A phase synchrony measure for quantifying dynamic functional integration in the brain

Atypical low-frequency cortical encoding of speech identifies children with developmental dyslexia

Slow cortical oscillations play a crucial role in processing the speech amplitude envelope, which is perceived atypically by children with developmental dyslexia. Here we use electroencephalography (EEG) recorded during natural speech listening to identify neural processing patterns involving slow oscillations that may characterize children with dyslexia. In a story listening paradigm, we find that atypical power dynamics and phase-amplitude coupling between delta and theta oscillations characterize dyslexic versus other child control groups (typically-developing controls, other language disorder controls). We further isolate EEG common spatial patterns (CSP) during speech listening across delta and theta oscillations that identify dyslexic children. A linear classifier using four delta-band CSP variables predicted dyslexia status (0.77 AUC). Crucially, these spatial patterns also identified children with dyslexia when applied to EEG measured during a rhythmic syllable processing task. This transfer effect (i.e., the ability to use neural features derived from a story listening task as input features to a classifier based on a rhythmic syllable task) is consistent with a core developmental deficit in neural processing of speech rhythm. The findings are suggestive of distinct atypical neurocognitive speech encoding mechanisms underlying dyslexia, which could be targeted by novel interventions.

Errors elicit frontoparietal theta-gamma coupling that is modulated by endogenous estradiol levels

Cognitive control-related error monitoring is intimately involved in behavioral adaptation, learning, and individual differences in a variety of psychological traits and disorders. Accumulating evidence suggests that a focus on women's health and ovarian hormones is critical to the study of such cognitive brain functions. Here we sought to identify a novel index of error monitoring using a time-frequency based phase amplitude coupling (t-f PAC) measure and examine its modulation by endogenous levels of estradiol in females. Forty-three healthy, naturally cycling young adult females completed a flanker task while continuous electroencephalogram was recorded on four occasions across the menstrual cycle. Results revealed significant error-related t-f PAC between theta phase generated in fronto-central areas and gamma amplitude generated in parietal-occipital areas. Moreover, this error-related theta-gamma coupling was enhanced by endogenous levels of estradiol both within females across the cycle as well as between females with higher levels of average circulating estradiol. While the role of frontal midline theta in error processing is well documented, this paper extends the extant literature by illustrating that error monitoring involves the coordination between multiple distributed systems with the slow midline theta activity modulating the power of gamma-band oscillatory activity in parietal regions. They further show enhancement of inter-regional coupling by endogenous estradiol levels, consistent with research indicating modulation of cognitive control neural functions by the endocrine system in females. Together, this work identifies a novel neurophysiological marker of cognitive control-related error monitoring in females that has implications for neuroscience and women's health.

NeuroFreq: a MATLAB Toolbox for Time-Frequency Analysis of M/EEG Data

Time-frequency (TF) analysis of M/EEG data enables rich understanding of cortical dynamics underlying cognition, health, and disease. There are many algorithms for time-frequency decomposition of M/EEG neural data, but they are implemented in an inconsistent manner and most existing toolboxes either 1) contain only one or a few transforms, or 2) are not adapted to analyze multichannel, multitrial M/EEG data. This makes entry into time-frequency daunting for new practitioners and limits the ability of the community to flexibly compare the performance of multiple TF methods on M/EEG data. This paper introduces the NeuroFreq toolbox for MATLAB, which includes multiple TF transformation algorithms that are implemented in a consistent fashion and produce consistent output. The toolbox includes TF decomposition algorithms of both linear and quadratic classes, utilities for resampling, averaging, and baseline correction of TF representations, and tools for visualizing and interacting with single-trial or averaged TF representations over multiple channels. This paper introduces these utilities, and applies them to synthetic and EEG data to demonstrate the NeuroFreq toolbox.

Measuring multivariate phase synchronization with symbolization and permutation

Phase synchronization is an important mechanism for the information processing of neurons in the brain. Most of the current phase synchronization measures are bivariate and focus on the synchronization between pairs of time series. However, these methods do not provide a full picture of global interactions in neural systems. Considering the prevalence and importance of multivariate neural signal analysis, there is an urgent need to quantify global phase synchronization (GPS) in neural networks. Therefore, we propose a new measure named symbolic phase difference and permutation entropy (SPDPE), which symbolizes the phase difference in multivariate neural signals and estimates GPS according to the permutation patterns of the symbolic sequences. The performance of SPDPE was evaluated using simulated data generated by Kuramoto and Rössler model. The results demonstrate that SPDPE exhibits low sensitivity to data length and outperforms existing methods in accurately characterizing GPS and effectively resisting noise. Moreover, to validate the method with real data, it was applied to classify seizures and non-seizures by calculating the GPS of stereoelectroencephalography (SEEG) data recorded from the onset zones of ten epilepsy patients. We believe that SPDPE will improve the estimation of GPS in many applications, such as EEG-based brain-computer interfaces, brain modeling, and simultaneous EEG-fMRI analysis.

Large-scale encoding of emotion concepts becomes increasingly similar between individuals from childhood to adolescence

Humans require a shared conceptualization of others' emotions for adaptive social functioning. A concept is a mental blueprint that gives our brains parameters for predicting what will happen next. Emotion concepts undergo refinement with development, but it is not known whether their neural representations change in parallel. Here, in a sample of 5-15-year-old children (n = 823), we show that the brain represents different emotion concepts distinctly throughout the cortex, cerebellum and caudate. Patterns of activation to each emotion changed little across development. Using a model-free approach, we show that activation patterns were more similar between older children than between younger children. Moreover, scenes that required inferring negative emotional states elicited higher default mode network activation similarity in older children than younger children. These results suggest that representations of emotion concepts are relatively stable by mid to late childhood and synchronize between individuals during adolescence.

Community detection in multi-frequency EEG networks

Functional connectivity networks of the human brain are commonly studied using tools from complex network theory. Existing methods focus on functional connectivity within a single frequency band. However, it is well-known that higher order brain functions rely on the integration of information across oscillations at different frequencies. Therefore, there is a need to study these cross-frequency interactions. In this paper, we use multilayer networks to model functional connectivity across multiple frequencies, where each layer corresponds to a different frequency band. We then introduce the multilayer modularity metric to develop a multilayer community detection algorithm. The proposed approach is applied to electroencephalogram (EEG) data collected during a study of error monitoring in the human brain. The differences between the community structures within and across different frequency bands for two response types, i.e. error and correct, are studied. The results indicate that following an error response, the brain organizes itself to form communities across frequencies, in particular between theta and gamma bands while a similar cross-frequency community formation is not observed following the correct response.

Cross-validated Adaboost Classification of Emotion Regulation Strategies Identified by Spectral Coherence in Resting-State

In the present study, quantitative relations between Cognitive Emotion Regulation strategies (CERs) and EEG synchronization levels have been investigated for the first time. For this purpose, spectral coherence (COH), phase locking value and mutual information have been applied to short segments of 62-channel resting state eyes-opened EEG data collected from healthy adults who use contrasting emotion regulation strategies (frequently and rarely use of rumination&distraction, frequently and rarely use of suppression&reappraisal). In tests, the individuals are grouped depending on their self-responses to both emotion regulation questionnaire (ERQ) and cognitive ERQ. Experimental data are downloaded from publicly available data-base, LEMON. Regarding EEG electrode pairs that placed on right and left cortical regions, inter-hemispheric dependency measures are computed for non-overlapped short segments of 2 sec at 2 min duration trials. In addition to full-band EEG analysis, dependency metrics are also obtained for both alpha and beta sub-bands. The contrasting groups are discriminated from each other with respect to the corresponding features using cross-validated adaboost classifiers. High classification accuracies (CA) of 99.44% and 98.33% have been obtained through instant classification driven by full-band COH estimations. Considering regional features that provide the high CA, CERs are found to be highly relevant with associative memory functions and cognition. The new findings may indicate the close relation between neuroplasticity and cognitive skills.

Recommendations and publication guidelines for studies using frequency domain and time-frequency domain analyses of neural time series

Since its beginnings in the early 20th century, the psychophysiological study of human brain function has included research into the spectral properties of electrical and magnetic brain signals. Now, dramatic advances in digital signal processing, biophysics, and computer science have enabled increasingly sophisticated methodology for neural time series analysis. Innovations in hardware and recording techniques have further expanded the range of tools available to researchers interested in measuring, quantifying, modeling, and altering the spectral properties of neural time series. These tools are increasingly used in the field, by a growing number of researchers who vary in their training, background, and research interests. Implementation and reporting standards also vary greatly in the published literature, causing challenges for authors, readers, reviewers, and editors alike. The present report addresses this issue by providing recommendations for the use of these methods, with a focus on foundational aspects of frequency domain and time-frequency analyses. It also provides publication guidelines, which aim to (1) foster replication and scientific rigor, (2) assist new researchers who wish to enter the field of brain oscillations, and (3) facilitate communication among authors, reviewers, and editors.

Time-frequency dynamics of error monitoring in childhood: An EEG study

Error monitoring allows individuals to monitor and adapt their behavior by detecting errors. Error monitoring is thought to develop throughout childhood and adolescence. However, most of this evidence comes from studies in late childhood and adolescence utilizing event-related potentials (ERPs). The current study utilizes time-frequency (TF) and connectivity analyses to provide a comprehensive examination of age-related changes in error-monitoring processes across early childhood (N = 326; 50.9% females; 4-9 years). ERP analyses indicated the presence of the error-related negativity (ERN) and error positivity (Pe) across all ages. Results showed no error-specific age-related changes in the ERN and the Pe. However, TF analyses suggested error-related frontocentral responses in delta and theta signal strength (power), delta consistency (intertrial phase synchrony), and delta synchrony (interchannel phase synchrony) between frontrocentral and frontolateral clusters-all of which increased with age. Additionally, the current study examines the reliability and effect size estimates of the ERP and TF measures. For most measures, more trials were needed to achieve acceptable reliability than what is commonly used in the psychophysiological literature. Resources to facilitate the measurement and reporting of reliability are provided. Overall, findings highlight the utility of TF analyses and provide useful information for future studies examining the development of error monitoring.

Behavior of olfactory-related frontal lobe oscillations in Alzheimer's disease and MCI: A pilot study

Slow-gamma (35-45 Hz) phase synchronization and the coupling between slow-gamma and low-frequency theta oscillations (4-8 Hz) are closely related to memory retrieval and cognitive functions. In this pilot study, we assess the Phase Amplitude Coupling (PAC) between theta and slow-gamma oscillatory bands and the quality of synchronization in slow-gamma oscillations using Phase Locking Value (PLV) on EEG data from healthy individuals and patients diagnosed with amnestic Mild Cognitive Impairment (aMCI) and Alzheimer's Disease (AD) during an oddball olfactory task. Our study indicates noticeable differences between the PLV and PAC values corresponding to olfactory stimulation in the three groups of participants. These differences can help explain the underlying processes involved in these cognitive disorders and the differences between aMCI and AD patients in performing cognitive tasks. Our study also proposes a diagnosis method for aMCI through comparing the brain's response characteristics during olfactory stimulation and rest. Early diagnosis of aMCI can potentially lead to its timely treatment and prevention from progression to AD.

Phase synchronization and measure of criticality in a network of neural mass models

Synchronization has an important role in neural networks dynamics that is mostly accompanied by cognitive activities such as memory, learning, and perception. These activities arise from collective neural behaviors and are not totally understood yet. This paper aims to investigate a cortical model from this perspective. Historically, epilepsy has been regarded as a functional brain disorder associated with excessive synchronization of large neural populations. Epilepsy is believed to arise as a result of complex interactions between neural networks characterized by dynamic synchronization. In this paper, we investigated a network of neural populations in a way the dynamics of each node corresponded to the Jansen-Rit neural mass model. First, we study a one-column Jansen-Rit neural mass model for four different input levels. Then, we considered a Watts-Strogatz network of Jansen-Rit oscillators. We observed an epileptic activity in the weak input level. The network is considered to change various parameters. The detailed results including the mean time series, phase spaces, and power spectrum revealed a wide range of different behaviors such as epilepsy, healthy, and a transition between synchrony and asynchrony states. In some points of coupling coefficients, there is an abrupt change in the order parameters. Since the critical state is a dynamic candidate for healthy brains, we considered some measures of criticality and investigated them at these points. According to our study, some markers of criticality can occur at these points, while others may not. This occurrence is a result of the nature of the specific order parameter selected to observe these markers. In fact, The definition of a proper order parameter is key and must be defined properly. Our view is that the critical points exhibit clear characteristics and invariance of scale, instead of some types of markers. As a result, these phase transition points are not critical as they show no evidence of scaling invariance.

Graph Theoretic Analysis of Multilayer EEG Connectivity Networks

Over the past twenty years, functional connectivity of the human brain has been studied in detail using tools from complex network theory. These methods include graph theoretic metrics ranging from the micro-scale such as the degree of a node to the macro-scale such as the small worldness of the brain network. However, most of these network models focus on average activity within a time window of interest and given frequency band. Therefore, they cannot capture the changes in network connectivity across time and different frequency bands. Recently, multilayer brain networks have attracted a lot of attention as they can capture the full view of neuronal connectivity. In this paper, we introduce a multilayer view of the functional connectivity network of the brain, where each layer corresponds to a different frequency band. We construct multi-frequency connectivity networks from electroencephalogram data where the intra-layer edges are quantified by phase synchrony while the inter-layer edges are quantified by phase-amplitude coupling. We then introduce multilayer degree, participation coefficient and clustering coefficient to quantify the centrality of nodes across frequency layers and to identify the importance of different frequency bands. The proposed framework is applied to electroencephalogram data collected during a study of error monitoring in the human brain.

Brain Structural and Functional Connectivity: A Review of Combined Works of Diffusion Magnetic Resonance Imaging and Electro-Encephalography

Implications of structural connections within and between brain regions for their functional counterpart are timely points of discussion. White matter microstructural organization and functional activity can be assessed in unison. At first glance, however, the corresponding findings appear variable, both in the healthy brain and in numerous neuro-pathologies. To identify consistent associations between structural and functional connectivity and possible impacts for the clinic, we reviewed the literature of combined recordings of electro-encephalography (EEG) and diffusion-based magnetic resonance imaging (MRI). It appears that the strength of event-related EEG activity increases with increased integrity of structural connectivity, while latency drops. This agrees with a simple mechanistic perspective: the nature of microstructural white matter influences the transfer of activity. The EEG, however, is often assessed for its spectral content. Spectral power shows associations with structural connectivity that can be negative or positive often dependent on the frequencies under study. Functional connectivity shows even more variations, which are difficult to rank. This might be caused by the diversity of paradigms being investigated, from sleep and resting state to cognitive and motor tasks, from healthy participants to patients. More challenging, though, is the potential dependency of findings on the kind of analysis applied. While this does not diminish the principal capacity of EEG and diffusion-based MRI co-registration, it highlights the urgency to standardize especially EEG analysis.

Exploring a sustainable building's impact on occupant mental health and cognitive function in a virtual environment

Even though people spend the majority of their time indoors, the role of buildings in shaping human experience is still not well understood. The objective of this experimental project is to develop, test, and validate a data-driven neuroscience approach to understand the built environment’s impact on occupant cognitive function and mental health. The present study utilized virtual environments and electroencephalogram (EEG) and event-related potential (ERP) approaches, to provide objective neurophysiological information about how sustainable buildings (SBs) impact people’s affective and cognitive functioning differently compared to conventional building (CBs). The long-term goal is to assess the validity of sustainable building design protocols in promoting and increasing mental health and well-being and the mechanism used to accomplish these increases. The findings showed test subjects demonstrated increased visual system engagement and modulated attentional focus and control processing in the SB compared to the CB environments. The findings can be explained by the cognitive load theory, which is consistent with the interpretation of greater focus on the present environment and reduced internal mental processing (cf. mindfulness), based on the observed increased theta/delta activities and greater engagement of visual systems and corresponding decreases in frontal activity in the SB environment. In addition, the combination of virtual environment (VE) and EEG/ERP has the potential to advance design methods by soliciting occupants’ responses prior to completion of the projects. Building design is more than aesthetics; expanding the horizon for neuroscience would eventually result in a new knowledge base for building design, particularly sustainable building design, since the sustainability of the building often needs to be quantified.

Differentiation of pain levels by deploying various EEG synchronization features and dynamic ensemble selection mechanism

OBJECTIVE

The target of this study is measuring the pain intensity in an objective manner by analysing the electroencephalogram (EEG) signals. Although this problem has attracted researchers' attention, increasing the resolution of this measurement, by increasing the number of pain states, significantly decreases the accuracy of pain level classification problem.

APPROACH

To overcome this drawback, we adopt state-of-the-art synchronization schemes to measure the linear, nonlinear and generalized synchronization between different EEG channels. 32 subjects executed the Cold Pressor Task (CPT) and experienced five defined levels of pain while recording their EEGs. Due to high number of synchronization features from 34 channels, the most discriminative features were selected using greedy overall relevancy (GOR) method. The selected features are applied to a dynamic ensemble selection system.

MAIN RESULTS

Our experiment provides 85.6% accuracy over the five classes, which significantly outperforms the results of past research. Moreover, we observe that the selected features belong to the channels placed over the ridge of cortex, the area responsible for processing somatic sensation arisen from nociceptive temperature. As expected, we noted that continuing the painful stimulus for minutes engaged regions beyond the sensorimotor cortex, e.g., the prefrontal cortex.

SIGNIFICANCE

We conclude that the amount of synchronization between scalp EEG channels is an informative tool in revealing the pain sensation.

Evaluation of synchronization measures for capturing the lagged synchronization between EEG channels: A cognitive task recognition approach

During cognitive, perceptual and sensory tasks, connectivity profile changes across different regions of the brain. Variations of such connectivity patterns between different cognitive tasks can be evaluated using pairwise synchronization measures applied to electrophysiological signals, such as electroencephalography (EEG). However, connectivity-based task recognition approaches achieving viable recognition performance have been lacking from the literature. By using several synchronization measures, we identify time lags between channel pairs during different cognitive tasks. We employed mutual information, cross correntropy, cross correlation, phase locking value, cosine similarity and nonlinear interdependence measures. In the training phase, for each type of cognitive task, we identify the time lags that maximize the average synchronization between channel pairs. These lags are used to calculate pairwise synchronization values with which we construct the train and test feature vectors for recognition of the cognitive task carried out using Fisher's linear discriminant (FLD) analysis. We tested our framework in a motor imagery activity recognition scenario on PhysioNet Motor Movement/Imagery and BCI Competition-III Ⅳa datasets. For PhysioNet dataset, average performance results ranging between % 51 and % 61 across 20 subjects. For BCI Competition-Ⅲ dataset, we achieve an average recognition performance of % 76 which is above the minimum reliable communication rate (% 70). We achieved an average accuracy over the minimum reliable communication rate on the BCI Competition-Ⅲ dataset. Performance levels were lower on the PhysioNet dataset. These results indicate that a viable task recognition system is achievable using pairwise synchronization measures evaluated at the proper task specific lags.

Neural Evidence of Superior Memory: How to Capture Brain Activities of Encoding Processes Underlying Superior Memory

Relatively little attention has been paid to the neural basis of superior memory despite its potential in providing important insight into efforts to improve memory in the general population or to offset age-related cognitive decline. The current study reports a rare opportunity to reproduce and isolate specific neural activities directly associated with exceptional memory. To capture the brain processes responsible for superior memory, we returned to a laboratory task and analytic approach used to explore the nature of exceptional memory, namely, digit-span task combined with verbal protocol analysis. One participant with average memory received approximately 50 h of digit-span training and the participant's digit-span increased from normative (8 digits) to exceptional (30 digits). Event-related potentials were recorded while the participant's digit span increased from 19 to 30 digits. Protocol analysis allowed us to identify direct behavioral indices of idiosyncratic encoding processes underlying the superior memory performance. EEG indices directly corresponding to the behavioral indices of encoding processes were identified. The results suggest that the early attention-related encoding processes were reflected in theta and delta whereas the later attention-independent encoding processes were reflected in time-domain slow-wave. This fine-grained approach offers new insights into studying neural mechanism mediating superior memory and the cognitive effort necessary to develop it.

Time-Frequency Based Phase-Amplitude Coupling Measure For Neuronal Oscillations

Oscillatory activity in the brain has been associated with a wide variety of cognitive processes including decision making, feedback processing, and working memory. The high temporal resolution provided by electroencephalography (EEG) enables the study of variation of oscillatory power and coupling across time. Various forms of neural synchrony across frequency bands have been suggested as the mechanism underlying neural binding. Recently, a considerable amount of work has focused on phase-amplitude coupling (PAC)– a form of cross-frequency coupling where the amplitude of a high frequency signal is modulated by the phase of low frequency oscillations. The existing methods for assessing PAC have some limitations including limited frequency resolution and sensitivity to noise, data length and sampling rate due to the inherent dependence on bandpass filtering. In this paper, we propose a new time-frequency based PAC (t-f PAC) measure that can address these issues. The proposed method relies on a complex time-frequency distribution, known as the Reduced Interference Distribution (RID)-Rihaczek distribution, to estimate both the phase and the envelope of low and high frequency oscillations, respectively. As such, it does not rely on bandpass filtering and possesses some of the desirable properties of time-frequency distributions such as high frequency resolution. The proposed technique is first evaluated for simulated data and then applied to an EEG speeded reaction task dataset. The results illustrate that the proposed time-frequency based PAC is more robust to varying signal parameters and provides a more accurate measure of coupling strength.

Non-negative Matrix Factorization Reveals Resting-State Cortical Alpha Network Abnormalities in the First-Episode Schizophrenia Spectrum

BACKGROUND

Little is known about neural oscillatory dynamics in first-episode psychosis. Pathophysiology of functional connectivity can be measured through network activity of alpha oscillations, reflecting long-range communication between distal brain regions.

METHODS

Resting magnetoencephalographic activity was collected from 31 individuals with first-episode schizophrenia spectrum psychosis and 22 healthy control individuals. Activity was projected to the realistic cortical surface, based on structural magnetic resonance imaging. The first principal component of activity in 40 Brodmann areas per hemisphere was Hilbert transformed within the alpha range. Non-negative matrix factorization was applied to single-trial alpha phase-locking values from all subjects to determine alpha networks. Within networks, energy and entropy were compared.

RESULTS

Four cortical alpha networks were pathological in individuals with first-episode schizophrenia spectrum psychosis. The networks involved the bilateral anterior and posterior cingulate; left auditory, medial temporal, and cingulate cortex; right inferior frontal gyrus and widespread areas; and right posterior parietal cortex and widespread areas. Energy and entropy were associated with the Positive and Negative Syndrome Scale total and thought disorder factors for the first three networks. In addition, the left posterior temporal network was associated with positive and negative factors, and the right inferior frontal network was associated with the positive factor.

CONCLUSIONS

Machine learning network analysis of resting alpha-band neural activity identified several aberrant networks in individuals with first-episode schizophrenia spectrum psychosis, including the left temporal, right inferior frontal, right posterior parietal, and bilateral cingulate cortices. Abnormal long-range alpha communication is evident at the first presentation for psychosis and may provide clues about mechanisms of dysconnectivity in psychosis and novel targets for noninvasive brain stimulation.

Time-frequency approaches to investigating changes in feedback processing during childhood and adolescence

Processing feedback from the environment is an essential function during development to adapt behavior in advantageous ways. One measure of feedback processing, the feedback negativity (FN), is an ERP observed following the presentation of feedback. Findings detailing developmental changes in the FN have been mixed, possibly due to limitations in traditional ERP measurement methods. Recent work shows that both theta and delta frequency activity contribute to the FN; utilizing time-frequency methods to measure change in power and phase in these frequency bands may provide more accurate representation of feedback processing development in childhood and adolescence. We employ time-frequency power and intertrial phase synchrony measures, in addition to conventional time-domain ERP methods, to examine the development of feedback processing in the theta (4-7 Hz) and delta (.1-3 Hz) bands throughout adolescence. A sample of 54 female participants (8-17 years old) completed a gambling task while EEG was recorded. As expected, time-domain ERP amplitudes showed no association with age. In contrast, significant effects were observed for the time-frequency measures, with theta power decreasing with age and delta power increasing with age. For intertrial phase synchrony, delta synchrony increased with age, while age-related changes in theta synchrony differed for gains and losses. Collectively, these findings highlight the importance of considering time-frequency dynamics when exploring how the processing of feedback develops through late childhood and adolescence. In particular, the role of delta band activity and theta synchrony appear central to understanding age-related changes in the neural response to feedback.

Assessment of Multivariate Neural Time Series by Phase Synchrony Clustering in a Time-Frequency-Topography Representation

Most EEG phase synchrony measures are of bivariate nature. Those that are multivariate focus on producing global indices of the synchronization state of the system. Thus, better descriptions of spatial and temporal local interactions are still in demand. A framework for characterization of phase synchrony relationships between multivariate neural time series is presented, applied either in a single epoch or over an intertrial assessment, relying on a proposed clustering algorithm, termed Multivariate Time Series Clustering by Phase Synchrony, which generates fuzzy clusters for each multivalued time sample and thereupon obtains hard clusters according to a circular variance threshold; such cluster modes are then depicted in Time-Frequency-Topography representations of synchrony state beyond mere global indices. EEG signals from P300 Speller sessions of four subjects were analyzed, obtaining useful insights of synchrony patterns related to the ERP and even revealing steady-state artifacts at 7.6 Hz. Further, contrast maps of Levenshtein Distance highlight synchrony differences between ERP and no-ERP epochs, mainly at delta and theta bands. The framework, which is not limited to one synchrony measure, allows observing dynamics of phase changes and interactions among channels and can be applied to analyze other cognitive states rather than ERP versus no ERP.

Utilizing time-frequency amplitude and phase synchrony measure to assess feedback processing in a gambling task

The neurophysiological mechanisms involved in the evaluation of performance feedback have been widely studied in the ERP literature over the past twenty years, but understanding has been limited by the use of traditional time-domain amplitude analytic approaches. Gambling outcome valence has been identified as an important factor modulating event-related potential (ERP) components, most notably the feedback negativity (FN). Recent work employing time-frequency analysis has shown that processes indexed by the FN are confounded in the time-domain and can be better represented as separable feedback-related processes in the theta (3-7 Hz) and delta (0-3 Hz) frequency bands. In addition to time-frequency amplitude analysis, phase synchrony measures have begun to further our understanding of performance evaluation by revealing how feedback information is processed within and between various brain regions. The current study aimed to provide an integrative assessment of time-frequency amplitude, inter-trial phase synchrony, and inter-channel phase synchrony changes following monetary feedback in a gambling task. Results revealed that time-frequency amplitude activity explained separable loss and gain processes confounded in the time-domain. Furthermore, phase synchrony measures explained unique variance above and beyond amplitude measures and demonstrated enhanced functional integration between medial prefrontal and bilateral frontal, motor, and occipital regions for loss relative to gain feedback. These findings demonstrate the utility of assessing time-frequency amplitude, inter-trial phase synchrony, and inter-channel phase synchrony together to better elucidate the neurophysiology of feedback processing.

Relationship between speech-evoked neural responses and perception of speech in noise in older adults

Speech-in-noise (SPIN) perception involves neural encoding of temporal acoustic cues. Cues include temporal fine structure (TFS) and envelopes that modulate at syllable (Slow-rate ENV) and fundamental frequency (F₀-rate ENV) rates. Here the relationship between speech-evoked neural responses to these cues and SPIN perception was investigated in older adults. Theta-band phase-locking values (PLVs) that reflect cortical sensitivity to Slow-rate ENV and peripheral/brainstem frequency-following responses phase-locked to F₀-rate ENV (FFR_{ENV_F0}) and TFS (FFR_TFS) were measured from scalp-electroencephalography responses to a repeated speech syllable in steady-state speech-shaped noise (SpN) and 16-speaker babble noise (BbN). The results showed that (1) SPIN performance and PLVs were significantly higher under SpN than BbN, implying differential cortical encoding may serve as the neural mechanism of SPIN performance that varies as a function of noise types; (2) PLVs and FFR_TFS at resolved harmonics were significantly related to good SPIN performance, supporting the importance of phase-locked neural encoding of Slow-rate ENV and TFS of resolved harmonics during SPIN perception; (3) FFR_{ENV_F0} was not associated to SPIN performance until audiometric threshold was controlled for, indicating that hearing loss should be carefully controlled when studying the role of neural encoding of F₀-rate ENV. Implications are drawn with respect to fitting auditory prostheses.

Influence of attention and bolus volume on brain organization during swallowing

It has been shown that swallowing involves certain attentional and cognitive resources which, when disrupted can influence swallowing function with in dysphagic patient. However, there are still open questions regarding the influence of attention and cognitive demands on brain activity during swallowing. In order to understand how brain regions responsible for attention influence brain activity during swallowing, we compared brain organization during no-distraction swallowing and swallowing with distraction. Fifteen healthy male adults participated in the data collection process. Participants performed ten 1 ml, ten 5 ml, and ten 10 ml water swallows under both no-distraction conditions and during distraction while EEG signals were recorded. After standard pre-processing of the EEG signals, brain networks were formed using the time-frequency based synchrony measure. The brain networks formed were then compared between the two sets of conditions. Results showed that there are differences in the Delta, Theta, Alpha, Beta, and Gamma frequency bands between no-distraction swallowing and swallowing with distraction. Differences in the Delta and Theta frequency bands can be attributed to changes in subliminal processes, while changes in the Alpha and Beta frequency bands are directly associated with the various levels of attention and cognitive demands during swallowing process, and changes in the Gamma frequency band are due to changes in motor activity. Furthermore, we showed that variations in bolus volume influenced the swallowing brain networks in the Delta, Theta, Alpha, Beta, and Gamma frequency bands. Changes in the Delta, Theta, and Alpha frequency bands are due to sensory perturbations evoked by the various bolus volumes. Changes in the Beta frequency band are due to reallocation of cognitive demands, while changes in the Gamma frequency band are due to changes in motor activity produced by variations in bolus volume. These findings could potentially lead to the development of better understanding of the nature of dysphagia and various rehabilitation strategies for patients with neurogenic dysphagia who have altered attention or impaired cognitive functions.

Differences in brain networks during consecutive swallows detected using an optimized vertex-frequency algorithm

Patients with dysphagia can have higher risks of aspiration after repetitive swallowing activity due to the "fatigue effect". However, it is still unknown how consecutive swallows affect brain activity. Therefore, we sought to investigate differences in swallowing brain networks formed during consecutive swallows using a signal processing on graph approach. Data were collected from 55 healthy people using electroencephalography (EEG) signals. Participants performed dry swallows (i.e., saliva swallows) and wet swallows (i.e., water, nectar-thick, and honey thick swallows). After standard pre-processing of the EEG time series, brain networks were formed using the time-frequency-based synchrony measure, while signals on graphs were formed as a line graph of the brain networks. For calculating the vertex frequency information from the signals on graphs, the proposed algorithm was based on the optimized window size for calculating the windowed graph Fourier transform and the graph S-transform. The proposed algorithms were tested using synthetic signals and showed improved energy concentration in comparison to the original algorithm. When applied to EEG swallowing data, the optimized windowed graph Fourier transform and the optimized graph S-transform showed that differences exist in brain activity between consecutive swallows. In addition, the results showed higher differences between consecutive swallows for thicker liquids.

A fast algorithm for vertex-frequency representations of signals on graphs

The windowed Fourier transform (short time Fourier transform) and the S-transform are widely used signal processing tools for extracting frequency information from non-stationary signals. Previously, the windowed Fourier transform had been adopted for signals on graphs and has been shown to be very useful for extracting vertex-frequency information from graphs. However, high computational complexity makes these algorithms impractical. We sought to develop a fast windowed graph Fourier transform and a fast graph S-transform requiring significantly shorter computation time. The proposed schemes have been tested with synthetic test graph signals and real graph signals derived from electroencephalography recordings made during swallowing. The results showed that the proposed schemes provide significantly lower computation time in comparison with the standard windowed graph Fourier transform and the fast graph S-transform. Also, the results showed that noise has no effect on the results of the algorithm for the fast windowed graph Fourier transform or on the graph S-transform. Finally, we showed that graphs can be reconstructed from the vertex-frequency representations obtained with the proposed algorithms.

Functional connectivity patterns of normal human swallowing: difference among various viscosity swallows in normal and chin-tuck head positions

Consuming thicker fluids and swallowing in the chin-tuck position has been shown to be advantageous for some patients with neurogenic dysphagia who aspirate due to various causes. The anatomical changes caused by these therapeutic techniques are well known, but it is unclear whether these changes alter the cerebral processing of swallow-related sensorimotor activity. We sought to investigate the effect of increased fluid viscosity and chin-down posture during swallowing on brain networks. 55 healthy adults performed water, nectar-thick, and honey thick liquid swallows in the neutral and chin-tuck positions while EEG signals were recorded. After pre-processing of the EEG timeseries, the time-frequency based synchrony measure was used for forming the brain networks to investigate whether there were differences among the brain networks between the swallowing of different fluid viscosities and swallowing in different head positions. We also investigated whether swallowing under various conditions exhibit small-world properties. Results showed that fluid viscosity affects the brain network in the Delta, Theta, Alpha, Beta, and Gamma frequency bands and that swallowing in the chin-tuck head position affects brain networks in the Alpha, Beta, and Gamma frequency bands. In addition, we showed that swallowing in all tested conditions exhibited small-world properties. Therefore, fluid viscosity and head positions should be considered in future swallowing EEG investigations.

What can time-frequency and phase coherence measures tell us about the genetic basis of P3 amplitude?

In a recent comprehensive investigation, we largely failed to identify significant genetic markers associated with P3 amplitude or to corroborate previous associations between P3 and specific single nucleotide polymorphisms (SNPs) or genes. In the present study we extended this line of investigation to examine time-frequency (TF) activity and intertrial phase coherence (ITPC) in the P3 time window, both of which are associated with P3 amplitude. Previous genome-wide research has reported associations between P3-related theta and delta activity and individual genetic variants. A large, population-based sample of 4211 subjects, comprising male and female adolescent twins and their parents, was genotyped for 527,828 single nucleotide polymorphisms (SNPs), from which over six million SNPs were accurately imputed. Heritability estimates were greater for TF energy than ITPC, whether based on biometric models or the combined influence of all measured SNPs (derived from genome-wide complex trait analysis). The magnitude of overlap in the specific SNPs associated with delta energy and ITPC and P3 amplitude was significant. A genome-wide analysis of all SNPs, accompanied by an analysis of approximately 17,600 genes, indicated a region of chromosome 2 around TEKT4 that was significantly associated with theta ITPC. Analysis of candidate SNPs and genes previously reported to be associated with P3 or related phenotypes yielded one association surviving correction for multiple tests: between theta energy and CRHR1. However, we did not obtain significant associations for SNPs implicated in previous genome-wide studies of TF measures. Identifying specific genetic variants associated with P3 amplitude remains a challenge.

Time-frequency phase-synchrony approaches with ERPs

Time-frequency signal processing approaches are well-developed, and have been widely employed for the study of the energy distribution of event-related potential (ERP) data across time and frequency. Wavelet time-frequency transform (TFT) and Cohen's class of time-frequency distributions (TFD) are the most widely used in the field. While ERP TFT approaches have been most extensively developed for amplitude measures, reflecting the magnitude of regional neuronal activity, time-frequency phase-synchrony measures have gained increased utility in recent years for the assessment of functional connectivity. Phase synchrony measures can be used to index the functional integration between regions (interregional), in addition to the consistency of activity within region (intertrial). In this paper, we focus on a particular class of time-frequency distributions belonging to Cohen's class, known as the Reduced Interference Distribution (RID) for quantifying functional connectivity, which we recently introduced (Aviyente et al., 2011). The present report first summarizes common time-frequency approaches to computing phase-synchrony with ERP data in order to highlight the similarities and differences relative to the RID. In previous work, we demonstrated differences between the RID and wavelet approaches to indexing phase-synchrony, and have applied the RID to demonstrate that RID-based time-frequency phase-synchrony measures can index increased functional connectivity between medial and lateral prefrontal regions during control processing, observed in the theta band during the error-related negativity (ERN). Because ERN amplitude measures have been associated with two other widely studied medial-frontal theta components (no-go N2; feedback negativity, FN), the application of the RID phase synchrony measure in the present report extends our previous work with ERN to include theta activity during the no-go N2 (inhibitory processing) and the feedback negativity (FN; loss feedback processing). Findings support the idea that similar medial-lateral prefrontal functional connectivity underlies the ERN, no-go N2, and FN components, and provide initial validation that the proposed RID-based time-frequency phase-synchrony measure can index this activity.

Stimulus sequence context differentially modulates inhibition-related theta and delta band activity in a go/no-go task

Recent work suggests that dissociable activity in theta and delta frequency bands underlies several common ERP components, including the no-go N2/P3 complex, which can better index separable functional processes than traditional time-domain measures. Reports have also demonstrated that neural activity can be affected by stimulus sequence context information (i.e., the number and type of preceding stimuli). Stemming from prior work demonstrating that theta and delta index separable processes during response inhibition, the current study assessed sequence context in a go/no-go paradigm in which the number of go stimuli preceding each no-go was selectively manipulated. Principal component analysis of time-frequency representations revealed differential modulation of evoked theta and delta related to sequence context, where delta increased robustly with additional preceding go stimuli, while theta did not. Findings are consistent with the view that theta indexes simpler initial salience-related processes, while delta indexes more varied and complex processes related to a variety of task parameters.

Characterizing functional connectivity patterns during saliva swallows in different head positions

BACKGROUND

The anatomical rationale and efficacy of the chin tuck in improving airway protection for some people with swallowing disorders have been well researched and established. However, there are still open questions regarding whether brain activity for swallowing control is altered while performing this chin-tuck maneuver.

METHODS

In this study, we collected EEG signals from 55 healthy adults while swallowing in the neutral and chin-tuck head positions. The time-frequency based synchrony measure was used to form brain networks. We investigated both the small-world properties of these brain networks and differences among the constructed brain networks for the two head positions during swallowing tasks.

RESULTS

We showed that brain networks for swallowing in both head positions exhibit small-world properties. Furthermore, we showed that swallowing in the chin-tuck head position affects brain networks in the Alpha and Gamma frequency bands.

CONCLUSIONS

According to these results, we can tell that the parameter of head position should be considered in future investigations which utilize EEG signals during swallowing activity.

Sending mixed signals: worry is associated with enhanced initial error processing but reduced call for subsequent cognitive control

Worry is reliably associated with overactive action-monitoring processes as measured by the error-related negativity (ERN). However, worry is not associated with error-related behavioral adjustments which are typically used to infer increased cognitive control following errors. We hypothesized that this disconnect between overactive action monitoring and unimproved post-error adjustments in worriers is the result of reduced functional integration between medial and lateral prefrontal regions during generation of the ERN, understood to have an important role in mediating controlled processing. To test this, we examined ERN amplitude and interchannel phase synchrony extracted from scalp-recorded electroencephalographic data during error processing in 77 undergraduates who performed a Flankers task. Correlational and path analytic results demonstrated that worry was related to both an enlarged ERN and reduced phase synchrony. Although not directly related to post-error behavioral adjustments, results also revealed that worry was indirectly related to poor post-error adjustments through its association with reduced phase synchrony. Therefore, worry seems to affect multiple components of the action-monitoring system. It is related not just with the initial response to the error, but also with the transmission of information between networks involved in cognitive control processes.

Network community structure detection for directional neural networks inferred from multichannel multisubject EEG data

In many neuroscience applications, one is interested in identifying the functional brain modules from multichannel, multiple subject neuroimaging data. However, most of the existing network community structure detection algorithms are limited to single undirected networks and cannot reveal the common community structure for a collection of directed networks. In this paper, we propose a community detection algorithm for weighted asymmetric (directed) networks representing the effective connectivity in the brain. Moreover, the issue of finding a common community structure across subjects is addressed by maximizing the total modularity of the group. Finally, the proposed community detection algorithm is applied to multichannel multisubject electroencephalogram data.

Statistical detection of EEG synchrony using empirical bayesian inference

There is growing interest in understanding how the brain utilizes synchronized oscillatory activity to integrate information across functionally connected regions. Computing phase-locking values (PLV) between EEG signals is a popular method for quantifying such synchronizations and elucidating their role in cognitive tasks. However, high-dimensionality in PLV data incurs a serious multiple testing problem. Standard multiple testing methods in neuroimaging research (e.g., false discovery rate, FDR) suffer severe loss of power, because they fail to exploit complex dependence structure between hypotheses that vary in spectral, temporal and spatial dimension. Previously, we showed that a hierarchical FDR and optimal discovery procedures could be effectively applied for PLV analysis to provide better power than FDR. In this article, we revisit the multiple comparison problem from a new Empirical Bayes perspective and propose the application of the local FDR method (locFDR; Efron, 2001) for PLV synchrony analysis to compute FDR as a posterior probability that an observed statistic belongs to a null hypothesis. We demonstrate the application of Efron's Empirical Bayes approach for PLV synchrony analysis for the first time. We use simulations to validate the specificity and sensitivity of locFDR and a real EEG dataset from a visual search study for experimental validation. We also compare locFDR with hierarchical FDR and optimal discovery procedures in both simulation and experimental analyses. Our simulation results showed that the locFDR can effectively control false positives without compromising on the power of PLV synchrony inference. Our results from the application locFDR on experiment data detected more significant discoveries than our previously proposed methods whereas the standard FDR method failed to detect any significant discoveries.

Traumatic brain injury detection using electrophysiological methods

Measuring neuronal activity with electrophysiological methods may be useful in detecting neurological dysfunctions, such as mild traumatic brain injury (mTBI). This approach may be particularly valuable for rapid detection in at-risk populations including military service members and athletes. Electrophysiological methods, such as quantitative electroencephalography (qEEG) and recording event-related potentials (ERPs) may be promising; however, the field is nascent and significant controversy exists on the efficacy and accuracy of the approaches as diagnostic tools. For example, the specific measures derived from an electroencephalogram (EEG) that are most suitable as markers of dysfunction have not been clearly established. A study was conducted to summarize and evaluate the statistical rigor of evidence on the overall utility of qEEG as an mTBI detection tool. The analysis evaluated qEEG measures/parameters that may be most suitable as fieldable diagnostic tools, identified other types of EEG measures and analysis methods of promise, recommended specific measures and analysis methods for further development as mTBI detection tools, identified research gaps in the field, and recommended future research and development thrust areas. The qEEG study group formed the following conclusions: (1) Individual qEEG measures provide limited diagnostic utility for mTBI. However, many measures can be important features of qEEG discriminant functions, which do show significant promise as mTBI detection tools. (2) ERPs offer utility in mTBI detection. In fact, evidence indicates that ERPs can identify abnormalities in cases where EEGs alone are non-disclosing. (3) The standard mathematical procedures used in the characterization of mTBI EEGs should be expanded to incorporate newer methods of analysis including non-linear dynamical analysis, complexity measures, analysis of causal interactions, graph theory, and information dynamics. (4) Reports of high specificity in qEEG evaluations of TBI must be interpreted with care. High specificities have been reported in carefully constructed clinical studies in which healthy controls were compared against a carefully selected TBI population. The published literature indicates, however, that similar abnormalities in qEEG measures are observed in other neuropsychiatric disorders. While it may be possible to distinguish a clinical patient from a healthy control participant with this technology, these measures are unlikely to discriminate between, for example, major depressive disorder, bipolar disorder, or TBI. The specificities observed in these clinical studies may well be lost in real world clinical practice. (5) The absence of specificity does not preclude clinical utility. The possibility of use as a longitudinal measure of treatment response remains. However, efficacy as a longitudinal clinical measure does require acceptable test-retest reliability. To date, very few test-retest reliability studies have been published with qEEG data obtained from TBI patients or from healthy controls. This is a particular concern because high variability is a known characteristic of the injured central nervous system.

Multiple subject analysis of functional brain network communities through co-regularized spectral clustering

In recent years, the human brain has been characterized as a complex network composed of segregated modules linked by short path lengths. In order to understand the organization of the network, it is important to determine these modules underlying the functional brain networks. However, the study of these modules is confounded by the fact that most neurophysiological studies consist of data collected from multiple subjects. Typically, this problem is addressed by either averaging the data across subjects which omits the variability across subjects or using consensus clustering methods which treats all subjects equally irrespective of outliers in the data. In this paper, we adapt a recently introduced co-regularized multiview spectral clustering approach to address these problems. The proposed framework is applied to EEG data collected during a study of error-related negativity (ERN) to better understand the functional networks involved in cognitive control and to compare between the network structure between error and correct responses.

The effects of increased fluid viscosity on stationary characteristics of EEG signal in healthy adults

Electroencephalography (EEG) systems can enable us to study cerebral activation patterns during performance of swallowing tasks and possibly infer about the nature of abnormal neurological conditions causing swallowing difficulties. While it is well known that EEG signals are non-stationary, there are still open questions regarding the stationarity of EEG during swallowing activities and how the EEG stationarity is affected by different viscosities of the fluids that are swallowed by subjects during these swallowing activities. In the present study, we investigated the EEG signal collected during swallowing tasks by collecting data from 55 healthy adults (ages 18-65). Each task involved the deliberate swallowing of boluses of fluids of different viscosities. Using time-frequency tests with surrogates, we showed that the EEG during swallowing tasks could be considered non-stationary. Furthermore, the statistical tests and linear regression showed that the parameters of fluid viscosity, sex, and different brain regions significantly influenced the index of non-stationarity values. Therefore, these parameters should be considered in future investigations which use EEG during swallowing activities.

Does electroencephalogram phase variability account for reduced P3 brain potential in externalizing disorders?

OBJECTIVE

Amplitude deficits of the P3 event-related potential (ERP) are associated with externalizing psychopathology but little is known about the nature of underlying brain electrical activity that accounts for this amplitude reduction. We sought to understand if group differences in task-induced phase-locking in electroencephalographic (EEG) delta and theta frequencies may account for P3-externalizing associations.

METHODS

Adult males (N=410) completed a visual oddball task and frontal and parietal P3-related delta- and theta-band phase-invariant evoked energy and inter-trial phase-locking measures were investigated with respect to the externalizing spectrum, including substance dependence, adult antisociality, and childhood disruptive disorders. We hypothesized that P3-related phase-locking is weaker in externalizing-diagnosed individuals and this might mediate prior findings of reduced evoked P3 energy.

RESULTS

Reductions in both evoked energy and phase-locking, in both frequency bands, at both scalp sites, were associated with greater odds of externalizing diagnoses. Generally, adding phase-locking to evoked energy came with better prediction model fit. Moreover, reduced theta-band phase-locking partially mediated the effects of within-frequency evoked energy on externalizing prediction.

CONCLUSIONS

Inter-trial phase-locking underlying P3 appears to be an important distinction between externalizing and control subjects.

SIGNIFICANCE

This cross-trial phase-variability for externalizing-diagnosed individuals might reflect deficient top-down "tuning" by neuromodulatory systems.

Computation of resting state networks from fMRI through a measure of phase synchrony

Resting-state fMRI (rs-fMRI) studies of the human brain have demonstrated that low-frequency fluctuations can define functionally relevant resting state networks (RSNs). The majority of these methods rely on Pearson's correlation for quantifying the functional connectivity between the time series from different regions. However, it is well-known that correlation is limited to quantifying only linear relationships between the time series and assumes stationarity of the underlying processes. Many empirical studies indicate nonstationarity of the BOLD signals. In this paper, we adapt a measure of time-varying phase synchrony to quantify the functional connectivity and modify it to distinguish between synchronization and desynchronization. The proposed measure is compared to the conventional Pearson's correlation method for rs-fMRI analyses on two subjects (six scans per subject) in terms of their reproducibility.

Theta and delta band activity explain N2 and P3 ERP component activity in a go/no-go task

OBJECTIVES

Recent work indicates that the feedback negativity and P3 components from gambling feedback tasks can be understood as mixtures of functionally distinct processes occurring separately in theta and delta frequency bands. The current study was conducted to assess whether dissociable processes occurring in the theta and delta bands would similarly account for activity underlying N2 and P3 components in a go/no-go task.

METHODS

The current study measured EEG signals from 66 participants during a go/no-go task, and a time-frequency principal components analysis decomposition approach was used to extract theta and delta measures from condition averages.

RESULTS

Theta and delta measures separately increased in relation to response inhibition, and were uniquely related to the N2 and P3 components, as predicted.

CONCLUSIONS

Findings support the view that the theta and delta measures indexed separable processes related to response inhibition, and better indexed the processes underlying N2 and P3 components in this go/no-go task.

SIGNIFICANCE

Theta and delta measures may index separable functional processes across other common ERP tasks, and may represent an improved target for research relative to standard time-domain components.

Disturbed resting state EEG synchronization in bipolar disorder: A graph-theoretic analysis

Disruption of functional connectivity may be a key feature of bipolar disorder (BD) which reflects disturbances of synchronization and oscillations within brain networks. We investigated whether the resting electroencephalogram (EEG) in patients with BD showed altered synchronization or network properties. Resting-state EEG was recorded in 57 BD type-I patients and 87 healthy control subjects. Functional connectivity between pairs of EEG channels was measured using synchronization likelihood (SL) for 5 frequency bands (δ, θ, α, β, and γ). Graph-theoretic analysis was applied to SL over the electrode array to assess network properties. BD patients showed a decrease of mean synchronization in the alpha band, and the decreases were greatest in fronto-central and centro-parietal connections. In addition, the clustering coefficient and global efficiency were decreased in BD patients, whereas the characteristic path length increased. We also found that the normalized characteristic path length and small-worldness were significantly correlated with depression scores in BD patients. These results suggest that BD patients show impaired neural synchronization at rest and a disruption of resting-state functional connectivity.

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Global synchronization of BD patients was reduced in the alpha-band at resting.

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De-synchronized connectivity was localized in fronto-centro-parietal connections.

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Global topology of BD had decreased network clustering and increased path length.

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BD showed the less efficient network processing.

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Network characteristics of BD patients were associated with depression severity.

A note on the phase locking value and its properties

We investigate the properties of the Phase Locking Value (PLV) and the Phase Lag Index (PLI) as metrics for quantifying interactions in bivariate local field potential (LFP), electroencephalography (EEG) and magnetoencephalography (MEG) data. In particular we describe the relationship between nonparametric estimates of PLV and PLI and the parameters of two distributions that can both be used to model phase interactions. The first of these is the von Mises distribution, for which the sample PLV is a maximum likelihood estimator. The second is the relative phase distribution associated with bivariate circularly symmetric complex Gaussian data. We derive an explicit expression for the PLV for this distribution and show that it is a function of the cross-correlation between the two signals. We compare the bias and variance of the sample PLV and the PLV computed from the cross-correlation. We also show that both the von Mises and Gaussian models are suitable for representing relative phase in application to LFP data from a visually-cued motor study in macaque. We then compare results using the two different PLV estimators and conclude that, for this data, the sample PLV provides equivalent information to the cross-correlation of the two complex time series.

Some sampling properties of common phase estimators

The instantaneous phase of neural rhythms is important to many neuroscience-related studies. In this letter, we show that the statistical sampling properties of three instantaneous phase estimators commonly employed to analyze neuroscience data share common features, allowing an analytical investigation into their behavior. These three phase estimators-the Hilbert, complex Morlet, and discrete Fourier transform-are each shown to maximize the likelihood of the data, assuming the observation of different neural signals. This connection, explored with the use of a geometric argument, is used to describe the bias and variance properties of each of the phase estimators, their temporal dependence, and the effect of model misspecification. This analysis suggests how prior knowledge about a rhythmic signal can be used to improve the accuracy of phase estimates.