Optimal band separation of extracellular field potentials

Data-Driven EEG Band Discovery with Decision Trees

Electroencephalography (EEG) is a brain imaging technique in which electrodes are placed on the scalp. EEG signals are commonly decomposed into frequency bands called delta, theta, alpha, and beta. While these bands have been shown to be useful for characterizing various brain states, their utility as a one-size-fits-all analysis tool remains unclear. The goal of this work is to outline an objective strategy for discovering optimal EEG bands based on signal power spectra. A two-step data-driven methodology is presented for objectively determining the best EEG bands for a given dataset. First, a decision tree is used to estimate the optimal frequency band boundaries for reproducing the signal’s power spectrum for a predetermined number of bands. The optimal number of bands is then determined using an Akaike Information Criterion (AIC)-inspired quality score that balances goodness-of-fit with a small band count. This data-driven approach led to better characterization of the underlying power spectrum by identifying bands that outperformed the more commonly used band boundaries by a factor of two. Additionally, key spectral components were isolated in dedicated frequency bands. The proposed method provides a fully automated and flexible approach to capturing key signal components and possibly discovering new indices of brain activity.

Effects of tDCS on spontaneous spike activity in a healthy ambulatory rat model

BACKGROUND

The neurophysiological effects of transcranial direct current stimulation (tDCS) are typically described with respect to changes in cortical excitability, defined by using transcranial magnetic stimulation pulses to determine changes in motor evoked potentials. However, how individual cortical neurons change firing patterns under the influence of tDCS is largely unknown. While the relatively weak currents produced in the brain by tDCS may not be adequate to directly depolarize neuronal membranes, ongoing neuronal activity, combined with subthreshold changes in membrane polarization might be sufficient to alter the threshold for neural firing.

OBJECTIVES

The purpose of this study was to determine the effects of tDCS on neurophysiological activity in motor cortex of freely moving, healthy rats.

METHODS

In nine healthy, ambulatory rats, each studied under six different stimulation conditions varying in current intensity (maximum current density = 39.8 A/m² at 0.4 mA) and polarity (anodal or cathodal), neural activity was analyzed in response to 20 min of tDCS applied through bone screws insulated from the overlying scalp.

RESULTS

After analysis of 480 multi-unit channels that satisfied a rigid set of neurophysiological criteria, we found no systematic effect of tDCS stimulation condition on firing rate or firing pattern. Restricting the analysis to the most responsive units, subtle, but statistically significant changes occurred only in the highest intensity anodal condition.

CONCLUSIONS

These results confirm that at current densities typically used in human or animal tDCS studies, observed effects of tDCS are likely to occur via mechanisms other than direct neuronal depolarization.

Two distinct profiles of fMRI and neurophysiological activity elicited by acetylcholine in visual cortex

fMRI changes are typically assumed to be due to changes in neural activity, although whether this remains valid under the influence of neuromodulators is relatively unknown. Here, we found evidence that intracortical acetylcholine elicits distinct profiles of fMRI and electrophysiological activity in visual cortex. Two patterns of cholinergic activity were observed, depending on the distance to the injection site, although neurovascular coupling was preserved. Our results illustrate the effects of neuromodulators on fMRI and electrophysiological responses and show that these depend on neuromodulator concentration and kinetics.

Cholinergic neuromodulation is involved in all aspects of sensory processing and is crucial for processes such as attention, learning and memory, etc. However, despite the known roles of acetylcholine (ACh), we still do not how to disentangle ACh contributions from sensory or task-evoked changes in functional magnetic resonance imaging (fMRI). Here, we investigated the effects of local injection of ACh on fMRI and neural signals in the primary visual cortex (V1) of anesthetized macaques by combining pharmaco-based MRI (phMRI) with electrophysiological recordings, using single electrodes and electrode arrays. We found that local injection of ACh elicited two distinct profiles of fMRI and neurophysiological activity, depending on the distance from the injector. Near the injection site, we observed an increase in the baseline blood oxygen-level-dependent (BOLD) and cerebral blood flow (CBF) responses, while their visual modulation decreased. In contrast, further from the injection site, we observed an increase in the visually induced BOLD and CBF modulation without changes in baseline. Neurophysiological recordings suggest that the spatial correspondence between fMRI responses and neural activity does not change in the gamma, high-gamma, and multiunit activity (MUA) bands. The results near the injection site suggest increased inhibitory drive and decreased metabolism, contrasting to the far region. These changes are thought to reflect the kinetics of ACh and its metabolism to choline.

Adaptive common average reference for in vivo multichannel local field potentials

For in vivo neural recording, local field potential (LFP) is often corrupted by spatially correlated artifacts, especially in awake/behaving subjects. A method named adaptive common average reference (ACAR) based on the concept of adaptive noise canceling (ANC) that utilizes the correlative features of common noise sources and implements with common average referencing (CAR), was proposed for removing the spatially correlated artifacts. Moreover, a correlation analysis was devised to automatically select appropriate channels before generating the CAR reference. The performance was evaluated in both synthesized data and real data from the hippocampus of pigeons, and the results were compared with the standard CAR and several previously proposed artifacts removal methods. Comparative testing results suggest that the ACAR performs better than the available algorithms, especially in a low SNR. In addition, feasibility of this method was provided theoretically. The proposed method would be an important pre-processing step for in vivo LFP processing.

Enhanced Gamma Oscillatory Activity in Rats with Chronic Inflammatory Pain

It has been reported that oscillatory gamma activity participates in brief acute pain and tonic ongoing pain. It is of great interest to determine whether the gamma activity is involved in chronic pain since chronic pain is a more severe pathological condition characterized by pain persistency. To investigate the oscillatory gamma activity in chronic pain, in the present study, we recorded spontaneous electrocorticogram (ECoG) signals during chronic pain development in rats with chronic inflammatory pain induced by monoarthritis. Power spectrum analysis of ECoG data showed that gamma power increased significantly at the late stage of chronic inflammatory pain. The increased gamma activity occurred mainly at electrodes over primary somatosensory cortices. In rats with chronic pain, the gamma power was positively correlated with the hyperalgesia measured by laser energy that elicited hindpaw withdrawal response. Furthermore, an increased coupling between the amplitude of gamma power and the phase of theta oscillations was observed in chronic inflammatory pain condition. These results indicate an enhanced spontaneous gamma activity in chronic pain and suggest a potential biomarker for the severity of chronic pain.

Laminar Distribution of Phase-Amplitude Coupling of Spontaneous Current Sources and Sinks

Although resting-state functional connectivity is a commonly used neuroimaging paradigm, the underlying mechanisms remain unknown. Thalamo-cortical and cortico-cortical circuits generate oscillations at different frequencies during spontaneous activity. However, it remains unclear how the various rhythms interact and whether their interactions are lamina-specific. Here we investigated intra- and inter-laminar spontaneous phase-amplitude coupling (PAC). We recorded local-field potentials using laminar probes inserted in the forelimb representation of rat area S1. We then computed time-series of frequency-band- and lamina-specific current source density (CSD), and PACs of CSD for all possible pairs of the classical frequency bands in the range of 1–150 Hz. We observed both intra- and inter-laminar spontaneous PAC. Of 18 possible combinations, 12 showed PAC, with the highest measures of interaction obtained for the pairs of the theta/gamma and delta/gamma bands. Intra- and inter-laminar PACs involving layers 2/3–5a were higher than those involving layer 6. Current sinks (sources) in the delta band were associated with increased (decreased) amplitudes of high-frequency signals in the beta to fast gamma bands throughout layers 2/3–6. Spontaneous sinks (sources) of the theta and alpha bands in layers 2/3–4 were on average linked to dipoles completed by sources (sinks) in layer 6, associated with high (low) amplitudes of the beta to fast-gamma bands in the entire cortical column. Our findings show that during spontaneous activity, delta, theta, and alpha oscillations are associated with periodic excitability, which for the theta and alpha bands is lamina-dependent. They further emphasize the differences between the function of layer 6 and that of the superficial layers, and the role of layer 6 in controlling activity in those layers. Our study links theories on the involvement of PAC in resting-state functional connectivity with previous work that revealed lamina-specific anatomical thalamo-cortico-cortical connections.

Modeling the Generation of Phase-Amplitude Coupling in Cortical Circuits: From Detailed Networks to Neural Mass Models

Phase-amplitude coupling (PAC), the phenomenon where the amplitude of a high frequency oscillation is modulated by the phase of a lower frequency oscillation, is attracting an increasing interest in the neuroscience community due to its potential relevance for understanding healthy and pathological information processing in the brain. PAC is a diverse phenomenon, having been experimentally detected in at least ten combinations of rhythms: delta-theta, delta-alpha, delta-beta, delta-gamma, theta-alpha, theta-beta, theta-gamma, alpha-beta, alpha-gamma, and beta-gamma. However, a complete understanding of the biophysical mechanisms generating this diversity is lacking. Here we review computational models of PAC generation that range from detailed models of neuronal networks, where each cell is described by Hodgkin-Huxley-type equations, to neural mass models (NMMs) where only the average activities of neuronal populations are considered. We argue that NMMs are an appropriate mathematical framework (due to the small number of parameters and variables involved and the richness of the dynamics they can generate) to study the PAC phenomenon.

Shifts of Gamma Phase across Primary Visual Cortical Sites Reflect Dynamic Stimulus-Modulated Information Transfer

Distributed neural processing likely entails the capability of networks to reconfigure dynamically the directionality and strength of their functional connections. Yet, the neural mechanisms that may allow such dynamic routing of the information flow are not yet fully understood. We investigated the role of gamma band (50-80 Hz) oscillations in transient modulations of communication among neural populations by using measures of direction-specific causal information transfer. We found that the local phase of gamma-band rhythmic activity exerted a stimulus-modulated and spatially-asymmetric directed effect on the firing rate of spatially separated populations within the primary visual cortex. The relationships between gamma phases at different sites (phase shifts) could be described as a stimulus-modulated gamma-band wave propagating along the spatial directions with the largest information transfer. We observed transient stimulus-related changes in the spatial configuration of phases (compatible with changes in direction of gamma wave propagation) accompanied by a relative increase of the amount of information flowing along the instantaneous direction of the gamma wave. These effects were specific to the gamma-band and suggest that the time-varying relationships between gamma phases at different locations mark, and possibly causally mediate, the dynamic reconfiguration of functional connections.

Stimulus dependence of local field potential spectra: experiment versus theory

The local field potential (LFP) captures different neural processes, including integrative synaptic dynamics that cannot be observed by measuring only the spiking activity of small populations. Therefore, investigating how LFP power is modulated by external stimuli can offer important insights into sensory neural representations. However, gaining such insight requires developing data-driven computational models that can identify and disambiguate the neural contributions to the LFP. Here, we investigated how networks of excitatory and inhibitory integrate-and-fire neurons responding to time-dependent inputs can be used to interpret sensory modulations of LFP spectra. We computed analytically from such models the LFP spectra and the information that they convey about input and used these analytical expressions to fit the model to LFPs recorded in V1 of anesthetized macaques (Macaca mulatta) during the presentation of color movies. Our expressions explain 60%-98% of the variance of the LFP spectrum shape and its dependency upon movie scenes and we achieved this with realistic values for the best-fit parameters. In particular, synaptic best-fit parameters were compatible with experimental measurements and the predictions of firing rates, based only on the fit of LFP data, correlated with the multiunit spike rate recorded from the same location. Moreover, the parameters characterizing the input to the network across different movie scenes correlated with cross-scene changes of several image features. Our findings suggest that analytical descriptions of spiking neuron networks may become a crucial tool for the interpretation of field recordings.

Modelling and analysis of local field potentials for studying the function of cortical circuits

The past decade has witnessed a renewed interest in cortical local field potentials (LFPs)--that is, extracellularly recorded potentials with frequencies of up to ~500 Hz. This is due to both the advent of multielectrodes, which has enabled recording of LFPs at tens to hundreds of sites simultaneously, and the insight that LFPs offer a unique window into key integrative synaptic processes in cortical populations. However, owing to its numerous potential neural sources, the LFP is more difficult to interpret than are spikes. Careful mathematical modelling and analysis are needed to take full advantage of the opportunities that this signal offers in understanding signal processing in cortical circuits and, ultimately, the neural basis of perception and cognition.