Statistical method for detection of phase-locking episodes in neural oscillations

Asymmetries of the subthalamic activity in Parkinson's disease: phase-amplitude coupling among local field potentials

The role of brain asymmetries of dopaminergic neurons in motor symptoms of Parkinson's disease is still undefined. Local field recordings from the subthalamic nucleus revealed some neurophysiological biomarkers of the disease: increased beta activity, increased low-frequency activity and high-frequency oscillations. Phase-amplitude coupling coordinates the timing of neuronal activity and allows determining the mechanism for communication within distinct regions of the brain. In this study, we discuss the use of phase-amplitude coupling to assess the differences between the two hemispheres in a cohort of 24 patients with Parkinson's disease before and after levodopa administration. Subthalamic low- (12-20 Hz) and high-beta (20-30 Hz) oscillations were compared with low- (30-45 Hz), medium- (70-100 Hz) and high-frequency (260-360 Hz) bands. We found a significant beta-phase-amplitude coupling asymmetry between left and right and an opposite-side-dependent effect of the pharmacological treatment, which is associated with the reduction of motor symptoms. In particular, high coupling between high frequencies and high-beta oscillations was found during the OFF condition (P < 0.01) and a low coupling during the ON state (P < 0.0001) when the right subthalamus was assessed; exactly the opposite happened when the left subthalamus was considered in the analysis, showing a lower coupling between high frequencies and high-beta oscillations during the OFF condition (P < 0.01), followed by a higher one during the ON state (P < 0.01). Interestingly, these asymmetries are independent of the motor onset side, either left or right. These findings have important implications for neural signals that may be used to trigger adaptive deep brain stimulation in Parkinson's and could provide more exhaustive insights into subthalamic dynamics.

Adolescents rats engage the orbitofrontal-striatal pathway differently than adults during impulsive actions

Adolescence is characterized by increased impulsive and risk-taking behaviors. To better understand the neural networks that subserves impulsivity in adolescents, we used a reward-guided behavioral model that quantifies age differences in impulsive actions in adult and adolescent rats of both sexes. Using chemogenetics, we identified orbitofrontal cortex (OFC) projections to the dorsomedial striatum (DMS) as a critical pathway for age-related execution of impulsive actions. Simultaneous recording of single units and local field potentials in the OFC and DMS during task performance revealed an overall muted response in adolescents during impulsive actions as well as age-specific differences in theta power and OFC-DMS functional connectivity. Collectively, these data reveal that the OFC-DMS pathway is critical for age-differences in reward-guided impulsive actions and provide a network mechanism to enhance our understanding of how adolescent and adult brains coordinate behavioral inhibition.

Changes in normalized mutual information in response to strength training: An ancillary analysis of a quasi-randomized controlled trial

The aim of the present investigation was twofold. (1) to assess test-retest reliability of normalized mutual information (NMI) values extracted from the surface electromyography (sEMG) signal of muscles pairs of the upper body during dynamic bench press at a high load, and (2) to assess changes in NMI values from before to after a five-week quasi-randomized controlled bench press training intervention. For test-retest reliability, 20 strength trained males (age 25 ± 2 years, height 1.81 ± 0.07 m) performed two three-repetition maximum (3RM) tests in bench press, while sEMG was recorded from six upper body muscles. Tests were separated by 8.2 ± 2.9 days. For the training intervention, 17 male participants (age 26 ± 5 years, height 1.80 ± 0.07 m) trained bench press specific strength training for 5 weeks (TRA), while 13 male participants (age 23 ± 3 years, height 1.80 ± 0.08 m) constituted a control group (CON). 3RM bench press test and sEMG recordings were carried out before and after the intervention period. The NMI values ranged from poor to almost perfect reliability, with the majority displaying substantial reliability. TRA displayed a significant decrease in NMI values during the concentric phase for two agonist-agonist muscle pairs, while one agonist-agonist and two agonist-antagonist muscle pairs increased the NMI values during the eccentric phase. The observed changes did not exceed the minimal detectable threshold, and we therefore cannot surely ascertain that the changes observed in NMI values reflect genuine neural adaptations.

Deep learning-based sleep stage classification with cardiorespiratory and body movement activities in individuals with suspected sleep disorders

Deep learning methods have gained significant attention in sleep science. This study aimed to assess the performance of a deep learning-based sleep stage classification model constructed using fewer physiological parameters derived from cardiorespiratory and body movement data. Overnight polysomnography (PSG) data from 123 participants (age: 19-82 years) with suspected sleep disorders were analyzed. Multivariate time series data, including heart rate, respiratory rate, cardiorespiratory coupling, and body movement frequency, were input into a bidirectional long short-term memory (biLSTM) network model to train and predict five-class sleep stages. The trained model's performance was evaluated using balanced accuracy, Cohen's κ coefficient, and F1 scores on an epoch-per-epoch basis and compared with the ground truth using the leave-one-out cross-validation scheme. The model achieved an accuracy of 71.2 ± 5.8%, Cohen's κ of 0.425 ± 0.115, and an F1 score of 0.650 ± 0.083 across all sleep stages, and all metrics were negatively correlated with the apnea-hypopnea index, as well as age, but positively correlated with sleep efficiency. Moreover, the model performance varied for each sleep stage, with the highest F1 score observed for N2 and the lowest for N3. Regression and Bland-Altman analyses between sleep parameters of interest derived from deep learning and PSG showed substantial correlations (r = 0.33-0.60) with low bias. The findings demonstrate the efficacy of the biLSTM deep learning model in accurately classifying sleep stages and in estimating sleep parameters for sleep structure analysis using a reduced set of physiological parameters. The current model without using EEG information may expand the application of unobtrusive in-home monitoring to clinically assess the prevalence of sleep disorders outside of a sleep laboratory.

EEG-based functional connectivity for tactile roughness discrimination

Tactile sensation and perception involve cooperation between different parts of the brain. Roughness discrimination is an important phase of texture recognition. In this study, we investigated how different roughness levels would influence the brain network characteristics. We recorded EEG signals from nine right-handed healthy subjects who underwent touching three surfaces with different levels of roughness. The experiment was separately repeated in 108 trials for each hand for both static and dynamic touch. For estimation of the functional connectivity between brain regions, the phase lag index method was employed. Frequency-specific connectivity patterns were observed in the ipsilateral and contralateral hemispheres to the hand of interest, for delta, theta, alpha, and beta frequency bands under the study. A number of connections were identified to be in charge of discrimination between surfaces in both alpha and beta frequency bands for the left hand in static touch and for the right hand in dynamic touch. In addition, common connections were determined in both hands for all three roughness in alpha band for static touch and in theta band for dynamic touch. The common connections were identified for the smooth surface in beta band for static touch and in delta and alpha bands for dynamic touch. As observed for static touch in alpha band and for dynamic touch in theta band, the number of common connections between the two hands was decreased by increasing the surface roughness. The results of this research would extend the current knowledge about tactile information processing in the brain.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09876-1.

The Effect of the Peristimulus α Phase on Visual Perception through Real-Time Phase-Locked Stimulus Presentation

The α phase has been theorized to reflect fluctuations in cortical excitability and thereby impose a cyclic influence on visual perception. Despite its appeal, this notion is not fully substantiated, as both supporting and opposing evidence has been recently reported. In contrast to previous research, this study examined the effect of the peristimulus instead of prestimulus phase on visual detection through a real-time phase-locked stimulus presentation (PLSP) approach. Specifically, we monitored phase data from magnetoencephalography (MEG) recordings over time, with a newly developed algorithm based on adaptive Kalman filtering (AKF). This information guided online presentations of masked stimuli that were phased-locked to different stages of the α cycle while healthy humans concurrently performed detection tasks. Behavioral evidence showed that the overall detection rate did not significantly vary according to the four predetermined peristimulus α phases. Nevertheless, the follow-up analyses highlighted that the phase at 90° relative to 180° likely enhanced detection. Corroborating neural parietal activity showed that early interaction between α phases and incoming stimuli orchestrated the neural representation of the hits and misses of the stimuli. This neural representation varied according to the phase and in turn shaped the behavioral outcomes. In addition to directly investigating to what extent fluctuations in perception can be ascribed to the α phases, this study suggests that phase-dependent perception is not as robust as previously presumed, and might also depend on how the stimuli are differentially processed as a result of a stimulus-phase interaction, in addition to reflecting alternations of the perceptual states between phases.

Oscillations in the dentate gyrus as a tool for the performance of the hippocampal functions: Healthy and epileptic brain

The dentate gyrus (DG) is part of the hippocampal formation and is essential for important cognitive processes such as navigation and memory. The oscillatory activity of the DG network is believed to play a critical role in cognition. DG circuits generate theta, beta, and gamma rhythms, which participate in the specific information processing performed by DG neurons. In the temporal lobe epilepsy (TLE), cognitive abilities are impaired, which may be due to drastic alterations in the DG structure and network activity during epileptogenesis. The theta rhythm and theta coherence are especially vulnerable in dentate circuits; disturbances in DG theta oscillations and their coherence may be responsible for general cognitive impairments observed during epileptogenesis. Some researchers suggested that the vulnerability of DG mossy cells is a key factor in the genesis of TLE, but others did not support this hypothesis. The aim of the review is not only to present the current state of the art in this field of research but to help pave the way for future investigations by highlighting the gaps in our knowledge to completely appreciate the role of DG rhythms in brain functions. Disturbances in oscillatory activity of the DG during TLE development may be a diagnostic marker in the treatment of this disease.

Analysis of Respiratory Sinus Arrhythmia and Directed Information Flow between Brain and Body Indicate Different Management Strategies of fMRI-Related Anxiety

Background: Respiratory sinus arrhythmia (RSA) denotes decrease of cardiac beat-to-beat intervals (RRI) during inspiration and RRI increase during expiration, but an inverse pattern (termed negative RSA) was also found in healthy humans with elevated anxiety. It was detected using wave-by-wave analysis of cardiorespiratory rhythms and was considered to reflect a strategy of anxiety management involving the activation of a neural pacemaker. Results were consistent with slow breathing, but contained uncertainty at normal breathing rates (0.2–0.4 Hz). Objectives and methods: We combined wave-by-wave analysis and directed information flow analysis to obtain information on anxiety management at higher breathing rates. We analyzed cardiorespiratory rhythms and blood oxygen level-dependent (BOLD) signals from the brainstem and cortex in 10 healthy fMRI participants with elevated anxiety. Results: Three subjects with slow respiratory, RRI, and neural BOLD oscillations showed 57 ± 26% negative RSA and significant anxiety reduction by 54 ± 9%. Six participants with breathing rate of ~0.3 Hz showed 41 ± 16% negative RSA and weaker anxiety reduction. They presented significant information flow from RRI to respiration and from the middle frontal cortex to the brainstem, which may result from respiration-entrained brain oscillations, indicating another anxiety management strategy. Conclusion: The two analytical approaches applied here indicate at least two different anxiety management strategies in healthy subjects.

Spatiotemporal organisation of human sensorimotor beta burst activity

Beta oscillations in human sensorimotor cortex are hallmark signatures of healthy and pathological movement. In single trials, beta oscillations include bursts of intermittent, transient periods of high-power activity. These burst events have been linked to a range of sensory and motor processes, but their precise spatial, spectral, and temporal structure remains unclear. Specifically, a role for beta burst activity in information coding and communication suggests spatiotemporal patterns, or travelling wave activity, along specific anatomical gradients. We here show in human magnetoencephalography recordings that burst activity in sensorimotor cortex occurs in planar spatiotemporal wave-like patterns that dominate along two axes either parallel or perpendicular to the central sulcus. Moreover, we find that the two propagation directions are characterised by distinct anatomical and physiological features. Finally, our results suggest that sensorimotor beta bursts occurring before and after a movement can be distinguished by their anatomical, spectral, and spatiotemporal characteristics, indicating distinct functional roles.

multiSyncPy: A Python package for assessing multivariate coordination dynamics

In order to support the burgeoning field of research into intra- and interpersonal synchrony, we present an open-source software package: multiSyncPy. Multivariate synchrony goes beyond the bivariate case and can be useful for quantifying how groups, teams, and families coordinate their behaviors, or estimating the degree to which multiple modalities from an individual become synchronized. Our package includes state-of-the-art multivariate methods including symbolic entropy, multidimensional recurrence quantification analysis, coherence (with an additional sum-normalized modification), the cluster-phase 'Rho' metric, and a statistical test based on the Kuramoto order parameter. We also include functions for two surrogation techniques to compare the observed coordination dynamics with chance levels and a windowing function to examine time-varying coordination for most of the measures. Taken together, our collation and presentation of these methods make the study of interpersonal synchronization and coordination dynamics applicable to larger, more complex and often more ecologically valid study designs. In this work, we summarize the relevant theoretical background and present illustrative practical examples, lessons learned, as well as guidance for the usage of our package - using synthetic as well as empirical data. Furthermore, we provide a discussion of our work and software and outline interesting further directions and perspectives. multiSyncPy is freely available under the LGPL license at: https://github.com/cslab-hub/multiSyncPy , and also available at the Python package index.

Advances in human intracranial electroencephalography research, guidelines and good practices

Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.

Amplitude and frequency modulation of subthalamic beta oscillations jointly encode the dopaminergic state in Parkinson's disease

Brain states in health and disease are classically defined by the power or the spontaneous amplitude modulation (AM) of neuronal oscillations in specific frequency bands. Conversely, the possible role of the spontaneous frequency modulation (FM) in defining pathophysiological brain states remains unclear. As a paradigmatic example of pathophysiological resting states, here we assessed the spontaneous AM and FM dynamics of subthalamic beta oscillations recorded in patients with Parkinson's disease before and after levodopa administration. Even though AM and FM are mathematically independent, they displayed negatively correlated dynamics. First, AM decreased while FM increased with levodopa. Second, instantaneous amplitude and instantaneous frequency were negatively cross-correlated within dopaminergic states, with FM following AM by approximately one beta cycle. Third, AM and FM changes were also negatively correlated between dopaminergic states. Both the slow component of the FM and the fast component (i.e. the phase slips) increased after levodopa, but they differently contributed to the AM-FM correlations within and between states. Finally, AM and FM provided information about whether the patients were OFF vs. ON levodopa, with partial redundancy and with FM being more informative than AM. AM and FM of spontaneous beta oscillations can thus both separately and jointly encode the dopaminergic state in patients with Parkinson's disease. These results suggest that resting brain states are defined not only by AM dynamics but also, and possibly more prominently, by FM dynamics of neuronal oscillations.

Modulation of Neuronal Activity and Saccades at Theta Rhythm During Visual Search in Non-human Primates

Active exploratory behaviors have often been associated with theta oscillations in rodents, while theta oscillations during active exploration in non-human primates are still not well understood. We recorded neural activities in the frontal eye field (FEF) and V4 simultaneously when monkeys performed a free-gaze visual search task. Saccades were strongly phase-locked to theta oscillations of V4 and FEF local field potentials, and the phase-locking was dependent on saccade direction. The spiking probability of V4 and FEF units was significantly modulated by the theta phase in addition to the time-locked modulation associated with the evoked response. V4 and FEF units showed significantly stronger responses following saccades initiated at their preferred phases. Granger causality and ridge regression analysis showed modulatory effects of theta oscillations on saccade timing. Together, our study suggests phase-locking of saccades to the theta modulation of neural activity in visual and oculomotor cortical areas, in addition to the theta phase locking caused by saccade-triggered responses.

Abnormal Cross Frequency Coupling of Brain Electroencephalographic Oscillations Related to Visual Oddball Task in Parkinson's Disease with Mild Cognitive Impairment

Parkinson's disease (PD) is a movement disorder caused by degeneration in dopaminergic neurons. During the disease course, most of PD patients develop mild cognitive impairment (PDMCI) and dementia, especially affecting frontal executive functions. In this study, we tested the hypothesis that PDMCI patients may be characterized by abnormal neurophysiological oscillatory mechanisms coupling frontal and posterior cortical areas during cognitive information processing. To test this hypothesis, event-related EEG oscillations (EROs) during counting visual target (rare) stimuli in an oddball task were recorded in healthy controls (HC; N = 51), cognitively unimpaired PD patients (N = 48), and PDMCI patients (N = 53). Hilbert transform served to estimate instantaneous phase and amplitude of EROs from delta to gamma frequency bands, while modulation index computed ERO phase-amplitude coupling (PAC) at electrode pairs. As compared to the HC and PD groups, the PDMCI group was characterized by (1) more posterior topography of the delta-theta PAC and (2) reversed delta-low frequency alpha PAC direction, ie, posterior-to-anterior rather than anterior-to-posterior. These results suggest that during cognitive demands, PDMCI patients are characterized by abnormal neurophysiological oscillatory mechanisms mainly led by delta frequencies underpinning functional connectivity from frontal to parietal cortical areas.

Emotional Brain Network Community Division Study Based on an Improved Immunogenetic Algorithm

Emotion analysis has emerged as one of the most prominent study areas in the field of Brain Computer Interface (BCI) due to the critical role that the human brain plays in the creation of human emotions. In this study, a Multi-objective Immunogenetic Community Division Algorithm Based on Memetic Framework (MFMICD) was suggested to study different emotions from the perspective of brain networks. To improve convergence and accuracy, MFMICD incorporates the unique immunity operator based on the traditional genetic algorithm and combines it with the taboo search algorithm. Based on this approach, we examined how the structure of people’s brain networks alters in response to different emotions using the electroencephalographic emotion database. The findings revealed that, in positive emotional states, more brain regions are engaged in emotion dominance, the information exchange between local modules is more frequent, and various emotions cause more varied patterns of brain area interactions than in negative brain states. A brief analysis of the connections between different emotions and brain regions shows that MFMICD is reliable in dividing emotional brain functional networks into communities.

Two mechanisms facilitate regional independence between brain regions based on an examination of alpha-band activity in healthy control adult males

BACKGROUND

At rest, 8 to 12 Hz alpha rhythms are the dominant rhythm in the brain, with a common peak alpha frequency (PAF = the frequency at which alpha generators show maximum power) observed across brain regions. Although a common PAF across brain regions should result in high between-region connectivity, especially connectivity measures assessing the phase-similarity between alpha generators, high inter-regional alpha connectivity has not been observed. The present study was conducted as an initial step toward identifying mechanisms that allow brain regions to maintain functional independence in the presence of a common PAF.

METHODS

MEG data were obtained from 16 healthy control male adults (mean age = 24 years; range 21 to 30 years). A task requiring participants to alternate between a 10 s eyes-closed condition and a 5 s eyes-open condition was used to drive parietal-occipital alpha generators, with the 10 s eyes-closed condition eliciting large-amplitude alpha activity and thus providing alpha measures with good signal-to-noise ratio for source localization. Alpha source-space measures were obtained using Vector-based Spatial-Temporal Analysis using L1-minimum-norm. In each participant, the four strongest parietal-occipital alpha generators were identified. Connectivity between sources was assessed via a measure of phase-based connectivity called inter-site phase clustering (ISPC).

RESULTS

Intra-class correlations (ICC) showed very high similarity in the average PAF (=computed using all eyes-closed data) between the four alpha sources (ICC single measure = 0.88, p < 0.001). Despite a common average PAF, across participants, significant ISPC was often observed no more than that expected by chance. Examination of the alpha time course data indicated that low ISPC was often due to instantaneous changes in alpha phase (phase slips). ISPC analyses removing data with phase slips indicated that low ISPC was also due to slight continuous changes in the alpha frequency, with frequency drift more likely in non-significant than significant ISPC trials.

CONCLUSIONS

The present exploratory effort suggested two processes underlying the lack of observed inter-regional alpha phase coherence that may help maintain regional functional independence even in the presence of a common PAF. In particular, although the alpha generators were observed to oscillate at the same rate on average, across time each alpha generator oscillated a little slower or faster, and about every one and a half seconds an alpha generator abruptly lost the beat. Because of this, functional independence among alpha generators (and thus brain regions) was the rule rather than the exception. Studies replicating these processes that allow brain regions to maintain functional independence, using different source localization methods and in different conditions (e.g., a true resting state), are warranted.

IMPACT STATEMENT

Using source localization to measure parietal-occipital alpha generator activity, two properties that limit between-region alpha functional connectivity are proposed. In particular, a model of alpha generator activity is offered where via transient phase slips occurring approximately every 1.5 s, as well as slight non-stationarity in the alpha frequency, brain regions retain a common alpha frequency while also maintaining regional identity and presumably functionality. Findings also suggest novel markers for use in studies examining changes in alpha activity across maturation as well as in studies examining alpha activity in patient populations where alpha abnormalities have been reported.

Theta and gamma hippocampal-neocortical oscillations during the episodic-like memory test: Impairment in epileptogenic rats

Cortical oscillations in different frequency bands have been shown to be intimately involved in exploration of environment and cognition. Here, the local field potentials in the hippocampus, the medial prefrontal cortex (mPFC), and the medial entorhinal cortex (mEC) were recorded simultaneously in rats during the execution of the episodic-like memory task. The power of theta (~4-10 Hz), slow gamma (~25-50 Hz), and fast gamma oscillations (~55-100 Hz) was analyzed in all structures examined. Particular attention was paid to the theta coherence between three mentioned structures. The modulation of the power of gamma rhythms by the phase of theta cycle during the execution of the episodic-like memory test by rats was also closely studied. Healthy rats and rats one month after kainate-induced status epilepticus (SE) were examined. Paroxysmal activity in the hippocampus (high amplitude interictal spikes), excessive excitability of animals, and the death of hippocampal and dentate granular cells in rats with kainate-evoked SE were observed, which indicated the development of seizure focus in the hippocampus (epileptogenesis). One month after SE, the rats exhibited a specific impairment of episodic memory for the what-where-when triad: unlike healthy rats, epileptogenic SE animals did not identify the objects during the test. This impairment was associated with the changes in the characteristics of theta and gamma rhythms and specific violation of theta coherence and theta/gamma coupling in these structures in comparison with the healthy animals. We believe that these disturbances in the cortical areas play a role in episodic memory dysfunction in kainate-treated animals. These findings can shed light on the mechanisms of cognitive deficit during epileptogenesis.

EEG Evidence Reveals Zolpidem-Related Alterations and Prognostic Value in Disorders of Consciousness

Effective treatment and accurate long-term prognostication of patients with disorders of consciousness (DOC) remain pivotal clinical issues and challenges in neuroscience. Previous studies have shown that zolpidem produces paradoxical recovery and induces similar change patterns in specific electrophysiological features in some DOC (∼6%). However, whether these specific features are neural markers of responders, and how neural features evolve over time remain unclear. Here, we capitalized on static and dynamic EEG analysis techniques to fully uncover zolpidem-induced alterations in eight patients with DOC and constructed machine-learning models to predict long-term outcomes at the single-subject level. We observed consistent patterns of change across all patients in several static features (e.g., decreased relative theta power and weakened alpha-band functional connectivity) after zolpidem administration, albeit none zolpidem responders. Based on the current evidence, previously published electrophysiological features are not neural markers for zolpidem responders. Moreover, we found that the temporal dynamics of the brain slowed down after zolpidem intake. Brain states before and after zolpidem administration could be completely characterized by the EEG features. Furthermore, long-term outcomes were accurately predicted using connectivity features. Our findings suggest that EEG neural signatures have huge potential to assess consciousness states and predict fine-grained outcomes. In summary, our results extend the understanding of the effects of zolpidem on the brain and open avenues for the application prospect of zolpidem and EEG in patients with DOC.

Using a Novel Functional Brain Network Approach to Locate Important Nodes for Working Memory Tasks

Working Memory (WM) is a short-term memory for processing and storing information. When investigating WM mechanisms using Electroencephalogram (EEG), its rhythmic synchronization properties inevitably become one of the focal features. To further leverage these features for better improve WM task performance, this paper uses a novel algorithm: Weight K-order propagation number (WKPN) to locate important brain nodes and their coupling characteristic in different frequency bands while subjects are proceeding French word retaining tasks, which is an intriguing but original experiment paradigm. Based on this approach, we investigated the node importance of PLV brain networks under different memory loads and found that the connectivity between frontal and parieto-occipital lobes in theta and beta frequency bands enhanced with increasing memory load. We used the node importance of the brain network as a feature vector of the SVM to classify different memory load states, and the highest classification accuracy of 95% is obtained in the beta band. Compared to the Weight degree centrality (WDC) and Weight Page Rank (WPR) algorithm, the SVM with the node importance of the brain network as the feature vector calculated by the WKPN algorithm has higher classification accuracy and shorter running time. It is concluded that the algorithm can effectively spot active central hubs so that researchers can later put more energy to study these areas where active hubs lie in such as placing Transcranial alternating current stimulation (tACS).

EEG phase-amplitude coupling to stratify encephalopathy severity in the developing brain

BACKGROUND

Neonatal hypoxic ischemic encephalopathy (HIE) is difficult to classify within the narrow therapeutic window of hypothermia. Neurophysiological biomarkers are needed for timely differentiation of encephalopathy severity within the short therapeutic window for initiation of hypothermia therapy.

METHODS

A novel analysis of mean Phase Amplitude Coupling index, PAC_m, of amplitudes high frequencies (12-30 Hz) coupled with phases of low (1,2 Hz) frequencies was calculated from the 6 h EEG recorded during the first day of life. PAC_m values were compared to identify differences between mild versus higher-grade HIE, respectively, for each of the EEG electrodes. A receiver operating characteristic curve was generated to examine the performance of PAC_m.

RESULTS

38 newborns with different HIE grades were enrolled in the first 6 h of life. Threshold PAC_m 0.001 at Fz, O1, O2, P3, and P4 had AUC >0.9 to differentiate HIE severity and predict the persistence of moderate to severe encephalopathy that requires treatment with hypothermia.

CONCLUSION

PAC is a promising biomarker to identify mild from higher severity of HIE after birth.

Long-Range Respiratory and Theta Oscillation Networks Depend on Spatial Sensory Context

Neural oscillations can couple networks of brain regions, especially at lower frequencies. The nasal respiratory rhythm, which elicits robust olfactory bulb oscillations, has been linked to episodic memory, locomotion, and exploration, along with widespread oscillatory coherence. The piriform cortex is implicated in propagating the olfactory-bulb-driven respiratory rhythm, but this has not been tested explicitly in the context of both hippocampal theta and nasal respiratory rhythm during exploratory behaviors. We investigated systemwide interactions during foraging behavior, which engages respiratory and theta rhythms. Local field potentials from the olfactory bulb, piriform cortex, dentate gyrus, and CA1 of hippocampus, primary visual cortex, and nasal respiration were recorded simultaneously from male rats. We compared interactions among these areas while rats foraged using either visual or olfactory spatial cues. We found high coherence during foraging compared with home cage activity in two frequency bands that matched slow and fast respiratory rates. Piriform cortex and hippocampus maintained strong coupling at theta frequency during periods of slow respiration, whereas other pairs showed coupling only at the fast respiratory frequency. Directional analysis shows that the modality of spatial cues was matched to larger influences in the network by the respective primary sensory area. Respiratory and theta rhythms also coupled to faster oscillations in primary sensory and hippocampal areas. These data provide the first evidence of widespread interactions among nasal respiration, olfactory bulb, piriform cortex, and hippocampus in awake freely moving rats, and support the piriform cortex as an integrator of respiratory and theta activity.SIGNIFICANCE STATEMENT Recent studies have shown widespread interactions between the nasally driven respiratory rhythm and neural oscillations in hippocampus and neocortex. With this study, we address how the respiratory rhythm interacts with ongoing slow brain rhythms across olfactory, hippocampal, and visual systems in freely moving rats. Patterns of network connectivity change with behavioral state, with stronger interactions at fast and slow respiratory frequencies during foraging as compared with home cage activity. Routing of interactions between sensory cortices depends on the modality of spatial cues present during foraging. Functional connectivity and cross-frequency coupling analyses suggest strong bidirectional interactions between olfactory and hippocampal systems related to respiration and point to the piriform cortex as a key area for mediating respiratory and theta rhythms.

Phase- targeted stimulation modulates phase-amplitude coupling in the motor cortex of the human brain

BACKGROUND

Phase-amplitude coupling (PAC) in which the amplitude of a faster field potential oscillation is coupled to the phase of a slower rhythm, is one of the most well-studied interactions between oscillations at different frequency bands. In a healthy brain, PAC accompanies cognitive functions such as learning and memory, and changes in PAC have been associated with neurological diseases including Parkinson's disease (PD), schizophrenia, obsessive-compulsive disorder, Alzheimer's disease, and epilepsy.

OBJECTIVE

/Hypothesis: In PD, normalization of PAC in the motor cortex has been reported in the context of effective treatments such as dopamine replacement therapy and deep brain stimulation (DBS), but the possibility of normalizing PAC through intervention at the cortex has not been shown in humans. Phase-targeted stimulation (PDS) has a strong potential to modulate PAC levels and potentially normalize it.

METHODS

We applied stimulation pulses triggered by specific phases of the beta oscillations, the low frequency oscillations that define phase of gamma amplitude in beta-gamma PAC, to the motor cortex of seven PD patients at rest during DBS lead placement surgery We measured the effect on PAC modulation in the motor cortex relative to stimulation-free periods.

RESULTS

We describe a system for phase-targeted stimulation locked to specific phases of a continuously updated slow local field potential oscillation (in this case, beta band oscillations) prediction. Stimulation locked to the phase of the peak of beta oscillations increased beta-gamma coupling both during and after stimulation in the motor cortex, and the opposite phase (trough) stimulation reduced the magnitude of coupling after stimulation.

CONCLUSION

These results demonstrate the capacity of cortical phase-targeted stimulation to modulate PAC without evoking motor activation, which could allow applications in the treatment of neurological disorders associated with abnormal PAC, such as PD.

The diagnosis of amnestic mild cognitive impairment by combining the characteristics of brain functional network and support vector machine classifier

BACKGROUND

Amnestic mild cognitive impairment (aMCI) is an essential stage of early detection and potential intervention for Alzheimer's disease (AD). Patients with aMCI exhibit partially abnormal functional brain connectivity and it is suggested that these features may represent a new diagnostic marker of early AD.

NEW METHOD

In this paper, we constructed two brain network models, a phase synchronization index (PSI) undirected network and a directed transfer function (DTF) directed network, to evaluate the cognitive function in patients with aMCI. We then built SVM classification models using the network clustering coefficient, global efficiency and average node degree as features to distinguish between aMCI patients and controls.

RESULTS

Our results reveal a classification accuracy and AUC of 66.6 ± 1.7% and 0.7475 and 80.0 ± 2.2% and 0.7825, respectively, for the two network models (PSI and DTF). As the directed network model performed better than the undirected model, we introduced an improved graph theory feature, efficiency density, which resulted in an increased classification accuracy and AUC value 86.6 ± 2.6% and 0.8295, respectively.

COMPARISON WITH EXISTING METHODS

The analysis of network models and the directionality of information flow is suitable for analysis of nonlinear EEG signals for assessment of the functional state of the brain. Compared with traditional network features, our proposed improved features more comprehensively evaluate transmission efficiency and density of the brain.

CONCLUSION

In this study, we demonstrate that an improved efficiency density feature is helpful for enhancing classification the accuracy of aMCI. Moreover, directed brain network models exhibit better classification for aMCI diagnosis than undirected networks.

Influence of inter-electrode distance on subthalamic nucleus local field potential recordings in Parkinson's disease

OBJECTIVES

To evaluate spectra and their correlations with clinical symptoms of local field potentials (LFP) acquired from wide- and close-spaced contacts (i.e. between contacts 0-3 or LFP03, and contacts 1-2 or LFP12 respectively) on the same DBS electrode within the subthalamus (STN) in Parkinson's disease (PD), before and after levodopa administration.

METHODS

LFP12 and LFP03 were recorded from 20 PD patients. We evaluated oscillatory power, local and switched phase-amplitude coupling (l- and Sw-PAC) and correlation with motor symptoms (UPDRSIII).

RESULTS

Before levodopa, both LFP03 and LFP12 power in the α band inversely correlated with UPDRSIII. Differences between contacts were found in the low-frequency bands power. After levodopa, differences in UPDRSIII were associated to changes in LFP03 low-β and LFP12 HFO (high frequency oscillations, 250-350 Hz) power, while a modulation of the low-β power and an increased β-LFO (low frequency oscillations, 15-45 Hz) PAC was found only for LFP12.

CONCLUSION

This study reveals differences in spectral pattern between LFP12 and LFP03 before and after levodopa administration, as well as different correlations with PD motor symptoms.

SIGNIFICANCE

Differences between LFP12 and LFP03 may offer an opportunity for optimizing adaptive deep brain stimulation (aDBS) protocols for PD. LFP12 can be used to detect β-HFO coupling and β power (i.e. bradykinesia), while LFP03 are optimal for low frequency oscillations (dyskinesias).

Phase-locking intermittency induced by dynamical heterogeneity in networks of thermosensitive neurons

In this work, we study the phase synchronization of a neural network and explore how the heterogeneity in the neurons' dynamics can lead their phases to intermittently phase-lock and unlock. The neurons are connected through chemical excitatory connections in a sparse random topology, feel no noise or external inputs, and have identical parameters except for different in-degrees. They follow a modification of the Hodgkin-Huxley model, which adds details like temperature dependence, and can burst either periodically or chaotically when uncoupled. Coupling makes them chaotic in all cases but each individual mode leads to different transitions to phase synchronization in the networks due to increasing synaptic strength. In almost all cases, neurons' inter-burst intervals differ among themselves, which indicates their dynamical heterogeneity and leads to their intermittent phase-locking. We argue then that this behavior occurs here because of their chaotic dynamics and their differing initial conditions. We also investigate how this intermittency affects the formation of clusters of neurons in the network and show that the clusters' compositions change at a rate following the degree of intermittency. Finally, we discuss how these results relate to studies in the neuroscience literature, especially regarding metastability.

Windowed multiscale synchrony: modeling time-varying and scale-localized interpersonal coordination dynamics

Social interactions are pervasive in human life with varying forms of interpersonal coordination emerging and spanning different modalities (e.g. behaviors, speech/language, and neurophysiology). However, during social interactions, as in any dynamical system, patterns of coordination form and dissipate at different scales. Historically, researchers have used aggregate measures to capture coordination over time. While those measures (e.g. mean relative phase, cross-correlation, coherence) have provided a wealth of information about coordination in social settings, some evidence suggests that multiscale coordination may change over the time course of a typical empirical observation. To address this gap, we demonstrate an underutilized method, windowed multiscale synchrony, that moves beyond quantifying aggregate measures of coordination by focusing on how the relative strength of coordination changes over time and the scales that comprise social interaction. This method involves using a wavelet transform to decompose time series into component frequencies (i.e. scales), preserving temporal information and then quantifying phase synchronization at each of these scales. We apply this method to both simulated and empirical interpersonal physiological and neuromechanical data. We anticipate that demonstrating this method will stimulate new insights on the mechanisms and functions of synchrony in interpersonal contexts using neurophysiological and behavioral measures.

Processing of Affective Pictures: A Study Based on Functional Connectivity Network in the Cerebral Cortex

Emotion plays an important role in people's life. However, the existing researches do not give a unified conclusion on the brain function network under different emotional states. In this study, pictures from the international affective picture system (IAPS) of different valences were presented to subjects with a fixed frequency blinking frequency to induce stable state visual evoked potential (SSVEP). With the source location method, the cerebral cortex source signal was reconstructed based on EEG signals, and then the difference in SSVEP amplitudes in key brain areas under different emotional states and the difference in brain function network connections among different brain areas were analysed in cortical space. The results of the study show that positive and negative emotions evoked greater activation intensities in the prefrontal, temporal, and parietal lobes compared with those of neutral emotion. The network connections with a significant difference between emotional states mainly appear in the alpha and gamma bands, and the network connections with significant differences between positive emotion and negative emotion mainly exist in the right middle temporal gyrus and the superior frontal gyrus on both sides. In addition, the long-range connections play an important role in the process of emotional processing, especially the connections among frontal gyrus, middle temporal gyrus, and middle occipital gyrus. The results of this study provide a reliable scientific basis for revealing and elucidating the neural mechanism of emotion processing and the selection of brain regions and frequency bands in emotion recognition based on EEG signals.

From Univariate to Multivariate Coupling Between Continuous Signals and Point Processes: A Mathematical Framework

Time series data sets often contain heterogeneous signals, composed of both continuously changing quantities and discretely occurring events. The coupling between these measurements may provide insights into key underlying mechanisms of the systems under study. To better extract this information, we investigate the asymptotic statistical properties of coupling measures between continuous signals and point processes. We first introduce martingale stochastic integration theory as a mathematical model for a family of statistical quantities that include the phase locking value, a classical coupling measure to characterize complex dynamics. Based on the martingale central limit theorem, we can then derive the asymptotic gaussian distribution of estimates of such coupling measure that can be exploited for statistical testing. Second, based on multivariate extensions of this result and random matrix theory, we establish a principled way to analyze the low-rank coupling between a large number of point processes and continuous signals. For a null hypothesis of no coupling, we establish sufficient conditions for the empirical distribution of squared singular values of the matrix to converge, as the number of measured signals increases, to the well-known Marchenko-Pastur (MP) law, and the largest squared singular value converges to the upper end of the MP support. This justifies a simple thresholding approach to assess the significance of multivariate coupling. Finally, we illustrate with simulations the relevance of our univariate and multivariate results in the context of neural time series, addressing how to reliably quantify the interplay between multichannel local field potential signals and the spiking activity of a large population of neurons.

Temporal patterns of synchrony in a pyramidal-interneuron gamma (PING) network

Synchronization in neural systems plays an important role in many brain functions. Synchronization in the gamma frequency band (30-100 Hz) is involved in a variety of cognitive phenomena; abnormalities of the gamma synchronization are found in schizophrenia and autism spectrum disorder. Frequently, the strength of synchronization is not high, and synchronization is intermittent even on short time scales (few cycles of oscillations). That is, the network exhibits intervals of synchronization followed by intervals of desynchronization. Neural circuit dynamics may show different distributions of desynchronization durations even if the synchronization strength is fixed. We use a conductance-based neural network exhibiting pyramidal-interneuron gamma rhythm to study the temporal patterning of synchronized neural oscillations. We found that changes in the synaptic strength (as well as changes in the membrane kinetics) can alter the temporal patterning of synchrony. Moreover, we found that the changes in the temporal pattern of synchrony may be independent of the changes in the average synchrony strength. Even though the temporal patterning may vary, there is a tendency for dynamics with short (although potentially numerous) desynchronizations, similar to what was observed in experimental studies of neural synchronization in the brain. Recent studies suggested that the short desynchronizations dynamics may facilitate the formation and the breakup of transient neural assemblies. Thus, the results of this study suggest that changes of synaptic strength may alter the temporal patterning of the gamma synchronization as to make the neural networks more efficient in the formation of neural assemblies and the facilitation of cognitive phenomena.

The origins and development of speech envelope tracking during the first months of life

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The adult brain tracks the modulation of the amplitude of speech, i.e. its envelope.

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We tested if preverbal infants, i.e. newborns & 6-month-olds, track the speech envelope.

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Infants track the envelope phase at both ages in the native language & in unfamiliar languages.

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Infants track the envelope amplitude in the native language at birth but not at 6 months.

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This suggests that phase tracking is unrelated to language experience, whereas amplitude tracking is shaped by experience.

When humans listen to speech, their neural activity tracks the slow amplitude fluctuations of the speech signal over time, known as the speech envelope. Studies suggest that the quality of this tracking is related to the quality of speech comprehension. However, a critical unanswered question is how envelope tracking arises and what role it plays in language development. Relatedly, its causal role in comprehension remains unclear, as some studies have found it to be present even for unintelligible speech. Using electroencephalography, we investigated whether the neural activity of newborns and 6-month-olds is able to track the speech envelope of familiar and unfamiliar languages in order to explore the developmental origins and functional role of envelope tracking. Our results show that amplitude and phase tracking take place at birth for familiar and unfamiliar languages alike, i.e. independently of prenatal experience. However, by 6 months language familiarity modulates the ability to track the amplitude of the speech envelope, while phase tracking continues to be universal. Our findings support the hypothesis that amplitude and phase tracking could represent two different neural mechanisms of oscillatory synchronisation and may thus play different roles in speech perception.

Disruption of neocortical synchronisation during slow-wave sleep in the rotenone model of Parkinson's disease

Parkinson's disease motor dysfunctions are associated with improperly organised neural oscillatory activity. The presence of such disruption at the early stages of the disease in which altered sleep is one of the main features could be a relevant predictive feature. Based on this, we aimed to investigate the neocortical synchronisation dynamics during slow-wave sleep (SWS) in the rotenone model of Parkinson's disease. After rotenone administration within the substantia nigra pars compacta, one group of male Wistar rats underwent sleep-wake recording. Considering the association between SWS oscillatory activity and memory consolidation, another group of rats underwent a memory test. The fine temporal structure of synchronisation dynamics was evaluated by a recently developed technique called first return map. We observed that rotenone administration decreased the time spent in SWS and altered the power spectrum within different frequency bands, whilst it increased the transition rate from a synchronised to desynchronised state. This neurotoxin also increased the probability of longer and decreased the probability of shorter desynchronisation events. At the same time, we observed impairment in object recognition memory. These findings depict an electrophysiological fingerprint represented by a disruption in the typical oscillatory activity within the neocortex at the early stages of Parkinson's disease, concomitant with a decrease in the time spent in SWS and impairment in recognition memory.

Muscle Synergies and Coherence Networks Reflect Different Modes of Coordination During Walking

When walking speed is increased, the frequency ratio between the arm and leg swing switches spontaneously from 2:1 to 1:1. We examined whether these switches are accompanied by changes in functional connectivity between multiple muscles. Subjects walked on a treadmill with their arms swinging along their body while kinematics and surface electromyography (EMG) of 26 bilateral muscles across the body were recorded. Walking speed was varied from very slow to normal. We decomposed EMG envelopes and intermuscular coherence spectra using non-negative matrix factorization (NMF), and the resulting modes were combined into multiplex networks and analyzed for their community structure. We found five relevant muscle synergies that significantly differed in activation patterns between 1:1 and 2:1 arm-leg coordination and the transition period between them. The corresponding multiplex network contained a single module indicating pronounced muscle co-activation patterns across the whole body during a gait cycle. NMF of the coherence spectra distinguished three EMG frequency bands: 4-8, 8-22, and 22-60 Hz. The community structure of the multiplex network revealed four modules, which clustered functional and anatomical linked muscles across modes of coordination. Intermuscular coherence at 4-22 Hz between upper and lower body and within the legs was particularly pronounced for 1:1 arm-leg coordination and was diminished when switching between modes of coordination. These findings suggest that the stability of arm-leg coordination is associated with modulations in long-distant neuromuscular connectivity.

Temporal patterns of dispersal-induced synchronization in population dynamics

The mechanisms and properties of synchronization of oscillating ecological populations attract attention because it is a fairly common phenomenon and because spatial synchrony may elevate a risk of extinction and may lead to other environmental impacts. Conditions for stable synchronization in a system of linearly coupled predator-prey oscillators have been considered in the past. However, the spatial dispersal coupling may be relatively weak and may not necessarily lead to a stable, complete synchrony. If the coupling between oscillators is too weak to induce a stable synchrony, oscillators may be engaged into intermittent synchrony, when episodes of synchronized dynamics are interspersed with the episodes of desynchronized dynamics. In the present study we consider the temporal patterning of this kind of intermittent synchronized dynamics in a system of two dispersal-coupled Rosenzweig-MacArthur predator-prey oscillators. We consider the properties of the distributions of durations of desynchronized intervals and their dependence on the model parameters. We show that the temporal patterning of synchronous dynamics (an ecological network phenomenon) may depend on the properties of individual predator-prey patch (individual oscillator) and may vary independently of the strength of dispersal. We also show that if the dynamics of predator is slow relative to the dynamics of the prey (a situation that may promote brief but large outbreaks), dispersal-coupled predator-prey oscillating populations exhibit numerous short desynchronizations (as opposed to few long desynchronizations) and may require weaker dispersal in order to reach strong synchrony.

Dysregulation of temporal dynamics of synchronous neural activity in adolescents on autism spectrum

Autism spectrum disorder is increasingly understood to be based on atypical signal transfer among multiple interconnected networks in the brain. Relative temporal patterns of neural activity have been shown to underlie both the altered neurophysiology and the altered behaviors in a variety of neurogenic disorders. We assessed brain network dynamics variability in autism spectrum disorders (ASD) using measures of synchronization (phase-locking) strength, and timing of synchronization and desynchronization of neural activity (desynchronization ratio) across frequency bands of resting-state electroencephalography (EEG). Our analysis indicated that frontoparietal synchronization is higher in ASD but with more short periods of desynchronization. It also indicates that the relationship between the properties of neural synchronization and behavior is different in ASD and typically developing populations. Recent theoretical studies suggest that neural networks with a high desynchronization ratio have increased sensitivity to inputs. Our results point to the potential significance of this phenomenon to the autistic brain. This sensitivity may disrupt the production of an appropriate neural and behavioral responses to external stimuli. Cognitive processes dependent on the integration of activity from multiple networks maybe, as a result, particularly vulnerable to disruption. Autism Res 2020, 13: 24-31. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Parts of the brain can work together by synchronizing the activity of the neurons. We recorded the electrical activity of the brain in adolescents with autism spectrum disorder and then compared the recording to that of their peers without the diagnosis. We found that in participants with autism, there were a lot of very short time periods of non-synchronized activity between frontal and parietal parts of the brain. Mathematical models show that the brain system with this kind of activity is very sensitive to external events.

Comparing Open-Source Toolboxes for Processing and Analysis of Spike and Local Field Potentials Data

Analysis of spike and local field potential (LFP) data is an essential part of neuroscientific research. Today there exist many open-source toolboxes for spike and LFP data analysis implementing various functionality. Here we aim to provide a practical guidance for neuroscientists in the choice of an open-source toolbox best satisfying their needs. We overview major open-source toolboxes for spike and LFP data analysis as well as toolboxes with tools for connectivity analysis, dimensionality reduction and generalized linear modeling. We focus on comparing toolboxes functionality, statistical and visualization tools, documentation and support quality. To give a better insight, we compare and illustrate functionality of the toolboxes on open-access dataset or simulated data and make corresponding MATLAB scripts publicly available.

Empirical analysis of phase-amplitude coupling approaches

Analysis of the coupling between the phases and amplitudes of oscillations within the same continuously sampled signal has provided interesting insights into the physiology of memory and other brain process, and, more recently, the pathophysiology of parkinsonism and other movement disorders. Technical aspects of the analysis have a significant impact on the results. We present an empirical exploration of a variety of analysis design choices that need to be considered when measuring phase-amplitude coupling (PAC). We studied three alternative filtering approaches to the commonly used Kullback-Leibler distance-based method of PAC analysis, including one method that uses wavelets, one that uses constant filter settings, and one in which filtering of the data is optimized for individual frequency bands. Additionally, we introduce a time-dependent PAC analysis technique that takes advantage of the inherent temporality of wavelets. We examined how the duration of the sampled data, the stability of oscillations, or the presence of artifacts affect the value of the "modulation index", a commonly used parameter describing the degree of PAC. We also studied the computational costs associated with calculating modulation indices by the three techniques. We found that wavelet-based PAC performs better with similar or less computational cost than the two other methods while also allowing to examine temporal changes of PAC. We also show that the reliability of PAC measurements strongly depends on the duration and stability of PAC, and the presence (or absence) of artifacts. The best parameters to be used for PAC analyses of long samples of data may differ, depending on data characteristics and analysis objectives. Prior to settling on a specific PAC analysis approach for a given set of data, it may be useful to conduct an initial analysis of the time-dependence of PAC using our time-resolved PAC analysis.

Brain Complex Network Characteristic Analysis of Fatigue during Simulated Driving Based on Electroencephalogram Signals

Fatigued driving is one of the major causes of traffic accidents. Frequent repetition of driving behavior for a long time may lead to driver fatigue, which is closely related to the central nervous system. In the present work, we designed a fatigue driving simulation experiment and collected the electroencephalogram (EEG) signals. Complex network theory was introduced to study the evolution of brain dynamics under different rhythms of EEG signals during several periods of the simulated driving. The results show that as the fatigue degree deepened, the functional connectivity and the clustering coefficients increased while the average shortest path length decreased for the delta rhythm. In addition, there was a significant increase of the degree centrality in partial channels on the right side of the brain for the delta rhythm. Therefore, it can be concluded that driving fatigue can cause brain complex network characteristics to change significantly for certain brain regions and certain rhythms. This exploration may provide a theoretical basis for further finding objective and effective indicators to evaluate the degree of driving fatigue and to help avoid fatigue driving.

Rifle Shooting Performance Correlates with Electroencephalogram Beta Rhythm Network Activity during Aiming

To study the relationship between brain network and shooting performance during shooting aiming, we collected electroencephalogram (EEG) signals from 40 skilled shooters during rifle shooting and calculated the EEG functional coupling, functional brain network topology, and correlation coefficients between these EEG characteristics and shooting performance. Our result shows a significant negative correlation between shooting performance and functional coupling between the prefrontal, frontal, and temporal regions of the right brain in the Beta1 and Beta2 frequency bands. Global and local brain network topology characteristics were also significantly correlated with shooting performance. These findings indicate that under these experimental conditions, shooters with higher shooting performances exhibit lower functional coupling, higher global, and lower local information integration efficiency during shooting. These conclusions may provide a theoretical basis of the EEG brain network for studying the mental status of shooters while shooting.

Evaluation on Directed Functional Brain Connectivity during the Expert Rifle Pre-shot Period

Experts require long-term and intense practice to acquire extraordinary motor skills that be known as the brain function regulator. However, the brain function network mechanism of rifle shooters is still unclear. The generalized orthogonalized partial directed coherence (gOPDC) algorithm and local efficiency significance analysis method (LESA) was applied to quantify the difference in directed functional networks between expert and novice rifle shooters during the pre-shot period. The more directed functional connections were observed in alpha and low-beta frequency bands as compared to other bands. Moreover, comparing with the novice's fluctuant connection, the values of connection (P3→C3) strength were increasing steadily in the experts during the pre-shot period. Fewer connections in left hemisphere networks were obtained in the experts than in the novices. The results validated the "neural efficiency" hypothesis in experts. Moreover, the strength of the functional connection (P3→C3) in the alpha and beta bands serves as a distinguishing feature between experts and novices.

COMT Inhibition Alters Cue-Evoked Oscillatory Dynamics during Alcohol Drinking in the Rat

Alterations in the corticostriatal system have been implicated in numerous substance use disorders, including alcohol use disorder (AUD). Adaptations in this neural system are associated with enhanced drug-seeking behaviors following exposure to cues predicting drug availability. Therefore, understanding how potential treatments alter neural activity in this system could lead to more refined and effective approaches for AUD. Local field potentials (LFPs) were acquired simultaneously in the prefrontal cortex (PFC) and nucleus accumbens (NA) of both alcohol preferring (P) and Wistar rats engaged in a Pavlovian conditioning paradigm wherein a light cue signaled the availability of ethanol (EtOH). On test days, the catechol-o-methyl-transferase (COMT) inhibitor tolcapone was administered prior to conditioning. Stimulus-evoked voltage changes were observed following the presentation of the EtOH cue in both strains and were most pronounced in the PFC of P rats. Phase analyses of LFPs in the θ band (5–11 Hz) revealed that PFC-NA synchrony was reduced in P rats relative to Wistars but was robustly increased during drinking. Presentation of the cue resulted in a larger phase reset in the PFC of P rats but not Wistars, an effect that was attenuated by tolcapone. Additionally, tolcapone reduced cued EtOH intake in P rat but not Wistars. These results suggest a link between corticostriatal synchrony and genetic risk for excessive drinking. Moreover, inhibition of COMT within these systems may result in reduced attribution of salience to reward paired stimuli via modulation of stimulus-evoked changes to cortical oscillations in genetically susceptible populations.

Reducing the Effect of Spurious Phase Variations in Neural Oscillatory Signals

The phase-reset model of oscillatory EEG activity has received a lot of attention in the last decades for decoding different cognitive processes. Based on this model, the ERPs are assumed to be generated as a result of phase reorganization in ongoing EEG. Alignment of the phase of neuronal activities can be observed within or between different assemblies of neurons across the brain. Phase synchronization has been used to explore and understand perception, attentional binding and considering it in the domain of neuronal correlates of consciousness. The importance of the topic and its vast exploration in different domains of the neuroscience presses the need for appropriate tools and methods for measuring the level of phase synchronization of neuronal activities. Measuring the level of instantaneous phase (IP) synchronization has been used extensively in numerous studies of ERPs as well as oscillatory activity for a better understanding of the underlying cognitive binding with regard to different set of stimulations such as auditory and visual. However, the reliability of results can be challenged as a result of noise artifact in IP. Phase distortion due to environmental noise artifacts as well as different pre-processing steps on signals can lead to generation of artificial phase jumps. One of such effects presented recently is the effect of low envelope on the IP of signal. It has been shown that as the instantaneous envelope of the analytic signal approaches zero, the variations in the phase increase, effectively leading to abrupt transitions in the phase. These abrupt transitions can distort the phase synchronization results as they are not related to any neurophysiological effect. These transitions are called spurious phase variation. In this study, we present a model to remove generated artificial phase variations due to the effect of low envelope. The proposed method is based on a simplified form of a Kalman smoother, that is able to model the IP behavior in narrow-bandpassed oscillatory signals. In this work we first explain the details of the proposed Kalman smoother for modeling the dynamics of the phase variations in narrow-bandpassed signals and then evaluate it on a set of synthetic signals. Finally, we apply the model on ongoing-EEG signals to assess the removal of spurious phase variations.

Measuring transient phase-amplitude coupling using local mutual information

Here we demonstrate the suitability of a local mutual information measure for estimating the temporal dynamics of cross-frequency coupling (CFC) in brain electrophysiological signals. In CFC, concurrent activity streams in different frequency ranges interact and transiently couple. A particular form of CFC, phase-amplitude coupling (PAC), has raised interest given the growing amount of evidence of its possible role in healthy and pathological brain information processing. Although several methods have been proposed for PAC estimation, only a few have addressed the estimation of the temporal evolution of PAC, and these typically require a large number of experimental trials to return a reliable estimate. Here we explore the use of mutual information to estimate a PAC measure (MIPAC) in both continuous and event-related multi-trial data. To validate these two applications of the proposed method, we first apply it to a set of simulated phase-amplitude modulated signals and show that MIPAC can successfully recover the temporal dynamics of the simulated coupling in either continuous or multi-trial data. Finally, to explore the use of MIPAC to analyze data from human event-related paradigms, we apply it to an actual event-related human electrocorticographic (ECoG) data set that exhibits strong PAC, demonstrating that the MIPAC estimator can be used to successfully characterize amplitude-modulation dynamics in electrophysiological data.

Phase Synchronization Dynamics of Neural Network during Seizures

Epilepsy has been considered as a network-level disorder characterized by recurrent seizures, which result from network reorganization with evolution of synchronization. In this study, the brain networks were established by calculating phase synchronization based on electrocorticogram (ECoG) signals from eleven refractory epilepsy patients. Results showed that there was a significant increase of synchronization prior to seizure termination and no significant difference of the transitions of network states among the preseizure, seizure, and postseizure periods. Those results indicated that synchronization might participate in termination of seizures, and the network states transitions might not dominate the seizure evolution.

Generalization of the primary tone phase variation method: An exclusive way of isolating the frequency-following response components

The primary tone phase variation (PTPV) technique combines selective sub-averaging with systematic variation of the phases of multitone stimuli. Each response component having a known phase relationship with the stimulus components phases can be isolated in the time domain. The method was generalized to the frequency-following response (FFR) evoked by a two-tone (f ₁ and f ₂) stimulus comprising both linear and non-linear, as well as transient components. The generalized PTPV technique isolated each spectral component present in the FFR, including those sharing the same frequency, allowing comparison of their latencies. After isolation of the envelope component f ₂ - f ₁ from its harmonic distortion 2f ₂ - 2f ₁ and from the transient auditory brainstem response, a computerized analysis of instantaneous amplitudes and phases was applied in order to objectively determine the onset and offset latencies of the response components. The successive activation of two generators separated by 3.7 ms could be detected in all (N = 12) awake adult normal subjects, but in none (N = 10) of the sleeping/sedated children with normal hearing thresholds. The method offers an unprecedented way of disentangling the various FFR subcomponents. These results open the way for renewed investigations of the FFR components in both human and animal research as well as for clinical applications.

Alterations of Coherent Theta and Gamma Network Oscillations as an Early Biomarker of Temporal Lobe Epilepsy and Alzheimer's Disease

Alzheimer's disease (AD) and temporal lobe epilepsy (TLE) are the most common forms of neurodegenerative disorders characterized by the loss of cells and progressive irreversible alteration of cognitive functions, such as attention and memory. AD may be an important cause of epilepsy in the elderly. Early diagnosis of diseases is very important for their successful treatment. Many efforts have been done for defining new biomarkers of these diseases. Significant advances have been made in the searching of some AD and TLE reliable biomarkers, including cerebrospinal fluid and plasma measurements and glucose positron emission tomography. However, there is a great need for the biomarkers that would reflect changes of brain activity within few milliseconds to obtain information about cognitive disturbances. Successful early detection of AD and TLE requires specific biomarkers capable of distinguishing individuals with the progressing disease from ones with other pathologies that affect cognition. In this article, we review recent evidence suggesting that magnetoencephalographic recordings and coherent analysis coupled with behavioral evaluation can be a promising approach to an early detection of AD and TLE. Highlights -Data reviewed include the results of clinical and experimental studies.-Theta and gamma rhythms are disturbed in epilepsy and AD.-Common and different behavioral and oscillatory features of pathologies are compared.-Coherent analysis can be useful for an early diagnostics of diseases.

Learning Task-Related Activities From Independent Local-Field-Potential Components Across Motor Cortex Layers

Motor cortical microcircuits receive inputs from dispersed cortical and subcortical regions in behaving animals. However, how these inputs contribute to learning and execution of voluntary sequential motor behaviors remains elusive. Here, we analyzed the independent components extracted from the local field potential (LFP) activity recorded at multiple depths of rat motor cortex during reward-motivated movement to study their roles in motor learning. Because slow gamma (30-50 Hz), fast gamma (60-120 Hz), and theta (4-10 Hz) oscillations temporally coordinate task-relevant motor cortical activities, we first explored the behavioral state- and layer-dependent coordination of motor behavior in these frequency ranges. Consistent with previous findings, oscillations in the slow and fast gamma bands dominated during distinct movement states, i.e., preparation and execution states, respectively. However, we identified a novel independent component that dominantly appeared in deep cortical layers and exhibited enhanced slow gamma activity during the execution state. Then, we used the four major independent components to train a recurrent network model for the same lever movements as the rats performed. We show that the independent components differently contribute to the formation of various task-related activities, but they also play overlapping roles in motor learning.

Synchronization of intrinsic 0.1-Hz blood-oxygen-level-dependent oscillations in amygdala and prefrontal cortex in subjects with increased state anxiety

Low-frequency oscillations with a dominant frequency at 0.1 Hz are one of the most influential intrinsic blood-oxygen-level-dependent (BOLD) signals. This raises the question if vascular BOLD oscillations (originating from blood flow in the brain) and intrinsic slow neural activity fluctuations (neural BOLD oscillations) can be differentiated. In this study, we report on two different approaches: first, on computing the phase-locking value in the frequency band 0.07-0.13 Hz between heart beat-to-beat interval (RRI) and BOLD oscillations and second, between multiple BOLD oscillations (functional connectivity) in four resting states in 23 scanner-naïve, anxious healthy subjects. The first method revealed that vascular 0.1-Hz BOLD oscillations preceded those in RRI signals by 1.7 ± 0.6 s and neural BOLD oscillations lagged RRI oscillations by 0.8 ± 0.5 s. Together, vascular BOLD oscillations preceded neural BOLD oscillations by ~90° or ~2.5 s. To verify this discrimination, connectivity patterns of neural and vascular 0.1-Hz BOLD oscillations were compared in 26 regions involved in processing of emotions. Neural BOLD oscillations revealed significant phase-coupling between amygdala and medial frontal cortex, while vascular BOLD oscillations showed highly significant phase-coupling between amygdala and multiple regions in the supply areas of the anterior and medial cerebral arteries. This suggests that not only slow neural and vascular BOLD oscillations can be dissociated but also that two strategies may exist to optimize regulation of anxiety, that is increased functional connectivity between amygdala and medial frontal cortex, and increased cerebral blood flow in amygdala and related structures.