Analysis methods
Using spatiotemporal source separation to identify prominent features in multichannel data without sinusoidal filters

Authors: Michael X Cohen

Abstract

The number of simultaneously recorded electrodes in neuroscience is steadily increasing, providing new opportunities for understanding brain function, but also new challenges for appropriately dealing with the increase in dimensionality. Multivariate source-separation analysis methods have been particularly effective at improving signal-to-noise ratio while reducing the dimensionality of the data, and are widely used for cleaning, classifying, and source-localizing multichannel neural time series data. Most source-separation methods produce a spatial component (that is, a weighted combination of channels to produce one time series); here, this is extended to apply source-separation to a time series, with the idea of obtaining a weighted combination of successive time points, such that the weights are optimized to match some criteria. This is achieved via a two-stage source-separation procedure, in which an optimal spatial filter is first constructed, and then its optimal temporal basis function is computed. This second stage is achieved with a time-delay-embedding matrix, in which additional rows of a matrix are created from time-delayed versions of existing rows. The optimal spatial and temporal weights can be obtained by solving a generalized eigendecomposition of covariance matrices. The method is demonstrated in simulated data and in an empirical EEG study on theta-band activity during response conflict. Spatiotemporal source separation has several advantages, including defining empirical filters without the need to apply sinusoidal narrowband filters.

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Multivariate cross-frequency coupling via generalized eigendecomposition

Authors: Michael X Cohen

Abstract

This paper presents a new framework for analyzing cross-frequency coupling in multichannel electrophysiological recordings. The generalized eigendecomposition-based cross-frequency coupling framework (gedCFC) is inspired by source separation algorithms combined with dynamics of mesoscopic neurophysiological processes. It is unaffected by factors that confound traditional CFC methods such as non-stationarities, non-sinusoidality, and non-uniform phase angle distributions—attractive properties considering that brain activity is neither stationary nor perfectly sinusoidal. The gedCFC framework opens new opportunities for conceptualizing CFC as network interactions with diverse spatial/topographical distributions. Five specific methods within the gedCFC framework are detailed, with validations in simulated data and applications in several empirical datasets. gedCFC accurately recovers physiologically plausible CFC patterns embedded in noise where traditional CFC methods perform poorly. It is also demonstrated that spike-field coherence in multichannel local field potential data can be analyzed using the gedCFC framework, with significant advantages over traditional spike-field coherence analyses. Null-hypothesis testing is also discussed.

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Comparison of linear spatial filters for identifying narrowband activity in multichannel data

Authors: Michael X Cohen

Abstract

Large-scale synchronous neural activity produces electrical fields that can be measured by electrodes outside the head, and volume conduction ensures that neural sources can be measured by many electrodes. However, most data analyses in M/EEG research are univariate, meaning each electrode is considered as a separate measurement. Several multivariate linear spatial filtering techniques have been introduced to the cognitive electrophysiology literature, but these techniques are not commonly used; comparisons across filters would be beneficial to the field. The purpose of this paper is to evaluate and compare the performance of several linear spatial filtering techniques, with a focus on those that use generalized eigendecomposition to facilitate dimensionality reduction and signal-to-noise ratio maximization. Simulated and empirical data were used to assess the accuracy, signal-to-noise ratio, and interpretability of the spatial filter results. When the simulated signal is powerful, different spatial filters provide convergent results. However, more subtle signals require carefully selected analysis parameters to obtain optimal results. Linear spatial filters can be powerful data analysis tools in cognitive electrophysiology, and should be applied more often; on the other hand, spatial filters can latch onto artifacts or produce uninterpretable results. Hypothesis-driven analyses, careful data inspection, and appropriate parameter selection are necessary to obtain high-quality results when using spatial filters.

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Rhythmic entrainment source separation: Optimizing analyses of neural responses to rhythmic sensory stimulation

Authors: Michael X Cohen, Rasa Gulbinaite

Abstract

Steady-state evoked potentials (SSEPs) are rhythmic brain responses to rhythmic sensory stimulation, and are often used to study perceptual and attentional processes. We present a data analysis method for maximizing the signal-to-noise ratio of the narrow-band steady-state response in the frequency and time-frequency domains. The method, termed rhythmic entrainment source separation (RESS), is based on denoising source separation approaches that take advantage of the simultaneous but differential projection of neural activity to multiple electrodes or sensors. Our approach is a combination and extension of existing multivariate source separation methods. We demonstrate that RESS performs well on both simulated and empirical data, and outperforms conventional SSEP analysis methods based on selecting electrodes with the strongest SSEP response, as well as several other linear spatial filters. We also discuss the potential confound of overfitting, whereby the filter captures noise in absence of a signal. Matlab scripts are available to replicate and extend our simulations and methods. We conclude with some practical advice for optimizing SSEP data analyses and interpreting the results.

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Effects of time lag and frequency matching on phase-based connectivity

Authors: Michael X Cohen

Abstract

The time- and frequency-varying dynamics of how brain regions interact is one of the fundamental mysteries of neuroscience. In electrophysiological data, functional connectivity is often measured through the consistency of oscillatory phase angles between two electrodes placed in or over different brain regions. However, due to volume conduction, the results of such analyses can be difficult to interpret, because mathematical estimates of connectivity can be driven both by true inter-regional connectivity, and by volume conduction from the same neural source. Generally, there are two approaches to attenuate artifacts due to volume conduction: spatial filtering in combination with standard connectivity methods, or connectivity methods such as the weighted phase lag index that are blind to instantaneous connectivity that may reflect volume conduction artifacts. The purpose of this paper is to compare these two approaches directly in the presence of different connectivity time lags (5 or 25 ms) and physiologically realistic frequency non-stationarities. The results show that standard connectivity methods in combination with Laplacian spatial filtering correctly identified simulated connectivity regardless of time lag or changes in frequency, although residual volume conduction artifacts were seen in the vicinity of the "seed" electrode. Weighted phase lag index under-estimated connectivity strength at small time lags and failed to identify connectivity in the presence of frequency mismatches or non-stationarities, but did not misidentify volume conduction as "connectivity." Both approaches have strengths and limitations, and this paper concludes with practical advice for when to use which approach in context of hypothesis testing and exploratory data analyses.

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Phase-clustering bias in phase-amplitude cross-frequency coupling and its removal

Authors: Joram van Driel, Roy Cox, Michael X Cohen

Abstract

BACKGROUND: Cross-frequency coupling methods allow for the identification of non-linear interactions across frequency bands, which are thought to reflect a fundamental principle of how electrophysiological brain activity is temporally orchestrated. In this paper we uncover a heretofore unknown source of bias in a commonly used method that quantifies cross-frequency coupling (phase-amplitude-coupling, or PAC).
NEW METHOD: We demonstrate that non-uniform phase angle distributions--a phenomenon that can readily occur in real data--can under some circumstances produce statistical errors and uninterpretable results when using PAC. We propose a novel debiasing procedure that, through a simple linear subtraction, effectively ameliorates this phase clustering bias.
RESULTS: Simulations showed that debiased PAC (dPAC) accurately detected the presence of coupling. This was true even in the presence of moderate noise levels, which inflated the phase clustering bias. Finally, dPAC was applied to intracranial sleep recordings from a macaque monkey, and to hippocampal LFP data from a freely moving rat, revealing robust cross-frequency coupling in both data sets.
COMPARISON WITH EXISTING METHODS: Compared to dPAC, regular PAC showed inflated or deflated estimations and statistically negative coupling values, depending on the strength of the bias and the angle of coupling. Noise increased these unwanted effects. Two other frequently used phase-amplitude coupling methods (the Modulation Index and Phase Locking Value) were also affected by the bias, though allowed for statistical inferences that were similar to dPAC.
CONCLUSION: We conclude that dPAC provides a simple modification of PAC, and thereby offers a cleaner and possibly more sensitive alternative method, to more accurately assess phase-amplitude coupling.

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Fluctuations in oscillation frequency control spike timing and coordinate neural networks

Authors: Michael X Cohen

Abstract

Neuroscience research spans multiple spatiotemporal scales, from subsecond dynamics of individual neurons to the slow coordination of billions of neurons during resting state and sleep. Here it is shown that a single functional principle-temporal fluctuations in oscillation peak frequency ("frequency sliding")—can be used as a common analysis approach to bridge multiple scales within neuroscience. Frequency sliding is demonstrated in simulated neural networks and in human EEG data during a visual task. Simulations of biophysically detailed neuron models show that frequency sliding modulates spike threshold and timing variability, as well as coincidence detection. Finally, human resting-state EEG data demonstrate that frequency sliding occurs endogenously and can be used to identify large-scale networks. Frequency sliding appears to be a general principle that regulates brain function on multiple spatial and temporal scales, from modulating spike timing in individual neurons to coordinating large-scale brain networks during cognition and resting state.

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Midfrontal theta, response conflict, and errors
Midfrontal theta tracks action monitoring over multiple interactive time scales

Authors: Michael X Cohen

Abstract

Quickly detecting and correcting mistakes is a crucial brain function. EEG studies have identified an idiosyncratic electrophysiological signature of online error correction, termed midfrontal theta. Midfrontal theta has so far been investigated over the fast time-scale of a few hundred milliseconds. But several aspects of behavior and brain activity unfold over multiple time scales, displaying "scale-free" dynamics that have been linked to criticality and optimal flexibility when responding to changing environmental demands. Here we used a novel line-tracking task to demonstrate that midfrontal theta is a transient yet non-phase-locked response that is modulated by task performance over at least three time scales: a few hundred milliseconds at the onset of a mistake, task performance over a fixed window of the previous 5s, and scale-free-like fluctuations over many tens of seconds. These findings provide novel evidence for a role of midfrontal theta in online behavioral adaptation, and suggest new approaches for linking EEG signatures of human executive functioning to its neurobiological underpinnings.

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S1, S2, S3, S5, S6, S7, S8, S9, S10, S12, S13, S14, S15, S16, S17, S18, S19, S20, S21 Each dataset (.mat file, eeglab format)
EEG source reconstruction reveals frontal-parietal dynamics of spatial conflict processing

Authors: Michael X Cohen, Richard Ridderinkhof

Abstract

Cognitive control requires the suppression of distracting information in order to focus on task-relevant information. We applied EEG source reconstruction via time-frequency linear constrained minimum variance beamforming to help elucidate the neural mechanisms involved in spatial conflict processing. Human subjects performed a Simon task, in which conflict was induced by incongruence between spatial location and response hand. We found an early (∼200 ms post-stimulus) conflict modulation in stimulus-contralateral parietal gamma (30-50 Hz), followed by a later alpha-band (8-12 Hz) conflict modulation, suggesting an early detection of spatial conflict and inhibition of spatial location processing. Inter-regional connectivity analyses assessed via cross-frequency coupling of theta (4-8 Hz), alpha, and gamma power revealed conflict-induced shifts in cortical network interactions: Congruent trials (relative to incongruent trials) had stronger coupling between frontal theta and stimulus-contrahemifield parietal alpha/gamma power, whereas incongruent trials had increased theta coupling between medial frontal and lateral frontal regions. These findings shed new light into the large-scale network dynamics of spatial conflict processing, and how those networks are shaped by oscillatory interactions.

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Dynamic interactions between large-scale brain networks predict behavioral adaptation after perceptual errors

Authors: Michael X Cohen, Simon van Gaal

Abstract

Failures to perceive visual stimuli lead to errors in decision making. Different theoretical accounts implicate either medial frontal (MF) cognitive control processes or prestimulus occipital (OC) cortical oscillatory dynamics in errors during perceptual tasks. Here, we show that these 2 previously unconnected theoretical accounts can be reconciled, and the brain regions described by the 2 theories have complimentary and interactive roles in supporting error adaptation. Using a perceptual discrimination task and time-frequency network-based analyses of electroencephalography data, we show that perceptual anticipation and posterror top-down control mechanisms recruit distinct but interacting brain networks. MF sites were a hub for theta-band networks and theta-alpha coupling elicited after errors, whereas occipital sites were a network hub during stimulus anticipation and alpha-gamma coupling. Granger causality analyses revealed that these networks communicate in their preferred direction and frequency band: response-related MF → OC interactions occurred in the theta band, whereas stimulus anticipation-related OC → MF interactions occurred in the alpha band. Subjects with stronger network interactions were more likely to improve performance after errors. These findings demonstrate that multiple large-scale brain networks interact dynamically and in a directionally specific manner in different frequency bands to support flexible behavior adaptation during perceptual decision making.

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Subthreshold muscle twitches dissociate oscillatory neural signatures of conflicts from errors

Authors: Michael X Cohen, Simon van Gaal

Abstract

We investigated the neural systems underlying conflict detection and error monitoring during rapid online error correction/monitoring mechanisms. We combined data from four separate cognitive tasks and 64 subjects in which EEG and EMG (muscle activity from the thumb used to respond) were recorded. In typical neuroscience experiments, behavioral responses are classified as "error" or "correct"; however, closer inspection of our data revealed that correct responses were often accompanied by "partial errors" - a muscle twitch of the incorrect hand ("mixed correct trials," ~13% of the trials). We found that these muscle twitches dissociated conflicts from errors in time-frequency domain analyses of EEG data. In particular, both mixed-correct trials and full error trials were associated with enhanced theta-band power (4-9Hz) compared to correct trials. However, full errors were additionally associated with power and frontal-parietal synchrony in the delta band. Single-trial robust multiple regression analyses revealed a significant modulation of theta power as a function of partial error correction time, thus linking trial-to-trial fluctuations in power to conflict. Furthermore, single-trial correlation analyses revealed a qualitative dissociation between conflict and error processing, such that mixed correct trials were associated with positive theta-RT correlations whereas full error trials were associated with negative delta-RT correlations. These findings shed new light on the local and global network mechanisms of conflict monitoring and error detection, and their relationship to online action adjustment.

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Midfrontal conflict-related theta-band power reflects neural oscillations that predict behavior

Authors: Michael X Cohen, Tobias Donner

Abstract

Action monitoring and conflict resolution require the rapid and flexible coordination of activity in multiple brain regions. Oscillatory neural population activity may be a key physiological mechanism underlying such rapid and flexible network coordination. EEG power modulations of theta-band (4-8 Hz) activity over the human midfrontal cortex during response conflict have been proposed to reflect neural oscillations that support conflict detection and resolution processes. However, it has remained unclear whether this frequency-band-specific activity reflects neural oscillations or nonoscillatory responses (i.e., event-related potentials). Here, we show that removing the phase-locked component of the EEG did not reduce the strength of the conflict-related modulation of the residual (i.e., non-phase-locked) theta power over midfrontal cortex. Furthermore, within-subject regression analyses revealed that the non-phase-locked theta power was a significantly better predictor of the conflict condition than was the time-domain phase-locked EEG component. Finally, non-phase-locked theta power showed robust and condition-specific (high- vs. low-conflict) cross-trial correlations with reaction time, whereas the phase-locked component did not. Taken together, our results indicate that most of the conflict-related and behaviorally relevant midfrontal EEG signal reflects a modulation of ongoing theta-band oscillations that occurs during the decision process but is not phase-locked to the stimulus or to the response.

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Op/ed
Where does EEG come from and what does it mean?

Author: Michael X Cohen

Abstract

Electroencephalography (EEG) has been instrumental in making discoveries about cognition, brain function, and dysfunction. But where does EEG come from and what does it mean? The purpose of this paper is to argue that we know shockingly little about the answer to this question, to highlight what we do know, how important the answers are, and how modern neuroscience technologies that allow us to measure and manipulate neural circuits with high spatiotemporal accuracy might finally bring us some answers. Neural oscillations are perhaps the best feature of EEG to use as anchors, because oscillations are observed and are studied at multiple spatiotemporal scales of the brain, in multiple species, and are widely implicated in cognition and in neural computations.

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A neural microcircuit for cognitive conflict detection and signaling

Authors: Michael X Cohen

Abstract

During human response conflict - competition between multiple conflicting actions when a mistake could be made - a specific pattern of brain electrical activity occurs over the medial frontal cortex (MFC), characterized by modulations of ongoing theta-band (∼6Hz) oscillations and synchronization with task-relevant brain regions. Despite the replicable and robust findings linking MFC theta to conflict processing, the significance of MFC theta for how neural microcircuits actually detect conflict and broadcast that signal is unknown. A neural MFC microcircuit model is proposed for processing conflict and generating theta oscillations. The model makes several novel predictions for the causes and consequences of MFC theta and conflict processing, and may be relevant for understanding the neural implementations of related cognitive processes.

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Rigor and replication in time-frequency analyses of cognitive electrophysiology data

Authors: Michael X Cohen

Abstract

Cognitive electrophysiology is a subfield of neuroscience that focused on linking M/EEG data to aspects of cognition and the neurophysiological processes that produce them. This field is growing in terms of the novelty and sophistication of findings, data, and data analysis methods. Simultaneously, many areas of modern sciences are experiencing a "replication crisis," prompting discussions of best practices to produce robust and replicable research. The purpose of this paper is to contribute to this discussion with a particular focus on cognitive electrophysiology. More issues are raised than are answered. Several recommendations are made, including (1) incorporate replications into new experiments, (2) write clear Methods and Results sections, and (3) publish null results.

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It's about time

Authors: Michael X Cohen

Abstract

The purpose of this review/opinion paper is to argue that human cognitive neuroscience has focused too little attention on how the brain may use time and time-based coding schemes to represent, process, and transfer information within and across brain regions. Instead, the majority of cognitive neuroscience studies rest on the assumption of functional localization. Although the functional localization approach has brought us a long way toward a basic characterization of brain functional organization, there are methodological and theoretical limitations of this approach. Further advances in our understanding of neurocognitive function may come from examining how the brain performs computations and forms transient functional neural networks using the rich multi-dimensional information available in time. This approach rests on the assumption that information is coded precisely in time but distributed in space; therefore, measures of rapid neuroelectrophysiological dynamics may provide insights into brain function that cannot be revealed using localization-based approaches and assumptions. Space is not an irrelevant dimension for brain organization; rather, a more complete understanding of how brain dynamics lead to behavior dynamics must incorporate how the brain uses time-based coding and processing schemes.

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