In the anticipation of threat: Neural regulatory activity indicated by delta-beta correlation and its relation to anxiety.

The anticipation of oncoming threats is emotionally challenging and related to anxiety. The current study aimed to investigate the neural regulatory processes during the anticipatory preparations in stressful situations in relation to trait anxiety, especially in an uncertainty-related stressful situation. To this end, we measured within-subjects delta-beta amplitude-amplitude correlation (AAC) and phase-amplitude coupling (PAC) with electroencephalography using a well-defined stress-inducing paradigm in 28 high-trait-anxiety (HTA) and 29 low-trait-anxiety (LTA) college students. Specifically, a threat probability task was conducted, where participants anticipated the future stimuli under the uncertain (i.e., an average of 50% electric shocks), certain (i.e., 100% electric shocks) and no threat conditions, as well as a resting state task. Results showed a generally larger delta-beta AAC in the LTA group relative to the HTA group across conditions, supporting the hypothesis that delta-beta AAC reflects the efficiency of stress regulation and trait anxiety could compromise this adaptive regulatory activity. Furthermore, a larger delta-beta PAC was found under the uncertain threat condition relative to the no threat condition, indicating the sensitivity of delta-beta PAC in reflecting state anxiety. These findings indicate that while delta-beta AAC is more related to trait anxiety and could distinguish between high and low trait anxiety irrespective of conditions, delta-beta PAC is more related to state anxiety and is sensitive enough to detect the uncertainty-related anxious state.

The frontooccipital interaction mechanism of high-frequency acoustoelectric signal.

Based on acoustoelectric effect, acoustoelectric brain imaging has been proposed, which is a high spatiotemporal resolution neural imaging method. At the focal spot, brain electrical activity is encoded by focused ultrasound, and corresponding high-frequency acoustoelectric signal is generated. Previous studies have revealed that acoustoelectric signal can also be detected in other non-focal brain regions. However, the processing mechanism of acoustoelectric signal between different brain regions remains sparse. Here, with acoustoelectric signal generated in the left primary visual cortex, we investigated the spatial distribution characteristics and temporal propagation characteristics of acoustoelectric signal in the transmission. We observed a strongest transmission strength within the frontal lobe, and the global temporal statistics indicated that the frontal lobe features in acoustoelectric signal transmission. Then, cross-frequency phase-amplitude coupling was used to investigate the coordinated activity in the AE signal band range between frontal and occipital lobes. The results showed that intra-structural cross-frequency coupling and cross-structural coupling co-occurred between these two lobes, and, accordingly, high-frequency brain activity in the frontal lobe was effectively coordinated by distant occipital lobe. This study revealed the frontooccipital long-range interaction mechanism of acoustoelectric signal, which is the foundation of improving the performance of acoustoelectric brain imaging.

Schizophrenia induces abnormal frequency-dependent patterns of dynamic brain network reconfiguration during an auditory oddball task.

Objective.Schizophrenia is a psychiatric disorder that has been shown to disturb the dynamic top-down processing of sensory information. Various imaging techniques have revealed abnormalities in brain activity associated with this disorder, both locally and between cerebral regions. However, there is increasing interest in investigating dynamic network response to novel and relevant events at the network level during an attention-demanding task with high-temporal-resolution techniques. The aim of the work was: (i) to test the capacity of a novel algorithm to detect recurrent brain meta-states from auditory oddball task recordings; and (ii) to evaluate how the dynamic activation and behavior of the aforementioned meta-states were altered in schizophrenia, since it has been shown to impair top-down processing of sensory information.Approach.A novel unsupervised method for the detection of brain meta-states based on recurrence plots and community detection algorithms, previously tested on resting-state data, was used on auditory oddball task recordings. Brain meta-states and several properties related to their activation during target trials in the task were extracted from electroencephalography data from patients with schizophrenia and cognitively healthy controls.Main results.The methodology successfully detected meta-states during an auditory oddball task, and they appeared to show both frequency-dependent time-locked and non-time-locked activity with respect to the stimulus onset. Moreover, patients with schizophrenia displayed higher network diversity, and showed more sluggish meta-state transitions, reflected in increased dwell times, less complex meta-state sequences, decreased meta-state space speed, and abnormal ratio of negative meta-state correlations.Significance.Abnormal cognition in schizophrenia is also reflected in decreased brain flexibility at the dynamic network level, which may hamper top-down processing, possibly indicating impaired decision-making linked to dysfunctional predictive coding. Moreover, the results showed the ability of the methodology to find meaningful and task-relevant changes in dynamic connectivity and pathology-related group differences.

Abnormal meta-state activation of dynamic brain networks across the Alzheimer spectrum.

The characterization of the distinct dynamic functional connectivity (dFC) patterns that activate in the brain during rest can help to understand the underlying time-varying network organization. The presence and behavior of these patterns (known as meta-states) have been widely studied by means of functional magnetic resonance imaging (fMRI). However, modalities with high-temporal resolution, such as electroencephalography (EEG), enable the characterization of fast temporally evolving meta-state sequences. Mild cognitive impairment (MCI) and dementia due to Alzheimer's disease (AD) have been shown to disrupt spatially localized activation and dFC between different brain regions, but not much is known about how they affect meta-state network topologies and their network dynamics. The main hypothesis of the study was that MCI and dementia due to AD alter normal meta-state sequences by inducing a loss of structure in their patterns and a reduction of their dynamics. Moreover, we expected that patients with MCI would display more flexible behavior compared to patients with dementia due to AD. Thus, the aim of the current study was twofold: (i) to find repeating, distinctly organized network patterns (meta-states) in neural activity; and (ii) to extract information about meta-state fluctuations and how they are influenced by MCI and dementia due to AD. To accomplish these goals, we present a novel methodology to characterize dynamic meta-states and their temporal fluctuations by capturing aspects based on both their discrete activation and the continuous evolution of their individual strength. These properties were extracted from 60-s resting-state EEG recordings from 67 patients with MCI due to AD, 50 patients with dementia due to AD, and 43 cognitively healthy controls. First, the instantaneous amplitude correlation (IAC) was used to estimate instantaneous functional connectivity with a high temporal resolution. We then extracted meta-states by means of graph community detection based on recurrence plots (RPs), both at the individual- and group-level. Subsequently, a diverse set of properties of the continuous and discrete fluctuation patterns of the meta-states was extracted and analyzed. The main novelty of the methodology lies in the usage of Louvain GJA community detection to extract meta-states from IAC-derived RPs and the extended analysis of their discrete and continuous activation. Our findings showed that distinct dynamic functional connectivity meta-states can be found on the EEG time-scale, and that these were not affected by the oscillatory slowing induced by MCI or dementia due to AD. However, both conditions displayed a loss of meta-state modularity, coupled with shorter dwell times and higher complexity of the meta-state sequences. Furthermore, we found evidence that meta-state sequencing is not entirely random; it shows an underlying structure that is partially lost in MCI and dementia due to AD. These results show evidence that AD progression is associated with alterations in meta-state switching, and a degradation of dynamic brain flexibility.

Tracking dynamic brain networks using high temporal resolution MEG measures of functional connectivity.

Fluctuations in functional interactions between brain regions typically occur at the millisecond time scale. Conventional connectivity metrics are not adequately time-resolved to detect such fast fluctuations in functional connectivity. At the same time, attempts to use conventional metrics in a time-resolved manner usually come with the selection of sliding windows of fixed arbitrary length. In the current work, we evaluated the use of high temporal resolution metrics of functional connectivity in conjunction with non-negative tensor factorisation to detect fast fluctuations in connectivity and temporally evolving subnetworks. To this end, we used the phase difference derivative, wavelet coherence, and we also introduced a new metric, the instantaneous amplitude correlation. In order to deal with the inherently noisy nature of magnetoencephalography data and large datasets, we make use of recurrence plots and we used pair-wise orthogonalisation to avoid spurious estimates of functional connectivity due to signal leakage. Firstly, metrics were evaluated in the context of dynamically coupled neural mass models in the presence and absence of delays and also compared to conventional static metrics with fixed sliding windows. Simulations showed that these high temporal resolution metrics outperformed conventional static connectivity metrics. Secondly, the sensitivity of the metrics to fluctuations in connectivity was analysed in post-movement beta rebound magnetoencephalography data, which showed time locked sensorimotor subnetworks that modulated with the post-movement beta rebound. Finally, sensitivity of the metrics was evaluated in resting-state magnetoencephalography, showing similar spatial patterns across metrics, thereby indicating the robustness of the current analysis. The current methods can be applied in cognitive experiments that involve fast modulations in connectivity in relation to cognition. In addition, these methods could also be used as input to temporal graph analysis to further characterise the rapid fluctuation in brain network topology.

Frontal delta-beta cross-frequency coupling in high and low social anxiety: An index of stress regulation?

Cross-frequency coupling (CFC) between frontal delta (1-4 Hz) and beta (14-30 Hz) oscillations has been suggested as a candidate neural correlate of social anxiety disorder, a disorder characterized by fear and avoidance of social and performance situations. Prior studies have used amplitude-amplitude correlation (AAC) as a CFC measure and hypothesized it as a candidate neural mechanism of affective control. However, using this metric has yielded inconsistent results regarding the direction of CFC, and the functional significance of coupling strength is uncertain. To offer a better understanding of CFC in social anxiety, we compared frontal delta-beta AAC with phase-amplitude coupling (PAC) - a mechanism for information transfer through neural circuits. Twenty high socially anxious (HSA) and 32 low socially anxious (LSA) female undergraduates participated in a social performance task (SPT). Delta-beta PAC and AAC were estimated during the resting state, as well as the anticipation and recovery conditions. Results showed significantly more AAC in LSA than HSA participants during early anticipation, as well as significant values during all conditions in LSA participants only. PAC did not distinguish between LSA and HSA participants, and instead was found to correlate with state nervousness during early anticipation, but in LSA participants only. Together, these findings are interpreted to suggest that delta-beta AAC is a plausible neurobiological index of adaptive stress regulation and can distinguish between trait high and low social anxiety during stress, while delta-beta PAC might be sensitive enough to reflect mild state anxiety in LSA participants.

Ghost interactions in MEG/EEG source space: A note of caution on inter-areal coupling measures.

When combined with source modeling, magneto- (MEG) and electroencephalography (EEG) can be used to study long-range interactions among cortical processes non-invasively. Estimation of such inter-areal connectivity is nevertheless hindered by instantaneous field spread and volume conduction, which artificially introduce linear correlations and impair source separability in cortical current estimates. To overcome the inflating effects of linear source mixing inherent to standard interaction measures, alternative phase- and amplitude-correlation based connectivity measures, such as imaginary coherence and orthogonalized amplitude correlation have been proposed. Being by definition insensitive to zero-lag correlations, these techniques have become increasingly popular in the identification of correlations that cannot be attributed to field spread or volume conduction. We show here, however, that while these measures are immune to the direct effects of linear mixing, they may still reveal large numbers of spurious false positive connections through field spread in the vicinity of true interactions. This fundamental problem affects both region-of-interest-based analyses and all-to-all connectome mappings. Most importantly, beyond defining and illustrating the problem of spurious, or "ghost" interactions, we provide a rigorous quantification of this effect through extensive simulations. Additionally, we further show that signal mixing also significantly limits the separability of neuronal phase and amplitude correlations. We conclude that spurious correlations must be carefully considered in connectivity analyses in MEG/EEG source space even when using measures that are immune to zero-lag correlations.

Corticostriatal coordination through coherent phase-amplitude coupling.

The corticostriatal axis is the main input stage of the basal ganglia and is crucial for their role in motor behavior. Synchronized oscillations might mediate interactions between cortex and striatum during behavior, yet direct evidence remains sparse. Here, we show that, during motor behavior, low- and high-frequency oscillations jointly couple cortex and striatum via cross-frequency interactions. We investigated neuronal oscillations along the corticostriatal axis in rats during rest and treadmill running. We found prominent theta and gamma oscillations in cortex and striatum, the peak frequencies of which scaled with motor demand. Theta and gamma oscillations were functionally coupled through phase-amplitude coupling. Furthermore, theta oscillations were phase coupled between structures. Together, local phase-amplitude coupling and corticostriatal theta phase coupling mediated the temporal correlation of gamma bursts between the cortex and striatum. The coordination of fast oscillations through coherent phase-amplitude coupling may be a general mechanism to regulate neuronal interactions along the corticostriatal axis and beyond.