How local is the local field potential?

Degeneracy in hippocampal physiology and plasticity

Degeneracy, defined as the ability of structurally disparate elements to perform analogous function, has largely been assessed from the perspective of maintaining robustness of physiology or plasticity. How does the framework of degeneracy assimilate into an encoding system where the ability to change is an essential ingredient for storing new incoming information? Could degeneracy maintain the balance between the apparently contradictory goals of the need to change for encoding and the need to resist change towards maintaining homeostasis? In this review, we explore these fundamental questions with the mammalian hippocampus as an example encoding system. We systematically catalog lines of evidence, spanning multiple scales of analysis that point to the expression of degeneracy in hippocampal physiology and plasticity. We assess the potential of degeneracy as a framework to achieve the conjoint goals of encoding and homeostasis without cross-interferences. We postulate that biological complexity, involving interactions among the numerous parameters spanning different scales of analysis, could establish disparate routes towards accomplishing these conjoint goals. These disparate routes then provide several degrees of freedom to the encoding-homeostasis system in accomplishing its tasks in an input- and state-dependent manner. Finally, the expression of degeneracy spanning multiple scales offers an ideal reconciliation to several outstanding controversies, through the recognition that the seemingly contradictory disparate observations are merely alternate routes that the system might recruit towards accomplishment of its goals.

Auditory deviance detection in the human insula: An intracranial EEG study

The human insula is known to be involved in auditory processing, but knowledge about its precise functional role and the underlying electrophysiology is limited. To assess its role in automatic auditory deviance detection we analyzed the EEG high frequency activity (HFA; 75-145 Hz) and ERPs from 90 intracranial insular channels across 16 patients undergoing pre-surgical intracranial monitoring for epilepsy treatment. Subjects passively listened to a stream of standard and deviant tones differing in four physical dimensions: intensity, frequency, location or time. HFA responses to auditory stimuli were found in the short and long gyri, and the anterior, superior, and inferior segments of the circular sulcus of the insular cortex. Only a subset of channels in the inferior segment of the circular sulcus of the insula showed HFA deviance detection responses, i.e., a greater and longer latency response to specific deviants relative to standards. Auditory deviancy processing was also later in the insula when compared with the superior temporal cortex. ERP results were more widespread and supported the HFA insular findings. These results provide evidence that the human insula is engaged during auditory deviance detection.

Single-cell selectivity and functional architecture of human lateral occipital complex

The human lateral occipital complex (LOC) is more strongly activated by images of objects compared to scrambled controls, but detailed information at the neuronal level is currently lacking. We recorded with microelectrode arrays in the LOC of 2 patients and obtained highly selective single-unit, multi-unit, and high-gamma responses to images of objects. Contrary to predictions derived from functional imaging studies, all neuronal properties indicated that the posterior subsector of LOC we recorded from occupies an unexpectedly high position in the hierarchy of visual areas. Notably, the response latencies of LOC neurons were long, the shape selectivity was spatially clustered, LOC receptive fields (RFs) were large and bilateral, and a number of LOC neurons exhibited three-dimensional (3D)-structure selectivity (a preference for convex or concave stimuli), which are all properties typical of end-stage ventral stream areas. Thus, our results challenge prevailing ideas about the position of the more posterior subsector of LOC in the hierarchy of visual areas.

Between persistently active and activity-silent frameworks: novel vistas on the cellular basis of working memory

Recent work has revealed important new discoveries on the cellular mechanisms of working memory (WM). These findings have motivated several seemingly conflicting theories on the mechanisms of short‐term memory maintenance. Here, we summarize the key insights gained from these new experiments and critically evaluate them in light of three hypotheses: classical persistent activity, activity‐silent, and dynamic coding. The experiments discussed include the first direct demonstration of persistently active neurons in the human medial temporal lobe that form static attractors with relevance to WM, single‐neuron recordings in the macaque prefrontal cortex that show evidence for both persistent and more dynamic types of WM representations, and noninvasive neuroimaging in humans that argues for activity‐silent representations. A key insight that emerges from these new results is that there are several neural mechanisms that support the maintenance of information in WM. Finally, based on established cognitive theories of WM, we propose a coherent model that encompasses these seemingly contradictory results. We propose that the three neuronal mechanisms of persistent activity, activity‐silent, and dynamic coding map well onto the cognitive levels of information processing (within focus of attention, activated long‐term memory, and central executive) that Cowan's WM model proposes.

Multiunit cluster firing patterns of piriform cortex and mediodorsal thalamus in absence epilepsy

OBJECTIVE

The objective of the study were to investigate patterns of multiunit cluster firing in the piriform cortex (PC) and mediodorsal thalamus (MDT) in a rat model of genetic generalized epilepsy (GGE) with absence seizures and to assess whether these regions contribute to the initiation or spread of generalized epileptiform discharges.

METHODS

Multiunit clusters and their corresponding local field potentials (LFPs) were recorded from microelectrode arrays implanted in the PC and MDT in urethane anesthetized Genetic Absence Epilepsy Rats from Strasbourg (GAERS) and nonepileptic control (NEC) rats. Peristimulus time histograms (PSTHs) and cross-correlograms were used to observe transient changes in both the rate of firing and synchrony over time. The phase locking of multiunit clusters to LFP signals (spike-LFP phase locking) was calculated for frequency bands associated with olfactory communication between the two brain regions.

RESULTS

There were significant increases in both rate of firing and synchronous activity at the onset of generalized epileptiform discharges in both PC and MDT. Prior to and following these increases in synchronous activity, there were periods of suppression. Significant increases in spike-LFP phase locking were observed within the PC prior to the onset of epileptiform discharges across all spectral bands. There were also significant increases in spike-LFP phase locking within the theta band of the MDT prior to onset. Between the two brain regions, there was a significant decrease in spike-LFP phase locking -0.5 s prior to onset in the theta band which coincided with a significant elevation in spike-LFP phase locking in the gamma band.

CONCLUSIONS

Both the PC and MDT are engaged in the absence epilepsy network. Early spike-LFP phase locking between these two brain regions suggests potential involvement in the initiation of seizure activity.

Spike-field Granger causality for hybrid neural data analysis

Neurotechnological innovations allow for simultaneous recording at various scales, ranging from spiking activity of individual neurons to large neural populations’ local field potentials (LFPs). This capability necessitates developing multiscale analysis of spike-field activity. A joint analysis of the hybrid neural data is crucial for bridging the scales between single neurons and local networks. Granger causality is a fundamental measure to evaluate directional influences among neural signals. However, it is mainly limited to inferring causal influence between the same type of signals—either LFPs or spike trains—and not well developed between two different signal types. Here we propose a model-free, nonparametric spike-field Granger causality measure for hybrid data analysis. Our measure is distinct from existing methods in that we use “binless” spikes (precise spike timing) rather than “binned” spikes (spike counts within small consecutive time windows). The latter clearly distort the information in the mixed analysis of spikes and LFP. Therefore, our spectral estimate of spike trains is directly applied to the neural point process itself, i.e., sequences of spike times rather than spike counts. Our measure is validated by an extensive set of simulated data. When the measure is applied to LFPs and spiking activity simultaneously recorded from visual areas V1 and V4 of monkeys performing a contour detection task, we are able to confirm computationally the long-standing experimental finding of the input-output relationship between LFPs and spikes. Importantly, we demonstrate that spike-field Granger causality can be used to reveal the modulatory effects that are inaccessible by traditional methods, such that spike→LFP Granger causality is modulated by the behavioral task, whereas LFP→spike Granger causality is mainly related to the average synaptic input.

NEW & NOTEWORTHY It is a pressing question to study the directional interactions between local field potential (LFP) and spiking activity. In this report, we propose a model-free, nonparametric spike-field Granger causality measure that can be used to reveal directional influences between spikes and LFPs. This new measure is crucial for bridging the scales between single neurons and neural networks; hence it represents an important step to explicate how the brain orchestrates information processing.

Modulation of local field potentials and neuronal activity in primate hippocampus during saccades

Primates use saccades to gather information about objects and their relative spatial arrangement, a process essential for visual perception and memory. It has been proposed that signals linked to saccades reset the phase of local field potential (LFP) oscillations in the hippocampus, providing a temporal window for visual signals to activate neurons in this region and influence memory formation. We investigated this issue by measuring hippocampal LFPs and spikes in two macaques performing different tasks with unconstrained eye movements. We found that LFP phase clustering (PC) in the alpha/beta (8-16 Hz) frequencies followed foveation onsets, while PC in frequencies lower than 8 Hz followed spontaneous saccades, even on a homogeneous background. Saccades to a solid grey background were not followed by increases in local neuronal firing, whereas saccades toward appearing visual stimuli were. Finally, saccade parameters correlated with LFPs phase and amplitude: saccade direction correlated with delta (≤4 Hz) phase, and saccade amplitude with theta (4-8 Hz) power. Our results suggest that signals linked to saccades reach the hippocampus, producing synchronization of delta/theta LFPs without a general activation of local neurons. Moreover, some visual inputs co-occurring with saccades produce LFP synchronization in the alpha/beta bands and elevated neuronal firing. Our findings support the hypothesis that saccade-related signals enact sensory input-dependent plasticity and therefore memory formation in the primate hippocampus.

Anatomical and physiological foundations of cerebello-hippocampal interaction

Multiple lines of evidence suggest that functionally intact cerebello-hippocampal interactions are required for appropriate spatial processing. However, how the cerebellum anatomically and physiologically engages with the hippocampus to sustain such communication remains unknown. Using rabies virus as a retrograde transneuronal tracer in mice, we reveal that the dorsal hippocampus receives input from topographically restricted and disparate regions of the cerebellum. By simultaneously recording local field potential from both the dorsal hippocampus and anatomically connected cerebellar regions, we additionally suggest that the two structures interact, in a behaviorally dynamic manner, through subregion-specific synchronization of neuronal oscillations in the 6-12 Hz frequency range. Together, these results reveal a novel neural network macro-architecture through which we can understand how a brain region classically associated with motor control, the cerebellum, may influence hippocampal neuronal activity and related functions, such as spatial navigation.

Microelectrode array electrical impedance tomography for fast functional imaging in the thalamus

Electrical Impedance Tomography (EIT) has the potential to be able to observe functional tomographic images of neural activity in the brain at millisecond time-scales. Prior modelling and experimental work has shown that EIT is capable of imaging impedance changes from neural depolarisation in rat somatosensory cortex. Here, we investigate the feasibility of EIT for imaging impedance changes using a stereotaxically implanted microelectrode array in the thalamus. Microelectrode array EIT was simulated using an anatomically accurate marmoset brain model. Impedance imaging was validated and detectability estimated using physiological noise recorded from the marmoset visual thalamus. The results suggest that visual-input-driven impedance changes in visual subcortical bodies within 300 μm of the implanted array could be reliably reconstructed and localised, comparable to local field potential measurements. Furthermore, we demonstrated that microelectrode array EIT could reconstruct concurrent activity in multiple subcortical bodies simultaneously.

Theta Oscillations Alternate With High Amplitude Neocortical Population Within Synchronized States

Synchronized states are marked by large-amplitude low-frequency oscillations in the cortex. These states can be seen during quiet waking or slow-wave sleep. Within synchronized states, previous studies have noted a plethora of different types of activity, including delta oscillations (0.5-4 Hz) and slow oscillations (<1 Hz) in the neocortex and large- and small- irregular activity in the hippocampus. However, it is not still fully characterized how neural populations contribute to the synchronized state. Here we apply independent component analysis to parse which populations are involved in different kinds of neocortical activity, and find two populations that alternate throughout synchronized states. One population broadly affects neocortical deep layers, and is associated with larger amplitude slower neocortical oscillations. The other population exhibits theta-frequency oscillations that are not easily observed in raw field potential recordings. These theta oscillations apparently come from below the neocortex, suggesting hippocampal origin, and are associated with smaller amplitude faster neocortical oscillations. Relative involvement of these two alternating populations may indicate different modes of operation within synchronized states.

Single-Cell Membrane Potential Fluctuations Evince Network Scale-Freeness and Quasicriticality

What information single neurons receive about general neural circuit activity is a fundamental question for neuroscience. Somatic membrane potential (V _m) fluctuations are driven by the convergence of synaptic inputs from a diverse cross-section of upstream neurons. Furthermore, neural activity is often scale-free, implying that some measurements should be the same, whether taken at large or small scales. Together, convergence and scale-freeness support the hypothesis that single V _m recordings carry useful information about high-dimensional cortical activity. Conveniently, the theory of "critical branching networks" (one purported explanation for scale-freeness) provides testable predictions about scale-free measurements that are readily applied to V _m fluctuations. To investigate, we obtained whole-cell current-clamp recordings of pyramidal neurons in visual cortex of turtles with unknown genders. We isolated fluctuations in V _m below the firing threshold and analyzed them by adapting the definition of "neuronal avalanches" (i.e., spurts of population spiking). The V _m fluctuations which we analyzed were scale-free and consistent with critical branching. These findings recapitulated results from large-scale cortical population data obtained separately in complementary experiments using microelectrode arrays described previously (Shew et al., 2015). Simultaneously recorded single-unit local field potential did not provide a good match, demonstrating the specific utility of V _m Modeling shows that estimation of dynamical network properties from neuronal inputs is most accurate when networks are structured as critical branching networks. In conclusion, these findings extend evidence of critical phenomena while also establishing subthreshold pyramidal neuron V _m fluctuations as an informative gauge of high-dimensional cortical population activity.SIGNIFICANCE STATEMENT The relationship between membrane potential (V _m) dynamics of single neurons and population dynamics is indispensable to understanding cortical circuits. Just as important to the biophysics of computation are emergent properties such as scale-freeness, where critical branching networks offer insight. This report makes progress on both fronts by comparing statistics from single-neuron whole-cell recordings with population statistics obtained with microelectrode arrays. Not only are fluctuations of somatic V _m scale-free, they match fluctuations of population activity. Thus, our results demonstrate appropriation of the brain's own subsampling method (convergence of synaptic inputs) while extending the range of fundamental evidence for critical phenomena in neural systems from the previously observed mesoscale (fMRI, LFP, population spiking) to the microscale, namely, V _m fluctuations.

Cortical Electrocorticogram (ECoG) Is a Local Signal

Electrocorticogram (ECoG), obtained by low-pass filtering the brain signal recorded from a macroelectrode placed on the cortex, is extensively used to find the seizure focus in drug-resistant epilepsy and is of growing importance in cognitive and brain-machine-interfacing studies. To accurately estimate the epileptogenic cortex or to make inferences about cognitive processes, it is important to determine the "spatial spread" of ECoG (i.e., the extent of cortical tissue that contributes to its activity). However, the ECoG spread is currently unknown; even the spread of local field potential (LFP) obtained from microelectrodes is debated, with estimates ranging from a few hundred micrometers to several millimeters. Spatial spread can be estimated by measuring the receptive field (RF) and multiplying by the cortical magnification factor, but this method overestimates the spread because RF size gets inflated due to several factors. This issue can be partially addressed using a model that compares the RFs of two measures, such as LFP and multi-unit activity (MUA). To use this approach for ECoG, we designed a customized array containing both microelectrodes and ECoG electrodes to simultaneously map MUA, LFP, and ECoG RFs from the primary visual cortex of awake monkeys (three female Macaca radiata). The spatial spread of ECoG was surprisingly local (diameter ∼3 mm), only 3 times that of the LFP. Similar results were obtained using a model to simulate ECoG as a sum of LFPs of varying electrode sizes. Our results further validate the use of ECoG in clinical and basic cognitive research.SIGNIFICANCE STATEMENT Brains signals capture different attributes of the neural network depending on the size and location of the recording electrode. Electrocorticogram (ECoG), obtained by placing macroelectrodes (typically 2-3 mm diameter) on the exposed surface of the cortex, is widely used by neurosurgeons to identify the source of seizures in drug-resistant epileptic patients. The brain area responsible for seizures is subsequently surgically removed. Accurate estimation of the epileptogenic cortex and its removal requires the estimation of spatial spread of ECoG. Here, we estimated the spatial spread of ECoG in five behaving monkeys using two different approaches. Our results suggest that ECoG is a local signal (diameter of ∼3 mm), which can provide a useful tool for clinical, cognitive neuroscience, and brain-machine-interfacing applications.

Volitional control of single-electrode high gamma local field potentials by people with paralysis

Intracortical brain-computer interfaces (BCIs) can enable individuals to control effectors, such as a computer cursor, by directly decoding the user's movement intentions from action potentials and local field potentials (LFPs) recorded within the motor cortex. However, the accuracy and complexity of effector control achieved with such "biomimetic" BCIs will depend on the degree to which the intended movements used to elicit control modulate the neural activity. In particular, channels that do not record distinguishable action potentials and only record LFP modulations may be of limited use for BCI control. In contrast, a biofeedback approach may surpass these limitations by letting the participants generate new control signals and learn strategies that improve the volitional control of signals used for effector control. Here, we show that, by using a biofeedback paradigm, three individuals with tetraplegia achieved volitional control of gamma LFPs (40-400 Hz) recorded by a single microelectrode implanted in the precentral gyrus. Control was improved over a pair of consecutive sessions up to 3 days apart. In all but one session, the channel used to achieve control lacked distinguishable action potentials. Our results indicate that biofeedback LFP-based BCIs may potentially contribute to the neural modulation necessary to obtain reliable and useful control of effectors. NEW & NOTEWORTHY Our study demonstrates that people with tetraplegia can volitionally control individual high-gamma local-field potential (LFP) channels recorded from the motor cortex, and that this control can be improved using biofeedback. Motor cortical LFP signals are thought to be both informative and stable intracortical signals and, thus, of importance for future brain-computer interfaces.

Novel Porous Brain Electrodes for Augmented Local Field Potential Signal Detection

Intracerebral local field potential (LFP) measurements are commonly used to monitor brain activity, providing insight into the flow of information across neural networks. Herein we describe synthesis and application of a neural electrode possessing a nano/micro-scale porous surface topology for improved LFP measurement. Compared with conventional brain electrodes, the porous electrodes demonstrate higher measured amplitudes with lower noise levels.

Correlation Structure in Micro-ECoG Recordings is Described by Spatially Coherent Components

Electrocorticography (ECoG) is becoming more prevalent due to improvements in fabrication and recording technology as well as its ease of implantation compared to intracortical electrophysiology, larger cortical coverage, and potential advantages for use in long term chronic implantation. Given the flexibility in the design of ECoG grids, which is only increasing, it remains an open question what geometry of the electrodes is optimal for an application. Conductive polymer, PEDOT:PSS, coated microelectrodes have an advantage that they can be made very small without losing low impedance. This makes them suitable for evaluating the required granularity of ECoG recording in humans and experimental animals. We used two-dimensional (2D) micro-ECoG grids to record intra-operatively in humans and during acute implantations in mouse with separation distance between neighboring electrodes (i.e., pitch) of 0.4 mm and 0.2/0.25 mm respectively. To assess the spatial properties of the signals, we used the average correlation between electrodes as a function of the pitch. In agreement with prior studies, we find a strong frequency dependence in the spatial scale of correlation. By applying independent component analysis (ICA), we find that the spatial pattern of correlation is largely due to contributions from multiple spatially extended, time-locked sources present at any given time. Our analysis indicates the presence of spatially structured activity down to the sub-millimeter spatial scale in ECoG despite the effects of volume conduction, justifying the use of dense micro-ECoG grids.

Electrocorticography (ECoG) is a type of electrophysiological monitoring that uses electrodes placed directly on the exposed surface of the brain. ECoG is a promising technique for studying the brain, and EcoG signals can be used to control brain-computer interfaces. Advances have made it possible to record simultaneously with an increasing number of smaller, and more closely spaced electrodes. However, a property of electrical recording from outside the brain is that common signals appear on different electrodes at different locations, and this affects decisions about how to best distribute a limited number of electrodes to maximize the information that can be gathered. Large spacing of electrodes around one centimeter apart on the brain’s surface has proven useful for clinical and research use, but how much benefit there is to recording from more locations in a smaller area remains to be answered. We found that we can explain the commonality between the different locations as the combination of different patterns of brain activity that are present at multiple electrode locations, and that signals recorded from very closely spaced electrodes, around a millimeter or less apart, are able to identify patterns that are at this small scale.

Automatic Sleep Stage Classification Based on Subthalamic Local Field Potentials

Deep brain stimulation (DBS) is an established treatment for patients with Parkinson's disease (PD). Sleep disorders are common complications of PD and affected by subthalamic DBS treatment. To achieve more precise neuromodulation, chronicsleepmonitoringand closed-loop DBS toward sleep-wake cycles could potentially be utilized. Local field potential (LFP) signals that are sensed by the DBS electrode could be processed as primary feedback signals. This is the first study to systematically investigate the sleep-stage classification based on LFPs in subthalamic nucleus (STN). With our newly developed recording and transmission system, STN-LFPs were collected from 12 PD patients during wakefulness and nocturnal polysomnography sleep monitoring at one month after DBS implantation. Automatic sleep-stage classificationmodels were built with robust and interpretable machine learning methods (support vector machine and decision tree). The accuracy, sensitivity, selectivity, and specificity of the classification reached high values (above90% at most measures) at group and individual levels. Features extracted in alpha (8-13 Hz), beta (13-35 Hz), and gamma (35-50 Hz) bandswere found to contribute the most to the classification. These results will directly guide the engineering development of implantable sleepmonitoring and closed-loopDBS and pave the way for a better understanding of the STN-LFP sleep patterns.

Slow insertion of silicon probes improves the quality of acute neuronal recordings

Neural probes designed for extracellular recording of brain electrical activity are traditionally implanted with an insertion speed between 1 µm/s and 1 mm/s into the brain tissue. Although the physical effects of insertion speed on the tissue are well studied, there is a lack of research investigating how the quality of the acquired electrophysiological signal depends on the speed of probe insertion. In this study, we used four different insertion speeds (0.002 mm/s, 0.02 mm/s, 0.1 mm/s, 1 mm/s) to implant high-density silicon probes into deep layers of the somatosensory cortex of ketamine/xylazine anesthetized rats. After implantation, various qualitative and quantitative properties of the recorded cortical activity were compared across different speeds in an acute manner. Our results demonstrate that after the slowest insertion both the signal-to-noise ratio and the number of separable single units were significantly higher compared with those measured after inserting probes at faster speeds. Furthermore, the amplitude of recorded spikes as well as the quality of single unit clusters showed similar speed-dependent differences. Post hoc quantification of the neuronal density around the probe track showed a significantly higher number of NeuN-labelled cells after the slowest insertion compared with the fastest insertion. Our findings suggest that advancing rigid probes slowly (~1 µm/s) into the brain tissue might result in less tissue damage, and thus in neuronal recordings of improved quality compared with measurements obtained after inserting probes with higher speeds.

Differential recordings of local field potential: A genuine tool to quantify functional connectivity

Local field potential (LFP) recording is a very useful electrophysiological method to study brain processes. However, this method is criticized for recording low frequency activity in a large area of extracellular space potentially contaminated by distal activity. Here, we theoretically and experimentally compare ground-referenced (RR) with differential recordings (DR). We analyze electrical activity in the rat cortex with these two methods. Compared with RR, DR reveals the importance of local phasic oscillatory activities and their coherence between cortical areas. Finally, we show that DR provides a more faithful assessment of functional connectivity caused by an increase in the signal to noise ratio, and of the delay in the propagation of information between two cortical structures.

Lamina-specific population encoding of cutaneous signals in the spinal dorsal horn using multi-electrode arrays

KEY POINTS

Traditional, widely used in vivo electrophysiological techniques for the investigation of spinal processing of somatosensory information fail to account for the diverse functions of each lamina. To overcome this oversimplification, we have used multi-electrode arrays, in vivo, to simultaneously record neuronal activity across all laminae of the spinal dorsal horn. Multi-electrode arrays are sensitive enough to detect lamina- and region-specific encoding of different subtypes of afferent fibres and to detect short-lived changes in synaptic plasticity as measured by the application of cutaneous electrical stimulation of varying intensity and frequency. Differential encoding of innocuous and noxious thermal and mechanical stimuli were also detected across the laminae with the technique, as were the effects of the application of capsaicin. This new approach to the study of the dorsal spinal cord produces significantly more information per experiment, permitting accelerated research whilst also permitting the effects of pharmacological tools to modulate network responses.

ABSTRACT

The dorsal horn (DH) of the spinal cord is a complex laminar structure integrating peripheral signals into the central nervous system. Spinal somatosensory processing is commonly measured electrophysiologically in vivo by recording the activity of individual wide-dynamic-range neurons in the deep DH and extrapolating their behaviour to all cells in every lamina. This fails to account for the specialized processes that occur in each lamina and the considerable heterogeneity in cellular phenotype within and between laminae. Here we overcome this oversimplification by employing linear multi-electrode arrays (MEAs) in the DH of anaesthetized rats to simultaneously measure activity across all laminae. The MEAs, comprising 16 channels, were inserted into the lumbar dorsal horn and peripheral neurons activated electrically via transcutaneous electrodes and ethologically with von Frey hairs (vFHs) or an aluminium heating block. Ascending electrical stimuli showed fibre thresholds with distinct dorsoventral innervation profiles. Wind up was observed across the DH during the C-fibre and post-discharge latencies following 0.5 Hz stimulation. Intrathecal application of morphine (5 ng/50 μl) significantly reduced Aδ- and C-fibre-evoked activity in deep and superficial DH. Light vFHs (≤10 g) predominantly activated intermediate and deep laminae whereas noxious vFHs (26 g) also activated the superficial laminae. Noxious heat (55°C) induced significantly greater activity in the superficial and deep laminae than the innocuous control (30°C). The application of these arrays produced the first description of the processing of innocuous and noxious stimuli throughout the intact DH.

Tonic thermonociceptive stimulation selectively modulates ongoing neural oscillations in the human posterior insula: Evidence from intracerebral EEG

The human insula is an important target for spinothalamic input, but there is still no consensus on its role in pain perception and nociception. In this study, we show that the human insula exhibits activity preferential for sustained thermonociception. Using intracerebral EEG recorded from the insula of 8 patients (2 females) undergoing a presurgical evaluation of focal epilepsy (53 contacts: 27 anterior, 26 posterior), we "frequency-tagged" the insular activity elicited by sustained thermonociceptive and vibrotactile stimuli, by periodically modulating stimulation intensity at a fixed frequency of 0.2 Hz during 75 s. Both types of stimuli elicited an insular response at the frequency of stimulation (0.2 Hz) and its harmonics, whose magnitude was significantly greater in the posterior insula compared to the anterior insula. Compared to vibrotactile stimulation, thermonociceptive stimulation exerted a markedly greater 0.2 Hz modulation of ongoing theta-band (4-8 Hz) and alpha-band (8-12 Hz) oscillations. These modulations were also more prominent in the posterior insula compared to the anterior insula. The identification of oscillatory activities preferential for thermonociception could lead to new insights into the physiological mechanisms of nociception and pain perception in humans.

VIOLA-A Multi-Purpose and Web-Based Visualization Tool for Neuronal-Network Simulation Output

Neuronal network models and corresponding computer simulations are invaluable tools to aid the interpretation of the relationship between neuron properties, connectivity, and measured activity in cortical tissue. Spatiotemporal patterns of activity propagating across the cortical surface as observed experimentally can for example be described by neuronal network models with layered geometry and distance-dependent connectivity. In order to cover the surface area captured by today's experimental techniques and to achieve sufficient self-consistency, such models contain millions of nerve cells. The interpretation of the resulting stream of multi-modal and multi-dimensional simulation data calls for integrating interactive visualization steps into existing simulation-analysis workflows. Here, we present a set of interactive visualization concepts called views for the visual analysis of activity data in topological network models, and a corresponding reference implementation VIOLA (VIsualization Of Layer Activity). The software is a lightweight, open-source, web-based, and platform-independent application combining and adapting modern interactive visualization paradigms, such as coordinated multiple views, for massively parallel neurophysiological data. For a use-case demonstration we consider spiking activity data of a two-population, layered point-neuron network model incorporating distance-dependent connectivity subject to a spatially confined excitation originating from an external population. With the multiple coordinated views, an explorative and qualitative assessment of the spatiotemporal features of neuronal activity can be performed upfront of a detailed quantitative data analysis of specific aspects of the data. Interactive multi-view analysis therefore assists existing data analysis workflows. Furthermore, ongoing efforts including the European Human Brain Project aim at providing online user portals for integrated model development, simulation, analysis, and provenance tracking, wherein interactive visual analysis tools are one component. Browser-compatible, web-technology based solutions are therefore required. Within this scope, with VIOLA we provide a first prototype.

Laminar profile of task-related plasticity in ferret primary auditory cortex

Rapid task-related plasticity is a neural correlate of selective attention in primary auditory cortex (A1). Top-down feedback from higher-order cortex may drive task-related plasticity in A1, characterized by enhanced neural representation of behaviorally meaningful sounds during auditory task performance. Since intracortical connectivity is greater within A1 layers 2/3 (L2/3) than in layers 4–6 (L4–6), we hypothesized that enhanced representation of behaviorally meaningful sounds might be greater in A1 L2/3 than L4–6. To test this hypothesis and study the laminar profile of task-related plasticity, we trained 2 ferrets to detect pure tones while we recorded laminar activity across a 1.8 mm depth in A1. In each experiment we analyzed high-gamma local field potentials (LFPs) and multi-unit spiking in response to identical acoustic stimuli during both passive listening and active task performance. We found that neural responses to auditory targets were enhanced during task performance, and target enhancement was greater in L2/3 than in L4–6. Spectrotemporal receptive fields (STRFs) computed from both high-gamma LFPs and multi-unit spiking showed similar increases in auditory target selectivity, also greatest in L2/3. Our results suggest that activity within intracortical networks plays a key role in the underlying neural mechanisms of selective attention.

Mapping stress networks using functional magnetic resonance imaging in awake animals

The neurobiology of stress is studied through behavioral neuroscience, endocrinology, neuronal morphology and neurophysiology. There is a shift in focus toward progressive changes throughout stress paradigms and individual susceptibility to stress that requires methods that allow for longitudinal study design and study of individual differences in stress response. Functional magnetic resonance imaging (fMRI), with the advantages of noninvasiveness and a large field of view, can be used for functionally mapping brain-wide regions and circuits critical to the stress response, making it suitable for longitudinal studies and understanding individual variability of short-term and long-term consequences of stress exposure. In addition, fMRI can be applied to both animals and humans, which is highly valuable in translating findings across species and examining whether the physiology and neural circuits involved in the stress response are conserved in mammals. However, compared to human fMRI studies, there are a number of factors that are essential for the success of fMRI studies in animals. This review discussed the use of fMRI in animal studies of stress. It reviewed advantages, challenges and technical considerations of the animal fMRI methodology as well as recent literature of stress studies using fMRI in animals. It also highlighted the development of combining fMRI with other methods and the future potential of fMRI in animal studies of stress. We conclude that animal fMRI studies, with their flexibility, low cost and short time frame compared to human studies, are crucial to advancing our understanding of the neurobiology of stress.

Functional Brain Connectivity Revealed by Sparse Coding of Large-Scale Local Field Potential Dynamics

Exploration of brain dynamics patterns has attracted increasing attention due to its fundamental significance in understanding the working mechanism of the brain. However, due to the lack of effective modeling methods, how the simultaneously recorded LFP can inform us about the brain dynamics remains a general challenge. In this paper, we propose a novel sparse coding based method to investigate brain dynamics of freely-behaving mice from the perspective of functional connectivity, using super-long local field potential (LFP) recordings from 13 distinct regions of the mouse brain. Compared with surrogate datasets, six and four reproducible common functional connectivities were discovered to represent the space of brain dynamics in the frequency bands of alpha and theta respectively. Modeled by a finite state machine, temporal transition framework of functional connectivities was inferred for each frequency band, and evident preference was discovered. Our results offer a novel perspective for analyzing neural recording data at such high temporal resolution and recording length, as common functional connectivities and their transition framework discovered in this work reveal the nature of the brain dynamics in freely behaving mice.

A Primer on Electroencephalography and Event-Related Potentials for Organizational Neuroscience

Electroencephalography (EEG) was the first of the noninvasive brain measures in neuroscience. Technical advances over the last 100 years or so have rendered EEG a true brain imaging technique. Here, we provide an accessible primer on the biophysics of EEG, on measurement aspects, and on the analysis of EEG data. We use the example of event-related potentials (ERPs), although the issues apply equally to other varieties of EEG signals, and provide an overview of analytic methods at the base of the so-called electrical neuroimaging framework. We detail the interpretational strengths of electrical neuroimaging for organizational researchers and describe some domains of ongoing technical developments. We likewise emphasize practical considerations with the use of EEG in more real-world settings. This primer is intended to provide organizational researchers specifically, and novices more generally, an access point to understanding how EEG may be applied in their research.

Electrophysiological signatures of the resting-state fMRI global signal: A simultaneous EEG-fMRI study

BACKGROUND

The global signal of resting-state functional magnetic resonance imaging (fMRI) constitutes an intrinsic fluctuation and presents an opportunity to characterize and understand the activity of the whole brain. Recently, evidence that the global signal contains neurophysiologic information has been growing, but the global signal of electroencephalography (EEG) has never been determined.

NEW METHODS

We developed a new method to obtain the EEG global signal. The EEG global signal was reconstructed by the reference electrode standardization technique and represented the outer cortical electrophysiological activity. To investigate its relationship with the global signal of resting-state fMRI, a simultaneous EEG-fMRI signal was recorded, and this was analyzed in 24 subjects.

RESULTS

We found that the global signal of resting-state fMRI showed a positive correlation with power fluctuations of the EEG global signal in the γ band (30-45 Hz) and a negative correlation in the low-frequency band (4-20 Hz).

COMPARISON WITH EXISTING METHOD(S)

Compared with the global signal of fMRI, the global signal of EEG provides more temporal information about outer cortical neural activity.

CONCLUSIONS

These results provide new evidence for the electrophysiology information of the global signal of resting-state fMRI. More importantly, due to its high correlation with the fMRI global signal, the EEG global signal may serve as a new biomarker for neurological disorders.

Neural Activity Predicts Reaction in Primates Long Before a Behavioral Response

How neural activity is linked to behavior is a critical question in neural engineering and cognitive neurosciences. It is crucial to predict behavior as early as possible, to plan a machine response in real-time brain computer interactions. However, previous studies have studied the neural readout of behavior only within a short time before the action is performed. This leaves unclear, if the neural activity long before a decision could predict the upcoming behavior. By recording extracellular neural activities from the visual cortex of behaving rhesus monkeys, we show that: (1) both, local field potentials (LFPs) and the rate of neural spikes long before (>2 s) a monkey responds to a change, foretell its behavioral performance in a spatially selective manner; (2) LFPs, the more accessible component of extracellular activity, are a stronger predictor of behavior; and (3) LFP amplitude is positively correlated while spiking activity is negatively correlated with behavioral reaction time (RT). These results suggest that field potentials could be used to predict behavior way before it is performed, an observation that could potentially be useful for brain computer interface applications, and that they contribute to the sensory neural circuit’s speed in information processing.

waveCSD: A method for estimating transmembrane currents originated from propagating neuronal activity in the neocortex: Application to study cortical spreading depression

BACKGROUND

Recent years have witnessed an upsurge in the development of methods for estimating current source densities (CSDs) in the neocortical tissue from their recorded local field potential (LFP) reflections using microelectrode arrays. Among these, methods utilizing linear arrays work under the assumption that CSDs vary as a function of cortical depth; whereas they are constant in the tangential direction, infinitely or in a confined cylinder. This assumption is violated in the analysis of neuronal activity propagating along the neocortical sheet, e.g. propagation of alpha waves or cortical spreading depression.

NEW METHOD

Here, we developed a novel mathematical method (waveCSD) for CSD analysis of LFPs associated with a planar wave of neocortical neuronal activity propagating at a constant velocity towards a linear probe.

RESULTS

Results show that the algorithm is robust to the presence of noise in LFP data and uncertainties in knowledge of propagation velocity. Also, results show high level of accuracy of the method in a wide range of electrode resolutions. Using in vivo experimental recordings from the rat neocortex, we employed waveCSD to characterize transmembrane currents associated with cortical spreading depressions.

COMPARISON WITH EXISTING METHOD(S)

Simulation results indicate that waveCSD has a significantly higher reconstruction accuracy compared to the widely-used inverse CSD method (iCSD), and the regularized kernel CSD method (kCSD), in the analysis of CSDs originating from propagating neuronal activity.

CONCLUSIONS

The waveCSD method provides a theoretical platform for estimation of transmembrane currents from their LFPs in experimental paradigms involving wave propagation.

Auditory modulation of spiking activity and local field potentials in area MT does not appear to underlie an audiovisual temporal illusion

The timing of brief stationary sounds has been shown to alter the perceived speed of visual apparent motion (AM), presumably by altering the perceived timing of the individual frames of the AM stimuli and/or the duration of the interstimulus intervals (ISIs) between those frames. To investigate the neural correlates of this "temporal ventriloquism" illusion, we recorded spiking and local field potential (LFP) activity from the middle temporal area (area MT) in awake, fixating macaques. We found that the spiking activity of most MT neurons (but not the LFP) was tuned for the ISI/speed (these parameters covaried) of our AM stimuli but that auditory timing had no effect on that tuning. We next asked whether the predicted changes in perceived timing were reflected in the timing of neuronal responses to the individual frames of the AM stimuli. Although spiking dynamics were significantly, if weakly, affected by auditory timing in a minority of neurons, the timing of spike responses did not systematically mirror the predicted perception of stimuli. Conversely, the duration of LFP responses in β- and γ-frequency bands was qualitatively consistent with human perceptual reports. We discovered, however, that LFP responses to auditory stimuli presented alone were robust and that responses to audiovisual stimuli were predicted by the linear sum of responses to auditory and visual stimuli presented individually. In conclusion, we find evidence of auditory input into area MT but not of the nonlinear audiovisual interactions we had hypothesized to underlie the illusion. NEW & NOTEWORTHY We utilized a set of audiovisual stimuli that elicit an illusion demonstrating "temporal ventriloquism" in visual motion and that have spatiotemporal intervals for which neurons within the middle temporal area are selective. We found evidence of auditory input into the middle temporal area but not of the nonlinear audiovisual interactions underlying this illusion. Our findings suggest that either the illusion was absent in our nonhuman primate subjects or the neuronal correlates of this illusion lie within other areas.

Effect of Stimulus Contrast and Visual Attention on Spike-Gamma Phase Relationship in Macaque Primary Visual Cortex

Brain signals often show rhythmic activity in the so-called gamma range (30-80 Hz), whose magnitude and center frequency are modulated by properties of the visual stimulus such as size and contrast, as well as by cognitive processes such as attention. How gamma rhythm can potentially influence cortical processing remains unclear; previous studies have proposed a scheme called phase coding, in which the intensity of the incoming stimulus is coded in the position of the spike relative to the rhythm. Using chronically implanted microelectrode arrays in the primary visual cortex (area V1) of macaques engaged in an attention task while presenting stimuli of varying contrasts, we tested whether the phase of the gamma rhythm relative to spikes varied as a function of stimulus contrast and attentional state. A previous study had found no evidence of gamma phase coding for either contrast or attention in V1, but in that study spikes and local field potential (LFP) were recorded from the same electrode, due to which spike-gamma phase estimation could have been biased. Further, the filtering operation to obtain LFP could also have biased the gamma phase. By analyzing spikes and LFP from different electrodes, we found a weak but significant effect of attention, but not stimulus contrast, on gamma phase relative to spikes. The results remained consistent even after correcting the filter induced lags, although the absolute magnitude of gamma phase shifted by up to ~15°. Although we found a significant effect of attention, we argue that a small magnitude of phase shift as well as the dependence of phase angles on gamma power and center frequency limits a potential role of gamma in phase coding in V1.

Optimal referencing for stereo-electroencephalographic (SEEG) recordings

Stereo-electroencephalography (SEEG) is an intracranial recording technique in which depth electrodes are inserted in the brain as part of presurgical assessments for invasive brain surgery. SEEG recordings can tap into neural signals across the entire brain and thereby sample both cortical and subcortical sites. However, even though signal referencing is important for proper assessment of SEEG signals, no previous study has comprehensively evaluated the optimal referencing method for SEEG. In our study, we recorded SEEG data from 15 human subjects during a motor task, referencing them against the average of two white matter contacts (monopolar reference). We then subjected these signals to 5 different re-referencing approaches: common average reference (CAR), gray-white matter reference (GWR), electrode shaft reference (ESR), bipolar reference, and Laplacian reference. The results from three different signal quality metrics suggest the use of the Laplacian re-reference for study of local population-level activity and low-frequency oscillatory activity.

A Robotic System for Adaptive Training and Function Assessment of Forelimb Retraction in Mice

Rodent models are decisive for translational research in healthy and pathological conditions of motor function thanks to specific similarities with humans. Here, we present an upgraded version of the M-Platform, a robotic device previously designed to train mice during forelimb retraction tasks. This new version significantly extends its possibilities for murine experiments during motor tasks: 1) an actuation system for friction adjustment allows to automatically adapt pulling difficulty; 2) the device can be used both for training, with a retraction task, and for assessment, with an isometric task; and 3) the platform can be integrated with a neurophysiology systems to record simultaneous cortical neural activity. Results of the validation experiments with healthy mice confirmed that the M-Platform permits precise adjustments of friction during the task, thus allowing to change its difficulty and that these variations induce a different improvement in motor performance, after specific training sessions. Moreover, simultaneous and high quality (high signal-to-noise ratio) neural signals can be recorded from the rostral forelimb area (RFA) during task execution. With the novel features presented herein, the M-Platform may allow to investigate the outcome of a customized motor rehabilitation protocol after neural injury, to analyze task-related signals from brain regions interested by neuroplastic events and to perform optogenetic silencing or stimulation during experiments in transgenic mice.

Distinct frequency bands in the local field potential are differently tuned to stimulus drift rate

Local field potential (LFP) recorded with a microelectrode reflects the activity of several neural processes, including afferent synaptic inputs, microcircuit-level computations, and spiking activity. Objectively probing their contribution requires a design that allows dissociation between these potential contributors. Earlier reports have shown that the primate lateral geniculate nucleus (LGN) has a higher temporal frequency (drift rate) cutoff than the primary visual cortex (V1), such that at higher drift rates inputs into V1 from the LGN continue to persist, whereas output ceases, permitting partial dissociation. Using chronic microelectrode arrays, we recorded spikes and LFP from V1 of passively fixating macaques while presenting sinusoidal gratings drifting over a wide range. We further optimized the gratings to produce strong gamma oscillations, since recent studies in rodent V1 have reported LGN-dependent narrow-band gamma oscillations. Consistent with earlier reports, power in higher LFP frequencies (above ~140 Hz) tracked the population firing rate and were tuned to preferred drift rates similar to those for spikes. Significantly, power in the lower (up to ~40 Hz) frequencies increased transiently in the early epoch after stimulus onset, even at high drift rates, and had preferred drift rates higher than for spikes/high gamma. Narrow-band gamma (50-80 Hz) power was not strongly correlated with power in high or low frequencies and had much lower preferred temporal frequencies. Our results demonstrate that distinct frequency bands of the V1 LFP show diverse tuning profiles, which may potentially convey different attributes of the underlying neural activity. NEW & NOTEWORTHY In recent years the local field potential (LFP) has been increasingly studied, but interpreting its rich frequency content has been difficult. We use a stimulus manipulation that generates different tuning profiles for low, gamma, and high frequencies of the LFP, suggesting contributions from potentially different sources. Our results have possible implications for design of better neural prosthesis systems and brain-machine interfacing applications.

Auditory motion does not modulate spiking activity in the middle temporal and medial superior temporal visual areas

The integration of multiple sensory modalities is a key aspect of brain function, allowing animals to take advantage of concurrent sources of information to make more accurate perceptual judgments. For many years, multisensory integration in the cerebral cortex was deemed to occur only in high-level "polysensory" association areas. However, more recent studies have suggested that cross-modal stimulation can also influence neural activity in areas traditionally considered to be unimodal. In particular, several human neuroimaging studies have reported that extrastriate areas involved in visual motion perception are also activated by auditory motion, and may integrate audiovisual motion cues. However, the exact nature and extent of the effects of auditory motion on the visual cortex have not been studied at the single neuron level. We recorded the spiking activity of neurons in the middle temporal (MT) and medial superior temporal (MST) areas of anesthetized marmoset monkeys upon presentation of unimodal stimuli (moving auditory or visual patterns), as well as bimodal stimuli (concurrent audiovisual motion). Despite robust, direction selective responses to visual motion, none of the sampled neurons responded to auditory motion stimuli. Moreover, concurrent moving auditory stimuli had no significant effect on the ability of single MT and MST neurons, or populations of simultaneously recorded neurons, to discriminate the direction of motion of visual stimuli (moving random dot patterns with varying levels of motion noise). Our findings do not support the hypothesis that direct interactions between MT, MST and areas low in the hierarchy of auditory areas underlie audiovisual motion integration.

Optogenetic Modulation of a Minor Fraction of Parvalbumin-Positive Interneurons Specifically Affects Spatiotemporal Dynamics of Spontaneous and Sensory-Evoked Activity in Mouse Somatosensory Cortex in Vivo

Parvalbumin (PV) positive interneurons exert strong effects on the neocortical excitatory network, but it remains unclear how they impact the spatiotemporal dynamics of sensory processing in the somatosensory cortex. Here, we characterized the effects of optogenetic inhibition and activation of PV interneurons on spontaneous and sensory-evoked activity in mouse barrel cortex in vivo. Inhibiting PV interneurons led to a broad-spectrum power increase both in spontaneous and sensory-evoked activity. Whisker-evoked responses were significantly increased within 20 ms after stimulus onset during inhibition of PV interneurons, demonstrating high temporal precision of PV-shaped inhibition. Multiunit activity was strongly enhanced in neighboring cortical columns, but not at the site of transduction, supporting a central and highly specific role of PV interneurons in lateral inhibition. Inversely, activating PV interneurons drastically decreased spontaneous and whisker-evoked activity in the principal column and exerted strong lateral inhibition. Histological assessment of transduced cells combined with quantitative modeling of light distribution and spike sorting revealed that only a minor fraction (~10%) of the local PV population comprising no more than a few hundred neurons is optogenetically modulated, mediating the observed prominent and widespread effects on neocortical processing.

Recording Spatially Restricted Oscillations in the Hippocampus of Behaving Mice

The local field potential (LFP) emerges from ion movements across neural membranes. Since the voltage recorded by LFP electrodes reflects the summed electrical field of a large volume of brain tissue, extracting information about local activity is challenging. Studying neuronal microcircuits, however, requires a reliable distinction between truly local events and volume-conducted signals originating in distant brain areas. Current source density (CSD) analysis offers a solution for this problem by providing information about current sinks and sources in the vicinity of the electrodes. In brain areas with laminar cytoarchitecture such as the hippocampus, one-dimensional CSD can be obtained by estimating the second spatial derivative of the LFP. Here, we describe a method to record multilaminar LFPs using linear silicon probes implanted into the dorsal hippocampus. CSD traces are computed along individual shanks of the probe. This protocol thus describes a procedure to resolve spatially restricted neuronal network oscillations in the hippocampus of freely moving mice.

Go/No-Go task engagement enhances population representation of target stimuli in primary auditory cortex

Primary sensory cortices are classically considered to extract and represent stimulus features, while association and higher-order areas are thought to carry information about stimulus meaning. Here we show that this information can in fact be found in the neuronal population code of the primary auditory cortex (A1). A1 activity was recorded in awake ferrets while they either passively listened or actively discriminated stimuli in a range of Go/No-Go paradigms, with different sounds and reinforcements. Population-level dimensionality reduction techniques reveal that task engagement induces a shift in stimulus encoding from a sensory to a behaviorally driven representation that specifically enhances the target stimulus in all paradigms. This shift partly relies on task-engagement-induced changes in spontaneous activity. Altogether, we show that A1 population activity bears strong similarities to frontal cortex responses. These findings indicate that primary sensory cortices implement a crucial change in the structure of population activity to extract task-relevant information during behavior.

Sensory areas are thought to process stimulus information while higher-order processing occurs in association cortices. Here the authors report that during task engagement population activity in ferret primary auditory cortex shifts away from encoding stimulus features toward detection of the behaviourally relevant targets.

Learning Task-Related Activities From Independent Local-Field-Potential Components Across Motor Cortex Layers

Motor cortical microcircuits receive inputs from dispersed cortical and subcortical regions in behaving animals. However, how these inputs contribute to learning and execution of voluntary sequential motor behaviors remains elusive. Here, we analyzed the independent components extracted from the local field potential (LFP) activity recorded at multiple depths of rat motor cortex during reward-motivated movement to study their roles in motor learning. Because slow gamma (30-50 Hz), fast gamma (60-120 Hz), and theta (4-10 Hz) oscillations temporally coordinate task-relevant motor cortical activities, we first explored the behavioral state- and layer-dependent coordination of motor behavior in these frequency ranges. Consistent with previous findings, oscillations in the slow and fast gamma bands dominated during distinct movement states, i.e., preparation and execution states, respectively. However, we identified a novel independent component that dominantly appeared in deep cortical layers and exhibited enhanced slow gamma activity during the execution state. Then, we used the four major independent components to train a recurrent network model for the same lever movements as the rats performed. We show that the independent components differently contribute to the formation of various task-related activities, but they also play overlapping roles in motor learning.

Investigating large-scale brain dynamics using field potential recordings: analysis and interpretation

New technologies to record electrical activity from the brain on a massive scale offer tremendous opportunities for discovery. Electrical measurements of large-scale brain dynamics, termed field potentials, are especially important to understanding and treating the human brain. Here, our goal is to provide best practices on how field potential recordings (electroencephalograms, magnetoencephalograms, electrocorticograms and local field potentials) can be analyzed to identify large-scale brain dynamics, and to highlight critical issues and limitations of interpretation in current work. We focus our discussion of analyses around the broad themes of activation, correlation, communication and coding. We provide recommendations for interpreting the data using forward and inverse models. The forward model describes how field potentials are generated by the activity of populations of neurons. The inverse model describes how to infer the activity of populations of neurons from field potential recordings. A recurring theme is the challenge of understanding how field potentials reflect neuronal population activity given the complexity of the underlying brain systems.

Source-reconstruction of the sensorimotor network from resting-state macaque electrocorticography

The discovery of hemodynamic (BOLD-fMRI) resting-state networks (RSNs) has brought about a fundamental shift in our thinking about the role of intrinsic brain activity. The electrophysiological underpinnings of RSNs remain largely elusive and it has been shown only recently that electric cortical rhythms are organized into the same RSNs as hemodynamic signals. Most electrophysiological studies into RSNs use magnetoencephalography (MEG) or scalp electroencephalography (EEG), which limits the spatial resolution with which electrophysiological RSNs can be observed. Due to their close proximity to the cortical surface, electrocorticographic (ECoG) recordings can potentially provide a more detailed picture of the functional organization of resting-state cortical rhythms, albeit at the expense of spatial coverage. In this study we propose using source-space spatial independent component analysis (spatial ICA) for identifying generators of resting-state cortical rhythms as recorded with ECoG and for reconstructing their functional connectivity. Network structure is assessed by two kinds of connectivity measures: instantaneous correlations between band-limited amplitude envelopes and oscillatory phase-locking. By simulating rhythmic cortical generators, we find that the reconstruction of oscillatory phase-locking is more challenging than that of amplitude correlations, particularly for low signal-to-noise levels. Specifically, phase-lags can both be over- and underestimated, which troubles the interpretation of lag-based connectivity measures. We illustrate the methodology on somatosensory beta rhythms recorded from a macaque monkey using ECoG. The methodology decomposes the resting-state sensorimotor network into three cortical generators, distributed across primary somatosensory and primary and higher-order motor areas. The generators display significant and reproducible amplitude correlations and phase-locking values with non-zero lags. Our findings illustrate the level of spatial detail attainable with source-projected ECoG and motivates wider use of the methodology for studying resting-state as well as event-related cortical dynamics in macaque and human.

Volume Conduction Coupling of Whisker-Evoked Cortical LFP in the Mouse Olfactory Bulb

Local field potentials (LFPs) are an important measure of brain activity and have been used to address various mechanistic and behavioral questions. We revealed a prominent whisker-evoked LFP signal in the olfactory bulb and investigated its physiology. This signal, dependent on barrel cortex activation and highly correlated with its local activity, represented a pure volume conduction signal that was sourced back to the activity in the ventro-lateral orbitofrontal cortex, located a few millimeters away. Thus, we suggest that special care should be taken when acquiring and interpreting LFP data.

Phase matters: A role for the subthalamic network during gait

The role of the subthalamic nucleus in human locomotion is unclear although relevant, given the troublesome management of gait disturbances with subthalamic deep brain stimulation in patients with Parkinson’s disease. We investigated the subthalamic activity and inter-hemispheric connectivity during walking in eight freely-moving subjects with Parkinson’s disease and bilateral deep brain stimulation. In particular, we compared the subthalamic power spectral densities and coherence, amplitude cross-correlation and phase locking value between resting state, upright standing, and steady forward walking. We observed a phase locking value drop in the β-frequency band (≈13-35Hz) during walking with respect to resting and standing. This modulation was not accompanied by specific changes in subthalamic power spectral densities, which was not related to gait phases or to striatal dopamine loss measured with [¹²³I]N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl)nortropane and single-photon computed tomography. We speculate that the subthalamic inter-hemispheric desynchronization in the β-frequency band reflects the information processing of each body side separately, which may support linear walking. This study also suggests that in some cases (i.e. gait) the brain signal, which could allow feedback-controlled stimulation, might derive from network activity.

Signal quality of simultaneously recorded endovascular, subdural and epidural signals are comparable

Recent work has demonstrated the feasibility of minimally-invasive implantation of electrodes into a cortical blood vessel. However, the effect of the dura and blood vessel on recording signal quality is not understood and may be a critical factor impacting implementation of a closed-loop endovascular neuromodulation system. The present work compares the performance and recording signal quality of a minimally-invasive endovascular neural interface with conventional subdural and epidural interfaces. We compared bandwidth, signal-to-noise ratio, and spatial resolution of recorded cortical signals using subdural, epidural and endovascular arrays four weeks after implantation in sheep. We show that the quality of the signals (bandwidth and signal-to-noise ratio) of the endovascular neural interface is not significantly different from conventional neural sensors. However, the spatial resolution depends on the array location and the frequency of recording. We also show that there is a direct correlation between the signal-noise-ratio and classification accuracy, and that decoding accuracy is comparable between electrode arrays. These results support the consideration for use of an endovascular neural interface in a clinical trial of a novel closed-loop neuromodulation technology.

Extrapolating meaning from local field potential recordings

Local field potentials (LFP) reflect the spatially weighted low-frequency activity nearest to a recording electrode. LFP recording is a window to a wide range of cellular activities and has gained increasing attention over recent years. We here review major conceptual issues related to LFP with the goal of creating a resource for non-experts considering implementing LFP into their research. We discuss the cellular activity that constitutes the local field potential; recording techniques, including recommendations and limitations; approaches to analysis of LFP data (with focus on power-banded analyses); and finally we discuss reports of the successful use of LFP in clinical applications.

Parallel detection of theta and respiration-coupled oscillations throughout the mouse brain

Slow brain oscillations are usually coherent over long distances and thought to link distributed cell assemblies. In mice, theta (5–10 Hz) stands as one of the most studied slow rhythms. However, mice often breathe at theta frequency, and we recently reported that nasal respiration leads to local field potential (LFP) oscillations that are independent of theta. Namely, we showed respiration-coupled oscillations in the hippocampus, prelimbic cortex, and parietal cortex, suggesting that respiration could impose a global brain rhythm. Here we extend these findings by analyzing LFPs from 15 brain regions recorded simultaneously with respiration during exploration and REM sleep. We find that respiration-coupled oscillations can be detected in parallel with theta in several neocortical regions, from prefrontal to visual areas, and also in subcortical structures such as the thalamus, amygdala and ventral hippocampus. They might have escaped attention in previous studies due to the absence of respiration monitoring, the similarity with theta oscillations, and the highly variable peak frequency. We hypothesize that respiration-coupled oscillations constitute a global brain rhythm suited to entrain distributed networks into a common regime. However, whether their widespread presence reflects local network activity or is due to volume conduction remains to be determined.

Olfactory inputs modulate respiration-related rhythmic activity in the prefrontal cortex and freezing behavior

Respiration and airflow through the nasal cavity are known to be correlated with rhythmic neural activity in the central nervous system. Here we show in rodents that during conditioned fear-induced freezing behavior, mice breathe at a steady rate (~4 Hz), which is correlated with a predominant 4-Hz oscillation in the prelimbic prefrontal cortex (plPFC), a structure critical for expression of conditioned fear behaviors. We demonstrate anatomical and functional connections between the olfactory pathway and plPFC via circuit tracing and optogenetics. Disruption of olfactory inputs significantly reduces the 4-Hz oscillation in the plPFC, but leads to prolonged freezing periods. Our results indicate that olfactory inputs can modulate rhythmic activity in plPFC and freezing behavior.

Nasal airflow and olfactory bulb activity are linked to oscillations in cortical areas. This study shows olfactory input and respiration are correlated with oscillation in the prefrontal cortex during freezing behavior in mice, and attenuation of olfactory inputs can increase behavioral freezing.