Dynamic large-scale network synchronization from perception to action

Distinct Hippocampal Oscillation Dynamics in Trace Eyeblink Conditioning Task for Retrieval and Consolidation of Associations

Trace eyeblink conditioning (TEBC) has been widely used to study associative learning in both animals and humans. In this paradigm, conditioned responses (CRs) to conditioned stimuli (CS) serve as a measure for retrieving learned associations between the CS and the unconditioned stimuli (US) within a trial. Memory consolidation, that is, learning over time, can be quantified as an increase in the proportion of CRs across training sessions. However, how hippocampal oscillations differentiate between successful memory retrieval within a session and consolidation across TEBC training sessions remains unknown. To address this question, we recorded local field potentials (LFPs) from the rat dorsal hippocampus during TEBC and investigated hippocampal oscillation dynamics associated with these two functions. We show that transient broadband responses to the CS were correlated with memory consolidation, as indexed by an increase in CRs across TEBC sessions. In contrast, induced alpha (8-10 Hz) and beta (16-20 Hz) band responses were correlated with the successful retrieval of the CS-US association within a session, as indexed by the difference in trials with and without CR.

Multiplex dynamic networks in the newborn brain disclose latent links with neurobehavioral phenotypes

The higher brain functions arise from coordinated neural activity between distinct brain regions, but the spatial, temporal, and spectral complexity of these functional connectivity networks (FCNs) has challenged the identification of correlates with neurobehavioral phenotypes. Characterizing behavioral correlates of early life FCNs is important to understand the activity dependent emergence of neurodevelopmental performance and for improving health outcomes. Here, we develop an analysis pipeline for identifying multiplex dynamic FCNs that combine spectral and spatiotemporal characteristics of the newborn cortical activity. This data-driven approach automatically uncovers latent networks that show robust neurobehavioral correlations and consistent effects by in utero drug exposure. Altogether, the proposed pipeline provides a robust end-to-end solution for an objective assessment and quantitation of neurobehaviorally meaningful network constellations in the highly dynamic cortical functions.

The influence of inter-regional delays in generating large-scale brain networks of phase synchronization

Large-scale networks of phase synchronization are considered to regulate the communication between brain regions fundamental to cognitive function, but the mapping to their structural substrates, i.e., the structure-function relationship, remains poorly understood. Biophysical Network Models (BNMs) have demonstrated the influences of local oscillatory activity and inter-regional anatomical connections in generating alpha-band (8-12 Hz) networks of phase synchronization observed with Electroencephalography (EEG) and Magnetoencephalography (MEG). Yet, the influence of inter-regional conduction delays remains unknown. In this study, we compared a BNM with standard "distance-dependent delays", which assumes constant conduction velocity, to BNMs with delays specified by two alternative methods accounting for spatially varying conduction velocities, "isochronous delays" and "mixed delays". We followed the Approximate Bayesian Computation (ABC) workflow, i) specifying neurophysiologically informed prior distributions of BNM parameters, ii) verifying the suitability of the prior distributions with Prior Predictive Checks, iii) fitting each of the three BNMs to alpha-band MEG resting-state data (N = 75) with Bayesian optimization for Likelihood-Free Inference (BOLFI), and iv) choosing between the fitted BNMs with ABC model comparison on a separate MEG dataset (N = 30). Prior Predictive Checks revealed the range of dynamics generated by each of the BNMs to encompass those seen in the MEG data, suggesting the suitability of the prior distributions. Fitting the models to MEG data yielded reliable posterior distributions of the parameters of each of the BNMs. Finally, model comparison revealed the BNM with "distance-dependent delays", as the most probable to describe the generation of alpha-band networks of phase synchronization seen in MEG. These findings suggest that distance-dependent delays might contribute to the neocortical architecture of human alpha-band networks of phase synchronization. Hence, our study illuminates the role of inter-regional delays in generating the large-scale networks of phase synchronization that might subserve the communication between regions vital to cognition.

Brain criticality predicts individual levels of inter-areal synchronization in human electrophysiological data

Neuronal oscillations and their synchronization between brain areas are fundamental for healthy brain function. Yet, synchronization levels exhibit large inter-individual variability that is associated with behavioral variability. We test whether individual synchronization levels are predicted by individual brain states along an extended regime of critical-like dynamics - the Griffiths phase (GP). We use computational modelling to assess how synchronization is dependent on brain criticality indexed by long-range temporal correlations (LRTCs). We analyze LRTCs and synchronization of oscillations from resting-state magnetoencephalography and stereo-electroencephalography data. Synchronization and LRTCs are both positively linearly and quadratically correlated among healthy subjects, while in epileptogenic areas they are negatively linearly correlated. These results show that variability in synchronization levels is explained by the individual position along the GP with healthy brain areas operating in its subcritical and epileptogenic areas in its supercritical side. We suggest that the GP is fundamental for brain function allowing individual variability while retaining functional advantages of criticality.

Modules in connectomes of phase-synchronization comprise anatomically contiguous, functionally related regions

Modules in brain functional connectomes are essential to balancing segregation and integration of neuronal activity. Connectomes are the complete set of pairwise connections between brain regions. Non-invasive Electroencephalography (EEG) and Magnetoencephalography (MEG) have been used to identify modules in connectomes of phase-synchronization. However, their resolution is suboptimal because of spurious phase-synchronization due to EEG volume conduction or MEG field spread. Here, we used invasive, intracerebral recordings from stereo-electroencephalography (SEEG, N = 67), to identify modules in connectomes of phase-synchronization. To generate SEEG-based group-level connectomes affected only minimally by volume conduction, we used submillimeter accurate localization of SEEG contacts and referenced electrode contacts in cortical gray matter to their closest contacts in white matter. Combining community detection methods with consensus clustering, we found that the connectomes of phase-synchronization were characterized by distinct and stable modules at multiple spatial scales, across frequencies from 3 to 320 Hz. These modules were highly similar within canonical frequency bands. Unlike the distributed brain systems identified with functional Magnetic Resonance Imaging (fMRI), modules up to the high-gamma frequency band comprised only anatomically contiguous regions. Notably, the identified modules comprised cortical regions involved in shared repertoires of sensorimotor and cognitive functions including memory, language and attention. These results suggest that the identified modules represent functionally specialised brain systems, which only partially overlap with the brain systems reported with fMRI. Hence, these modules might regulate the balance between functional segregation and functional integration through phase-synchronization.

Early brain activity: Translations between bedside and laboratory

Neural activity is both a driver of brain development and a readout of developmental processes. Changes in neuronal activity are therefore both the cause and consequence of neurodevelopmental compromises. Here, we review the assessment of neuronal activities in both preclinical models and clinical situations. We focus on issues that require urgent translational research, the challenges and bottlenecks preventing translation of biomedical research into new clinical diagnostics or treatments, and possibilities to overcome these barriers. The key questions are (i) what can be measured in clinical settings versus animal experiments, (ii) how do measurements relate to particular stages of development, and (iii) how can we balance practical and ethical realities with methodological compromises in measurements and treatments.

Electroencephalographic Microstates are Correlated with Global Functioning in Schizophrenia But Not in Bipolar Disorder

Objectives. Microstate studies of electroencephalograms (EEGs) on schizophrenia (SCZ) and bipolar disorder (BD) demonstrated categorical differences. The relationship between microstate indices and clinical symptoms in each group, however, remained unclear. Our objective was to examine associations between EEG microstates and the core features of SCZ and BD. Methods. This study examined the resting EEG data of 40 patients with SCZ, 19 patients with BD (12 BD type I and 7 BD type II), and 16 healthy controls. EEG topographic maps were divided into four canonical microstate classes: A, B, C, and D. The Positive and Negative Syndrome Scale (PANSS), Young Mania Rating Scale, Hamilton Depression Rating Scale (HAMD), and Global Assessment of Functioning (GAF) were used to measure clinical symptoms and global functioning. Results. There was a significant inverse correlation between the proportion of time spent in microstate class A and GAF in patients with SCZ but not BD. Furthermore, the occurrence of microstate class A was positively correlated with the Positive Scale scores of the PANSS. Nevertheless, there were no group differences between the microstate classes. Conclusions. The results of this study indicate a negative correlation between microstate class A and global functioning in SCZ but not in BD. The association may be mediated by positive symptoms of SZ. Neural mechanisms underlying this relationship require further investigation.

Connectomics of human electrophysiology

We present both a scientific overview and conceptual positions concerning the challenges and assets of electrophysiological measurements in the search for the nature and functions of the human connectome. We discuss how the field has been inspired by findings and approaches from functional magnetic resonance imaging (fMRI) and informed by a small number of significant multimodal empirical studies, which show that the canonical networks that are commonplace in fMRI are in fact rooted in electrophysiological processes. This review is also an opportunity to produce a brief, up-to-date critical survey of current data modalities and analytical methods available for deriving both static and dynamic connectomes from electrophysiology. We review hurdles that challenge the significance and impact of current electrophysiology connectome research. We then encourage the field to take a leap of faith and embrace the wealth of electrophysiological signals, despite their apparent, disconcerting complexity. Our position is that electrophysiology connectomics is poised to inform testable mechanistic models of information integration in hierarchical brain networks, constructed from observable oscillatory and aperiodic signal components and their polyrhythmic interactions.

Pre-stimulus alpha oscillation and post-stimulus cortical activity differ in localization between consciously perceived and missed near-threshold somatosensory stimuli

Conscious perception of a near‐threshold (NT) stimulus is characterized by the pre‐ and post‐stimulus brain state. However, the power of pre‐stimulus neural oscillations and strength of post‐stimulus cortical activity that lead to conscious perception have rarely been examined in individual cortical areas. This is because most previous electro‐ and magnetoencephalography (EEG and MEG, respectively) studies involved scalp‐ and sensor‐level analyses. Therefore, we recorded MEG during a continuous NT somatosensory stimulus detection task and applied the reconstructed source data in order to identify cortical areas where the post‐stimulus cortical activity and pre‐stimulus alpha oscillation predict the conscious perception of NT somatosensory stimuli. We found that the somatosensory hierarchical processing areas, prefrontal areas and cortical areas belonging to the default mode network showed stronger cortical activity for consciously perceived trials in the post‐stimulus period, but the cortical activity in primary somatosensory area (SI) is independent of conscious perception during the early stage of NT stimulus processing. In addition, we revealed that the pre‐stimulus alpha oscillation only in SI is predictive of conscious perception. These findings suggest that the bottom‐up stream of somatosensory information flow following SI and pre‐stimulus alpha activity fluctuation in SI as a top‐down modulation are crucial constituents of conscious perception.

Spatiotemporal Characteristics of Neural Dynamics in Theta Oscillations Related to the Inhibition of Habitual Behavior

The human brain carries out cognitive control for the inhibition of habitual behaviors by suppressing some familiar but inappropriate behaviors instead of engaging specific goal-directed behavior flexibly in a given situation. To examine the characteristics of neural dynamics related to such inhibition of habitual behaviors, we used a modified rock–paper–scissors (RPS) task that consisted of a basic, a lose-, and a win-conditioned game. Spectral and phase synchrony analyses were conducted to examine the acquired electroencephalogram signals across the entire brain during all RPS tasks. Temporal variations in frontal theta power activities were directly in line with the stream of RPS procedures in accordance with the task conditions. The lose-conditioned RPS task gave rise to increases in the local frontal power and global phase-synchronized pairs of theta oscillations. The activation of the global phase-synchronized network preceded the activation of frontal theta power. These results demonstrate that the frontal regions play a pivotal role in the inhibition of habitual behaviors—stereotyped and ingrained stimulus–response mappings that have been established over time. This study suggests that frontal theta oscillations may be engaged during the cognitive inhibition of habitual behaviors and that these oscillations characterize the degree of cognitive load required to inhibit habitual behaviors.

Use of complex visual stimuli allows controlled recruitment of cortical networks in infants

OBJECTIVE

To characterize cortical networks activated by patterned visual stimuli in infants, and to evaluate their potential for assessment of visual processing and their associations with neurocognitive development.

METHODS

Three visual stimuli, orientation reversal (OR), global form (GF), and global motion (GM), were presented to cohort of five-month-old infants (N = 26). Eye tracker was used to guide the stimulation and to choose epochs for analysis. Visual responses were recorded with electroencephalography and analysed in source space using weighted phase lag index as the connectivity measure. The networks were quantified using several metrics that were compared between stimuli and correlated to cognitive outcomes.

RESULTS

Responses to OR/GF/GM stimuli were observed in nearly all (96/100/100%) recordings. All stimuli recruited cortical networks that were partly condition-specific in their characteristics. The more complex GF and GM conditions recruited wider global networks than OR. Additionally, strength of the GF network showed positive association with later cognitive performance.

CONCLUSIONS

Network analysis suggests that visual stimulation recruits large-scale cortical networks that extend far beyond the conventional visual streams and that differ between stimulation conditions.

SIGNIFICANCE

The method allows controlled recruitment of wide cortical networks, which holds promise for the early assessment of visual processing and its related higher-order cognitive processes.

Spatiotemporal Integrity and Spontaneous Nonlinear Dynamic Properties of the Salience Network Revealed by Human Intracranial Electrophysiology: A Multicohort Replication

The salience network (SN) plays a critical role in cognitive control and adaptive human behaviors, but its electrophysiological foundations and millisecond timescale dynamic temporal properties are poorly understood. Here, we use invasive intracranial EEG (iEEG) from multiple cohorts to investigate the neurophysiological underpinnings of the SN and identify dynamic temporal properties that distinguish it from the default mode network (DMN) and dorsolateral frontal-parietal network (FPN), two other large-scale brain networks that play important roles in human cognition. iEEG analysis of network interactions revealed that the anterior insula and anterior cingulate cortex, which together anchor the SN, had stronger intranetwork interactions with each other than cross-network interactions with the DMN and FPN. Analysis of directionality of information flow between the SN, DMN, and FPN revealed causal outflow hubs in the SN consistent with its role in fast temporal switching of network interactions. Analysis of regional iEEG temporal fluctuations revealed faster temporal dynamics and higher entropy of neural activity within the SN, compared to the DMN and FPN. Critically, these results were replicated across multiple cohorts. Our findings provide new insights into the neurophysiological basis of the SN, and more broadly, foundational mechanisms underlying the large-scale functional organization of the human brain.

Genuine cross-frequency coupling networks in human resting-state electrophysiological recordings

Phase synchronization of neuronal oscillations in specific frequency bands coordinates anatomically distributed neuronal processing and communication. Typically, oscillations and synchronization take place concurrently in many distinct frequencies, which serve separate computational roles in cognitive functions. While within-frequency phase synchronization has been studied extensively, less is known about the mechanisms that govern neuronal processing distributed across frequencies and brain regions. Such integration of processing between frequencies could be achieved via cross-frequency coupling (CFC), either by phase–amplitude coupling (PAC) or by n:m-cross–frequency phase synchrony (CFS). So far, studies have mostly focused on local CFC in individual brain regions, whereas the presence and functional organization of CFC between brain areas have remained largely unknown. We posit that interareal CFC may be essential for large-scale coordination of neuronal activity and investigate here whether genuine CFC networks are present in human resting-state (RS) brain activity. To assess the functional organization of CFC networks, we identified brain-wide CFC networks at mesoscale resolution from stereoelectroencephalography (SEEG) and at macroscale resolution from source-reconstructed magnetoencephalography (MEG) data. We developed a novel, to our knowledge, graph-theoretical method to distinguish genuine CFC from spurious CFC that may arise from nonsinusoidal signals ubiquitous in neuronal activity. We show that genuine interareal CFC is present in human RS activity in both SEEG and MEG data. Both CFS and PAC networks coupled theta and alpha oscillations with higher frequencies in large-scale networks connecting anterior and posterior brain regions. CFS and PAC networks had distinct spectral patterns and opposing distribution of low- and high-frequency network hubs, implying that they constitute distinct CFC mechanisms. The strength of CFS networks was also predictive of cognitive performance in a separate neuropsychological assessment. In conclusion, these results provide evidence for interareal CFS and PAC being 2 distinct mechanisms for coupling oscillations across frequencies in large-scale brain networks.

Genuine interareal cross-frequency coupling (CFC) can be identified from human resting state activity using magnetoencephalography, stereoelectroencephalography, and novel network approaches. CFC couples slow theta and alpha oscillations to faster oscillations across brain regions.

Influence of multiple hypothesis testing on reproducibility in neuroimaging research: A simulation study and Python-based software

BACKGROUND

Reproducibility of research findings has been recently questioned in many fields of science, including psychology and neurosciences. One factor influencing reproducibility is the simultaneous testing of multiple hypotheses, which entails false positive findings unless the analyzed p-values are carefully corrected. While this multiple testing problem is well known and studied, it continues to be both a theoretical and practical problem.

NEW METHOD

Here we assess reproducibility in simulated experiments in the context of multiple testing. We consider methods that control either the family-wise error rate (FWER) or false discovery rate (FDR), including techniques based on random field theory (RFT), cluster-mass based permutation testing, and adaptive FDR. Several classical methods are also considered. The performance of these methods is investigated under two different models.

RESULTS

We found that permutation testing is the most powerful method among the considered approaches to multiple testing, and that grouping hypotheses based on prior knowledge can improve power. We also found that emphasizing primary and follow-up studies equally produced most reproducible outcomes.

COMPARISON WITH EXISTING METHOD(S)

We have extended the use of two-group and separate-classes models for analyzing reproducibility and provide a new open-source software "MultiPy" for multiple hypothesis testing.

CONCLUSIONS

Our simulations suggest that performing strict corrections for multiple testing is not sufficient to improve reproducibility of neuroimaging experiments. The methods are freely available as a Python toolkit "MultiPy" and we aim this study to help in improving statistical data analysis practices and to assist in conducting power and reproducibility analyses for new experiments.

Intrinsic connectome organization across temporal scales: New insights from cross-modal approaches

The discovery of a stable, whole-brain functional connectivity organization that is largely independent of external events has drastically extended our view of human brain function. However, this discovery has been primarily based on functional magnetic resonance imaging (fMRI). The role of this whole-brain organization in fast oscillation-based connectivity as measured, for example, by electroencephalography (EEG) and magnetoencephalography (MEG) is only beginning to emerge. Here, we review studies of intrinsic connectivity and its whole-brain organization in EEG, MEG, and intracranial electrophysiology with a particular focus on direct comparisons to connectome studies in fMRI. Synthesizing this literature, we conclude that irrespective of temporal scale over four orders of magnitude, intrinsic neurophysiological connectivity shows spatial similarity to the connectivity organization commonly observed in fMRI. A shared structural connectivity basis and cross-frequency coupling are possible mechanisms contributing to this similarity. Acknowledging that a stable whole-brain organization governs long-range coupling across all timescales of neural processing motivates researchers to take "baseline" intrinsic connectivity into account when investigating brain-behavior associations, and further encourages more widespread exploration of functional connectomics approaches beyond fMRI by using EEG and MEG modalities.

Neuronal correlates of full and partial visual conscious perception

Stimuli may induce only partial consciousness-an intermediate between null and full consciousness-where the presence but not identity of an object can be reported. The differences in the neuronal basis of full and partial consciousness are poorly understood. We investigated if evoked and oscillatory activity could dissociate full from partial conscious perception. We recorded human cortical activity with magnetoencephalography (MEG) during a visual perception task in which stimulus could be either partially or fully perceived. Partial consciousness was associated with an early increase in evoked activity and theta/low-alpha-band oscillations while full consciousness was also associated with late evoked activity and beta-band oscillations. Full from partial consciousness was dissociated by stronger evoked activity and late increase in theta oscillations that were localized to higher-order visual regions and posterior parietal and prefrontal cortices. Our results reveal both evoked activity and theta oscillations dissociate partial and full consciousness.

Heartbeat Induces a Cortical Theta-Synchronized Network in the Resting State

In the resting state, heartbeats evoke cortical responses called heartbeat-evoked responses (HERs), which reflect cortical cardiac interoceptive processing. While previous studies have reported that the heartbeat evokes cortical responses at a regional level, whether the heartbeat induces synchronization between regions to form a network structure remains unknown. Using resting-state MEG data from 85 human subjects of both genders, we first showed that heartbeat increases the phase synchronization between cortical regions in the theta frequency but not in other frequency bands. This increase in synchronization between cortical regions formed a network structure called the heartbeat-induced network (HIN), which did not reflect artificial phase synchronization. In the HIN, the left inferior temporal gyrus and parahippocampal gyrus played a central role as hubs. Furthermore, the HIN was modularized, containing five subnetworks called modules. In particular, module 1 played a central role in between-module interactions in the HIN. Furthermore, synchronization within module 1 had a positive association with the mood of an individual. In this study, we show the existence of the HIN and its network properties, advancing the current understanding of cortical heartbeat processing and its relationship with mood, which was previously confined to region level.

The Common Rhythm of Action and Perception

Research in the last decade has undermined the idea of perception as a continuous process, providing strong empirical support for its rhythmic modulation. More recently, it has been revealed that the ongoing motor processes influence the rhythmic sampling of sensory information. In this review, we will focus on a growing body of evidence suggesting that oscillation-based mechanisms may structure the dynamic interplay between the motor and sensory system and provide a unified temporal frame for their effective coordination. We will describe neurophysiological data, primarily collected in animals, showing phase-locking of neuronal oscillations to the onset of (eye) movements. These data are complemented by novel evidence in humans, which demonstrate the behavioral relevance of these oscillatory modulations and their domain-general nature. Finally, we will discuss the possible implications of these modulations for action-perception coupling mechanisms.

Electrophysiological Brain Connectivity: Theory and Implementation

We review the theory and algorithms of electrophysiological brain connectivity analysis. This tutorial is aimed at providing an introduction to brain functional connectivity from electrophysiological signals, including electroencephalography (EEG), magnetoencephalography (MEG), electrocorticography (ECoG), stereoelectroencephalography (SEEG). Various connectivity estimators are discussed, and algorithms introduced. Important issues for estimating and mapping brain functional connectivity with electrophysiology are discussed.

Dynamic large-scale network synchronization from perception to action

Sensory-guided actions entail the processing of sensory information, generation of perceptual decisions, and the generation of appropriate actions. Neuronal activity underlying these processes is distributed into sensory, fronto-parietal, and motor brain areas, respectively. How the neuronal processing is coordinated across these brain areas to support functions from perception to action remains unknown. We investigated whether phase synchronization in large-scale networks coordinate these processes. We recorded human cortical activity with magnetoencephalography (MEG) during a task in which weak somatosensory stimuli remained unperceived or were perceived. We then assessed dynamic evolution of phase synchronization in large-scale networks from source-reconstructed MEG data by using advanced analysis approaches combined with graph theory. Here we show that perceiving and reporting of weak somatosensory stimuli is correlated with sustained strengthening of large-scale synchrony concurrently in delta/theta (3-7 Hz) and gamma (40-60 Hz) frequency bands. In a data-driven network localization, we found this synchronization to dynamically connect the task-relevant, that is, the fronto-parietal, sensory, and motor systems. The strength and temporal pattern of interareal synchronization were also correlated with the response times. These data thus show that key brain areas underlying perception, decision-making, and actions are transiently connected by large-scale dynamic phase synchronization in the delta/theta and gamma bands.