Spatiotemporal resonance in mouse primary visual cortex.

Human primary visual cortex (V1) responds more strongly, or resonates, when exposed to ∼10, ∼15-20, and ∼40-50 Hz rhythmic flickering light. Full-field flicker also evokes the perception of hallucinatory geometric patterns, which mathematical models explain as standing-wave formations emerging from periodic forcing at resonant frequencies of the simulated neural network. However, empirical evidence for such flicker-induced standing waves in the visual cortex was missing. We recorded cortical responses to flicker in awake mice using high-spatial-resolution widefield imaging in combination with high-temporal-resolution glutamate-sensing fluorescent reporter (iGluSnFR). The temporal frequency tuning curves in the mouse V1 were similar to those observed in humans, showing a banded structure with multiple resonance peaks (8, 15, and 33 Hz). Spatially, all flicker frequencies evoked responses in V1 corresponding to retinotopic stimulus location, but some evoked additional peaks. These flicker-induced cortical patterns displayed standing-wave characteristics and matched linear wave equation solutions in an area restricted to the visual cortex. Taken together, the interaction of periodic traveling waves with cortical area boundaries leads to spatiotemporal activity patterns that may affect perception.

Serotonin transporter knockout in rats reduces beta- and gamma-band functional connectivity between the orbitofrontal cortex and amygdala during auditory discrimination.

The orbitofrontal cortex and amygdala collaborate in outcome-guided decision-making through reciprocal projections. While serotonin transporter knockout (SERT-/-) rodents show changes in outcome-guided decision-making, and in orbitofrontal cortex and amygdala neuronal activity, it remains unclear whether SERT genotype modulates orbitofrontal cortex-amygdala synchronization. We trained SERT-/- and SERT+/+ male rats to execute a task requiring to discriminate between two auditory stimuli, one predictive of a reward (CS+) and the other not (CS-), by responding through nose pokes in opposite-side ports. Overall, task acquisition was not influenced by genotype. Next, we simultaneously recorded local field potentials in the orbitofrontal cortex and amygdala of both hemispheres while the rats performed the task. Behaviorally, SERT-/- rats showed a nonsignificant trend for more accurate responses to the CS-. Electrophysiologically, orbitofrontal cortex-amygdala synchronization in the beta and gamma frequency bands during response selection was significantly reduced and associated with decreased hubness and clustering coefficient in both regions in SERT-/- rats compared to SERT+/+ rats. Conversely, theta synchronization at the time of behavioral response in the port associated with reward was similar in both genotypes. Together, our findings reveal the modulation by SERT genotype of the orbitofrontal cortex-amygdala functional connectivity during an auditory discrimination task.

Similarity of brain activity patterns during learning and subsequent resting state predicts memory consolidation.

Spontaneous reactivation of brain activity from learning to a subsequent off-line period has been implicated as a neural mechanism underlying memory consolidation. However, similarities in brain activity may also emerge as a result of individual, trait-like characteristics. Here, we introduced a novel approach for analyzing continuous electroencephalography (EEG) data to investigate learning-induced changes as well as trait-like characteristics in brain activity underlying memory consolidation. Thirty-one healthy young adults performed a learning task, and their performance was retested after a short (∼1 h) delay. Consolidation of two distinct types of information (serial-order and probability) embedded in the task were tested to reveal similarities in functional networks that uniquely predict the changes in the respective memory performance. EEG was recorded during learning and pre- and post-learning rest periods. To investigate brain activity associated with consolidation, we quantified similarities in EEG functional connectivity between learning and pre-learning rest (baseline similarity) and learning and post-learning rest (post-learning similarity). While comparable patterns of these two could indicate trait-like similarities, changes from baseline to post-learning similarity could indicate learning-induced changes, possibly spontaneous reactivation. Higher learning-induced changes in alpha frequency connectivity (8.5-9.5 Hz) were associated with better consolidation of serial-order information, particularly for long-range connections across central and parietal sites. The consolidation of probability information was associated with learning-induced changes in delta frequency connectivity (2.5-3 Hz) specifically for more local, short-range connections. Furthermore, there was a substantial overlap between the baseline and post-learning similarities and their associations with consolidation performance, suggesting robust (trait-like) differences in functional connectivity networks underlying memory processes.

Decreased Beta Power and OFC-STN Phase Synchronization during Reactive Stopping in Freely Behaving Rats.

During natural behavior, an action often needs to be suddenly stopped in response to an unexpected sensory input-referred to as reactive stopping. Reactive stopping has been mostly investigated in humans, which led to hypotheses about the involvement of different brain structures, in particular the hyperdirect pathway. Here, we directly investigate the contribution and interaction of two key regions of the hyperdirect pathway, the orbitofrontal cortex (OFC) and subthalamic nucleus (STN), using dual-area, multielectrode recordings in male rats performing a stop-signal task. In this task, rats have to initiate movement to a go-signal, and occasionally stop their movement to the go-signal side after a stop-signal, presented at various stop-signal delays. Both the OFC and STN show near-simultaneous field potential reductions in the beta frequency range (12-30 Hz) compared with the period preceding the go-signal and the movement period. These transient reductions (∼200 ms) only happen during reactive stopping, which is when the stop-signal was received after action initiation, and are well timed after stop-signal onset and before the estimated time of stopping. Phase synchronization analysis also showed a transient attenuation of synchronization between the OFC and STN in the beta range during reactive stopping. The present results provide the first direct quantification of local neural oscillatory activity in the OFC and STN and interareal synchronization specifically timed during reactive stopping.

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.

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.

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.

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.