Decomposing neural synchrony: toward an explanation for near-zero phase-lag in cortical oscillatory networks

Cortical Synchrony and Information Flow during Transition from Wakefulness to Light Non-Rapid Eye Movement Sleep

Sleep is a highly stereotyped phenomenon, requiring robust spatiotemporal coordination of neural activity. Understanding how the brain coordinates neural activity with sleep onset can provide insights into the physiological functions subserved by sleep and the pathologic phenomena associated with sleep onset. We quantified whole-brain network changes in synchrony and information flow during the transition from wakefulness to light non-rapid eye movement (NREM) sleep, using MEG imaging in a convenient sample of 14 healthy human participants (11 female; mean 63.4 years [SD 11.8 years]). We furthermore performed computational modeling to infer excitatory and inhibitory properties of local neural activity. The transition from wakefulness to light NREM was identified to be encoded in spatially and temporally specific patterns of long-range synchrony. Within the delta band, there was a global increase in connectivity from wakefulness to light NREM, which was highest in frontoparietal regions. Within the theta band, there was an increase in connectivity in fronto-parieto-occipital regions and a decrease in temporal regions from wakefulness to Stage 1 sleep. Patterns of information flow revealed that mesial frontal regions receive hierarchically organized inputs from broad cortical regions upon sleep onset, including direct inflow from occipital regions and indirect inflow via parieto-temporal regions within the delta frequency band. Finally, biophysical neural mass modeling demonstrated changes in the anterior-to-posterior distribution of cortical excitation-to-inhibition with increased excitation-to-inhibition model parameters in anterior regions in light NREM compared with wakefulness. Together, these findings uncover whole-brain corticocortical structure and the orchestration of local and long-range, frequency-specific cortical interactions in the sleep-wake transition.SIGNIFICANCE STATEMENT Our work uncovers spatiotemporal cortical structure of neural synchrony and information flow upon the transition from wakefulness to light non-rapid eye movement sleep. Mesial frontal regions were identified to receive hierarchically organized inputs from broad cortical regions, including both direct inputs from occipital regions and indirect inputs via the parieto-temporal regions within the delta frequency range. Biophysical neural mass modeling revealed a spatially heterogeneous, anterior-posterior distribution of cortical excitation-to-inhibition. Our findings shed light on the orchestration of local and long-range cortical neural structure that is fundamental to sleep onset, and support an emerging view of cortically driven regulation of sleep homeostasis.

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks.

Disrupted communication of the temporoparietal junction in patients with major depressive disorder

Patients with major depressive disorder (MDD) suffer impairment in the transmission and integration of internal and external information sources. Accumulating evidence suggests that the temporoparietal junction (TPJ) is important for multiple cognitive and social functions and may act as a key node for the integration of internal and external information. Therefore, the TPJ's aberrant interaction mechanism may underpin MDD psychopathology. To answer this question, we conducted a comprehensive study using resting-state functional magnetic imaging data recorded from 74 patients with MDD and 69 normal controls. First, we examined whether TPJ was the most prominent region with altered functional/effective connectivity with multiple depression-related regions/networks, based on either zero-lag correlations or temporal mutual information (total interdependence and Granger causality) measurements. Accordingly, we derived a network model that depicts alterations of TPJ-connectivity in patients with MDD. Lastly, we performed a cross-approach comparison demonstrating more conducive indicators in delineating the network alteration model. Functional/effective connectivity between the TPJ and major functional networks that govern internal and external-driven information resources was attenuated in patients with MDD. TPJ acts like a key node for information-inflow and integration of multiple information streams. Therefore, dysfunctional connectivity indicators may serve as effective biomarkers for MDD. MDD is associated with the breakdown of the TPJ interaction model and its connections with the default mode network and the task-positive network.

Gamma coherence mediates interhemispheric integration during multiple object tracking

Our ability to track the paths of multiple visual objects moving between the hemifields requires effective integration of information between the two cerebral hemispheres. Coherent neural oscillations in the gamma band (35-70 Hz) are hypothesized to drive this information transfer. Here we manipulated the need for interhemispheric integration using a novel multiple object tracking (MOT) task in which stimuli either moved between the two visual hemifields, requiring interhemispheric integration, or moved within separate visual hemifields. We used electroencephalography (EEG) to measure interhemispheric coherence during the task. Human observers (21 women; 20 men) were poorer at tracking objects between versus within hemifields, reflecting a cost of interhemispheric integration. Critically, gamma coherence was greater in trials requiring interhemispheric integration, particularly between sensors over parieto-occipital areas. In approximately half of the participants, the observed cost of integration was associated with a failure of the cerebral hemispheres to become coherent in the gamma band. Moreover, individual differences in this integration cost correlated with endogenous gamma coherence at these same sensors, although with generally opposing relationships for the real and imaginary part of coherence. The real part (capturing synchronization with a near-zero phase lag) benefited between-hemifield tracking; imaginary coherence was detrimental. Finally, instantaneous phase coherence over the tracking period uniquely predicted between-hemifield tracking performance, suggesting that effective integration benefits from sustained interhemispheric synchronization. Our results show that gamma coherence mediates interhemispheric integration during MOT and add to a growing body of work demonstrating that coherence drives communication across cortically distributed neural networks.NEW & NOTEWORTHY Using a multiple object tracking paradigm, we were able to manipulate the need for interhemispheric integration on a per-trial basis, while also having an objective measure of integration efficacy (i.e., tracking performance). We show that tracking performance reflects a cost of integration, which correlates with individual differences in interhemispheric EEG coherence. Gamma coherence appears to uniquely benefit between-hemifield tracking, predicting performance both across participants and across trials.

Ketamine induced converged synchronous gamma oscillations in the cortico-basal ganglia network of nonhuman primates

N-methyl-d-aspartate (NMDA) antagonists are widely used in anesthesia, pain management, and schizophrenia animal model studies, and recently as potential antidepressants. However, the mechanisms underlying their anesthetic, psychotic, cognitive, and emotional effects are still elusive. The basal ganglia (BG) integrate input from different cortical domains through their dopamine-modulated connections to achieve optimal behavior control. NMDA antagonists have been shown to induce gamma oscillations in human EEG recordings and in rodent cortical and BG networks. However, network relations and implications to the primate brain are still unclear. We recorded local field potentials (LFPs) simultaneously from the primary motor cortex (M1) and the external globus pallidus (GPe) of four vervet monkeys (26 sessions, 97 and 76 cortical and pallidal LFPs, respectively) before and after administration of ketamine (NMDA antagonist, 10 mg/kg im). Ketamine induced robust, spontaneous gamma (30-50 Hz) oscillations in M1 and GPe. These oscillations were initially modulated by ultraslow oscillations (~0.3 Hz) and were highly synchronized within and between M1 and the GPe (mean coherence magnitude = 0.76, 0.88, and 0.41 for M1-M1, GPe-GPe, and M1-GPe pairs). Phase differences were distributed evenly around zero with broad and very narrow distribution for the M1-M1 and GPe-GPe pairs (-3.5 ± 31.8° and -0.4 ± 6.0°), respectively. The distribution of M1-GPe phase shift was skewed to the left with a mean of -18.4 ± 20.9°. The increased gamma coherence between M1 and GPe, two central stages in the cortico-BG loops, suggests a global abnormal network phenomenon with a unique spectral signature, which is enabled by the BG funneling architecture.NEW & NOTEWORTHY This study is the first to show spontaneous gamma oscillations under NMDA antagonist in nonhuman primates. These oscillations appear in synchrony in the cortex and the basal ganglia. Phase analysis refutes the confounding effects of volume conduction and supports the funneling and amplifying architecture of the cortico-basal ganglia loops. These results suggest an abnormal network phenomenon with a unique spectral signature that could account for pathological mental and neurological states.

Local and Distant Dysregulation of Synchronization Around Interictal Spikes in BECTS

Objective: High Density electroencephalography (HD EEG) is the reference non-invasive technique to investigate the dynamics of neuronal networks in Benign Epilepsy with Centro-Temporal Spikes (BECTS). Analysis of local dynamic changes surrounding Interictal Epileptic Spikes (IES) might improve our knowledge of the mechanisms that propel neurons to the hypersynchronization of IES in BECTS. Transient distant changes in the dynamics of neurons populations may also interact with neuronal networks involved in various functions that are impaired in BECTS patients.

Methods: HD EEG (64 electrodes) of eight well-characterized BECTS patients (8 males; mean age: 7.2 years, range: 5–9 years) were analyzed. Unilateral IES were selected in 6 patients. They were bilateral and independent in 2 other patients. This resulted in a total of 10 groups of IES. Time-frequency analysis was performed on HD EEG epochs around the peak of the IES (±1000 ms), including phase-locked and non-phase-locked activities to the IES. The time frequency analyses were calculated for the frequencies between 4 and 200 Hz.

Results: Time-frequency analysis revealed two patterns of dysregulation of the synchronization between neuronal networks preceding and following hypersynchronization of interictal spikes (±400 ms) in the epileptogenic zone. Dysregulation consists of either desynchronization (n = 6) or oscillating synchronization (n = 4) (4–50 Hz) surrounding the IES. The 2 patients with bilateral IES exhibited only local desynchronization whatever the IES considered. Distant desynchronization in low frequencies within the same window occurs simultaneously in bilateral frontal, temporal and occipital areas (n = 7).

Significance: Using time-frequency analysis of HD EEG data in a well-defined population of BECTS, we demonstrated repeated complex changes in the dynamics of neuronal networks not only during, but also, before and after the IES. In the epileptogenic zone, our results found more complex reorganization of the local network than initially thought. In line with previous results obtained at a microscopic or macroscopic level, these changes suggested the variability strategies of neuronal assemblies to raise IES. Distant changes from the epileptogenic zone in desynchronization observed in the same time window suggested interactions between larger embedded networks and opened new avenues about their possible role in the underlying mechanism leading to cognitive deficits.

EEG based zero-phase phase-locking value (PLV) and effects of spatial filtering during actual movement

Phase-locking value (PLV) is a well-known feature in sensorimotor rhythm (SMR) based BCI. Zero-phase PLV has not been explored because it is generally regarded as the result of volume conduction. Because spatial filters are often used to enhance the amplitude (square root of band power (BP)) feature and attenuate volume conduction, they are frequently applied as pre-processing methods when computing PLV. However, the effects of spatial filtering on PLV are ambiguous. Therefore, this article aims to explore whether zero-phase PLV is meaningful and how this is influenced by spatial filtering. Based on archival EEG data of left and right hand movement tasks for 32 subjects, we compared BP and PLV feature using data with and without pre-processing by a large Laplacian. Results showed that using ear-referenced data, zero-phase PLV provided unique information independent of BP for task prediction which was not explained by volume conduction and was significantly decreased when a large Laplacian was applied. In other words, the large Laplacian eliminated the useful information in zero-phase PLV for task prediction suggesting that it contains effects of both amplitude and phase. Therefore, zero-phase PLV may have functional significance beyond volume conduction. The interpretation of spatial filtering may be complicated by effects of phase.

From connectome to cognition: The search for mechanism in human functional brain networks

Recent developments in functional connectivity research have expanded the scope of human neuroimaging, from identifying changes in regional activation amplitudes to detailed mapping of large-scale brain networks. However, linking network processes to a clear role in cognition demands advances in the theoretical frameworks, algorithms, and experimental approaches applied. This would help evolve the field from a descriptive to an explanatory state, by targeting network interactions that can mechanistically account for cognitive effects. In the present review, we provide an explicit framework to aid this search for "network mechanisms", which anchors recent methodological advances in functional connectivity estimation to a renewed emphasis on careful experimental design. We emphasize how this framework can address specific questions in network neuroscience. These span ambiguity over the cognitive relevance of resting-state networks, how to characterize task-evoked and spontaneous network dynamics, how to identify directed or "effective" connections, and how to apply multivariate pattern analysis at the network level. In parallel, we apply the framework to highlight the mechanistic interaction of network components that remain "stable" across task domains and more "flexible" components associated with on-task reconfiguration. By emphasizing the need to structure the use of diverse analytic approaches with sound experimentation, our framework promotes an explanatory mapping between the workings of the cognitive mind and the large-scale network mechanisms of the human brain.

Primary motor and sensory cortical areas communicate via spatiotemporally coordinated networks at multiple frequencies

Skilled movements rely on sensory information to shape optimal motor responses, for which the sensory and motor cortical areas are critical. How these areas interact to mediate sensorimotor integration is largely unknown. Here, we measure intercortical coherence between the orofacial motor (MIo) and somatosensory (SIo) areas of cortex as monkeys learn to generate tongue-protrusive force. We report that coherence between MIo and SIo is reciprocal and that neuroplastic changes in coherence gradually emerge over a few days. These functional networks of coherent spiking and local field potentials exhibit frequency-specific spatiotemporal properties. During force generation, theta coherence (2-6 Hz) is prominent and exhibited by numerous paired signals; before or after force generation, coherence is evident in alpha (6-13 Hz), beta (15-30 Hz), and gamma (30-50 Hz) bands, but the functional networks are smaller and weaker. Unlike coherence in the higher frequency bands, the distribution of the phase at peak theta coherence is bimodal with peaks near 0° and ±180°, suggesting that communication between somatosensory and motor areas is coordinated temporally by the phase of theta coherence. Time-sensitive sensorimotor integration and plasticity may rely on coherence of local and large-scale functional networks for cortical processes to operate at multiple temporal and spatial scales.

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

Multielectrode voltage data are usually recorded against a common reference. Such data are frequently used without further treatment to assess patterns of functional connectivity between neuronal populations and between brain areas. It is important to note from the outset that such an approach is valid only when the reference electrode is nearly electrically silent. In practice, however, the reference electrode is generally not electrically silent, thereby adding a common signal to the recorded data. Volume conduction further complicates the problem. In this study we demonstrate the adverse effects of common signals on the estimation of Granger causality, which is a statistical measure used to infer synaptic transmission and information flow in neural circuits from multielectrode data. We further test the hypothesis that the problem can be overcome by utilizing bipolar derivations where the difference between two nearby electrodes is taken and treated as a representation of local neural activity. Simulated data generated by a neuronal network model where the connectivity pattern is known were considered first. This was followed by analyzing data from three experimental preparations where a priori predictions regarding the patterns of causal interactions can be made: (1) laminar recordings from the hippocampus of an anesthetized rat during theta rhythm, (2) laminar recordings from V4 of an awake-behaving macaque monkey during alpha rhythm, and (3) ECoG recordings from electrode arrays implanted in the middle temporal lobe and prefrontal cortex of an epilepsy patient during fixation. For both simulation and experimental analysis the results show that bipolar derivations yield the expected connectivity patterns whereas the untreated data (referred to as unipolar signals) do not. In addition, current source density signals, where applicable, yield results that are close to the expected connectivity patterns, whereas the commonly practiced average re-reference method leads to erroneous results.

Diversity of LFPs Activated in Different Target Regions by a Common CA3 Input

Identifying the pathways contributing to local field potential (LFP) events and oscillations is essential to determine whether synchronous interregional patterns indicate functional connectivity. Here, we studied experimentally and numerically how different target structures receiving input from a common population shape their LFPs. We focused on the bilateral CA3 that sends gamma-paced excitatory packages to the bilateral CA1, the lateral septum, and itself (recurrent input). The CA3-specific contribution was isolated from multisite LFPs in target regions using spatial discrimination techniques. We found strong modulation of LFPs by target-specific features, including the morphology and population arrangement of cells, the timing of CA3 inputs, volume conduction from nearby targets, and co-activated inhibition. Jointly they greatly affect the LFP amplitude, profile, and frequency characteristics. For instance, ipsilateral (Schaffer) LFPs occluded contralateral ones, and septal LFPs arise mostly from remote sources while local contribution from CA3 input was minor. In the CA3 itself, gamma waves have dual origin from local networks: in-phase excitatory and nearly antiphase inhibitory. Also, waves may have different duration and varying phase in different targets. These results indicate that to explore the cellular basis of LFPs and the functional connectivity between structures, besides identifying the origin population/s, target modifiers should be considered.

Synchrony in schizophrenia: a window into circuit-level pathophysiology

As a complex neuropsychiatric disease with both hereditary and environmental components, schizophrenia must be understood across multiple biological scales, from genes through cells and circuits to behaviors. The key to evaluating candidate explanatory models, therefore, is to establish causal links between disease-related phenomena observed across these scales. To this end, there has been a resurgence of interest in the circuit-level pathophysiology of schizophrenia, which has the potential to link molecular and cellular data from risk factor and post-mortem studies with the behavioral phenomena that plague patients. The demonstration that patients with schizophrenia frequently have deficits in neuronal synchrony, including deficits in local oscillations and long-range functional connectivity, offers a promising opportunity to forge such links across scales.

Analyzing coherent brain networks with Granger causality

Multielectrode neurophysiological recording and functional brain imaging produce massive quantities of data. Multivariate time series analysis provides the basic framework for analyzing the patterns of neural interactions in these data. Neural interactions are directional. Being able to assess the directionality of neuronal interactions is thus a highly desired capability for understanding the cooperative nature of neural computation. Research over the last few years has identified Granger causality as a promising technique to furnish this capability. In this paper, we first introduce the concept of Granger causality and then present results from the application of this technique to multichannel local field potential data from an awake-behaving monkey.

EEG-based automatic classification of 'awake' versus 'anesthetized' state in general anesthesia using Granger causality

BACKGROUND

General anesthesia is a reversible state of unconsciousness and depression of reflexes to afferent stimuli induced by administration of a "cocktail" of chemical agents. The multi-component nature of general anesthesia complicates the identification of the precise mechanisms by which anesthetics disrupt consciousness. Devices that monitor the depth of anesthesia are an important aide for the anesthetist. This paper investigates the use of effective connectivity measures from human electrical brain activity as a means of discriminating between 'awake' and 'anesthetized' state during induction and recovery of consciousness under general anesthesia.

METHODOLOGY/PRINCIPAL FINDINGS

Granger Causality (GC), a linear measure of effective connectivity, is utilized in automated classification of 'awake' versus 'anesthetized' state using Linear Discriminant Analysis and Support Vector Machines (with linear and non-linear kernel). Based on our investigations, the most characteristic change of GC observed between the two states is the sharp increase of GC from frontal to posterior regions when the subject was anesthetized, and reversal at recovery of consciousness. Features derived from the GC estimates resulted in classification of 'awake' and 'anesthetized' states in 21 patients with maximum average accuracies of 0.98 and 0.95, during loss and recovery of consciousness respectively. The differences in linear and non-linear classification are not statistically significant, implying that GC features are linearly separable, eliminating the need for a complex and computationally expensive non-linear classifier. In addition, the observed GC patterns are particularly interesting in terms of a physiological interpretation of the disruption of consciousness by anesthetics. Bidirectional interaction or strong unidirectional interaction in the presence of a common input as captured by GC are most likely related to mechanisms of information flow in cortical circuits.

CONCLUSIONS/SIGNIFICANCE

GC-based features could be utilized effectively in a device for monitoring depth of anesthesia during surgery.

Exploring resting-state functional connectivity with total interdependence

Resting-state fMRI has become a powerful tool for studying network mechanisms of normal brain functioning and its impairments by neurological and psychiatric disorders. Analytically, independent component analysis and seed-based cross correlation are the main methods for assessing the connectivity of resting-state fMRI time series. A feature common to both methods is that they exploit the covariation structures of contemporaneously (zero-lag) measured data but ignore temporal relations that extend beyond the zero-lag. To examine whether data covariations across different lags can contribute to our understanding of functional brain networks, a measure that can uncover the overall temporal relationship between two resting-state BOLD signals is needed. In this paper we propose such a measure referred as total interdependence (TI). Comparing TI with zero-lag cross correlation (CC) we report three results. First, when combined with a random permutation procedure, TI can reveal the amount of temporal relationship between two resting-state BOLD time series that is not captured by CC. Second, comparing resting-state data with task-state data recorded in the same scanning session, we demonstrate that the resting-state functional networks constructed with TI match more precisely the networks activated by the task. Third, TI is shown to be more statistically sensitive than CC and provides better feature vectors for network clustering analysis.

More discussions for granger causality and new causality measures

Granger causality (GC) has been widely applied in economics and neuroscience to reveal causality influence of time series. In our previous paper (Hu et al., in IEEE Trans on Neural Netw, 22(6), pp. 829-844, 2011), we proposed new causalities in time and frequency domains and particularly focused on new causality in frequency domain by pointing out the shortcomings/limitations of GC or Granger-alike causality metrics and the advantages of new causality. In this paper we continue our previous discussions and focus on new causality and GC or Granger-alike causality metrics in time domain. Although one strong motivation was introduced in our previous paper (Hu et al., in IEEE Trans on Neural Netw, 22(6), pp. 829-844, 2011) we here present additional motivation for the proposed new causality metric and restate the previous motivation for completeness. We point out one property of conditional GC in time domain and the shortcomings/limitations of conditional GC which cannot reveal the real strength of the directional causality among three time series. We also show the shortcomings/limitations of directed causality (DC) or normalize DC for multivariate time series and demonstrate it cannot reveal real causality at all. By calculating GC and new causality values for an example we demonstrate the influence of one of the time series on the other is linearly increased as the coupling strength is linearly increased. This fact further supports reasonability of new causality metric. We point out that larger instantaneous correlation does not necessarily mean larger true causality (e.g., GC and new causality), or vice versa. Finally we conduct analysis of statistical test for significance and asymptotic distribution property of new causality metric by illustrative examples.

Multiscale causal connectivity analysis by canonical correlation: theory and application to epileptic brain

Multivariate Granger causality is a well-established approach for inferring information flow in complex systems, and it is being increasingly applied to map brain connectivity. Traditional Granger causality is based on vector autoregressive (AR) or mixed autoregressive moving average (ARMA) model, which are potentially affected by errors in parameter estimation and may be contaminated by zero-lag correlation, notably when modeling neuroimaging data. To overcome this issue, we present here an extended canonical correlation approach to measure multivariate Granger causal interactions among time series. The procedure includes a reduced rank step for calculating canonical correlation analysis (CCA), and extends the definition of causality including instantaneous effects, thus avoiding the potential estimation problems of AR (or ARMA) models. We tested this approach on simulated data and confirmed its practical utility by exploring local network connectivity at different scales in the epileptic brain analyzing scalp and depth-EEG data during an interictal period.

Differential effects produced by ketamine on oscillatory activity recorded in the rat hippocampus, dorsal striatum and nucleus accumbens

Previously, we showed that NMDA antagonists enhance high-frequency oscillations (130-180 Hz) in the nucleus accumbens. However, whether NMDA antagonists can enhance high-frequency oscillations in other brain regions remains unclear. Here, we used monopolar, bipolar and inverse current source density techniques to examine oscillatory activity in the hippocampus, a region known to generate spontaneous ripples (∼200 Hz), its surrounding tissue, and the dorsal striatum, neuroanatomically related to the nucleus accumbens. In monopolar recordings, ketamine-induced increases in the power of high-frequency oscillations were detected in all structures, although the power was always substantially larger in the nucleus accumbens. In bipolar recordings, considered to remove common-mode input, high-frequency oscillations associated with ketamine injection were not present in the regions we investigated outside the nucleus accumbens. In line with this, inverse current source density showed the greatest changes in current to occur in the vicinity of the nucleus accumbens and a monopolar structure of the generator. We found little spatial localisation of ketamine high-frequency oscillations in other areas. In contrast, sharp-wave ripples, which were well localized to the hippocampus, occurred less frequently after ketamine. Notably, we also found ketamine produced small, but significant, changes in the power of 30-90 Hz gamma oscillations (an increase in the hippocampus and a decrease in the nucleus accumbens).

It's about Time

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.

Abnormalities in resting-state functional connectivity in early human immunodeficiency virus infection

Limited information is available concerning changes that occur in the brain early in human immunodeficiency virus (HIV) infection. This investigation evaluated resting-state functional connectivity, which is based on correlations of spontaneous blood oxygen level-dependent functional magnetic resonance imaging (fMRI) oscillations between brain regions, in 15 subjects within the first year of HIV infection and in 15 age-matched controls. Resting-state fMRI data for each session were concatenated in time across subjects to create a single 4D dataset and decomposed into 36 independent component analysis (ICA) using Multivariate Exploratory Linear Optimized Decomposition into Independent Components. ICA components were back-reconstructed for each subject's 4D data to estimate subject-specific spatial maps using the dual-regression technique. Comparison of spatial maps between HIV and controls revealed significant differences in the lateral occipital cortex (LOC) network. Reduced coactivation in left inferior parietal cortex within the LOC network was identified in the HIV subjects. Connectivity strength within this region correlated with performance on tasks involving visual-motor coordination (Grooved Pegboard and Rey Figure Copy) in the HIV group. The findings indicate prominent changes in resting-state functional connectivity of visual networks early in HIV infection. This network may sustain injury in association with the intense viremia and brain viral invasion before immune defenses can contain viral replication. Resting-state functional connectivity may have utility as a noninvasive neuroimaging biomarker for central nervous system impairment in early HIV infection.

Incremental mutual information: a new method for characterizing the strength and dynamics of connections in neuronal circuits

Understanding the computations performed by neuronal circuits requires characterizing the strength and dynamics of the connections between individual neurons. This characterization is typically achieved by measuring the correlation in the activity of two neurons. We have developed a new measure for studying connectivity in neuronal circuits based on information theory, the incremental mutual information (IMI). By conditioning out the temporal dependencies in the responses of individual neurons before measuring the dependency between them, IMI improves on standard correlation-based measures in several important ways: 1) it has the potential to disambiguate statistical dependencies that reflect the connection between neurons from those caused by other sources (e.g. shared inputs or intrinsic cellular or network mechanisms) provided that the dependencies have appropriate timescales, 2) for the study of early sensory systems, it does not require responses to repeated trials of identical stimulation, and 3) it does not assume that the connection between neurons is linear. We describe the theory and implementation of IMI in detail and demonstrate its utility on experimental recordings from the primate visual system.

Corticomuscular coherence between motor cortex, somatosensory areas and forearm muscles in the monkey

Corticomuscular coherence has previously been reported between primary motor cortex (M1) and contralateral muscles. We examined whether such coherence could also be seen from somatosensory areas. Local field potentials (LFPs) were recorded from primary somatosensory cortex (S1; areas 3a and 2) and posterior parietal cortex (PPC; area 5) simultaneously with M1 LFP and forearm EMG activity in two monkeys during an index finger flexion task. Significant beta-band ( approximately 20 Hz) corticomuscular coherence was found in all areas investigated. Directed coherence (Granger causality) analysis was used to investigate the direction of effects. Surprisingly, the strongest beta-band directed coherence was in the direction from S1/PPC to muscle; it was much weaker in the ascending direction. Examination of the phase of directed coherence provided estimates of the time delay from cortex to muscle. Delays were longer from M1 ( approximately 62 ms for the first dorsal interosseous muscle) than from S1/PPC ( approximately 36 ms). We then looked at coherence and directed coherence between M1 and S1 for clues to this discrepancy. Directed coherence showed large beta-band effects from S1/PPC to M1, with smaller directed coherence in the reverse direction. The directed coherence phase suggested a delay of approximately 40 ms from M1 to S1. Corticomuscular coherence from S1/PPC could involve multiple pathways; the most important is probably common input from M1 to S1/PPC and muscles. If correct, this implies that somatosensory cortex receives oscillatory efference copy information from M1 about the motor command. This could allow sensory inflow to be interpreted in the light of its motor context.

Assessing and compensating for zero-lag correlation effects in time-lagged Granger causality analysis of FMRI

Effective connectivity in brain networks can be studied using Granger causality analysis, which is based on temporal precedence, while functional connectivity is usually derived using zero-lag correlation. Due to the smoothing of the neuronal activity by the hemodynamic response inherent in the functional magnetic resonance imaging (fMRI) acquisition process, Granger causality, as normally computed from fMRI data, may be contaminated by zero-lag correlation. Simulations performed in this paper showed that the zero-lag correlation does "leak" into estimates of time-lagged causality. To eliminate this leak, we introduce a method in which the zero-lag influences are explicitly modeled in the vector autoregressive model but omitted while calculating Granger causality. The effectiveness of this method is demonstrated using fMRI data obtained from healthy humans performing a verbal working memory task.

From prestimulus alpha oscillation to visual-evoked response: an inverted-U function and its attentional modulation

Understanding the relation between prestimulus neural activity and subsequent stimulus processing has become an area of active investigation. Computational modeling, as well as in vitro and in vivo single-unit recordings in animal preparations, have explored mechanisms by which background synaptic activity can influence the responsiveness of cortical neurons to afferent input. How these mechanisms manifest in humans is not well understood. Although numerous EEG/MEG studies have considered the role of prestimulus alpha oscillations in the genesis of visual-evoked potentials, no consensus has emerged, and divergent reports continue to appear. The present work addresses this problem in three stages. First, a theoretical model was developed in which the background synaptic activity and the firing rate of a neural ensemble are related through a sigmoidal function. The derivative of this function, referred to as local gain, has an inverted-U shape and is postulated to be proportional to the trial-by-trial response evoked by a transient stimulus. Second, the theoretical model was extended to noninvasive studies of human visual processing, where the model variables are reinterpreted in terms of ongoing EEG oscillations and event-related potentials. Predictions were derived from the model and tested by recording high-density scalp EEG from healthy volunteers performing a trial-by-trial cued spatial visual attention task. Finally, enhanced stimulus processing by attention was linked to an increase in the overall slope of the sigmoidal function. The commonly observed reduction of alpha magnitude with attention was interpreted as signaling a shift of the underlying neural ensemble toward an optimal excitability state that enables the increase in global gain.

Detection of a Weak Somatosensory Stimulus: Role of the Prestimulus Mu Rhythm and Its Top–Down Modulation

The ongoing neural activity in human primary somatosensory cortex (SI) is characterized by field potential oscillations in the 7–13 Hz range known as the mu rhythm. Recent work has shown that the magnitude of the mu oscillation immediately preceding the onset of a weak stimulus has a significant impact on its detection. The neural mechanisms mediating this impact remain not well understood. In particular, whether and how somatosensory mu rhythm is modulated by executive areas prior to stimulus onset for improved behavioral performance has not been investigated. We addressed these issues by recording 128-channel scalp electroencephalogram from normal volunteers performing a somatosensory perception experiment in which they reported the detection of a near-threshold electrical stimulus (∼50% detection rate) delivered to the right index finger. Three results were found. First, consistent with numerous previous reports, the N1 component (∼140 msec) of the somatosensory-evoked potential was significantly enhanced for perceived stimulus compared to unperceived stimulus. Second, the prestimulus mu power and the evoked N1 amplitude exhibited an inverted-U relationship, suggesting that an intermediate level of prestimulus mu oscillatory activity is conducive to stimulus processing and perception. Third, a Granger causality analysis revealed that the prestimulus causal influence in the mu band from prefrontal cortex to SI was significantly higher for perceived stimulus than for unperceived stimulus, indicating that frontal executive structures, via ongoing mu oscillations, exert cognitive control over posterior sensory cortices to facilitate somatosensory processing.

Global synchronization in the theta band during mental imagery of navigation in humans

Visual mental imagery is critical for successfully navigating the environment, which in turn activates many cortical regions simultaneously. Theta oscillation is implicated in navigation and brain synchronization. In this study, EEG coherence was analyzed during 3 tasks: subjects (1) mentally simulated jogging along the walls of a gym and pressed a button when they imagined arriving at a corner (jogging imagery task), (2) thought of and memorized one digit after pressing a button 5 times and recalled the digits sequentially after pressing the button again (digit imagery task), and (3) pressed a button (button pressing task). The results indicated that theta-wave (4-8 Hz) power was significantly higher in the frontal and parietal regions during the digit and jogging imagery tasks. Coherence at the theta band showed almost no differences between the button pressing and digit imagery tasks. Coherence between the distant regions, especially between the frontal and parieto-occipital regions and between interhemispheric regions, was significantly higher during the jogging imagery task. Increase in theta power during the jogging imagery task reflects working memory load to manipulate internal information. Theta oscillation appears to play an important role in large-scale synchronization to form the functional neuronal networks required for mental navigation.

Denoising neural data with state-space smoothing: method and application

Neural data are inevitably contaminated by noise. When such noisy data are subjected to statistical analysis, misleading conclusions can be reached. Here we attempt to address this problem by applying a state-space smoothing method, based on the combined use of the Kalman filter theory and the Expectation-Maximization algorithm, to denoise two datasets of local field potentials recorded from monkeys performing a visuomotor task. For the first dataset, it was found that the analysis of the high gamma band (60-90 Hz) neural activity in the prefrontal cortex is highly susceptible to the effect of noise, and denoising leads to markedly improved results that were physiologically interpretable. For the second dataset, Granger causality between primary motor and primary somatosensory cortices was not consistent across two monkeys and the effect of noise was suspected. After denoising, the discrepancy between the two subjects was significantly reduced.