Assessing Granger Causality in Electrophysiological Data: Removing the Adverse Effects of Common Signals via Bipolar Derivations

Recording of single-unit activities with flexible micro-electrocorticographic array in rats for decoding of whole-body navigation

Objective.Micro-electrocorticographic (μECoG) arrays are able to record neural activities from the cortical surface, without the need to penetrate the brain parenchyma. Owing in part to small electrode sizes, previous studies have demonstrated that single-unit spikes could be detected from the cortical surface, and likely from Layer I neurons of the neocortex. Here we tested the ability to useμECoG arrays to decode, in rats, body position during open field navigation, through isolated single-unit activities.Approach. μECoG arrays were chronically implanted onto primary motor cortex (M1) of Wistar rats, and neural recording was performed in awake, behaving rats in an open-field enclosure. The signals were band-pass filtered between 300-3000 Hz. Threshold-crossing spikes were identified and sorted into distinct units based on defined criteria including waveform morphology and refractory period. Body positions were derived from video recordings. We used gradient-boosting machine to predict body position based on previous 100 ms of spike data, and correlation analyses to elucidate the relationship between position and spike patterns.Main results.Single-unit spikes could be extracted during chronic recording fromμECoG, and spatial position could be decoded from these spikes with a mean absolute error of prediction of 0.135 and 0.090 in the x- and y- dimensions (of a normalized range from 0 to 1), and Pearson's r of 0.607 and 0.571, respectively.Significance. μECoG can detect single-unit activities that likely arise from superficial neurons in the cortex and is a promising alternative to intracortical arrays, with the added benefit of scalability to cover large cortical surface with minimal incremental risks. More studies should be performed in human related to its use as brain-machine interface.

Causal interactions in brain networks predict pain levels in trigeminal neuralgia

Trigeminal neuralgia (TN) is a highly debilitating facial pain condition. Magnetic resonance imaging (MRI) is the main method for generating insights into the central mechanisms of TN pain in humans. Studies have found both structural and functional abnormalities in various brain structures in TN patients as compared with healthy controls. Whereas studies have also examined aberrations in brain networks in TN, no studies have to date investigated causal interactions in these brain networks and related these causal interactions to the levels of TN pain. We recorded fMRI data from 39 TN patients who either rested comfortably in the scanner during the resting state session or tracked their pain levels during the pain tracking session. Applying Granger causality to analyze the data and requiring consistent findings across the two scanning sessions, we found 5 causal interactions, including: (1) Thalamus → dACC, (2) Caudate → Inferior temporal gyrus, (3) Precentral gyrus → Inferior temporal gyrus, (4) Supramarginal gyrus → Inferior temporal gyrus, and (5) Bankssts → Inferior temporal gyrus, that were consistently associated with the levels of pain experienced by the patients. Utilizing these 5 causal interactions as predictor variables and the pain score as the predicted variable in a linear multiple regression model, we found that in both pain tracking and resting state sessions, the model was able to explain ∼36 % of the variance in pain levels, and importantly, the model trained on the 5 causal interaction values from one session was able to predict pain levels using the 5 causal interaction values from the other session, thereby cross-validating the models. These results, obtained by applying novel analytical methods to neuroimaging data, provide important insights into the pathophysiology of TN and could inform future studies aimed at developing innovative therapies for treating TN.

Inferring Neural Communication Dynamics from Field Potentials Using Graph Diffusion Autoregression

Estimating dynamic network communication is attracting increased attention, spurred by rapid advancements in multi-site neural recording technologies and efforts to better understand cognitive processes. Yet, traditional methods, which infer communication from statistical dependencies among distributed neural recordings, face core limitations: they do not model neural interactions in a biologically plausible way, neglect spatial information from the recording setup, and yield predominantly static estimates that cannot capture rapid changes in the brain. To address these issues, we introduce a graph diffusion autoregressive model. Designed for distributed field potential recordings, our model combines vector autoregression with a network communication process to produce a high-resolution communication signal. We successfully validated the model on simulated neural activity and recordings from subdural and intracortical micro-electrode arrays placed in macaque sensorimotor cortex demonstrating its ability to describe rapid communication dynamics induced by optogenetic stimulation, changes in resting state communication, and the trial-by-trial variability during a reach task.

Decision and response monitoring during working memory are sequentially represented in the human insula

Emerging research supports a role of the insula in human cognition. Here, we used intracranial EEG to investigate the spatiotemporal dynamics in the insula during a verbal working memory (vWM) task. We found robust effects for theta, beta, and high frequency activity (HFA) during probe presentation requiring a decision. Theta band activity showed differential involvement across left and right insulae while sequential HFA modulations were observed along the anteroposterior axis. HFA in anterior insula tracked decision making and subsequent HFA was observed in posterior insula after the behavioral response. Our results provide electrophysiological evidence of engagement of different insula subregions in both decision-making and response monitoring during vWM and expand our knowledge of the role of the insula in complex human behavior.

Aversive memory formation in humans involves an amygdala-hippocampus phase code

Memory for aversive events is central to survival but can become maladaptive in psychiatric disorders. Memory enhancement for emotional events is thought to depend on amygdala modulation of hippocampal activity. However, the neural dynamics of amygdala-hippocampal communication during emotional memory encoding remain unknown. Using simultaneous intracranial recordings from both structures in human patients, here we show that successful emotional memory encoding depends on the amygdala theta phase to which hippocampal gamma activity and neuronal firing couple. The phase difference between subsequently remembered vs. not-remembered emotional stimuli translates to a time period that enables lagged coherence between amygdala and downstream hippocampal gamma. These results reveal a mechanism whereby amygdala theta phase coordinates transient amygdala -hippocampal gamma coherence to facilitate aversive memory encoding. Pacing of lagged gamma coherence via amygdala theta phase may represent a general mechanism through which the amygdala relays emotional content to distant brain regions to modulate other aspects of cognition, such as attention and decision-making.

Information flows from hippocampus to auditory cortex during replay of verbal working memory items

The maintenance of items in working memory (WM) relies on a widespread network of cortical areas and hippocampus where synchronization between electrophysiological recordings reflects functional coupling. We investigated the direction of information flow between auditory cortex and hippocampus while participants heard and then mentally replayed strings of letters in WM by activating their phonological loop. We recorded local field potentials from the hippocampus, reconstructed beamforming sources of scalp EEG, and – additionally in four participants – recorded from subdural cortical electrodes. When analyzing Granger causality, the information flow was from auditory cortex to hippocampus with a peak in the [4 8] Hz range while participants heard the letters. This flow was subsequently reversed during maintenance while participants maintained the letters in memory. The functional interaction between hippocampus and the cortex and the reversal of information flow provide a physiological basis for the encoding of memory items and their active replay during maintenance.

Every day, the brain’s ability to temporarily store and recall information – called working memory – enables us to reason, solve complex problems or to speak. Holding pieces of information in working memory for short periods of times is a skill that relies on communication between neural circuits that span several areas of the brain. The hippocampus, a seahorse-shaped area at the centre of the brain, is well-known for its role in learning and memory. Less clear, however, is how brain regions that process sensory inputs, including visual stimuli and sounds, contribute to working memory.

To investigate, Dimakopoulos et al. studied the flow of information between the hippocampus and the auditory cortex, which processes sound. To do so, various types of electrodes were placed on the scalp or surgically implanted in the brains of people with drug-resistant epilepsy. These electrodes measured the brain activity of participants as they read, heard and then mentally replayed strings of up to 8 letters. The electrical signals analysed reflected the flow of information between brain areas.

When participants read and heard the sequence of letters, brain signals flowed from the auditory cortex to the hippocampus. The flow of electrical activity was reversed while participants recalled the letters. This pattern was found only in the left side of the brain, as expected for a language related task, and only if participants recalled the letters correctly.

This work by Dimakopoulos et al. provides the first evidence of bidirectional communication between brain areas that are active when people memorise and recall information from their working memory. In doing so, it provides a physiological basis for how the brain encodes and replays information stored in working memory, which evidently relies on the interplay between the hippocampus and sensory cortex.

qEEG Analysis in the Diagnosis of Alzheimer’s Disease: A Comparison of Functional Connectivity and Spectral Analysis

Alzheimer’s disease (AD) is a brain disorder that is mainly characterized by a progressive degeneration of neurons in the brain and decline of cognitive abilities. This study compared an FFT-based spectral analysis against a functional connectivity analysis for the diagnosis of AD. Both quantitative methods were applied on an EEG dataset including 20 diagnosed AD patients and 20 age-matched healthy controls (HC). The obtained results showed an advantage of the functional connectivity analysis when compared to the spectral analysis; while the latter could not find any significant differences between the AD and HC groups, the functional connectivity analysis showed statistically higher synchronization levels in the AD group in the lower frequency bands (delta and theta), suggesting a ‘phase-locked’ state in AD-affected brains. Further comparison of functional connectivity between the homotopic regions confirmed that the traits of AD were localized to the centro-parietal and centro-temporal areas in the theta frequency band (4–8 Hz). This study applies a neural metric for Alzheimer’s detection from a data science perspective rather than from a neuroscience one and shows that the combination of bipolar derivations with phase synchronization yields similar results to comparable studies employing alternative analysis methods.

Saccade-related neural communication in the human medial temporal lobe is modulated by the social relevance of stimuli

Humans predominantly explore their environment by moving their eyes. To optimally communicate and process visual information, neural activity needs to be coordinated with the execution of eye movements. We investigated the coordination between visual exploration and interareal neural communication by analyzing local field potentials and single neuron activity in patients with epilepsy. We demonstrated that during the free viewing of images, neural communication between the human amygdala and hippocampus is coordinated with the execution of eye movements. The strength and direction of neural communication and hippocampal saccade-related phase alignment were strongest for fixations that landed on human faces. Our results argue that the state of the human medial temporal lobe network is selectively coordinated with motor behavior. Interareal neural communication was facilitated for social stimuli as indexed by the category of the attended information.

Stimulus dependence of directed information exchange between cortical layers in macaque V1

Perception and cognition require the integration of feedforward sensory information with feedback signals. Using different sized stimuli, we isolate spectral signatures of feedforward and feedback signals, and their effect on communication between layers in primary visual cortex of male macaque monkeys. Small stimuli elicited gamma frequency oscillations predominantly in the superficial layers. These Granger-causally originated in upper layer 4 and lower supragranular layers. Unexpectedly, large stimuli generated strong narrow band gamma oscillatory activity across cortical layers. They Granger-causally arose in layer 5, were conveyed through layer six to superficial layers, and violated existing models of feedback spectral signatures. Equally surprising, with large stimuli, alpha band oscillatory activity arose predominantly in granular and supragranular layers and communicated in a feedforward direction. Thus, oscillations in specific frequency bands are dynamically modulated to serve feedback and feedforward communication and are not restricted to specific cortical layers in V1.

Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality

The study of the interdependence relationships of the variables of an examined system is of great importance and remains a challenging task. There are two distinct cases of interdependence. In the first case, the variables evolve in synchrony, connections are undirected and the connectivity is examined based on symmetric measures, such as correlation. In the second case, a variable drives another one and they are connected with a causal relationship. Therefore, directed connections entail the determination of the interrelationships based on causality measures. The main open question that arises is the following: can symmetric correlation measures or directional causality measures be applied to infer the connectivity network of an examined system? Using simulations, we demonstrate the performance of different connectivity measures in case of contemporaneous or/and temporal dependencies. Results suggest the sensitivity of correlation measures when temporal dependencies exist in the data. On the other hand, causality measures do not spuriously indicate causal effects when data present only contemporaneous dependencies. Finally, the necessity of introducing effective instantaneous causality measures is highlighted since they are able to handle both contemporaneous and causal effects at the same time. Results based on instantaneous causality measures are promising; however, further investigation is required in order to achieve an overall satisfactory performance.

Consciousness depends on integration between parietal cortex, striatum, and thalamus

The neural substrates of consciousness remain elusive. Competing theories that attempt to explain consciousness disagree on the contribution of frontal versus posterior cortex and omit subcortical influences. This lack of understanding impedes the ability to monitor consciousness, which can lead to adverse clinical consequences. To test substrates and measures of consciousness, we recorded simultaneously from frontal cortex, parietal cortex, and subcortical structures, the striatum and thalamus, in awake, sleeping, and anesthetized macaques. We manipulated consciousness on a finer scale using thalamic stimulation, rousing macaques from continuously administered anesthesia. Our results show that, unlike measures targeting complexity, a measure additionally capturing neural integration (Φ∗) robustly correlated with changes in consciousness. Machine learning approaches show parietal cortex, striatum, and thalamus contributed more than frontal cortex to decoding differences in consciousness. These findings highlight the importance of integration between parietal and subcortical structures and challenge a key role for frontal cortex in consciousness.

Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning

OBJECTIVE

Brain-machine interfaces (BMIs) seek to restore lost motor functions in individuals with neurological disorders by enabling them to control external devices directly with their thoughts. This work aims to improve robustness and decoding accuracy that currently become major challenges in the clinical translation of intracortical BMIs.

APPROACH

We propose entire spiking activity (ESA) -an envelope of spiking activity that can be extracted by a simple, threshold-less, and automated technique- as the input signal. We couple ESA with deep learning-based decoding algorithm that uses quasi-recurrent neural network (QRNN) architecture. We evaluate comprehensively the performance of ESA-driven QRNN decoder for decoding hand kinematics from neural signals chronically recorded from the primary motor cortex area of three non-human primates performing different tasks.

MAIN RESULTS

Our proposed method yields consistently higher decoding performance than any other combinations of the input signal and decoding algorithm previously reported across long term recording sessions. It can sustain high decoding performance even when removing spikes from the raw signals, when using the different number of channels, and when using a smaller amount of training data.

SIGNIFICANCE

Overall results demonstrate exceptionally high decoding accuracy and chronic robustness, which is highly desirable given it is an unresolved challenge in BMIs.

Impact of referencing scheme on decoding performance of LFP-based brain-machine interface

OBJECTIVE

There has recently been an increasing interest in local field potential (LFP) for brain-machine interface (BMI) applications due to its desirable properties (signal stability and low bandwidth). LFP is typically recorded with respect to a single unipolar reference which is susceptible to common noise. Several referencing schemes have been proposed to eliminate the common noise, such as bipolar reference, current source density (CSD), and common average reference (CAR). However, to date, there have not been any studies to investigate the impact of these referencing schemes on decoding performance of LFP-based BMIs.

APPROACH

To address this issue, we comprehensively examined the impact of different referencing schemes and LFP features on the performance of hand kinematic decoding using a deep learning method. We used LFPs chronically recorded from the motor cortex area of a monkey while performing reaching tasks.

MAIN RESULTS

Experimental results revealed that local motor potential (LMP) emerged as the most informative feature regardless of the referencing schemes. Using LMP as the feature, CAR was found to yield consistently better decoding performance than other referencing schemes over long-term recording sessions. Significance Overall, our results suggest the potential use of LMP coupled with CAR for enhancing the decoding performance of LFP-based BMIs.

Layer and rhythm specificity for predictive routing

An established theoretical model, predictive coding, states that the brain is constantly building models (signifying changing predictions) of the environment. The brain does this by forming predictions and signaling sensory inputs which deviate from predictions (“prediction errors”). Various hypotheses exist about how predictive coding could be implemented in the brain. We recorded neural spiking and oscillations with laminar resolution in a network of cortical areas as monkeys performed a working memory task with changing stimulus predictability. Predictability modulated the patterns of feedforward/feedback flow, cortical layers, and oscillations used to process a visual stimulus. These data support the theory of predictive coding but suggest an alternate model for its neural implementation: predictive routing.

In predictive coding, experience generates predictions that attenuate the feeding forward of predicted stimuli while passing forward unpredicted “errors.” Different models have suggested distinct cortical layers, and rhythms implement predictive coding. We recorded spikes and local field potentials from laminar electrodes in five cortical areas (visual area 4 [V4], lateral intraparietal [LIP], posterior parietal area 7A, frontal eye field [FEF], and prefrontal cortex [PFC]) while monkeys performed a task that modulated visual stimulus predictability. During predictable blocks, there was enhanced alpha (8 to 14 Hz) or beta (15 to 30 Hz) power in all areas during stimulus processing and prestimulus beta (15 to 30 Hz) functional connectivity in deep layers of PFC to the other areas. Unpredictable stimuli were associated with increases in spiking and in gamma-band (40 to 90 Hz) power/connectivity that fed forward up the cortical hierarchy via superficial-layer cortex. Power and spiking modulation by predictability was stimulus specific. Alpha/beta power in LIP, FEF, and PFC inhibited spiking in deep layers of V4. Area 7A uniquely showed increases in high-beta (∼22 to 28 Hz) power/connectivity to unpredictable stimuli. These results motivate a conceptual model, predictive routing. It suggests that predictive coding may be implemented via lower-frequency alpha/beta rhythms that “prepare” pathways processing-predicted inputs by inhibiting feedforward gamma rhythms and associated spiking.

Granger Causality of the Electroencephalogram Reveals Abrupt Global Loss of Cortical Information Flow during Propofol-induced Loss of Responsiveness

It is a commonly held view that information flow between widely separated regions of the cerebral cortex is a necessary component in the generation of wakefulness (also termed “connected” consciousness). This study therefore hypothesized that loss of wakefulness caused by propofol anesthesia should be associated with loss of information flow, as estimated by the effective connectivity in the scalp electroencephalogram (EEG) signal.

In healthy adult volunteers, propofol anesthesia–induced loss of consciousness was associated with an abrupt, substantial, and global decrease in connectivity. These changes are comparably reversed at regain of consciousness. These observations suggest that information flow is an important indicator of wakefulness.

Supplemental Digital Content is available in the text.

Thalamus Modulates Consciousness via Layer-Specific Control of Cortex

Functional MRI and electrophysiology studies suggest that consciousness depends on large-scale thalamocortical and corticocortical interactions. However, it is unclear how neurons in different cortical layers and circuits contribute. We simultaneously recorded from central lateral thalamus (CL) and across layers of the frontoparietal cortex in awake, sleeping, and anesthetized macaques. We found that neurons in thalamus and deep cortical layers are most sensitive to changes in consciousness level, consistent across different anesthetic agents and sleep. Deep-layer activity is sustained by interactions with CL. Consciousness also depends on deep-layer neurons providing feedback to superficial layers (not to deep layers), suggesting that long-range feedback and intracolumnar signaling are important. To show causality, we stimulated CL in anesthetized macaques and effectively restored arousal and wake-like neural processing. This effect was location and frequency specific. Our findings suggest layer-specific thalamocortical correlates of consciousness and inform how targeted deep brain stimulation can alleviate disorders of consciousness.

EMD-Based, Mean-Phase Coherence Analysis to Assess Instantaneous Phase-Synchrony Dynamics in Epilepsy Patients

In this paper, an adaptive, non-linear, analytical methodology is proposed in order to quantitatively evaluate the instantaneous phase-synchrony dynamics in epilepsy patients. A group of finite neuronal oscillators is extracted from a multichannel electrocorticographic (ECoG) data, using the empirical mode decomposition (EMD). The instantaneous phases of the extracted oscillators are measured using the Hilbert transform in order to be utilized in the mean-phase coherence analysis. Finally, the dynamical evolution of phase-synchrony among the extracted neuronal oscillators within 1-600 Hz frequency range is assessed using eigenvalue decomposition. A different phasesynchrony dynamics was observed in two patients with frontal vs. temporal lobe epilepsy, as their seizures evolve. However, experimental results demonstrated a hypersynchrony level at seizure offset for both types of epilepsy during the ictal periods. This result suggests that hypersynchronization of the epileptic network may be a crucial, self-regulatory mechanism by which the brain terminate seizures.

Using Interictal HFOs to Improve the Identification of Epileptogenic Zones in Preparation for Epilepsy Surgery

For the more than 15 million patients who have drug-resistant epilepsy, surgical resection of the region where seizure arise is often the only alternative therapy. However, the identification of this epileptogenic zone (EZ) is often imprecise. Generally, insufficient EZ identification and resection may cause seizures to continue and too much resection may lead to unnecessary neurological deficits. In this paper, an automatic high frequency oscillations (HFOs) detection method based on noise-assisted multivariate EMD (NA-MEMD) is proposed to improve the localization of the EZ for epilepsy patients. In this method, different detected HFO types such as fast-ripple (FR), ripple (R), and fast-ripple concurrent with ripple (FRandR) are utilized to investigate their clinical relevance in identifying EZ. The proposed method may significantly improve the precision by which pathological brain tissue can be identified.

Differences in theta coherence between spatial and nonspatial attention using intracranial electroencephalographic signals in humans

A number of previous studies revealed the importance of the frontoparietal network for attention and preparatory top-down control. Here, we investigated the theta (7-9 Hz) coherence of the right frontoparietal networks to explore the differences in connectivity changes for the right frontoparietal regions during spatial attention (i.e., attention to a specific location rather than a specific feature) and nonspatial attention (i.e., attention to a specific feature rather than a specific location) tasks. The theta coherence in both tasks was primarily maintained at a preparatory state, decreases after stimulus onset, and recovers to the level of the preparatory state after the response time. However, the theta coherence of the frontoparietal network during spatial attention was immediately maintained after cue-onset, whereas for the case of nonspatial attention, it was immediately decreased after cue-onset. In addition, the connectivity of the right frontoparietal network, including the middle frontal gyrus and superior parietal lobe, were significantly higher for spatial attention rather than for nonspatial attention, suggesting that the dorsal parts of right frontoparietal network are more engaged in spatial-specific attention from the preparatory state. These findings also suggest that these two attention systems involve the use of different regional connectivity patterns, not only in the cognitive state, but in the preparatory state as well.

Identification of Hidden Sources by Estimating Instantaneous Causality in High-Dimensional Biomedical Time Series

The study of connectivity patterns of a system’s variables, such as multi-channel electroencephalograms (EEG), is of utmost importance towards a better understanding of its internal evolutionary mechanisms. Here, the problem of estimating the connectivity network from multivariate time series in the presence of prominent unobserved variables is addressed. The causality measure of partial mutual information from mixed embedding (PMIME), designed to estimate direct lag-causal effects in the presence of many observed variables, is adapted to estimate also zero-lag effects, the so-called instantaneous causality. We term the proposed advanced method, PMIME0. The estimation of instantaneous causality by PMIME0 is a signature of the presence of hidden source in the observed system, as demonstrated analytically in a toy model. It is further demonstrated that the PMIME0 identifies the true instantaneous with great accuracy in a variety of high-dimensional dynamical systems. The method is applied to EEG data with epileptiform discharges (EDs), and the results imply a strong impact of unobserved confounders during the EDs. This finding comes as a possible explanation for the increased levels of causality during epileptic seizures estimated by some measures affected by the presence of a common source.

Electrophysiological Brain Connectivity: Theory and Implementation

We review the theory and algorithms of electrophysiological brain connectivity analysis. This tutorial is aimed at providing an introduction to brain functional connectivity from electrophysiological signals, including electroencephalography (EEG), magnetoencephalography (MEG), electrocorticography (ECoG), stereoelectroencephalography (SEEG). Various connectivity estimators are discussed, and algorithms introduced. Important issues for estimating and mapping brain functional connectivity with electrophysiology are discussed.

Inferring the direction of rhythmic neural transmission via inter-regional phase-amplitude coupling (ir-PAC)

Phase-amplitude coupling (PAC) estimates the statistical dependence between the phase of a low-frequency component and the amplitude of a high-frequency component of local field potentials (LFP). To date PAC has been mainly applied to one signal. In this work, we introduce a new application of PAC to two LFPs and suggest that it can be used to infer the direction and strength of rhythmic neural transmission between distinct brain networks. This hypothesis is based on the accumulating evidence that transmembrane currents related to action potentials contribute a broad-band component to LFP in the high-gamma band, and PAC calculated between the amplitude of high-gamma (>60 Hz) in one LFP and the phase of a low-frequency oscillation (e.g., theta) in another would therefore relate the output (spiking) of one area to the input (somatic/dendritic postsynaptic potentials) of the other. We tested the hypothesis on theta-band long range communications between hippocampus and prefrontal cortex (PFC) and theta-band short range communications between dentate gyrus (DG) and the Ammon’s horn (CA1) within the hippocampus. The ground truth was provided by the known anatomical connections predicting hippocampus → PFC and DG → CA1, i.e., theta transmission is unidirectional in both cases: from hippocampus to PFC and from DG to CA1 along the tri-synaptic pathway within hippocampus. We found that (1) hippocampal high-gamma amplitude was significantly coupled to PFC theta phase, but not vice versa; (2) similarly, DG high-gamma amplitude was significantly coupled to CA1 theta phase, but not vice versa, and (3) the DG high-gamma-CA1 theta PAC was significantly correlated with DG → CA1 Granger causality, a well-established analytical measure of directional neural transmission. These results support the hypothesis that inter-regional PAC (ir-PAC) can be used to relate the output of a rhythmic “driver” network (i.e., high gamma) to the input of a rhythmic “receiver” network (i.e., theta) and thereby establish the direction and strength of rhythmic neural transmission.

Integrated analysis of anatomical and electrophysiological human intracranial data

Human intracranial electroencephalography (iEEG) recordings provide data with much greater spatiotemporal precision than is possible from data obtained using scalp EEG, magnetoencephalography (MEG), or functional MRI. Until recently, the fusion of anatomical data (MRI and computed tomography (CT) images) with electrophysiological data and their subsequent analysis have required the use of technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that enables complex raw human iEEG data to be converted into more readily comprehensible illustrative representations. The protocol uses an open-source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and used by a large research community. In this protocol, we describe how to analyze complex iEEG datasets by providing an intuitive and rapid approach that can handle both neuroanatomical information and large electrophysiological datasets. We provide a worked example using an example dataset. We also explain how to automate the protocol and adjust the settings to enable analysis of iEEG datasets with other characteristics. The protocol can be implemented by a graduate student or postdoctoral fellow with minimal MATLAB experience and takes approximately an hour to execute, excluding the automated cortical surface extraction.

Neural mechanisms of internal distraction suppression in visual attention

When performing a demanding cognitive task, internal distraction in the form of task-irrelevant thoughts and mind wandering can shift our attention away from the task, negatively affecting task performance. Behaviorally, individuals with higher executive function indexed by higher working memory capacity (WMC) exhibit less mind wandering during cognitive tasks, but the underlying neural mechanisms are unknown. To address this problem, we recorded functional magnetic resonance imaging (fMRI) data from subjects performing a cued visual attention task, and assessed their WMC in a separate experiment. Applying machine learning and time-series analysis techniques, we showed that (1) higher WMC individuals experienced lower internal distraction through stronger suppression of posterior cingulate cortex (PCC) activity, (2) higher WMC individuals had better neural representations of attended information as evidenced by higher multivoxel decoding accuracy of cue-related activities in the dorsal attention network (DAN), (3) the positive relationship between WMC and DAN decoding accuracy was mediated by suppression of PCC activity, (4) the dorsal anterior cingulate (dACC) was a source of top-down signals that regulate PCC activity as evidenced by the negative association between Granger-causal influence dACC→PCC and PCC activity levels, and (5) higher WMC individuals exhibiting stronger dACC→PCC Granger-causal influence. These results shed light on the neural mechanisms underlying the executive suppression of internal distraction in tasks requiring externally oriented attention and provide an explanation of the individual differences in such suppression.

Layer 3 Dynamically Coordinates Columnar Activity According to Spatial Context

To reduce statistical redundancy of natural inputs and increase the sparseness of coding, neurons in primary visual cortex (V1) show tuning for stimulus size and surround suppression. This integration of spatial information is a fundamental, context-dependent neural operation involving extensive neural circuits that span across all cortical layers of a V1 column, and reflects both feedforward and feedback processing. However, how spatial integration is dynamically coordinated across cortical layers remains poorly understood. We recorded single- and multiunit activity and local field potentials across V1 layers of awake mice (both sexes) while they viewed stimuli of varying size and used dynamic Bayesian model comparisons to identify when laminar activity and interlaminar functional interactions showed surround suppression, the hallmark of spatial integration. We found that surround suppression is strongest in layer 3 (L3) and L4 activity, where suppression is established within ∼10 ms after response onset, and receptive fields dynamically sharpen while suppression strength increases. Importantly, we also found that specific directed functional connections were strongest for intermediate stimulus sizes and suppressed for larger ones, particularly for connections from L3 targeting L5 and L1. Together, the results shed light on the different functional roles of cortical layers in spatial integration and on how L3 dynamically coordinates activity across a cortical column depending on spatial context.SIGNIFICANCE STATEMENT Neurons in primary visual cortex (V1) show tuning for stimulus size, where responses to stimuli exceeding the receptive field can be suppressed (surround suppression). We demonstrate that functional connectivity between V1 layers can also have a surround-suppressed profile. A particularly prominent role seems to have layer 3, the functional connections to layers 5 and 1 of which are strongest for stimuli of optimal size and decreased for large stimuli. Our results therefore point toward a key role of layer 3 in coordinating activity across the cortical column according to spatial context.

Spectral Imprints of Working Memory for Everyday Associations in the Frontoparietal Network

How does the human brain rapidly process incoming information in working memory? In growing divergence from a single-region focus on the prefrontal cortex (PFC), recent work argues for emphasis on how distributed neural networks are rapidly coordinated in support of this central neurocognitive function. Previously, we showed that working memory for everyday “what,” “where,” and “when” associations depends on multiplexed oscillatory systems, in which signals of different frequencies simultaneously link the PFC to parieto-occipital and medial temporal regions, pointing to a complex web of sub-second, bidirectional interactions. Here, we used direct brain recordings to delineate the frontoparietal oscillatory correlates of working memory with high spatiotemporal precision. Seven intracranial patients with electrodes simultaneously localized to prefrontal and parietal cortices performed a visuospatial working memory task that operationalizes the types of identity and spatiotemporal information we encounter every day. First, task-induced oscillations in the same delta-theta (2–7 Hz) and alpha-beta (9–24 Hz) frequency ranges previously identified using scalp electroencephalography (EEG) carried information about the contents of working memory. Second, maintenance was linked to directional connectivity from the parietal cortex to the PFC. However, presentation of the test prompt to cue identity, spatial, or temporal information changed delta-theta coordination from a unidirectional, parietal-led system to a bidirectional, frontoparietal system. Third, the processing of spatiotemporal information was more bidirectional in the delta-theta range than was the processing of identity information, where alpha-beta connectivity did not exhibit sensitivity to the contents of working memory. These findings implicate a bidirectional delta-theta mechanism for frontoparietal control over the contents of working memory.

Noise-Assisted Multivariate EMD-Based Mean-Phase Coherence Analysis to Evaluate Phase-Synchrony Dynamics in Epilepsy Patients

Spatiotemporal evolution of synchrony dynamics among neuronal populations plays an important role in decoding complicated brain function in normal cognitive processing as well as during pathological conditions such as epileptic seizures. In this paper, a non-linear analytical methodology is proposed to quantitatively evaluate the phase-synchrony dynamics in epilepsy patients. A set of finite neuronal oscillators was adaptively extracted from a multi-channel electrocorticographic (ECoG) dataset utilizing noise-assisted multivariate empirical mode de-composition (NA-MEMD). Next, the instantaneous phases of the oscillatory functions were extracted using the Hilbert transform in order to be utilized in the mean-phase coherence analysis. The phase-synchrony dynamics were then assessed using eigenvalue decomposition. The extracted neuronal oscillators were grouped with respect to their frequency range into wideband (1-600 Hz), ripple (80-250 Hz), and fast-ripple (250-600 Hz) bands in order to investigate the dynamics of ECoG activity in these frequency ranges as seizures evolve. Drug-refractory patients with frontal and temporal lobe epilepsy demonstrated a reduction in phase-synchrony around seizure onset. However, the network phase-synchrony started to increase toward seizure end and achieved its maximum level at seizure offset for both types of epilepsy. This result suggests that hyper-synchronization of the epileptic network may be an essential self-regulatory mechanism by which the brain terminates seizures.

Assessing the performance of Granger-Geweke causality: Benchmark dataset and simulation framework

Nonparametric methods based on spectral factorization offer well validated tools for estimating spectral measures of causality, called Granger–Geweke Causality (GGC). In Pagnotta et al. (2018) [1] we benchmarked nonparametric GGC methods using EEG data recorded during unilateral whisker stimulations in ten rats; here, we include detailed information about the benchmark dataset. In addition, we provide codes for estimating nonparametric GGC and a simulation framework to evaluate the effects on GGC analyses of potential problems, such as the common reference problem, signal-to-noise ratio (SNR) differences between channels, and the presence of additive noise. We focus on nonparametric methods here, but these issues also affect parametric methods, which can be tested in our framework as well. Our examples allow showing that time reversal testing for GGC (tr-GGC) mitigates the detrimental effects due to SNR imbalance and presence of mixed additive noise, and illustrate that, when using a common reference, tr-GGC unambiguously detects the causal influence׳s dominant spectral component, irrespective of the characteristics of the common reference signal. Finally, one of our simulations provides an example that nonparametric methods can overcome a pitfall associated with the implementation of conditional GGC in traditional parametric methods.

The Effect of Common Signals on Power, Coherence and Granger Causality: Theoretical Review, Simulations, and Empirical Analysis of Fruit Fly LFPs Data

When analyzing neural data it is important to consider the limitations of the particular experimental setup. An enduring issue in the context of electrophysiology is the presence of common signals. For example a non-silent reference electrode adds a common signal across all recorded data and this adversely affects functional and effective connectivity analysis. To address the common signals problem, a number of methods have been proposed, but relatively few detailed investigations have been carried out. As a result, our understanding of how common signals affect neural connectivity estimation is incomplete. For example, little is known about recording preparations involving high spatial-resolution electrodes, used in linear array recordings. We address this gap through a combination of theoretical review, simulations, and empirical analysis of local field potentials recorded from the brains of fruit flies. We demonstrate how a framework that jointly analyzes power, coherence, and quantities based on Granger causality reveals the presence of common signals. We further show that subtracting spatially adjacent signals (bipolar derivations) largely removes the effects of the common signals. However, in some special cases this operation itself introduces a common signal. We also show that Granger causality is adversely affected by common signals and that a quantity referred to as “instantaneous interaction” is increased in the presence of common signals. The theoretical review, simulation, and empirical analysis we present can readily be adapted by others to investigate the nature of the common signals in their data. Our contributions improve our understanding of how common signals affect power, coherence, and Granger causality and will help reduce the misinterpretation of functional and effective connectivity analysis.

Detecting multivariate cross-correlation between brain regions

The problem of identifying functional connectivity from multiple time series data recorded in each of two or more brain areas arises in many neuroscientific investigations. For a single stationary time series in each of two brain areas statistical tools such as cross-correlation and Granger causality may be applied. On the other hand, to examine multivariate interactions at a single time point, canonical correlation, which finds the linear combinations of signals that maximize the correlation, may be used. We report here a new method that produces interpretations much like these standard techniques and, in addition, 1) extends the idea of canonical correlation to 3-way arrays (with dimensionality number of signals by number of time points by number of trials), 2) allows for nonstationarity, 3) also allows for nonlinearity, 4) scales well as the number of signals increases, and 5) captures predictive relationships, as is done with Granger causality. We demonstrate the effectiveness of the method through simulation studies and illustrate by analyzing local field potentials recorded from a behaving primate. NEW & NOTEWORTHY Multiple signals recorded from each of multiple brain regions may contain information about cross-region interactions. This article provides a method for visualizing the complicated interdependencies contained in these signals and assessing them statistically. The method combines signals optimally but allows the resulting measure of dependence to change, both within and between regions, as the responses evolve dynamically across time. We demonstrate the effectiveness of the method through numerical simulations and by uncovering a novel connectivity pattern between hippocampus and prefrontal cortex during a declarative memory task.

Coding of self-motion-induced and self-independent visual motion in the rat dorsomedial striatum

Evolutionary development of vision has provided us with the capacity to detect moving objects. Concordant shifts of visual features suggest movements of the observer, whereas discordant changes are more likely to be indicating independently moving objects, such as predators or prey. Such distinction helps us to focus attention, adapt our behavior, and adjust our motor patterns to meet behavioral challenges. However, the neural basis of distinguishing self-induced and self-independent visual motions is not clarified in unrestrained animals yet. In this study, we investigated the presence and origin of motion-related visual information in the striatum of rats, a hub of action selection and procedural memory. We found that while almost half of the neurons in the dorsomedial striatum are sensitive to visual motion congruent with locomotion (and that many of them also code for spatial location), only a small subset of them are composed of fast-firing interneurons that could also perceive self-independent visual stimuli. These latter cells receive their visual input at least partially from the secondary visual cortex (V2). This differential visual sensitivity may be an important support in adjusting behavior to salient environmental events. It emphasizes the importance of investigating visual motion perception in unrestrained animals.

Dynamic frontotemporal systems process space and time in working memory

How do we rapidly process incoming streams of information in working memory, a cognitive mechanism central to human behavior? Dominant views of working memory focus on the prefrontal cortex (PFC), but human hippocampal recordings provide a neurophysiological signature distinct from the PFC. Are these regions independent, or do they interact in the service of working memory? We addressed this core issue in behavior by recording directly from frontotemporal sites in humans performing a visuospatial working memory task that operationalizes the types of identity and spatiotemporal information we encounter every day. Theta band oscillations drove bidirectional interactions between the PFC and medial temporal lobe (MTL; including the hippocampus). MTL theta oscillations directed the PFC preferentially during the processing of spatiotemporal information, while PFC theta oscillations directed the MTL for all types of information being processed in working memory. These findings reveal an MTL theta mechanism for processing space and time and a domain-general PFC theta mechanism, providing evidence that rapid, dynamic MTL–PFC interactions underlie working memory for everyday experiences.

How do we rapidly process incoming streams of information in working memory? Dominant views of working memory focus on the prefrontal cortex (PFC), but other data suggest a role for the medial temporal lobe (MTL). To delineate whether (and how) these brain regions interact during working memory, we recorded directly from PFC and MTL sites in humans performing a task that tests working memory for the types of “what,” “where,” and “when” information encountered every day. MTL oscillations in the theta band (3–7 Hz) directed PFC activity during the processing of spatiotemporal information, while PFC theta oscillations directed MTL activity for all types of information. This functional dissociation provides the first demonstration of bidirectional communication between the PFC and MTL during working memory. Our findings reveal that rapid, dynamic interactions between these two regions underlie working memory for everyday experiences, challenging dominant views on the central role of the PFC.

Isoflurane Impairs Low-Frequency Feedback but Leaves High-Frequency Feedforward Connectivity Intact in the Fly Brain

Hierarchically organized brains communicate through feedforward (FF) and feedback (FB) pathways. In mammals, FF and FB are mediated by higher and lower frequencies during wakefulness. FB is preferentially impaired by general anesthetics in multiple mammalian species. This suggests FB serves critical functions in waking brains. The brain of Drosophila melanogaster (fruit fly) is also hierarchically organized, but the presence of FB in these brains is not established. Here, we studied FB in the fly brain, by simultaneously recording local field potentials (LFPs) from low-order peripheral structures and higher-order central structures. We analyzed the data using Granger causality (GC), the first application of this analysis technique to recordings from the insect brain. Our analysis revealed that low frequencies (0.1–5 Hz) mediated FB from the center to the periphery, while higher frequencies (10–45 Hz) mediated FF in the opposite direction. Further, isoflurane anesthesia preferentially reduced FB. Our results imply that the spectral characteristics of FF and FB may be a signature of hierarchically organized brains that is conserved from insects to mammals. We speculate that general anesthetics may induce unresponsiveness across species by targeting the mechanisms that support FB.

Lateral hypothalamic activity indicates hunger and satiety states in humans

Lateral hypothalamic area (LHA) local field potentials (LFPs) were recorded in a Prader–Willi patient undergoing deep brain stimulation (DBS) for obesity. During hunger, exposure to food‐related cues induced an increase in beta/low‐gamma activity. In contrast, recordings during satiety were marked by prominent alpha rhythms. Based on these findings, we have delivered alpha‐frequency DBS prior to and during food intake. Despite reporting an early sensation of fullness, the patient continued to crave food. This suggests that the pattern of activity in LHA may indicate hunger/satiety states in humans but attest to the complexity of conducting neuromodulation studies in obesity.

Top-Down Beta Enhances Bottom-Up Gamma

Several recent studies have demonstrated that the bottom-up signaling of a visual stimulus is subserved by interareal gamma-band synchronization, whereas top-down influences are mediated by alpha-beta band synchronization. These processes may implement top-down control of stimulus processing if top-down and bottom-up mediating rhythms are coupled via cross-frequency interaction. To test this possibility, we investigated Granger-causal influences among awake macaque primary visual area V1, higher visual area V4, and parietal control area 7a during attentional task performance. Top-down 7a-to-V1 beta-band influences enhanced visually driven V1-to-V4 gamma-band influences. This enhancement was spatially specific and largest when beta-band activity preceded gamma-band activity by ∼0.1 s, suggesting a causal effect of top-down processes on bottom-up processes. We propose that this cross-frequency interaction mechanistically subserves the attentional control of stimulus selection.SIGNIFICANCE STATEMENT Contemporary research indicates that the alpha-beta frequency band underlies top-down control, whereas the gamma-band mediates bottom-up stimulus processing. This arrangement inspires an attractive hypothesis, which posits that top-down beta-band influences directly modulate bottom-up gamma band influences via cross-frequency interaction. We evaluate this hypothesis determining that beta-band top-down influences from parietal area 7a to visual area V1 are correlated with bottom-up gamma frequency influences from V1 to area V4, in a spatially specific manner, and that this correlation is maximal when top-down activity precedes bottom-up activity. These results show that for top-down processes such as spatial attention, elevated top-down beta-band influences directly enhance feedforward stimulus-induced gamma-band processing, leading to enhancement of the selected stimulus.

EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks

Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a "reach/saccade to spatial target" cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI.

Tuning Up the Old Brain with New Tricks: Attention Training via Neurofeedback

Neurofeedback (NF) is a form of biofeedback that uses real-time (RT) modulation of brain activity to enhance brain function and behavioral performance. Recent advances in Brain-Computer Interfaces (BCI) and cognitive training (CT) have provided new tools and evidence that NF improves cognitive functions, such as attention and working memory (WM), beyond what is provided by traditional CT. More published studies have demonstrated the efficacy of NF, particularly for treating attention deficit hyperactivity disorder (ADHD) in children. In contrast, there have been fewer studies done in older adults with or without cognitive impairment, with some notable exceptions. The focus of this review is to summarize current success in RT NF training of older brains aiming to match those of younger brains during attention/WM tasks. We also outline potential future advances in RT brainwave-based NF for improving attention training in older populations. The rapid growth in wireless recording of brain activity, machine learning classification and brain network analysis provides new tools for combating cognitive decline and brain aging in older adults. We optimistically conclude that NF, combined with new neuro-markers (event-related potentials and connectivity) and traditional features, promises to provide new hope for brain and CT in the growing older population.

Time-course of coherence in the human basal ganglia during voluntary movements

We are interested in characterizing how brain networks interact and communicate with each other during voluntary movements. We recorded electrical activities from the globus pallidus pars interna (GPi), subthalamic nucleus (STN) and the motor cortex during voluntary wrist movements. Seven patients with dystonia and six patients with Parkinson’s disease underwent bilateral deep brain stimulation (DBS) electrode placement. Local field potentials from the DBS electrodes and scalp EEG from the electrodes placed over the motor cortices were recorded while the patients performed externally triggered and self-initiated movements. The coherence calculated between the motor cortex and STN or GPi was found to be coupled to its power in both the beta and the gamma bands. The association of coherence with power suggests that a coupling in neural activity between the basal ganglia and the motor cortex is required for the execution of voluntary movements. Finally, we propose a mathematical model involving coupled neural oscillators which provides a possible explanation for how inter-regional coupling takes place.