LFP publications

The mismatch negativity (MMN) is a key biomarker of automatic deviance detection thought to emerge from 2 cortical sources. First, the auditory cortex (AC) encodes spectral regularities and reports frequency-specific deviances. Then, more abstract representations in the prefrontal cortex (PFC) allow to detect contextual changes of potential behavioral relevance. However, the precise location and time asynchronies between neuronal correlates underlying this frontotemporal network remain unclear and elusive. Our study presented auditory oddball paradigms along with “no-repetition” controls to record mismatch responses in neuronal spiking activity and local field potentials at the rat medial PFC. Whereas mismatch responses in the auditory system are mainly induced by stimulus-dependent effects, we found that auditory responsiveness in the PFC was driven by unpredictability, yielding context-dependent, comparatively delayed, more robust and longer-lasting mismatch responses mostly comprised of prediction error signaling activity. This characteristically different composition discarded that mismatch responses in the PFC could be simply inherited or amplified downstream from the auditory system. Conversely, it is more plausible for the PFC to exert top-down influences on the AC, since the PFC exhibited flexible and potent predictive processing, capable of suppressing redundant input more efficiently than the AC. Remarkably, the time course of the mismatch responses we observed in the spiking activity and local field potentials of the AC and the PFC combined coincided with the time course of the large-scale MMN-like signals reported in the rat brain, thereby linking the microscopic, mesoscopic, and macroscopic levels of automatic deviance detection.

Changes in brain temperature have been reported to affect various brain functions. However, little is known about the effects of temperature on the neural activity at the network level, where multiple inputs are integrated. In this study, we recorded cortical evoked potentials while altering the local brain temperature in anesthetized rats. We delivered electrical stimulations to the midbrain dopamine area and measured the evoked potentials in the frontal cortex, the temperature of which was locally altered using a thermal control device. We focused on the maximum negative peaks, which was presumed to result mainly from polysynaptic responses, to examine the effect of local temperature on network activity. We showed that focal cortical cooling increased the amplitude of evoked potentials (negative correlation, >17°C); further cooling decreased their amplitude. This relationship would be graphically represented as an inverted-U-shaped curve. The pharmacological blockade of GABAergic inhibitory inputs eliminated the negative correlation (>17°C) and even showed a positive correlation when the concentration of GABA A receptor antagonist was sufficiently high. Blocking the glutamatergic excitatory inputs decreased the amplitude but did not cause such inversion. Our results suggest that the negative correlation between the amplitude of evoked potentials and the near-physiological local temperature is caused by the alteration of the balance of contribution between excitatory and inhibitory inputs to the evoked potentials, possibly due to higher temperature sensitivity of inhibitory inputs.

Neurophysiological biomarkers that correlate with motor symptoms or disease severity are vital to improve our understanding of the pathophysiology in Parkinson’s disease (PD) and for the development of more effective treatments, including deep brain stimulation (DBS). This work provides direct insight into the application of these biomarkers in training classifiers to discriminate between brain states, which is a first step toward developing closed-loop DBS systems.

Although the activity from the dentate gyrus is known to have strong connections with other hippocampal layers, the functionality of these connections, that is, the degree to which it drives activity in the downstream regions of the hippocampus, is not well understood. This question is particularly relevant for mesoscale localfield potential (LFP) rhythms such as gamma oscillations. Following the hypothesis that fundamental features of the LFP are consistent with turbulent dynamics, we investigate the crosslayer relationship between the CA1 layers and the dentate gyrus as a function of running speed. In agreement with previous studies, same-layer spectral and bispectral analyses show that increasing input (rat speed) results in an increase of power and nonlinearity (phase coupling) between theta and gamma. The effectiveness of the connection between the 2 regions is investigated using cross-bicoherence analysis. Based on the turbulence interpretation of the evolution of spectra and bispectra as a function of the power input rate, we propose a measure for estimating the strength of the cross-frequency, cross-layer nonlinear forcing, that compares the magnitude of bicoherence (same-layer) and cross-bicoherence (cross-layer). Our results suggest that at moderate speeds gamma in CA1 is mainly driven by local theta, while the coupling of the CA1 gamma to the dentate-gyrus gamma becomes significant. Overall, these data are consistent with the hypothesis of theta-to-gamma energy cascade model for the organization of hippocampal LFP, with theta playing the role of a global pacemaker across the entire hippocampus while gamma is a local oscillation generated by through local anatomical connections. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Understanding the cytoarchitecture and wiring of the brain requires improved methods to record and stimulate large groups of neurons with cellular specificity. This requires miniaturized neural interfaces that integrate into brain tissue without altering its properties. Existing neural interface technologies have been shown to provide high-resolution electrophysiological recording with high signal-to-noise ratio. However, with single implantation, the physical properties of these devices limit their access to one, small brain region. To overcome this limitation, we developed a platform that provides three-dimensional coverage of brain tissue through multisite multifunctional fiber-based neural probes guided in a helical scaffold. Chronic recordings from the spatially expandable fiber probes demonstrate the ability of these fiber probes capturing brain activities with a single-unit resolution for long observation times. Furthermore, using Thy1-ChR2-YFP mice we demonstrate the application of our probes in simultaneous recording and optical/chemical modulation of brain activities across distant regions. Similarly, varying electrographic brain activities from different brain regions were detected by our customizable probes in a mouse model of epilepsy, suggesting the potential of using these probes for the investigation of brain disorders such as epilepsy. Ultimately, this technique enables three-dimensional manipulation and mapping of brain activities across distant regions in the deep brain with minimal tissue damage, which can bring new insights for deciphering complex brain functions and dynamics in the near future.

Significance The noise pollution accompanying industrialization is a risk factor to human health. Here, we show in a rodent model that even moderate-level noise at ∼65 dB SPL that has little effect on stress status can substantially impair hippocampus-related learning and memory by altering the plasticity of synaptic transmission. It is possible that because moderately loud noise does not affect peripheral hearing per se, the negative impacts of chronic exposure to such noise are currently not well characterized. Our results indicate the importance of more thoroughly defining these possibly hitherto unappreciated hazards of noise pollution in modern human environments.

Oscillations in the granule cell layer (GCL) of the cerebellar cortex have been related to behavior and could facilitate communication with the cerebral cortex. These local field potential (LFP) oscillations, strong at 4-12 Hz in the rodent cerebellar cortex during awake immobility, should also be an indicator of an underlying influence on the patterns of the cerebellar cortex neuronal firing during rest. To address this hypothesis, cerebellar cortex LFPs and simultaneous single-neuron activity were collected during LFP oscillatory periods in the GCL of awake resting rats. During these oscillatory episodes, different types of units across the GCL and Purkinje cell layers showed variable phase-relation with the oscillatory cycles. Overall, 74% of the Golgi cell firing and 54% of the Purkinje cell simple spike (SS) firing were phase-locked with the oscillations, displaying a clear phase relationship. Despite this tendency, fewer Golgi cells (50%) and Purkinje cell's SSs (25%) showed an oscillatory firing pattern. Oscillatory phase-locked spikes for the Golgi and Purkinje cells occurred towards the peak of the LFP cycle. GCL LFP oscillations had a strong capacity to predict the timing of Golgi cell spiking activity, indicating a strong influence of this oscillatory phenomenon over the GCL. Phase-locking was not as prominent for the Purkinje cell SS firing, indicating a weaker influence over the Purkinje cell layer, yet a similar phase relation. Overall, synaptic activity underlying GCL LFP oscillations likely exert an influence on neuronal population firing patterns in the cerebellar cortex in the awake resting state and could have a preparatory neural network shaping capacity serving as a neural baseline for upcoming cerebellar operations.

Cortical synchrony is theorized to contribute to communication between connected networks during executive functioning. To understand the functional role of neural synchrony in cognitive flexibility, we recorded from auditory cortex (AC) and medial prefrontal cortex (mPFC), while mice performed an auditory extinction learning task. We found that while animals gradually showed reduced responding to the unrewarded tone over hundreds of trials, the power of local field potential (LFP) oscillations (8-18 Hz, centered at alpha/beta frequencies) in AC and mPFC exhibited immediate and robust increases, prior to behavioral changes. The strength of LFP alpha/beta power in the mPFC, but not AC, was strongly correlated with the behavioral performance that mice would achieve later in the training session. Further, we found that coherence between AC and mPFC at 8-18Hz was selectively enhanced only after mice learned to suppress licking, and this LFP coherence increase coincided with a reduction in spiking rate for the unrewarded tone in AC. These results reveal that enhanced interactions between PFC and AC is an inherent property of auditory discrimination learning, and that coordinated alpha/beta oscillations contribute to cognitive flexibility.

Background: Hippocampal and cortical slice-based models are widely used to study seizures and epilepsy. Seizure detection and quantification are essential components for studying mechanisms of epilepsy and assessing therapeutic interventions. To obtain meaningful signals and maximize experimental throughput, variability should be minimized. Some electrical recording methods require insertion of an electrode into neuronal tissue, change in slice chemical microenvironment, and transients in temperature and pH. These perturbations can cause acute and long-term alterations of the neuronal network which may be reflected in the variability of the recorded signal. New method: In this study we investigated the effect of experimental perturbations in three local field potential (LFP) recording methods including substrate micro-wires (s-MWs), multiple electrode arrays (MEAs), and inserted micro wire electrodes (i-MW). These methods enabled us to isolate effects of different perturbations. We used organotypic hippocampal slices (OHCs) as an in-vitro model of posttraumatic epilepsy. To investigate the effect of the disturbances caused by the recording method on the paroxysmal events, we introduced jitter analysis, which is sensitive to small differences in the seizure spike timing. Results: Medium replacement can introduce long-lasting perturbations. Electrode insertion increased variability on a shorter time scale. OHCs also underwent spontaneous state transitions characterized by transient increases in variability. Comparison with existing methods: This new method of seizure waveform analysis allows for more sensitive assessment of variability of ictal events than simply measuring seizure frequency and duration. Conclusion: We demonstrated that some of the variability in OHC recordings are due to experimental perturbations while some are spontaneous and independent of recording method.

Background: Parkinson’s disease (PD) neuropathology is characterized by intraneuronal protein aggregates composed of misfolded α-Synuclein (α-Syn), as well as degeneration of substantia nigra dopamine neurons. Deficits in olfactory perception and aggregation of α-Syn in the olfactory bulb (OB) are observed during early stages of PD, and have been associated with the PD prodrome, before onset of the classic motor deficits. α-Syn fibrils injected into the OB of mice cause progressive propagation of α-Syn pathology throughout the olfactory system and are coupled to olfactory perceptual deficits. Objective: We hypothesized that accumulation of pathogenic α-Syn in the OB impairs neural activity in the olfactory system. Methods: To address this, we monitored spontaneous and odor-evoked local field potential dynamics in awake wild type mice simultaneously in the OB and piriform cortex (PCX) one, two, and three months following injection of pathogenic preformed α-Syn fibrils in the OB. Results: We detected α-Syn pathology in both the OB and PCX. We also observed that α-Syn fibril injections influenced odor-evoked activity in the OB. In particular, α-Syn fibril-injected mice displayed aberrantly high odor-evoked power in the beta spectral range. A similar change in activity was not detected in the PCX, despite high levels of α-Syn pathology. Conclusion: Together, this work provides evidence that synucleinopathy impacts in vivo neural activity in the olfactory system at the network-level.

Objective. Brain-Machine Interfaces (BMIs) hold great promises for advancing neuroprosthetics, robotics, and for providing treatment options for severe neurological diseases. The objective of this work is the development and in vivo evaluation of electrodes for BMIs that meet the needs to record brain activity at sub-millimeter resolution over a large area of the cortex while being soft and electromechanically robust (i.e. stretchable). Approach. Current electrodes require a trade-off between high spatiotemporal resolution and cortical coverage area. To address the needs for simultaneous high resolution and large cortical coverage, the prototype electrode array developed in this study employs a novel bilayer routing of soft and stretchable lead wires from the recording sites on the surface of the brain (electrocorticography, ECoG) to the data acquisition system. Main results. To validate the recording characteristics, the array was implanted in healthy felines for up to 5 months. Neural signals recorded from both layers of the device showed elevated mid-frequency structures typical of local field potential (LFP) signals that were stable in amplitude over implant duration, and also exhibited consistent frequency-dependent modulation after anesthesia induction by Telazol. Significance. The successful development of a soft and stretchable large-area, high resolution micro ECoG electrode array ( lahrμ ECoG) is an important step to meet the neurotechnological needs of advanced BMI applications.

A major frontier in neuroscience is to find neural correlates of perception, learning, decision making, and a variety of other types of behavior. In the last decades, modern devices allow simultaneous recordings of different operant responses and the electrical activity of large neuronal populations. However, the commercially available instruments for studying operant conditioning are expensive, and the design of low-cost chambers has emerged as an appealing alternative to resource-limited laboratories engaged in animal behavior. In this article, we provide a full description of a platform that records the operant behavior and synchronizes it with the electrophysiological activity. The programming of this platform is open source, flexible, and adaptable to a wide range of operant conditioning tasks. We also show results of operant conditioning experiments with freely moving rats with simultaneous electrophysiological recordings.

Transcranial alternating current stimulation increases neural spike entrainment and burstiness in the awake primate brain.

In speech, listeners extract continuously-varying spectrotemporal cues from the acoustic signal to perceive discrete phonetic categories. Spectral cues are spatially encoded in the amplitude of responses in phonetically-tuned neural populations in auditory cortex. It remains unknown whether similar neurophysiological mechanisms encode temporal cues like voice-onset time (VOT), which distinguishes sounds like / b / and/ p /. We used direct brain recordings in humans to investigate the neural encoding of temporal speech cues with a VOT continuum from / ba / to / pa /. We found that distinct neural populations respond preferentially to VOTs from one phonetic category, and are also sensitive to sub-phonetic VOT differences within a population’s preferred category. In a simple neural network model, simulated populations tuned to detect either temporal gaps or coincidences between spectral cues captured encoding patterns observed in real neural data. These results demonstrate that a spatial/amplitude neural code underlies the cortical representation of both spectral and temporal speech cues.

BHA (aka high gamma) correlates with neuronal firing, but is generated mainly by dendritic processes separable from firing.

Field potential (FP) oscillations are believed to coordinate brain activity over large spatiotemporal scales, with specific features (e.g., phase and power) in discrete frequency bands correlated with motor output. Furthermore, complex correlations between oscillations in distinct frequency bands (phase-amplitude, amplitude-amplitude, and phase-phase coupling) are commonly observed. However, the mechanisms underlying FP-behavior correlations and cross-frequency coupling remain unknown. The thalamus plays a central role in generating many circuit-level neural oscillations, and single-unit activity in motor thalamus (Mthal) is correlated with behavioral output. We, therefore, hypothesized that motor thalamic spiking coordinates motor system FPs and underlies FP-behavior correlations. To investigate this possibility, we recorded wideband motor thalamic (Mthal) electrophysiology as healthy rats performed a two-alternative forced-choice task. Delta (1-4 Hz), beta (13-30 Hz), low gamma (30-70 Hz), and high gamma (70-200 Hz) power were strongly modulated by task performance. As in the cortex, the delta phase was correlated with beta/low gamma power and reaction time. Most interestingly, subpopulations of Mthal neurons defined by their relationship to the behavior exhibited distinct relationships with FP features. Specifically, neurons whose activity was correlated with action selection and movement speed were entrained to delta oscillations. Furthermore, changes in their activity anticipated power fluctuations in beta/low gamma bands. These complex relationships suggest mechanisms for commonly observed FP-FP and spike-FP correlations, as well as subcortical influences on motor output.

Mammalian cortex has both local and cross-area connections, suggesting vital roles for both local and cross-area neural population dynamics in cortically-dependent tasks, like movement learning. Prior studies of movement learning have focused on how single-area population dynamics change during short-term adaptation. It is unclear how cross-area dynamics contribute to movement learning, particularly long-term learning and skill acquisition. Using simultaneous recordings of rodent motor (M1) and premotor (M2) cortex and computational methods, we show how cross-area activity patterns evolve during reach-to-grasp learning in rats. The emergence of reach-related modulation in cross-area activity correlates with skill acquisition, and single-trial modulation in cross-area activity predicts reaction time and reach duration. Local M2 neural activity precedes local M1 activity, supporting top–down hierarchy between the regions. M2 inactivation preferentially affects cross-area dynamics and behavior, with minimal disruption of local M1 dynamics. Together, these results indicate that cross-area population dynamics are necessary for learned motor skills.

The neural mechanisms underlying forward suppression in the auditory cortex remain a puzzle. Little attention is paid to thalamic contribution despite the important fact that the thalamus gates upstreaming information to the auditory cortex. This study compared the time courses of forward suppression in the auditory thalamus, thalamocortical inputs and cortex using the two-tone stimulus paradigm. The preceding and succeeding tones were 20-ms long. Their frequency and amplitude were set at the characteristic frequency and 20 dB above the minimum threshold of given neurons, respectively. In the ventral division of the medial geniculate body of the thalamus, we found that the duration of complete forward suppression was about 75 ms and the duration of partial suppression was from 75 ms to about 300 ms after the onset of the preceding tone. We also found that during the partial suppression period, the responses to the succeeding tone were further suppressed in the primary auditory cortex. The forward suppression of thalamocortical field excitatory postsynaptic potentials was between those of thalamic and cortical neurons but much closer to that of thalamic ones. Our results indicate that early suppression in the cortex could result from complete suppression in the thalamus whereas later suppression may involve thalamocortical and intracortical circuitry. This suggests that the complete suppression that occurs in the thalamus provides the cortex with a "silence" window that could potentially benefit cortical processing and/or perception of the information carried by the preceding sound.

Neural implants that deliver multi-site electrical stimulation to the nervous systems are no longer the last resort but routine treatment options for various neurological disorders. Multi-site electrical stimulation is also widely used to study nervous system function and neural circuit transformations. These technologies increasingly demand dynamic electrical stimulation and closed-loop feedback control for real-time assessment of neural function, which is technically challenging since stimulus-evoked artifacts overwhelm the small neural signals of interest. We report a novel and versatile artifact removal method that can be applied in a variety of settings, from single- to multi-site stimulation and recording and for current waveforms of arbitrary shape and size. The method capitalizes on linear electrical coupling between stimulating currents and recording artifacts, which allows us to estimate a multi-channel linear Wiener filter to predict and subsequently remove artifacts via subtraction. We confirm and verify the linearity assumption and demonstrate feasibility in a variety of recording modalities, including in vitro sciatic nerve stimulation, bilateral cochlear implant stimulation, and multi-channel stimulation and recording between the auditory midbrain and cortex. We demonstrate a vast enhancement in the recording quality with a typical artifact reduction of 25-40 dB. The method is efficient and can be scaled to arbitrary number of stimulus and recording sites, making it ideal for applications in large-scale arrays, closed-loop implants, and high-resolution multi-channel brain-machine interfaces.

Sleep homeostasis manifests as a relative constancy of its daily amount and intensity. Theoretical descriptions define ‘Process S’, a variable with dynamics dependent on global sleep-wake history, and reflected in electroencephalogram (EEG) slow wave activity (SWA, 0.5–4 Hz) during sleep. The notion of sleep as a local, activity-dependent process suggests that activity history must be integrated to determine the dynamics of global Process S. Here, we developed novel mathematical models of Process S based on cortical activity recorded in freely behaving mice, describing local Process S as a function of the deviation of neuronal firing rates from a locally defined set-point, independent of global sleep-wake state. Averaging locally derived Processes S and their rate parameters yielded values resembling those obtained from EEG SWA and global vigilance states. We conclude that local Process S dynamics reflects neuronal activity integrated over time, and global Process S reflects local processes integrated over space.