Phase Lag Index Research Articles

Motor network connectivity predicts neuroplastic response following theta burst stimulation in healthy adults.

A patterned repetitive transcranial magnetic stimulation protocol, known as continuous theta burst stimulation (cTBS), can suppress corticospinal excitability via mechanisms that appear similar to long-term depression synaptic plasticity. Despite much potential, this technique is currently limited by substantial response variability. The purpose of this study was to investigate whether baseline resting state functional connectivity is a determinant of response to cTBS. Eighteen healthy young adults participated in up to three experimental sessions. Single-pulse transcranial magnetic stimulation was used to quantify change in corticospinal excitability following cTBS. Three minutes of resting electroencephalographic activity was recorded, and functional connectivity was estimated using the debiased weighted phase lag index across different frequency bands. Partial least squares regression identified models of connectivity between a seed region (C3) and the whole scalp that maximally accounted for variance in cTBS responses. There was no group-level effect of a single cTBS train or spaced cTBS trains on corticospinal excitability (p = 0.092). A low beta frequency band model of connectivity accounted for the largest proportion of variance in spaced cTBS response (R2 = 0.50). Based on the low beta frequency model, a-priori regions of interest were identified and predicted 39% of variance in response to spaced cTBS at a subsequent session. Importantly, weaker connectivity between the seed electrode (C3) and a cluster approximating a frontocentral region was associated with greater spaced cTBS response (p = 0.02). It appears M1-frontocentral networks may have an important role in determining the effects of cTBS on corticospinal excitability.

Altered evoked low-frequency connectivity from SI to ACC following nerve injury in rats

Objective. Despite decades of research on central processing of pain, there are still several unanswered questions, in particular regarding the brain regions that may contribute to this alerting sensation. Since it is generally accepted that more than one cortical area is responsible for pain processing, there is an increasing focus on the interaction between areas known to be involved. Approach. In this study, we aimed to investigate the bidirectional information flow from the primary somatosensory cortex (SI) to the anterior cingulate cortex (ACC) in an animal model of neuropathic pain.19 rats (nine controls and ten intervention) had an intracortical electrode implanted with six pins in SI and six pins in ACC, and a cuff stimulation electrode around the sciatic nerve. The intervention rats were subjected to the spared nerve injury (SNI) after baseline recordings. Electrical stimulation at three intensities of both noxious and non-noxious stimulation was used to record electrically evoked cortical potentials. To investigate information flow, two connectivity measures were used: phase lag index (PLI) and granger prediction (GP). The rats were anesthetized during the entire study. Main results. Immediately after the intervention (<5 min after intervention), the high frequency (γ and γ+) PLI was significantly decreased compared to controls. In the last recording cycle (3–4 h after intervention), the GP increased consistently in the intervention group. Peripheral nerve injury, as a model of neuropathic pain, resulted in an immediate decrease in information flow between SI and ACC, possibly due to decreased sensory input from the injured nerve. Hours after injury, the connectivity between SI and ACC increased, likely indicating hypersensitivity of this pathway. Significance. We have shown that both a directed and non-directed connectivity between SI and ACC approach can be used to show the acute changes resulting from the SNI model.

Differential classification of states of consciousness using envelope- and phase-based functional connectivity

The development of sophisticated computational tools to quantify changes in the brain's oscillatory dynamics across states of consciousness have included both envelope- and phase-based measures of functional connectivity (FC), but there are very few direct comparisons of these techniques using the same dataset. The goal of this study was to compare an envelope-based (i.e. Amplitude Envelope Correlation, AEC) and a phase-based (i.e. weighted Phase Lag Index, wPLI) measure of FC in their classification of states of consciousness. Nine healthy participants underwent a three-hour experimental anesthetic protocol with propofol induction and isoflurane maintenance, in which five minutes of 128-channel electroencephalography were recorded before, during, and after anesthetic-induced unconsciousness, at the following time points: Baseline; light sedation with propofol (Light Sedation); deep unconsciousness following three hours of surgical levels of anesthesia with isoflurane (Unconscious); five minutes prior to the recovery of consciousness (Pre-ROC); and three hours following the recovery of consciousness (Recovery). Support vector machine classification was applied to the source-localized EEG in the alpha (8–13 Hz) frequency band in order to investigate the ability of AEC and wPLI (separately and together) to discriminate i) the four states from Baseline; ii) Unconscious (“deep” unconsciousness) vs. Pre-ROC (“light” unconsciousness); and iii) responsiveness (Baseline, Light Sedation, Recovery) vs. unresponsiveness (Unconscious, Pre-ROC). AEC and wPLI yielded different patterns of global connectivity across states of consciousness, with AEC showing the strongest network connectivity during the Unconscious epoch, and wPLI showing the strongest connectivity during full consciousness (i.e., Baseline and Recovery). Both measures also demonstrated differential predictive contributions across participants and used different brain regions for classification. AEC showed higher classification accuracy overall, particularly for distinguishing anesthetic-induced unconsciousness from Baseline (83.7 ± 0.8%). AEC also showed stronger classification accuracy than wPLI when distinguishing Unconscious from Pre-ROC (i.e., “deep” from “light” unconsciousness) (AEC: 66.3 ± 1.2%; wPLI: 56.2 ± 1.3%), and when distinguishing between responsiveness and unresponsiveness (AEC: 76.0 ± 1.3%; wPLI: 63.6 ± 1.8%). Classification accuracy was not improved compared to AEC when both AEC and wPLI were combined. This analysis of source-localized EEG data demonstrates that envelope- and phase-based FC provide different information about states of consciousness but that, on a group level, AEC is better able to detect relative alterations in brain FC across levels of anesthetic-induced unconsciousness compared to wPLI.

Functional Connectivity Alterations Based on the Weighted Phase Lag Index: An Exploratory Electroencephalography Study on Alzheimer's Disease.

Numerous electroencephalography (EEG) studies focus on the alteration of electrical activity in patients with Alzheimer's Disease (AD), but there are no consistent results especially regarding functional connectivity. We supposed that the weighted Phase Lag Index (w- PLI), as phase-based measures of functional connectivity, may be used as an auxiliary diagnostic method for AD. We enrolled 30 patients with AD, 30 patients with Mild Cognitive Impairment (MCI), and 30 Healthy Controls (HC). EEGs were recorded in all participants at baseline during relaxed wakefulness. Following EEG preprocessing, Power Spectral Density (PSD) and wPLI parameters were determined to further analyze whether they were correlated to cognitive scores. In the patients with AD, the increased PSD in theta band was presented compared with MCI and HC groups, which was associated with disturbances of the directional, computational, and delayed memory capacity. Furthermore, the wPLI revealed a distinctly lower connection strength between frontal and distant areas in the delta band and a higher connection strength of the central and temporo-occipital region in the theta band for AD patients. Moreover,we found a significant negative correlation between theta functional connectivity and cognitive scores. Increased theta PSD and decreased delta wPLI may be one of the earliest changes in AD and associated with disease severity. The parameter wPLI is a novel measurement of phase synchronization and has potentials in understanding underlying functional connectivity and aiding in the diagnostics of AD.

Multi-Sessions Outcome for EEG Feature Extraction and Classification Methods in a Motor Imagery Task

The purpose of this research is to evaluate the performances of some features extraction methods and classification algorithms for the electroencephalographic (EEG) signals recorded in a motor task imagery paradigm. The sessions were performed by the same subject in eight consecutive years. Modeling the EEG signal as an autoregressive process (by means of Itakura distance and symmetric Itakura distance), amplitude modulation (using the amplitude modulation energy index) and phase synchronization (measuring phase locking value, phase lag index and weighted phase lag index) are the methods used for getting the appropriate information. The extracted features are classified using linear discriminant analysis, quadratic discriminant analysis, Mahalanobis distance, support vector machine and k nearest neighbor classifiers. The highest classifications rates are achieved when Itakura distance with Mahalanobis distance based classifier are applied. The outcomes of this research may improve the design of assistive devices for restoration of movement and communication strength for physically disabled patients in order to rehabilitate their lost motor abilities and to improve the quality of their daily life.

Measures of resting state EEG rhythms for clinical trials in Alzheimer's disease: Recommendations of an expert panel.

The Electrophysiology Professional Interest Area (EPIA) and Global Brain Consortium endorsed recommendations on candidate electroencephalography (EEG) measures for Alzheimer's disease (AD) clinical trials. The Panel reviewed the field literature. As most consistent findings, AD patients with mild cognitive impairment and dementia showed abnormalities in peak frequency, power, and "interrelatedness" at posterior alpha (8-12Hz) and widespread delta (<4Hz) and theta (4-8Hz) rhythms in relation to disease progression and interventions. The following consensus statements were subscribed: (1) Standardization of instructions to patients, resting state EEG (rsEEG) recording methods, and selection of artifact-free rsEEG periods are needed; (2) power density and "interrelatedness" rsEEG measures (e.g., directed transfer function, phase lag index, linear lagged connectivity, etc.) at delta, theta, and alpha frequency bands may be use for stratification of AD patients and monitoring of disease progression and intervention; and (3) international multisectoral initiatives are mandatory for regulatory purposes.

Effects of transcranial static magnetic stimulation over the primary motor cortex on local and network spontaneous electroencephalogram oscillations

Transcranial static magnetic stimulation (tSMS) is a novel non-invasive brain stimulation technique that reduces cortical excitability at the stimulation site. We investigated the effects of tSMS over the left primary motor cortex (M1) for 20 min on the local electroencephalogram (EEG) power spectrum and interregional EEG coupling. Twelve right-handed healthy subjects participated in this crossover, double-blind, sham-controlled study. Resting-state EEG data were recorded for 3 min before the intervention and 17 min after the beginning of the intervention. The power spectrum at the left central electrode (C3) and the weighted phase lag index (wPLI) between C3 and the other electrodes was calculated for theta (4–8 Hz), alpha (8–12 Hz), and beta (12–30 Hz) frequencies. The tSMS significantly increased theta power at C3 and the functional coupling in the theta band between C3 and the parietal midline electrodes. The tSMS over the left M1 for 20 min exhibited modulatory effects on local cortical activity and interregional functional coupling in the theta band. The neural oscillations in the theta band may have an important role in the neurophysiological effects induced by tSMS over the frontal cortex.

Hardware architecture for real-time EEG-based functional brain connectivity parameter extraction

Objective. Design a novel architecture for real-time quantitative characterization of functional brain connectivity (FC) networks derived from wearable electroencephalogram (EEG). Approach. We performed an algorithm to architecture mapping for the calculation of phase lag index to form the functional connectivity networks and the extraction of a set of graph-theoretic parameters to quantitatively characterize these networks. This mapping was optimized using approximations in the mathematical definitions of the algorithms which reduce its computational complexity and produce a more hardware amenable implementation. Main results. The architecture was developed for a 19-channel EEG system. The system can calculate all the functional connectivity parameters in a total time of 131 µs, utilizes 71% of the total logic resources in the FPGA, and shows 51.84 mW dynamic power consumption at 22.16 MHz operation frequency when implemented in a Stratix IV EP4SGX230K FPGA. Our analysis also showed that the system occupies an area equivalent to approximately 937 K 2-input NAND gates, with an estimated power consumption of 39.3 mW at 0.9 V supply using a 90 nm CMOS application specific integrated circuit technology. Significance. The proposed architecture can calculate the FC and extract the graph-theoretic parameters in real-time with low power consumption. This characteristic makes the architecture ideal for applications such as a wearable closed-loop neurofeedback systems, where constant monitoring of the brain activity and fast processing of EEG is necessary to control the appropriate feedback.

Relation between the phase-lag index and lagged coherence for assessing interactions in EEG and MEG data

Over the last two decades, a large number of estimators have been proposed to assess brain connectivity from electroencephalography (EEG) and magnetoencephalography (MEG) data. The statistical theory underlying these estimators, however, is relatively underdeveloped. In particular, the theoretical relationships between different estimators were unknown until very recently. In a recent preprint, Nolte et al. derived formulas for such relationships under the assumption that the data are Gaussian. One of the results was that the phase-lag index and the lagged coherence concentrate around identical values for large sample sizes, i.e. have identical asymptotic limits. A proof of this statement, however, was only sketched, the model assumptions were not checked in experimental data, and sampling properties of the estimators were not considered. We derive the probability density of the relative-phase in the Gaussian model and use it to provide an alternative proof of the asymptotic equality of the phase-lag index and the lagged coherence. The proof is based on power series expansions of the Fourier coefficients of the phase-lag index and the lagged coherence, which are demonstrated to be hypergeometric functions. We also assess the sampling properties of the phase-lag index and the lagged coherence through numerical simulations from the Gaussian model. These demonstrate that throughout the entire parameter space of the model and for all sample sizes, the standard error of the phase-lag index is higher than that of the lagged coherence and thus establish that the lagged coherence is a uniformly better estimator than the phase-lag index. We use experimental EEG and MEG data to verify to what extent the Gaussian assumption is appropriate. Deviations from normality were observed precisely at frequencies for which EEG/MEG power spectra had local maxima, i.e. at oscillatory resonances. Depending on the data-set, the resonances were located in the delta, alpha, and beta frequency bands and correspond to the respective brain rhythms. Based on these observations, we propose to model EEG/MEG data with exponential power densities, which include the Gaussian and Laplace densities as special cases. Lastly, we demonstrate that the asymptotic equality of the phase-lag index and the lagged coherence, as well as the large relative standard errors of the phase lag index, also hold in experimental EEG/MEG data. This establishes that the lagged coherence is not only a better estimator for Gaussian data, but for experimental EEG/MEG data as well.

Brain network motifs are markers of loss and recovery of consciousness

Motifs are patterns of inter-connections between nodes of a network, and have been investigated as building blocks of directed networks. This study explored the re-organization of 3-node motifs during loss and recovery of consciousness. Nine healthy subjects underwent a 3-h anesthetic protocol while 128-channel electroencephalography (EEG) was recorded. In the alpha (8–13 Hz) band, 5-min epochs of EEG were extracted for: Baseline; Induction; Unconscious; 30-, 10- and 5-min pre-recovery of responsiveness; 30- and 180-min post-recovery of responsiveness. We constructed a functional brain network using the weighted and directed phase lag index, on which we calculated the frequency and topology of 3-node motifs. Three motifs (motifs 1, 2 and 5) were significantly present across participants and epochs, when compared to random networks (p < 0.05). The topology of motifs 1 and 5 changed significantly between responsive and unresponsive epochs (p-values < 0.01; Kendall’s W = 0.664 (motif 1) and 0.529 (motif 5)). Motif 1 was constituted of long-range chain-like connections, while motif 5 was constituted of short-range, loop-like connections. Our results suggest that anesthetic-induced unconsciousness is associated with a topological re-organization of network motifs. As motif topological re-organization may precede (motif 5) or accompany (motif 1) the return of responsiveness, motifs could contribute to the understanding of the neural correlates of consciousness.

Lack of redundancy between electrophysiological measures of long-range neuronal communication

BackgroundCommunication between brain areas has been implicated in a wide range of cognitive and emotive functions and is impaired in numerous mental disorders. In rodent models, various metrics have been used to quantify inter-regional neuronal communication. However, in individual studies, typically, only very few measures of coupling are reported and, hence, redundancy across such indicators is implicitly assumed.ResultsIn order to test this assumption, we here comparatively assessed a broad range of directional and non-directional metrics like coherence, Weighted Phase Lag Index (wPLI), phase-locking value (PLV), pairwise phase consistency (PPC), parametric and non-parametric Granger causality (GC), partial directed coherence (PDC), directed transfer function (DTF), spike-phase coupling (SPC), cross-regional phase-amplitude coupling, amplitude cross-correlations and others. We applied these analyses to simultaneous field recordings from the prefrontal cortex and the ventral and dorsal hippocampus in the schizophrenia-related Gria1-knockout mouse model which displays a robust novelty-induced hyperconnectivity phenotype. Using the detectability of coupling deficits in Gria1−/− mice and bivariate correlations within animals as criteria, we found that across such measures, there is a considerable lack of functional redundancy. Except for three pairwise correlations—PLV with PPC, PDC with DTF and parametric with non-parametric Granger causality—almost none of the analysed metrics consistently co-varied with any of the other measures across the three connections and two genotypes analysed. Notable exceptions to this were the correlation of coherence with PPC and PLV that was found in most cases, and partial correspondence between these three measures and Granger causality. Perhaps most surprisingly, partial directed coherence and Granger causality—sometimes regarded as equivalent measures of directed influence—diverged profoundly. Also, amplitude cross-correlation, spike-phase coupling and theta-gamma phase-amplitude coupling each yielded distinct results compared to all other metrics.ConclusionsOur analysis highlights the difficulty of quantifying real correlates of inter-regional information transfer, underscores the need to assess multiple coupling measures and provides some guidelines which metrics to choose for a comprehensive, yet non-redundant characterization of functional connectivity.

Reliance on Visual Input for Balance Skill Transfer in Older Adults: EEG Connectome Analysis Using Minimal Spanning Tree.

Skill transfer from trained balance exercises is critical to reduce the rate of falls in older adults, who rely more on vision to control postural responses due to age-dependent sensory reweighting. With an electroencephalography (EEG) minimum spanning tree (MST) structure, the purpose of this study was to compare the organization of supraspinal neural networks of transfer effect after postural training using full and intermittent visual feedbacks for older adults. Thirty-two older adults were randomly assigned to the stroboscopic vision (SV) (n = 16; age = 64.7 ± 3.0 years) and control (16; 66.3 ± 2.7 years) groups for balance training on a stabilometer (target task) with on-line visual feedback. Center-of-pressure characteristics and an MST-based connectome of the weighted phase-lag index during the bilateral stance on a foam surface (transfer task) were compared before and after stabilometer training. The results showed that both the SV and control groups showed improvements in postural stability in the trained task (p < 0.001). However, unlike the control group (p = 0.030), the SV group who received intermittent visual feedback during the stabilometer training failed to reduce the size of postural sway in the anteroposterior direction of the postural transfer task (unstable stance on the foam surface) in the post-test (p = 0.694). In addition, network integration for the transfer task in the post-test was absent in the SV group (p > 0.05). For the control group in the post-test, it manifested with training-related increases in leaf fraction in beta band (p = 0.015) and maximum betweenness in alpha band (p = 0.018), but a smaller diameter in alpha (p = 0.006)/beta (p = 0.021) bands and average eccentricity in alpha band (p = 0.028). In conclusion, stabilometer training with stroboscopic vision impairs generalization of postural skill to unstable stance for older adults. Adequate visual information is a key mediating factor of supraspinal neural networks to carry over balance skill in older adults.

Revisiting Functional Connectivity for Infraslow Scale-Free Brain Dynamics Using Complex Wavelets.

The analysis of human brain functional networks is achieved by computing functional connectivity indices reflecting phase coupling and interactions between remote brain regions. In magneto- and electroencephalography, the most frequently used functional connectivity indices are constructed based on Fourier-based cross-spectral estimation applied to specific fast and band-limited oscillatory regimes. Recently, infraslow arrhythmic fluctuations (below the 1 Hz) were recognized as playing a leading role in spontaneous brain activity. The present work aims to propose to assess functional connectivity from fractal dynamics, thus extending the assessment of functional connectivity to the infraslow arrhythmic or scale-free temporal dynamics of M/EEG-quantified brain activity. Instead of being based on Fourier analysis, new Imaginary Coherence and weighted Phase Lag indices are constructed from complex-wavelet representations. Their performances are first assessed on synthetic data by means of Monte-Carlo simulations, and they are then compared favorably against the classical Fourier-based indices. These new assessments of functional connectivity indices are also applied to MEG data collected on 36 individuals both at rest and during the learning of a visual motion discrimination task. They demonstrate a higher statistical sensitivity, compared to their Fourier counterparts, in capturing significant and relevant functional interactions in the infraslow regime and modulations from rest to task. Notably, the consistent overall increase in functional connectivity assessed from fractal dynamics from rest to task correlated with a change in temporal dynamics as well as with improved performance in task completion, which suggests that the complex-wavelet weighted Phase Lag index is the sole index is able to capture brain plasticity in the infraslow scale-free regime.

Brain Functional Networks Based on Resting-State EEG Data for Major Depressive Disorder Analysis and Classification.

If the brain is regarded as a system, it will be one of the most complex systems in the universe. Traditional analysis and classification methods of major depressive disorder (MDD) based on electroencephalography (EEG) feature-levels often regard electrode as isolated node and ignore the correlation between them, so it's difficult to find alters of abnormal topological architecture in brain. To solve this problem, we propose a brain functional network framework for MDD of analysis and classification based on resting state EEG. The phase lag index (PLI) was calculated based on the 64-channel resting state EEG to construct the function connection matrix to reduce and avoid the volume conductor effect. Then binarization of brain function network based on small world index was realized. Statistical analyses were performed on different EEG frequency band and different brain regions. The results showed that significant alterations of brain synchronization occurred in frontal, temporal, parietal-occipital regions of left brain and temporal region of right brain. And average shortest path length and clustering coefficient in left central region of theta band and node betweenness centrality in right parietal-occipital region were significantly correlated with PHQ-9 score of MDD, which indicates these three network metrics may be served as potential biomarkers to effectively distinguish MDD from controls and the highest classification accuracy can reach 93.31%. Our findings also point out that the brain function network of MDD patients shows a random trend, and small world characteristics appears to weaken.

The effect of density thresholding on the EEG network construction

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.

Performance monitoring in obsessive–compulsive disorder: Insights from internal capsule/nucleus accumbens deep brain stimulation

BackgroundSymptoms of obsessive–compulsive disorder (OCD) are partly related to impaired cognitive control processes and theta modulations constitute an important electrophysiological marker for cognitive control processes such as signaling negative performance feedback in a fronto-striatal network. Deep brain stimulation (DBS) targeting the anterior limb of the internal capsule (ALIC)/nucleus accumbens (NAc) shows clinical efficacy in OCD, while the exact influence on the performance monitoring system remains largely unknown. MethodsSeventeen patients with treatment-refractory OCD performed a probabilistic reinforcement learning task. Analyses were focused on 4–8 Hz (theta) power, intertrial phase coherence (ITPC) and debiased weighted Phase-Lag Index (dwPLI) in response to negative performance feedback. Combined EEG and local field potential (LFP) recordings were obtained shortly after DBS electrode implantation to investigate fronto-striatal network modulations. To assess the impact of clinically effective DBS on negative performance feedback modulations, EEG recordings were obtained pre-surgery and at follow-up with DBS on and off. ResultsMedial frontal cortex ITPC, striatal ITPC and striato-frontal dwPLI were increased following negative performance feedback. Decreased right-lateralized dwPLI was associated with pre-surgery symptom severity. ITPC was globally decreased during DBS-off. ConclusionWe observed a theta phase coherence mediated fronto-striatal performance monitoring network. Within this network, decreased connectivity was related to increased OCD symptomatology, consistent with the idea of impaired cognitive control in OCD. While ALIC/NAc DBS decreased theta network activity globally, this effect was unrelated to clinical efficacy and performance monitoring.

Prediction of Cerebral Palsy in Newborns With Hypoxic-Ischemic Encephalopathy Using Multivariate EEG Analysis and Machine Learning

This study was carried out to investigate whether the quantitative analysis of electroencephalogram (EEG) signals of infants with hypoxic-ischemic encephalopathy (HIE) can be used for early prediction of cerebral palsy (CP). We computed sample entropy (SampEn), permutation entropy (PEn), and spectral entropy (SpEn) measures to reflect the signal’s complexity and the graph-theoretic parameters derived from weighted phase-lag index (WPLI) to measure functional brain connectivity. Both feature sets were calculated in the noise-assisted multivariate empirical mode decomposition (N-A MEMD) domain to characterize the tempo-spectral integration of information and thus provide novel insight into the physiological mechanisms of the brain. Statistical analysis results showed a general deficit in the CP signals at the alpha-band component characterized by a decrease in the complexity measures and an increase in the graph-theoretic parameters specified by the diameter feature. The proposed set of features have also been evaluated using the random under-sampling boosting (RUSBoost) classifier, which was trained and tested on the feature vectors of a cohort of 26 infants - 6 who developed CP by the age of 24 months and 20 with normal neuromotor outcome. A good performance of 84.6% classification accuracy (ACC), 83% sensitivity (SNS), 85% specificity (SPC) and 0.87 area under curve (AUC) was obtained using the entropy features extracted from the alpha-band component. A close result of 80.8% ACC, 67% SNS, 85% SPC and 0.79 AUC was also achieved using the diameter feature calculated from the same frequency range. Therefore, it was concluded that the obtained brain functions’ characteristics successfully discriminate between the two groups of infants. These characteristics could be considered potential biomarkers of cerebral cellular damage and, therefore, could be employed in practical clinical applications for early CP prediction.

Auditory Noise Leads to Increased Visual Brain-Computer Interface Performance: A Cross-Modal Study.

Noise has been proven to have a beneficial role in non-linear systems, including the human brain, based on the stochastic resonance (SR) theory. Several studies have been implemented on single-modal SR. Cross-modal SR phenomenon has been confirmed in different human sensory systems. In our study, a cross-modal SR enhanced brain–computer interface (BCI) was proposed by applying auditory noise to visual stimuli. Fast Fourier transform and canonical correlation analysis methods were used to evaluate the influence of noise, results of which indicated that a moderate amount of auditory noise could enhance periodic components in visual responses. Directed transfer function was applied to investigate the functional connectivity patterns, and the flow gain value was used to measure the degree of activation of specific brain regions in the information transmission process. The results of flow gain maps showed that moderate intensity of auditory noise activated the brain area to a greater extent. Further analysis by weighted phase-lag index (wPLI) revealed that the phase synchronization between visual and auditory regions under auditory noise was significantly enhanced. Our study confirms the existence of cross-modal SR between visual and auditory regions and achieves a higher accuracy for recognition, along with shorter time window length. Such findings can be used to improve the performance of visual BCIs to a certain extent.

Electroencephalographic functional connectivity is altered in persons with Down syndrome and Alzheimer's disease.

Persons with Down syndrome (DS) are at increased risk of developing Alzheimer's dementia (DS-AD). Due to heterogeneity in the functioning in persons with DS, it is difficult to use cognitive testing to assess whether a person with DS has developed dementia due to AD. Electroencephalography (EEG) functional connectivity has shown promising results as a diagnostic tool for AD in persons without DS. In the current exploratory study, we investigated whether EEG functional connectivity could be used as a diagnostic marker of AD in persons with DS and the association with symptoms. Electroencephalography from 12 persons with DS and 16 persons with DS-AD were analysed, and both coherence and weighted phase lag index were calculated. In addition, we calculated the average coherence for fronto-parietal and temporo-parietal connections. Lastly, we investigated the correlation between the informant-based Dementia Screening Questionnaire in Intellectual Disability (DSQIID) and total alpha coherence. Decreased alpha and increased delta coherence and weighted phase lag index were observed in DS-AD as compared with DS. The decrease in alpha coherence was more marked in the fronto-parietal connections as compared with the temporo-parietal connections. No significant correlation was found between DSQIID and total alpha coherence (P value=0.095, rho=-0.335). The decreased alpha coherence and weighted phase lag index have previously been found in AD. The increased delta coherence and weighted phase lag index may indicate a different initial neurophysiological presentation as compared with patients with AD or may be a sign of more advanced disease. Larger studies are needed to confirm the current findings.

Test-Retest Reliability of Magnetoencephalography Resting-State Functional Connectivity in Schizophrenia.

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.