Rhythms for Cognition: Communication through Coherence

Theta-gamma tACS modulates attention network synchronization, not isolated network performance

As the brain ages, oscillatory changes disrupt neuronal communication, contributing to cognitive decline in key areas such as parts of the attention network system. This study explores the effects of multi-session low-intensity transcranial Alternating Current Stimulation (tACS) on the efficiency of the alerting, orienting, and executive control networks in older adults. Using a 16-session theta-gamma tACS protocol targeting the prefrontal cortex, we examined its impact on Attention Network Task (ANT) performance of 76 participants aged 55 to 84 in a randomized, double-blind, sham-controlled design. To account for the influence of brain state, both active and sham tACS groups underwent cognitive n-back training during stimulation. Despite no significant modulations in attention network efficiencies, generalized linear mixed-effect modeling revealed that active tACS negatively influenced overall reaction time (RT) improvements, resulting in poorer ANT performance compared to the sham group. Additionally, active tACS disrupted network correlations post-intervention, particularly affecting the alerting network's interactions with the orienting and executive networks. These findings provide further evidence for the involvement of theta-gamma coupling in attention processes, though without network-specific effects. The results underscore the potential of frequency-specific neurostimulation to modulate cognitive functions but also emphasize the need for caution, as such interventions may inadvertently impair brain network dynamics.

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.

Closed-loop control of theta oscillations enhances human hippocampal network connectivity

Theta oscillations are implicated in regulating information flow within cortico-hippocampal networks to support memory and cognition. However, causal evidence tying theta oscillations to network communication in humans is lacking. Here we report experimental findings using a closed-loop, phase-locking algorithm to apply direct electrical stimulation to neocortical nodes of the hippocampal network precisely timed to ongoing hippocampal theta rhythms in human neurosurgical patients. We show that repetitive stimulation of lateral temporal cortex synchronized to hippocampal theta increases hippocampal theta while it is delivered, suggesting theta entrainment of hippocampal neural activity. After stimulation, network connectivity is persistently increased relative to baseline, as indicated by theta-phase synchrony of hippocampus to neocortex and increased amplitudes of the hippocampal evoked response to isolated neocortical stimulation. These indicators of network connectivity are not affected by control stimulation delivered with approximately the same rhythm but without phase locking to hippocampal theta. These findings support the causal role of theta oscillations in routing neural signals across the hippocampal network and suggest phase-synchronized stimulation as a promising method to modulate theta- and hippocampal-dependent behaviors.

Interictal network dysfunction and cognitive impairment in epilepsy

Epilepsy is diagnosed when neural networks become capable of generating excessive or hypersynchronous activity patterns that result in observable seizures. In many cases, epilepsy is associated with cognitive comorbidities that persist between seizures and negatively impact quality of life. Dysregulation of the coordinated physiological network interactions that are required for cognitive function has been implicated in mediating these enduring symptoms, but the causal mechanisms are often elusive. Here, we provide an overview of neural network abnormalities with the potential to contribute to cognitive dysfunction in epilepsy. We examine these pathological interactions across spatial and temporal scales, additionally highlighting the dynamics that arise in response to the brain's intrinsic capacity for plasticity. Understanding these processes will facilitate development of network-level interventions to address cognitive comorbidities that remain undertreated by currently available epilepsy therapeutics.

The effects of gamma-tACS on cognitive impairment in multiple sclerosis: A randomized, double-blind, sham-controlled, pilot study

BACKGROUND

Multiple sclerosis (MS) often causes impairment in working memory (WM), information processing speed (IPS), and verbal memory (VM). These deficits are linked to disrupted neural oscillatory activity. Transcranial alternating current stimulation (tACS), which modulates cortical oscillations, may hold promise for treating cognitive impairment in MS.

OBJECTIVES

To evaluate online and offline effects of gamma (γ)-tACS on WM, IPS, and VM while assessing changes in brain rhythms using electroencephalography (EEG).

METHODS

Thirty-six MS patients with single-domain impairment in WM (12), IPS (13), or VM (11) underwent γ-tACS and sham-tACS over the left dorsolateral prefrontal cortex (DLPFC) (WM, IPS) or precuneus (VM). Cognitive performance was assessed pre-tACS (T0), during (T1), and post-tACS (T2) using the Digit Span Backward (DSBW) for WM, Symbol Digit Modalities Test (SDMT) for IPS, and Rey Auditory Verbal Learning Test (RAVLT) for VM. EEG was recorded at T0 and T2 to analyze local power spectral density and local-to-global connectivity.

RESULTS

DSBW, SDMT, and RAVLT scores transiently improved during γ-tACS and not during sham. IPS-impaired patients showed a reduction in spectral power across all frequency bands, at the stimulation site, post-DLPFC γ-tACS.

CONCLUSION

γ-tACS briefly improves WM, IPS, and VM in MS patients, warranting further trials of this non-invasive intervention.

Biologically realistic mean field model of spiking neural networks with fast and slow inhibitory synapses

We present a mean field model for a spiking neural network of excitatory and inhibitory neurons with fast GABAA and nonlinear slow GABAB inhibitory conductance-based synapses. This mean field model can predict the spontaneous and evoked response of the network to external stimulation in asynchronous irregular regimes. The model displays theta oscillations for sufficiently strong GABAB conductance. Optogenetic activation of interneurons and an increase of GABAB conductance caused opposite effects on the emergence of gamma oscillations in the model. In agreement with direct numerical simulations of neural networks and experimental data, the mean field model predicts that an increase of GABAB conductance reduces gamma oscillations. Furthermore, the slow dynamics of GABAB synapses regulates the appearance and duration of transient gamma oscillations, namely gamma bursts, in the mean field model. Finally, we show that nonlinear GABAB synapses play a major role to stabilize the network from the emergence of epileptic seizures.

A neural computational framework for face processing in the human temporal lobe

A key question in cognitive neuroscience is how unified identity representations emerge from visual inputs. Here, we recorded intracranial electroencephalography (iEEG) from the human ventral temporal cortex (VTC) and medial temporal lobe (MTL), as well as single-neuron activity in the MTL, to demonstrate how dense feature-based representations in the VTC are translated into sparse identity-based representations in the MTL. First, we characterized the spatiotemporal neural dynamics of face coding in the VTC and MTL. The VTC, particularly the fusiform gyrus, exhibits robust axis-based feature coding. Remarkably, MTL neurons encode a receptive field within the VTC neural feature space, constructed using VTC neural axes, thereby bridging dense feature and sparse identity representations. We further validated our findings using recordings from a macaque. Lastly, inter-areal interactions between the VTC and MTL provide the physiological basis of this computational framework. Together, we reveal the neurophysiological underpinnings of a computational framework that explains how perceptual information is translated into face identities.

Anatomically resolved oscillatory bursts reveal dynamic motifs of thalamocortical activity during naturalistic stimulus viewing

Natural vision requires circuit mechanisms which process complex spatiotemporal stimulus features in parallel. In the mammalian forebrain, one signature of circuit activation is fast oscillatory dynamics, reflected in the local field potential (LFP). Using data from the Allen Neuropixels Visual Coding project, we show that local visual features in naturalistic stimuli induce in mouse primary visual cortex (V1) retinotopically specific oscillations in various frequency bands and V1 layers. Specifically, layer 4 (L4) narrowband gamma was linked to luminance, low-gamma to optic flow, and L4/L5 epsilon oscillations to contrast. These feature-specific oscillations were associated with distinct translaminar spike-phase coupling patterns, which were conserved across a range of stimuli containing the relevant visual features, suggesting that they might constitute feature-specific circuit motifs. Our findings highlight visually induced fast oscillations as markers of dynamic circuit motifs, which may support differential and multiplexed coding of complex visual input and thalamocortical information propagation.

A general framework for characterizing optimal communication in brain networks

Efficient communication in brain networks is foundational for cognitive function and behavior. However, how communication efficiency is defined depends on the assumed model of signaling dynamics, e.g., shortest path signaling, random walker navigation, broadcasting, and diffusive processes. Thus, a general and model-agnostic framework for characterizing optimal neural communication is needed. We address this challenge by assigning communication efficiency through a virtual multi-site lesioning regime combined with game theory, applied to large-scale models of human brain dynamics. Our framework quantifies the exact influence each node exerts over every other, generating optimal influence maps given the underlying model of neural dynamics. These descriptions reveal how communication patterns unfold if regions are set to maximize their influence over one another. Comparing these maps with a variety of brain communication models showed that optimal communication closely resembles a broadcasting regime in which regions leverage multiple parallel channels for information dissemination. Moreover, we found that the brain's most influential regions are its rich-club, exploiting their topological vantage point by broadcasting across numerous pathways that enhance their reach even if the underlying connections are weak. Altogether, our work provides a rigorous and versatile framework for characterizing optimal brain communication, and uncovers the most influential brain regions, and the topological features underlying their influence.

Pre-movement sensorimotor oscillations shape the sense of agency by gating cortical connectivity

Our sense of agency, the subjective experience of controlling our actions, is a crucial component of self-awareness and motor control. It is thought to originate from the comparison between intentions and actions across broad cortical networks. However, the underlying neural mechanisms are still not fully understood. We hypothesized that oscillations in the theta-alpha range, thought to orchestrate long-range neural connectivity, may mediate sensorimotor comparisons. To test this, we manipulated the relation between intentions and actions in a tetraplegic user of a brain machine interface (BMI), decoding primary motor cortex (M1) activity to restore hand functionality. We found that the pre-movement phase of low-alpha oscillations in M1 predicted the participant's agency judgements. Further, using EEG-BMI in healthy participants, we found that pre-movement alpha oscillations in M1 and supplementary motor area (SMA) correlated with agency ratings, and with changes in their functional connectivity with parietal, temporal and prefrontal areas. These findings argue for phase-driven gating as a key mechanism for sensorimotor integration and sense of agency.

Time-Resolved Aperiodic and Oscillatory Dynamics during Human Visual Memory Encoding

Biological neural networks translate sensory information into neural code that is held in memory over long timescales. Theories for how this occurs often posit a functional role of neural oscillations. However, recent advances show that neural oscillations are often confounded with non-oscillatory, aperiodic neural activity. Here we analyze a dataset of intracranial human EEG recordings (N = 13; 10 female) to test the hypothesis that aperiodic activity plays a role in visual memory, independent and distinct from oscillations. By leveraging a new approach to time-resolved parameterization of neural spectral activity, we find event-related changes in both oscillations and aperiodic activity during memory encoding. During memory encoding, aperiodic-adjusted alpha oscillatory power significantly decreases while, simultaneously, aperiodic neural activity "flattens out". These results provide novel evidence for task-related dynamics of both aperiodic and oscillatory activity in human memory, paving the way for future investigations into the unique functional roles of these two neural processes in human cognition.

Biased Information Routing Through the Basolateral Amygdala, Altered Valence Processing, and Impaired Affective States Associated With Psychiatric Illnesses

Accumulating evidence supports a role for altered circuit function in impaired valence processing and altered affective states as a core feature of psychiatric illnesses. We review the circuit mechanisms underlying normal valence processing and highlight evidence supporting altered function of the basolateral amygdala, valence processing, and affective states across psychiatric illnesses. The mechanisms controlling network activity that governs valence processing are reviewed in the context of potential pathophysiological mechanisms mediating circuit dysfunction and impaired valence processing in psychiatric illnesses. Finally, we review emerging data demonstrating experience-dependent, biased information routing through the basolateral amygdala promoting negative valence processing and discuss the potential relevance to impaired affective states and psychiatric illnesses.

Examining the role of novelty detection in 20- and 40-Hz auditory steady-state responses

An auditory steady-state response (ASSR) is an electrophysiological response to periodic stimuli that reflects the synchronization of endogenous oscillations. The 40-Hz ASSR is reduced in patients with schizophrenia, bipolar disorder, and autism spectrum disorder, making it a candidate biomarker for these psychiatric disorders. Previous studies have revealed that experimental conditions such as stimulus duration and inter-stimulus interval tend to affect ASSR, suggesting that novelty detection may play an important role in determining the magnitude of ASSR. The present study is the first to investigate the effect of novelty detection on 20- and 40-Hz ASSRs in healthy individuals. Magnetoencephalography recordings were obtained from 30 healthy adults exposed to auditory stimuli at 20 and 40 Hz. The stimuli were presented in three paradigms: 20- and 40-Hz repetitive presentations and random presentation, the latter being categorized by whether the preceding stimulus was the same (S trials) or different (D trials). The ASSR amplitude and inter-trial phase coherence (ITPC) were assessed via time-frequency analysis. The results revealed that the 20-Hz ASSR was suppressed with increased novelty, with the highest amplitude and ITPC observed during repetitive presentation. In contrast, the 40-Hz ASSR was enhanced by increased novelty, with the greatest measures observed during the D trials. These findings show that novelty detection modulates 20- and 40-Hz ASSRs in opposite directions, highlighting its critical role in shaping stimulus-induced oscillatory responses. This frequency-specific modulation pattern may provide a novel perspective for understanding ASSR abnormalities in psychiatric disorders.

Representational shifts from feedforward to feedback rhythms index phenomenological integration in naturalistic vision

How does the brain integrate complex and dynamic visual inputs into phenomenologically seamless percepts? Previous results demonstrate that when visual inputs are organized coherently across space and time, they are more strongly encoded in feedback-related alpha rhythms, and less strongly in feedforward-related gamma rhythms. Here, we tested whether this representational shift from feedforward to feedback rhythms is linked to the phenomenological experience of coherence. In an Electroencephalography (EEG) study, we manipulated the degree of spatiotemporal coherence by presenting two segments from the same video across visual hemifields, either synchronously or asynchronously (with a delay between segments). We asked participants whether they perceived the stimulus as coherent or incoherent. When stimuli were presented at the perceptual threshold (i.e., when the same stimulus was judged as coherent 50% of times), perception co-varied with stimulus coding across alpha and gamma rhythms: When stimuli were perceived as coherent, they were represented in alpha activity; when stimuli were perceived as incoherent, they were represented in gamma activity. Whether the same visual input is perceived as coherent or incoherent thus depends on representational shifts between feedback-related alpha and feedforward-related gamma rhythms.

Dynamic prefrontal coupling coordinates adaptive decision-making

Adaptive decision-making requires flexibly maintaining or changing behavior in response to uncertainty. While the dorsomedial (dmPFC) and dorsolateral (dIPFC) prefrontal cortex are each essential for this ability, how they coordinate to drive adaptation remains unknown. Using intracranial EEG recordings from human participants performing a dynamic reward task, we identified distinct, frequency-specific computations: dmPFC high-gamma activity encoded uncertainty before stay decisions but transitioned to prediction error before switches, while theta activity shifted from uncertainty to value representation. In contrast, dIPFC theta activity signaled both value and uncertainty before stays, but predominantly value before switches. Crucially, these regions coordinated through two temporally specific coupling mechanisms that predicted behavioral changes: theta-theta amplitude coupling during feedback processing and theta-gamma phase coupling before decisions. Both coupling mechanisms strengthened before switches, suggesting that changing behavior requires greater dmPFC-dIPFC integration than maintaining. These findings reveal how the dorsal prefrontal cortex employs frequency-specific computations and precise temporal coordination to guide adaptive behavior.

Multiplexed subspaces route neural activity across brain-wide networks

Cognition is flexible, allowing behavior to change on a moment-by-moment basis. Such flexibility relies on the brain's ability to route information through different networks of brain regions to perform different cognitive computations. However, the mechanisms that determine which network of regions is active are not well understood. Here, we combined cortex-wide calcium imaging with high-density electrophysiological recordings in eight cortical and subcortical regions of mice to understand the interactions between regions. We found different dimensions within the population activity of each region were functionally connected with different cortex-wide 'subspace networks' of regions. These subspace networks were multiplexed; each region was functionally connected with multiple independent, yet overlapping, subspace networks. The subspace network that was active changed from moment-to-moment. These changes were associated with changes in the geometric relationship between the neural response within a region and the subspace dimensions: when neural responses were aligned with (i.e., projected along) a subspace dimension, neural activity was increased in the associated regions. Together, our results suggest that changing the geometry of neural representations within a brain region may allow the brain to flexibly engage different brain-wide networks, thereby supporting cognitive flexibility.

Oscillatory control over representational geometry of sequence working memory in macaque frontal cortex

To process sequential streams of information, e.g., language, the brain must encode multiple items in sequence working memory (SWM) according to their ordinal relationship. While the geometry of neural states could represent sequential events in the frontal cortex, the control mechanism over these neural states remains unclear. Using high-throughput electrophysiology recording in the macaque frontal cortex, we observed widespread theta responses after each stimulus entry. Crucially, by applying targeted dimensionality reduction to extract task-relevant neural subspaces from both local field potential (LFP) and spike data, we found that theta power transiently encoded each sequentially presented stimulus regardless of its order. At the same time, theta-spike interaction was rank-selectively associated with memory subspaces, thereby potentially supporting the binding of items to appropriate ranks. Furthermore, this putative theta control can generalize to length-variable and error sequences, predicting behavior. Thus, decomposed entry/rank-WM subspaces and theta-spike interactions may underlie the control of SWM.

A magnitude-independent neural code for linguistic information during sentence production

Humans are the only species with the ability to convey an unbounded number of novel thoughts by combining words into sentences. This process is guided by complex syntactic and semantic processes. Despite their centrality to human cognition, the neural mechanisms underlying these systems remain obscured by inherent limitations of non-invasive brain measures and a near total focus on comprehension paradigms. Here, we address these limitations with high-resolution neurosurgical recordings (electrocorticography) and a controlled sentence production experiment. We uncover distinct cortical networks encoding word-level, semantic, and syntactic information. These networks are broadly distributed across traditional language areas, but with focal sensitivity to syntactic structure in middle and inferior frontal gyri. In contrast to previous findings from comprehension studies, we find that these networks are largely non-overlapping, each specialized for just one of the three linguistic constructs we investigate. Most strikingly, our data reveal an unexpected property of syntax and semantics: it is encoded independent of neural activity levels. We hypothesize that this "magnitude-independent" coding scheme likely reflects the distributed nature of these higher-order cognitive constructs.

On variability in local field potentials

Neuronal coding and decoding would be compromised if neuronal responses were highly variable. Intriguingly, neuronal spike counts (SCs) show a reduction in across-trial variance ( ATV ) in response to sensory stimulation, when SC variance is normalized by SC mean, that is, when using the Fano factor 1. Inspired by this seminal finding, ATV has also been studied in electroencephalography (EEG) signals, revealing effects of various stimulus and cognitive factors as well as disease states. Here, we empirically show that outside of evoked potentials, the ATV of the EEG or local field potential (LFP) is very highly correlated to the intra-trial variance ( ITV ), which corresponds to the well-known power metric. We propose that the LFP power, rather than the raw LFP signal, should be considered with regard to putative changes of its variability. We quantify LFP power variability as the standard deviation of the logarithm of the power ratio between an active and a baseline condition, normalized by the mean of that log(power ratio), that is the coefficient of variation (CV) of the log(power ratio). This CV(log(power ratio)) is reduced for gamma and alpha power when they are enhanced by stimulation, and it is enhanced for alpha power when it is reduced by stimulation. This suggests a potential inverse relation between changes in band-limited power and the corresponding CV. We propose that the CV(log(power ratio)) is a useful metric that can be computed for numerous existing and future LFP, EEG or MEG datasets, which will provide insights into those signals' frequency-specific variability and how they might be used for neuronal coding and decoding.

The Interoceptive Brain: Confidence Ratings and Accuracy Scores are Independently and Differently Associated With Task-Related Alpha Power During the Heartbeat Tracking Task

Cardiac interoception is important for health and can be assessed in terms of accuracy (IAcc) and sensibility (IS), at least. While IAcc measures the correspondence between recorded and perceived heartbeats, IS means the confidence in interoceptive perceptions during the task. The present study investigated if brain activity during the heartbeat tracking task is associated with IAcc as well as IS. Specifically, we were interested if task-related power (TRP) in the alpha band (8-12 Hz), known to indicate task-specific cognitive functions such as semantic, attentional, and sensory processes, is associated with IAcc and IS, respectively. In a sample of 30 participants, we found relatively higher TRP in the alpha band over left temporal and parietal areas (vs. right) during the interoception task. Furthermore, we observed a negative association between TRP in the alpha band and IS. Lower TRP in the alpha band might indicate that more pronounced cognitive and sensory processes are linked to higher IS. Furthermore, we found a positive effect for IAcc (independent from IS), which might indicate that more internal attention during the interoception task is beneficial for IAcc. We further discuss the findings in the context of methodological issues of the heartbeat tracking task. Taken together, the pattern of findings favors the investigation of task-related IS (i.e., confidence ratings) in combination with IAcc to gain a better access to interoceptive processes and to improve our understanding of the neural underpinnings of (cardiac) interoception.

Impaired emotion recognition in Cntnap2-deficient mice is associated with hyper-synchronous prefrontal cortex neuronal activity

Individuals diagnosed with autism spectrum disorder (ASD) show difficulty in recognizing emotions in others, a process termed emotion recognition. While human fMRI studies linked multiple brain areas to emotion recognition, the specific mechanisms underlying impaired emotion recognition in ASD are not clear. Here, we employed an emotional state preference (ESP) task to show that Cntnap2-knockout (KO) mice, an established ASD model, do not distinguish between conspecifics according to their emotional state. We assessed brain-wide local-field potential (LFP) signals during various social behavior tasks and found that Cntnap2-KO mice exhibited higher LFP theta and gamma rhythmicity than did C57BL/6J mice, even at rest. Specifically, Cntnap2-KO mice showed increased theta coherence, especially between the prelimbic cortex (PrL) and the hypothalamic paraventricular nucleus, during social behavior. Moreover, we observed significantly increased Granger causality of theta rhythmicity between these two brain areas, across several types of social behavior tasks. Finally, optogenetic stimulation of PrL pyramidal neurons in C57BL/6J mice impaired their social discrimination abilities, including in ESP. Together, these results suggest that increased rhythmicity of PrL pyramidal neuronal activity and its hyper-synchronization with specific brain regions are involved in the impaired emotion recognition exhibited by Cntnap2-KO mice.

Application and research progress of different frequency tACS in stroke rehabilitation: A systematic review

After a stroke, abnormal changes in neural oscillations that are related to the severity and prognosis of the disease can occur. Resetting these abnormal neural oscillations is a potential approach for stroke rehabilitation. Transcranial alternating current stimulation (tACS) can modulate intrinsic neural oscillations noninvasively and has attracted attention as a possible technique to improve multiple post-stroke symptoms, including deficits in speech, vision, and motor ability and overall neurological recovery. The clinical effect of tACS varies according to the selected frequency. Therefore, choosing an appropriate frequency to optimize outcomes for specific dysfunctions is essential. This review focuses on the current research status and possibilities of tACS with different frequencies in stroke rehabilitation. We also discuss the possible mechanisms of tACS in stroke to provide a theoretical foundation for the method and highlight the controversial aspects that need further exploration. Although tACS has great potential, few clinical studies have applied it in the treatment of stroke, and no consensus has been reached. We analyze limitations in experimental designs and identify potential tACS approaches worthy of exploration in the future.

Exploring the neuromagnetic signatures of cognitive decline from mild cognitive impairment to Alzheimer's disease dementia

BACKGROUND

Developing non-invasive and affordable biomarkers to detect Alzheimer's disease (AD) at a prodromal stage is essential, particularly in the context of new disease-modifying therapies. Mild cognitive impairment (MCI) is a critical stage preceding dementia, but not all patients with MCI will progress to AD. This study explores the potential of magnetoencephalography (MEG) to predict cognitive decline from MCI to AD dementia.

METHODS

We analysed resting-state MEG data from the BioFIND dataset including 117 patients with MCI among whom 64 developed AD dementia (AD progression), while 53 remained cognitively stable (stable MCI), using spectral analysis. Logistic regression models estimated the additive explanation of selected clinical, MEG, and MRI variables for AD progression risk. We then built a high-dimensional classification model to combine all modalities and variables of interest.

FINDINGS

MEG 16-38Hz spectral power, particularly over parieto-occipital magnetometers, was significantly reduced in the AD progression group. In logistic regression models, decreased MEG 16-38Hz spectral power and reduced hippocampal volume/total grey matter ratio on MRI were independently linked to higher AD progression risk. The data-driven classification model confirmed, among other factors, the complementary information of MEG covariance (AUC = 0.74, SD = 0.13) and MRI cortical volumes (AUC = 0.77, SD = 0.14) to predict AD progression. Combining all inputs led to markedly improved classification scores (AUC = 0.81, SD = 0.12).

INTERPRETATION

These findings highlight the potential of spectral power and covariance as robust non-invasive electrophysiological biomarkers to predict AD progression, complementing other diagnostic measures, including cognitive scores and MRI.

FUNDING

This work was supported by: Fondation pour la Recherche Médicale (grant FDM202106013579).

Phase of firing does not reflect temporal order in sequence memory of humans and recurrent neural networks

The temporal order of a sequence of events has been thought to be reflected in the ordered firing of neurons at different phases of theta oscillations. Here we assess this by measuring single neuron activity (1,420 neurons) and local field potentials (921 channels) in the medial temporal lobe of 16 patients with epilepsy performing a working-memory task for temporal order. During memory maintenance, we observe theta oscillations, preferential firing of single neurons to theta phase and a close relationship between phase of firing and item position. However, the firing order did not match item order. Training recurrent neural networks to perform an analogous task, we also show the generation of theta oscillations, theta phase-dependent firing related to item position and, again, no match between firing and item order. Rather, our results suggest a mechanistic link between phase order, stimulus timing and oscillation frequency. In both biological and artificial neural networks, we provide evidence supporting the role of phase of firing in working-memory processing.

Top-down and bottom-up interactions rely on nested brain oscillations to shape rhythmic visual attention sampling

Adaptive visual processing is enabled through the dynamic interplay between top-down and bottom-up (feedback/feedforward) information exchange, presumably propagated through brain oscillations. Here, we causally tested for the oscillatory mechanisms governing this interaction in the human visual system. Using concurrent transcranial magnetic stimulation-electroencephalography (TMS-EEG), we emulated top-down signals by a single TMS pulse over the frontal eye field (right FEF), while manipulating the strength of sensory input through the presentation of moving concentric gratings (compared to a control-TMS site). FEF-TMS without sensory input led to a top-down modulated occipital phase realignment, alongside higher fronto-occipital phase connectivity, in the alpha/beta band. Sensory input in the absence of FEF-TMS increased occipital gamma activity. Crucially, testing the interaction between top-down and bottom-up processes (FEF-TMS during sensory input) revealed an increased nesting of the bottom-up gamma activity in the alpha/beta-band cycles. This establishes a causal link between phase-to-power coupling and top-down modulation of feedforward signals, providing novel mechanistic insights into how attention interacts with sensory input at the neural level, shaping rhythmic sampling.

Induced zero-phase synchronization as a potential neural code for optimized visuomotor integration

BACKGROUND

Goal-directed behavior requires the integration of information from the outside world and internal (somatosensory) sources about our own actions. Expectations (or 'internal models') are generated from prior knowledge and constantly updated based on sensory feedback. This optimized information integration ('predictive coding') results in a global behavioral advantage of anticipated action in the presence of uncertainty. Our goal was to probe the effect of phase entrainment of the sensorimotor mu-rhythm on visuomotor integration.

METHODS

Participants received transcranial alternating current stimulation over bilateral motor cortices (M1) while performing a visually-guided force adjustment task during functional magnetic resonance imaging.

RESULTS

Inter-hemispheric zero-phase entrainment resulted in effector-specific modulation of performance precision and effector-generic minimization of force signal complexity paralleled by BOLD activation changes in bilateral caudate and increased functional connectivity between the right M1 and contralateral putamen, inferior parietal, and medial temporal regions. While effector-specific changes in performance precision were associated with contralateral caudate and hippocampal activation decreases, only the global reduction in force signal complexity was associated with increased functional M1 connectivity with bilateral striatal regions.

CONCLUSION

We propose that zero-phase synchronization represents a neural mode of optimized information integration related to internal model updating within the recursive perception-action continuum associated with predictive coding.

Integrated phenomenology and brain connectivity demonstrate changes in nonlinear processing in jhana advanced meditation

We present a neurophenomenological case study investigating distinct neural connectivity regimes during an advanced concentrative absorption meditation called jhana (ACAM-J),characterized by highly-stable attention and mental absorption. Using EEG recordings and phenomenological ratings (29 sessions) from a meditator with +20,000 hours of practice, we evaluated connectivity metrics tracking distinct large-scale neural interactions: nonlinear (WSMI and Directed Information), capturing non-oscillatory dynamics; and linear (WPLI) connectivity metrics, capturing oscillatory synchrony. Results demonstrate ACAM-J are better distinguished by non-oscillatory compared to oscillatory dynamics across multiple frequency ranges. Furthermore, combining attention-related phenomenological ratings with WSMI improves Bayesian decoding of ACAM-J compared to neural metrics alone. Crucially, deeper ACAM-J indicate an equalization of feedback and feedforward processes, suggesting a balance of internally- and externally-driven information processing. The results from this intensively sampled case study are a promising initial step in revealing the distinct neural dynamics during ACAM-J, offering insights into refined conscious states and highlighting the value of nonlinear neurophenomenological approaches to studying attentional states.

Electroencephalography-driven brain-network models for personalized interpretation and prediction of neural oscillations

OBJECTIVE

Develop an encephalography (EEG)-driven method that integrates interpretability, predictiveness, and personalization to assess the dynamics of the brain network, with a focus on pathological conditions such as pharmacoresistant epilepsy.

METHODS

We propose a method to identify dominant coherent oscillations from EEG recordings. It relies on the Koopman operator theory to achieve individualized EEG prediction and electrophysiological interpretability. We extend it with concepts from adiabatic theory to address the nonstationary and noisy EEG signals.

RESULTS

By simultaneously capturing the local spectral and connectivity aspects of patient-specific oscillatory dynamics, we are able to clarify the underlying dynamical mechanism. We use it to construct the corresponding generative models of the brain network. We demonstrate the proposed approach on recordings of patients in status epilepticus.

CONCLUSIONS

The proposed EEG-driven method opens new perspectives on integrating interpretability, predictiveness, and personalization within a unified framework. It provides a quantitative approach for assessing EEG recordings, crucial for understanding and modulating pathological brain activity.

SIGNIFICANCE

This work bridges theoretical neuroscience and clinical practice, offering a novel framework for understanding and predicting brain network dynamics. The resulting approach paves the way for data-driven insights into brain network mechanisms and the design of personalized neuromodulation therapies.

Targeting Neural Oscillations for Cognitive Enhancement in Alzheimer's Disease

Alzheimer's disease (AD), the most prevalent form of dementia, is marked by progressive cognitive decline, affecting memory, language, orientation, and behavior. Pathological hallmarks include extracellular amyloid plaques and intracellular tau tangles, which disrupt synaptic function and connectivity. Neural oscillations, the rhythmic synchronization of neuronal activity across frequency bands, are integral to cognitive processes but become dysregulated in AD, contributing to network dysfunction and memory impairments. Targeting these oscillations has emerged as a promising therapeutic strategy. Preclinical studies have demonstrated that specific frequency modulations can restore oscillatory balance, improve synaptic plasticity, and reduce amyloid and tau pathology. In animal models, interventions, such as gamma entrainment using sensory stimulation and transcranial alternating current stimulation (tACS), have shown efficacy in enhancing memory function and modulating neuroinflammatory responses. Clinical trials have reported promising cognitive improvements with repetitive transcranial magnetic stimulation (rTMS) and deep brain stimulation (DBS), particularly when targeting key hubs in memory-related networks, such as the default mode network (DMN) and frontal-parietal network. Moreover, gamma-tACS has been linked to increased cholinergic activity and enhanced network connectivity, which are correlated with improved cognitive outcomes in AD patients. Despite these advancements, challenges remain in optimizing stimulation parameters, individualizing treatment protocols, and understanding long-term effects. Emerging approaches, including transcranial pulse stimulation (TPS) and closed-loop adaptive neuromodulation, hold promise for refining therapeutic strategies. Integrating neuromodulation with pharmacological and lifestyle interventions may maximize cognitive benefits. Continued interdisciplinary efforts are essential to refine these approaches and translate them into clinical practice, advancing the potential for neural oscillation-based therapies in AD.

Circuit dynamics of approach-avoidance conflict in humans

Debilitating anxiety is pervasive in the modern world. Choices to approach or avoid are common in everyday life and excessive avoidance is a cardinal feature of anxiety disorders. Here, we used intracranial EEG to define a distributed prefrontal-limbic circuit supporting approach and avoidance. Presurgical epilepsy patients (n=20) performed a continuous-choice, approach-avoidance conflict decision-making task inspired by the arcade game Pac-Man, where patients trade-off harvesting rewards against potential losses from attack by the ghost. As patients approached increasing rewards and threats, we found evidence of a limbic circuit mediated by increased theta power in the hippocampus, amygdala, orbitofrontal cortex (OFC) and anterior cingulate cortex (ACC), that drops rapidly during avoidance. Theta band connectivity within this circuit and with the lateral prefrontal cortex increases during approach and falls during avoidance, and amygdala and lateral frontal activity granger-caused the theta oscillations in both the OFC and ACC. Importantly, the degree of network connectivity predicted how long patients approach, with enhanced network synchronicity extending approach times. Finally, when threat is imminent, the system dynamically switches to a sustained increase in high-frequency activity (70-150Hz) in the middle frontal gyrus (MFG), tracking the degree of threat. The results provide evidence for a distributed prefrontal-limbic circuit, mediated by theta oscillations and high frequency activity, underlying approach-avoidance conflict in humans.

Neural Dynamics of Visual Stream Interactions During Memory-Guided Actions Investigated by Intracranial EEG

The dorsal and ventral visual streams have been considered to play distinct roles in visual processing for action: the dorsal stream is assumed to support real-time actions, while the ventral stream facilitates memory-guided actions. However, recent evidence suggests a more integrated function of these streams. We investigated the neural dynamics and functional connectivity between them during memory-guided actions using intracranial EEG. We tracked neural activity in the inferior parietal lobule in the dorsal stream, and the ventral temporal cortex in the ventral stream as well as the hippocampus during a delayed action task involving object identity and location memory. We found increased alpha power in both streams during the delay, indicating their role in maintaining spatial visual information. In addition, we recorded increased alpha power in the hippocampus during the delay, but only when both object identity and location needed to be remembered. We also recorded an increase in theta band phase synchronization between the inferior parietal lobule and ventral temporal cortex and between the inferior parietal lobule and hippocampus during the encoding and delay. Granger causality analysis indicated dynamic and frequency-specific directional interactions among the inferior parietal lobule, ventral temporal cortex, and hippocampus that varied across task phases. Our study provides unique electrophysiological evidence for close interactions between dorsal and ventral streams, supporting an integrated processing model in which both streams contribute to memory-guided actions.

Entrainment by transcranial alternating current stimulation: Insights from models of cortical oscillations and dynamical systems theory

Signature of neuronal oscillations can be found in nearly every brain function. However, abnormal oscillatory activity is linked with several brain disorders. Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation technique that can potentially modulate neuronal oscillations and influence behavior both in health and disease. Yet, a complete understanding of how interacting networks of neurons are affected by tACS remains elusive. Entrainment effects by which tACS synchronizes neuronal oscillations is one of the main hypothesized mechanisms, as evidenced in animals and humans. Computational models of cortical oscillations may shed light on the entrainment effects of tACS, but current modeling studies lack specific guidelines to inform experimental investigations. This study addresses the existing gap in understanding the mechanisms of tACS effects on rhythmogenesis within the brain by providing a comprehensive overview of both theoretical and experimental perspectives. We explore the intricate interactions between oscillators and periodic stimulation through the lens of dynamical systems theory. Subsequently, we present a synthesis of experimental findings that demonstrate the effects of tACS on both individual neurons and collective oscillatory patterns in animal models and humans. Our review extends to computational investigations that elucidate the interplay between tACS and neuronal dynamics across diverse cortical network models. To illustrate these concepts, we conclude with a simple oscillatory neuron model, showcasing how fundamental theories of oscillatory behavior derived from dynamical systems, such as phase response of neurons to external perturbation, can account for the entrainment effects observed with tACS. Studies reviewed here render the necessity of integrated experimental and computational approaches for effective neuromodulation by tACS in health and disease.

Preclinical insights into gamma-tACS: foundations for clinical translation in neurodegenerative diseases

Gamma transcranial alternating current stimulation (gamma-tACS) represents a novel neuromodulation technique with promising therapeutic applications across neurodegenerative diseases. This mini-review consolidates recent preclinical and clinical findings, examining the mechanisms by which gamma-tACS influences neural oscillations, enhances synaptic plasticity, and modulates neuroimmune responses. Preclinical studies have demonstrated the capacity of gamma-tACS to synchronize neuronal firing, support long-term neuroplasticity, and reduce markers of neuroinflammation, suggesting its potential to counteract neurodegenerative processes. Early clinical studies indicate that gamma-tACS may improve cognitive functions and network connectivity, underscoring its ability to restore disrupted oscillatory patterns central to cognitive performance. Given the intricate and multifactorial nature of gamma oscillations, the development of tailored, optimized tACS protocols informed by extensive animal research is crucial. Overall, gamma-tACS presents a promising avenue for advancing treatments that support cognitive resilience in a range of neurodegenerative conditions.

Beyond the neural underpinnings of action emulation in expert athletes: An EEG study

Athletes specializing in sports demanding rapid predictions and hand-eye coordination are highly trained in predicting the consequences of motor commands. This can be framed as highly efficient action emulation, but the neural underpinnings of this remain elusive. We examined the neural processes linked to the training effect of athletes (4000 h of training) by employing a continuous pursuit tracking task and EEG data. We manipulated feedback availability by intermittently occluding the cursor. As a performance measure, we used the distance between cursor and target (position error), the angle between the cursor and target movement direction (direction error) and the magnitude of cursor acceleration (acceleration error) to quantify movement strategy. In EEG data, we investigated beta, alpha, and theta frequency band oscillations. Athletes' position error is lower than non-athletes' when there is no feedback about the cursor location, but direction error is not. We found no quantitative power differences in the investigated frequency bands, but evidence that athletes and non-athletes accomplish action emulation through different functional neuroanatomical structures, especially when alpha and beta band activity is concerned. We surmise that non-athletes seemed to rely on top-down inhibitory control to predict guesses on cursor trajectories in the absence of cursor position feedback. In contrast, athletes might benefit from enhanced inhibitory gating mechanisms in the ventral stream and the integration of sensory and motor processes in the insular cortex, which could provide them with processing advantages in computing forward models. We further reflect that this advantage might be supported by alpha band activity in athletes' motor cortex, suggesting less inhibitory gating and a higher likelihood of executing integrated sensorimotor programs. We posit that current framings of neuroanatomical structures and neurophysiological processes in the action emulation framework must be revised to better capture superior motor performance.

Characteristics of spontaneous anterior-posterior oscillation-frequency convergences in the alpha band

Anterior-posterior interactions in the alpha band (8-12 Hz) have been implicated in a variety of functions including perception, attention, and working memory. The underlying neural communication can be flexibly controlled by adjusting phase relations when activities across anterior-posterior regions oscillate at a matched frequency. We thus investigated how alpha oscillation frequencies spontaneously converged along anterior-posterior regions by tracking oscillatory EEG activity while participants rested. As more anterior-posterior regions (scalp sites) frequency-converged, the probability of additional regions joining the frequency convergence increased, and so did oscillatory synchronization at participating regions (measured as oscillatory power), suggesting that anterior-posterior frequency convergences are driven by inter-regional entrainment. Notably, frequency convergences were accompanied by two types of approximately linear phase gradients, one progressively phase-lagged in the anterior direction-the posterior-to-anterior (P-A) gradient-and the other progressively phase-lagged in the posterior direction-the anterior-to-posterior (A-P) gradient. These gradients implied traveling waves propagating in the feedforward and feedback directions, respectively. Interestingly, while in natural viewing frequency convergences were accompanied by both gradient types (occurring at different frequencies) regardless of anterior-posterior routes, when the eyes were closed, the P-A and A-P gradients spatially segregated, channeling feedforward flows of information primarily through the midline and feedback flows primarily through each hemisphere. Future research may investigate how eye closure organizes information flows in this way and how it influences hierarchical information processing. Future research may also investigate the functional roles of frequency-convergence contingent traveling waves in contrast to those generated by other mechanisms.Significance Statement Anterior-posterior interactions in the alpha band (8-12 Hz) have been implicated in a variety of functions including perception, attention, and working memory. While alpha frequencies differ across anterior-posterior regions, they also dynamically converge while people rest. Our EEG study investigated the mechanisms and functions of spontaneous alpha-frequency convergences. Our results suggest that anterior-posterior frequency convergences are driven by inter-regional entrainment. Notably, frequency convergences were accompanied by approximately linear posterior-to-anterior and anterior-to-posterior phase gradients, likely facilitating feedforward and feedback information flows via travelling waves. Interestingly, closing eyes spatially organized these information flows, channeling feedforward flows through the midline and feedback flows through each hemisphere. Future research may investigate the behavioral significance of these frequency-convergence contingent flows of information.

Deciphering temporal scales of visual awareness: insights from flicker frequency modulation in continuous flash suppression

Evidence from temporal regularities in perception, temporal phenomenology, and neural dynamics indicate that our awareness evolves and devolves over several timescales. However, most theories of consciousness posit a single timescale of processing at the end of which a percept is rendered conscious. To show evidence for multiple timescales, we utilized continuous flash suppression (CFS). Based on a hierarchical framework of temporal phenomenology, we reasoned that different flicker rates (1, 4, 10, and 25 Hz) of the suppressor should be able to perturb phenomenologically distinct tasks. We designed four experiments that used different perceptual tasks (N = 48). The results showed that entry of contents into conscious awareness, their attentional sampling, perceptual grouping, and exiting from awareness were all maximally perturbed at distinct flicker frequencies of the suppressor in a CFS paradigm. Our demonstration shows that different flicker frequencies perturb different phenomenological aspects of awareness, and these flicker frequencies systematically map onto temporal hierarchies of timing of awareness.

Functional Connectivity in Chronic Schizophrenia: An EEG Resting-State Study with Corrected Imaginary Phase-Locking

Schizophrenia is a complex disorder characterized by altered brain functional connectivity, detectable during both task and resting state conditions using different neuroimaging methods. To this day, electroencephalography (EEG) studies have reported inconsistent results, showing both hyper- and hypo-connectivity with diverse topographical distributions. Interpretation of these findings is complicated by volume-conduction effects, where local brain activity fluctuations project simultaneously to distant scalp regions (zero-phase lag), inducing spurious inter-electrode correlations.

AIM

In the present study, we explored the network dynamics of schizophrenia using a novel functional connectivity metric-corrected imaginary phase locking value (ciPLV)-which is insensitive to changes in amplitude as well as interactions at zero-phase lag. This method, which is less prone to volume conduction effects, provides a more reliable estimate of sensor-space functional network connectivity in schizophrenia.

METHODS

We employed a cross-sectional design, utilizing resting state EEG recordings from two adult groups: individuals diagnosed with chronic schizophrenia (n = 30) and a control group of healthy participants (n = 30), all aged between 18 and 55 years old.

RESULTS

Our observations revealed that schizophrenia is characterized by a prevalence of excess theta (4-8 Hz) power localized to centroparietal electrodes. This was accompanied by significant alterations in inter- and intra-hemispheric functional network connectivity patterns, mainly between frontotemporal regions within the theta band and frontoparietal regions within beta/gamma bands.

CONCLUSIONS

Our findings suggest that patients with schizophrenia demonstrate long-range electrophysiological connectivity abnormalities that are independent of spectral power (i.e., volume conduction). Overall, distinct hemispheric differences were present in frontotemporo-parietal networks in theta and beta/gamma bands. While preliminary, these alterations could be promising new candidate biomarkers of chronic schizophrenia.

GABAergic Progenitor Cell Graft Rescues Cognitive Deficits in Fragile X Syndrome Mice

Fragile X syndrome (FXS) is an inherited neurodevelopmental disorder characterized by a range of clinical manifestations with no effective treatment strategy to date. Here, transplantation of GABAergic precursor cells from the medial ganglionic eminence (MGE) is demonstrated to significantly improve cognitive performance in Fmr1 knockout (KO) mice. Within the hippocampus of Fmr1-KO mice, MGE-derived cells from wild-type donor mice survive, migrate, differentiate into functionally mature interneurons, and form inhibitory synaptic connections with host pyramidal neurons. MGE cell transplantation restores Ras-PKB signaling in pyramidal neurons, enhances AMPA receptor trafficking, rescues synaptic plasticity, and corrects abnormal hippocampal neural oscillations. These findings highlight the potential of GABAergic precursor cell transplantation as a promising therapeutic strategy for FXS.

Oscillatory But Not Aperiodic Frontal Brain Activity Predicts the Development of Executive Control From Infancy to Toddlerhood

Executive control (EC) emerges in the first year of life, with the ability to inhibit prepotent responses (inhibitory control [IC]) and to flexibly readapt (cognitive flexibility [CF]) steadily improving. Simultaneously, electrophysiological brain activity undergoes profound reconfiguration, which has been linked to individual variability in EC. However, most studies exploring this relationship have used relative/absolute power and tasks that combine different executive processes. In addition, brain activity conflates aperiodic and oscillatory activity, which hinders the interpretation of the relationship between power and cognition. In the current study, we used the Early Childhood Inhibitory Touchscreen Task (ECITT) to examine the development of EC skills from 9 to 16 months in a longitudinal sample, and related performance of the task to resting-state EEG (rs-EEG) power, separating oscillatory and aperiodic activity. Our results showed improvement in IC but not in CF with age. In addition, alpha and theta oscillatory activity were concurrent (9-mo.) and longitudinal predictors of CF in toddlerhood, whereas the aperiodic exponent of the EEG signal did not contribute to EC. These findings demonstrate the relevance of oscillatory brain activity for cognitive development and provide an early brain marker for the early development of EC.

Decoding the rhythmic representation and communication of visual contents

Rhythmic neural activity is considered essential for adaptively modulating responses in the visual system. In this opinion article we posit that visual brain rhythms also serve a key function in the representation and communication of visual contents. Collating a set of recent studies that used multivariate decoding methods on rhythmic brain signals, we highlight such rhythmic content representations in visual perception, imagery, and prediction. We argue that characterizing representations across frequency bands allows researchers to elegantly disentangle content transfer in feedforward and feedback directions. We further propose that alpha dynamics are central to content-specific feedback propagation in the visual system. We conclude that considering rhythmic content codes is pivotal for understanding information coding in vision and beyond.

Prefrontal working memory signal controls phase-coded information within extrastriate cortex

In order to understand how prefrontal cortex provides the benefits of working memory (WM) for visual processing we examined the influence of WM on the representation of visual signals in V4 neurons in two macaque monkeys. We found that WM induces strong β oscillations in V4 and that the timing of action potentials relative to this oscillation reflects sensory information- i.e., a phase coding of visual information. Pharmacologically inactivating the Frontal Eye Field part of prefrontal cortex, we confirmed the necessity of prefrontal signals for the WM-driven boost in phase coding of visual information. Indeed, changes in the average firing rate of V4 neurons were correlated with WM-induced oscillatory changes. We present a network model to describe how WM signals can recruit sensory areas by inducing oscillations within these areas and discuss the implications of these findings for a sensory recruitment theory of WM through coherence.

Deciphering neuronal variability across states reveals dynamic sensory encoding

Influenced by non-stationary factors such as brain states and behavior, neurons exhibit substantial response variability even to identical stimuli. However, it remains unclear how their relative impact on neuronal variability evolves over time. To address this question, we designed an encoding model conditioned on latent states to partition variability in the mouse visual cortex across internal brain dynamics, behavior, and external visual stimulus. Applying a hidden Markov model to local field potentials, we consistently identified three distinct oscillation states, each with a unique variability profile. Regression models within each state revealed a dynamic composition of factors influencing spiking variability, with the dominant factor switching within seconds. The state-conditioned regression model uncovered extensive diversity in source contributions across units, varying in accordance with anatomical hierarchy and internal state. This heterogeneity in encoding underscores the importance of partitioning variability over time, particularly when considering the influence of non-stationary factors on sensory processing.

Mind in others' shoes: Neuroscientific protocol for external referent decision awareness (ERDA) in organizations

This study investigates the neural and physiological mechanisms underlying External Referent Decision Awareness (ERDA) within organizational contexts, focusing on hierarchical roles (Head, Peer, Staff). Twenty-two professionals participated, and electroencephalographic (EEG frequency band: Delta, Theta, Alpha, Beta, Gamma) and autonomic indices (skin conductance and cardiovascular indices) were recorded, while personality traits and decision-making styles were assessed. Results revealed higher Delta and Theta activation in the left temporo-parietal junction (TPJ) during Peer-related decisions, reflecting increased social cognition and ambiguity regulation in those contexts. Gamma activity, associated with high-order cognitive processes, was prominent in the left frontal cortex across all roles, indicating complex decision evaluation. These findings underscore the complexity of low-frequency bands (Delta and Theta), involved in emotional regulation and social cognition, while high-frequency bands (Gamma) reflect cognitive integration and decision complexity. Furthermore, autonomic data showed higher Skin Conductance Levels (SCL) for Head decisions, suggesting greater emotional involvement.The findings revealed a significant negative correlation between avoidant decision-making styles and the neural and behavioral evaluations of leader decisions, suggesting reduced engagement of neurocognitive systems involved in reward processing and evaluative judgment in individuals with a tendency to avoid decision-making. Additionally, higher extraversion correlated with more favorable evaluations of decisions made by Staff, potentially indicating greater activation in neural circuits associated with social reward and group dynamics. In conclusion, these findings suggest that neural activity and personality traits interact to shape hierarchical decision-making awareness, highlighting the need for tailored leadership and decision-making strategies in organizations.

Causal evidence for increased theta and gamma phase consistency in a parieto-frontal network during the maintenance of visual attention

Endogenous visuo-spatial attention is under the control of a fronto-parietal network of brain regions. One key node in this network, the intra-parietal sulcus (IPS), plays a crucial role in maintaining endogenous attention, but little is known about its ongoing physiology and network dynamics during different attentional states. Here, we investigated the reactivity of the left IPS in response to brain stimulation under different states of selective attention. We recorded electroencephalography (EEG) in response to single pulses of transcranial magnetic stimulation (TMS) of the IPS, while participants (N = 44) viewed bilateral random-dot motion displays. Individual MRI-guided TMS pulses targeted the left IPS, while the left primary somatosensory cortex (S1) served as an active control site. In separate blocks of trials, participants were cued to attend covertly to the motion display in one hemifield (left or right) and to report brief coherent motion targets. The perceptual load of the task was manipulated by varying the degree of motion coherence of the targets. Excitability, variability and information content of the neural responses to TMS were assessed by analysing TMS-evoked potential (TEP) amplitude and inter-trial phase clustering (ITPC), and by performing multivariate decoding of attentional state. Results revealed that a left posterior region displayed reduced variability in the phase of theta and gamma oscillations following TMS of the IPS, but not of S1, when attention was directed contralaterally, rather than ipsilaterally to the stimulation site. A right frontal cluster also displayed reduced theta variability and increased amplitude of TEPs when attention was directed contralaterally rather than ipsilaterally, after TMS of the IPS but not S1. Reliable decoding of attentional state was achieved after TMS pulses of both S1 and IPS. Taken together, our findings suggest that endogenous control of visuo-spatial attention leads to changes in the intrinsic oscillatory properties of the IPS and its associated fronto-parietal network.

Neurochemical and Neurophysiological Effects of Intravenous Administration of N,N-Dimethyltryptamine in Rats

N,N-dimethyltryptamine (DMT) is a serotonergic psychedelic that is being investigated clinically for the treatment of psychiatric disorders. Although the neurophysiological effects of DMT in humans are well-characterized, similar studies in animal models as well as data on the neurochemical effects of DMT are generally lacking, which are critical for mechanistic understanding. In the current study, we combined behavioral analysis, high-density (32-channel) electroencephalography, and ultra-high-performance liquid chromatography-tandem mass spectrometry to simultaneously quantify changes in behavior, cortical neural dynamics, and levels of 17 neurochemicals in medial prefrontal and somatosensory cortices before, during, and after intravenous administration of three different doses of DMT (0.75 mg/kg, 3.75 mg/kg, 7.5 mg/kg) in male and female adult rats. All three doses of DMT produced head twitch response with most twitches observed after the low dose. DMT caused dose-dependent increases in serotonin and dopamine levels in both cortical sites along with a reduction in EEG spectral power in theta (4-10 Hz) and low gamma (25-55 Hz), and increase in power in delta (1-4 Hz), medium gamma (65-115 ), and high gamma (125-155 Hz) bands. Functional connectivity decreased in the delta band and increased across the gamma bands. In addition, we provide the first measurements of endogenous DMT in these cortical sites at levels comparable to serotonin and dopamine, which together with a previous study in occipital cortex, suggests a physiological role for endogenous DMT. This study represents one of the most comprehensive characterizations of psychedelic drug action in rats and the first to be conducted with DMT.

Significance Statement

N,N-dimethyltryptamine (DMT) is a serotonergic psychedelic with potential as a tool for probing the neurobiology of consciousness and as a therapeutic agent for psychiatric disorders. However, the neurochemical and neurophysiological effects of DMT in rat, a preferred animal model for mechanistic studies, are unclear. We demonstrate that intravenous DMT caused a dose-dependent increase in serotonin and dopamine in medial prefrontal and somatosensory cortices, and simultaneously increased gamma functional connectivity. Similar effects have been shown for other serotonergic and atypical psychedelics, suggesting a shared mechanism of drug action. Additionally, we report DMT during normal wakefulness in two spatially and functionally distinct cortical sites - prefrontal, somatosensory - at levels comparable to those of serotonin and dopamine, supporting a physiological role for endogenous DMT.

Exploring the role of dimensionality transformation in episodic memory

Episodic memory must accomplish two adversarial goals: encoding and storing a multitude of experiences without exceeding the finite neuronal structure of the brain, and recalling memories in vivid detail. Dimensionality reduction and expansion ('dimensionality transformation') enable the brain to meet these demands. Reduction compresses sensory input into simplified, storable codes, while expansion reconstructs vivid details. Although these processes are essential to memory, their neural mechanisms for episodic memory remain unclear. Drawing on recent insights from cognitive psychology, systems neuroscience, and neuroanatomy, we propose two accounts of how dimensionality transformation occurs in the brain: structurally (via corticohippocampal pathways) and functionally (through neural oscillations). By examining cross-species evidence, we highlight neural mechanisms that may support episodic memory and identify crucial questions for future research.

On the varieties of conscious experiences: Altered Beliefs Under Psychedelics (ALBUS)

How is it that psychedelics so profoundly impact brain and mind? According to the model of "Relaxed Beliefs Under Psychedelics" (REBUS), 5-HT2a agonism is thought to help relax prior expectations, thus making room for new perspectives and patterns. Here, we introduce an alternative (but largely compatible) perspective, proposing that REBUS effects may primarily correspond to a particular (but potentially pivotal) regime of very high levels of 5-HT2a receptor agonism. Depending on both a variety of contextual factors and the specific neural systems being considered, we suggest opposite effects may also occur in which synchronous neural activity becomes more powerful, with accompanying "Strengthened Beliefs Under Psychedelics" (SEBUS) effects. Such SEBUS effects are consistent with the enhanced meaning-making observed in psychedelic therapy (e.g. psychological insight and the noetic quality of mystical experiences), with the imposition of prior expectations on perception (e.g. hallucinations and pareidolia), and with the delusional thinking that sometimes occurs during psychedelic experiences (e.g. apophenia, paranoia, engendering of inaccurate interpretations of events, and potentially false memories). With "Altered Beliefs Under Psychedelics" (ALBUS), we propose that the manifestation of SEBUS vs. REBUS effects may vary across the dose-response curve of 5-HT2a signaling. While we explore a diverse range of sometimes complex models, our basic idea is fundamentally simple: psychedelic experiences can be understood as kinds of waking dream states of varying degrees of lucidity, with similar underlying mechanisms. We further demonstrate the utility of ALBUS by providing neurophenomenological models of psychedelics focusing on mechanisms of conscious perceptual synthesis, dreaming, and episodic memory and mental simulation.

Bifocal tACS over the primary sensorimotor cortices increases interhemispheric inhibition and improves bimanual dexterity

Concurrent application of transcranial alternating current stimulation over distant cortical regions has been shown to modulate functional connectivity between stimulated regions; however, the precise mechanisms remain unclear. Here, we investigated how bifocal transcranial alternating current stimulation applied over the bilateral primary sensorimotor cortices modulates connectivity between the left and right primary motor cortices (M1). Using a cross-over sham-controlled triple-blind design, 37 (27 female, age: 18 to 37 yrs) healthy participants received transcranial alternating current stimulation (1.0 mA, 20 Hz, 20 min) over the bilateral sensorimotor cortices. Before and after transcranial alternating current stimulation, functional connectivity between the left and right M1s was assessed using imaginary coherence measured via resting-state electroencephalography and interhemispheric inhibition via dual-site transcranial magnetic stimulation protocol. Additionally, manual dexterity was assessed using the Purdue pegboard task. While imaginary coherence remained unchanged after stimulation, beta (20 Hz) power decreased during the transcranial alternating current stimulation session. Bifocal transcranial alternating current stimulation but not sham strengthened interhemispheric inhibition between the left and right M1s and improved bimanual assembly performance. These results suggest that improvement in bimanual performance may be explained by modulation in interhemispheric inhibition, rather than by coupling in the oscillatory activity. As functional connectivity underlies many clinical symptoms in neurological and psychiatric disorders, these findings are invaluable in developing noninvasive therapeutic interventions that target neural networks to alleviate symptoms.

Brain-body dysconnectivity: deficient autonomic regulation of cortical function in first-episode schizophrenia

BACKGROUND

An accumulating body of evidence indicates that peripheral physiological rhythms help regulate and organize large-scale brain activity. Given that schizophrenia (SZ) is characterized by marked abnormalities in oscillatory cortical activity as well as changes in autonomic function, the present study aimed to identify mechanisms by which central and autonomic nervous system deficits may be related. We evaluated phase-amplitude coupling (PAC) as a physiological mechanism through which autonomic nervous system (ANS) and central nervous system (CNS) activity are integrated and that may be disrupted in SZ.

METHODS

PAC was measured between high-frequency heart rate variability (HF-HRV) as an index of parasympathetic activity and electroencephalography (EEG) oscillations in 36 individuals with first-episode SZ and 38 healthy comparison participants at rest.

RESULTS

HRV-EEG coupling was lower in SZ in the alpha and theta bands, and HRV-EEG coupling uniquely predicted group membership, whereas HRV and EEG power alone did not. HRV-EEG coupling in the alpha band correlated with measures of sustained attention in SZ. Granger causality analyses indicated a stronger heart-to-brain effect than brain-to-heart effect, consistent across groups.

CONCLUSIONS

Lower HRV-EEG coupling provides evidence of deficient autonomic regulation of cortical activity in SZ, suggesting that patterns of dysconnectivity observed in brain networks extend to brain-body interactions. Deficient ANS-CNS integration in SZ may foster a breakdown in the spatiotemporal organization of cortical activity, which may contribute to core cognitive impairments in SZ such as dysregulated attention. These findings encourage pursuit of therapies targeting autonomic function for the treatment of SZ.

Gustatory cortex neurons perform reliability-dependent integration of multisensory flavor inputs

Flavor is the quintessential multisensory experience, combining gustatory, retronasal olfactory, and texture qualities to inform food perception and consumption behavior. However, the computations that govern multisensory integration of flavor components and their underlying neural mechanisms remain elusive. Here, we use rats as a model system to test the hypothesis that taste and smell components of flavor are integrated in a reliability-dependent manner to inform hedonic judgments and that this computation is performed by neurons in the primary taste cortex. Using a series of two-bottle preference tests, we demonstrate that hedonic judgments of taste + smell mixtures are a weighted average of the component judgments, and that the weight of the components depends on their relative reliability. Using extracellular recordings of single-neuron spiking and local field potential activity in combination with decoding analysis, we reveal a correlate of this computation in gustatory cortex (GC). GC neurons weigh bimodal taste and smell inputs based on their reliability, with more reliable inputs contributing more strongly to taste + smell mixture responses. Input reliability was associated with less variable responses and stronger network-level synchronization in the gamma band. Together, our findings establish a quantitative framework for understanding hedonic multisensory flavor judgments and identify the neural computations that underlie them.