On cross-frequency phase-phase coupling between theta and gamma oscillations in the hippocampus

Multimodal Integration of EEG and Near-Infrared Spectroscopy for Robust Cross-Frequency Coupling Estimation

Neuroimaging techniques have had a major impact on medical science, allowing advances in the research of many neurological diseases and improving their diagnosis. In this context, multimodal neuroimaging approaches, based on the neurovascular coupling phenomenon, exploit their individual strengths to provide complementary information on the neural activity of the brain cortex. This work proposes a novel method for combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to explore the functional activity of the brain processes related to low-level language processing of skilled and dyslexic seven-year-old readers. We have transformed EEG signals into image sequences considering the interaction between different frequency bands by means of cross-frequency coupling (CFC), and applied an activation mask sequence obtained from the local functional brain activity inferred from simultaneously recorded fNIRS signals. Thus, the resulting image sequences preserve spatial and temporal information of the communication and interaction between different neural processes and provide discriminative information that allows differentiation between controls and dyslexic subjects with an AUC of 77.1%. Finally, explainability is improved by introducing an easily comprehensible representation of the SHAP values obtained for the classification method in the brainSHAP maps.

A Novel Real-time Phase Prediction Network in EEG Rhythm

Closed-loop neuromodulation, especially using the phase of the electroencephalography (EEG) rhythm to assess the real-time brain state and optimize the brain stimulation process, is becoming a hot research topic. Because the EEG signal is non-stationary, the commonly used EEG phase-based prediction methods have large variances, which may reduce the accuracy of the phase prediction. In this study, we proposed a machine learning-based EEG phase prediction network, which we call EEG phase prediction network (EPN), to capture the overall rhythm distribution pattern of subjects and map the instantaneous phase directly from the narrow-band EEG data. We verified the performance of EPN on pre-recorded data, simulated EEG data, and a real-time experiment. Compared with widely used state-of-the-art models (optimized multi-layer filter architecture, auto-regress, and educated temporal prediction), EPN achieved the lowest variance and the greatest accuracy. Thus, the EPN model will provide broader applications for EEG phase-based closed-loop neuromodulation.

Phase-Amplitude Coupling in Response to Transcutaneous Vagus Nerve Stimulation: Focus on Regions Implicated in Mood and Memory

OBJECTIVE

This study aimed to investigate whether transcutaneous vagus nerve stimulation (tVNS) at different frequencies affects phase-amplitude coupling among regions of the brain linked to mood and memory disorders using simultaneous magnetoencephalography (MEG) in healthy participants.

MATERIALS AND METHODS

Phase-amplitude coupling was measured among brain areas in response to different stimulation frequencies of tVNS using concurrent MEG and tVNS in 17 healthy participants. The 4 protocols were: 24 Hz cymba concha, 1 Hz cymba concha, PFM cymba concha, and 24 Hz ear lobe. A driven autoregressive method was used to estimate the coupling among brain areas in different physiological frequency bands in response to these protocols.

RESULTS

Different tVNS stimulation protocols led to alterations in phase-amplitude coupling among multiple brain regions linked to mood and memory, notably the prefrontal cortex, hippocampus, and temporal pole. Stimulation delivered at 24 Hz was observed to decrease delta-gamma coupling within the temporal pole and cingulate cortex when contrasted with 24-Hz sham stimulation. Increased alpha-gamma coupling was observed between the hippocampus and prefrontal cortex when contrasting 24 Hz with pulse-frequency-modulated stimulation. Finally, a comparison of 24-Hz with low-frequency 1-Hz stimulation showed an increase in theta-gamma coupling within the prefrontal cortex.

SIGNIFICANCE

To our knowledge, this study represents the first attempt to quantify phase-amplitude coupling in response to tVNS and suggests that different stimulation frequencies can modulate coupling between different areas of the brain. Abnormal phase-amplitude coupling has been linked to multiple mood and memory disorders. Further investigations using different stimulation frequencies of tVNS to alter phase-amplitude coupling may lead to the development of tVNS as a therapeutic option for different medical conditions.

Novel cyclic homogeneous oscillation detection method for high accuracy and specific characterization of neural dynamics

Determining the presence and frequency of neural oscillations is essential to understanding dynamic brain function. Traditional methods that detect peaks over 1/f noise within the power spectrum fail to distinguish between the fundamental frequency and harmonics of often highly non-sinusoidal neural oscillations. To overcome this limitation, we define fundamental criteria that characterize neural oscillations and introduce the cyclic homogeneous oscillation (CHO) detection method. We implemented these criteria based on an autocorrelation approach to determine an oscillation's fundamental frequency. We evaluated CHO by verifying its performance on simulated non-sinusoidal oscillatory bursts and validated its ability to determine the fundamental frequency of neural oscillations in electrocorticographic (ECoG), electroencephalographic (EEG), and stereoelectroencephalographic (SEEG) signals recorded from 27 human subjects. Our results demonstrate that CHO outperforms conventional techniques in accurately detecting oscillations. In summary, CHO demonstrates high precision and specificity in detecting neural oscillations in time and frequency domains. The method's specificity enables the detailed study of non-sinusoidal characteristics of oscillations, such as the degree of asymmetry and waveform of an oscillation. Furthermore, CHO can be applied to identify how neural oscillations govern interactions throughout the brain and to determine oscillatory biomarkers that index abnormal brain function.

Prestimulus Alpha Phase Modulates Visual Temporal Integration

When presented shortly after another, discrete pictures are naturally perceived as continuous. The neuronal mechanism underlying such continuous or discrete perception is not well understood. While continuous alpha oscillations are a candidate for orchestrating such neuronal mechanisms, recent evidence is mixed. In this study, we investigated the influence of prestimulus alpha oscillation on visual temporal perception. Specifically, we were interested in whether prestimulus alpha phase modulates neuronal and perceptual processes underlying discrete or continuous perception. Participants had to report the location of a missing object in a visual temporal integration task, while simultaneously MEG data were recorded. Using source reconstruction, we evaluated local phase effects by contrasting phase angle values between correctly and incorrectly integrated trials. Our results show a phase opposition cluster between -0.8 and -0.5 s (relative to stimulus presentation) and between 6 and 20 Hz. These momentary phase angle values were correlated with behavioral performance and event-related potential amplitude. There was no evidence that frequency defined a window of temporal integration.

Novel Cyclic Homogeneous Oscillation Detection Method for High Accuracy and Specific Characterization of Neural Dynamics

Detecting temporal and spectral features of neural oscillations is essential to understanding dynamic brain function. Traditionally, the presence and frequency of neural oscillations are determined by identifying peaks over 1/f noise within the power spectrum. However, this approach solely operates within the frequency domain and thus cannot adequately distinguish between the fundamental frequency of a non-sinusoidal oscillation and its harmonics. Non-sinusoidal signals generate harmonics, significantly increasing the false-positive detection rate - a confounding factor in the analysis of neural oscillations. To overcome these limitations, we define the fundamental criteria that characterize a neural oscillation and introduce the Cyclic Homogeneous Oscillation (CHO) detection method that implements these criteria based on an auto-correlation approach that determines the oscillation's periodicity and fundamental frequency. We evaluated CHO by verifying its performance on simulated sinusoidal and non-sinusoidal oscillatory bursts convolved with 1/f noise. Our results demonstrate that CHO outperforms conventional techniques in accurately detecting oscillations. Specifically, we determined the sensitivity and specificity of CHO as a function of signal-to-noise ratio (SNR). We further assessed CHO by testing it on electrocorticographic (ECoG, 8 subjects) and electroencephalographic (EEG, 7 subjects) signals recorded during the pre-stimulus period of an auditory reaction time task and on electrocorticographic signals (6 SEEG subjects and 6 ECoG subjects) collected during resting state. In the reaction time task, the CHO method detected auditory alpha and pre-motor beta oscillations in ECoG signals and occipital alpha and pre-motor beta oscillations in EEG signals. Moreover, CHO determined the fundamental frequency of hippocampal oscillations in the human hippocampus during the resting state (6 SEEG subjects). In summary, CHO demonstrates high precision and specificity in detecting neural oscillations in time and frequency domains. The method's specificity enables the detailed study of non-sinusoidal characteristics of oscillations, such as the degree of asymmetry and waveform of an oscillation. Furthermore, CHO can be applied to identify how neural oscillations govern interactions throughout the brain and to determine oscillatory biomarkers that index abnormal brain function.

Cognitive and Neuropathophysiological Outcomes of Gamma-tACS in Dementia: A Systematic Review

Despite the numerous pharmacological interventions targeting dementia, no disease-modifying therapy is available, and the prognosis remains unfavorable. A promising perspective involves tackling high-frequency gamma-band (> 30 Hz) oscillations involved in hippocampal-mediated memory processes, which are impaired from the early stages of typical Alzheimer's Disease (AD). Particularly, the positive effects of gamma-band entrainment on mouse models of AD have prompted researchers to translate such findings into humans using transcranial alternating current stimulation (tACS), a methodology that allows the entrainment of endogenous cortical oscillations in a frequency-specific manner. This systematic review examines the state-of-the-art on the use of gamma-tACS in Mild Cognitive Impairment (MCI) and dementia patients to shed light on its feasibility, therapeutic impact, and clinical effectiveness. A systematic search from two databases yielded 499 records resulting in 10 included studies and a total of 273 patients. The results were arranged in single-session and multi-session protocols. Most of the studies demonstrated cognitive improvement following gamma-tACS, and some studies showed promising effects of gamma-tACS on neuropathological markers, suggesting the feasibility of gamma-tACS in these patients anyhow far from the strong evidence available for mouse models. Nonetheless, the small number of studies and their wide variability in terms of aims, parameters, and measures, make it difficult to draw firm conclusions. We discuss results and methodological limitations of the studies, proposing possible solutions and future avenues to improve research on the effects of gamma-tACS on dementia.

Blood pressure pulsations modulate central neuronal activity via mechanosensitive ion channels

The transmission of the heartbeat through the cerebral vascular system causes intracranial pressure pulsations. We discovered that arterial pressure pulsations can directly modulate central neuronal activity. In a semi-intact rat brain preparation, vascular pressure pulsations elicited correlated local field oscillations in the olfactory bulb mitral cell layer. These oscillations did not require synaptic transmission but reflected baroreceptive transduction in mitral cells. This transduction was mediated by a fast excitatory mechanosensitive ion channel and modulated neuronal spiking activity. In awake animals, the heartbeat entrained the activity of a subset of olfactory bulb neurons within ~20 milliseconds. Thus, we propose that this fast, intrinsic interoceptive mechanism can modulate perception-for example, during arousal-within the olfactory bulb and possibly across various other brain areas.

Trained recurrent neural networks develop phase-locked limit cycles in a working memory task

Neural oscillations are ubiquitously observed in many brain areas. One proposed functional role of these oscillations is that they serve as an internal clock, or 'frame of reference'. Information can be encoded by the timing of neural activity relative to the phase of such oscillations. In line with this hypothesis, there have been multiple empirical observations of such phase codes in the brain. Here we ask: What kind of neural dynamics support phase coding of information with neural oscillations? We tackled this question by analyzing recurrent neural networks (RNNs) that were trained on a working memory task. The networks were given access to an external reference oscillation and tasked to produce an oscillation, such that the phase difference between the reference and output oscillation maintains the identity of transient stimuli. We found that networks converged to stable oscillatory dynamics. Reverse engineering these networks revealed that each phase-coded memory corresponds to a separate limit cycle attractor. We characterized how the stability of the attractor dynamics depends on both reference oscillation amplitude and frequency, properties that can be experimentally observed. To understand the connectivity structures that underlie these dynamics, we showed that trained networks can be described as two phase-coupled oscillators. Using this insight, we condensed our trained networks to a reduced model consisting of two functional modules: One that generates an oscillation and one that implements a coupling function between the internal oscillation and external reference. In summary, by reverse engineering the dynamics and connectivity of trained RNNs, we propose a mechanism by which neural networks can harness reference oscillations for working memory. Specifically, we propose that a phase-coding network generates autonomous oscillations which it couples to an external reference oscillation in a multi-stable fashion.

Cross-frequency and inter-regional phase synchronization in explicit transitive inference

Explicit logical reasoning, like transitive inference, is a hallmark of human intelligence. This study investigated cortical oscillations and their interactions in transitive inference with EEG. Participants viewed premises describing abstract relations among items. They accurately recalled the relationship between old pairs of items, effectively inferred the relationship between new pairs of items, and discriminated between true and false relationships for new pairs. First, theta (4-7 Hz) and alpha oscillations (8-15 Hz) had distinct functional roles. Frontal theta oscillations distinguished between new and old pairs, reflecting the inference of new information. Parietal alpha oscillations changed with serial position and symbolic distance of the pairs, representing the underlying relational structure. Frontal alpha oscillations distinguished between true and false pairs, linking the new information with the underlying relational structure. Second, theta and alpha oscillations interacted through cross-frequency and inter-regional phase synchronization. Frontal theta-alpha 1:2 phase locking appeared to coordinate spectrally diverse neural activity, enhanced for new versus old pairs and true versus false pairs. Alpha-band frontal-parietal phase coherence appeared to coordinate anatomically distributed neural activity, enhanced for new versus old pairs and false versus true pairs. It suggests that cross-frequency and inter-regional phase synchronization among theta and alpha oscillations supports human transitive inference.

40-Hz optogenetic stimulation rescues functional synaptic plasticity after stroke

Evoked brain oscillations in the gamma range have been shown to assist in stroke recovery. However, the causal relationship between evoked oscillations and neuroprotection is not well understood. We have used optogenetic stimulation to investigate how evoked gamma oscillations modulate cortical dynamics in the acute phase after stroke. Our results reveal that stimulation at 40 Hz drives activity in interneurons at the stimulation frequency and phase-locked activity in principal neurons at a lower frequency, leading to increased cross-frequency coupling. In addition, 40-Hz stimulation after stroke enhances interregional communication. These effects are observed up to 24 h after stimulation. Our stimulation protocol also rescues functional synaptic plasticity 24 h after stroke and leads to an upregulation of plasticity genes and a downregulation of cell death genes. Together these results suggest that restoration of cortical dynamics may confer neuroprotection after stroke.

Neuronal functional connectivity is impaired in a layer dependent manner near the chronically implanted microelectrodes

Objective

This study aims to reveal longitudinal changes in functional network connectivity within and across different brain structures near the chronically implanted microelectrode. While it is well established that the foreign-body response (FBR) contributes to the gradual decline of the signals recorded from brain implants over time, how does the FBR impact affect the functional stability of neural circuits near implanted Brain-Computer Interfaces (BCIs) remains unknown. This research aims to illuminate how the chronic FBR can alter local neural circuit function and the implications for BCI decoders.

Approach

This study utilized multisite Michigan-style microelectrodes that span all cortical layers and the hippocampal CA1 region to collect spontaneous and visually-evoked electrophysiological activity. Alterations in neuronal activity near the microelectrode were tested assessing cross-frequency synchronization of LFP and spike entrainment to LFP oscillatory activity throughout 16 weeks after microelectrode implantation.

Main Results

The study found that cortical layer 4, the input-receiving layer, maintained activity over the implantation time. However, layers 2/3 rapidly experienced severe impairment, leading to a loss of proper intralaminar connectivity in the downstream output layers 5/6. Furthermore, the impairment of interlaminar connectivity near the microelectrode was unidirectional, showing decreased connectivity from Layers 2/3 to Layers 5/6 but not the reverse direction. In the hippocampus, CA1 neurons gradually became unable to properly entrain to the surrounding LFP oscillations.

Significance

This study provides a detailed characterization of network connectivity dysfunction over long-term microelectrode implantation periods. This new knowledge could contribute to the development of targeted therapeutic strategies aimed at improving the health of the tissue surrounding brain implants and potentially inform engineering of adaptive decoders as the FBR progresses. Our study's understanding of the dynamic changes in the functional network over time opens the door to developing interventions for improving the long-term stability and performance of intracortical microelectrodes.

Pro-myelinating clemastine administration improves recording performance of chronically implanted microelectrodes and nearby neuronal health

Intracortical microelectrodes have become a useful tool in neuroprosthetic applications in the clinic and to understand neurological disorders in basic neurosciences. Many of these brain-machine interface technology applications require successful long-term implantation with high stability and sensitivity. However, the intrinsic tissue reaction caused by implantation remains a major failure mechanism causing loss of recorded signal quality over time. Oligodendrocytes remain an underappreciated intervention target to improve chronic recording performance. These cells can accelerate action potential propagation and provides direct metabolic support for neuronal health and functionality. However, implantation injury causes oligodendrocyte degeneration and leads to progressive demyelination in surrounding brain tissue. Previous work highlighted that healthy oligodendrocytes are necessary for greater electrophysiological recording performance and the prevention of neuronal silencing around implanted microelectrodes over the chronic implantation period. Thus, we hypothesize that enhancing oligodendrocyte activity with a pharmaceutical drug, Clemastine, will prevent the chronic decline of microelectrode recording performance. Electrophysiological evaluation showed that the promyelination Clemastine treatment significantly elevated the signal detectability and quality, rescued the loss of multi-unit activity, and increased functional interlaminar connectivity over 16-weeks of implantation. Additionally, post-mortem immunohistochemistry showed that increased oligodendrocyte density and myelination coincided with increased survival of both excitatory and inhibitory neurons near the implant. Overall, we showed a positive relationship between enhanced oligodendrocyte activity and neuronal health and functionality near the chronically implanted microelectrode. This study shows that therapeutic strategy that enhance oligodendrocyte activity is effective for integrating the functional device interface with brain tissue over chronic implantation period.

Linking temporal coordination of hippocampal activity to memory function

Oscillations in neural activity are widespread throughout the brain and can be observed at the population level through the local field potential. These rhythmic patterns are associated with cycles of excitability and are thought to coordinate networks of neurons, in turn facilitating effective communication both within local circuits and across brain regions. In the hippocampus, theta rhythms (4-12 Hz) could contribute to several key physiological mechanisms including long-range synchrony, plasticity, and at the behavioral scale, support memory encoding and retrieval. While neurons in the hippocampus appear to be temporally coordinated by theta oscillations, they also tend to fire in sequences that are developmentally preconfigured. Although loss of theta rhythmicity impairs memory, these sequences of spatiotemporal representations persist in conditions of altered hippocampal oscillations. The focus of this review is to disentangle the relative contribution of hippocampal oscillations from single-neuron activity in learning and memory. We first review cellular, anatomical, and physiological mechanisms underlying the generation and maintenance of hippocampal rhythms and how they contribute to memory function. We propose candidate hypotheses for how septohippocampal oscillations could support memory function while not contributing directly to hippocampal sequences. In particular, we explore how theta rhythms could coordinate the integration of upstream signals in the hippocampus to form future decisions, the relevance of such integration to downstream regions, as well as setting the stage for behavioral timescale synaptic plasticity. Finally, we leverage stimulation-based treatment in Alzheimer's disease conditions as an opportunity to assess the sufficiency of hippocampal oscillations for memory function.

Cross-Frequency Coupling and Intelligent Neuromodulation

Cross-frequency coupling (CFC) reflects (nonlinear) interactions between signals of different frequencies. Evidence from both patient and healthy participant studies suggests that CFC plays an essential role in neuronal computation, interregional interaction, and disease pathophysiology. The present review discusses methodological advances and challenges in the computation of CFC with particular emphasis on potential solutions to spurious coupling, inferring intrinsic rhythms in a targeted frequency band, and causal interferences. We specifically focus on the literature exploring CFC in the context of cognition/memory tasks, sleep, and neurological disorders, such as Alzheimer's disease, epilepsy, and Parkinson's disease. Furthermore, we highlight the implication of CFC in the context and for the optimization of invasive and noninvasive neuromodulation and rehabilitation. Mainly, CFC could support advancing the understanding of the neurophysiology of cognition and motor control, serve as a biomarker for disease symptoms, and leverage the optimization of therapeutic interventions, e.g., closed-loop brain stimulation. Despite the evident advantages of CFC as an investigative and translational tool in neuroscience, further methodological improvements are required to facilitate practical and correct use in cyborg and bionic systems in the field.

Glycolysis inhibition partially resets epilepsy-induced alterations in the dorsal hippocampus-basolateral amygdala circuit involved in anxiety-like behavior

Pharmacoresistant temporal lobe epilepsy affects millions of people around the world with uncontrolled seizures and comorbidities, like anxiety, being the most problematic aspects calling for novel therapies. The intrahippocampal kainic acid model of temporal lobe epilepsy is an appropriate rodent model to evaluate the effects of novel interventions, including glycolysis inhibition, on epilepsy-induced alterations. Here, we investigated kainic acid-induced changes in the dorsal hippocampus (dHPC) and basolateral amygdala (BLA) circuit and the efficiency of a glycolysis inhibitor, 2-deoxy D-glucose (2-DG), in resetting such alterations using simultaneous local field potentials (LFP) recording and elevated zero-maze test. dHPC theta and gamma powers were lower in epileptic groups, both in the baseline and anxiogenic conditions. BLA theta power was higher in baseline condition while it was lower in anxiogenic condition in epileptic animals and 2-DG could reverse it. dHPC-BLA coherence was altered only in anxiogenic condition and 2-DG could reverse it only in gamma frequency. This coherence was significantly correlated with the time in which the animals exposed themselves to the anxiogenic condition. Further, theta-gamma phase-locking was lower in epileptic groups in the dHPC-BLA circuit and 2-DG could considerably increase it.

Over and above frequency: Gamma oscillations as units of neural circuit operations

Gamma oscillations (∼30-150 Hz) are widespread correlates of neural circuit functions. These network activity patterns have been described across multiple animal species, brain structures, and behaviors, and are usually identified based on their spectral peak frequency. Yet, despite intensive investigation, whether gamma oscillations implement causal mechanisms of specific brain functions or represent a general dynamic mode of neural circuit operation remains unclear. In this perspective, we review recent advances in the study of gamma oscillations toward a deeper understanding of their cellular mechanisms, neural pathways, and functional roles. We discuss that a given gamma rhythm does not per se implement any specific cognitive function but rather constitutes an activity motif reporting the cellular substrates, communication channels, and computational operations underlying information processing in its generating brain circuit. Accordingly, we propose shifting the attention from a frequency-based to a circuit-level definition of gamma oscillations.

Nonlinear Theta-Gamma Coupling between the Anterior Thalamus and Hippocampus Increases as a Function of Running Speed

The hippocampal theta rhythm strongly correlates to awake behavior leading to theories that it represents a cognitive state of the brain. As theta has been observed in other regions of the Papez circuit, it has been theorized that activity propagates in a reentrant manner. These observations complement the energy cascade hypothesis in which large-amplitude, slow-frequency oscillations reflect activity propagating across a large population of neurons. Higher frequency oscillations, such as gamma, are related to the speed with which inhibitory and excitatory neurons interact and distribute activity on the local level. The energy cascade hypothesis suggests that the larger anatomic loops, maintaining theta, drive the smaller loops. As hippocampal theta increases in power with running speed, so does the power and frequency of the gamma rhythm. If theta is propagated through the circuit, it stands to reason that the local field potential (LFP) recorded in other regions would be coupled to the hippocampal theta, with the coupling increasing with running speed. We explored this hypothesis using open-source simultaneous recorded data from the CA1 region of the hippocampus and the anterior dorsal and anterior ventral thalamus. Cross-regional theta coupling increased with running speed. Although the power of the gamma rhythm was lower in the anterior thalamus, there was an increase in the coupling of hippocampal theta to anterior thalamic gamma. Broadly, the data support models of how activity moves across the nervous system, suggesting that the brain uses large-scale volleys of activity to support higher cognitive processes.

Supt16 haploinsufficiency causes neurodevelopment disorder by disrupting MAPK pathway in neural stem cells

Chromatin regulators constitute a fundamental means of transcription regulation, which have been implicated in neurodevelopment and neurodevelopment disorders (NDDs). Supt16, one of candidate genes for NDDs, encodes the large subunit of facilitates chromatin transcription. However, the underlying mechanisms remain poorly understood. Here, Supt16+/- mice was generated, modeling the neurodevelopment disorder. Abnormal cognitive and social behavior was observed in the Supt16  +/- mice. Simultaneously, the number of neurocytes in the cerebral cortex and hippocampus is decreased, which might be resulted from the impairment of mouse neural stem cells (mNSCs) in the SVZ. Supt16 haploinsufficiency affects the proliferation and apoptosis of mNSCs. As the RNA-seq and chromatic immunoprecipitation sequencing assays showed, Supt16 haploinsufficiency disrupts the stemness of mNSCs by inhibiting MAPK signal pathway. Thus, this study demonstrates a critical role of Supt16 gene in the proliferation and apoptosis of mNSCs and provides a novel insight in the pathogenesis of NDDs.

Pro-myelinating Clemastine administration improves recording performance of chronically implanted microelectrodes and nearby neuronal health

Intracortical microelectrodes have become a useful tool in neuroprosthetic applications in the clinic and to understand neurological disorders in basic neurosciences. Many of these brain-machine interface technology applications require successful long-term implantation with high stability and sensitivity. However, the intrinsic tissue reaction caused by implantation remains a major failure mechanism causing loss of recorded signal quality over time. Oligodendrocytes remain an underappreciated intervention target to improve chronic recording performance. These cells can accelerate action potential propagation and provides direct metabolic support for neuronal health and functionality. However, implantation injury causes oligodendrocyte degeneration and leads to progressive demyelination in surrounding brain tissue. Previous work highlighted that healthy oligodendrocytes are necessary for greater electrophysiological recording performance and the prevention of neuronal silencing around implanted microelectrodes over chronic implantation. Thus, we hypothesize that enhancing oligodendrocyte activity with a pharmaceutical drug, Clemastine, will prevent the chronic decline of microelectrode recording performance. Electrophysiological evaluation showed that the promyelination Clemastine treatment significantly elevated the signal detectability and quality, rescued the loss of multi-unit activity, and increased functional interlaminar connectivity over 16-weeks of implantation. Additionally, post-mortem immunohistochemistry showed that increased oligodendrocyte density and myelination coincided with increased survival of both excitatory and inhibitory neurons near the implant. Overall, we showed a positive relationship between enhanced oligodendrocyte activity and neuronal health and functionality near the chronically implanted microelectrode. This study shows that therapeutic strategy that enhance oligodendrocyte activity is effective for integrating the functional device interface with brain tissue over chronic implantation period.

Abstract Figure

Theta dominates cross-frequency coupling in hippocampal-medial entorhinal circuit during awake-behavior in rats

Hippocampal theta and gamma rhythms are hypothesized to play a role in the physiology of higher cognition. Prior research has reported that an offset in theta cycles between the entorhinal cortex, CA3, and CA1 regions promotes independence of population activity across the hippocampus. In line with this idea, it has recently been observed that CA1 pyramidal cells can establish and maintain coordinated place cell activity intrinsically, with minimal reliance on afferent input. Counter to these observations is the contemporary hypothesis that CA1 neuron activity is driven by a gamma oscillation arising from the medial entorhinal cortex (MEC) that relays information by providing precisely timed synchrony between MEC and CA1. Reinvestigating this in rats during appetitive track running, we found that theta is the dominant frequency of cross-frequency coupling between the MEC and hippocampus, with hippocampal gamma largely independent of entorhinal gamma.

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Theta, theta harmonic, and gamma power increase with running speed in the HPC and MEC

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Intra-regionally, theta-theta harmonic and theta-gamma coupling increases with speed

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Cross-regionally, theta is the dominant frequency of coupling between HPC and MEC

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Marginal gamma coupling can be explained by local gamma modulated by coherent theta

Advances in human intracranial electroencephalography research, guidelines and good practices

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

Hippocampal-prefrontal theta coupling develops as mice become proficient in associative odorant discrimination learning

Learning and memory requires coordinated activity between different regions of the brain. Here we studied the interaction between infralimbic medial prefrontal cortex (mPFC) and hippocampal dorsal CA1 during associative odorant discrimination learning in the mouse. We found that as the animal learns to discriminate odorants in a go-no go task, the coupling of high frequency neural oscillations to the phase of theta oscillations (theta-referenced phase-amplitude coupling or tPAC) changes in a manner that results in divergence between rewarded and unrewarded odorant-elicited changes in the theta-phase referenced power (tPRP) for beta and gamma oscillations. In addition, in the proficient animal there was a decrease in the coordinated oscillatory activity between CA1 and mPFC in the presence of the unrewarded odorant. Furthermore, the changes in tPAC resulted in a marked increase in the accuracy for decoding contextual odorant identity from tPRP when the animal became proficient. Finally, we studied the role of Ca2+/calmodulin-dependent protein kinase II α (CaMKIIα), a protein involved in learning and memory, in oscillatory neural processing in this task. We find that the accuracy for decoding the contextual odorant identity from tPRP decreases in CaMKIIα knockout mice and that this accuracy correlates with behavioral performance. These results implicate a role for tPAC and CaMKIIα in olfactory go-no go associative learning in the hippocampal-prefrontal circuit.Significance statementCoupling of neural oscillations within and between hippocampal CA1 and medial prefrontal cortex (mPFC) is involved in spatial learning and memory, but the role of oscillation coupling for other learning tasks is not well understood. Here we performed local field potential recording in CA1 and mPFC in mice learning to differentiate rewarded from unrewarded odorants in an associative learning task. We find that odorant-elicited changes in the power of bursts of gamma oscillations at distinct phases of theta oscillations become divergent as the animal becomes proficient allowing decoding of contextual odorant identity. Finally, we find that the accuracy to decode contextual odorant identity decreases in mice deficient for the expression of Ca2+/calmodulin-dependent protein kinase II α, a protein involved in synaptic plasticity.

Theta activity in semantic priming under visual crowding as revealed by magnetoencephalography

Crowding refers to impaired object recognition of peripheral visual targets caused by nearby flankers. It has been shown that the response to a word was faster when it was preceded by a semantically related than unrelated crowded prime, demonstrating that semantic priming survives crowding. This study examines neural correlates of semantic priming under visual crowding using magnetoencephalography with four conditions: prime (isolated, crowded) x prime-target relationship (related, unrelated). Participants judged whether the target was a word or a nonword. We found significant differences in θ activity at the left inferior frontal gyrus (IFG) for both isolated and crowded primes when comparing the unrelated and related conditions, although the activation was delayed with the crowded prime compared to the isolated prime. The locations within the IFG were also different: theta-band activation was at BA 45 in the isolated condition and at BA 47 in the crowded condition. Phase-locking-value analysis revealed that bilateral IFG was more synchronized with unrelated prime-target pairs than related pairs regardless of whether the primes were isolated or crowded, indicating the recruitment of the right hemisphere when the prime-target semantic relationship was remote. Finally, the distinct waveform patterns found in the isolated and crowded conditions from both the source localization and PLV analysis suggest different neural mechanisms for processing semantic information with isolated primes versus crowded primes.

Understanding Harmonic Structures Through Instantaneous Frequency

The analysis of harmonics and non-sinusoidal waveform shape in time-series data is growing in importance. However, a precise definition of what constitutes a harmonic is lacking. In this paper, we propose a rigorous definition of when to consider signals to be in a harmonic relationship based on an integer frequency ratio, constant phase, and a well-defined joint instantaneous frequency. We show this definition is linked to extrema counting and Empirical Mode Decomposition (EMD). We explore the mathematics of our definition and link it to results from analytic number theory. This naturally leads to us to define two classes of harmonic structures, termed strong and weak, with different extrema behaviour. We validate our framework using both simulations and real data. Specifically, we look at the harmonic structures in shallow water waves, the FitzHugh-Nagumo neuronal model, and the non-sinusoidal theta oscillation in rat hippocampus local field potential data. We further discuss how our definition helps to address mode splitting in nonlinear time-series decomposition methods. A clear understanding of when harmonics are present in signals will enable a deeper understanding of the functional roles of non-sinusoidal oscillations.

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.

Harmoni: a Method for Eliminating Spurious Interactions due to the Harmonic Components in Neuronal Data

Cross-frequency synchronization (CFS) has been proposed as a mechanism for integrating spatially and spectrally distributed information in the brain. However, investigating CFS in Magneto- and Electroencephalography (MEG/EEG) is hampered by the presence of spurious neuronal interactions due to the non-sinusoidal waveshape of brain oscillations. Such waveshape gives rise to the presence of oscillatory harmonics mimicking genuine neuronal oscillations. Until recently, however, there has been no methodology for removing these harmonics from neuronal data. In order to address this long-standing challenge, we introduce a novel method (called HARMOnic miNImization - Harmoni) that removes the signal components which can be harmonics of a non-sinusoidal signal. Harmoni's working principle is based on the presence of CFS between harmonic components and the fundamental component of a non-sinusoidal signal. We extensively tested Harmoni in realistic EEG simulations. The simulated couplings between the source signals represented genuine and spurious CFS and within-frequency phase synchronization. Using diverse evaluation criteria, including ROC analyses, we showed that the within- and cross-frequency spurious interactions are suppressed significantly, while the genuine activities are not affected. Additionally, we applied Harmoni to real resting-state EEG data revealing intricate remote connectivity patterns which are usually masked by the spurious connections. Given the ubiquity of non-sinusoidal neuronal oscillations in electrophysiological recordings, Harmoni is expected to facilitate novel insights into genuine neuronal interactions in various research fields, and can also serve as a steppingstone towards the development of further signal processing methods aiming at refining within- and cross-frequency synchronization in electrophysiological recordings.

Bidirectionally Regulating Gamma Oscillations in Wilson-Cowan Model by Self-Feedback Loops: A Computational Study

The Wilson-Cowan model can emulate gamma oscillations, and thus is extensively used to research the generation of gamma oscillations closely related to cognitive functions. Previous studies have revealed that excitatory and inhibitory inputs to the model can modulate its gamma oscillations. Inhibitory and excitatory self-feedback loops are important structural features of the model, however, its functional role in the regulation of gamma oscillations in the model is still unclear. In the present study, bifurcation analysis and spectrum analysis are employed to elucidate the regulating mechanism of gamma oscillations underlined by the inhibitory and excitatory self-feedback loops, especially how the two self-feedback loops cooperate to generate the gamma oscillations and regulate the oscillation frequency. The present results reveal that, on one hand, the inhibitory self-feedback loop is not conducive to the generation of gamma oscillations, and increased inhibitory self-feedback strength facilitates the enhancement of the oscillation frequency. On the other hand, the excitatory self-feedback loop promotes the generation of gamma oscillations, and increased excitatory self-feedback strength leads to the decrease of oscillation frequency. Finally, theoretical analysis is conducted to provide explain on how the two self-feedback loops play a crucial role in the generation and regulation of neural oscillations in the model. To sum up, Inhibitory and excitatory self-feedback loops play a complementary role in generating and regulating the gamma oscillation in Wilson-Cowan model, and cooperate to bidirectionally regulate the gamma-oscillation frequency in a more flexible manner. These results might provide testable hypotheses for future experimental research.

Slow oscillation-spindle coupling strength predicts real-life gross-motor learning in adolescents and adults

Previously, we demonstrated that precise temporal coordination between slow oscillations (SOs) and sleep spindles indexes declarative memory network development (Hahn et al., 2020). However, it is unclear whether these findings in the declarative memory domain also apply in the motor memory domain. Here, we compared adolescents and adults learning juggling, a real-life gross-motor task. Juggling performance was impacted by sleep and time of day effects. Critically, we found that improved task proficiency after sleep lead to an attenuation of the learning curve, suggesting a dynamic juggling learning process. We employed individualized cross-frequency coupling analyses to reduce inter- and intragroup variability of oscillatory features. Advancing our previous findings, we identified a more precise SO-spindle coupling in adults compared to adolescents. Importantly, coupling precision over motor areas predicted overnight changes in task proficiency and learning curve, indicating that SO-spindle coupling relates to the dynamic motor learning process. Our results provide first evidence that regionally specific, precisely coupled sleep oscillations support gross-motor learning.

The Functional Interactions between Cortical Regions through Theta-Gamma Coupling during Resting-State and a Visual Working Memory Task

Theta phase-gamma amplitude coupling (TGC) plays an important role in several different cognitive processes. Although spontaneous brain activity at the resting state is crucial in preparing for cognitive performance, the functional role of resting-state TGC remains unclear. To investigate the role of resting-state TGC, electroencephalogram recordings were obtained for 56 healthy volunteers while they were in the resting state, with their eyes closed, and then when they were engaged in a retention interval period in the visual memory task. The TGCs of the two different conditions were calculated and compared. The results indicated that the modulation index of TGC during the retention interval of the visual working memory (VWM) task was not higher than that during the resting state; however, the topographical distribution of TGC during the resting state was negatively correlated with TGC during VWM task at the local level. The topographical distribution of TGC during the resting state was negatively correlated with TGC coordinates’ engagement of brain areas in local and large-scale networks and during task performance at the local level. These findings support the view that TGC reflects information-processing and signal interaction across distant brain areas. These results demonstrate that TGC could explain the efficiency of competing brain networks.

Endothelial peroxynitrite causes disturbance of neuronal oscillations by targeting caspase-1 in the arcuate nucleus

Severe anorexia limits the clinical application of cisplatin, and even leads to the discontinuation of treatment. However, the mechanisms underlying cisplatin-induced anorexia are unknown. Herein, we demonstrated that cisplatin could affect neuronal gamma oscillations and induce abnormal neuronal theta-gamma phase-amplitude coupling in the arcuate nucleus (Arc) of the hypothalamus, and these findings were associated with significantly decreased food intake and weight loss in mice. Chemogenetic activation of AgRP neurons in the Arc reversed the cisplatin-induced food intake reduction in mice. We further demonstrated that endothelial peroxynitrite (ONOO⁻) formation in the Arc induced nitrosative stress following cisplatin treatment via a previously uncharacterized pathway involving neuronal caspase-1 activation. Strikingly, treatment with the ONOO⁻ scavenger uric acid (UA) reversed the reduced action potential (AP) frequency of AgRP neurons and increased the AP frequency of POMC neurons induced by SIN1, a donor of ONOO⁻, in the Arc, as determined by whole-cell patch-clamp electrophysiological recording. Consistent with these findings, UA treatment effectively alleviated cisplatin-induced dysfunction of neuronal oscillations and neuronal theta-gamma phase-amplitude coupling in the Arc of mice. Taken together, these results suggest, for the first time, that targeting the overproduction of endothelial ONOO⁻ can regulate cisplatin-induced neurotoxicity through neuronal caspase-1, and thereby serve as a potential therapeutic approach to alleviate chemotherapy-induced anorexia and weight loss.

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Endothelial ONOO^– induced the abnormal neuronal oscillations following cisplatin treatment through caspase-1 in the Arc.

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ONOO^– scavenger UA could attenuate cisplatin-induced neurotoxicity and caspase-1 activation in the Arc.

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Targeting endothelial ONOO^– provided a promising approach to alleviate chemotherapy-induced anorexia and weight loss.

Measuring Phase-Amplitude Coupling Based on the Jensen-Shannon Divergence and Correlation Matrix

Phase-amplitude coupling (PAC) measures the relationship between the phase of low-frequency oscillations (LFO) and the amplitude of high-frequency oscillations (HFO). It plays an important functional role in neural information processing and cognition. Thus, we propose a novel method based on the Jensen-Shannon (JS) divergence and correlation matrix. The method takes the amplitude distributions of the HFO located in the corresponding phase bins of the LFO as multichannel inputs to construct a correlation matrix, where the elements are calculated based on the JS divergence between pairs of amplitude distributions. Then, the omega complexity extracted from the correlation matrix is used to estimate the PAC strength. The simulation results demonstrate that the proposed method can effectively reflect the PAC strength and slightly vary with the data length. Moreover, it outperforms five frequently used algorithms in the performance against additive white Gaussian noise and spike noise and the ability of detecting PAC in wide frequency ranges. To validate our proposed method with real data, it was applied to analyze the local field potential recorded from the dorsomedial striatum in a male Sprague-Dawley rat. It can replicate previous results obtained with other PAC metrics. In conclusion, these results suggest that our proposed method is a powerful tool for measuring the PAC between neural oscillations.

Large-Scale and Multiscale Networks in the Rodent Brain during Novelty Exploration

Neural activity is coordinated across multiple spatial and temporal scales, and these patterns of coordination are implicated in both healthy and impaired cognitive operations. However, empirical cross-scale investigations are relatively infrequent, because of limited data availability and to the difficulty of analyzing rich multivariate datasets. Here, we applied frequency-resolved multivariate source-separation analyses to characterize a large-scale dataset comprising spiking and local field potential (LFP) activity recorded simultaneously in three brain regions (prefrontal cortex, parietal cortex, hippocampus) in freely-moving mice. We identified a constellation of multidimensional, inter-regional networks across a range of frequencies (2-200 Hz). These networks were reproducible within animals across different recording sessions, but varied across different animals, suggesting individual variability in network architecture. The theta band (∼4-10 Hz) networks had several prominent features, including roughly equal contribution from all regions and strong inter-network synchronization. Overall, these findings demonstrate a multidimensional landscape of large-scale functional activations of cortical networks operating across multiple spatial, spectral, and temporal scales during open-field exploration.

Spectrum Degradation of Hippocampal LFP During Euthanasia

The hippocampal local field potential (LFP) exhibits a strong correlation with behavior. During rest, the theta rhythm is not prominent, but during active behavior, there are strong rhythms in the theta, theta harmonics, and gamma ranges. With increasing running velocity, theta, theta harmonics and gamma increase in power and in cross-frequency coupling, suggesting that neural entrainment is a direct consequence of the total excitatory input. While it is common to study the parametric range between the LFP and its complementing power spectra between deep rest and epochs of high running velocity, it is also possible to explore how the spectra degrades as the energy is completely quenched from the system. Specifically, it is unknown whether the 1/f slope is preserved as synaptic activity becomes diminished, as low frequencies are generated by large pools of neurons while higher frequencies comprise the activity of more local neuronal populations. To test this hypothesis, we examined rat LFPs recorded from the hippocampus and entorhinal cortex during barbiturate overdose euthanasia. Within the hippocampus, the initial stage entailed a quasi-stationary LFP state with a power-law feature in the power spectral density. In the second stage, there was a successive erosion of power from high- to low-frequencies in the second stage that continued until the only dominant remaining power was <20 Hz. This stage was followed by a rapid collapse of power spectrum toward the absolute electrothermal noise background. As the collapse of activity occurred later in hippocampus compared with medial entorhinal cortex, it suggests that the ability of a neural network to maintain the 1/f slope with decreasing energy is a function of general connectivity. Broadly, these data support the energy cascade theory where there is a cascade of energy from large cortical populations into smaller loops, such as those that supports the higher frequency gamma rhythm. As energy is pulled from the system, neural entrainment at gamma frequency (and higher) decline first. The larger loops, comprising a larger population, are fault-tolerant to a point capable of maintaining their activity before a final collapse.

Effects of Several Classes of Voltage-Gated Ion Channel Conductances on Gamma and Theta Oscillations in a Hippocampal Microcircuit Model

Gamma and theta oscillations have been functionally associated with cognitive processes, such as learning and memory. Synaptic conductances play an important role in the generation of intrinsic network rhythmicity, but few studies have examined the effects of voltage-gated ion channels (VGICs) on these rhythms. In this report, we have used a pyramidal-interneuron-gamma (PING) network consisting of excitatory pyramidal cells and two types of inhibitory interneurons. We have constructed a conductance-based neural network incorporating a persistent sodium current (I_NaP), a delayed rectifier potassium current (I_KDR), a inactivating potassium current (I_A) and a hyperpolarization-activated current (I_H). We have investigated the effects of several conductances on network theta and gamma frequency oscillations. Variation of all conductances of interest changed network rhythmicity. Theta power was altered by all conductances tested. Gamma rhythmogenesis was dependent on I_A and I_H. The I_KDR currents in excitatory pyramidal cells as well as both types of inhibitory interneurons were essential for theta rhythmogenesis and altered gamma rhythm properties. Increasing I_NaP suppressed both gamma and theta rhythms. Addition of noise did not alter these patterns. Our findings suggest that VGICs strongly affect brain network rhythms. Further investigations in vivo will be of great interest, including potential effects on neural function and cognition.

Nonlinear System Identification of Neural Systems from Neurophysiological Signals

The human nervous system is one of the most complicated systems in nature. Complex nonlinear behaviours have been shown from the single neuron level to the system level. For decades, linear connectivity analysis methods, such as correlation, coherence and Granger causality, have been extensively used to assess the neural connectivities and input-output interconnections in neural systems. Recent studies indicate that these linear methods can only capture a certain amount of neural activities and functional relationships, and therefore cannot describe neural behaviours in a precise or complete way. In this review, we highlight recent advances in nonlinear system identification of neural systems, corresponding time and frequency domain analysis, and novel neural connectivity measures based on nonlinear system identification techniques. We argue that nonlinear modelling and analysis are necessary to study neuronal processing and signal transfer in neural systems quantitatively. These approaches can hopefully provide new insights to advance our understanding of neurophysiological mechanisms underlying neural functions. These nonlinear approaches also have the potential to produce sensitive biomarkers to facilitate the development of precision diagnostic tools for evaluating neurological disorders and the effects of targeted intervention.

Event-related components are structurally represented by intrinsic event-related potentials

The detection of event-related potentials (ERPs) through electroencephalogram (EEG) analysis is a well-established method for understanding brain functions during a cognitive process. To increase the signal-to-noise ratio (SNR) and stationarity of the data, ERPs are often filtered to a wideband frequency range, such as 0.05–30 Hz. Alternatively, a natural-filtering procedure can be performed through empirical mode decomposition (EMD), which yields intrinsic mode functions (IMFs) for each trial of the EEG data, followed by averaging over trials to generate the event-related modes. However, although the EMD-based filtering procedure has advantages such as a high SNR, suitable waveform shape, and high statistical power, one fundamental drawback of the procedure is that it requires the selection of an IMF (or a partial sum of a range of IMFs) to determine an ERP component effectively. Therefore, in this study, we propose an intrinsic ERP (iERP) method to overcome the drawbacks and retain the advantages of event-related mode analysis for investigating ERP components. The iERP method can reveal multiple ERP components at their characteristic time scales and suitably cluster statistical effects among modes by using a tailored definition of each mode’s neighbors. We validated the iERP method by using realistic EEG data sets acquired from a face perception task and visual working memory task. By using these two data sets, we demonstrated how to apply the iERP method to a cognitive task and incorporate existing cluster-based tests into iERP analysis. Moreover, iERP analysis revealed the statistical effects between (or among) experimental conditions more effectively than the conventional ERP method did.

Allergic rhinitis impairs working memory in association with drop of hippocampal - Prefrontal coupling

Allergic rhinitis (AR) is a chronic inflammatory disease frequently associated with a deficit in learning and memory. Working memory is an important system for decision making and guidance, which depends on interactions between the ventral hippocampus (vHipp) and the prelimbic prefrontal cortex (plPFC). It is still unclear whether AR influences the activity and coupling of these brain areas, which consequently may impair working memory. The current study aimed to examine alterations of the vHipp-plPFC circuit in a rat model of AR. Our results show decreased working memory performance in AR animals, accompanied by a reduction of theta and gamma oscillations in plPFC. Also, AR reduces coherence between vHipp and plPFC in both theta and gamma frequency bands. Cross-frequency coupling analyses confirmed a reduced interaction between hippocampal theta and plPFC gamma oscillations. Granger causality analysis revealed a reduction in the causal effects of vHipp activity on plPFC oscillations and vice versa. A significant correlation was found between working memory performance with disruption of functional connectivity in AR animals. In summary, our data show that in AR, there is a deficit of functional coupling between hippocampal and prefrontal network, and suggest that this mechanism might contribute to working memory impairment in individuals with AR.

Coupling between fast and slow oscillator circuits in Cancer borealis is temperature-compensated

Coupled oscillatory circuits are ubiquitous in nervous systems. Given that most biological processes are temperature-sensitive, it is remarkable that the neuronal circuits of poikilothermic animals can maintain coupling across a wide range of temperatures. Within the stomatogastric ganglion (STG) of the crab, Cancer borealis, the fast pyloric rhythm (~1 Hz) and the slow gastric mill rhythm (~0.1 Hz) are precisely coordinated at ~11°C such that there is an integer number of pyloric cycles per gastric mill cycle (integer coupling). Upon increasing temperature from 7°C to 23°C, both oscillators showed similar temperature-dependent increases in cycle frequency, and integer coupling between the circuits was conserved. Thus, although both rhythms show temperature-dependent changes in rhythm frequency, the processes that couple these circuits maintain their coordination over a wide range of temperatures. Such robustness to temperature changes could be part of a toolbox of processes that enables neural circuits to maintain function despite global perturbations.

VEGF Modulates the Neural Dynamics of Hippocampal Subregions in Chronic Global Cerebral Ischemia Rats

Theta and gamma rhythms in hippocampus are important to cognitive performance. The cognitive impairments following cerebral ischemia is linked with the dysfunction of theta and gamma oscillations. As the primary mechanism for learning and memory, synaptic plasticity is in connection with these neural oscillations. Although vascular endothelial growth factor (VEGF) is thought to protect synaptic function in the ischemia rats to relieve cognitive impairment, little has been done on its effect of neural dynamics with this process. The present study investigated whether the alternation of neural oscillations in the hippocampus of ischemia rats is one of the potential neuroprotective mechanisms of VEGF. Rats were treated with the intranasal administration of VEGF at 72 h following chronic global cerebral ischemia procedure. Then local field potentials (LFPs) in hippocampal CA1 and CA3 regions were recorded and analyzed. Our results showed that VEGF can improve the power of theta and gamma rhythms in CA1 region after ischemia. Chronic global cerebral ischemia reduced the theta-gamma phase-amplitude coupling (PAC) not only within CA1 area but also in the pathway from CA3 to CA1, while VEGF alleviated the decreased coupling strength. Despite these notable differences, there were no obvious changes in the PAC within CA3 region. Surprisingly, the ischemia state did not affect the phase-phase interaction of hippocampus. In conclusion, our findings demonstrated that VEGF enhanced the theta-gamma PAC strength of CA3-CA1 pathway in ischemia rats, which may futher improve the information transmission within the hippocampus. These results illustrated the potential electrophysiologic mechanism of VEGF on cognitive improvement.

Improved Cognitive Promotion through Accelerated Magnetic Stimulation

Noninvasive brain stimulation to enhance cognition is an area of increasing research interest. Theta burst stimulation (TBS) is a novel accelerated form of stimulation, which more closely mimics the brain's natural firing patterns and may have greater effects on cognitive performance. We report here the comparative assessment of the effect of conventional high-frequency repetitive transcranial magnetic stimulation (HF-rTMS) protocols and TBS protocols on cognition enhancement in healthy controls. Sixty healthy adults (34 males and 26 females) were randomized and counterbalanced and assigned to HF-rTMS (n = 20), TBS (n = 20), or sham (n = 20) groups. The promotion effects of different parameters of prefrontal stimulation on working memory and executive function were compared, as assessed by performance in N-back tasks and the Wisconsin Card Sorting Test (WCST). Both HF-rTMS and intermittent TBS (iTBS) groups displayed a significant improvement in N-back tasks, with an effect size of 0.79 and 1.50, respectively. Furthermore, the iTBS group displayed a significant improvement in the WCST, with an effect size of 0.84. The iTBS group demonstrated higher effect sizes than the HF-rTMS group (t = 2.68, p = 0.011), with an effect size of 0.85. However, no improvement in other tasks was observed (p > 0.05). Intermittent TBS has a stronger cognitive promoting effect than conventional rTMS. In summary, our findings provide direct evidence that iTBS may be a superior protocol for cognitive promotion.

High-gamma oscillations precede visual steady-state responses: A human electrocorticography study

The robust steady-state cortical activation elicited by flickering visual stimulation has been exploited by a wide range of scientific studies. As the fundamental neural response inherits the spectral properties of the gazed flickering, the paradigm has been used to chart cortical characteristics and their relation to pathologies. However, despite its widespread adoption, the underlying neural mechanisms are not well understood. Here, we show that the fundamental response is preceded by high-gamma (55-125 Hz) oscillations which are also synchronised to the gazed frequency. Using a subdural recording of the primary and associative visual cortices of one human subject, we demonstrate that the latencies of the high-gamma and fundamental components are highly correlated on a single-trial basis albeit that the latter is consistently delayed by approximately 55 ms. These results corroborate previous reports that top-down feedback projections are involved in the generation of the fundamental response, but, in addition, we show that trial-to-trial variability in fundamental latency is paralleled by a highly similar variability in high-gamma latency. Pathology- or paradigm-induced alterations in steady-state responses could thus originate either from deviating visual gamma responses or from aberrations in the neural feedback mechanism. Experiments designed to tease apart the two processes are expected to provide deeper insights into the studied paradigm.

The Effect of Transcranial Pulsed Current Stimulation at 4 and 75 Hz on Electroencephalography Theta and High Gamma Band Power: A Pilot Study

Introduction: Transcranial pulsed current stimulation (tPCS) is an emerging noninvasive brain stimulation technique that has shown significant effects on cortical excitability. To date, electrophysiological measures of the efficiency of monophasic tPCS have not been reported. Objective: We aimed to explore the effects of monophasic anodal and cathodal-tPCS (a-tPCS/c-tPCS) at theta (4 Hz) and gamma (75 Hz) frequencies on theta and high gamma electroencephalography (EEG) oscillatory power. Methods: In a single-blind, randomized, sham-controlled crossover design, 15 healthy participants were randomly assigned into 5 experimental sessions in which they received a-PCS/c-tPCS at 4 and 75 Hz or sham stimulation over the left primary motor cortex (M1) for 15 min at an intensity of 1.5 mA. Changes in theta and high gamma oscillatory power were recorded at baseline, immediately after, and 30 min after stimulation using EEG at rest with eyes open. Results: a-tPCS at 4 Hz showed a significant increase in theta power compared with sham, whereas c-tPCS at 4 Hz had no significant effect on theta power. a-tPCS at 75 Hz produced no changes in high gamma power compared with sham. Importantly, c-tPCS at 75 Hz led to a significant reduction in high gamma power compared with baseline, as well as compared with c-tPCS at 4 Hz and sham stimulation. Conclusion: The results demonstrate the modulation of oscillatory brain activity by monophasic tPCS, and highlight the need for future studies on a larger scale to confirm these initial findings. Impact statement Transcranial pulsed current stimulation (tPCS) is a novel brain stimulation technique. Recently, tPCS has been introduced to directly modulate brain oscillations by applying pulsatile current over the target brain area. Using both anodal and cathodal monophasic tPCS at theta and gamma frequencies, we demonstrate the ability of the stimulation to modulate brain activity. The present findings are the first direct electroencephalography evidence of an interaction between tPCS and ongoing oscillatory activity in the human motor cortex. Our work recommends tPCS as a tool for investigating human brain oscillations and open more studies in this area.

Different theta frameworks coexist in the rat hippocampus and are coordinated during memory-guided and novelty tasks

Hippocampal firing is organized in theta sequences controlled by internal memory processes and by external sensory cues, but how these computations are coordinated is not fully understood. Although theta activity is commonly studied as a unique coherent oscillation, it is the result of complex interactions between different rhythm generators. Here, by separating hippocampal theta activity in three different current generators, we found epochs with variable theta frequency and phase coupling, suggesting flexible interactions between theta generators. We found that epochs of highly synchronized theta rhythmicity preferentially occurred during behavioral tasks requiring coordination between internal memory representations and incoming sensory information. In addition, we found that gamma oscillations were associated with specific theta generators and the strength of theta-gamma coupling predicted the synchronization between theta generators. We propose a mechanism for segregating or integrating hippocampal computations based on the flexible coordination of different theta frameworks to accommodate the cognitive needs.

Analyzing human sleep EEG: A methodological primer with code implementation

Recent years have witnessed a surge in human sleep electroencephalography (EEG) studies, employing increasingly sophisticated analysis strategies to relate electrophysiological activity to cognition and disease. However, properly calculating and interpreting metrics used in contemporary sleep EEG requires attention to numerous theoretical and practical signal-processing details that are not always obvious. Moreover, the vast number of outcome measures that can be derived from a single dataset inflates the risk of false positives and threatens replicability. We review several methodological issues related to 1) spectral analysis, 2) montage choice, 3) extraction of phase and amplitude information, 4) surrogate construction, and 5) minimizing false positives, illustrating both the impact of methodological choices on downstream results, and the importance of checking processing steps through visualization and simplified examples. By presenting these issues in non-mathematical form, with sleep-specific examples, and with code implementation, this paper aims to instill a deeper appreciation of methodological considerations in novice and non-technical audiences, and thereby help improve the quality of future sleep EEG studies.

Theta Phase-dependent Modulation of Perception by Concurrent Transcranial Alternating Current Stimulation and Periodic Visual Stimulation

Sensory perception can be modulated by the phase of neural oscillations, especially in the theta and alpha ranges. Oscillatory activity in the visual cortex can be entrained by transcranial alternating current stimulation (tACS) as well as periodic visual stimulation (i.e., flicker). Combined tACS and visual flicker stimulation modulates BOLD response, and concurrent 4-Hz auditory click train, and tACS modulate auditory perception in a phase-dependent way. In this study, we investigated whether phase synchrony between concurrent tACS and periodic visual stimulation (i.e., flicker) can modulate performance on a visual matching task. Participants completed a visual matching task on a flickering visual stimulus while receiving either in-phase (0°) or asynchronous (180°, 90°, or 270°) tACS at alpha or theta frequency. Stimulation was applied over either occipital cortex or dorsolateral pFC. Visual performance was significantly better during theta frequency tACS over the visual cortex when it was in-phase (0°) with visual stimulus flicker, compared with antiphase (180°). This effect did not appear with alpha frequency flicker or with dorsolateral pFC stimulation. Furthermore, a control sham group showed no effect. There were no significant performance differences among the asynchronous (180°, 90°, and 270°) phase conditions. Extending previous studies on visual and auditory perception, our results support a crucial role of oscillatory phase in sensory perception and demonstrate a behaviorally relevant combination of visual flicker and tACS. The spatial and frequency specificity of our results have implications for research on the functional organization of perception.

Theta-Nested Gamma Oscillations in Next Generation Neural Mass Models

Theta-nested gamma oscillations have been reported in many areas of the brain and are believed to represent a fundamental mechanism to transfer information across spatial and temporal scales. In a series of recent experiments in vitro it has been possible to replicate with an optogenetic theta frequency stimulation several features of cross-frequency coupling (CFC) among theta and gamma rhythms observed in behaving animals. In order to reproduce the main findings of these experiments we have considered a new class of neural mass models able to reproduce exactly the macroscopic dynamics of spiking neural networks. In this framework, we have examined two set-ups able to support collective gamma oscillations: namely, the pyramidal interneuronal network gamma (PING) and the interneuronal network gamma (ING). In both set-ups we observe the emergence of theta-nested gamma oscillations by driving the system with a sinusoidal theta-forcing in proximity of a Hopf bifurcation. These mixed rhythms always display phase amplitude coupling. However, two different types of nested oscillations can be identified: one characterized by a perfect phase locking between theta and gamma rhythms, corresponding to an overall periodic behavior; another one where the locking is imperfect and the dynamics is quasi-periodic or even chaotic. From our analysis it emerges that the locked states are more frequent in the ING set-up. In agreement with the experiments, we find theta-nested gamma oscillations for forcing frequencies in the range [1:10] Hz, whose amplitudes grow proportionally to the forcing intensity and which are clearly modulated by the theta phase. Furthermore, analogously to the experiments, the gamma power and the frequency of the gamma-power peak increase with the forcing amplitude. At variance with experimental findings, the gamma-power peak does not shift to higher frequencies by increasing the theta frequency. This effect can be obtained, in our model, only by incrementing, at the same time, also the stimulation power. An effect achieved by increasing the amplitude either of the noise or of the forcing term proportionally to the theta frequency. On the basis of our analysis both the PING and the ING mechanism give rise to theta-nested gamma oscillations with almost identical features.

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.

Nonlinear interaction decomposition (NID): A method for separation of cross-frequency coupled sources in human brain

Cross-frequency coupling (CFC) between neuronal oscillations reflects an integration of spatially and spectrally distributed information in the brain. Here, we propose a novel framework for detecting such interactions in Magneto- and Electroencephalography (MEG/EEG), which we refer to as Nonlinear Interaction Decomposition (NID). In contrast to all previous methods for separation of cross-frequency (CF) sources in the brain, we propose that the extraction of nonlinearly interacting oscillations can be based on the statistical properties of their linear mixtures. The main idea of NID is that nonlinearly coupled brain oscillations can be mixed in such a way that the resulting linear mixture has a non-Gaussian distribution. We evaluate this argument analytically for amplitude-modulated narrow-band oscillations which are either phase-phase or amplitude-amplitude CF coupled. We validated NID extensively with simulated EEG obtained with realistic head modelling. The method extracted nonlinearly interacting components reliably even at SNRs as small as -15 dB. Additionally, we applied NID to the resting-state EEG of 81 subjects to characterize CF phase-phase coupling between alpha and beta oscillations. The extracted sources were located in temporal, parietal and frontal areas, demonstrating the existence of diverse local and distant nonlinear interactions in resting-state EEG data. All codes are available publicly via GitHub.