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

Quantifying local field potential dynamics with amplitude and frequency stability between ON and OFF medication and stimulation in Parkinson's disease

Neural oscillations are critical to understanding the synchronisation of neural activities and their relevance to neurological disorders. For instance, the amplitude of beta oscillations in the subthalamic nucleus has gained extensive attention, as it has been found to correlate with medication status and the therapeutic effects of continuous deep brain stimulation in people with Parkinson's disease. However, the frequency stability of subthalamic nucleus beta oscillations, which has been suggested to be associated with dopaminergic information in brain states, has not been well explored. Moreover, the administration of medicine can have inverse effects on changes in frequency and amplitude. In this study, we proposed a method based on the stationary wavelet transform to quantify the amplitude and frequency stability of subthalamic nucleus beta oscillations and evaluated the method using simulation and real data for Parkinson's disease patients. The results suggest that the amplitude and frequency stability quantification has enhanced sensitivity in distinguishing pathological conditions in Parkinson's disease patients. Our quantification shows the benefit of combining frequency stability information with amplitude and provides a new potential feedback signal for adaptive deep brain stimulation.

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

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

Neural Decoding and Feature Selection Techniques for Closed-Loop Control of Defensive Behavior

Objective

Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors.

Approach

We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance.

Main results

Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low time complexity, requiring <110 ms for training and <1 ms for inference.

Significance

Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.

A mutual information measure of phase-amplitude coupling using gamma generalized linear models

Introduction

Cross frequency coupling (CFC) between electrophysiological signals in the brain is a long-studied phenomenon and its abnormalities have been observed in conditions such as Parkinson's disease and epilepsy. More recently, CFC has been observed in stomach-brain electrophysiologic studies and thus becomes an enticing possible target for diseases involving aberrations of the gut-brain axis. However, current methods of detecting coupling, specifically phase-amplitude coupling (PAC), do not attempt to capture the phase and amplitude statistical relationships.

Methods

In this paper, we first demonstrate a method of modeling these joint statistics with a flexible parametric approach, where we model the conditional distribution of amplitude given phase using a gamma distributed generalized linear model (GLM) with a Fourier basis of regressors. We perform model selection with minimum description length (MDL) principle, demonstrate a method for assessing goodness-of-fit (GOF), and showcase the efficacy of this approach in multiple electroencephalography (EEG) datasets. Secondly, we showcase how we can utilize the mutual information, which operates on the joint distribution, as a canonical measure of coupling, as it is non-zero and non-negative if and only if the phase and amplitude are not statistically independent. In addition, we build off of previous work by Martinez-Cancino et al., and Voytek et al., and show that the information density, evaluated using our method along the given sample path, is a promising measure of time-resolved PAC.

Results

Using synthetically generated gut-brain coupled signals, we demonstrate that our method outperforms the existing gold-standard methods for detectable low-levels of phase-amplitude coupling through receiver operating characteristic (ROC) curve analysis. To validate our method, we test on invasive EEG recordings by generating comodulograms, and compare our method to the gold standard PAC measure, Modulation Index, demonstrating comparable performance in exploratory analysis. Furthermore, to showcase its use in joint gut-brain electrophysiology data, we generate topoplots of simultaneous high-density EEG and electrgastrography recordings and reproduce seminal work by Richter et al. that demonstrated the existence of gut-brain PAC. Using simulated data, we validate our method for different types of time-varying coupling and then demonstrate its performance to track time-varying PAC in sleep spindle EEG and mismatch negativity (MMN) datasets.

Conclusions

Our new measure of PAC using Gamma GLMs and mutual information demonstrates a promising new way to compute PAC values using the full joint distribution on amplitude and phase. Our measure outperforms the most common existing measures of PAC, and show promising results in identifying time varying PAC in electrophysiological datasets. In addition, we provide for using our method with multiple comparisons and show that our measure potentially has more statistical power in electrophysiologic recordings using simultaneous gut-brain datasets.

Gait-related beta-gamma phase amplitude coupling in the subthalamic nucleus of parkinsonian patients

Analysis of coupling between the phases and amplitudes of neural oscillations has gained increasing attention as an important mechanism for large-scale brain network dynamics. In Parkinson's disease (PD), preliminary evidence indicates abnormal beta-phase coupling to gamma-amplitude in different brain areas, including the subthalamic nucleus (STN). We analyzed bilateral STN local field potentials (LFPs) in eight subjects with PD chronically implanted with deep brain stimulation electrodes during upright quiet standing and unperturbed walking. Phase-amplitude coupling (PAC) was computed using the Kullback-Liebler method, based on the modulation index. Neurophysiological recordings were correlated with clinical and kinematic measurements and individual molecular brain imaging studies ([123I]FP-CIT and single-photon emission computed tomography). We showed a dopamine-related increase in subthalamic beta-gamma PAC from standing to walking. Patients with poor PAC modulation and low PAC during walking spent significantly more time in the stance and double support phase of the gait cycle. Our results provide new insights into the subthalamic contribution to human gait and suggest cross-frequency coupling as a gateway mechanism to convey patient-specific information of motor control for human locomotion.

Biomimetic Deep Learning Networks With Applications to Epileptic Spasms and Seizure Prediction

OBJECTIVE

In this study, we present a novel biomimetic deep learning network for epileptic spasms and seizure prediction and compare its performance with state-of-the-art conventional machine learning models.

METHODS

Our proposed model incorporates modular Volterra kernel convolutional networks and bidirectional recurrent networks in combination with the phase amplitude cross-frequency coupling features derived from scalp EEG. They are applied to the standard CHB-MIT dataset containing focal epilepsy episodes as well as two other datasets from the Montefiore Medical Center and the University of California Los Angeles that provide data of patients experiencing infantile spasm (IS) syndrome.

RESULTS

Overall, in this study, the networks can produce accurate predictions (100%) and significant detection latencies (10 min). Furthermore, the biomimetic network outperforms conventional ones by producing no false positives.

SIGNIFICANCE

Biomimetic neural networks utilize extensive knowledge about processing and learning in the electrical networks of the brain. Predicting seizures in adults can improve their quality of life. Epileptic spasms in infants are part of a particular seizure type that needs identifying when suspicious behaviors are noticed in babies. Predicting epileptic spasms within a given time frame (the prediction horizon) suggests their existence and allows an epileptologist to flag an EEG trace for future review.

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

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

Transcranial Alternating Current Stimulation to Modulate Alpha Activity: A Systematic Review

BACKGROUND

Transcranial alternating current stimulation (tACS) has been one of numerous investigation methods used for their potential to modulate brain oscillations; however, such investigations have given contradictory results and a lack of standardization.

OBJECTIVES

In this systematic review, we aimed to assess the potential of tACS to modulate alpha spectral power. The secondary outcome was the identification of tACS methodologic key parameters, adverse effects, and sensations.

MATERIALS AND METHODS

Studies in healthy adults who were receiving active and sham tACS intervention or any differential condition were included. The main outcome assessed was the increase/decrease of alpha spectral power through either electroencephalography or magnetoencephalography. Secondary outcomes were methodologic parameters, sensation reporting, and adverse effects. Risks of bias and the study quality were assessed with the Cochrane assessment tool.

RESULTS

We obtained 1429 references, and 20 met the selection criteria. A statistically significant alpha-power increase was observed in nine studies using continuous tACS stimulation and two using intermittent tACS stimulation set at a frequency within the alpha range. A statistically significant alpha-power increase was observed in three more studies using a stimulation frequency outside the alpha range. Heterogeneity among stimulation parameters was recognized. Reported adverse effects were mild. The implementation of double blind was identified as challenging using tACS, in part owing to electrical artifacts generated by stimulation on the recorded signal.

CONCLUSIONS

Most assessed studies reported that tACS has the potential to modulate brain alpha power. The optimization of this noninvasive brain stimulation method is of interest mostly for its potential clinical applications with neurological conditions associated with perturbations in alpha brain activity. However, more research efforts are needed to standardize optimal parameters to achieve lasting modulation effects, develop methodologic alternatives to reduce experimental bias, and improve the quality of studies using tACS to modulate brain activity.

Aberrations in temporal dynamics of cognitive processing induced by Parkinson's disease and Levodopa

The motor symptoms of Parkinson's disease (PD) have been shown to significantly improve by Levodopa. However, despite the widespread adoption of Levodopa as a standard pharmaceutical drug for the treatment of PD, cognitive impairments linked to PD do not show visible improvement with Levodopa treatment. Furthermore, the neuronal and network mechanisms behind the PD-induced cognitive impairments are not clearly understood. In this work, we aim to explain these cognitive impairments, as well as the ones exacerbated by Levodopa, through examining the differential dynamic patterns of the phase-amplitude coupling (PAC) during cognitive functions. EEG data recorded in an auditory oddball task performed by a cohort consisting of controls and a group of PD patients during both on and off periods of Levodopa treatment were analyzed to derive the temporal dynamics of the PAC across the brain. We observed distinguishing patterns in the PAC dynamics, as an indicator of information binding, which can explain the slower cognitive processing associated with PD in the form of a latency in the PAC peak time. Thus, considering the high-level connections between the hippocampus, the posterior and prefrontal cortices established through the dorsal and ventral striatum acting as a modulatory system, we posit that the primary issue with cognitive impairments of PD, as well as Levodopa's cognitive deficit side effects, can be attributed to the changes in temporal dynamics of dopamine release influencing the modulatory function of the striatum.

Individual differences in information processing during sleep and wake predict sleep-based memory consolidation of complex rules

Memory is critical for many cognitive functions, from remembering facts, to learning complex environmental rules. While memory encoding occurs during wake, memory consolidation is associated with sleep-related neural activity. Further, research suggests that individual differences in alpha frequency during wake (∼7 - 13 Hz) modulate memory processes, with higher individual alpha frequency (IAF) associated with greater memory performance. However, the relationship between wake-related EEG individual differences, such as IAF, and sleep-related neural correlates of memory consolidation has been largely unexplored, particularly in a complex rule-based memory context. Here, we aimed to investigate whether wake-derived IAF and sleep neurophysiology interact to influence rule learning in a sample of 35 healthy adults (16 males; mean age = 25.4, range: 18 - 40). Participants learned rules of a modified miniature language prior to either 8hrs of sleep or wake, after which they were tested on their knowledge of the rules in a grammaticality judgement task. Results indicate that sleep neurophysiology and wake-derived IAF do not interact but modulate memory for complex linguistic rules separately. Phase-amplitude coupling between slow oscillations and spindles during non-rapid eye-movement (NREM) sleep also promoted memory for rules that were analogous to the canonical English word order. As an exploratory analysis, we found that rapid eye-movement (REM) sleep theta power at posterior regions interacts with IAF to predict rule learning and proportion of time in REM sleep predicts rule learning differentially depending on grammatical rule type. Taken together, the current study provides behavioural and electrophysiological evidence for a complex role of NREM and REM sleep neurophysiology and wake-derived IAF in the consolidation of rule-based information.

Non-Stationary Dynamic Mode Decomposition

Many physical processes display complex high-dimensional time-varying behavior, from global weather patterns to brain activity. An outstanding challenge is to express high dimensional data in terms of a dynamical model that reveals their spatiotemporal structure. Dynamic Mode Decomposition is a means to achieve this goal, allowing the identification of key spatiotemporal modes through the diagonalization of a finite dimensional approximation of the Koopman operator. However, DMD methods apply best to time-translationally invariant or stationary data, while in many typical cases, dynamics vary across time and conditions. To capture this temporal evolution, we developed a method, Non-Stationary Dynamic Mode Decomposition (NS-DMD), that generalizes DMD by fitting global modulations of drifting spatiotemporal modes. This method accurately predicts the temporal evolution of modes in simulations and recovers previously known results from simpler methods. To demonstrate its properties, the method is applied to multi-channel recordings from an awake behaving non-human primate performing a cognitive task.

The effect of HD-tDCS on brain oscillations and frontal synchronicity during resting-state EEG in violent offenders with a substance dependence

Violence is a major problem in our society and therefore research into the neural underpinnings of aggression has grown exponentially. Although in the past decade the biological underpinnings of aggressive behavior have been examined, research on neural oscillations in violent offenders during resting-state electroencephalography (rsEEG) remains scarce. In this study we aimed to investigate the effect of high-definition transcranial direct current stimulation (HD-tDCS) on frontal theta, alpha and beta frequency power, asymmetrical frontal activity, and frontal synchronicity in violent offenders. Fifty male violent forensic patients diagnosed with a substance dependence were included in a double-blind sham-controlled randomized study. The patients received 20 minutes of HD-tDCS two times a day on five consecutive days. Before and after the intervention, the patients underwent a rsEEG task. Results showed no effect of HD-tDCS on the power in the different frequency bands. Also, no increase in asymmetrical activity was found. However, we found increased synchronicity in frontal regions in the alpha and beta frequency bands indicating enhanced connectivity in frontal brain regions as a result of the HD-tDCS-intervention. This study has enhanced our understanding of the neural underpinnings of aggression and violence, pointing to the importance of alpha and beta frequency bands and their connectivity in frontal brain regions. Although future studies should further investigate the complex neural underpinnings of aggression in different populations and using whole-brain connectivity, it can be suggested with caution, that HD-tDCS could be an innovative method to regain frontal synchronicity in neurorehabilitation.

Community detection in multi-frequency EEG networks

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

Epileptic seizures detection and the analysis of optimal seizure prediction horizon based on frequency and phase analysis

Changes in the frequency composition of the human electroencephalogram are associated with the transitions to epileptic seizures. Cross-frequency coupling (CFC) is a measure of neural oscillations in different frequency bands and brain areas, and specifically phase-amplitude coupling (PAC), a form of CFC, can be used to characterize these dynamic transitions. In this study, we propose a method for seizure detection and prediction based on frequency domain analysis and PAC combined with machine learning. We analyzed two databases, the Siena Scalp EEG database and the CHB-MIT database, and used the frequency features and modulation index (MI) for time-dependent quantification. The extracted features were fed to a random forest classifier for classification and prediction. The seizure prediction horizon (SPH) was also analyzed based on the highest-performing band to maximize the time for intervention and treatment while ensuring the accuracy of the prediction. Under comprehensive consideration, the results demonstrate that better performance could be achieved at an interval length of 5 min with an average accuracy of 85.71% and 95.87% for the Siena Scalp EEG database and the CHB-MIT database, respectively. As for the adult database, the combination of PAC analysis and classification can be of significant help for seizure detection and prediction. It suggests that the rarely used SPH also has a major impact on seizure detection and prediction and further explorations for the application of PAC are needed.

FSTL1-knockdown improves neural oscillation via decreasing neuronal-inflammation regulating apoptosis in Aβ1-42 induced AD model mice

Follistatin like protein 1 (FSTL1) is a famous growth regulatory protein. FSTL1 has been noticed in many diseases, including heart and lung ischemia, cerebral ischemia, glioma, schizophrenia, and Autism. The role of FSTL1 has been declared in the genetics and development of the central nervous system. Therefore, we designed this study to investigate the function and the role of FSTL1 in Alzheimer's disease. Firstly, we noticed upregulated expression level of FSTL1 among four to six-month-old 5XFAD AD mice. Accordingly, we hypothesized that FSTL1-Knockdown improved AD model mice's cognitive function and recover from Alzheimer's disease. Thus, AD model mice were made by single intracerebroventricular injections of Aβ_1-42 peptides in FSTL1^+/- and CON mice. Next, our results concluded that FSTL1-knockdown effectively improved cognitive functions. FSTL1-knockdown enhanced the pattern of neural oscillations, and synaptic plasticity in Aβ_1-42 treated FSTL1-Knockdown mice compared to Aβ_1-42 induced AD model mice. Next, FSTL1-Knockdown inhibited the activation of microglia and binding of TLR-4 with microglia. Further, inactivated microglia stopped the formation of MyD88. Thus, our data revealed that FSTL1-Knockdown is slowing down the caspase/BAX/Bcl-2/TLR-4 regulating apoptosis pathway, and the expression of inflammatory cytokines in the hippocampus of Aβ_1-42 inserted FSTL1-Knockdown mice. Overall, all these data illuminate the clinical significance role of down-regulated FSTL1. FSTL1-Knockdown reduced the amyloid-beta by affecting microglia, neural-inflammation and apoptosis in AD-like model mice. Finally, down regulation of FSTL1 improved synaptic plasticity, neural oscillations, and cognitive behaviours in the Aβ_1-42 induced AD model mice.

Large-scale frontoparietal theta, alpha, and beta phase synchronization: A set of EEG differential characteristics for freezing of gait in Parkinson's disease?

Freezing of gait (FOG) is a complex gait disturbance in Parkinson's disease (PD), during which the patient is not able to effectively initiate gait or continue walking. The mystery of the FOG phenomenon is still unsolved. Recent studies have revealed abnormalities in cortical activities associated with FOG, which highlights the importance of cortical and cortical-subcortical network dysfunction in PD patients with FOG. In this paper, phase-locking value (PLV) of eight frequency sub-bands between 0.05 Hz and 35 Hz over frontal, motor, and parietal areas [during an ankle dorsiflexion (ADF) task] is used to investigate EEG phase synchronization. PLV was investigated over both superficial and deeper networks by analyzing EEG signals preprocessed with and without Surface Laplacian (SL) spatial filter. Four groups of participants were included: PD patients with severe FOG (N = 5, 5 males), PD patients with mild FOG (N = 7, 6 males), PD patients without FOG (N = 14, 13 males), and healthy age-matched controls (N = 13, 10 males). Fifteen trials were recorded from each participant. At superficial layers, frontoparietal theta phase synchrony was a unique feature present in PD with FOG groups. At deeper networks, significant dominance of interhemispheric frontoparietal alpha phase synchrony in PD with FOG, in contrast to beta phase synchrony in PD without FOG, was identified. Alpha phase synchrony was more distributed in PD with severe FOG, with higher levels of frontoparietal alpha phase synchrony. In addition to FOG-related abnormalities in PLV analysis, phase-amplitude coupling (PAC) analysis was also performed on frequency bands with PLV abnormalities. PAC analysis revealed abnormal coupling between theta and low beta frequency bands in PD with severe FOG at the superficial layers over frontal areas. At deeper networks, theta and alpha frequency bands show high PAC over parietal areas in PD with severe FOG. Alpha and low beta also presented PAC over frontal areas in PD groups with FOG. The results introduced significant phase synchrony differences between PD with and without FOG and provided important insight into a possible unified underlying mechanism for FOG. These results thus suggest that PLV and PAC can potentially be used as EEG-based biomarkers for FOG.

Developments in the mechanistic understanding and clinical application of deep brain stimulation for Parkinson's disease

INTRODUCTION. Deep brain stimulation (DBS) is a life-changing treatment for patients with Parkinson's disease (PD) and gives the unique opportunity to directly explore how basal ganglia work. Despite the rapid technological innovation of the last years, the untapped potential of DBS is still high.

AREAS COVERED. This review summarizes the developments in the mechanistic understanding of DBS and the potential clinical applications of cutting-edge technological advances. Rather than a univocal local mechanism, DBS exerts its therapeutic effects through several multimodal mechanisms and involving both local and network-wide structures, although crucial questions remain unexplained. Nonetheless, new insights in mechanistic understanding of DBS in PD have provided solid bases for advances in preoperative selection phase, prediction of motor and non-motor outcomes, leads placement and postoperative stimulation programming.

EXPERT OPINION. DBS has not only strong evidence of clinical effectiveness in PD treatment, but technological advancements are revamping its role of neuromodulation of brain circuits and key to better understanding PD pathophysiology. In the next few years, the worldwide use of new technologies in clinical practice will provide large data to elucidate their role and to expand their applications for PD patients, providing useful insights to personalize DBS treatment and follow-up.

Network synchronization deficits caused by dementia and Alzheimer's disease serve as topographical biomarkers: a pilot study

Mild cognitive impairment (MCI) is known as an early stage of cognitive decline. Amnestic MCI (aMCI) is considered as the preliminary stage of dementia which may progress to Alzheimer’s disease (AD). While some aMCI patients may stay in this condition for years, others might develop dementia associated with AD. Early detection of MCI allows for potential treatments to prevent or decelerate the process of developing dementia. Standard methods of diagnosing MCI and AD employ structural (imaging), behavioral (cognitive tests), and genetic or molecular (blood or CSF tests) techniques. Our study proposes network-level neural synchronization parameters as topographical markers for diagnosing aMCI and AD. We conducted a pilot study based on EEG data recorded during an olfactory task from a group of elderly participants consisting of healthy individuals and patients of aMCI and AD to assess the value of different indicators of network-level phase and amplitude synchronization in differentiating the three groups. Significant differences were observed in the percent phase locking value, theta-gamma phase-amplitude coupling, and amplitude coherence between the groups, and classifiers were developed to differentiate the three groups based on these parameters. The observed differences in these indicators of network-level functionality of the brain can help explain the underlying processes involved in aMCI and AD.

Brain network analysis of interictal epileptiform discharges from ECoG to identify epileptogenic zone in pediatric patients with epilepsy and focal cortical dysplasia type II: A retrospective study

Objective

For patients with drug–resistant focal epilepsy, intracranial monitoring remains the gold standard for surgical intervention. Focal cortical dysplasia (FCD) is the most common cause of pharmacoresistant focal epilepsy in pediatric patients who usually develop seizures in early childhood. Timely removal of the epileptogenic zone (EZ) is necessary to achieve lasting seizure freedom and favorable developmental and cognitive outcomes to improve the quality of life. We applied brain network analysis to investigate potential biomarkers for the diagnosis of EZ that will aid in the resection for pediatric focal epilepsy patients with FCD type II.

Methods

Ten pediatric patients with focal epilepsy diagnosed as FCD type II and that had a follow–up after resection surgery (Engel class I [n = 9] and Engel class II [n = 1]) were retrospectively included. Time–frequency analysis of phase transfer entropy, graph theory analysis, and power spectrum compensation were combined to calculate brain network parameters based on interictal epileptiform discharges from ECoG.

Results

Clustering coefficient, local efficiency, node out–degree, and node out–strength with higher values are the most reliable biomarkers for the delineation of EZ, and the differences between EZ and margin zone (MZ), and EZ and normal zone (NZ) were significant (p &lt; 0.05; Mann–Whitney U-test, two–tailed). In particular, the difference between MZ and NZ was significant for patients with frontal FCD (MZ &gt; NZ; p &lt; 0.05) but was not significant for patients with extra–frontal FCD.

Conclusions

Brain network analysis, based on the combination of time–frequency analysis of phase transfer entropy, graph theory analysis, and power spectrum compensation, can aid in the diagnosis of EZ for pediatric focal epilepsy patients with FCD type II.

Brain-wide neural co-activations in resting human

Spontaneous neural activity in human as assessed with resting-state functional magnetic resonance imaging (fMRI) exhibits brain-wide coordinated patterns in the frequency of < 0.1 Hz. However, understanding of fast brain-wide networks at the timescales of neuronal events (milliseconds to sub-seconds) and their spatial, spectral, and transitional characteristics remain limited due to the temporal constraints of hemodynamic signals. With milli-second resolution and whole-head coverage, scalp-based electroencephalography (EEG) provides a unique window into brain-wide networks with neuronal-timescale dynamics, shedding light on the organizing principles of brain functions. Using the state-of-the-art signal processing techniques, we reconstructed cortical neural tomography from resting-state EEG and extracted component-based co-activation patterns (cCAPs). These cCAPs revealed brain-wide intrinsic networks and their dynamics, indicating the configuration/reconfiguration of resting human brains into recurring and transitional functional states, which are featured with the prominent spatial phenomena of global patterns and anti-state pairs of co-(de)activations. Rich oscillational structures across a wide frequency band (i.e., 0.6 Hz, 5 Hz, and 10 Hz) were embedded in the nonstationary dynamics of these functional states. We further identified a superstructure that regulated between-state immediate and long-range transitions involving the entire set of identified cCAPs and governed a significant aspect of brain-wide network dynamics. These findings demonstrated how resting-state EEG data can be functionally decomposed using cCAPs to reveal rich dynamic structures of brain-wide human neural activations.

Spontaneous Spiking Is Governed by Broadband Fluctuations

Populations of cortical neurons generate rhythmic fluctuations in their ongoing spontaneous activity. These fluctuations can be seen in the local field potential (LFP), which reflects summed return currents from synaptic activity in the local population near a recording electrode. The LFP is spectrally broad, and many researchers view this breadth as containing many narrowband oscillatory components that may have distinct functional roles. This view is supported by the observation that the phase of narrowband oscillations is often correlated with cortical excitability and can relate to the timing of spiking activity and the fidelity of sensory evoked responses. Accordingly, researchers commonly tune in to these channels by narrowband filtering the LFP. Alternatively, neural activity may be fundamentally broadband and composed of transient, nonstationary rhythms that are difficult to approximate as oscillations. In this view, the instantaneous state of the broad ensemble relates directly to the excitability of the local population with no particular allegiance to any frequency band. To test between these alternatives, we asked whether the spiking activity of neocortical neurons in marmoset of either sex is better aligned with the phase of the LFP within narrow frequency bands or with a broadband measure. We find that the phase of broadband LFP fluctuations provides a better predictor of spike timing than the phase after filtering in narrow bands. These results challenge the view of the neocortex as a system composed of narrowband oscillators and supports a view in which neural activity fluctuations are intrinsically broadband.SIGNIFICANCE STATEMENT Research into the dynamical state of neural populations often attributes unique significance to the state of narrowband oscillatory components. However, rhythmic fluctuations in cortical activity are nonstationary and broad spectrum. We find that the timing of spontaneous spiking activity is better captured by the state of broadband fluctuations over any latent oscillatory component. These results suggest narrowband interpretations of rhythmic population activity may be limited, and broader representations may provide higher fidelity in describing moment-to-moment fluctuations in cortical activity.

Pathological and neurophysiological outcomes of seeding human-derived tau pathology in the APP-KI NL-G-F and NL-NL mouse models of Alzheimer's Disease

The two main histopathological hallmarks that characterize Alzheimer’s Disease are the presence of amyloid plaques and neurofibrillary tangles. One of the current approaches to studying the consequences of amyloid pathology relies on the usage of transgenic animal models that incorporate the mutant humanized form of the amyloid precursor protein (hAPP), with animal models progressively developing amyloid pathology as they age. However, these mice models generally overexpress the hAPP protein to facilitate the development of amyloid pathology, which has been suggested to elicit pathological and neuropathological changes unrelated to amyloid pathology. In this current study, we characterized APP knock-in (APP-KI) animals, that do not overexpress hAPP but still develop amyloid pathology to understand the influence of protein overexpression. We also induced tau pathology via human-derived tau seeding material to understand the neurophysiological effects of amyloid and tau pathology. We report that tau-seeded APP-KI animals progressively develop tau pathology, exacerbated by the presence of amyloid pathology. Interestingly, older amyloid-bearing, tau-seeded animals exhibited more amyloid pathology in the entorhinal area, isocortex and hippocampus, but not thalamus, which appeared to correlate with impairments in gamma oscillations before seeding. Tau-seeded animals also featured immediate deficits in power spectra values and phase-amplitude indices in the hippocampus after seeding, with gamma power spectra deficits persisting in younger animals. Both deficits in hippocampal phase-amplitude coupling and gamma power differentiate tau-seeded, amyloid-positive animals from buffer controls. Based on our results, impairments in gamma oscillations appear to be strongly associated with the presence and development of amyloid and tau pathology, and may also be an indicator of neuropathology, network dysfunction, and even potential disposition to the future development of amyloid pathology.

Brains in Sync: Practical Guideline for Parent-Infant EEG During Natural Interaction

Parent-infant EEG is a novel hyperscanning paradigm to measure social interaction simultaneously in the brains of parents and infants. The number of studies using parent-infant dual-EEG as a theoretical framework to measure brain-to-brain synchrony during interaction is rapidly growing, while the methodology for measuring synchrony is not yet uniform. While adult dual-EEG methodology is quickly improving, open databases, tutorials, and methodological validations for dual-EEG with infants are largely missing. In this practical guide, we provide a step-by-step manual on how to implement and run parent-infant EEG paradigms in a neurodevelopmental laboratory in naturalistic settings (e.g., free interactions). Next, we highlight insights on the variety of choices that can be made during (pre)processing dual-EEG data, including recommendations on interpersonal neural coupling metrics and interpretations of the results. Moreover, we provide an exemplar dataset of two mother-infant dyads during free interactions ("free play") that may serve as practice material. Instead of providing a critical note, we would like to move the field of parent-infant EEG forward and be transparent about the challenges that come along with the exciting opportunity to study the development of our social brain within the naturalistic context of dual-EEG.

Neural Synchrony and Network Dynamics in Social Interaction: A Hyper-Brain Cell Assembly Hypothesis

Mounting neurophysiological evidence suggests that interpersonal interaction relies on continual communication between cell assemblies within interacting brains and continual adjustments of these neuronal dynamic states between the brains. In this Hypothesis and Theory article, a Hyper-Brain Cell Assembly Hypothesis is suggested on the basis of a conceptual review of neural synchrony and network dynamics and their roles in emerging cell assemblies within the interacting brains. The proposed hypothesis states that such cell assemblies can emerge not only within, but also between the interacting brains. More precisely, the hyper-brain cell assembly encompasses and integrates oscillatory activity within and between brains, and represents a common hyper-brain unit, which has a certain relation to social behavior and interaction. Hyper-brain modules or communities, comprising nodes across two or several brains, are considered as one of the possible representations of the hypothesized hyper-brain cell assemblies, which can also have a multidimensional or multilayer structure. It is concluded that the neuronal dynamics during interpersonal interaction is brain-wide, i.e., it is based on common neuronal activity of several brains or, more generally, of the coupled physiological systems including brains.

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

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

Prefrontal theta phase-dependent rTMS-induced plasticity of cortical and behavioral responses in human cortex

BACKGROUND

Prefrontal theta oscillations are involved in neuronal information transfer and retention. Phases along the theta cycle represent varied excitability states, whereby high-excitability states correspond to high-frequency neuronal activity and heightened capacity for plasticity induction, as demonstrated in animal studies. Human studies corroborate this model and suggest a core role of prefrontal theta activity in working memory (WM).

OBJECTIVE/HYPOTHESIS

We aimed at modulating prefrontal neuronal excitability and WM performance in healthy humans, using real-time EEG analysis for triggering repetitive transcranial magnetic stimulation (rTMS) theta-phase synchronized to the left dorsomedial prefrontal cortex.

METHODS

16 subjects underwent 3 different rTMS interventions on separate days, with pulses triggered according to the individual's real-time EEG activity: 400 rTMS gamma-frequency (100 Hz) triplet bursts applied during either the negative peak of the prefrontal theta oscillation, the positive peak, or at random phase. Changes in cortical excitability were assessed with EEG responses following single-pulse TMS, and behavioral effects by using a WM task.

RESULTS

Negative-peak rTMS increased single-pulse TMS-induced prefrontal theta power and theta-gamma phase-amplitude coupling, and decreased WM response time. In contrast, positive-peak rTMS decreased prefrontal theta power, while no changes were observed after random-phase rTMS.

CONCLUSION

Findings point to the feasibility of EEG-TMS technology in a theta-gamma phase-amplitude coupling mode for effectively modifying WM networks in human prefrontal cortex, with potential for therapeutic applications.

Transcorneal Electrical Stimulation Induces Long-Lasting Enhancement of Brain Functional and Directional Connectivity in Retinal Degeneration Mice

To investigate neuromodulation of functional and directional connectivity features in both visual and non-visual brain cortices after short-term and long-term retinal electrical stimulation in retinal degeneration mice. We performed spontaneous electrocorticography (ECoG) in retinal degeneration (rd) mice following prolonged transcorneal electrical stimulation (pTES) at varying currents (400, 500 and 600 μA) and different time points (transient or day 1 post-stimulation, 1-week post-stimulation and 2-weeks post-stimulation). We also set up a sham control group of rd mice which did not receive any electrical stimulation. Subsequently we analyzed alterations in cross-frequency coupling (CFC), coherence and directional connectivity of the primary visual cortex and the prefrontal cortex. It was observed that the sham control group did not display any significant changes in brain connectivity across all stages of electrical stimulation. For the stimulated groups, we observed that transient electrical stimulation of the retina did not significantly alter brain coherence and connectivity. However, for 1-week post-stimulation, we identified enhanced increase in theta-gamma CFC. Meanwhile, enhanced coherence and directional connectivity appeared predominantly in theta, alpha and beta oscillations. These alterations occurred in both visual and non-visual brain regions and were dependent on the current amplitude of stimulation. Interestingly, 2-weeks post-stimulation demonstrated long-lasting enhancement in network coherence and connectivity patterns at the level of cross-oscillatory interaction, functional connectivity and directional inter-regional communication between the primary visual cortex and prefrontal cortex. Application of electrical stimulation to the retina evidently neuromodulates brain coherence and connectivity of visual and non-visual cortices in retinal degeneration mice and the observed alterations are largely maintained. pTES holds strong possibility of modulating higher cortical functions including pathways of cognition, awareness, emotion and memory.

A ketogenic diet affects brain volume and metabolome in juvenile mice

Ketogenic diet (KD) is a high-fat and low-carbohydrate therapy for medically intractable epilepsy, and its applications in other neurological conditions, including those occurring in children, have been increasingly tested. However, how KD affects childhood neurodevelopment, a highly sensitive and plastic process, is not clear. In this study, we explored structural, metabolic, and functional consequences of a brief treatment of a strict KD (weight ratio of fat to carbohydrate plus protein is approximately 6.3:1) in naive juvenile mice of different inbred strains, using a multidisciplinary approach. Systemic measurements using magnetic resonance imaging revealed that unexpectedly, the volumes of most brain structures in KD-fed mice were about 90% of those in mice of the same strain but fed a standard diet. The reductions in volumes were nonselective, including different regions throughout the brain, the ventricles, and the white matter. The relative volumes of different brain structures were unaltered. Additionally, as KD is a metabolism-based treatment, we performed untargeted metabolomic profiling to explore potential means by which KD affected brain growth and to identify metabolic changes in the brain. We found that brain metabolomic profile was significantly impacted by KD, through both distinct and common pathways in different mouse strains. To explore whether the volumetric and metabolic changes induced by this KD treatment were associated with functional consequences, we recorded spontaneous EEG to measure brain network activity. Results demonstrated limited alterations in EEG patterns in KD-fed animals. In addition, we observed that cortical levels of brain-derived neurotrophic factor (BDNF), a critical molecule in neurodevelopment, did not change in KD-fed animals. Together, these findings indicate that a strict KD could affect volumetric development and metabolic profile of the brain in inbred juvenile mice, while global network activities and BDNF signaling in the brain were mostly preserved. Whether the volumetric and metabolic changes are related to any core functional consequences during neurodevelopment and whether they are also observed in humans need to be further investigated. In addition, our results indicate that certain outcomes of KD are specific to the individual mouse strains tested, suggesting that the physiological profiles of individuals may need to be examined to maximize the clinical benefit of KD.

Graph Theoretic Analysis of Multilayer EEG Connectivity Networks

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

Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis

Objective Dyslexia diagnosis is a challenging task, since traditional diagnosis methods are not based on biological markers but on behavioural tests. Although dyslexia diagnosis has been addressed by these tests in clinical practice, it is difficult to extract information about the brain processes involved in the different tasks and, then, to go deeper into its biological basis. Thus, the use of biomarkers can contribute not only to the diagnosis but also to a better understanding of specific learning disorders such as dyslexia. In this work, we use Electroencephalography (EEG) signals to discover differences among controls and dyslexic subjects using signal processing and artificial intelligence techniques. Specifically, we measure phase synchronization among channels, to reveal the functional brain network activated during auditory processing. On the other hand, to explore synchronicity patterns risen by low-level auditory processing, we used specific stimuli consisting in band-limited white noise, modulated in amplitude at different frequencies. The differential information contained in the functional (i.e., synchronization) network has been processed by an anomaly detection system that addresses the problem of subjects variability by an outlier-detection method based on vector quantization. The results, obtained for 7 years-old children, show that the proposed method constitutes an useful tool for clinical use, with the area under ROC curve (AUC) values up to 0.95 in differential diagnosis tasks.

Impoverished Inhibitory Control Exacerbates Multisensory Impairments in Older Fallers

Impaired temporal perception of multisensory cues is a common phenomenon observed in older adults that can lead to unreliable percepts of the external world. For instance, the sound induced flash illusion (SIFI) can induce an illusory percept of a second flash by presenting a beep close in time to an initial flash-beep pair. Older adults that have enhanced susceptibility to a fall demonstrate significantly stronger illusion percepts during the SIFI task compared to those older adults without any history of falling. We hypothesize that a global inhibitory deficit may be driving the impairments across both postural stability and multisensory function in older adults with a fall history (FH). We investigated oscillatory activity and perceptual performance during the SIFI task, to understand how active sensory processing, measured by gamma (30–80 Hz) power, was regulated by alpha activity (8–13 Hz), oscillations that reflect inhibitory control. Compared to young adults (YA), the FH and non-faller (NF) groups demonstrated enhanced susceptibility to the SIFI. Further, the FH group had significantly greater illusion strength compared to the NF group. The FH group also showed significantly impaired performance relative to YA during congruent trials (2 flash-beep pairs resulting in veridical perception of 2 flashes). In illusion compared to non-illusion trials, the NF group demonstrated reduced alpha power (or diminished inhibitory control). Relative to YA and NF, the FH group showed reduced phase-amplitude coupling between alpha and gamma activity in non-illusion trials. This loss of inhibitory capacity over sensory processing in FH compared to NF suggests a more severe change than that consequent of natural aging.

Alpha and high gamma phase amplitude coupling during motor imagery and weighted cross-frequency coupling to extract discriminative cross-frequency patterns

Motor imagery modulates specific neural oscillations like actual movement does. Representatively, suppression of the alpha power (e.g., event-related desynchronization [ERD]) is the typical pattern of motor imagery in the motor cortex. However, in addition to this amplitude-based feature, the coupling across frequencies includes important information about the brain functions and the existence of such complex information has been reported in various invasive studies. Yet, the interaction across multiple frequencies during motor imagery processing is still unclear and has not been widely studied, particularly concerning the non-invasive signals. In this study, we provide empirical evidence of the comodulation between the phase of alpha rhythm and the amplitude of high gamma rhythm during the motor imagery process. We used electroencephalography (EEG) in our investigation during the imagination of left- or right-hand movement recorded from 52 healthy subjects, and quantified the ERD of alpha and phase-amplitude coupling (PAC) which is a relative change of modulation index to the base line period (before the cue). As a result, we found that the coupling between the phase of alpha (8-12 Hz) and the amplitude of high gamma (70-120 Hz) and this PAC decreases during motor imagery and then rebounds to the baseline like alpha ERD (r = 0.29 to 0.42). This correlation between PAC and ERD was particularly stronger in the ipsilateral area. In addition, trials that demonstrated higher alpha power during the ready period (before the cue) showed a larger ERD during motor imagery and similarly, trials with higher modulation index during the ready period yielded a greater decrease in PAC during imagery. In the classification analysis, we found that the effective phase frequency that showed better decoding accuracy in left and right-hand imagery, varied across subjects. Motivated by result, we proposed a weighted cross-frequency coupling (WCFC) method that extracts the maximal discriminative feature by combining band power and CFC. In the evaluation, WCFC with only two electrodes yielded a performance comparable to the conventional algorithm with 64 electrodes in classifying left and right-hand motor imagery. These results indicate that the phase-amplitude frequency plays an important role in motor imagery, and that optimizing this frequency ranges is crucial for extracting information features to decode the motor imagery types.

Multivariate Analysis of Bivariate Phase-Amplitude Coupling in EEG Data Using Tensor Robust PCA

Cross-frequency coupling is emerging as a crucial mechanism that coordinates the integration of spectrally and spatially distributed neuronal oscillations. Recently, phase-amplitude coupling, a form of cross-frequency coupling, where the phase of a slow oscillation modulates the amplitude of a fast oscillation, has gained attention. Existing phase-amplitude coupling measures are mostly confined to either coupling within a region or between pairs of brain regions. Given the availability of multi-channel electroencephalography recordings, a multivariate analysis of phase amplitude coupling is needed to accurately quantify the coupling across multiple frequencies and brain regions. In the present work, we propose a tensor based approach, i.e., higher order robust principal component analysis, to identify response-evoked phase-amplitude coupling across multiple frequency bands and brain regions. Our experiments on both simulated and electroencephalography data demonstrate that the proposed multivariate phase-amplitude coupling method can capture the spatial and spectral dynamics of phase-amplitude coupling more accurately compared to existing methods. Accordingly, we posit that the proposed higher order robust principal component analysis based approach filters out the background phase-amplitude coupling activity and predominantly captures the event-related phase-amplitude coupling dynamics to provide insight into the spatially distributed brain networks across different frequency bands.

Addressing Pitfalls in Phase-Amplitude Coupling Analysis with an Extended Modulation Index Toolbox

Phase-amplitude coupling (PAC) is proposed to play an essential role in coordinating the processing of information on local and global scales. In recent years, the methods able to reveal trustworthy PAC has gained considerable interest. However, the intrinsic features of some signals can lead to the identification of spurious or waveform-dependent coupling. This prompted us to develop an easily accessible tool that could be used to differentiate spurious from authentic PAC. Here, we propose a new tool for more reliable detection of PAC named the Extended Modulation Index (eMI) based on the classical Modulation Index measure of coupling. eMI is suitable both for continuous and epoched data and allows estimation of the statistical significance of each pair of frequencies for phase and for amplitude in the whole comodulogram in the framework of extreme value statistics. We compared eMI with the reference PAC measures-direct PAC estimator (a modification of Mean Vector Length) and standard Modulation Index. All three methods were tested using computer-simulated data and actual local field potential recordings from freely moving rats. All methods exhibited similar properties in terms of sensitivity and specificity of PAC detection. eMI proved to be more selective in the dimension of frequency for phase. One of the novelty's offered by eMI is a heuristic algorithm for classification of PAC as Reliable or Ambiguous. It relies on analysis of the relation between the spectral properties of the signal and the detected coupling. Moreover, eMI generates visualizations that support further evaluation of the coupling properties. It also introduces the concept of the polar phase-histogram to study phase relations of coupled slow and fast oscillations. We discuss the extent to which eMI addresses the known problems of interpreting PAC. The Matlab® toolbox implementing eMI framework, and the two reference PAC estimators is freely available as EEGLAB plugin at https://github.com/GabrielaJurkiewicz/ePAC .

Generative models, linguistic communication and active inference

This paper presents a biologically plausible generative model and inference scheme that is capable of simulating communication between synthetic subjects who talk to each other. Building on active inference formulations of dyadic interactions, we simulate linguistic exchange to explore generative models that support dialogues. These models employ high-order interactions among abstract (discrete) states in deep (hierarchical) models. The sequential nature of language processing mandates generative models with a particular factorial structure-necessary to accommodate the rich combinatorics of language. We illustrate linguistic communication by simulating a synthetic subject who can play the 'Twenty Questions' game. In this game, synthetic subjects take the role of the questioner or answerer, using the same generative model. This simulation setup is used to illustrate some key architectural points and demonstrate that many behavioural and neurophysiological correlates of linguistic communication emerge under variational (marginal) message passing, given the right kind of generative model. For example, we show that theta-gamma coupling is an emergent property of belief updating, when listening to another.

Application of Global Coherence Measure to Characterize Coordinated Neural Activity during Frontal and Temporal Lobe Epilepsy

Time- and frequency-domain studies of EEG signals are most commonly employed to study the electrical activities of the brain in order to diagnose potential neurological disorders. In this work, we applied the global coherence approach to help estimating the neural synchrony across multiple nodes in the brain, prior and during a seizure. The ratio of the largest eigenvalue to the sum of the eigenvalues of the cross spectral matrix at a certain frequency and time allowed detecting a strong coordinated neural activity in alpha sub-band for the frontal lobe epilepsy. Kruskal Wallis test reveals that global coherence is an efficient tool before the seizure for the temporal lobe epilepsy in a wide range of frequencies from Delta to Beta sub-bands.Clinical Relevance-The work introduces global coherence as a new and efficient feature in prediction of seizure and specifically for the frontal lobe epilepsy.

Multielectrode array analysis of EEG biomarkers in a mouse model of Fragile X Syndrome

Fragile X Syndrome (FXS) is a leading known genetic cause of intellectual disability with symptoms that include increased anxiety and social and sensory processing deficits. Recent EEG studies in humans with FXS have identified neural oscillation deficits that include increased resting state gamma power, increased amplitude of auditory evoked potentials, and reduced inter-trial phase coherence of sound-evoked gamma oscillations. Identification of comparable EEG biomarkers in mouse models of FXS could facilitate the pre-clinical to clinical therapeutic pipeline. However, while human EEG studies have involved 128-channel scalp EEG acquisition, no mouse studies have been performed with more than three EEG channels. In the current study, we employed a recently developed 30-channel mouse multielectrode array (MEA) system to record and analyze resting and stimulus-evoked EEG signals in WT vs. Fmr1 KO mice. Using this system, we now report robust MEA-derived phenotypes including higher resting EEG power, altered event-related potentials (ERPs) and reduced inter-trial phase coherence to auditory chirp stimuli in Fmr1 KO mice that are remarkably similar to those reported in humans with FXS. We propose that the MEA system can be used for: (i) derivation of higher-level EEG parameters; (ii) EEG biomarkers for drug testing; and (ii) mechanistic studies of FXS pathophysiology.