaque deposition process as early as 3 months of the disease onset by identifying the subtle patterns in the network activities. Lastly, the individual and ensemble models were found to be robust when evaluated by randomly masking channels to mimic the presence of artefacts.

Differential thalamocortical interactions in slow and fast spindle generation: A computational model.

Cortical slow oscillations (SOs) and thalamocortical sleep spindles are two prominent EEG rhythms of slow wave sleep. These EEG rhythms play an essential role in memory consolidation. In humans, sleep spindles are categorized into slow spindles (8-12 Hz) and fast spindles (12-16 Hz), with different properties. Slow spindles that couple with the up-to-down phase of the SO require more experimental and computational investigation to disclose their origin, functional relevance and most importantly their relation with SOs regarding memory consolidation. To examine slow spindles, we propose a biophysical thalamocortical model with two independent thalamic networks (one for slow and the other for fast spindles). Our modeling results show that fast spindles lead to faster cortical cell firing, and subsequently increase the amplitude of the cortical local field potential (LFP) during the SO down-to-up phase. Slow spindles also facilitate cortical cell firing, but the response is slower, thereby increasing the cortical LFP amplitude later, at the SO up-to-down phase of the SO cycle. Neither the SO rhythm nor the duration of the SO down state is affected by slow spindle activity. Furthermore, at a more hyperpolarized membrane potential level of fast thalamic subnetwork cells, the activity of fast spindles decreases, while the slow spindles activity increases. Together, our model results suggest that slow spindles may facilitate the initiation of the following SO cycle, without however affecting expression of the SO Up and Down states.

Movement decoding using spatio-spectral features of cortical and subcortical local field potentials

The first commercially sensing enabled deep brain stimulation (DBS) devices for the treatment of movement disorders have recently become available. In the future, such devices could leverage machine learning based brain signal decoding strategies to individualize and adapt therapy in real-time. As multi-channel recordings become available, spatial information may provide an additional advantage for informing machine learning models. To investigate this concept, we compared decoding performances from single channels vs. spatial filtering techniques using intracerebral multitarget electrophysiology in Parkinson's disease patients undergoing DBS implantation. We investigated the feasibility of spatial filtering in invasive neurophysiology and the putative utility of combined cortical ECoG and subthalamic local field potential signals for decoding grip-force, a well-defined and continuous motor readout. We found that adding spatial information to the model can improve decoding (6% gain in decoding), but the spatial patterns and additional benefit was highly individual. Beyond decoding performance results, spatial filters and patterns can be used to obtain meaningful neurophysiological information about the brain networks involved in target behavior. Our results highlight the importance of individualized approaches for brain signal decoding, for which multielectrode recordings and spatial filtering can improve precision medicine approaches for clinical brain computer interfaces.

Microscale electrophysiological functional connectivity in human cortico-basal ganglia network

ObjectiveWe investigated the electrophysiological relationships in the cortico-basal ganglia network on a sub-centimeter scale to increase our understanding of neural functional relationships in Parkinson’s disease (PD). MethodsData was intraoperatively recorded from 2 sources in the human brain—a microelectrode in the subthalamic nucleus (STN) and a micro-electrocorticography grid on the motor association cortex—during bilateral deep brain stimulation (DBS) electrode placement. STN neurons and local field potential (LFP) were defined as functionally connected when the 99.7% confidence intervals of the action potential (AP)-aligned average LFP and control did not overlap. ResultsAPs from STN neurons were functionally connected to the STN LFP for 18/46 STN neurons. This functional connection was observed between STN neuron APs and cortical LFP for 25/46 STN neurons. The cortical patterns of electrophysiological functional connectivity differed for each neuron. ConclusionsA subset of single neurons in the STN exhibited functional connectivity with electrophysiological activity in the STN and at a distance with the motor association cortex surveyed on a sub-centimeter spatial scale. These connections show a per neuron differential topography on the cortex. SignificanceThe cortico-basal ganglia circuit is organized on a sub-centimeter scale, and plays an important role in the mechanisms of PD and DBS.

Reward-dependent Graded Suppression of Sensorimotor Beta-band Local Field Potentials During an Arm Reaching Task in NHP.

A better understanding of reward signaling in the sensorimotor cortices can aid in developing Reinforcement Learning-based Brain-Computer Interfaces (RLBCI) for restoration of movement functions with fewer implants. Brain-computer interfaces (BCIs) using local field potentials (LFPs) have recently achieved performance comparable to spike-BCIs [1]. With superior stability over time, LFPs may be the preferred signal for BCIs. We show that sensorimotor LFPs can provide reward level information (R1 - R3) like spikes[2]. We used a cued reward-level reaching task in which reward information was temporally dissociated from movement information. This allowed the study of reward- and movement-related modulations in LFPs. We recorded simultaneously from contralateral primary -somatosensory (S1), -motor (M1), and the dorsal premotor (PMd) cortices in a female Macaca Mulatta. We found that all three cortices' average beta band (14-30 Hz) amplitude showed robust modulation with reward levels during the cue presentation period. Such modulation was consistently observed after controlling for cue color, differences in behavioral variables, and electromyogram (EMG) activity. Statistical amplitude analysis showed that reward level could be extracted from the simple LFP feature of beta band amplitude, even before a reaching target appeared, and no specific reach plan could be developed. Clinical Relevance - The availability of reward-related signals in the sensorimotor cortical (S1, M1,and PMd) LFPs' prior to movement planning opens new avenues to build RLBCIs with fewer implants recording fewer sites among different cortices Reward and motivational representations derived from LFPs compared to spikes allow the development of long-term clinical applications given LFP's stability and ease of recording over long periods.

The Spatial Reach of Neuronal Coherence and Spike-Field Coupling across the Human Neocortex.

Neuronal coherence is thought to be a fundamental mechanism of communication in the brain, where synchronized field potentials coordinate synaptic and spiking events to support plasticity and learning. Although the spread of field potentials has garnered great interest, little is known about the spatial reach of phase synchronization, or neuronal coherence. Functional connectivity between different brain regions is known to occur across long distances, but the locality of synchronization across the neocortex is understudied. Here we used simultaneous recordings from electrocorticography (ECoG) grids and high-density microelectrode arrays to estimate the spatial reach of neuronal coherence and spike-field coherence (SFC) across frontal, temporal, and occipital cortices during cognitive tasks in humans. We observed the strongest coherence within a 2-3 cm distance from the microelectrode arrays, potentially defining an effective range for local communication. This range was relatively consistent across brain regions, spectral frequencies, and cognitive tasks. The magnitude of coherence showed power law decay with increasing distance from the microelectrode arrays, where the highest coherence occurred between ECoG contacts, followed by coherence between ECoG and deep cortical local field potential (LFP), and then SFC (i.e., ECoG > LFP > SFC). The spectral frequency of coherence also affected its magnitude. Alpha coherence (8-14 Hz) was generally higher than other frequencies for signals nearest the microelectrode arrays, whereas delta coherence (1-3 Hz) was higher for signals that were farther away. Action potentials in all brain regions were most coherent with the phase of alpha oscillations, which suggests that alpha waves could play a larger, more spatially local role in spike timing than other frequencies. These findings provide a deeper understanding of the spatial and spectral dynamics of neuronal synchronization, further advancing knowledge about how activity propagates across the human brain.SIGNIFICANCE STATEMENT Coherence is theorized to facilitate information transfer across cerebral space by providing a convenient electrophysiological mechanism to modulate membrane potentials in spatiotemporally complex patterns. Our work uses a multiscale approach to evaluate the spatial reach of phase coherence and spike-field coherence during cognitive tasks in humans. Locally, coherence can reach up to 3 cm around a given area of neocortex. The spectral properties of coherence revealed that alpha phase-field and spike-field coherence were higher within ranges <2 cm, whereas lower-frequency delta coherence was higher for contacts farther away. Spatiotemporally shared information (i.e., coherence) across neocortex seems to reach farther than field potentials alone.

Functional Interactions Between the Parafascicular Thalamic Nucleus and Motor Cortex Are Altered in Hemiparkinsonian Rat.

Parkinson’s disease (PD) is characterized by aberrant discharge patterns and exaggerated oscillatory activity within basal ganglia-thalamocortical circuits. We have previously observed substantial alterations in spike and local field potential (LFP) activities recorded in the thalamic parafascicular nucleus (PF) and motor cortex (M1), respectively, of hemiparkinsonian rats during rest or catching movements. This study explored whether the mutual effects of the PF and M1 depended on the amplitude and phase relationship in their identified neuron spikes or group rhythmic activities. Microwire electrode arrays were paired and implanted in the PF and M1 of rats with unilateral dopaminergic cell lesions. The results showed that the identified PF neurons exhibited aberrant cell type-selective firing rates and preferential and excessive phase-locked firing to cortical LFP oscillations mainly at 12–35 Hz (beta frequencies), consistent with the observation of identified M1 neurons with ongoing PF LFP oscillations. Experimental evidence also showed a decrease in phase-locking at 0.7–12 Hz and 35–70 Hz in the PF and M1 circuits in the hemiparkinsonian rats. Furthermore, anatomical evidence was provided for the existence of afferent and efferent bidirectional reciprocal connectivity pathways between the PF and M1 using an anterograde and retrograde neuroanatomical tracing virus. Collectively, our results suggested that multiple alterations may be present in regional anatomical and functional modes with which the PF and M1 interact, and that parkinsonism-associated changes in PF integrate M1 activity in a manner that varies with frequency, behavioral state, and integrity of the dopaminergic system.

Altered cortical processing of sensory input in Huntington disease mouse models

Huntington disease (HD), a hereditary neurodegenerative disorder, manifests as progressively impaired movement and cognition. Although early abnormalities of neuronal activity in striatum are well established in HD models, there are fewer in vivo studies of the cortex. Here, we record local field potentials (LFPs) in YAC128 HD model mice versus wild-type mice. In multiple cortical areas, limb sensory stimulation evokes a greater change in LFP power in YAC128 mice. Mesoscopic imaging using voltage-sensitive dyes reveals more extensive spread of evoked sensory signals across the cortical surface in YAC128 mice. YAC128 layer 2/3 sensory cortical neurons ex vivo show increased excitatory events, which could contribute to enhanced sensory responses in vivo. Cortical LFP responses to limb stimulation, visual and auditory input are also significantly increased in zQ175 HD mice. Results presented here extend knowledge of HD beyond ex vivo studies of individual neurons to the intact cortical network.

Visual Recognition Is Heralded by Shifts in Local Field Potential Oscillations and Inhibitory Networks in Primary Visual Cortex.

Learning to recognize and filter familiar, irrelevant sensory stimuli eases the computational burden on the cerebral cortex. Inhibition is a candidate mechanism in this filtration process, and oscillations in the cortical local field potential (LFP) serve as markers of the engagement of different inhibitory neurons. We show here that LFP oscillatory activity in visual cortex is profoundly altered as male and female mice learn to recognize an oriented grating stimulus—low-frequency (∼15 Hz peak) power sharply increases, whereas high-frequency (∼65 Hz peak) power decreases. These changes report recognition of the familiar pattern as they disappear when the stimulus is rotated to a novel orientation. Two-photon imaging of neuronal activity reveals that parvalbumin-expressing inhibitory neurons disengage with familiar stimuli and reactivate to novelty, whereas somatostatin-expressing inhibitory neurons show opposing activity patterns. We propose a model in which the balance of two interacting interneuron circuits shifts as novel stimuli become familiar.SIGNIFICANCE STATEMENT Habituation, familiarity, and novelty detection are fundamental cognitive processes that enable organisms to adaptively filter meaningless stimuli and focus attention on potentially important elements of their environment. We have shown that this process can be studied fruitfully in the mouse primary visual cortex by using simple grating stimuli for which novelty and familiarity are defined by orientation and by measuring stimulus-evoked and continuous local field potentials. Altered event-related and spontaneous potentials, and deficient habituation, are well-documented features of several neurodevelopmental psychiatric disorders. The paradigm described here will be valuable to interrogate the origins of these signals and the meaning of their disruption more deeply.

Decoding Saccade Intention From Primate Prefrontal Cortical Local Field Potentials Using Spectral, Spatial, and Temporal Dimensionality Reduction.

Most invasive Brain Computer Interfaces (iBCIs) use spike and Local Field Potentials (LFPs) from the motor or parietal cortices to decode movement intentions. It has been debated whether harvesting signals from other brain areas that encode global cognitive variables, such as the allocation of attention and eye movement goals in a variety of spatial reference frames, may improve the outcome of iBCIs. Here, we explore the ability of LFP signals, sampled from the lateral prefrontal cortex (LPFC) of macaque monkeys, to encode eye-movement intention during the pre-movement fixation period of a delayed saccade task. We use spectral dimensionality reduction to examine the spatiotemporal properties of the extracted non-rhythmic broadband activity and explore its usefulness in decoding saccade goals. The dynamics of the broadband signal in low spatial dimensions across the pre-movement fixation period uncovered saccade target separation; its discriminative potential was confirmed using support vector machine classifications. These findings reveal that broadband LFP from the LPFC can be used to decode intended saccade target location during pre-movement periods. We further provide a general workflow that can be implemented in iBCIs and it is relatively robust to the loss of spikes in individual electrodes.

Movement-related changes in pallidocortical synchrony differentiate action execution and observation in humans

ObjectiveSuppression of local and network alpha and beta oscillations in the human basal ganglia-thalamocortical (BGTC) circuit is a prominent feature of movement, including suppression of local alpha/beta power, cross-region beta phase coupling, and cortical and subcortical phase-amplitude coupling (PAC). We hypothesized that network-level coupling is more directly related to movement execution than local power changes, given the role of pathological network hypersynchrony in movement disorders such as Parkinson disease (PD). Understanding the specificity of these movement-related signals is important for designing novel therapeutics. MethodsWe recorded globus pallidus internus (GPi) and motor cortical local field potentials during movement execution, passive movement observation and rest in 12 patients with PD undergoing deep brain stimulator implantation. ResultsLocal alpha/beta power is suppressed in the globus pallidus and motor cortex during both action execution and action observation, although less so during action observation. In contrast, pallidocortical phase synchrony and GPi and motor cortical alpha/beta-gamma PAC are suppressed only during action execution. ConclusionsThe functional dissociation across tasks in pallidocortical network activity suggests a particularly important role of network coupling in motor execution. SignificanceNetwork level recordings provide important specificity in differentiating motor behavior and may provide significant value for future closed loop therapies.

Error-Related Activity in Striatal Local Field Potentials and Medial Frontal Cortex: Evidence From Patients With Severe Opioid Abuse Disorder.

For successful goal-directed behavior, a performance monitoring system is essential. It detects behavioral errors and initiates behavioral adaptations to improve performance. Two electrophysiological potentials are known to follow errors in reaction time tasks: the error-related negativity (ERN), which is linked to error processing, and the error positivity (Pe), which is associated with subjective error awareness. Furthermore, the correct-related negativity (CRN) is linked to uncertainty about the response outcome. Here we attempted to identify the involvement of the nucleus accumbens (NAc) in the aforementioned performance monitoring processes. To this end, we simultaneously recorded cortical activity (EEG) and local field potentials (LFP) during a flanker task performed by four patients with severe opioid abuse disorder who underwent electrode implantation in the NAc for deep brain stimulation. We observed significant accuracy-related modulations in the LFPs at the time of the ERN/CRN in two patients and at the time of Pe in three patients. These modulations correlated with the ERN in 2/8, with CRN in 5/8 and with Pe in 6/8, recorded channels, respectively. Our results demonstrate the functional interrelation of striatal and cortical processes in performance monitoring specifically related to error processing and subjective error awareness.

Soft Printable Electrode Coating for Neural Interfaces.

The mechanical mismatch between implantable interfaces and neural tissues may be reduced by employing soft polymeric materials. Here, we report on a simple strategy to prepare and pattern a soft electrode coating of neural interfacing devices based on a screen-printable conducting hydrogel. The coating formulation, based on polyacrylamide and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate, is suitable to additive manufacturing and exhibits excellent adhesion to polydimethylsiloxane, an elastomer commonly used as a substrate in soft neural interfaces. The soft conductive coating displays a tunable elastic modulus in the 10-100 kPa range and electrochemical properties on a par with stiff conductive inks while supporting good neural cell attachment and proliferation in vitro. Next, the soft printable hydrogel is integrated within a 4 × 4 microelectrode array for electrocorticography with 250 μm-diameter contacts. Acute recording of cortical local field potentials and electrochemical characterization preimplantation and postimplantation highlight the stability of the soft organic conductor. The overall robustness of the soft coating and its patterning method provide a promising route for a range of implantable bioelectronic applications.