Interactive effects of pain and arousal state on heart rate and cortical activity in the mouse anterior cingulate and somatosensory cortices.

Sensory disconnection is a hallmark of sleep, yet the cortex retains some ability to process sensory information. Acute noxious stimulation during sleep increases the heart rate and the likelihood of awakening, indicating that certain mechanisms for pain sensing and processing remain active. However, processing of somatosensory information, including pain, during sleep remains underexplored. To assess somatosensation in natural sleep, we simultaneously recorded heart rate and local field potentials in the anterior cingulate (ACC) and somatosensory (S1) cortices of naïve, adult male mice, while applying noxious and non-noxious stimuli to their hind paws throughout their sleep-wake cycle. Noxious stimuli evoked stronger heart rate increases in both wake and non-rapid eye movement sleep (NREMS), and resulted in larger awakening probability in NREMS, as compared to non-noxious stimulation, suggesting differential processing of noxious and non-noxious information during sleep. Somatosensory information differentially reached S1 and ACC in sleep, eliciting complex transient and sustained responses in the delta, alpha, and gamma frequency bands as well as somatosensory evoked potentials. These dynamics depended on sleep state, the behavioral response to the stimulation and stimulation intensity (non-noxious vs. noxious). Furthermore, we found a correlation of the heart rate with the gamma band in S1 in the absence of a reaction in wake and sleep for noxious stimulation. These findings confirm that somatosensory information, including nociception, is sensed and processed during sleep even in the absence of a behavioral response.

Two modes of midfrontal theta suggest a role in conflict and error processing.

Midfrontal theta increases during scenarios when conflicts are successfully resolved. Often considered a generic signal of cognitive control, its temporal nature has hardly been investigated. Using advanced spatiotemporal techniques, we uncover that midfrontal theta occurs as a transient oscillation or "event" at single trials with their timing reflecting computationally distinct modes. Single-trial analyses of electrophysiological data from participants performing the Flanker (N = 24) and Simon task (N = 15) were used to probe the relationship between theta and metrics of stimulus-response conflict. We specifically investigated "partial errors", in which a small burst of muscle activity in the incorrect response effector occurred, quickly followed by a correction. We found that transient theta events in single trials could be categorized into two distinct theta modes based on their relative timing to different task events. Theta events from the first mode occurred briefly after the task stimulus and might reflect conflict-related processing of the stimulus. In contrast, theta events from the second mode were more likely to occur around the time partial errors were committed, suggesting they were elicited by a potential upcoming error. Importantly, in trials in which a full error was committed, this "error-related theta" occurred too late with respect to the onset of the erroneous muscle response, supporting the role of theta also in error correction. We conclude that different modes of transient midfrontal theta can be adopted in single trials not only to process stimulus-response conflict, but also to correct erroneous responses.

Distinct cortico-muscular coupling between step and stance leg during reactive stepping responses.

Balance recovery often relies on successful stepping responses, which presumably require precise and rapid interactions between the cerebral cortex and the leg muscles. Yet, little is known about how cortico-muscular coupling (CMC) supports the execution of reactive stepping. We conducted an exploratory analysis investigating time-dependent CMC with specific leg muscles in a reactive stepping task. We analyzed high density EEG, EMG, and kinematics of 18 healthy young participants while exposing them to balance perturbations at different intensities, in the forward and backward directions. Participants were instructed to maintain their feet in place, unless stepping was unavoidable. Muscle-specific Granger causality analysis was conducted on single step- and stance-leg muscles over 13 EEG electrodes with a midfrontal scalp distribution. Time-frequency Granger causality analysis was used to identify CMC from cortex to muscles around perturbation onset, foot-off and foot strike events. We hypothesized that CMC would increase compared to baseline. In addition, we expected to observe different CMC between step and stance leg because of their functional role during the step response. In particular, we expected that CMC would be most evident for the agonist muscles while stepping, and that CMC would precede upregulation in EMG activity in these muscles. We observed distinct Granger gain dynamics over theta, alpha, beta, and low/high-gamma frequencies during the reactive balance response for all leg muscles in each step direction. Interestingly, between-leg differences in Granger gain were almost exclusively observed following the divergence of EMG activity. Our results demonstrate cortical involvement in the reactive balance response and provide insights into its temporal and spectral characteristics. Overall, our findings suggest that higher levels of CMC do not facilitate leg-specific EMG activity. Our work is relevant for clinical populations with impaired balance control, where CMC analysis may elucidate the underlying pathophysiological mechanisms.

Midfrontal theta dynamics index the monitoring of postural stability.

Stepping is a common strategy to recover postural stability and maintain upright balance. Postural perturbations have been linked to neuroelectrical markers such as the N1 potential and theta frequency dynamics. Here, we investigated the role of cortical midfrontal theta dynamics of balance monitoring, driven by balance perturbations at different initial standing postures. We recorded electroencephalography, electromyography, and motion tracking of human participants while they stood on a platform that delivered a range of forward and backward whole-body balance perturbations. The participants' postural threat was manipulated prior to the balance perturbation by instructing them to lean forward or backward while keeping their feet-in-place in response to the perturbation. We hypothesized that midfrontal theta dynamics index the engagement of a behavioral monitoring system and, therefore, that perturbation-induced theta power would be modulated by the initial leaning posture and perturbation intensity. Targeted spatial filtering in combination with mixed-effects modeling confirmed our hypothesis and revealed distinct modulations of theta power according to postural threat. Our results provide novel evidence that midfrontal theta dynamics subserve action monitoring of human postural balance. Understanding of cortical mechanisms of balance control is crucial for studying balance impairments related to aging and neurological conditions (e.g. stroke).

Cortical midfrontal theta dynamics following foot strike may index response adaptation during reactive stepping.

Reactive balance recovery often requires stepping responses to regain postural stability following a sudden change in posture. The monitoring of postural stability has been linked to neuroelectrical markers such as the N1 potential and midfrontal theta frequency dynamics. Here, we investigated the role of cortical midfrontal theta dynamics during balance monitoring following foot landing of a reactive stepping response to recover from whole-body balance perturbations. We hypothesized that midfrontal theta dynamics reflect the engagement of a behavioral monitoring system, and therefore that theta would increase time-locked to the moment of foot strike after a stepping response, coinciding with a re-assessment of postural balance to determine if an additional step is necessary. We recorded high-density EEG and kinematic data of 15 healthy young participants while they stood on a platform that delivered multi-directional balance perturbations. Participants were instructed to recover balance with a single step utilizing either their left or right leg (in separate blocks). We used targeted spatial filtering (generalized eigen decomposition) in combination with time-frequency analysis of the EEG data to investigate whether theta dynamics increase following foot strike event. In line with our hypothesis, the results indicate that the foot strike event elicits a midfrontal theta power increase, though only for backward stepping. Counter to our expectations, however, this theta power increase was positively correlated with the margin of stability at foot strike, suggesting a different role of foot strike related theta from monitoring stability. Post-hoc analysis suggests that midfrontal theta dynamics following foot landing may instead facilitate adaptation of stability margins at subsequent stepping responses. We speculate that increase of theta power following foot strikes was not related to stability monitoring but instead may indicate cortical dynamics related to performance monitoring of the balance response.

Intra- and inter-regional dynamics in cortical-striatal-tegmental networks.

It is increasingly recognized that networks of brain areas work together to accomplish computational goals. However, functional connectivity networks are not often compared between different behavioral states and across different frequencies of electrical oscillatory signals. In addition, connectivity is always defined as the strength of signal relatedness between two atlas-based anatomical locations. Here, we performed an exploratory analysis using data collected from high-density arrays in the prefrontal cortex (PFC), striatum (STR), and ventral tegmental area (VTA) of male rats. These areas have all been implicated in a wide range of different tasks and computations including various types of memory as well as reward valuation, habit formation and execution, and skill learning. Novel intraregional clustering analyses identified patterns of spatially restricted, temporally coherent, and frequency-specific signals that were reproducible across days and were modulated by behavioral states. Multiple clusters were identified within each anatomical region, indicating a mesoscopic scale of organization. Generalized eigendecomposition (GED) was used to dimension-reduce each cluster to a single component time series. Dense intercluster connectivity was modulated by behavioral state, with connectivity becoming reduced when the animals were exposed to a novel object, compared with a baseline condition. Behavior-modulated connectivity changes were seen across the spectrum, with δ, θ, and γ all being modulated. These results demonstrate the brain's ability to reorganize functionally at both the intra- and inter-regional levels during different behavioral states.NEW & NOTEWORTHY We applied novel clustering techniques to discover functional subregional anatomical patches that changed with behavioral conditions but were frequency specific and stable across days. By taking into account these changes in intraregional signal generator location and extent, we were able to reveal a richer picture of inter-regional functional connectivity than would otherwise have been possible. These findings reveal that the brain's functional organization changes with state at multiple levels of scale.

Recommendations and publication guidelines for studies using frequency domain and time-frequency domain analyses of neural time series.

Since its beginnings in the early 20th century, the psychophysiological study of human brain function has included research into the spectral properties of electrical and magnetic brain signals. Now, dramatic advances in digital signal processing, biophysics, and computer science have enabled increasingly sophisticated methodology for neural time series analysis. Innovations in hardware and recording techniques have further expanded the range of tools available to researchers interested in measuring, quantifying, modeling, and altering the spectral properties of neural time series. These tools are increasingly used in the field, by a growing number of researchers who vary in their training, background, and research interests. Implementation and reporting standards also vary greatly in the published literature, causing challenges for authors, readers, reviewers, and editors alike. The present report addresses this issue by providing recommendations for the use of these methods, with a focus on foundational aspects of frequency domain and time-frequency analyses. It also provides publication guidelines, which aim to (1) foster replication and scientific rigor, (2) assist new researchers who wish to enter the field of brain oscillations, and (3) facilitate communication among authors, reviewers, and editors.

Power spectrum slope confounds estimation of instantaneous oscillatory frequency.

Oscillatory neural dynamics are highly non-stationary and require methods capable of quantifying time-resolved changes in oscillatory activity in order to understand neural function. Recently, a method termed 'frequency sliding' was introduced to estimate the instantaneous frequency of oscillatory activity, providing a means of tracking temporal changes in the dominant frequency within a sub-band of field potential recordings. Here, the ability of frequency sliding to recover ground-truth oscillatory frequency in simulated data is tested while the exponent (slope) of the 1/fx component of the signal power spectrum is systematically varied, mimicking real electrophysiological data. The results show that 1) in the presence of 1/f activity, frequency sliding systematically underestimates the true frequency of the signal, 2) the magnitude of underestimation is correlated with the steepness of the slope, suggesting that, if unaccounted for, slope changes could be misinterpreted as frequency changes, 3) the impact of slope on frequency estimates interacts with oscillation amplitude, indicating that changes in oscillation amplitude alone may also influence instantaneous frequency estimates in the presence of strong 1/f activity; and 4) analysis parameters such as filter bandwidth and location also mediate the influence of slope on estimated frequency, indicating that these settings should be considered when interpreting estimates obtained via frequency sliding. The origin of these biases resides in the output of the filtering step of frequency sliding, whose energy is biased towards lower frequencies precisely because of the 1/f structure of the data. We discuss several strategies to mitigate these biases and provide a proof-of-principle for a 1/f normalization strategy.

A tutorial on generalized eigendecomposition for denoising, contrast enhancement, and dimension reduction in multichannel electrophysiology.

The goal of this paper is to present a theoretical and practical introduction to generalized eigendecomposition (GED), which is a robust and flexible framework used for dimension reduction and source separation in multichannel signal processing. In cognitive electrophysiology, GED is used to create spatial filters that maximize a researcher-specified contrast. For example, one may wish to exploit an assumption that different sources have different frequency content, or that sources vary in magnitude across experimental conditions. GED is fast and easy to compute, performs well in simulated and real data, and is easily adaptable to a variety of specific research goals. This paper introduces GED in a way that ties together myriad individual publications and applications of GED in electrophysiology, and provides sample MATLAB and Python code that can be tested and adapted. Practical considerations and issues that often arise in applications are discussed.

Characterizing neural phase-space trajectories via Principal Louvain Clustering.

BACKGROUND: With the growing size and richness of neuroscience datasets in terms of dimension, volume, and resolution, identifying spatiotemporal patterns in those datasets is increasingly important. Multivariate dimension-reduction methods are particularly adept at addressing these challenges. NEW METHOD: In this paper, we propose a novel method, which we refer to as Principal Louvain Clustering (PLC), to identify clusters in a low-dimensional data subspace, based on time-varying trajectories of spectral dynamics across multisite local field potential (LFP) recordings in awake behaving mice. Data were recorded from prefrontal cortex, hippocampus, and parietal cortex in eleven mice while they explored novel and familiar environments. RESULTS: PLC-identified subspaces and clusters showed high consistency across animals, and were modulated by the animals' ongoing behavior. CONCLUSIONS: PLC adds to an important growing literature on methods for characterizing dynamics in high-dimensional datasets, using a smaller number of parameters. The method is also applicable to other kinds of datasets, such as EEG or MEG.

Synchronization between Keyboard Typing and Neural Oscillations.

Rhythmic neural activity synchronizes with certain rhythmic behaviors, such as breathing, sniffing, saccades, and speech. The extent to which neural oscillations synchronize with higher-level and more complex behaviors is largely unknown. Here, we investigated electrophysiological synchronization with keyboard typing, which is an omnipresent behavior daily engaged by an uncountably large number of people. Keyboard typing is rhythmic, with frequency characteristics roughly the same as neural oscillatory dynamics associated with cognitive control, notably through midfrontal theta (4-7 Hz) oscillations. We tested the hypothesis that synchronization occurs between typing and midfrontal theta and breaks down when errors are committed. Thirty healthy participants typed words and sentences on a keyboard without visual feedback, while EEG was recorded. Typing rhythmicity was investigated by interkeystroke interval analyses and by a kernel density estimation method. We used a multivariate spatial filtering technique to investigate frequency-specific synchronization between typing and neuronal oscillations. Our results demonstrate theta rhythmicity in typing (around 6.5 Hz) through the two different behavioral analyses. Synchronization between typing and neuronal oscillations occurred at frequencies ranging from 4 to 15 Hz, but to a larger extent for lower frequencies. However, peak synchronization frequency was idiosyncratic across participants, therefore not specific to theta nor to midfrontal regions, and correlated somewhat with peak typing frequency. Errors and trials associated with stronger cognitive control were not associated with changes in synchronization at any frequency. As a whole, this study shows that brain-behavior synchronization does occur during keyboard typing but is not specific to midfrontal theta.

Dynamics of Neural Microstates in the VTA-Striatal-Prefrontal Loop during Novelty Exploration in the Rat.

Neural activity at the large-scale population level has been suggested to be consistent with a sequence of brief, quasistable spatial patterns. These "microstates" and their temporal dynamics have been linked to myriad cognitive functions and brain diseases. Most of this research has been performed using EEG, leaving many questions, such as the existence, dynamics, and behavioral relevance of microstates at the level of local field potentials (LFPs), unaddressed. Here, we adapted the standard EEG microstate analysis to triple-area LFP recordings from 192 electrodes in rats to investigate the mesoscopic dynamics of neural microstates within and across brain regions during novelty exploration. We performed simultaneous recordings from the prefrontal cortex, striatum, and ventral tegmental area in male rats during awake behavior (object novelty and exploration). We found that the LFP data can be accounted for by multiple, recurring microstates that were stable for ∼60-100 ms. The simultaneous microstate activity across brain regions revealed rhythmic patterns of coactivations, which we interpret as a novel indicator of inter-regional, mesoscale synchronization. Furthermore, these rhythmic coactivation patterns across microstates were modulated by behavioral states such as movement and exploration of a novel object. These results support the existence of a functional mesoscopic organization across multiple brain areas and present a possible link of the origin of macroscopic EEG microstates to zero-lag neuronal synchronization within and between brain areas, which is of particular interest to the human research community.SIGNIFICANCE STATEMENT The coordination of neural activity across the entire brain has remained elusive. Here we combine large-scale neural recordings at fine spatial resolution with the analysis of microstates (i.e., short-lived, recurring spatial patterns of neural activity). We demonstrate that the local activity in different brain areas can be accounted for by only a few microstates per region. These microstates exhibited temporal dynamics that were correlated across regions in rhythmic patterns. We demonstrate that these microstates are linked to behavior and exhibit different properties in the frequency domain during different behavioral states. In summary, LFP microstates provide an insightful approach to studying both mesoscopic and large-scale brain activation within and across regions.

Large-Scale and Multiscale Networks in the Rodent Brain during Novelty Exploration.

Neural activity is coordinated across multiple spatial and temporal scales, and these patterns of coordination are implicated in both healthy and impaired cognitive operations. However, empirical cross-scale investigations are relatively infrequent, because of limited data availability and to the difficulty of analyzing rich multivariate datasets. Here, we applied frequency-resolved multivariate source-separation analyses to characterize a large-scale dataset comprising spiking and local field potential (LFP) activity recorded simultaneously in three brain regions (prefrontal cortex, parietal cortex, hippocampus) in freely-moving mice. We identified a constellation of multidimensional, inter-regional networks across a range of frequencies (2-200 Hz). These networks were reproducible within animals across different recording sessions, but varied across different animals, suggesting individual variability in network architecture. The theta band (∼4-10 Hz) networks had several prominent features, including roughly equal contribution from all regions and strong inter-network synchronization. Overall, these findings demonstrate a multidimensional landscape of large-scale functional activations of cortical networks operating across multiple spatial, spectral, and temporal scales during open-field exploration.

Beta2 Oscillations in Hippocampal-Cortical Circuits During Novelty Detection.

Novelty detection is a core feature of behavioral adaptation and involves cascades of neuronal responses-from initial evaluation of the stimulus to the encoding of new representations-resulting in the behavioral ability to respond to unexpected inputs. In the past decade, a new important novelty detection feature, beta2 (~20-30 Hz) oscillations, has been described in the hippocampus (HC). However, the interactions between beta2 and the hippocampal network are unknown, as well as the role-or even the presence-of beta2 in other areas involved with novelty detection. In this work, we combined multisite local field potential (LFP) recordings with novelty-related behavioral tasks in mice to describe the oscillatory dynamics associated with novelty detection in the CA1 region of the HC, parietal cortex, and mid-prefrontal cortex. We found that transient beta2 power increases were observed only during interaction with novel contexts and objects, but not with familiar contexts and objects. Also, robust theta-gamma phase-amplitude coupling was observed during the exploration of novel environments. Surprisingly, bursts of beta2 power had strong coupling with the phase of delta-range oscillations. Finally, the parietal and mid-frontal cortices had strong coherence with the HC in both theta and beta2. These results highlight the importance of beta2 oscillations in a larger hippocampal-cortical circuit, suggesting that beta2 plays a role in the mechanism for detecting and modulating behavioral adaptation to novelty.

Cortical responses to whole-body balance perturbations index perturbation magnitude and predict reactive stepping behavior.

The goal of this study was to determine whether the cortical responses elicited by whole-body balance perturbations were similar to established cortical markers of action monitoring. Postural changes imposed by balance perturbations elicit a robust negative potential (N1) and a brisk increase of theta activity in the electroencephalogram recorded over midfrontal scalp areas. Because action monitoring is a cognitive function proposed to detect errors and initiate corrective adjustments, we hypothesized that the possible cortical markers of action monitoring during balance control (N1 potential and theta rhythm) scale with perturbation intensity and the eventual execution of reactive stepping responses (as opposed to feet-in-place responses). We recorded high-density electroencephalogram from eleven young individuals, who participated in an experimental balance assessment. The participants were asked to recover balance following anteroposterior translations of the support surface at various intensities, while attempting to maintain both feet in place. We estimated source-resolved cortical activity using independent component analysis. Combining time-frequency decomposition and group-level general linear modeling of single-trial responses, we found a significant relation of the interaction between perturbation intensity and stepping responses with multiple cortical features from the midfrontal cortex, including the N1 potential, and theta, alpha, and beta rhythms. Our findings suggest that the cortical responses to balance perturbations index the magnitude of a deviation from a stable postural state to predict the need for reactive stepping responses. We propose that the cortical control of balance may involve cognitive control mechanisms (i.e., action monitoring) that facilitate postural adjustments to maintain postural stability.

A data-driven method to identify frequency boundaries in multichannel electrophysiology data.

BACKGROUND: Electrophysiological recordings of the brain often exhibit neural oscillations, defined as narrowband bumps that deviate from the background power spectrum. These narrowband dynamics are grouped into frequency ranges, and the study of how activities in these ranges are related to cognition and disease is a major part of the neuroscience corpus. Frequency ranges are nearly always defined according to integer boundaries, such as 4-8 Hz for the theta band and 8-12 Hz for the alpha band. NEW METHOD: A data-driven multivariate method is presented to identify empirical frequency boundaries based on clustering of spatiotemporal similarities across a range of frequencies. The method, termed gedBounds, identifies patterns in covariance matrices that maximally separate narrowband from broadband activity, and then identifies clusters in the correlation matrix of those spatial patterns over all frequencies, using the dbscan clustering algorithm. Those clusters are empirically derived frequency bands, from which boundaries can be extracted. RESULTS: gedBounds recovers ground truth results in simulated data with high accuracy. The method was tested on EEG resting-state data from Parkinson's patients and control, and several features of the frequency components differed between patients and controls. COMPARISON WITH EXISTING METHODS: The proposed method offers higher precision in defining subject-specific frequency boundaries compared to the current standard approach. CONCLUSIONS: gedBounds can increase the precision and feature extraction of spectral dynamics in electrophysiology data.

Narrowband multivariate source separation for semi-blind discovery of experiment contrasts.

BACKGROUND: Electrophysiological recordings contain mixtures of signals from distinct neural sources, impeding a straightforward interpretation of the sensor-level data. This mixing is particularly detrimental when distinct sources resonate in overlapping frequencies. Fortunately, the mixing is linear and instantaneous. Multivariate source separation methods may therefore successfully separate statistical sources, even with overlapping spatial distributions. NEW METHOD: We demonstrate a feature-guided multivariate source separation method that is tuned to narrowband frequency content as well as binary condition differences. This method - comparison scanning generalized eigendecomposition, csGED - harnesses the covariance structure of multichannel data to find directions (i.e., eigenvectors) that maximally separate two subsets of data. To drive condition specificity and frequency specificity, our data subsets were taken from different task conditions and narrowband-filtered prior to applying GED. RESULTS: To validate the method, we simulated MEG data in two conditions with shared noise characteristics and unique signal. csGED outperformed the best sensor at reconstructing the ground truth signals, even in the presence of large amounts of noise. We next applied csGED to a published empirical MEG dataset on visual perception vs. imagery. csGED identified sources in alpha, beta, and gamma bands, and successfully separated distinct networks in the same frequency band. COMPARISON WITH EXISTING METHOD(S): GED is a flexible feature-guided decomposition method that has previously successfully been applied. Our combined frequency- and condition-tuning is a novel adaptation that extends the power of GED in cognitive electrophysiology. CONCLUSIONS: We demonstrate successful condition-specific source separation by applying csGED to simulated and empirical data.

Low-cost and versatile electrodes for extracellular chronic recordings in rodents.

Electrophysiological data are used to investigate fundamental properties of brain function, its relation to cognition, and its dysfunction in diseases. The development of reliable and open-source systems for electrophysiological data acquisition is decreasing the total cost of constructing and operating an electrophysiology laboratory, and facilitates low-cost methods to extract and analyze the data (Siegle et al., 2017). Here we detail our method of building custom-designed low-cost electrodes. These electrodes can be customized and manufactured by any researcher to address a broad set of research questions, further decreasing the final cost of an implanted animal. Finally, we present data showing such an electrode has a good signal quality to record LFP.

Multiple Midfrontal Thetas Revealed by Source Separation of Simultaneous MEG and EEG.

Theta-band (∼6 Hz) rhythmic activity within and over the medial PFC ("midfrontal theta") has been identified as a distinctive signature of "response conflict," the competition between multiple actions when only one action is goal-relevant. Midfrontal theta is traditionally conceptualized and analyzed under the assumption that it is a unitary signature of conflict that can be uniquely identified at one electrode (typically FCz). Here we recorded simultaneous MEG and EEG (total of 328 sensors) in 9 human subjects (7 female) and applied a feature-guided multivariate source-separation decomposition to determine whether conflict-related midfrontal theta is a unitary or multidimensional feature of the data. For each subject, a generalized eigendecomposition yielded spatial filters (components) that maximized the ratio between theta and broadband activity. Components were retained based on significance thresholding and midfrontal EEG topography. All of the subjects individually exhibited multiple (mean 5.89, SD 2.47) midfrontal components that contributed to sensor-level midfrontal theta power during the task. Component signals were temporally uncorrelated and asynchronous, suggesting that each midfrontal theta component was unique. Our findings call into question the dominant notion that midfrontal theta represents a unitary process. Instead, we suggest that midfrontal theta spans a multidimensional space, indicating multiple origins, but can manifest as a single feature at the sensor level because of signal mixing.SIGNIFICANCE STATEMENT "Midfrontal theta" is a rhythmic electrophysiological signature of the competition between multiple response options. Midfrontal theta is traditionally considered to reflect a single process. However, this assumption could be erroneous because of "mixing" (multiple sources contributing to the activity recorded at a single electrode). We investigated the dimensionality of midfrontal theta by applying advanced multivariate analysis methods to a multimodal MEG/EEG dataset. We identified multiple topographically overlapping neural sources that drove response conflict-related midfrontal theta. Midfrontal theta thus reflects multiple uncorrelated signals that manifest with similar EEG scalp projections. In addition to contributing to the cognitive control literature, we demonstrate both the feasibility and the necessity of signal demixing to understand the narrowband neural dynamics underlying cognitive processes.

Mesoscopic-scale functional networks in the primate amygdala.

The primate amygdala performs multiple functions that may be related to the anatomical heterogeneity of its nuclei. Individual neurons with stimulus- and task-specific responses are not clustered in any of the nuclei, suggesting that single-units may be too-fine grained to shed light on the mesoscale organization of the amygdala. We have extracted from local field potentials recorded simultaneously from multiple locations within the primate (Macaca mulatta) amygdala spatially defined and statistically separable responses to visual, tactile, and auditory stimuli. A generalized eigendecomposition-based method of source separation isolated coactivity patterns, or components, that in neurophysiological terms correspond to putative subnetworks. Some component spatial patterns mapped onto the anatomical organization of the amygdala, while other components reflected integration across nuclei. These components differentiated between visual, tactile, and auditory stimuli suggesting the presence of functionally distinct parallel subnetworks.