Brain Oscillations and the Importance of Waveform Shape

Filtered point processes tractably capture rhythmic and broadband power spectral structure in neural electrophysiological recordings

Objective. Neural electrophysiological recordings arise from interacting rhythmic (oscillatory) and broadband (aperiodic) biological subprocesses. Both rhythmic and broadband processes contribute to the neural power spectrum, which decomposes the variance of a neural recording across frequencies. While rhythms in various diseases and brain states continue to be well studied, researchers only recently have systematically studied broadband effects in the power spectrum. Broadband effects include shifts in power across all frequencies, which correlate with changes in local firing rates, and changes in the overall shape of the power spectrum, such as the spectral slope. Shape changes are evident in various conditions and brain states, influenced by factors such as excitation-to-inhibition balance, age, and diseases; additionally, it is increasingly recognized that broadband and rhythmic effects can interact on a sub-second timescale. As such, modeling tools that explicitly deal with both rhythmic and broadband contributors to the power spectrum and capture their interactions are essential to improving the interpretability of power spectral effects.Approach. Here, we introduce a tractable stochastic forward modeling framework designed to capture both narrowband and broadband spectral effects when prior knowledge about the primary biophysical processes involved is available. Population-level neural recordings are modeled as the sum of filtered point processes (FPPs), each representing the contribution of a different biophysical process such as action potentials or postsynaptic potentials.Main results. Our approach builds on prior neuroscience FPP work by allowing multiple interacting processes, time-varying firing rates, and deriving theoretical power spectra and cross-spectra. We demonstrate several properties of the models, including that they divide the power spectrum into frequency ranges dominated by rhythmic and broadband effects and capture spectral effects across multiple timescales, including sub-second cross-frequency coupling.Significance. The framework can interpret empirically observed power spectra and cross-frequency coupling effects in biophysical terms, bridging theoretical models and experimental results.

Transcranial alternating current stimulation for investigating complex oscillatory dynamics and interactions

Neural oscillations play a fundamental role in human cognition and behavior. While electroencephalography (EEG) and related methods provide precise temporal recordings of these oscillations, they are limited in their ability to generate causal conclusions. Transcranial alternating current stimulation (tACS) has emerged as a promising non-invasive neurostimulation technique to modulate neural oscillations, which offers insights into their functional role and relation to human cognition and behavior. Originally, tACS is applied between two or more electrodes at a given frequency. However, recent advances have aimed to apply different current waveforms to target specific oscillatory dynamics. This systematic review evaluates the efficacy of non-standard tACS applications designed to investigate oscillatory patterns beyond simple sinusoidal stimulation. We categorized these approaches into three key domains: (1) phase synchronization techniques, including in-phase, anti-phase, and traveling wave stimulation; (2) non-sinusoidal tACS, which applies alternative waveforms such as composite, broadband or triangular oscillations; and (3) amplitude-modulated tACS and temporal interference stimulation, which allow for concurrent EEG recordings and deeper cortical targeting. While a number of studies provide evidence for the added value of these non-standard tACS procedures, other studies show opposing or null findings. Crucially, the number of studies for most applications is currently low, and as such, the goal of this review is to highlight both the promise and current limitations of these techniques, providing a foundation for future research in neurostimulation.

Endogenous oscillatory rhythms and interactive contingencies jointly influence infant attention during early infant-caregiver interaction

Almost all early cognitive development takes place in social contexts. At the moment, however, we know little about the neural and micro-interactive mechanisms that support infants' attention during social interactions. Recording EEG during naturalistic caregiver-infant interactions (N=66), we compare two different accounts. Traditional, didactic perspectives emphasise the role of the caregiver in structuring the interaction, whilst active learning models focus on motivational factors, endogenous to the infant, that guide their attention. Our results show that, already by 12 months, intrinsic cognitive processes control infants' attention: fluctuations in endogenous oscillatory neural activity associated with changes in infant attentiveness. In comparison, infant attention was not forwards-predicted by caregiver gaze or vocal behaviours. Instead, caregivers rapidly modulated their behaviours in response to changes in infant attention and cognitive engagement, and greater reactive changes associated with longer infant attention. Our findings suggest that shared attention develops through interactive but asymmetric, infant-led processes that operate across the caregiver-child dyad.

Decoding the Narcissistic Brain

There is a substantial knowledge gap in the narcissism literature: Less than 1% of the nearly 12,000 articles on narcissism have addressed its neural basis. To help fill this gap, we asked whether the multifacetedness of narcissism could be decoded from spontaneous neural oscillations. We attempted to do so by applying a machine learning approach (multivariate pattern analysis) to the resting-state EEG data of 162 participants who also completed a comprehensive battery of narcissism scales assessing agentic, admirative, rivalrous, communal, and vulnerable forms. Consistent with the agency-communion model of narcissism, agentic and communal forms of grandiose narcissism were reflected in distinct, non-overlapping patterns of spontaneous neural oscillations. Furthermore, consistent with a narcissistic admiration and rivalry concept model of narcissism, we observed largely non-overlapping patterns of spontaneous neural oscillations for admirative and rivalrous forms of narcissism. Vulnerable narcissism was negatively associated with power across fast and slow wave frequency bands. Taken together, the results suggest that the diverse forms of narcissism can be reliably predicted from spontaneous neural oscillations. The findings contribute to the burgeoning field of personality neuroscience.

Processes and measurements: a framework for understanding neural oscillations in field potentials

Various neuroscientific theories maintain that brain oscillations are important for neuronal computation, but opposing views claim that these macroscale dynamics are 'exhaust fumes' of more relevant processes. Here, we approach the question of whether oscillations are functional or epiphenomenal by distinguishing between measurements and processes, and by reviewing whether causal or inferentially useful links exist between field potentials, electric fields, and neurobiological events. We introduce a vocabulary for the role of brain signals and their underlying processes, demarcating oscillations as a distinct entity where both processes and measurements can exhibit periodicity. Leveraging this distinction, we suggest that electric fields, oscillating or not, are causally and computationally relevant, and that field potential signals can carry information even without causality.

Distinguishing major depressive disorder from bipolar disorder using alpha-band activity in resting-state electroencephalogram

BACKGROUND

Bipolar disorder (BD) and major depressive disorder (MDD) share overlapping depressive symptoms, which pose challenges for achieving rapid and accurate differential diagnosis in clinical practice. This study aims to investigate whether alpha sub-band activity in electroencephalogram (EEG) can serve as a discriminative feature between MDD and BD, thereby improving diagnostic accuracy in mood disorders.

METHODS

This study recruited a total of 103 participants, comprising 37 patients diagnosed with MDD, 36 patients with BD, and 30 healthy controls (HC). All participants were matched in terms of gender and age. EEG data were acquired during both eyes-open and eyes-closed states over a 5-minute duration to examine whether different sub-oscillations in the alpha band can differentiate between MDD and BD.

RESULTS

We found that at the group level, the peak frequency of the HC group was in the low alpha band, the BD group in the medium alpha band, and the MDD group in the high alpha band. Our results indicate that the MDD and BD groups display the most pronounced differences in the high alpha band, irrespective of whether the eyes are open or closed. In contrast, the HC group exhibits some distinctions from the MDD and BD groups in the low alpha band.

CONCLUSIONS

This study provides novel insights into the differential characteristics of alpha sub-band oscillations in MDD from BD as compared to healthy controls. These observations suggest distinct neural signatures for MDD and BD, highlighting the potential value of alpha sub-band analyses in diagnostic classification.

The Awakening Brain is Characterized by a Widespread and Spatiotemporally Heterogeneous Increase in High Frequencies

Morning awakening is part of everyday life. Surprisingly, information remains scarce on its underlying neurophysiological correlates. Here simultaneous polysomnography and stereo-electroencephalography recordings from 18 patients are used to assess the spectral and connectivity content of the process of awakening at a local level 15 min before and after the awakening. Awakenings from non-rapid eye movement sleep are accompanied by a widespread increase in ripple (>80 Hz) power in the fronto-temporal and parieto-insular regions, with connectivity showing an almost exclusive increase in the ripple band in the somatomotor, default, dorsal attention, and frontoparietal networks. Awakenings from rapid eye movement sleep are characterized by a widespread and almost exclusive increase in the ripple band in all available brain lobes, and connectivity increases mainly in the low ripple band in the limbic system as well as the default, dorsal attention, somatomotor, and frontoparietal networks.

Resting-state alpha and mu rhythms change shape across development but lack diagnostic sensitivity for ADHD and autism

In the human brain, the alpha rhythm in occipital cortex and the mu rhythm in sensorimotor cortex are among the most prominent rhythms, with both rhythms functionally implicated in gating modality-specific information. Separation of these rhythms is non-trivial due to the spatial mixing of these oscillations in sensor space. Using a computationally efficient processing pipeline requiring no manual data cleaning, we isolated alpha and/or mu rhythms from electroencephalography recordings performed on 1605 children aged 5-18. Using the extracted time series for each rhythm, we characterized the waveform shape on a cycle-by-cycle basis and examined whether and how the waveform shape differs across development. We demonstrate that alpha and mu rhythms both exhibit nonsinusoidal waveform shape that changes significantly across development, in addition to the known large changes in oscillatory frequency. This dataset also provided an opportunity to assess oscillatory measures for attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). We found no differences in the resting-state features of these alpha-band rhythms for either ADHD or ASD in comparison to typically developing participants in this dataset. While waveform shape is ignored by traditional Fourier spectral analyses, these nonsinusoidal properties may be informative for building more constrained generative models for different types of alpha-band rhythms, yielding more specific insight into their generation.

Phase-Amplitude Coupling of NREM Sleep Oscillations Shows Between-Night Stability and is Related to Overnight Memory Gains

There is growing evidence in humans linking the temporal coupling between spindles and slow oscillations during NREM sleep with the overnight stabilization of memories encoded from daytime experiences in humans. However, whether the type and strength of learning influence that relationship is still unknown. Here we tested whether the amount or type of verbal word-pair learning prior to sleep affects subsequent phase-amplitude coupling (PAC) between spindles and slow oscillations (SO). We measured the strength and preferred timing of such coupling in the EEG of 41 healthy human participants over a post-learning and control night to compare intra-individual changes with inter-individual differences. We leveraged learning paradigms of varying word-pair (WP) load: 40 WP learned to a minimum criterion of 60% correct (n = 11); 40 WP presented twice (n = 15); 120 WP presented twice (n = 15). There were no significant differences in the preferred phase or strength between the control and post-learning nights, in all learning conditions. We observed an overnight consolidation effect (improved performance at delayed recall) for the criterion learning condition only, and only in this condition was the overnight change in memory performance significantly positively correlated with the phase of SO-spindle coupling. These results suggest that the coupling of brain oscillations during human NREM sleep is stable traits that are not modulated by the amount of pre-sleep learning, yet are implicated in the sleep-dependent consolidation of memory-especially when overnight gains in memory are observed.

Unleashing the potential: 40 Hz multisensory stimulation therapy for cognitive impairment

Cognitive impairment encompasses a spectrum of disorders marked by acquired deficits in cognitive function, potentially leading to diminished daily functioning and work capacity, often accompanied by psychiatric and behavioral disturbances. Alzheimer's disease (AD) and Post-stroke cognitive impairment (PSCI) are significant causes of cognitive decline. With the global population getting older, AD and PSCI are becoming major health concerns, underscoring the critical necessity for successful treatment options. In recent years, various non-invasive biophysical stimulation techniques, including ultrasound, light, electric, and magnetic stimulation, have been developed for the treatment of central nervous system diseases. Preliminary clinical studies have demonstrated the feasibility and safety of these techniques. This review discuss the impact of 40 Hz multisensory stimulation on cerebral function, behavioral outcomes, and disease progression in both animal models and individuals exhibiting cognitive deficits, such as AD and PSCI. Furthermore, it summarizes the potential neural pathways involved in this therapeutic modality by synthesizing evidence from a variety of studies within the field. Subsequently, it evaluates the existing constraints of this technique and underscores the potential advantages of 40 Hz multisensory stimulation therapy for individuals with cognitive deficits, with the goal of enhancing the management and care of AD and PSCI.

GREEN: A lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration with EEG signals

Spectral analysis using wavelets is widely used for identifying biomarkers in EEG signals. Recently, Riemannian geometry has provided an effective mathematical framework for predicting biomedical outcomes from multichannel electroencephalography (EEG) recordings while showing concord with neuroscientific domain knowledge. However, these methods rely on handcrafted rules and sequential optimization. In contrast, deep learning (DL) offers end-to-end trainable models achieving state-of-the-art performance on various prediction tasks but lacks interpretability and interoperability with established neuroscience concepts. We introduce Gabor Riemann EEGNet (GREEN), a lightweight neural network that integrates wavelet transforms and Riemannian geometry for processing raw EEG data. Benchmarking on six prediction tasks across four datasets with over 5,000 participants, GREEN outperformed non-deep state-of-the-art models and performed favorably against large DL models while using orders-of-magnitude fewer parameters. Computational experiments showed that GREEN facilitates learning sparse representations without compromising performance. By integrating domain knowledge, GREEN combines a desirable complexity-performance trade-off with interpretable representations.

The relationship between behavioral inhibition and resting electroencephalography: A neuroelectrophysiological study

Investigating the neurophysiological indicators of behavioral inhibition is crucial; however, despite numerous studies on the relationship between behavioral inhibition and resting-state electroencephalography (rs-EEG), the findings have yielded inconsistent results. Furthermore, these investigations primarily focused on reactive inhibition while neglecting intentional inhibition. Therefore, this study aimed to reassess the correlation between reactive inhibition and rs-EEG metrics while also exploring the association between intentional inhibition and rs-EEG. Power spectrum analysis and microstate analysis were employed to extract rs-EEG, whereas the Free Two-Choice Oddball task was utilized for assessing both reactive and intentional inhibition among 95 participants. The results revealed no significant correlations between reactive inhibition and rs-EEG metrics. However, intentional inhibition exhibited a negative correlation with relative power in delta and beta bands but a positive correlation with relative power in alpha band. Moreover, intentional inhibition demonstrated a negative correlation with occurrence rate and contribution of microstate A but a positive correlation with duration of microstate D. Additionally, it displayed a negative relationship with the transition probability between microstate A and C but a positive relationship with the transition probability between microstate C and D. The regression analysis revealed that the occurrence rate of microstate A can negatively predict intentional inhibition. Overall, this study advances theoretical understanding as well as empirical research in this field by addressing gaps in rs-EEG evidence for intentional inhibition while providing potential neuropsychological indicators for its assessment.

Dissociation-related behaviors in mice emerge from the inhibition of retrosplenial cortex parvalbumin interneurons

Dissociation, characterized by altered consciousness and perception, underlies multiple mental disorders, but the specific neuronal subtypes involved remain elusive. In mice, we find that dissociation-inducing doses of ketamine significantly inhibit retrosplenial cortex (RSC) parvalbumin interneurons (PV-INs), enhancing delta oscillations (1-3 Hz) and delta-gamma phase-amplitude coupling (δ-γ PAC) and inducing dissociation-like behaviors. Optogenetic inhibition of RSC PV-INs triggers delta oscillations, δ-γ PAC, and some dissociation-like behaviors without ketamine. Furthermore, activation of RSC PV-INs or knockdown of the N-methyl-D-aspartate receptor subunit NR1 and the hyperpolarization-activated cyclic nucleotide-gated channel 1 (HCN1) in RSC PV-INs attenuates ketamine-induced delta oscillations, δ-γ PAC, and certain dissociation-like behaviors. These findings reveal that PV-INs regulate delta oscillations and δ-γ PAC and identify NR1 and HCN1 as ketamine targets in PV-INs that may cooperatively affect dissociation, possibly providing potential therapeutic targets for dissociative symptoms.

Pigs as a translational animal model for the study of peak alpha frequency

The most characteristic feature of the human electroencephalogram is the peak alpha frequency (PAF). While PAF has been proposed as a biomarker in several diseases and disorders, the disease mechanisms modulating PAF, as well as its physiological substrates, remain elusive. This has partly been due to challenges related to experimental manipulation and invasive procedures in human neuroscience, as well as the scarcity of animal models where PAF is consistently present in resting-state. With the potential inclusion of PAF in clinical screening and decision-making, advancing the mechanistic understanding of PAF is warranted. In this paper, we propose the female Danish Landrace pig as a suitable animal model to probe the mechanisms of PAF and its feature as a biomarker. We show that somatosensory alpha oscillations are present in anesthetized pigs using electrocorticography and intracortical electrodes located at the sensorimotor cortex. This was evident when looking at the time-domain as well as the spectral morphology of spontaneous recordings. We applied the FOOOF-algorithm to extract the spectral characteristics and implemented a robustness threshold for any periodic component. Using this conservative threshold, PAF was present in 18/20 pigs with a normal distribution of the peak frequency between 8-12 Hz, producing similar findings to human recordings. We show that PAF was present in 69.6 % of epochs of approximately six-minute-long resting-state recordings. In sum, we propose that the pig is a suitable candidate for investigating the neural mechanisms of PAF as a biomarker for disease and disorders such as pain, neuropsychiatric disorders, and response to pharmacotherapy.

Mystery of gamma wave stimulation in brain disorders

Neuronal oscillations refer to rhythmic and periodic fluctuations of electrical activity in the central nervous system that arise from the cellular properties of diverse neuronal populations and their interactions. Specifically, gamma oscillations play a crucial role in governing the connectivity between distinct brain regions, which are essential in perception, motor control, memory, and emotions. In this context, we recapitulate various current stimulation methods to induce gamma entrainment. These methods include sensory stimulation, optogenetic modulation, photobiomodulation, and transcranial electrical or magnetic stimulation. Simultaneously, we explore the association between abnormal gamma oscillations and central nervous system disorders such as Alzheimer's disease, Parkinson's disease, stroke, schizophrenia, and autism spectrum disorders. Evidence suggests that gamma entrainment-inducing stimulation methods offer notable neuroprotection, although somewhat controversial. This review comprehensively discusses the functional role of gamma oscillations in higher-order brain activities from both physiological and pathological perspectives, emphasizing gamma entrainment as a potential therapeutic approach for neuropsychiatric disorders. Additionally, we discuss future opportunities and challenges in implementing such strategies.

SSVEP modulation via non-volitional neurofeedback: anin silicoproof of concept

Objective.Neuronal oscillatory patterns are believed to underpin multiple cognitive mechanisms. Accordingly, compromised oscillatory dynamics were shown to be associated with neuropsychiatric conditions. Therefore, the possibility of modulating, or controlling, oscillatory components of brain activity as a therapeutic approach has emerged. Typical non-invasive brain-computer interfaces based on EEG have been used to decode volitional motor brain signals for interaction with external devices. Here we aimed at feedback through visual stimulation which returns directly back to the visual cortex.Approach.Our architecture permits the implementation of feedback control-loops capable of controlling, or at least modulating, visual cortical activity. As this type of neurofeedback depends on early visual cortical activity, mainly driven by external stimulation it is called non-volitional or implicit neurofeedback. Because retino-cortical 40-100 ms delays in the feedback loop severely degrade controller performance, we implemented a predictive control system, called a Smith-Predictor (SP) controller, which compensates for fixed delays in the control loop by building an internal model of the system to be controlled, in this case the EEG response to stimuli in the visual cortex.Main results. Response models were obtained by analyzing, EEG data (n= 8) of experiments using periodically inverting stimuli causing prominent parieto-occipital oscillations, the steady-state visual evoked potentials (SSVEPs). Averaged subject-specific SSVEPs, and associated retina-cortical delays, were subsequently used to obtain the SP controller's linear, time-invariant models of individual responses. The SSVEP models were first successfully validated against the experimental data. When placed in closed loop with the designed SP controller configuration, the SSVEP amplitude level oscillated around several reference values, accounting for inter-individual variability.Significance. In silicoandin vivodata matched, suggesting model's robustness, paving the way for the experimental validation of this non-volitional neurofeedback system to control the amplitude of abnormal brain oscillations in autism and attention and hyperactivity deficits.

Resting-state EEG spectral and fractal features in dementia with Lewy bodies with and without visual hallucinations

OBJECTIVE

Complex visual hallucinations (VH) are a core feature of dementia with Lewy bodies (DLB), though they may not occur in all patients. Power spectral density (PSD) analysis of resting-state EEG (rs-EEG) shows associations between some frequency bands (e.g., theta), individual alpha frequency (IAF) and VH. However, new tools that improve early differential diagnosis and symptom-based stratification with higher sensitivity and specificity, even within the DLB population, are desirable. We aimed to assess differences in rs-EEG data between DLB patients with VH (DLB-VH+) and without VH (DLB-VH-), comparing innovative non-linear approaches with more traditional linear ones.

METHODS

We retrospectively analyzed rs-EEG recordings of DLB-VH+, DLB-VH-, Alzheimer's disease patients and age-matched healthy controls. EEG was analyzed using the nonlinear Higuchi's Fractal Dimension (FD) measure, and the results were compared with those of entropy and standard linear methods based on PSD and IAF.

RESULTS

Only the FD measure could discriminate between DLB-VH+ and DLB-VH-.

CONCLUSIONS

In conclusion, rs-EEG differences between DLB-VH+ and DLB-VH- are better characterized by FD analysis than by a more traditional power spectrum approach.

SIGNIFICANCE

This suggests that the presence of complex VH is associated with less complex brain dynamics at rest, as reflected by the FD measure.

The therapeutic potential of low-intensity focused ultrasound for treating substance use disorder

Substance use disorder (SUD) is a persistent public health issue that necessitates the exploration of novel therapeutic interventions. Low-intensity focused ultrasound (LIFU) is a promising modality for precise and invasive modulation of brain activity, capable of redefining the landscape of SUD treatment. The review overviews effective LIFU neuromodulatory parameters and molecular mechanisms, focusing on the modulation of reward pathways in key brain regions in animal and human models. Integration of LIFU with established therapeutics holds promise for augmenting treatment outcomes in SUD. The current research examines LIFU's efficacy in reducing cravings and withdrawal symptoms. LIFU shows promise for reducing cravings, modulating reward circuitry, and addressing interoceptive dysregulation and emotional distress. Selecting optimal parameters, encompassing frequency, burst patterns, and intensity, is pivotal for balancing therapeutic efficacy and safety. However, inconsistencies in empirical findings warrant further research on optimal treatment parameters, physiological action mechanisms, and long-term effects. Collaborative interdisciplinary investigations are imperative to fully realize LIFU's potential in revolutionizing SUD treatment paradigms and enhancing patient outcomes.

Filtered Point Processes Tractably Capture Rhythmic And Broadband Power Spectral Structure in Neural Electrophysiological Recordings

Neural electrophysiological recordings arise from interacting rhythmic (oscillatory) and broadband (aperiodic) biological subprocesses. Both rhythmic and broadband processes contribute to the neural power spectrum, which decomposes the variance of a neural recording across frequencies. Although an extensive body of literature has successfully studied rhythms in various diseases and brain states, researchers only recently have systematically studied the characteristics of broadband effects in the power spectrum. Broadband effects can generally be categorized as 1) shifts in power across all frequencies, which correlate with changes in local firing rates and 2) changes in the overall shape of the power spectrum, such as the spectral slope or power law exponent. Shape changes are evident in various conditions and brain states, influenced by factors such as excitation to inhibition balance, age, and various diseases. It is increasingly recognized that broadband and rhythmic effects can interact on a sub-second timescale. For example, broadband power is time-locked to the phase of <1 Hz rhythms in propofol induced unconsciousness. Modeling tools that explicitly deal with both rhythmic and broadband contributors to the power spectrum and that capture their interactions are essential to help improve the interpretability of power spectral effects. Here, we introduce a tractable stochastic forward modeling framework designed to capture both narrowband and broadband spectral effects when prior knowledge or theory about the primary biophysical processes involved is available. Population-level neural recordings are modeled as the sum of filtered point processes (FPPs), each representing the contribution of a different biophysical process such as action potentials or postsynaptic potentials of different types. Our approach builds on prior neuroscience FPP work by allowing multiple interacting processes and time-varying firing rates and by deriving theoretical power spectra and cross-spectra. We demonstrate several properties of the models, including that they divide the power spectrum into frequency ranges dominated by rhythmic and broadband effects, and that they can capture spectral effects across multiple timescales, including sub-second cross-frequency coupling. The framework can be used to interpret empirically observed power spectra and cross-frequency coupling effects in biophysical terms, which bridges the gap between theoretical models and experimental results.

Differential effects of haloperidol on neural oscillations during wakefulness and sleep

The electrical activity of the brain, characterized by its frequency components, reflects a complex interplay between periodic (oscillatory) and aperiodic components. These components are associated with various neurophysiological processes, such as the excitation-inhibition balance (aperiodic activity) or interregional communication (oscillatory activity). However, we do not fully understand whether these components are truly independent or if different neuromodulators affect them in different ways. The dopaminergic system has a critical role for cognition and motivation, being a potential modulator of these power spectrum components. To improve our understanding of these questions, we investigated the differential effects of this system on these components using electrocorticogram recordings in cats, which show clear oscillations and aperiodic 1/f activity. Specifically, we focused on the effects of haloperidol (a D2 receptor antagonist) on oscillatory and aperiodic dynamics during wakefulness and sleep. By parameterizing the power spectrum into these two components, our findings reveal a robust modulation of oscillatory activity by the D2 receptor across the brain. Surprisingly, aperiodic activity was not significantly affected and exhibited inconsistent changes across the brain. This suggests a nuanced interplay between neuromodulation and the distinct components of brain oscillations, providing insights into the selective regulation of oscillatory dynamics in awake states.

Aberrant neural oscillations in poststroke aphasia

Neural oscillations are electrophysiological indicators of synchronous neuronal activity in the brain. Recent work suggests aberrant patterns of neuronal activity in patients with poststroke aphasia. Yet, there is a lack of systematic explorations of neural oscillations in poststroke aphasia. Investigating changes in the dynamics of neuronal activity after stroke may be helpful to identify neural markers of aphasia and language recovery and increase the current understanding of successful language rehabilitation. This review summarizes research on neural oscillations in poststroke aphasia and evaluates their potential as biomarkers for specific linguistic processes. We searched the literature through PubMed, Web of Science, and EBSCO, and selected 31 studies that met the inclusion criteria. Our analyses focused on neural oscillation activity in each frequency band, brain connectivity, and therapy-induced changes during language recovery. Our review highlights potential neurophysiological markers; however, the literature remains confounded, casting doubt on the reliability of these findings. Future research must address these confounds to confirm the robustness of cross-study findings on neural oscillations in poststroke aphasia.

Visual Processing by Hierarchical and Dynamic Multiplexing

The complexity of natural environments requires highly flexible mechanisms for adaptive processing of single and multiple stimuli. Neuronal oscillations could be an ideal candidate for implementing such flexibility in neural systems. Here, we present a framework for structuring attention-guided processing of complex visual scenes in humans, based on multiplexing and phase coding schemes. Importantly, we suggest that the dynamic fluctuations of excitability vary rapidly in terms of magnitude, frequency and wave-form over time, i.e., they are not necessarily sinusoidal or sustained oscillations. Different elements of single objects would be processed within a single cycle (burst) of alpha activity (7-14 Hz), allowing for the formation of coherent object representations while separating multiple objects across multiple cycles. Each element of an object would be processed separately in time-expressed as different gamma band bursts (>30 Hz)-along the alpha phase. Since the processing capacity per alpha cycle is limited, an inverse relationship between object resolution and size of attentional spotlight ensures independence of the proposed mechanism from absolute object complexity. Frequency and wave-shape of those fluctuations would depend on the nature of the object that is processed and on cognitive demands. Multiple objects would further be organized along the phase of slower fluctuations (e.g., theta), potentially driven by saccades. Complex scene processing, involving covert attention and eye movements, would therefore be associated with multiple frequency changes in the alpha and lower frequency range. This framework embraces the idea of a hierarchical organization of visual processing, independent of environmental temporal dynamics.

Harnessing the sensing and stimulation function of deep brain-machine interfaces: a new dawn for overcoming substance use disorders

Substance use disorders (SUDs) imposes profound physical, psychological, and socioeconomic burdens on individuals, families, communities, and society as a whole, but the available treatment options remain limited. Deep brain-machine interfaces (DBMIs) provide an innovative approach by facilitating efficient interactions between external devices and deep brain structures, thereby enabling the meticulous monitoring and precise modulation of neural activity in these regions. This pioneering paradigm holds significant promise for revolutionizing the treatment landscape of addictive disorders. In this review, we carefully examine the potential of closed-loop DBMIs for addressing SUDs, with a specific emphasis on three fundamental aspects: addictive behaviors-related biomarkers, neuromodulation techniques, and control policies. Although direct empirical evidence is still somewhat limited, rapid advancements in cutting-edge technologies such as electrophysiological and neurochemical recordings, deep brain stimulation, optogenetics, microfluidics, and control theory offer fertile ground for exploring the transformative potential of closed-loop DBMIs for ameliorating symptoms and enhancing the overall well-being of individuals struggling with SUDs.

Cycling reduces the entropy of neuronal activity in the human adult cortex

Brain Complexity (BC) have successfully been applied to study the brain electroencephalographic signal (EEG) in health and disease. In this study, we employed recurrence entropy to quantify BC associated with the neurophysiology of movement by comparing BC in both resting state and cycling movement. We measured EEG in 24 healthy adults and placed the electrodes on occipital, parietal, temporal and frontal sites on both the right and left sides of the brain. We computed the recurrence entropy from EEG measurements during cycling and resting states. Entropy is higher in the resting state than in the cycling state for all brain regions analysed. This reduction in complexity is a result of the repetitive movements that occur during cycling. These movements lead to continuous sensorial feedback, resulting in reduced entropy and sensorimotor processing.

Age-related differences in how the shape of alpha and beta oscillations change during reaction time tasks

While the shape of cortical oscillations is increasingly recognised to be physiologically and functionally informative, its relevance to the aging motor system has not been established. We therefore examined the shape of alpha and beta band oscillations recorded at rest, as well as during performance of simple and go/no-go reaction time tasks, in 33 young (23.3 ± 2.9 years, 27 females) and 27 older (60.0 ± 5.2 years, 23 females) adults. The shape of individual oscillatory cycles was characterised using a recently developed pipeline involving empirical mode decomposition, before being decomposed into waveform motifs using principal component analysis. This revealed four principal components that were uniquely influenced by task and/or age. These described specific dimensions of shape and tended to be modulated during the reaction phase of each task. Our results suggest that although oscillation shape is task-dependent, the nature of this effect is altered by advancing age, possibly reflecting alterations in cortical activity. These outcomes demonstrate the utility of this approach for understanding the neurophysiological effects of ageing.

DENOISING: Dynamic enhancement and noise overcoming in multimodal neural observations via high-density CMOS-based biosensors

Large-scale multimodal neural recordings on high-density biosensing microelectrode arrays (HD-MEAs) offer unprecedented insights into the dynamic interactions and connectivity across various brain networks. However, the fidelity of these recordings is frequently compromised by pervasive noise, which obscures meaningful neural information and complicates data analysis. To address this challenge, we introduce DENOISING, a versatile data-derived computational engine engineered to adjust thresholds adaptively based on large-scale extracellular signal characteristics and noise levels. This facilitates the separation of signal and noise components without reliance on specific data transformations. Uniquely capable of handling a diverse array of noise types (electrical, mechanical, and environmental) and multidimensional neural signals, including stationary and non-stationary oscillatory local field potential (LFP) and spiking activity, DENOISING presents an adaptable solution applicable across different recording modalities and brain networks. Applying DENOISING to large-scale neural recordings from mice hippocampal and olfactory bulb networks yielded enhanced signal-to-noise ratio (SNR) of LFP and spike firing patterns compared to those computed from raw data. Comparative analysis with existing state-of-the-art denoising methods, employing SNR and root mean square noise (RMS), underscores DENOISING's performance in improving data quality and reliability. Through experimental and computational approaches, we validate that DENOISING improves signal clarity and data interpretation by effectively mitigating independent noise in spatiotemporally structured multimodal datasets, thus unlocking new dimensions in understanding neural connectivity and functional dynamics.

Spatial prediction modulates the rhythm of attentional sampling

Recent studies demonstrate that behavioral performance during visual spatial attention fluctuates at theta (4 to 8 Hz) and alpha (8 to 16 Hz) frequencies, linked to phase-amplitude coupling of neural oscillations within the visual and attentional system depending on task demands. To investigate the influence of prior spatial prediction, we employed an adaptive discrimination task with variable cue-target onset asynchronies (300 to 1,300 ms) and different cue validity (100% & 50%). We recorded electroencephalography concurrently and adopted adaptive electroencephalography data analytical methods, namely, Holo-Holo-Hilbert spectral analysis and Holo-Hilbert cross-frequency phase clustering. Our findings indicate that response precision for near-threshold Landolt rings fluctuates at the theta band (4 Hz) under certain predictions and at alpha & beta bands (15 & 19 Hz) with uncertain predictions. Furthermore, spatial prediction strengthens theta-alpha modulations at parietal-occipital areas, frontal theta/parietal-occipital alpha phase-amplitude coupling, and within frontal theta-alpha phase-amplitude coupling. Notably, during the pretarget period, beta-modulated gamma oscillations in parietal-occipital areas predict response precision under uncertain prediction, while frontal theta/parietal-occipital alpha phase-amplitude coupling predicts response precision in spatially certain conditions. In conclusion, our study highlights the critical role of spatial prediction in attentional sampling rhythms with both behavioral and electroencephalography evidence.

Resting-state frontal electroencephalography (EEG) biomarkers for detecting the severity of chronic neuropathic pain

Increasing evidence is present to enable pain measurement by using frontal channel EEG-based signals with spectral analysis and phase-amplitude coupling. To identify frontal channel EEG-based biomarkers for quantifying pain severity, we investigated band-power features to more complex features and employed various machine learning algorithms to assess the viability of these features. We utilized a public EEG dataset obtained from 36 patients with chronic pain during an eyes-open resting state and performed correlation analysis between clinically labelled pain scores and EEG features from Fp1 and Fp2 channels (EEG band-powers, phase-amplitude couplings (PAC), and its asymmetry features). We also conducted regression analysis with various machine learning models to predict patients' pain intensity. All the possible feature sets combined with five machine learning models (Linear Regression, random forest and support vector regression with linear, non-linear and polynomial kernels) were intensively checked, and regression performances were measured by adjusted R-squared value. We found significant correlations between beta power asymmetry (r = -0.375), gamma power asymmetry (r = -0.433) and low beta to low gamma coupling (r = -0.397) with pain scores while band power features did not show meaningful results. In the regression analysis, Support Vector Regression with a polynomial kernel showed the best performance (R squared value = 0.655), enabling the regression of pain intensity within a clinically usable error range. We identified the four most selected features (gamma power asymmetry, PAC asymmetry of theta to low gamma, low beta to low/high gamma). This study addressed the importance of complex features such as asymmetry and phase-amplitude coupling in pain research and demonstrated the feasibility of objectively observing pain intensity using the frontal channel-based EEG, that are clinically crucial for early intervention.

Brain wave modulation and EEG power changes during auditory beats stimulation

Auditory beats stimulation (ABS) has received increased attention for its potential to modulate neural oscillations through a phenomenon described as brain entrainment (i.e synchronization of brain's electrocortical activity to external stimuli at a specific frequency). Recently, a new form of ABS has emerged, inspired by isochronic tones stimulation (ITd). This study investigated neural oscillatory responses induced by ITd in comparison with formerly well-established ABS protocols, such as gamma-binaural beats (BB) and white noise (WN). We recorded the electroencephalographic brain activity in 28 participants during 4 min of BB, ITd, and WN presentation. Data demonstrated that while both BB and WN enhanced oscillatory power on the EEG gamma band, consistently with the expected brain entrainment effect, ITd yielded greater changes in EEG power (p < 0.001). This was confirmed by time-based analysis, which showed a progressive increase in normalized EEG power within the ITd window compared to BB (p < 0.05). Findings also revealed that ITd elicited acute changes in the alpha band of EEG oscillations, through a progressive decrease in power over time, which was distinctly different from the pattern observed while listening BB and WN. Such dual alpha-gamma effects underline the promising and unique potential of ITd to modulate neural oscillations which selectively differ from BB and WN. This study contributes to the evolution of ABS research, highlighting the promise of ITd for cognitive enhancement and clinical applications.

Neural oscillation in bipolar disorder: a systematic review of resting-state electroencephalography studies

Bipolar disorder (BD) is a severe psychiatric disease with high rates of misdiagnosis and underdiagnosis, resulting in a significant disease burden on both individuals and society. Abnormal neural oscillations have garnered significant attention as potential neurobiological markers of BD. However, untangling the mechanisms that subserve these baseline alternations requires measurement of their electrophysiological underpinnings. This systematic review investigates consistent abnormal resting-state EEG power of BD and conducted an initial exploration into how methodological approaches might impact the study outcomes. This review was conducted in Pubmed-Medline and Web-of-Science in March 2024 to summarize the oscillation changes in resting-state EEG (rsEEG) of BD. We focusing on rsEEG to report spectral power in different frequency bands. We identified 10 studies, in which neural oscillations was compared with healthy individuals (HCs). We found that BD patients had abnormal oscillations in delta, theta, beta, and gamma bands, predominantly characterized by increased power, indicating potential widespread neural dysfunction, involving multiple neural networks and cognitive processes. However, the outcomes regarding alpha oscillation in BD were more heterogeneous, which is thought to be potentially influenced by the disease severity and the diversity of samples. Furthermore, we conducted an initial exploration into how demographic and methodological elements might impact the study outcomes, underlining the importance of implementing standardized data collection methods. Key aspects we took into account included gender, age, medication usage, medical history, the method of frequency band segmentation, and situation of eye open/eye close during the recordings. Therefore, in the face of abnormal multiple oscillations in BD, we need to adopt a comprehensive research approach, consider the multidimensional attributes of the disease and the heterogeneity of samples, and pay attention to the standardized experimental design to improve the reliability and reproducibility of the research results.

Machine learning of brain-specific biomarkers from EEG

BACKGROUND

Electroencephalography (EEG) has a long history as a clinical tool to study brain function, and its potential to derive biomarkers for various applications is far from exhausted. Machine learning (ML) can guide future innovation by harnessing the wealth of complex EEG signals to isolate relevant brain activity. Yet, ML studies in EEG tend to ignore physiological artefacts, which may cause problems for deriving biomarkers specific to the central nervous system (CNS).

METHODS

We present a framework for conceptualising machine learning from CNS versus peripheral signals measured with EEG. A signal representation based on Morlet wavelets allowed us to define traditional brain activity features (e.g. log power) and alternative inputs used by state-of-the-art ML approaches based on covariance matrices. Using more than 2600 EEG recordings from large public databases (TUAB, TDBRAIN), we studied the impact of peripheral signals and artefact removal techniques on ML models in age and sex prediction analyses.

FINDINGS

Across benchmarks, basic artefact rejection improved model performance, whereas further removal of peripheral signals using ICA decreased performance. Our analyses revealed that peripheral signals enable age and sex prediction. However, they explained only a fraction of the performance provided by brain signals.

INTERPRETATION

We show that brain signals and body signals, both present in the EEG, allow for prediction of personal characteristics. While these results may depend on specific applications, our work suggests that great care is needed to separate these signals when the goal is to develop CNS-specific biomarkers using ML.

FUNDING

All authors have been working for F. Hoffmann-La Roche Ltd.

Return of intracranial beta oscillations and traveling waves with recovery from traumatic brain injury

Traumatic brain injury (TBI) remains a pervasive clinical problem associated with significant morbidity and mortality. However, TBI remains clinically and biophysically ill-defined, and prognosis remains difficult even with the standardization of clinical guidelines and advent of multimodality monitoring. Here we leverage a unique data set from TBI patients implanted with either intracranial strip electrodes during craniotomy or quad-lumen intracranial bolts with depth electrodes as part of routine clinical practice. By extracting spectral profiles of this data, we found that the presence of narrow-band oscillatory activity in the beta band (12-30 Hz) closely corresponds with the neurological exam as quantified with the standard Glasgow Coma Scale (GCS). Further, beta oscillations were distributed over the cortical surface as traveling waves, and the evolution of these waves corresponded to recovery from coma, consistent with the putative role of waves in perception and cognitive activity. We consequently propose that beta oscillations and traveling waves are potential biomarkers of recovery from TBI. In a broader sense, our findings suggest that emergence from coma results from recovery of thalamo-cortical interactions that coordinate cortical beta rhythms.

Bridging model and experiment in systems neuroscience with Cleo: the Closed-Loop, Electrophysiology, and Optophysiology simulation testbed

Systems neuroscience has experienced an explosion of new tools for reading and writing neural activity, enabling exciting new experiments such as all-optical or closed-loop control that effect powerful causal interventions. At the same time, improved computational models are capable of reproducing behavior and neural activity with increasing fidelity. Unfortunately, these advances have drastically increased the complexity of integrating different lines of research, resulting in the missed opportunities and untapped potential of suboptimal experiments. Experiment simulation can help bridge this gap, allowing model and experiment to better inform each other by providing a low-cost testbed for experiment design, model validation, and methods engineering. Specifically, this can be achieved by incorporating the simulation of the experimental interface into our models, but no existing tool integrates optogenetics, two-photon calcium imaging, electrode recording, and flexible closed-loop processing with neural population simulations. To address this need, we have developed Cleo: the Closed-Loop, Electrophysiology, and Optophysiology experiment simulation testbed. Cleo is a Python package enabling injection of recording and stimulation devices as well as closed-loop control with realistic latency into a Brian spiking neural network model. It is the only publicly available tool currently supporting two-photon and multi-opsin/wavelength optogenetics. To facilitate adoption and extension by the community, Cleo is open-source, modular, tested, and documented, and can export results to various data formats. Here we describe the design and features of Cleo, validate output of individual components and integrated experiments, and demonstrate its utility for advancing optogenetic techniques in prospective experiments using previously published systems neuroscience models.

Blindly separated spontaneous network-level oscillations predict corticospinal excitability

Objective.The corticospinal responses of the motor network to transcranial magnetic stimulation (TMS) are highly variable. While often regarded as noise, this variability provides a way of probing dynamic brain states related to excitability. We aimed to uncover spontaneously occurring cortical states that alter corticospinal excitability.Approach.Electroencephalography (EEG) recorded during TMS registers fast neural dynamics-unfortunately, at the cost of anatomical precision. We employed analytic Common Spatial Patterns technique to derive excitability-related cortical activity from pre-TMS EEG signals while overcoming spatial specificity issues.Main results.High corticospinal excitability was predicted by alpha-band activity, localized adjacent to the stimulated left motor cortex, and suggesting a travelling wave-like phenomenon towards frontal regions. Low excitability was predicted by alpha-band activity localized in the medial parietal-occipital and frontal cortical regions.Significance.We established a data-driven approach for uncovering network-level neural activity that modulates TMS effects. It requires no prior anatomical assumptions, while being physiologically interpretable, and can be employed in both exploratory investigation and brain state-dependent stimulation.

Characterization of antiseizure medications effects on the EEG neurodynamic by fractal dimension

Objectives

An important challenge in epilepsy is to define biomarkers of response to treatment. Many electroencephalography (EEG) methods and indices have been developed mainly using linear methods, e.g., spectral power and individual alpha frequency peak (IAF). However, brain activity is complex and non-linear, hence there is a need to explore EEG neurodynamics using nonlinear approaches. Here, we use the Fractal Dimension (FD), a measure of whole brain signal complexity, to measure the response to anti-seizure therapy in patients with Focal Epilepsy (FE) and compare it with linear methods.

Materials

Twenty-five drug-responder (DR) patients with focal epilepsy were studied before (t1, named DR-t1) and after (t2, named DR-t2) the introduction of the anti-seizure medications (ASMs). DR-t1 and DR-t2 EEG results were compared against 40 age-matched healthy controls (HC).

Methods

EEG data were investigated from two different angles: frequency domain-spectral properties in δ, θ, α, β, and γ bands and the IAF peak, and time-domain-FD as a signature of the nonlinear complexity of the EEG signals. Those features were compared among the three groups.

Results

The δ power differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The θ power differed between DR-t1 and DR-t2 (p = 0.015) and between DR-t1 and HC (p = 0.01). The α power, similar to the δ, differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The IAF value was lower for DR-t1 than DR-t2 (p = 0.048) and HC (p = 0.042). The FD value was lower in DR-t1 than in DR-t2 (p = 0.015) and HC (p = 0.011). Finally, Bayes Factor analysis showed that FD was 195 times more likely to separate DR-t1 from DR-t2 than IAF and 231 times than θ.

Discussion

FD measured in baseline EEG signals is a non-linear brain measure of complexity more sensitive than EEG power or IAF in detecting a response to ASMs. This likely reflects the non-oscillatory nature of neural activity, which FD better describes.

Conclusion

Our work suggests that FD is a promising measure to monitor the response to ASMs in FE.

Age-dependent coupling characteristics of bilateral frontal EEG during desflurane anesthesia

Objectives.The purpose of this study is to investigate the age dependence of bilateral frontal electroencephalogram (EEG) coupling characteristics, and find potential age-independent depth of anesthesia monitoring indicators for the elderlies.Approach.We recorded bilateral forehead EEG data from 41 patients (ranged in 19-82 years old), and separated into three age groups: 18-40 years (n= 12); 40-65 years (n= 14), >65 years (n= 15). All these patients underwent desflurane maintained general anesthesia (GA). We analyzed the age-related EEG spectra, phase amplitude coupling (PAC), coherence and phase lag index (PLI) of EEG data in the states of awake, GA, and recovery.Main results.The frontal alpha power shows age dependence in the state of GA maintained by desflurane. Modulation index in slow oscillation-alpha and delta-alpha bands showed age dependence and state dependence in varying degrees, the PAC pattern also became less pronounced with increasing age. In the awake state, the coherence in delta, theta and alpha frequency bands were all significantly higher in the >65 years age group than in the 18-40 years age group (p< 0.05 for three frequency bands). The coherence in alpha-band was significantly enhanced in all age groups in GA (p< 0.01) and then decreased in recovery state. Notably, the PLI in the alpha band was able to significantly distinguish the three states of awake, GA and recovery (p< 0.01) and the results of PLI in delta and theta frequency bands had similar changes to those of coherence.Significance.We found the EEG coupling and synchronization between bilateral forehead are age-dependent. The PAC, coherence and PLI portray this age-dependence. The PLI and coherence based on bilateral frontal EEG functional connectivity measures and PAC based on frontal single-channel are closely associated with anesthesia-induced unconsciousness.

[Formula: see text]-[Formula: see text]: A Nonparametric Model for Neural Power Spectra Decomposition

The power spectra estimated from the brain recordings are the mixed representation of aperiodic transient activity and periodic oscillations, i.e., aperiodic component (AC) and periodic component (PC). Quantitative neurophysiology requires precise decomposition preceding parameterizing each component. However, the shape, statistical distribution, scale, and mixing mechanism of AC and PCs are unclear, challenging the effectiveness of current popular parametric models such as FOOOF, IRASA, BOSC, etc. Here, ξ- π was proposed to decompose the neural spectra by embedding the nonparametric spectra estimation with penalized Whittle likelihood and the shape language modeling into the expectation maximization framework. ξ- π was validated on the synthesized spectra with loss statistics and on the sleep EEG and the large sample iEEG with evaluation metrics and neurophysiological evidence. Compared to FOOOF, both the simulation presenting shape irregularities and the batch simulation with multiple isolated peaks indicated that ξ- π improved the fit of AC and PCs with less loss and higher F1-score in recognizing the centering frequencies and the number of peaks; the sleep EEG revealed that ξ- π produced more distinguishable AC exponents and improved the sleep state classification accuracy; the iEEG showed that ξ- π approached the clinical findings in peak discovery. Overall, ξ- π offered good performance in the spectra decomposition, which allows flexible parameterization using descriptive statistics or kernel functions. ξ- π is a seminal tool for brain signal decoding in fields such as cognitive neuroscience, brain-computer interface, neurofeedback, and brain diseases.

Control of working memory by phase-amplitude coupling of human hippocampal neurons

Retaining information in working memory is a demanding process that relies on cognitive control to protect memoranda-specific persistent activity from interference1,2. However, how cognitive control regulates working memory storage is unclear. Here we show that interactions of frontal control and hippocampal persistent activity are coordinated by theta-gamma phase-amplitude coupling (TG-PAC). We recorded single neurons in the human medial temporal and frontal lobe while patients maintained multiple items in their working memory. In the hippocampus, TG-PAC was indicative of working memory load and quality. We identified cells that selectively spiked during nonlinear interactions of theta phase and gamma amplitude. The spike timing of these PAC neurons was coordinated with frontal theta activity when cognitive control demand was high. By introducing noise correlations with persistently active neurons in the hippocampus, PAC neurons shaped the geometry of the population code. This led to higher-fidelity representations of working memory content that were associated with improved behaviour. Our results support a multicomponent architecture of working memory1,2, with frontal control managing maintenance of working memory content in storage-related areas3-5. Within this framework, hippocampal TG-PAC integrates cognitive control and working memory storage across brain areas, thereby suggesting a potential mechanism for top-down control over sensory-driven processes.

Rhythmicity of neuronal oscillations delineates their cortical and spectral architecture

Neuronal oscillations are commonly analyzed with power spectral methods that quantify signal amplitude, but not rhythmicity or 'oscillatoriness' per se. Here we introduce a new approach, the phase-autocorrelation function (pACF), for the direct quantification of rhythmicity. We applied pACF to human intracerebral stereoelectroencephalography (SEEG) and magnetoencephalography (MEG) data and uncovered a spectrally and anatomically fine-grained cortical architecture in the rhythmicity of single- and multi-frequency neuronal oscillations. Evidencing the functional significance of rhythmicity, we found it to be a prerequisite for long-range synchronization in resting-state networks and to be dynamically modulated during event-related processing. We also extended the pACF approach to measure 'burstiness' of oscillatory processes and characterized regions with stable and bursty oscillations. These findings show that rhythmicity is double-dissociable from amplitude and constitutes a functionally relevant and dynamic characteristic of neuronal oscillations.

Oscillatory Waveform Shape and Temporal Spike Correlations Differ across Bat Frontal and Auditory Cortex

Neural oscillations are associated with diverse computations in the mammalian brain. The waveform shape of oscillatory activity measured in the cortex relates to local physiology and can be informative about aberrant or dynamically changing states. However, how waveform shape differs across distant yet functionally and anatomically related cortical regions is largely unknown. In this study, we capitalize on simultaneous recordings of local field potentials (LFPs) in the auditory and frontal cortices of awake, male Carollia perspicillata bats to examine, on a cycle-by-cycle basis, waveform shape differences across cortical regions. We find that waveform shape differs markedly in the fronto-auditory circuit even for temporally correlated rhythmic activity in comparable frequency ranges (i.e., in the delta and gamma bands) during spontaneous activity. In addition, we report consistent differences between areas in the variability of waveform shape across individual cycles. A conceptual model predicts higher spike-spike and spike-LFP correlations in regions with more asymmetric shapes, a phenomenon that was observed in the data: spike-spike and spike-LFP correlations were higher in the frontal cortex. The model suggests a relationship between waveform shape differences and differences in spike correlations across cortical areas. Altogether, these results indicate that oscillatory activity in the frontal and auditory cortex possesses distinct dynamics related to the anatomical and functional diversity of the fronto-auditory circuit.

Gamma oscillatory complexity conveys behavioral information in hippocampal networks

The hippocampus and entorhinal cortex exhibit rich oscillatory patterns critical for cognitive functions. In the hippocampal region CA1, specific gamma-frequency oscillations, timed at different phases of the ongoing theta rhythm, are hypothesized to facilitate the integration of information from varied sources and contribute to distinct cognitive processes. Here, we show that gamma elements -a multidimensional characterization of transient gamma oscillatory episodes- occur at any frequency or phase relative to the ongoing theta rhythm across all CA1 layers in male mice. Despite their low power and stochastic-like nature, individual gamma elements still carry behavior-related information and computational modeling suggests that they reflect neuronal firing. Our findings challenge the idea of rigid gamma sub-bands, showing that behavior shapes ensembles of irregular gamma elements that evolve with learning and depend on hippocampal layers. Widespread gamma diversity, beyond randomness, may thus reflect complexity, likely functional but invisible to classic average-based analyses.

Noisy Delay Denoises Biochemical Oscillators

Genetic oscillations are generated by delayed transcriptional negative feedback loops, wherein repressor proteins inhibit their own synthesis after a temporal production delay. This delay is distributed because it arises from a sequence of noisy processes, including transcription, translocation, translation, and folding. Because the delay determines repression timing and, therefore, oscillation period, it has been commonly believed that delay noise weakens oscillatory dynamics. Here, we demonstrate that noisy delay can surprisingly denoise genetic oscillators. Specifically, moderate delay noise improves the signal-to-noise ratio and sharpens oscillation peaks, all without impacting period and amplitude. We show that this denoising phenomenon occurs in a variety of well-studied genetic oscillators, and we use queueing theory to uncover the universal mechanisms that produce it.

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.

Phase-Amplitude Coupling between Theta Rhythm and High-Frequency Oscillations in the Hippocampus of Pigeons during Navigation

Navigation is a complex task in which the hippocampus (Hp), which plays an important role, may be involved in interactions between different frequency bands. However, little is known whether this cross-frequency interaction exists in the Hp of birds during navigation. Therefore, we examined the electrophysiological characteristics of hippocampal cross-frequency interactions of domestic pigeons (Columba livia domestica) during navigation. Two goal-directed navigation tasks with different locomotor modes were designed, and the local field potentials (LFPs) were recorded for analysis. We found that the amplitudes of high-frequency oscillations in Hp were dynamically modulated by the phase of co-occurring theta-band oscillations both during ground-based maze and outdoor flight navigation. The high-frequency amplitude sub-frequency bands modulated by the hippocampal theta phase were different at different tasks, and this process was independent of the navigation path and goal. These results suggest that phase-amplitude coupling (PAC) in the avian Hp may be more associated with the ongoing cognitive demands of navigational processes. Our findings contribute to the understanding of potential mechanisms of hippocampal PAC on multi-frequency informational interactions in avian navigation and provide valuable insights into cross-species evolution.

MEP and TEP features variability: is it just the brain-state?

Objective.The literature investigating the effects of alpha oscillations on corticospinal excitability is divergent. We believe inconsistency in the findings may arise, among others, from the electroencephalography (EEG) processing for brain-state determination. Here, we provide further insights in the effects of the brain-state on cortical and corticospinal excitability and quantify the impact of different EEG processing.Approach.Corticospinal excitability was measured using motor evoked potential (MEP) peak-to-peak amplitudes elicited with transcranial magnetic stimulation (TMS); cortical responses were studied through TMS-evoked potentials' TEPs features. A TMS-EEG-electromyography (EMG) dataset of 18 young healthy subjects who received 180 single-pulse (SP) and 180 paired pulses (PP) to determine short-intracortical inhibition (SICI) was investigated. To study the effect of different EEG processing, we compared the brain-state estimation deriving from three published methods. The influence of presence of neural oscillations was also investigated. To evaluate the effect of the brain-state on MEP and TEP features variability, we defined the brain-state based on specific EEG phase and power combinations, only in trials where neural oscillations were present. The relationship between TEPs and MEPs was further evaluated.Main results.The presence of neural oscillations resulted in more consistent results regardless of the EEG processing approach. Nonetheless, the latter still critically affected the outcomes, making conclusive claims complex. With our approach, the MEP amplitude was positively modulated by the alpha power and phase, with stronger responses during the trough phase and high power. Power and phase also affected TEP features. Importantly, similar effects were observed in both TMS conditions.Significance.These findings support the view that the brain state of alpha oscillations is associated with the variability observed in cortical and corticospinal responses to TMS, with a tight correlation between the two. The results further highlight the importance of closed-loop stimulation approaches while underlining that care is needed in designing experiments and choosing the analytical approaches, which should be based on knowledge from offline studies to control for the heterogeneity originating from different EEG processing strategies.

Multilevel and multifaceted brain response features in spiking, ERP and ERD: experimental observation and simultaneous generation in a neuronal network model with excitation-inhibition balance

Brain as a dynamic system responds to stimulations with specific patterns affected by its inherent ongoing dynamics. The patterns are manifested across different levels of organization-from spiking activity of neurons to collective oscillations in local field potential (LFP) and electroencephalogram (EEG). The multilevel and multifaceted response activities show patterns seemingly distinct and non-comparable from each other, but they should be coherently related because they are generated from the same underlying neural dynamic system. A coherent understanding of the interrelationships between different levels/aspects of activity features is important for understanding the complex brain functions. Here, based on analysis of data from human EEG, monkey LFP and neuronal spiking, we demonstrated that the brain response activities from different levels of neural system are highly coherent: the external stimulus simultaneously generated event-related potentials, event-related desynchronization, and variation in neuronal spiking activities that precisely match with each other in the temporal unfolding. Based on a biologically plausible but generic network of conductance-based integrate-and-fire excitatory and inhibitory neurons with dense connections, we showed that the multiple key features can be simultaneously produced at critical dynamical regimes supported by excitation-inhibition (E-I) balance. The elucidation of the inherent coherency of various neural response activities and demonstration of a simple dynamical neural circuit system having the ability to simultaneously produce multiple features suggest the plausibility of understanding high-level brain function and cognition from elementary and generic neuronal dynamics.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09889-w.

Assessment of mental workload across cognitive tasks using a passive brain-computer interface based on mean negative theta-band amplitudes

Brain-computer interfaces (BCI) can provide real-time and continuous assessments of mental workload in different scenarios, which can subsequently be used to optimize human-computer interaction. However, assessment of mental workload is complicated by the task-dependent nature of the underlying neural signals. Thus, classifiers trained on data from one task do not generalize well to other tasks. Previous attempts at classifying mental workload across different cognitive tasks have therefore only been partially successful. Here we introduce a novel algorithm to extract frontal theta oscillations from electroencephalographic (EEG) recordings of brain activity and show that it can be used to detect mental workload across different cognitive tasks. We use a published data set that investigated subject dependent task transfer, based on Filter Bank Common Spatial Patterns. After testing, our approach enables a binary classification of mental workload with performances of 92.00 and 92.35%, respectively for either low or high workload vs. an initial no workload condition, with significantly better results than those of the previous approach. It, nevertheless, does not perform beyond chance level when comparing high vs. low workload conditions. Also, when an independent component analysis was done first with the data (and before any additional preprocessing procedure), even though we achieved more stable classification results above chance level across all tasks, it did not perform better than the previous approach. These mixed results illustrate that while the proposed algorithm cannot replace previous general-purpose classification methods, it may outperform state-of-the-art algorithms in specific (workload) comparisons.

Neurophysiological mechanisms of deep brain stimulation across spatiotemporal resolutions

Deep brain stimulation is a neuromodulatory treatment for managing the symptoms of Parkinson's disease and other neurological and psychiatric disorders. Electrodes are chronically implanted in disease-relevant brain regions and pulsatile electrical stimulation delivery is intended to restore neurocircuit function. However, the widespread interest in the application and expansion of this clinical therapy has preceded an overarching understanding of the neurocircuit alterations invoked by deep brain stimulation. Over the years, various forms of neurophysiological evidence have emerged which demonstrate changes to brain activity across spatiotemporal resolutions; from single neuron, to local field potential, to brain-wide cortical network effects. Though fruitful, such studies have often led to debate about a singular putative mechanism. In this Update we aim to produce an integrative account of complementary instead of mutually exclusive neurophysiological effects to derive a generalizable concept of the mechanisms of deep brain stimulation. In particular, we offer a critical review of the most common historical competing theories, an updated discussion on recent literature from animal and human neurophysiological studies, and a synthesis of synaptic and network effects of deep brain stimulation across scales of observation, including micro-, meso- and macroscale circuit alterations.

Different Methods to Estimate the Phase of Neural Rhythms Agree But Only During Times of Low Uncertainty

Rhythms are a common feature of brain activity. Across different types of rhythms, the phase has been proposed to have functional consequences, thus requiring its accurate specification from noisy data. Phase is conventionally specified using techniques that presume a frequency band-limited rhythm. However, in practice, observed brain rhythms are typically nonsinusoidal and amplitude modulated. How these features impact methods to estimate phase remains unclear. To address this, we consider three phase estimation methods, each with different underlying assumptions about the rhythm. We apply these methods to rhythms simulated with different generative mechanisms and demonstrate inconsistency in phase estimates across the different methods. We propose two improvements to the practice of phase estimation: (1) estimating confidence in the phase estimate, and (2) examining the consistency of phase estimates between two (or more) methods.

Adaptive oscillators support Bayesian prediction in temporal processing

Humans excel at predictively synchronizing their behavior with external rhythms, as in dance or music performance. The neural processes underlying rhythmic inferences are debated: whether predictive perception relies on high-level generative models or whether it can readily be implemented locally by hard-coded intrinsic oscillators synchronizing to rhythmic input remains unclear and different underlying computational mechanisms have been proposed. Here we explore human perception for tone sequences with some temporal regularity at varying rates, but with considerable variability. Next, using a dynamical systems perspective, we successfully model the participants behavior using an adaptive frequency oscillator which adjusts its spontaneous frequency based on the rate of stimuli. This model better reflects human behavior than a canonical nonlinear oscillator and a predictive ramping model-both widely used for temporal estimation and prediction-and demonstrate that the classical distinction between absolute and relative computational mechanisms can be unified under this framework. In addition, we show that neural oscillators may constitute hard-coded physiological priors-in a Bayesian sense-that reduce temporal uncertainty and facilitate the predictive processing of noisy rhythms. Together, the results show that adaptive oscillators provide an elegant and biologically plausible means to subserve rhythmic inference, reconciling previously incompatible frameworks for temporal inferential processes.