A Study of Robust Partial Correlation between Electroencephalogram Connectivity and Human Arithmetic Abilities

The reliability of magnetoencephalography (MEG) resting-state functional connectivity in schizophrenia (SZ) is unknown as previous research has focused on healthy controls (HC). Here, we examined reliability in 26 participants (13-SZ, 13-HC). Eyes opened and eyes closed resting-state data were collected on 4 separate occasions during 2 visits, 1 week apart. For source modeling, we used minimum norm software to apply dynamic statistical parametric mapping. Source analyses compared the following functional connectivity metrics from each data run: coherence (coh), imaginary coherence (imcoh), pairwise phase consistency (ppc), phase-locking value (plv), phase lag index (pli), weighted phase lag index (wpli), and weighted phase lag index debiased (wpli2). Intraclass correlation coefficients (ICCs) were calculated for whole brain, network, and network pair averages. For reliability, ICCs above 0.75 = excellent, above 0.60 = good, above 0.40 = fair, and below 0.40 = poor reliability. We found the reliability of these metrics varied greatly depending on frequency band, network, network pair, and participant group examined. Broadband (1–58 Hz) whole brain averages in both HC and SZ showed excellent reliability for wpli2, and good to fair reliability for ppc, plv, and coh. Broadband network averages showed excellent to good reliability across 1 hour and 1 week for coh, imcoh, ppc, plv, wpli within default mode, cognitive control, and visual networks in HC, while the same metrics had excellent to fair reliability in SZ. Regional network pair averages showed good to fair reliability for coh, ppc, plv within default mode, cognitive control and visual network pairs in HC and SZ. In general, HC had higher reliability compared to SZ, and the default mode, cognitive control, and visual networks had higher reliability compared to somatosensory and auditory networks. Similar reliability levels occurred for both eyes opened and eyes closed resting-states for most metrics. The functional connectivity metrics of coh, ppc, and plv performed best across 1 hour and 1 week in HC and SZ. We also found that SZ had reduced coh, plv, and ppc in the dmn average and pair values indicating dysconnectivity in SZ. These findings encourage collecting both eyes opened and eyes closed resting-state MEG, while demonstrating that clinical populations may differ in reliability.

The procedure of thresholding for graph construction is one of the common steps in calculating networks of brain connections. However, this procedure can lead to incomparable results from different studies. In the present study we aim to test the effect of thresholding or algorithmic reduction of the number of connected nodes on the construction of a set of widely used connectivity graph metrics derived from EEG data. 164 people took part in our study. Participants were recruited via social networks. EEG was recorded during resting state. At the beginning of the procedure each participant was asked to relax and not to think about anything. Source reconstruction was performed using standard source localization pipeline from MNE-package. Desikan-Killiany Atlas was used for cortical parcellation with 34 ROI per hemisphere. Synchronization was estimated with weighted phase lag index in 4–30 Hz frequency range for eyes closed and eyes open separately. We have found that All metrics except average participation coefficient vary monotonously as a function of density level (moreover, we have found, that for Cluster Coefficient, more than 95% and for the Characteristic Path Length ∼50% of the variance is related to thresholding cut-off). The different data-driven approaches to the network construction leads to significant changes in the group-level graph metrics and can eliminate the variance in the data that can be crucial for individual differences studies.

Most brain-computer interfaces (BCIs) utilize univariate features (i.e., power spectrum or amplitude) for motor imagery (MI) pattern recognition, while less attention has been paid on multivariate analysis that considers information flow between various brain areas through brain connectivity estimations. Most recently, researches have proved that connectivity features were able to characterize different MI tasks. In this study, we investigated the performance of functional connectivity features measured by phase lag index (PLI), weighted phase lag index(wPLI) and phase-locking value(PLV) on MI classification. The widely-used filter-bank common spatial pattern (FBCSP) approach was employed here for performance assessment. A linear support vector machine was trained to classify different MI tasks using two publicly-available datasets from BCI Competition III and IV. The classification results showed that connectivity features achieved classification accuracy >85% in most cases and PLI outperformed all other methods including FBCSP. Our work suggested that functional connectivity features could be utilized as a powerful tool for recognizing different motor intention.

AimsVagus nerve stimulation (VNS) is a neuromodulation therapy for children with drug‐resistant epilepsy (DRE). The efficacy of VNS is heterogeneous. A prediction model is needed to predict the efficacy before implantation.MethodsWe collected data from children with DRE who underwent VNS implantation and received regular programming for at least 1 year. Preoperative clinical information and scalp video electroencephalography (EEG) were available in 88 children. Synchronization features, including phase lag index (PLI), weighted phase lag index (wPLI), and phase‐locking value (PLV), were compared between responders and non‐responders. We further adapted a support vector machine (SVM) classifier selected from 25 clinical and 18 synchronization features to build a prediction model for efficacy in a discovery cohort (n = 70) and was tested in an independent validation cohort (n = 18).ResultsIn the discovery cohort, the average interictal awake PLI in the high beta band was significantly higher in responders than non‐responders (p < 0.05). The SVM classifier generated from integrating both clinical and synchronization features had the best prediction efficacy, demonstrating an accuracy of 75.7%, precision of 80.8% and area under the receiver operating characteristic (AUC) of 0.766 on 10‐fold cross‐validation. In the validation cohort, the prediction model demonstrated an accuracy of 61.1%.ConclusionThis study established the first prediction model integrating clinical and baseline synchronization features for preoperative VNS responder screening among children with DRE. With further optimization of the model, we hope to provide an effective and convenient method for identifying responders before VNS implantation.

: Weighted Phase Lag Index (WPLI) is a methodology used to identify nonzero phase lag statistical interdependencies between electroencephalography (EEG) time series from pairs of electrodes. Identifying nonzero phase lags may be useful to identify neural interaction among regions based on the known delay for brain region-to-region communication. This project applies WPLI analysis to previously collected EEG data from a Soldier performing a shooting task. WPLI was examined on EEG data epoched around trigger pull events, which includes both brain activity and movement artifacts from weapon recoil. This functional connectivity measure was compared to a traditional time-frequency analysis at individual electrode sites. In the individual studied, WPLI identified a left lateralization in the network communication as well as neural activity from the occipital electrodes. Both of these findings were not evident in the channel-based analysis. This project suggests that WPLI may be able to detect task-related functional networks, despite artifactual contamination that is typical in real-world environments.

Functional brain connectivity (FC) can be estimated using electroencephalography (EEG). However, there is considerable variability across studies in the FC measures used and in data (pre-)processing methods, leading to difficulties comparing and amalgamating results between studies. Thus, standardisation of EEG (pre-)processing for the measurement and reporting of FC is needed.We aimed to assess differences in FC estimates produced by different settings across multiple EEG pre-processing steps, (including re-referencing and epoching) to validate a reliable methodological pipeline for assessing EEG-FC in simulated EEG data. We simulated EEG-FC data where the 'ground truth' of the connections is known and compared estimates of FC from this ground truth data across multiple FC measures and variations in multiple pre-processing steps. Our results indicated that pre-processing steps that included segmenting the data into 40 or more epochs that were 6s or more in length provided the most accurate estimation of the simulated FC. With regards to the data re-referencing, the Reference Electrode Standardization Technique or the common average re-referencing appeared best when used in conjunction with imaginary coherence and weighted phase lag index metrics. However, the magnitude-squared coherence FC measure performed best with the Current Source Density reference free techniques. Our paper provides an evidence-base for the influence of referencing, epoch length and number, controls for volume conduction, and different FC metrics on EEG-FC measurement. Using this evidence, we present an initial and promising account of the best performing (pre-)processing choices for robust EEG-FC assessment.

A large number of traffic accidents due to driver drowsiness have been under more attention of many countries. The organization of the functional brain network is associated with drowsiness, but little is known about the brain network topology that is modulated by drowsiness. To clarify this problem, in this study, we introduce a novel approach to detect driver drowsiness. Electroencephalogram (EEG) signals have been measured during a simulated driving task, in which participants are recruited to undergo both alert and drowsy states. The filtered EEG signals are then decomposed into multiple frequency bands by wavelet packet transform. Functional connectivity between all pairs of channels for multiple frequency bands is assessed using the phase lag index (PLI). Based on this, PLI-weighted networks are subsequently calculated, from which minimum spanning trees are constructed-a graph method that corrects for comparison bias. Statistical analyses are performed on graph-derived metrics as well as on the PLI connectivity values. The major finding is that significant differences in the delta frequency band for three graph metrics and in the theta frequency band for five graph metrics suggesting network integration and communication between network nodes are increased from alertness to drowsiness. Together, our findings also suggest a more line-like configuration in alert states and a more star-like topology in drowsy states. Collectively, our findings point to a more proficient configuration in drowsy state for lower frequency bands. Graph metrics relate to the intrinsic organization of functional brain networks, and these graph metrics may provide additional insights on driver drowsiness detection for reducing and preventing traffic accidents and further understanding the neural mechanisms of driver drowsiness.

SummaryThe political economy literature has put forward a multitude of hypotheses regarding the drivers of structural reforms, but few, if any, empirically robust findings have emerged thus far. To make progress, we draw a parallel with model uncertainty in the growth literature and provide a new version of the Bayesian averaging of maximum likelihood estimates (BAMLE) technique tailored to binary logit models. Relying on a new database of major past labor and product market reforms in advanced countries, we test a large set of variables for robust correlation with reform in each area. We find widespread support for the crisis‐induces‐reform hypothesis, as high unemployment and economic crises are robustly correlated to structural reforms. We also find evidence of reform convergence—that is, countries with tighter regulation are more prone to liberalize. Reforms are more likely when other countries also undertake them and when there is formal pressure to implement them. Other robust correlates are more specific to certain areas—for example, international pressure and political factors are most relevant for product market and job protection reforms, respectively.

Connectivity analyses are widely used to assess the interaction brain networks. This type of analyses is usually conducted considering the well-known classical frequency bands: delta, theta, alpha, beta, and gamma. However, this parcellation of the frequency content can bias the analyses, since it does not consider the between-subject variability or the particular idiosyncrasies of the connectivity patterns that occur within a band. In this study, we addressed these limitations by introducing the High Frequential Resolution Networks (HFRNs). HFRNs were constructed, using a narrow-bandwidth FIR bank filter of 1 Hz bandwidth, for two different connectivity metrics (Amplitude Envelope Correlation, AEC, and Phase Lag index, PLI) and for 3 different databases of MEG and EEG recordings. Results showed a noticeable similarity between the frequential evolution of PLI, AEC, and the Power Spectral Density (PSD) from MEG and EEG signals. Nonetheless, some technical remarks should be considered: (i) results at the gamma band should exclude the frequency range around 50 Hz due to abnormal connectivity patterns, consequence of the previously applied 50 Hz notch-filter; (ii) HFRNs patterns barely vary with the connection distance; and (iii) a low sampling frequency can exert a remarkable influence on HFRNs. To conclude, we proposed a new framework to perform connectivity analyses that allow to further analyze the frequency-based distribution of brain networks.

Effects of familiarity on child brain networks when listening to a storybook reading: A magneto-encephalographic study

The goal of the present study is to propose the use of global connectivity measures as quantitative indicators of long-term medication in pediatric patients with Attention-Deficit-Hyperactivity Disorder, combined type (ADHD-C). For this purpose, graph theoretical brain connectivity indices are computed from connectivity estimations across eyes-opened resting-state EEG recordings measured before and after the treatment with osmotic release oral system-methylphenidate for a month in 18 boys (aged between 7–12 years). In order to present the reliable results, neurofunctional correlations are firstly estimated in time (Pearson Correlation (PC), Spearman Correlation), frequency (Directed Transfer Function, Partial Directed Coherence) and phase (Phase Locking Value, Phase Lag Index) domains in between short segments of 2sec over single trials of 1min. Later, transitivity, clustering coefficients, assortativity, global efficiency and modularity are computed from EEG based connectivity matrices produced by each approach. Since the highest classification accuracy of 83.79% is provided by PC, statistical tests (one-way Anova, pair-wise multiple comparison) and step-wise logistic regression modelling are all examined to detect significant differences between pre- and post- treatment relevant connectivity measures. Statistical boxplots are also shown, as well. Overall results reveal that global brain connectivity can be increased by long-term medication in pediatric ADHD-C in terms of increased segregation & resilience. This is the first study to demonstrate that long-term medication can normalize the functional brain connectivity in ADHD, which is characterized by decreased connectivity compared to controls.

Machine learning-based algorithm of drug-resistant prediction in newly diagnosed patients with temporal lobe epilepsy.

IntroductionDepression is a mental disorder characterized by aberrant exploratory behavior. Environmental factors, such as chronic stress, are commonly used to induce depression-like behavior in rodent models. The medial prefrontal cortex (mPFC) and the basolateral amygdala (BLA) are crucial sites in subjects with chronic stress-induced depression. The transmission of amplitude information from the mPFC to the BLA was abated during exploratory behavior in depressive rats; however, the nature of the phase interaction between these two sites remains unknown.MethodsWe used chronic unpredictable mild stress (CUMS) to model depression in rats and acquired local field potentials (LFPs) via multiple electrodes implanted in the mPFC and the BLA while rats (both the control and CUMS groups, respectively) were allowed to explore freely in an open field. The weighted phase lag index (WPLI) within the mPFC and the BLA and phase transfer entropy (PTE) from the mPFC to BLA were computed for two groups of rats (control and CUMS rats) to quantify the phase information transmission.ResultsRats subjected to CUMS showed a decrease in exploratory behavior. The WPLI within the mPFC and the BLA showed strikingly higher phase synchrony at theta frequencies (4–12 Hz) than other frequency bands during exploratory behavior in both the control and CUMS groups. The results of theta PTE from the mPFC to BLA showed that PTE was significantly decreased in the CUMS group compared with the control group.DiscussionsThese findings demonstrated that attenuated phase information transfer might restrain exploratory behavior in CUMS rats.

The study of robustness and connectivity properties are important in the analysis of complex networks. This paper reports on an effort to compare different network topologies according to their algebraic connectivity, network criticality, average node degree, and average node betweenness. We consider different network types and study the behavior of these various metrics as scale is increased. Based on extensive simulations, we suggest some guidelines for the design and simplification of networks. The main finding is that, algebraic connectivity, network criticality, average degree, and average node betweenness capture different properties of a graph. Depending on the nature of the problem at hand, one needs to select which one is appropriate to use as the main metric for network analysis.

Diagnosing and treating dementia, including mild cognitive impairment (MCI), is challenging due to diverse disease types and overlapping symptoms. Early MCI detection is vital as it can precede dementia, yet distinguishing it from later stage dementia is intricate due to subtle symptoms. The primary objective of this study is to adopt a complex network perspective to unravel the underlying pathophysiological mechanisms of dementia-related disorders. Leveraging the extensive availability of electroencephalogram (EEG) data, our study focuses on the meticulous identification and analysis of EEG-based brain functional network (BFNs) associated with dementia-related disorders. To achieve this, we employ the Phase Lag Index (PLI) as a connectivity measure, offering a comprehensive view of neural interactions. To enhance the analytical rigor, we introduce a data-driven threshold selection technique. This innovative approach allows us to compare the topological structures of the formulated BFNs using complex network measures quantitatively and statistically. Furthermore, we harness the power of these BFNs by utilizing them as pre-defined graph inputs for a Graph Convolution Network (GCN-net) based approach. The results demonstrate that graph theory metrics, such as the rich-club coefficient, transitivity, and assortativity coefficients, effectively distinguish between MCI, Alzheimer's disease (AD) and vascular dementia (VD). Furthermore, GCN-net achieves high accuracy (95.07% delta, 80.62% theta) and F1-scores (0.92 delta, 0.67 theta), highlighting the effectiveness of EEG-based BFNs in the analysis of dementia-related disorders.