Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex

Experience Dependence of Alpha Rhythms and Neural Dynamics in the Mouse Visual Cortex

The role of experience in the development and maintenance of emergent network properties such as cortical oscillations and states is poorly understood. To define how early-life experience affects cortical dynamics in the visual cortex of adult, head-fixed mice, we examined the effects of two forms of blindness initiated before eye opening and continuing through recording: (1) bilateral loss of retinal input (enucleation) and (2) degradation of visual input (eyelid suture). Neither form of deprivation fundamentally altered the state-dependent regulation of firing rates or local field potentials. However, each deprivation caused unique changes in network behavior. Laminar analysis revealed two different generative mechanisms for low-frequency synchronization: one prevalent during movement and the other during quiet wakefulness. The former was absent in enucleated mice, suggesting a mouse homolog of human alpha oscillations. In addition, neurons in enucleated animals were less correlated and fired more regularly, but no change in mean firing rate. Eyelid suture decreased firing rates during quiet wakefulness, but not during movement, with no effect on neural correlations or regularity. Sutured animals showed a broadband increase in depth EEG power and an increased occurrence, but reduced central frequency, of narrowband gamma oscillations. The complementary-rather than additive-effects of lid suture and enucleation suggest that the development of emergent network properties does not require vision but is plastic to modified input. Our results suggest a complex interaction of internal set points and experience determines mature cortical activity, with low-frequency synchronization being particularly susceptible to early deprivation.

Imbalance in positive and negative acceleration ratio of alpha oscillation in patients with complex regional pain syndrome

Objectives

To elucidate the functional characteristics of the brain in the presence of chronic pain using electroencephalography (EEG), with a focus on the dynamics of neural excitation and inhibition.

Methods

Resting-state EEG was performed in: 17 patients with complex regional pain syndrome (CRPS) who exhibited chronic pain higher than 20 on the visual analogue scale (VAS), 6 patients with reduced CRPS symptoms and chronic pain less than 20 on VAS, and healthy age-matched controls. For the analysis, 50 s of electroencephalogram (EEG) signals were extracted from EEG recordings during wakefulness and rest with eyes closed. The envelope of the alpha frequency band was calculated by examining the positive and negative accelerations of the envelope oscillation, ratio of positive (Ap) to negative (An) accelerations (Ap-An ratio), and mean amplitude of the envelope. Comparisons were made between patients and controls, and correlations between these EEG measures and the subjective pain VAS were evaluated.Significant differences in the value of Ap, An and Ap-An ratio were observed at temporal and central electrodes between patients with pain symptoms and controls. Those with reduced CRPS symptoms exhibited a distinct Ap-An ratio at the majority of electrodes when compared with those exhibiting chronic pain.

Conclusions

Distinct patterns in alpha wave envelope dynamics, reflecting excitatory and inhibitory activities, were associated with chronic pain in patients with CRPS. The pain-relieved state of CRPS suggested that a new balance of activities was established. This relationship indicated a potential association between altered alpha oscillation characteristics and the subjective experience of pain.

Significance

This study introduces a novel method for analyzing alpha oscillation envelopes, providing new insights into the neural pathophysiology of chronic pain in CRPS patients. This approach has the potential to enhance our understanding of the alterations in brain function that occur under chronic pain conditions.

Predictive coding for the actions and emotions of others and its deficits in autism spectrum disorders

Traditionally, the neural basis of social perception has been studied by showing participants brief examples of the actions or emotions of others presented in randomized order to prevent participants from anticipating what others do and feel. This approach is optimal to isolate the importance of information flow from lower to higher cortical areas. The degree to which feedback connections and Bayesian hierarchical predictive coding contribute to how mammals process more complex social stimuli has been less explored, and will be the focus of this review. We illustrate paradigms that start to capture how participants predict the actions and emotions of others under more ecological conditions, and discuss the brain activity measurement methods suitable to reveal the importance of feedback connections in these predictions. Together, these efforts draw a richer picture of social cognition in which predictive coding and feedback connections play significant roles. We further discuss how the notion of predicting coding is influencing how we think of autism spectrum disorder.

Oscillations in an artificial neural network convert competing inputs into a temporal code

The field of computer vision has long drawn inspiration from neuroscientific studies of the human and non-human primate visual system. The development of convolutional neural networks (CNNs), for example, was informed by the properties of simple and complex cells in early visual cortex. However, the computational relevance of oscillatory dynamics experimentally observed in the visual system are typically not considered in artificial neural networks (ANNs). Computational models of neocortical dynamics, on the other hand, rarely take inspiration from computer vision. Here, we combine methods from computational neuroscience and machine learning to implement multiplexing in a simple ANN using oscillatory dynamics. We first trained the network to classify individually presented letters. Post-training, we added temporal dynamics to the hidden layer, introducing refraction in the hidden units as well as pulsed inhibition mimicking neuronal alpha oscillations. Without these dynamics, the trained network correctly classified individual letters but produced a mixed output when presented with two letters simultaneously, indicating a bottleneck problem. When introducing refraction and oscillatory inhibition, the output nodes corresponding to the two stimuli activate sequentially, ordered along the phase of the inhibitory oscillations. Our model implements the idea that inhibitory oscillations segregate competing inputs in time. The results of our simulations pave the way for applications in deeper network architectures and more complicated machine learning problems.

Cortical dynamics of perception as trains of coherent gamma oscillations, with the pulvinar as central coordinator

Synchronization of spikes carried by the visual streams is strategic for the proper binding of cortical assemblies, hence for the perception of visual objects as coherent units. Perception of a complex visual scene involves multiple trains of gamma oscillations, coexisting at each stage in visual and associative cortex. Here, we analyze how this synchrony is managed, so that the perception of each visual object can emerge despite this complex interweaving of cortical activations. After a brief review of structural and temporal facts, we analyze the interactions which make the oscillations coherent for the visual elements related to the same object. We continue with the propagation of these gamma oscillations within the sensory chain. The dominant role of the pulvinar and associated reticular thalamic nucleus as cortical coordinator is the common thread running through this step-by-step description. Synchronization mechanisms are analyzed in the context of visual perception, although the present considerations are not limited to this sense. A simple experiment is described, with the aim of assessing the validity of the elements developed here. A first set of results is provided, together with a proposed method to go further in this investigation.

Working memory expedites the processing of visual signals within the extrastriate cortex

Working memory is the ability to maintain information in the absence of sensory input. In this study, we investigated how working memory benefits processing in visual areas. Using a measure of phase consistency to detect the arrival time of visual signals to the middle temporal (MT) area, we assessed the impact of working memory on the speed of sensory processing. We recorded from MT neurons in two monkeys during a spatial working memory task with visual probes. When the memorized location closely matches the receptive field center of the recording site, visual input arrives sooner, but if the memorized location does not match the receptive field center then the arrival of visual information is delayed. Thus, working memory expedites the arrival of visual input in MT. These results reveal that even in the absence of firing rate changes, working memory can still benefit the processing of information within sensory areas.

Distinct oscillatory patterns differentiate between segregation and integration processes in perceptual grouping

Recently, there has been a resurgence in experimental and conceptual efforts to understand how brain rhythms can serve to organize visual information. Oscillations can provide temporal structure for neuronal processing and form a basis for integrating information across brain areas. Here, we use a bistable paradigm and a data-driven approach to test the hypothesis that oscillatory modulations associate with the integration or segregation of visual elements. Spectral signatures of perception of bound and unbound configurations of visual moving stimuli were studied using magnetoencephalography (MEG) in ambiguous and unambiguous conditions. Using a 2 × 2 design, we were able to isolate correlates from visual integration, either perceptual or stimulus-driven, from attentional and ambiguity-related activity. Two frequency bands were found to be modulated by visual integration: an alpha/beta frequency and a higher frequency gamma-band. Alpha/beta power was increased in several early visual cortical and dorsal visual areas during visual integration, while gamma-band power was surprisingly increased in the extrastriate visual cortex during segregation. This points to an integrative role for alpha/beta activity, likely from top-down signals maintaining a single visual representation. On the other hand, when more representations have to be processed in parallel gamma-band activity is increased, which is at odds with the notion that gamma oscillations are related to perceptual coherence. These modulations were confirmed in intracranial EEG recordings and partially originate from distinct brain areas. Our MEG and stereo-EEG data confirms predictions of binding mechanisms depending on low-frequency activity for long-range integration and for organizing visual processing while refuting a straightforward correlation between gamma-activity and perceptual binding. PRACTITIONER POINTS: Distinct neurophysiological signals underlie competing bistable percepts. Increased alpha/beta activity correlate with visual integration while gamma correlates with segmentation. Ambiguous percepts drive alpha/beta activity in the posterior cingulate cortex.

The right posterior parietal cortex mediates spatial reorienting of attentional choice bias

Attention facilitates behavior by enhancing perceptual sensitivity (sensory processing) and choice bias (decisional weighting) for attended information. Whether distinct neural substrates mediate these distinct components of attention remains unknown. We investigate the causal role of key nodes of the right posterior parietal cortex (rPPC) in the forebrain attention network in sensitivity versus bias control. Two groups of participants performed a cued attention task while we applied either inhibitory, repetitive transcranial magnetic stimulation (n = 28) or 40 Hz transcranial alternating current stimulation (n = 26) to the dorsal rPPC. We show that rPPC stimulation - with either modality - impairs task performance by selectively altering attentional modulation of bias but not sensitivity. Specifically, participants' bias toward the uncued, but not the cued, location reduced significantly following rPPC stimulation - an effect that was consistent across both neurostimulation cohorts. In sum, the dorsal rPPC causally mediates the reorienting of choice bias, one particular component of visual spatial attention.

Why brain organoids are not conscious yet

Rapid advances in human brain organoid technologies have prompted the question of their consciousness. Although brain organoids resemble many facets of the brain, their shortcomings strongly suggest that they do not fit any of the operational definitions of consciousness. As organoids gain internal processing systems through statistical learning and closed loop algorithms, interact with the external world, and become embodied through fusion with other organ systems, questions of biosynthetic consciousness will arise.

Cortical Layer-Dependent Signaling in Cognition: Three Computational Modes of the Canonical Circuit

The cerebral cortex performs computations via numerous six-layer modules. The operational dynamics of these modules were studied primarily in early sensory cortices using bottom-up computation for response selectivity as a model, which has been recently revolutionized by genetic approaches in mice. However, cognitive processes such as recall and imagery require top-down generative computation. The question of whether the layered module operates similarly in top-down generative processing as in bottom-up sensory processing has become testable by advances in the layer identification of recorded neurons in behaving monkeys. This review examines recent advances in laminar signaling in these two computations, using predictive coding computation as a common reference, and shows that each of these computations recruits distinct laminar circuits, particularly in layer 5, depending on the cognitive demands. These findings highlight many open questions, including how different interareal feedback pathways, originating from and terminating at different layers, convey distinct functional signals.

Laminar pattern of sensory-evoked dynamic high-frequency oscillatory activity in the macaque auditory cortex

High-frequency (>60 Hz) neuroelectric signals likely have functional roles distinct from low-frequency (<30 Hz) signals. While high-gamma activity (>60 Hz) does not simply equate to neuronal spiking, they are highly correlated, having similar information encoding. High-gamma activity is typically considered broadband and poorly phase-locked to sensory stimuli and thus is typically analyzed after transformations into absolute amplitude or spectral power. However, those analyses discard signal polarity, compromising the interpretation of neuroelectric events that are essentially dipolar. In the spectrotemporal profiles of field potentials in auditory cortex, we show high-frequency spectral peaks not phase-locked to sound onset, which follow the broadband peak of phase-locked onset responses. Isolating the signal components comprising the high-frequency peaks reveals narrow-band high-frequency oscillatory events, whose instantaneous frequency changes rapidly from >150 to 60 Hz, which may underlie broadband high-frequency spectral peaks in previous reports. The laminar amplitude distributions of the isolated activity had two peak positions, while the laminar phase patterns showed a counterphase relationship between those peaks, indicating the formation of dipoles. Our findings suggest that nonphase-locked HGA arises in part from oscillatory or recurring activity of supragranular-layer neuronal ensembles in auditory cortex.

Neural manifolds in V1 change with top-down signals from V4 targeting the foveal region

High-dimensional brain activity is often organized into lower-dimensional neural manifolds. However, the neural manifolds of the visual cortex remain understudied. Here, we study large-scale multi-electrode electrophysiological recordings of macaque (Macaca mulatta) areas V1, V4, and DP with a high spatiotemporal resolution. We find that the population activity of V1 contains two separate neural manifolds, which correlate strongly with eye closure (eyes open/closed) and have distinct dimensionalities. Moreover, we find strong top-down signals from V4 to V1, particularly to the foveal region of V1, which are significantly stronger during the eyes-open periods. Finally, in silico simulations of a balanced spiking neuron network qualitatively reproduce the experimental findings. Taken together, our analyses and simulations suggest that top-down signals modulate the population activity of V1. We postulate that the top-down modulation during the eyes-open periods prepares V1 for fast and efficient visual responses, resulting in a type of visual stand-by state.

Speech and music recruit frequency-specific distributed and overlapping cortical networks

To what extent does speech and music processing rely on domain-specific and domain-general neural networks? Using whole-brain intracranial EEG recordings in 18 epilepsy patients listening to natural, continuous speech or music, we investigated the presence of frequency-specific and network-level brain activity. We combined it with a statistical approach in which a clear operational distinction is made between shared, preferred, and domain-selective neural responses. We show that the majority of focal and network-level neural activity is shared between speech and music processing. Our data also reveal an absence of anatomical regional selectivity. Instead, domain-selective neural responses are restricted to distributed and frequency-specific coherent oscillations, typical of spectral fingerprints. Our work highlights the importance of considering natural stimuli and brain dynamics in their full complexity to map cognitive and brain functions.

Laminar dissociation of feedforward and feedback in high-level ventral visual cortex during imagery and perception

Visual imagery and perception share neural machinery but rely on different information flow. While perception is driven by the integration of sensory feedforward and internally generated feedback information, imagery relies on feedback only. This suggests that although imagery and perception may activate overlapping brain regions, they do so in informationally distinctive ways. Using lamina-resolved MRI at 7 T, we measured the neural activity during imagery and perception of faces and scenes in high-level ventral visual cortex at the mesoscale of laminar organization that distinguishes feedforward from feedback signals. We found distinctive laminar profiles for imagery and perception of scenes and faces in the parahippocampal place area and the fusiform face area, respectively. Our findings provide insight into the neural basis of the phenomenology of visual imagery versus perception and shed new light into the mesoscale organization of feedforward and feedback information flow in high-level ventral visual cortex.

Orthogonality of sensory and contextual categorical dynamics embedded in a continuum of responses from the second somatosensory cortex

How does the brain simultaneously process signals that bring complementary information, like raw sensory signals and their transformed counterparts, without any disruptive interference? Contemporary research underscores the brain's adeptness in using decorrelated responses to reduce such interference. Both neurophysiological findings and artificial neural networks support the notion of orthogonal representation for signal differentiation and parallel processing. Yet, where, and how raw sensory signals are transformed into more abstract representations remains unclear. Using a temporal pattern discrimination task in trained monkeys, we revealed that the second somatosensory cortex (S2) efficiently segregates faithful and transformed neural responses into orthogonal subspaces. Importantly, S2 population encoding for transformed signals, but not for faithful ones, disappeared during a nondemanding version of this task, which suggests that signal transformation and their decoding from downstream areas are only active on-demand. A mechanistic computation model points to gain modulation as a possible biological mechanism for the observed context-dependent computation. Furthermore, individual neural activities that underlie the orthogonal population representations exhibited a continuum of responses, with no well-determined clusters. These findings advocate that the brain, while employing a continuum of heterogeneous neural responses, splits population signals into orthogonal subspaces in a context-dependent fashion to enhance robustness, performance, and improve coding efficiency.

Decoding familiar visual object categories in the mu rhythm oscillatory response

Whilst previous research has linked attenuation of the mu rhythm to the observation of specific visual categories, and even to a potential role in action observation via a putative mirror neuron system, much of this work has not considered what specific type of information might be coded in this oscillatory response when triggered via vision. Here, we sought to determine whether the mu rhythm contains content-specific information about the identity of familiar (and also unfamiliar) graspable objects. In the present study, right-handed participants (N = 27) viewed images of both familiar (apple, wine glass) and unfamiliar (cubie, smoothie) graspable objects, whilst performing an orthogonal task at fixation. Multivariate pattern analysis (MVPA) revealed significant decoding of familiar, but not unfamiliar, visual object categories in the mu rhythm response. Thus, simply viewing familiar graspable objects may automatically trigger activation of associated tactile and/or motor properties in sensorimotor areas, reflected in the mu rhythm. In addition, we report significant attenuation in the central beta band for both familiar and unfamiliar visual objects, but not in the mu rhythm. Our findings highlight how analysing two different aspects of the oscillatory response - either attenuation or the representation of information content - provide complementary views on the role of the mu rhythm in response to viewing graspable object categories.

Application of EEG in the Diagnosis and Classification of Migraine: A Scoping Review

Migraine is a chronic debilitating disease affecting a significant number of people, more often women than men. The gold standard for diagnosis is the International Classification of Headache Disorders-3 (ICHD-3). Authors have identified multiple tight spots in the present method of diagnosis. An alternative method of diagnosis has always been coveted. Electroencephalogram (EEG) is one of the most researched of such alternatives. The visually evoked potential is the most studied; auditory evoked potentials and transcranial direct current stimulation are also being studied. Cortical hyperexcitability and habituation deficit to sensory stimuli are some of the consistent findings. Alpha oscillations are among the most frequently studied bands; spectral analysis of EEG waves has often shown more reliable and consistent results than features read off the EEG directly. EEG microstate is a novel and promising method showing characteristic identifiable features that may help diagnose Migraine patients. An alternative to the ICHD-3 criterion for diagnosing Migraines would be instrumental in promptly diagnosing the disease. EEG is one of the most explored alternatives within which enumerable features can be used to identify Migraines, of which the most promising are EEG microstates.

The neural oscillatory mechanism underlying human brain fingerprint recognition using a portable EEG acquisition device

In recent years, brainprint recognition has emerged as a novel method of personal identity verification. Although studies have demonstrated the feasibility of this technology, some limitations hinder its further development into the society, such as insufficient efficiency (extended wear time for multi-channel EEG cap), complex experimental paradigms (more time in learning and completing experiments), and unclear neurobiological characteristics (lack of intuitive biomarkers and an inability to eliminate the impact of noise on individual differences). Overall, these limitations are due to the incomplete understanding of the underlying neural mechanisms. Therefore, this study aims to investigate the neural mechanisms behind brainwave recognition and simplify the operation process. We recorded prefrontal resting-state EEG data from 40 participants, which is followed up over nine months using a single-channel portable brainwave device. We found that portable devices can effectively and stably capture the characteristics of different subjects in the alpha band (8-13Hz) over long periods, as well as capturing their individual differences (no alpha peak, 1 alpha peak, or 2 alpha peaks). Through correlation analysis, alpha-band activity can reveal the uniqueness of the subjects compared to others within one minute. We further used a descriptive model to dissect the oscillatory and non-oscillatory components in the alpha band, demonstrating the different contributions of fine oscillatory features to individual differences (especially amplitude and bandwidth). Our study validated the feasibility of portable brainwave devices in brainwave recognition and the underlying neural oscillation mechanisms. The fine characteristics of various alpha oscillations will contribute to the accuracy of brainwave recognition, providing new insights for the development of future brainwave recognition technology.

Interacting ventral temporal gradients of timescales and functional connectivity and their relationships to visual behavior

Cortical gradients in endogenous and stimulus-evoked neurodynamic timescales, and long-range cortical interactions, provide organizational constraints to the brain and influence neural populations' roles in cognition. It is unclear how these functional gradients interrelate and which influence behavior. Here, intracranial recordings from 4,090 electrode contacts in 35 individuals map gradients of neural timescales and functional connectivity to assess their interactions along category-selective ventral temporal cortex. Endogenous and stimulus-evoked information processing timescales were not significantly correlated with one another suggesting that local neural timescales are context dependent and may arise through distinct neurophysiological mechanisms. Endogenous neural timescales correlated with functional connectivity even after removing the effects of shared anatomical gradients. Neural timescales and functional connectivity correlated with how strongly a population's activity predicted behavior in a simple visual task. These results suggest both interrelated and distinct neurophysiological processes give rise to different functional connectivity and neural timescale gradients, which together influence behavior.

Dissociation of attentional state and behavioral outcome using local field potentials

Successful behavior depends on attentional state and other factors related to decision-making, which may modulate neuronal activity differently. Here, we investigated whether attentional state and behavioral outcome (i.e., whether a target is detected or missed) are distinguishable using the power and phase of local field potential (LFP) recorded bilaterally from area V4 of monkeys performing a cued visual attention task. To link each trial's outcome to pairwise measures of attention that are typically averaged across trials, we used several methods to obtain single-trial estimates of spike count correlation and phase consistency. Surprisingly, while attentional location was best discriminated using gamma and high-gamma power, behavioral outcome was best discriminated by alpha power and steady-state visually evoked potential. Power outperformed absolute phase in attentional/behavioral discriminability, although single-trial gamma phase consistency provided reasonably high attentional discriminability. Our results suggest a dissociation between the neuronal mechanisms that regulate attentional focus and behavioral outcome.

Analysis of the alpha activity envelope in electroencephalography in relation to the ratio of excitatory to inhibitory neural activity

Alpha waves, one of the major components of resting and awake cortical activity in human electroencephalography (EEG), are known to show waxing and waning, but this phenomenon has rarely been analyzed. In the present study, we analyzed this phenomenon from the viewpoint of excitation and inhibition. The alpha wave envelope was subjected to secondary differentiation. This gave the positive (acceleration positive, Ap) and negative (acceleration negative, An) values of acceleration and their ratio (Ap-An ratio) at each sampling point of the envelope signals for 60 seconds. This analysis was performed on 36 participants with Alzheimer's disease (AD), 23 with frontotemporal dementia (FTD) and 29 age-matched healthy participants (NC) whose data were provided as open datasets. The mean values of the Ap-An ratio for 60 seconds at each EEG electrode were compared between the NC and AD/FTD groups. The AD (1.41 ±0.01 (SD)) and FTD (1.40 ±0.02) groups showed a larger Ap-An ratio than the NC group (1.38 ±0.02, p<0.05). A significant correlation between the envelope amplitude of alpha activity and the Ap-An ratio was observed at most electrodes in the NC group (Pearson's correlation coefficient, r = -0.92 ±0.15, mean for all electrodes), whereas the correlation was disrupted in AD (-0.09 ±0.21, p<0.05) and disrupted in the frontal region in the FTD group. The present method analyzed the envelope of alpha waves from a new perspective, that of excitation and inhibition, and it could detect properties of the EEG, Ap-An ratio, that have not been revealed by existing methods. The present study proposed a new method to analyze the alpha activity envelope in electroencephalography, which could be related to excitatory and inhibitory neural activity.

Representations of imaginary scenes and their properties in cortical alpha activity

Imagining natural scenes enables us to engage with a myriad of simulated environments. How do our brains generate such complex mental images? Recent research suggests that cortical alpha activity carries information about individual objects during visual imagery. However, it remains unclear if more complex imagined contents such as natural scenes are similarly represented in alpha activity. Here, we answer this question by decoding the contents of imagined scenes from rhythmic cortical activity patterns. In an EEG experiment, participants imagined natural scenes based on detailed written descriptions, which conveyed four complementary scene properties: openness, naturalness, clutter level and brightness. By conducting classification analyses on EEG power patterns across neural frequencies, we were able to decode both individual imagined scenes as well as their properties from the alpha band, showing that also the contents of complex visual images are represented in alpha rhythms. A cross-classification analysis between alpha power patterns during the imagery task and during a perception task, in which participants were presented images of the described scenes, showed that scene representations in the alpha band are partly shared between imagery and late stages of perception. This suggests that alpha activity mediates the top-down re-activation of scene-related visual contents during imagery.

Common and distinct neural mechanisms of attention

Despite a constant deluge of sensory stimulation, only a fraction of it is used to guide behavior. This selective processing is generally referred to as attention, and much research has focused on the neural mechanisms controlling it. Recently, research has broadened to include more ways by which different species selectively process sensory information, whether due to the sensory input itself or to different behavioral and brain states. This work has produced a complex and disjointed body of evidence across different species and forms of attention. However, it has also provided opportunities to better understand the breadth of attentional mechanisms. Here, we summarize the evidence that suggests that different forms of selective processing are supported by mechanisms both common and distinct.

Single session cross-frequency bifocal tACS modulates visual motion network activity in young healthy population and stroke patients

BACKGROUND

Phase synchronization over long distances underlies inter-areal communication and importantly, modulates the flow of information processing to adjust to cognitive demands.

OBJECTIVE

This study investigates the impact of single-session, cross-frequency (Alpha-Gamma) bifocal transcranial alternating current stimulation (cf-tACS) to the cortical visual motion network on inter-areal coupling between the primary visual cortex (V1) and the medio-temporal area (MT) and on motion direction discrimination.

METHODS

Based on the well-established phase-amplitude coupling (PAC) mechanism driving information processing in the visual system, we designed a novel directionally tuned cf-tACS protocol. Directionality of information flow was inferred from the area receiving low-frequency tACS (e.g., V1) projecting onto the area receiving high-frequency tACS (e.g., MT), in this case, promoting bottom-up information flow (Forward-tACS). The control condition promoted the opposite top-down connection (from MT to V1, called Backward-tACS), both compared to a Sham-tACS condition. Task performance and EEG activity were recorded from 45 young healthy subjects. An additional cohort of 16 stroke patients with occipital lesions and impairing visual processing was measured to assess the influence of a V1 lesion on the modulation of V1-MT coupling.

RESULTS

The results indicate that Forward cf-tACS successfully modulated bottom-up PAC (V1 α-phase-MT ɣ-amplitude) in both cohorts, while producing opposite effects on the reverse MT-to-V1 connection. Backward-tACS did not change V1-MT PAC in either direction in healthy participants but induced a slight decrease in bottom-up PAC in stroke patients. However, these changes in inter-areal coupling did not translate into cf-tACS-specific behavioural improvements.

CONCLUSIONS

Single session cf-tACS can alter inter-areal coupling in intact and lesioned brains but is probably not enough to induce longer-lasting behavioural effects in these cohorts. This might suggest that a longer daily visual training protocol paired with tACS is needed to unveil the relationship between externally applied oscillatory activity and behaviourally relevant brain processing.

Distractor inhibition by alpha oscillations is controlled by an indirect mechanism governed by goal-relevant information

The role of alpha oscillations (8-13 Hz) in cognition is intensively investigated. While intracranial animal recordings demonstrate that alpha oscillations are associated with decreased neuronal excitability, it is been questioned whether alpha oscillations are under direct control from frontoparietal areas to suppress visual distractors. We here point to a revised mechanism in which alpha oscillations are controlled by an indirect mechanism governed by the load of goal-relevant information - a view compatible with perceptual load theory. We will outline how this framework can be further tested and discuss the consequences for network dynamics and resource allocation in the working brain.

Neural assemblies coordinated by cortical waves are associated with waking and hallucinatory brain states

The relationship between sensory stimuli and perceptions is brain-state dependent: in wakefulness, suprathreshold stimuli evoke perceptions; under anesthesia, perceptions are abolished; and during dreaming and in dissociated states, percepts are internally generated. Here, we exploit this state dependence to identify brain activity associated with internally generated or stimulus-evoked perceptions. In awake mice, visual stimuli phase reset spontaneous cortical waves to elicit 3-6 Hz feedback traveling waves. These stimulus-evoked waves traverse the cortex and entrain visual and parietal neurons. Under anesthesia as well as during ketamine-induced dissociation, visual stimuli do not disrupt spontaneous waves. Uniquely, in the dissociated state, spontaneous waves traverse the cortex caudally and entrain visual and parietal neurons, akin to stimulus-evoked waves in wakefulness. Thus, coordinated neuronal assemblies orchestrated by traveling cortical waves emerge in states in which perception can manifest. The awake state is privileged in that this coordination is reliably elicited by external visual stimuli.

Effects of neural oscillation power and phase on discrimination performance in a visual tilt illusion

Neural oscillations reflect fluctuations in the relative excitation/inhibition of neural systems1,2,3,4,5 and are theorized to play a critical role in canonical neural computations6,7,8,9 and cognitive processes.10,11,12,13,14 These theories have been supported by findings that detection of visual stimuli fluctuates with the phase of oscillations prior to stimulus onset.15,16,17,18,19,20,21,22,23 However, null results have emerged in studies seeking to demonstrate these effects in visual discrimination tasks,24,25,26,27 raising questions about the generalizability of these phenomena to wider neural processes. Recently, we suggested that methodological limitations may mask effects of phase in higher-level sensory processing.28 To test the generality of phasic influences on perception requires a task that involves stimulus discrimination while also depending on early sensory processing. Here, we examined the influence of oscillation phase on the visual tilt illusion, in which a center grating has its perceived orientation biased away from the orientation of a surround grating29 due to lateral inhibitory interactions in early visual processing.30,31,32 We presented center gratings at participants' subjective vertical angle and had participants report whether the grating appeared tilted clockwise or counterclockwise from vertical on each trial while measuring their brain activity with electroencephalography (EEG). In addition to effects of alpha power and aperiodic slope, we observed robust associations between orientation perception and alpha and theta phase, consistent with fluctuating illusion magnitude across the oscillatory cycle. These results confirm that oscillation phase affects the complex processing involved in stimulus discrimination, consistent with its purported role in canonical computations that underpin cognition.

Dependence of Contextual Modulation in Macaque V1 on Interlaminar Signal Flow

In visual cortex, neural correlates of subjective perception can be generated by modulation of activity from beyond the classical receptive field (CRF). In macaque V1, activity generated by nonclassical receptive field (nCRF) stimulation involves different intracortical circuitry than activity generated by CRF stimulation, suggesting that interactions between neurons across V1 layers differ under CRF and nCRF stimulus conditions. We measured border ownership modulation within large populations of V1 neurons. We found that neurons in single columns preferred the same side of objects located outside of the CRF. In addition, we found that interactions between pairs of neurons situated across feedback/horizontal and input layers differed between CRF and nCRF stimulation. Furthermore, the magnitude of border ownership modulation was predicted by greater information flow from feedback/horizontal to input layers. These results demonstrate that the flow of signals between layers covaries with the degree to which neurons integrate information from beyond the CRF.

Top-down modulation of visual cortical stimulus encoding and gamma independent of firing rates

Neurons in primary visual cortex integrate sensory input with signals reflecting the animal's internal state to support flexible behavior. Internal variables, such as expectation, attention, or current goals, are imposed in a top-down manner via extensive feedback projections from higher-order areas. We optogenetically activated a high-order visual area, area 21a, in the lightly anesthetized cat (OptoTD), while recording from neuronal populations in V1. OptoTD induced strong, up to several fold, changes in gamma-band synchronization together with much smaller changes in firing rate, and the two effects showed no correlation. OptoTD effects showed specificity for the features of the simultaneously presented visual stimuli. OptoTD-induced changes in gamma synchronization, but not firing rates, were predictive of simultaneous changes in the amount of encoded stimulus information. Our findings suggest that one important role of top-down signals is to modulate synchronization and the information encoded by populations of sensory neurons.

Evolutionary origin of alpha rhythms in vertebrates

The purpose of this review extends beyond the traditional triune brain model, aiming to elucidate the evolutionary aspects of alpha rhythms in vertebrates. The forebrain, comprising the telencephalon (pallium) and diencephalon (thalamus, hypothalamus), is a common feature in the brains of all vertebrates. In mammals, evolution has prioritized the development of the forebrain, especially the neocortex, over the midbrain (mesencephalon) optic tectum, which serves as the prototype for the visual brain. This evolution enables mammals to process visual information in the retina-thalamus (lateral geniculate nucleus)-occipital cortex pathway. The origin of posterior-dominant alpha rhythms observed in mammals in quiet and dark environments is not solely attributed to cholinergic pontine nuclei cells functioning as a 10 Hz pacemaker in the brainstem. It also involves the ability of the neocortex's cortical layers to generate traveling waves of alpha rhythms with waxing and waning characteristics. The utilization of alpha rhythms might have facilitated the shift of attention from external visual inputs to internal cognitive processes as an adaptation to thrive in dark environments. The evolution of alpha rhythms might trace back to the dinosaur era, suggesting that enhanced cortical connectivity linked to alpha bands could have facilitated the development of nocturnal awakening in the ancestors of mammals. In fishes, reptiles, and birds, the pallium lacks a cortical layer. However, there is a lack of research clearly observing dominant alpha rhythms in the pallium or organized nuclear structures in fishes, reptiles, or birds. Through convergent evolution, the pallium of birds, which exhibits cortex-like fiber architecture, has not only acquired advanced cognitive and motor abilities but also the capability to generate low-frequency oscillations (4-25 Hz) resembling alpha rhythms. This suggests that the origins of alpha rhythms might lie in the pallium of a common ancestor of birds and mammals.

Orbitofrontal high-gamma reflects spike-dissociable value and decision mechanisms

The orbitofrontal cortex (OFC) plays a crucial role in value-based decision-making. While previous research has focused on spiking activity in OFC neurons, the role of OFC local field potentials (LFPs) in decision-making remains unclear. LFPs are important because they can reflect synaptic and subthreshold activity not directly coupled to spiking, and because they are potential targets for less invasive forms of brain-machine interface (BMI). We recorded LFPs and spiking activity using multi-channel vertical probes while monkeys performed a two-option value-based decision-making task. We compared the value- and decision-coding properties of high-gamma range LFPs (HG, 50-150 Hz) to the coding properties of spiking multi-unit activity (MUA) recorded concurrently on the same electrodes. Results show that HG and MUA both represent the values of decision targets, and that their representations have similar temporal profiles in a trial. However, we also identified value-coding properties of HG that were dissociable from the concurrently-measured MUA. On average across channels, HG amplitude increased monotonically with value, whereas the average value encoding in MUA was net neutral. HG also encoded a signal consistent with a comparison between the values of the two targets, a signal which was much weaker in MUA. In individual channels, HG was better able to predict choice outcomes than MUA; however, when simultaneously recorded channels were combined in population-based decoder, MUA provided more accurate predictions than HG. Interestingly, HG value representations were accentuated in channels in or near shallow cortical layers, suggesting a dissociation between neuronal sources of HG and MUA. In summary, we find that HG signals are dissociable from MUA with respect to cognitive variables encoded in prefrontal cortex, evident in the monotonic encoding of value, stronger encoding of value comparisons, and more accurate predictions about behavior. High-frequency LFPs may therefore be a viable - or even preferable - target for BMIs to assist cognitive function, opening the possibility for less invasive access to mental contents that would otherwise be observable only with spike-based measures.

A Role for Bottom-Up Alpha Oscillations in Temporal Integration

Neural oscillations in the 8-12 Hz alpha band are thought to represent top-down inhibitory control and to influence temporal resolution: Individuals with faster peak frequencies segregate stimuli appearing closer in time. Recently, this theory has been challenged. Here, we investigate a special case in which alpha does not correlate with temporal resolution: when stimuli are presented amidst strong visual drive. Based on findings regarding alpha rhythmogenesis and wave spatial propagation, we suggest that stimulus-induced, bottom-up alpha oscillations play a role in temporal integration. We propose a theoretical model, informed by visual persistence, lateral inhibition, and network refractory periods, and simulate physiologically plausible scenarios of the interaction between bottom-up alpha and the temporal segregation. Our simulations reveal that different features of oscillations, including frequency, phase, and power, can influence temporal perception and provide a theoretically informed starting point for future empirical studies.

Distinct Cortical Networks Subserve Spatio-temporal Sampling in Vision through Different Oscillatory Rhythms

Although visual input arrives continuously, sensory information is segmented into (quasi-)discrete events. Here, we investigated the neural correlates of spatiotemporal binding in humans with magnetoencephalography using two tasks where separate flashes were presented on each trial but were perceived, in a bistable way, as either a single or two separate events. The first task (two-flash fusion) involved judging one versus two flashes, whereas the second task (apparent motion: AM) involved judging coherent motion versus two stationary flashes. Results indicate two different functional networks underlying two unique aspects of temporal binding. In two-flash fusion trials, involving an integration window of ∼50 msec, evoked responses differed as a function of perceptual interpretation by ∼25 msec after stimuli offset. Multivariate decoding of subjective perception based on prestimulus oscillatory phase was significant for alpha-band activity in the right medial temporal (V5/MT) area, with the strength of prestimulus connectivity between early visual areas and V5/MT being predictive of performance. In contrast, the longer integration window (∼130 msec) for AM showed evoked field differences only ∼250 msec after stimuli offset. Phase decoding of the perceptual outcome in AM trials was significant for theta-band activity in the right intraparietal sulcus. Prestimulus theta-band connectivity between V5/MT and intraparietal sulcus best predicted AM perceptual outcome. For both tasks, phase effects found could not be accounted by concomitant variations in power. These results show a strong relationship between specific spatiotemporal binding windows and specific oscillations, linked to the information flow between different areas of the where and when visual pathways.

Coherent categorical information triggers integration-related alpha dynamics

To create coherent visual experiences, the brain spatially integrates the complex and dynamic information it receives from the environment. We previously demonstrated that feedback-related alpha activity carries stimulus-specific information when two spatially and temporally coherent naturalistic inputs can be integrated into a unified percept. In this study, we sought to determine whether such integration-related alpha dynamics are triggered by categorical coherence in visual inputs. In an EEG experiment, we manipulated the degree of coherence by presenting pairs of videos from the same or different categories through two apertures in the left and right visual hemifields. Critically, video pairs could be video-level coherent (i.e., stem from the same video), coherent in their basic-level category, coherent in their superordinate category, or incoherent (i.e., stem from videos from two entirely different categories). We conducted multivariate classification analyses on rhythmic EEG responses to decode between the video stimuli in each condition. As the key result, we significantly decoded the video-level coherent and basic-level coherent stimuli, but not the superordinate coherent and incoherent stimuli, from cortical alpha rhythms. This suggests that alpha dynamics play a critical role in integrating information across space, and that cortical integration processes are flexible enough to accommodate information from different exemplars of the same basic-level category.NEW & NOTEWORTHY Our brain integrates dynamic inputs across the visual field to create coherent visual experiences. Such integration processes have previously been linked to cortical alpha dynamics. In this study, the integration-related alpha activity was observed not only when snippets from the same video were presented, but also when different video snippets from the same basic-level category were presented, highlighting the flexibility of neural integration processes.

The laminar organization of cell types in macaque cortex and its relationship to neuronal oscillations

The canonical microcircuit (CMC) has been hypothesized to be the fundamental unit of information processing in cortex. Each CMC unit is thought to be an interconnected column of neurons with specific connections between excitatory and inhibitory neurons across layers. Recently, we identified a conserved spectrolaminar motif of oscillatory activity across the primate cortex that may be the physiological consequence of the CMC. The spectrolaminar motif consists of local field potential (LFP) gamma-band power (40-150 Hz) peaking in superficial layers 2 and 3 and alpha/beta-band power (8-30 Hz) peaking in deep layers 5 and 6. Here, we investigate whether specific conserved cell types may produce the spectrolaminar motif. We collected laminar histological and electrophysiological data in 11 distinct cortical areas spanning the visual hierarchy: V1, V2, V3, V4, TEO, MT, MST, LIP, 8A/FEF, PMD, and LPFC (area 46), and anatomical data in DP and 7A. We stained representative slices for the three main inhibitory subtypes, Parvalbumin (PV), Calbindin (CB), and Calretinin (CR) positive neurons, as well as pyramidal cells marked with Neurogranin (NRGN). We found a conserved laminar structure of PV, CB, CR, and pyramidal cells. We also found a consistent relationship between the laminar distribution of inhibitory subtypes with power in the local field potential. PV interneuron density positively correlated with gamma (40-150 Hz) power. CR and CB density negatively correlated with alpha (8-12 Hz) and beta (13-30 Hz) oscillations. The conserved, layer-specific pattern of inhibition and excitation across layers is therefore likely the anatomical substrate of the spectrolaminar motif.

Significance Statement

Neuronal oscillations emerge as an interplay between excitatory and inhibitory neurons and underlie cognitive functions and conscious states. These oscillations have distinct expression patterns across cortical layers. Does cellular anatomy enable these oscillations to emerge in specific cortical layers? We present a comprehensive analysis of the laminar distribution of the three main inhibitory cell types in primate cortex (Parvalbumin, Calbindin, and Calretinin positive) and excitatory pyramidal cells. We found a canonical relationship between the laminar anatomy and electrophysiology in 11 distinct primate areas spanning from primary visual to prefrontal cortex. The laminar anatomy explained the expression patterns of neuronal oscillations in different frequencies. Our work provides insight into the cortex-wide cellular mechanisms that generate neuronal oscillations in primates.

Brain-wide human oscillatory local field potential activity during visual working memory

Oscillatory activity in the local field potential (LFP) is thought to be a marker of cognitive processes. To understand how it differentiates tasks and brain areas in humans, we recorded LFPs in 15 adults with intracranial depth electrodes, as they performed visual-spatial and shape working memory tasks. Stimulus appearance produced widespread, broad-band activation, including in occipital, parietal, temporal, insular, and prefrontal cortex, and the amygdala and hippocampus. Occipital cortex was characterized by most elevated power in the high-gamma (100-150 Hz) range during the visual stimulus presentation. The most consistent feature of the delay period was a systematic pattern of modulation in the beta frequency (16-40 Hz), which included a decrease in power of variable timing across areas, and rebound during the delay period. These results reveal the widespread nature of oscillatory activity across a broad brain network and region-specific signatures of oscillatory processes associated with visual working memory.

Target Engagement and Brain State Dependence of Transcranial Magnetic Stimulation: Implications for Clinical Practice

Transcranial magnetic stimulation (TMS) is capable of noninvasively inducing lasting neuroplastic changes when applied repetitively across multiple treatment sessions. In recent years, repetitive TMS has developed into an established evidence-based treatment for various neuropsychiatric disorders such as depression. Despite significant advancements in our understanding of the mechanisms of action of TMS, there is still much to learn about how these mechanisms relate to the clinical effects observed in patients. If there is one thing about TMS that we know for sure, it is that TMS effects are state dependent. In this review, we describe how the effects of TMS on brain networks depend on various factors, including cognitive brain state, oscillatory brain state, and recent brain state history. These states play a crucial role in determining the effects of TMS at the moment of stimulation and are therefore directly linked to what is referred to as target engagement in TMS therapy. There is no control over target engagement without considering the different brain state dependencies of our TMS intervention. Clinical TMS protocols are largely ignoring this fundamental principle, which may explain the large variability and often still limited efficacy of TMS treatments. We propose that after almost 30 years of research on state dependency of TMS, it is time to change standard clinical practice by taking advantage of this fundamental principle. Rather than ignoring TMS state dependency, we can use it to our clinical advantage to improve the effectiveness of TMS treatments.

Local field potentials, spiking activity, and receptive fields in human visual cortex

The concept of receptive field (RF) is central to sensory neuroscience. Neuronal RF properties have been substantially studied in animals, while those in humans remain nearly unexplored. Here, we measured neuronal RFs with intracranial local field potentials (LFPs) and spiking activity in human visual cortex (V1/V2/V3). We recorded LFPs via macro-contacts and discovered that RF sizes estimated from low-frequency activity (LFA, 0.5-30 Hz) were larger than those estimated from low-gamma activity (LGA, 30-60 Hz) and high-gamma activity (HGA, 60-150 Hz). We then took a rare opportunity to record LFPs and spiking activity via microwires in V1 simultaneously. We found that RF sizes and temporal profiles measured from LGA and HGA closely matched those from spiking activity. In sum, this study reveals that spiking activity of neurons in human visual cortex could be well approximated by LGA and HGA in RF estimation and temporal profile measurement, implying the pivotal functions of LGA and HGA in early visual information processing.

Distinct neural signatures underlying information maintenance and manipulation in working memory

Previous working memory research has demonstrated robust stimulus representations during memory maintenance in both voltage and alpha-band activity in electroencephalography. However, the exact functions of these 2 neural signatures have remained controversial. Here we systematically investigated their respective contributions to memory manipulation. Human participants either maintained a previously seen spatial location, or manipulated the location following a mental rotation cue over a delay. Using multivariate decoding, we observed robust location representations in low-frequency voltage and alpha-band oscillatory activity with distinct spatiotemporal dynamics: location representations were most evident in posterior channels in alpha-band activity, but were most prominent in the more anterior, central channels in voltage signals. Moreover, the temporal emergence of manipulated representation in central voltage preceded that in posterior alpha-band activity, suggesting that voltage might carry stimulus-specific source signals originated internally from anterior cortex, whereas alpha-band activity might reflect feedback signals in posterior cortex received from higher-order cortex. Lastly, while location representations in both signals were coded in a low-dimensional neural subspace, location representation in central voltage was higher-dimensional and underwent a representational transformation that exclusively predicted memory behavior. Together, these results highlight the crucial role of central voltage in working memory, and support functional distinctions between voltage and alpha-band activity.

A ubiquitous spectrolaminar motif of local field potential power across the primate cortex

The mammalian cerebral cortex is anatomically organized into a six-layer motif. It is currently unknown whether a corresponding laminar motif of neuronal activity patterns exists across the cortex. Here we report such a motif in the power of local field potentials (LFPs). Using laminar probes, we recorded LFPs from 14 cortical areas across the cortical hierarchy in five macaque monkeys. The laminar locations of recordings were histologically identified by electrolytic lesions. Across all areas, we found a ubiquitous spectrolaminar pattern characterized by an increasing deep-to-superficial layer gradient of high-frequency power peaking in layers 2/3 and an increasing superficial-to-deep gradient of alpha-beta power peaking in layers 5/6. Laminar recordings from additional species showed that the spectrolaminar pattern is highly preserved among primates-macaque, marmoset and human-but more dissimilar in mouse. Our results suggest the existence of a canonical layer-based and frequency-based mechanism for cortical computation.

Metabolic state modulates neural processing of odors in the human olfactory bulb

The olfactory and endocrine systems have recently been shown to reciprocally shape the homeostatic processes of energy intake. As demonstrated in animal models, the individual's metabolic state dynamically modulates how the olfactory bulb process odor stimuli using a range of endocrine signals. Here we aimed to determine whether the neural processing of odors in human olfactory bulb is modulated by metabolic state. Participants were exposed to food-associated odors, in separate sessions being hungry and sated, while neural responses from the olfactory bulb was obtained using electrobulbogram. We found significantly higher gamma power activity (51-100 Hz) in the OB's response to odors during the Hunger compared to Sated condition. Specifically, EBG gamma power were elevated while hungry already at 100 ms after odor onset, thereby suggesting intra-bulbar modulation according to metabolic state. These results demonstrate that, akin to other animal models, hunger state affects OB activity in humans. Moreover, we show that the EBG method has the potential to measure internal metabolic states and, as such, could be used to study specificities in olfactory processing of individuals suffering from pathologies such as obesity or anorexia.

The pulvinar as a hub of visual processing and cortical integration

The pulvinar nucleus of the thalamus is a crucial component of the visual system and plays significant roles in sensory processing and cognitive integration. The pulvinar's extensive connectivity with cortical regions allows for bidirectional communication, contributing to the integration of sensory information across the visual hierarchy. Recent findings underscore the pulvinar's involvement in attentional modulation, feature binding, and predictive coding. In this review, we highlight recent advances in clarifying the pulvinar's circuitry and function. We discuss the contributions of the pulvinar to signal modulation across the global cortical network and place these findings within theoretical frameworks of cortical processing, particularly the global neuronal workspace (GNW) theory and predictive coding.

Propofol-mediated Unconsciousness Disrupts Progression of Sensory Signals through the Cortical Hierarchy

A critical component of anesthesia is the loss of sensory perception. Propofol is the most widely used drug for general anesthesia, but the neural mechanisms of how and when it disrupts sensory processing are not fully understood. We analyzed local field potential and spiking recorded from Utah arrays in auditory cortex, associative cortex, and cognitive cortex of nonhuman primates before and during propofol-mediated unconsciousness. Sensory stimuli elicited robust and decodable stimulus responses and triggered periods of stimulus-related synchronization between brain areas in the local field potential of Awake animals. By contrast, propofol-mediated unconsciousness eliminated stimulus-related synchrony and drastically weakened stimulus responses and information in all brain areas except for auditory cortex, where responses and information persisted. However, we found stimuli occurring during spiking Up states triggered weaker spiking responses than in Awake animals in auditory cortex, and little or no spiking responses in higher order areas. These results suggest that propofol's effect on sensory processing is not just because of asynchronous Down states. Rather, both Down states and Up states reflect disrupted dynamics.

Phase of neural oscillations as a reference frame for attention-based routing in visual cortex

Selective attention allows the brain to efficiently process the image projected onto the retina, selectively focusing neural processing resources on behaviorally relevant visual information. While previous studies have documented the crucial role of the action potential rate of single neurons in relaying such information, little is known about how the activity of single neurons relative to their neighboring network contributes to the efficient representation of attended stimuli and transmission of this information to downstream areas. Here, we show in the dorsal visual pathway of monkeys (medial superior temporal area) that neurons fire spikes preferentially at a specific phase of the ongoing population beta (∼20 Hz) oscillations of the surrounding local network. This preferred spiking phase shifts towards a later phase when monkeys selectively attend towards (rather than away from) the receptive field of the neuron. This shift of the locking phase is positively correlated with the speed at which animals report a visual change. Furthermore, our computational modeling suggests that neural networks can manipulate the preferred phase of coupling by imposing differential synaptic delays on postsynaptic potentials. This distinction between the locking phase of neurons activated by the spatially attended stimulus vs. that of neurons activated by the unattended stimulus, may enable the neural system to discriminate relevant from irrelevant sensory inputs and consequently filter out distracting stimuli information by aligning the spikes which convey relevant/irrelevant information to distinct phases linked to periods of better/worse perceptual sensitivity for higher cortices. This strategy may be used to reserve the narrow windows of highest perceptual efficacy to the processing of the most behaviorally relevant information, ensuring highly efficient responses to attended sensory events.

A modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets

Neuroscience is moving toward a more integrative discipline where understanding brain function requires consolidating the accumulated evidence seen across experiments, species, and measurement techniques. A remaining challenge on that path is integrating such heterogeneous data into analysis workflows such that consistent and comparable conclusions can be distilled as an experimental basis for models and theories. Here, we propose a solution in the context of slow-wave activity (<1 Hz), which occurs during unconscious brain states like sleep and general anesthesia and is observed across diverse experimental approaches. We address the issue of integrating and comparing heterogeneous data by conceptualizing a general pipeline design that is adaptable to a variety of inputs and applications. Furthermore, we present the Collaborative Brain Wave Analysis Pipeline (Cobrawap) as a concrete, reusable software implementation to perform broad, detailed, and rigorous comparisons of slow-wave characteristics across multiple, openly available electrocorticography (ECoG) and calcium imaging datasets.

Information representation in an oscillating neural field model modulated by working memory signals

We study how stimulus information can be represented in the dynamical signatures of an oscillatory model of neural activity-a model whose activity can be modulated by input akin to signals involved in working memory (WM). We developed a neural field model, tuned near an oscillatory instability, in which the WM-like input can modulate the frequency and amplitude of the oscillation. Our neural field model has a spatial-like domain in which an input that preferentially targets a point-a stimulus feature-on the domain will induce feature-specific activity changes. These feature-specific activity changes affect both the mean rate of spikes and the relative timing of spiking activity to the global field oscillation-the phase of the spiking activity. From these two dynamical signatures, we define both a spike rate code and an oscillatory phase code. We assess the performance of these two codes to discriminate stimulus features using an information-theoretic analysis. We show that global WM input modulations can enhance phase code discrimination while simultaneously reducing rate code discrimination. Moreover, we find that the phase code performance is roughly two orders of magnitude larger than that of the rate code defined for the same model solutions. The results of our model have applications to sensory areas of the brain, to which prefrontal areas send inputs reflecting the content of WM. These WM inputs to sensory areas have been established to induce oscillatory changes similar to our model. Our model results suggest a mechanism by which WM signals may enhance sensory information represented in oscillatory activity beyond the comparatively weak representations based on the mean rate activity.

Alpha and SSVEP power outperform gamma power in capturing attentional modulation in human EEG

Attention typically reduces power in the alpha (8-12 Hz) band and increases power in gamma (>30 Hz) band in brain signals, as reported in macaque local field potential (LFP) and human electro/magneto-encephalogram (EEG/MEG) studies. In addition, EEG studies often use flickering stimuli that produce a specific measure called steady-state-visually-evoked-potential (SSVEP), whose power also increases with attention. However, effectiveness of these neural measures in capturing attentional modulation is unknown since stimuli and task paradigms vary widely across studies. In a recent macaque study, attentional modulation was more salient in the gamma band of the LFP, compared to alpha or SSVEP. To compare this with human EEG, we designed an orientation change detection task where we presented both static and counterphasing stimuli of matched difficulty levels to 26 subjects and compared attentional modulation of various measures under similar conditions. We report two main results. First, attentional modulation was comparable for SSVEP and alpha. Second, non-foveal stimuli produced weak gamma despite various stimulus optimizations and showed negligible attentional modulation although full-screen gratings showed robust gamma activity. Our results are useful for brain-machine-interfacing studies where suitable features are used for decoding attention, and also provide clues about spatial scales of neural mechanisms underlying attention.

Traveling waves shape neural population dynamics enabling predictions and internal model updating

The brain generates predictions based on statistical regularities in our environment. However, it is unclear how predictions are optimized through iterative interactions with the environment. Because traveling waves (TWs) propagate across the cortex shaping neural excitability, they can carry information to serve predictive processing. Using human intracranial recordings, we show that anterior-to-posterior alpha TWs correlated with prediction strength. Learning about priors altered neural state space trajectories, and how much it altered correlated with trial-by-trial prediction strength. Learning involved mismatches between predictions and sensory evidence triggering alpha-phase resets in lateral temporal cortex, accompanied by stronger alpha phase-high gamma amplitude coupling and high-gamma power. The mismatch initiated posterior-to-anterior alpha TWs and change in the subsequent trial's state space trajectory, facilitating model updating. Our findings suggest a vital role of alpha TWs carrying both predictions to sensory cortex and mismatch signals to frontal cortex for trial-by-trial fine-tuning of predictive models.

Predicting the subjective intensity of imagined experiences from electrophysiological measures of oscillatory brain activity

Most people can conjure images and sounds that they experience in their minds. There are, however, marked individual differences. Some people report that they cannot generate imagined sensory experiences at all (aphantasics) and others report that they have unusually intense imagined experiences (hyper-phantasics). These individual differences have been linked to activity in sensory brain regions, driven by feedback. We would therefore expect imagined experiences to be associated with specific frequencies of oscillatory brain activity, as these can be a hallmark of neural interactions within and across regions of the brain. Replicating a number of other studies, relative to a Resting-State we find that the act of engaging in auditory or in visual imagery is linked to reductions in the power of oscillatory brain activity across a broad range of frequencies, with prominent peaks in the alpha band (8-12 Hz). This oscillatory activity, however, did not predict individual differences in the subjective intensity of imagined experiences. For audio imagery, these were rather predicted by reductions within the theta (6-9 Hz) and gamma (33-38 Hz) bands, and by increases in beta (15-17 Hz) band activity. For visual imagery these were predicted by reductions in lower (14-16 Hz) and upper (29-32 Hz) beta band activity, and by an increase in mid-beta band (24-26 Hz) activity. Our data suggest that there is sufficient ground truth in the subjective reports people use to describe the intensity of their imagined sensory experiences to allow these to be linked to the power of distinct rhythms of brain activity. In future, we hope to combine this approach with better measures of the subjective intensity of imagined sensory experiences to provide a clearer picture of individual differences in the subjective intensity of imagined experiences, and of why these eventuate.

Computational components of visual predictive coding circuitry

If a full visual percept can be said to be a 'hypothesis', so too can a neural 'prediction' - although the latter addresses one particular component of image content (such as 3-dimensional organisation, the interplay between lighting and surface colour, the future trajectory of moving objects, and so on). And, because processing is hierarchical, predictions generated at one level are conveyed in a backward direction to a lower level, seeking to predict, in fact, the neural activity at that prior stage of processing, and learning from errors signalled in the opposite direction. This is the essence of 'predictive coding', at once an algorithm for information processing and a theoretical basis for the nature of operations performed by the cerebral cortex. Neural models for the implementation of predictive coding invoke specific functional classes of neuron for generating, transmitting and receiving predictions, and for producing reciprocal error signals. Also a third general class, 'precision' neurons, tasked with regulating the magnitude of error signals contingent upon the confidence placed upon the prediction, i.e., the reliability and behavioural utility of the sensory data that it predicts. So, what is the ultimate source of a 'prediction'? The answer is multifactorial: knowledge of the current environmental context and the immediate past, allied to memory and lifetime experience of the way of the world, doubtless fine-tuned by evolutionary history too. There are, in consequence, numerous potential avenues for experimenters seeking to manipulate subjects' expectation, and examine the neural signals elicited by surprising, and less surprising visual stimuli. This review focuses upon the predictive physiology of mouse and monkey visual cortex, summarising and commenting on evidence to date, and placing it in the context of the broader field. It is concluded that predictive coding has a firm grounding in basic neuroscience and that, unsurprisingly, there remains much to learn.