A measure centrality index for systematic empirical comparison of consciousness theories

Consciousness science is marred by disparate constructs and methodologies, making it challenging to systematically compare theories. This foundational crisis casts doubts on the scientific character of the field itself. Addressing it, we propose a framework for systematically comparing consciousness theories by introducing a novel inter-theory classification interface, the Measure Centrality Index (MCI). Recognizing its gradient distribution, the MCI assesses the degree of importance a specific empirical measure has for a given consciousness theory. We apply the MCI to probe how the empirical measures of the Global Neuronal Workspace Theory (GNW), Integrated Information Theory (IIT), and Temporospatial Theory of Consciousness (TTC) would fare within the context of the other two. We demonstrate that direct comparison of IIT, GNW, and TTC is meaningful and valid for some measures like Lempel-Ziv Complexity (LZC), Autocorrelation Window (ACW), and possibly Mutual Information (MI). In contrast, it is problematic for others like the anatomical and physiological neural correlates of consciousness (NCC) due to their MCI-based differential weightings within the structure of the theories. In sum, we introduce and provide proof-of-principle of a novel systematic method for direct inter-theory empirical comparisons, thereby addressing isolated evolution of theories and confirmatory bias issues in the state-of-the-art neuroscience of consciousness.

Neuroecological links of the exposome and One Health

This NeuroView assesses the interplay among exposome, One Health, and brain capital in health and disease. Physical and social exposomes affect brain health, and green brain skills are required for environmental health strategies. Ibanez et al. address current gaps and strategies needed in research, policy, and technology, offering a road map for stakeholders.

"What" and "when" predictions modulate auditory processing in a mutually congruent manner

Introduction

Extracting regularities from ongoing stimulus streams to form predictions is crucial for adaptive behavior. Such regularities exist in terms of the content of the stimuli and their timing, both of which are known to interactively modulate sensory processing. In real-world stimulus streams such as music, regularities can occur at multiple levels, both in terms of contents (e.g., predictions relating to individual notes vs. their more complex groups) and timing (e.g., pertaining to timing between intervals vs. the overall beat of a musical phrase). However, it is unknown whether the brain integrates predictions in a manner that is mutually congruent (e.g., if "beat" timing predictions selectively interact with "what" predictions falling on pulses which define the beat), and whether integrating predictions in different timing conditions relies on dissociable neural correlates.

Methods

To address these questions, our study manipulated "what" and "when" predictions at different levels - (local) interval-defining and (global) beat-defining - within the same stimulus stream, while neural activity was recorded using electroencephalogram (EEG) in participants (N = 20) performing a repetition detection task.

Results

Our results reveal that temporal predictions based on beat or interval timing modulated mismatch responses to violations of "what" predictions happening at the predicted time points, and that these modulations were shared between types of temporal predictions in terms of the spatiotemporal distribution of EEG signals. Effective connectivity analysis using dynamic causal modeling showed that the integration of "what" and "when" predictions selectively increased connectivity at relatively late cortical processing stages, between the superior temporal gyrus and the fronto-parietal network.

Discussion

Taken together, these results suggest that the brain integrates different predictions with a high degree of mutual congruence, but in a shared and distributed cortical network. This finding contrasts with recent studies indicating separable mechanisms for beat-based and memory-based predictive processing.

Stable perceptual phenotype of the magnitude of history biases even in the face of global task complexity

According to a Bayesian framework, visual perception requires active interpretation of noisy sensory signals in light of prior information. One such mechanism, serial dependence, is thought to promote perceptual stability by assimilating current percepts with recent stimulus history. Combining a delayed orientation-adjustment paradigm with predictable (study 1) or unpredictable (study 2) task structure, we test two key predictions of this account in a novel context: first, that serial dependence should persist even in variable environments, and, second, that, within a given observer and context, this behavioral bias should be stable from one occasion to the next. Relying on data of 41 human volunteers and two separate experimental sessions, we confirm both hypotheses. Group-level, attractive serial dependence remained strong even in the face of volatile settings with multiple, unpredictable types of tasks, and, despite considerable interindividual variability, within-subject patterns of attractive and repulsive stimulus-history biases were highly stable from one experimental session to the next. In line with the hypothesized functional role of serial dependence, we propose that, together with previous work, our findings suggest the existence of a more general individual-specific fingerprint with which the past shapes current perception. Congruent with the Bayesian account, interindividual differences may then result from differential weighting of sensory evidence and prior information.

A shared linguistic space for transmitting our thoughts from brain to brain in natural conversations

Effective communication hinges on a mutual understanding of word meaning in different contexts. The embedding space learned by large language models can serve as an explicit model of the shared, context-rich meaning space humans use to communicate their thoughts. We recorded brain activity using electrocorticography during spontaneous, face-to-face conversations in five pairs of epilepsy patients. We demonstrate that the linguistic embedding space can capture the linguistic content of word-by-word neural alignment between speaker and listener. Linguistic content emerged in the speaker's brain before word articulation, and the same linguistic content rapidly reemerged in the listener's brain after word articulation. These findings establish a computational framework to study how human brains transmit their thoughts to one another in real-world contexts.

Statistical learning in patients in the minimally conscious state

When listening to speech, cortical activity can track mentally constructed linguistic units such as words, phrases, and sentences. Recent studies have also shown that the neural responses to mentally constructed linguistic units can predict the outcome of patients with disorders of consciousness (DoC). In healthy individuals, cortical tracking of linguistic units can be driven by both long-term linguistic knowledge and online learning of the transitional probability between syllables. Here, we investigated whether statistical learning could occur in patients in the minimally conscious state (MCS) and patients emerged from the MCS (EMCS) using electroencephalography (EEG). In Experiment 1, we presented to participants an isochronous sequence of syllables, which were composed of either 4 real disyllabic words or 4 reversed disyllabic words. An inter-trial phase coherence analysis revealed that the patient groups showed similar word tracking responses to real and reversed words. In Experiment 2, we presented trisyllabic artificial words that were defined by the transitional probability between words, and a significant word-rate EEG response was observed for MCS patients. These results suggested that statistical learning can occur with a minimal conscious level. The residual statistical learning ability in MCS patients could potentially be harnessed to induce neural plasticity.

Tracking transitional probabilities and segmenting auditory sequences are dissociable processes in adults and neonates

Since speech is a continuous stream with no systematic boundaries between words, how do pre-verbal infants manage to discover words? A proposed solution is that they might use the transitional probability between adjacent syllables, which drops at word boundaries. Here, we tested the limits of this mechanism by increasing the size of the word-unit to four syllables, and its automaticity by testing asleep neonates. Using markers of statistical learning in neonates' EEG, compared to adult behavioral performances in the same task, we confirmed that statistical learning is automatic enough to be efficient even in sleeping neonates. We also revealed that: (1) Successfully tracking transition probabilities (TP) in a sequence is not sufficient to segment it. (2) Prosodic cues, as subtle as subliminal pauses, enable to recover words segmenting capacities. (3) Adults' and neonates' capacities to segment streams seem remarkably similar despite the difference of maturation and expertise. Finally, we observed that learning increased the overall similarity of neural responses across infants during exposure to the stream, providing a novel neural marker to monitor learning. Thus, from birth, infants are equipped with adult-like tools, allowing them to extract small coherent word-like units from auditory streams, based on the combination of statistical analyses and auditory parsing cues. RESEARCH HIGHLIGHTS: Successfully tracking transitional probabilities in a sequence is not always sufficient to segment it. Word segmentation solely based on transitional probability is limited to bi- or tri-syllabic elements. Prosodic cues, as subtle as subliminal pauses, enable to recover chunking capacities in sleeping neonates and awake adults for quadriplets.

An adversarial collaboration protocol for testing contrasting predictions of global neuronal workspace and integrated information theory

The relationship between conscious experience and brain activity has intrigued scientists and philosophers for centuries. In the last decades, several theories have suggested different accounts for these relationships. These theories have developed in parallel, with little to no cross-talk among them. To advance research on consciousness, we established an adversarial collaboration between proponents of two of the major theories in the field, Global Neuronal Workspace and Integrated Information Theory. Together, we devised and preregistered two experiments that test contrasting predictions of these theories concerning the location and timing of correlates of visual consciousness, which have been endorsed by the theories' proponents. Predicted outcomes should either support, refute, or challenge these theories. Six theory-impartial laboratories will follow the study protocol specified here, using three complementary methods: Functional Magnetic Resonance Imaging (fMRI), Magneto-Electroencephalography (M-EEG), and intracranial electroencephalography (iEEG). The study protocol will include built-in replications, both between labs and within datasets. Through this ambitious undertaking, we hope to provide decisive evidence in favor or against the two theories and clarify the footprints of conscious visual perception in the human brain, while also providing an innovative model of large-scale, collaborative, and open science practice.

Spatiotemporal dynamics of human high gamma discriminate naturalistic behavioral states

In analyzing the neural correlates of naturalistic and unstructured behaviors, features of neural activity that are ignored in a trial-based experimental paradigm can be more fully studied and investigated. Here, we analyze neural activity from two patients using electrocorticography (ECoG) and stereo-electroencephalography (sEEG) recordings, and reveal that multiple neural signal characteristics exist that discriminate between unstructured and naturalistic behavioral states such as “engaging in dialogue” and “using electronics”. Using the high gamma amplitude as an estimate of neuronal firing rate, we demonstrate that behavioral states in a naturalistic setting are discriminable based on long-term mean shifts, variance shifts, and differences in the specific neural activity’s covariance structure. Both the rapid and slow changes in high gamma band activity separate unstructured behavioral states. We also use Gaussian process factor analysis (GPFA) to show the existence of salient spatiotemporal features with variable smoothness in time. Further, we demonstrate that both temporally smooth and stochastic spatiotemporal activity can be used to differentiate unstructured behavioral states. This is the first attempt to elucidate how different neural signal features contain information about behavioral states collected outside the conventional experimental paradigm.

Advances in human intracranial electroencephalography research, guidelines and good practices

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

Simultaneous mnemonic and predictive representations in the auditory cortex

Recent studies have shown that stimulus history can be decoded via the use of broadband sensory impulses to reactivate mnemonic representations.^1-4. However, memories of previous stimuli can also be used to form sensory predictions about upcoming stimuli.^5,6 Predictive mechanisms allow the brain to create a probable model of the outside world, which can be updated when errors are detected between the model predictions and external inputs. ^7-10 Direct recordings in the auditory cortex of awake mice established neural mechanisms for how encoding mechanisms might handle working memory and predictive processes without "overwriting" recent sensory events in instances where predictive mechanisms are triggered by oddballs within a sequence.¹¹ However, it remains unclear whether mnemonic and predictive information can be decoded from cortical activity simultaneously during passive, implicit sequence processing, even in anesthetized models. Here, we recorded neural activity elicited by repeated stimulus sequences using electrocorticography (ECoG) in the auditory cortex of anesthetized rats, where events within the sequence (referred to henceforth as "vowels," for simplicity) were occasionally replaced with a broadband noise burst or omitted entirely. We show that both stimulus history and predicted stimuli can be decoded from neural responses to broadband impulses, at overlapping latencies but based on independent and uncorrelated data features. We also demonstrate that predictive representations are dynamically updated over the course of stimulation.

Shared computational principles for language processing in humans and deep language models

Departing from traditional linguistic models, advances in deep learning have resulted in a new type of predictive (autoregressive) deep language models (DLMs). Using a self-supervised next-word prediction task, these models generate appropriate linguistic responses in a given context. In the current study, nine participants listened to a 30-min podcast while their brain responses were recorded using electrocorticography (ECoG). We provide empirical evidence that the human brain and autoregressive DLMs share three fundamental computational principles as they process the same natural narrative: (1) both are engaged in continuous next-word prediction before word onset; (2) both match their pre-onset predictions to the incoming word to calculate post-onset surprise; (3) both rely on contextual embeddings to represent words in natural contexts. Together, our findings suggest that autoregressive DLMs provide a new and biologically feasible computational framework for studying the neural basis of language.

Deep language models have revolutionized natural language processing. The paper discovers three computational principles shared between deep language models and the human brain, which can transform our understanding of the neural basis of language.

Event boundaries shape temporal organization of memory by resetting temporal context

In memory, our continuous experiences are broken up into discrete events. Boundaries between events are known to influence the temporal organization of memory. However, how and through which mechanism event boundaries shape temporal order memory (TOM) remains unknown. Across four experiments, we show that event boundaries exert a dual role: improving TOM for items within an event and impairing TOM for items across events. Decreasing event length in a list enhances TOM, but only for items at earlier local event positions, an effect we term the local primacy effect. A computational model, in which items are associated to a temporal context signal that drifts over time but resets at boundaries captures all behavioural results. Our findings provide a unified algorithmic mechanism for understanding how and why event boundaries affect TOM, reconciling a long-standing paradox of why both contextual similarity and dissimilarity promote TOM.

Our memory is temporally organized, but our internal clock can be distorted. The authors demonstrate how environmental changes (termed event boundaries) affect memory for event order, and provide a computational model to explain these effects.

Effects of hippocampal interictal discharge timing, duration, and spatial extent on list learning

Interictal epileptiform discharges (IEDs) can impair memory. The properties of IEDs most detrimental to memory, however, are undefined. We studied the impact of temporal and spatial characteristics of IEDs on list learning. Subjects completed a memory task during intracranial EEG recordings including hippocampal depth and temporal neocortical subdural electrodes. Subjects viewed a series of objects, and after a distracting task, recalled the objects from the list. The impacts of IED presence, duration, and propagation to neocortex during encoding of individual stimuli were assessed. The effects of IED total number and duration during maintenance and recall periods on delayed recall performance were also determined. The influence of IEDs during recall was further investigated by comparing the likelihood of IEDs preceding correctly recalled items vs. periods of no verbal response. Across 6 subjects, we analyzed 28 hippocampal and 139 lateral temporal contacts. Recall performance was poor, with a median of 17.2% correct responses (range 10.4-21.9%). Interictal epileptiform discharges during encoding, maintenance, and recall did not significantly impact task performance, and there was no significant difference between the likelihood of IEDs during correct recall vs. periods of no response. No significant effects of discharge duration during encoding, maintenance, or recall were observed. Interictal epileptiform discharges with spread to lateral temporal cortex during encoding did not adversely impact recall. A post hoc analysis refining model assumptions indicated a negative impact of IED count during the maintenance period, but otherwise confirmed the above results. Our findings suggest no major effect of hippocampal IEDs on list learning, but study limitations, such as baseline hippocampal dysfunction, should be considered. The impact of IEDs during the maintenance period may be a focus of future research.

The Influence of Auditory Attention on Rhythmic Speech Tracking: Implications for Studies of Unresponsive Patients

Language comprehension relies on integrating words into progressively more complex structures, like phrases and sentences. This hierarchical structure-building is reflected in rhythmic neural activity across multiple timescales in E/MEG in healthy, awake participants. However, recent studies have shown evidence for this “cortical tracking” of higher-level linguistic structures also in a proportion of unresponsive patients. What does this tell us about these patients’ residual levels of cognition and consciousness? Must the listener direct their attention toward higher level speech structures to exhibit cortical tracking, and would selective attention across levels of the hierarchy influence the expression of these rhythms? We investigated these questions in an EEG study of 72 healthy human volunteers listening to streams of monosyllabic isochronous English words that were either unrelated (scrambled condition) or composed of four-word-sequences building meaningful sentences (sentential condition). Importantly, there were no physical cues between four-word-sentences. Rather, boundaries were marked by syntactic structure and thematic role assignment. Participants were divided into three attention groups: from passive listening (passive group) to attending to individual words (word group) or sentences (sentence group). The passive and word groups were initially naïve to the sentential stimulus structure, while the sentence group was not. We found significant tracking at word- and sentence rate across all three groups, with sentence tracking linked to left middle temporal gyrus and right superior temporal gyrus. Goal-directed attention to words did not enhance word-rate-tracking, suggesting that word tracking here reflects largely automatic mechanisms, as was shown for tracking at the syllable-rate before. Importantly, goal-directed attention to sentences relative to words significantly increased sentence-rate-tracking over left inferior frontal gyrus. This attentional modulation of rhythmic EEG activity at the sentential rate highlights the role of attention in integrating individual words into complex linguistic structures. Nevertheless, given the presence of high-level cortical tracking under conditions of lower attentional effort, our findings underline the suitability of the paradigm in its clinical application in patients after brain injury. The neural dissociation between passive tracking of sentences and directed attention to sentences provides a potential means to further characterise the cognitive state of each unresponsive patient.

Decoding the Content of Auditory Sensory Memory Across Species

In contrast to classical views of working memory (WM) maintenance, recent research investigating activity-silent neural states has demonstrated that persistent neural activity in sensory cortices is not necessary for active maintenance of information in WM. Previous studies in humans have measured putative memory representations indirectly, by decoding memory contents from neural activity evoked by a neutral impulse stimulus. However, it is unclear whether memory contents can also be decoded in different species and attentional conditions. Here, we employ a cross-species approach to test whether auditory memory contents can be decoded from electrophysiological signals recorded in different species. Awake human volunteers (N = 21) were exposed to auditory pure tone and noise burst stimuli during an auditory sensory memory task using electroencephalography. In a closely matching paradigm, anesthetized female rats (N = 5) were exposed to comparable stimuli while neural activity was recorded using electrocorticography from the auditory cortex. In both species, the acoustic frequency could be decoded from neural activity evoked by pure tones as well as neutral frozen noise burst stimuli. This finding demonstrates that memory contents can be decoded in different species and different states using homologous methods, suggesting that the mechanisms of sensory memory encoding are evolutionarily conserved across species.

Learning hierarchical sequence representations across human cortex and hippocampus

Sensory input arrives in continuous sequences that humans experience as segmented units, e.g., words and events. The brain's ability to discover regularities is called statistical learning. Structure can be represented at multiple levels, including transitional probabilities, ordinal position, and identity of units. To investigate sequence encoding in cortex and hippocampus, we recorded from intracranial electrodes in human subjects as they were exposed to auditory and visual sequences containing temporal regularities. We find neural tracking of regularities within minutes, with characteristic profiles across brain areas. Early processing tracked lower-level features (e.g., syllables) and learned units (e.g., words), while later processing tracked only learned units. Learning rapidly shaped neural representations, with a gradient of complexity from early brain areas encoding transitional probability, to associative regions and hippocampus encoding ordinal position and identity of units. These findings indicate the existence of multiple, parallel computational systems for sequence learning across hierarchically organized cortico-hippocampal circuits.

Covert Speech Comprehension Predicts Recovery From Acute Unresponsive States

OBJECTIVE

Patients with traumatic brain injury who fail to obey commands after sedation-washout pose one of the most significant challenges for neurological prognostication. Reducing prognostic uncertainty will lead to more appropriate care decisions and ensure provision of limited rehabilitation resources to those most likely to benefit. Bedside markers of covert residual cognition, including speech comprehension, may reduce this uncertainty.

METHODS

We recruited 28 patients with acute traumatic brain injury who were 2 to 7 days sedation-free and failed to obey commands. Patients heard streams of isochronous monosyllabic words that built meaningful phrases and sentences while their brain activity via electroencephalography (EEG) was recorded. In healthy individuals, EEG activity only synchronizes with the rhythm of phrases and sentences when listeners consciously comprehend the speech. This approach therefore provides a measure of residual speech comprehension in unresponsive patients.

RESULTS

Seventeen and 16 patients were available for assessment with the Glasgow Outcome Scale Extended (GOSE) at 3 months and 6 months, respectively. Outcome significantly correlated with the strength of patients' acute cortical tracking of phrases and sentences (r > 0.6, p < 0.007), quantified by inter-trial phase coherence. Linear regressions revealed that the strength of this comprehension response (beta = 0.603, p = 0.006) significantly improved the accuracy of prognoses relative to clinical characteristics alone (eg, Glasgow Coma Scale [GCS], computed tomography [CT] grade).

INTERPRETATION

A simple, passive, auditory EEG protocol improves prognostic accuracy in a critical period of clinical decision making. Unlike other approaches to probing covert cognition for prognostication, this approach is entirely passive and therefore less susceptible to cognitive deficits, increasing the number of patients who may benefit. ANN NEUROL 2021;89:646-656.

Dissociation and Brain Rhythms: Pitfalls and Promises

Recently, Vesuna et al. proposed a novel circuit mechanism underlying dissociative states using optogenetics and pharmacology in mice in combination with intracranial recordings and electrical stimulation in an epilepsy patient. Specifically, the authors identified a posteromedial cortical delta-rhythm that underlies states of dissociation. In the following, we would like to critically review these findings in the context of the human literature on dissociation as well as highlight the challenges in translational neuroscience to link complex behavioral phenotypes in psychiatric syndromes to circumscribed circuit mechanisms.

Memory guidance of value-based decision making at an abstract level of representation

Value-based decisions about alternatives we have never experienced can be guided by associations between current choice options and memories of prior reward. A critical question is how similar memories need to be to the current situation to effectively guide decisions. We address this question in the context of associative learning of faces using a sensory preconditioning paradigm. We find that memories of reward spread along established associations between faces to guide decision making. While memory guidance is specific for associated facial identities, it does not only occur for the specific images that were originally encountered. Instead, memory guidance generalizes across different images of the associated identities. This suggests that memory guidance does not rely on a pictorial format of representation but on a higher, view-invariant level of abstraction. Thus, memory guidance operates on a level of representation that neither over- nor underspecifies associative relationships in the context of obtaining reward.

Perceptual Gains and Losses in Synesthesia and Schizophrenia

Individual differences in perception are widespread. Considering inter-individual variability, synesthetes experience stable additional sensations; schizophrenia patients suffer perceptual deficits in, eg, perceptual organization (alongside hallucinations and delusions). Is there a unifying principle explaining inter-individual variability in perception? There is good reason to believe perceptual experience results from inferential processes whereby sensory evidence is weighted by prior knowledge about the world. Perceptual variability may result from different precision weighting of sensory evidence and prior knowledge. We tested this hypothesis by comparing visibility thresholds in a perceptual hysteresis task across medicated schizophrenia patients (N = 20), synesthetes (N = 20), and controls (N = 26). Participants rated the subjective visibility of stimuli embedded in noise while we parametrically manipulated the availability of sensory evidence. Additionally, precise long-term priors in synesthetes were leveraged by presenting either synesthesia-inducing or neutral stimuli. Schizophrenia patients showed increased visibility thresholds, consistent with overreliance on sensory evidence. In contrast, synesthetes exhibited lowered thresholds exclusively for synesthesia-inducing stimuli suggesting high-precision long-term priors. Additionally, in both synesthetes and schizophrenia patients explicit, short-term priors-introduced during the hysteresis experiment-lowered thresholds but did not normalize perception. Our results imply that perceptual variability might result from differences in the precision afforded to prior beliefs and sensory evidence, respectively.

Dual mechanisms of ictal high frequency oscillations in human rhythmic onset seizures

High frequency oscillations (HFOs) are bursts of neural activity in the range of 80 Hz or higher, recorded from intracranial electrodes during epileptiform discharges. HFOs are a proposed biomarker of epileptic brain tissue and may also be useful for seizure forecasting. Despite such clinical utility of HFOs, the spatial context and neuronal activity underlying these local field potential (LFP) events remains unclear. We sought to further understand the neuronal correlates of ictal high frequency LFPs using multielectrode array recordings in the human neocortex and mesial temporal lobe during rhythmic onset seizures. These multiscale recordings capture single cell, multiunit, and LFP activity from the human brain. We compare features of multiunit firing and high frequency LFP from microelectrodes and macroelectrodes during ictal discharges in both the seizure core and penumbra (spatial seizure domains defined by multiunit activity patterns). We report differences in spectral features, unit-local field potential coupling, and information theoretic characteristics of high frequency LFP before and after local seizure invasion. Furthermore, we tie these time-domain differences to spatial domains of seizures, showing that penumbral discharges are more broadly distributed and less useful for seizure localization. These results describe the neuronal and synaptic correlates of two types of pathological HFOs in humans and have important implications for clinical interpretation of rhythmic onset seizures.

Dissociation of broadband high-frequency activity and neuronal firing in the neocortex

Broadband high-frequency activity (BHA; 70 to 150 Hz), also known as "high gamma," a key analytic signal in human intracranial (electrocorticographic) recordings, is often assumed to reflect local neural firing [multiunit activity (MUA)]. As the precise physiological substrates of BHA are unknown, this assumption remains controversial. Our analysis of laminar multielectrode data from V1 and A1 in monkeys outlines two components of stimulus-evoked BHA distributed across the cortical layers: an "early-deep" and "late-superficial" response. Early-deep BHA has a clear spatial and temporal overlap with MUA. Late-superficial BHA was more prominent and accounted for more of the BHA signal measured near the cortical pial surface. However, its association with local MUA is weak and often undetectable, consistent with the view that it reflects dendritic processes separable from local neuronal firing.

Visual and somatosensory information contribute to distortions of the body model

Distorted representations of the body are observed in healthy individuals as well as in neurological and psychiatric disorders. Distortions of the body model have been attributed to the somatotopic cerebral representation. Recently, it has been demonstrated that visual biases also contribute to those distortions. To better understand the sources of such distortions, we compared the metric representations across five body parts affording different degrees of tactile sensitivity and visual accessibility. We evaluated their perceived dimensions using a Line Length Judgment task. We found that most body parts were underestimated in their dimensions. The estimation error relative to their length was predicted by their tactile acuity, supporting the influence of the cortical somatotopy on the body model. However, tactile acuity did not explain the distortions observed for the width. Visual accessibility in turn does appear to mediate body distortions, as we observed that the dimensions of the dorsal portion of the neck were the only ones accurately perceived. Coherent with the multisensory nature of body representations, we argue that the perceived dimensions of body parts are estimated by integrating visual and somatosensory information, each weighted differently, based on their availability for a given body part and a given spatial dimension.

Immediate neurophysiological effects of transcranial electrical stimulation

Noninvasive brain stimulation techniques are used in experimental and clinical fields for their potential effects on brain network dynamics and behavior. Transcranial electrical stimulation (TES), including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), has gained popularity because of its convenience and potential as a chronic therapy. However, a mechanistic understanding of TES has lagged behind its widespread adoption. Here, we review data and modelling on the immediate neurophysiological effects of TES in vitro as well as in vivo in both humans and other animals. While it remains unclear how typical TES protocols affect neural activity, we propose that validated models of current flow should inform study design and artifacts should be carefully excluded during signal recording and analysis. Potential indirect effects of TES (e.g., peripheral stimulation) should be investigated in more detail and further explored in experimental designs. We also consider how novel technologies may stimulate the next generation of TES experiments and devices, thus enhancing validity, specificity, and reproducibility.

Patient-Specific Pose Estimation in Clinical Environments

Reliable posture labels in hospital environments can augment research studies on neural correlates to natural behaviors and clinical applications that monitor patient activity. However, many existing pose estimation frameworks are not calibrated for these unpredictable settings. In this paper, we propose a semi-automated approach for improving upper-body pose estimation in noisy clinical environments, whereby we adapt and build around an existing joint tracking framework to improve its robustness to environmental uncertainties. The proposed framework uses subject-specific convolutional neural network models trained on a subset of a patient's RGB video recording chosen to maximize the feature variance of each joint. Furthermore, by compensating for scene lighting changes and by refining the predicted joint trajectories through a Kalman filter with fitted noise parameters, the extended system yields more consistent and accurate posture annotations when compared with the two state-of-the-art generalized pose tracking algorithms for three hospital patients recorded in two research clinics.

Mooney face stimuli for visual perception research

In 1957, Craig Mooney published a set of human face stimuli to study perceptual closure: the formation of a coherent percept on the basis of minimal visual information. Images of this type, now known as "Mooney faces", are widely used in cognitive psychology and neuroscience because they offer a means of inducing variable perception with constant visuo-spatial characteristics (they are often not perceived as faces if viewed upside down). Mooney's original set of 40 stimuli has been employed in several studies. However, it is often necessary to use a much larger stimulus set. We created a new set of over 500 Mooney faces and tested them on a cohort of human observers. We present the results of our tests here, and make the stimuli freely available via the internet. Our test results can be used to select subsets of the stimuli that are most suited for a given experimental purpose.

Low frequency transcranial electrical stimulation does not entrain sleep rhythms measured by human intracranial recordings

Transcranial electrical stimulation has widespread clinical and research applications, yet its effect on ongoing neural activity in humans is not well established. Previous reports argue that transcranial alternating current stimulation (tACS) can entrain and enhance neural rhythms related to memory, but the evidence from non-invasive recordings has remained inconclusive. Here, we measure endogenous spindle and theta activity intracranially in humans during low-frequency tACS and find no stable entrainment of spindle power during non-REM sleep, nor of theta power during resting wakefulness. As positive controls, we find robust entrainment of spindle activity to endogenous slow-wave activity in 66% of electrodes as well as entrainment to rhythmic noise-burst acoustic stimulation in 14% of electrodes. We conclude that low-frequency tACS at common stimulation intensities neither acutely modulates spindle activity during sleep nor theta activity during waking rest, likely because of the attenuated electrical fields reaching the cortical surface.

Characterizing Neural Entrainment to Hierarchical Linguistic Units using Electroencephalography (EEG)

To understand speech, listeners have to combine the words they hear into phrases and sentences. Recent magnetoencephalography (MEG) and electrocorticography (ECoG) studies show that cortical activity is concurrently entrained/synchronized to the rhythms of multiple levels of linguistic units including words, phrases, and sentences. Here we investigate whether this phenomenon can be observed using electroencephalography (EEG), a technique that is more widely available than MEG and ECoG. We show that the EEG responses concurrently track the rhythms of hierarchical linguistic units such as syllables/words, phrases, and sentences. The strength of the sentential-rate response correlates with how well each subject can detect random words embedded in a sequence of sentences. In contrast, only a syllabic-rate response is observed for an unintelligible control stimulus. In sum, EEG provides a useful tool to characterize neural encoding of hierarchical linguistic units, potentially even in individual participants.

Increasing suppression of saccade-related transients along the human visual hierarchy

A key hallmark of visual perceptual awareness is robustness to instabilities arising from unnoticeable eye and eyelid movements. In previous human intracranial (iEEG) work (Golan et al., 2016) we found that excitatory broadband high-frequency activity transients, driven by eye blinks, are suppressed in higher-level but not early visual cortex. Here, we utilized the broad anatomical coverage of iEEG recordings in 12 eye-tracked neurosurgical patients to test whether a similar stabilizing mechanism operates following small saccades. We compared saccades (1.3°−3.7°) initiated during inspection of large individual visual objects with similarly-sized external stimulus displacements. Early visual cortex sites responded with positive transients to both conditions. In contrast, in both dorsal and ventral higher-level sites the response to saccades (but not to external displacements) was suppressed. These findings indicate that early visual cortex is highly unstable compared to higher-level visual regions which apparently constitute the main target of stabilizing extra-retinal oculomotor influences.

Manipulating stored phonological input during verbal working memory

Verbal working memory (vWM) involves storing and manipulating information in phonological sensory input. An influential theory of vWM proposes that manipulation is carried out by a central executive while storage is performed by two interacting systems: a phonological input buffer that captures sound-based information and an articulatory rehearsal system that controls speech motor output. Whether, when and how neural activity in the brain encodes these components remains unknown. Here we read out the contents of vWM from neural activity in human subjects as they manipulated stored speech sounds. As predicted, we identified storage systems that contained both phonological sensory and articulatory motor representations. Unexpectedly, however, we found that manipulation did not involve a single central executive but rather involved two systems with distinct contributions to successful manipulation. We propose, therefore, that multiple subsystems comprise the central executive needed to manipulate stored phonological input for articulatory motor output in vWM.

Human intracranial recordings link suppressed transients rather than 'filling-in' to perceptual continuity across blinks

We hardly notice our eye blinks, yet an externally generated retinal interruption of a similar duration is perceptually salient. We examined the neural correlates of this perceptual distinction using intracranially measured ECoG signals from the human visual cortex in 14 patients. In early visual areas (V1 and V2), the disappearance of the stimulus due to either invisible blinks or salient blank video frames ('gaps') led to a similar drop in activity level, followed by a positive overshoot beyond baseline, triggered by stimulus reappearance. Ascending the visual hierarchy, the reappearance-related overshoot gradually subsided for blinks but not for gaps. By contrast, the disappearance-related drop did not follow the perceptual distinction - it was actually slightly more pronounced for blinks than for gaps. These findings suggest that blinks' limited visibility compared with gaps is correlated with suppression of blink-related visual activity transients, rather than with "filling-in" of the occluded content during blinks.

Rule-based and Word-level Statistics-based Processing of Language: Insights from Neuroscience

To flexibly convey meaning, the human language faculty iteratively combines smaller units such as words into larger structures such as phrases based on grammatical principles. During comprehension, however, it remains unclear how the brain encodes the relationship between words and combines them into phrases. One hypothesis is that internal grammatical principles governing language generation are also used to parse the hierarchical syntactic structure of spoken language during comprehension. An alternative hypothesis suggests, in contrast, that decoding language during comprehension solely relies on statistical relationships between words or strings of words, i.e., the N-gram statistics, while grammatical rules are not used and no hierarchical linguistic structures are constructed. Here, we briefly review distinctions between rule-based hierarchical models and statistics-based linear string models for comprehension, and how the neurolinguistic approach can shed light on this debate. Recent neurolinguistic studies show that tracking of probabilistic relationships between words is not sufficient to explain cortical encoding of linguistic constituent structure and support the involvement of rule-based processing during language comprehension.

Rule-based and Word-level Statistics-based Processing of Language: Insights from Neuroscience

To flexibly convey meaning, the human language faculty iteratively combines smaller units such as words into larger structures such as phrases based on grammatical principles. During comprehension, however, it remains unclear how the brain encodes the relationship between words and combines them into phrases. One hypothesis is that internal grammatical principles governing language generation are also used to parse the hierarchical syntactic structure of spoken language during comprehension. An alternative hypothesis suggests, in contrast, that decoding language during comprehension solely relies on statistical relationships between words or strings of words, i.e., the N-gram statistics, while grammatical rules are not used and no hierarchical linguistic structures are constructed. Here, we briefly review distinctions between rule-based hierarchical models and statistics-based linear string models for comprehension, and how the neurolinguistic approach can shed light on this debate. Recent neurolinguistic studies show that tracking of probabilistic relationships between words is not sufficient to explain cortical encoding of linguistic constituent structure and support the involvement of rule-based processing during language comprehension.

Interpretations of Frequency Domain Analyses of Neural Entrainment: Periodicity, Fundamental Frequency, and Harmonics

Brain activity can follow the rhythms of dynamic sensory stimuli, such as speech and music, a phenomenon called neural entrainment. It has been hypothesized that low-frequency neural entrainment in the neural delta and theta bands provides a potential mechanism to represent and integrate temporal information. Low-frequency neural entrainment is often studied using periodically changing stimuli and is analyzed in the frequency domain using the Fourier analysis. The Fourier analysis decomposes a periodic signal into harmonically related sinusoids. However, it is not intuitive how these harmonically related components are related to the response waveform. Here, we explain the interpretation of response harmonics, with a special focus on very low-frequency neural entrainment near 1 Hz. It is illustrated why neural responses repeating at f Hz do not necessarily generate any neural response at f Hz in the Fourier spectrum. A strong neural response at f Hz indicates that the time scales of the neural response waveform within each cycle match the time scales of the stimulus rhythm. Therefore, neural entrainment at very low frequency implies not only that the neural response repeats at f Hz but also that each period of the neural response is a slow wave matching the time scale of a f Hz sinusoid.

Early effects of previous experience on conscious perception

Constructive theories of brain function such as predictive coding posit that prior knowledge affects our experience of the world quickly and directly. However, it is yet unknown how swiftly prior knowledge impacts the neural processes giving rise to conscious experience. Here we used an experimental paradigm where prior knowledge augmented perception and measured the timing of this effect with magnetoencephalography (MEG). By correlating the perceptual benefits of prior knowledge with the MEG activity, we found that prior knowledge took effect in the time-window 80–95 ms after stimulus onset, thus reflecting an early influence on conscious perception. The sources of this effect were localized to occipital and posterior parietal regions. These results are in line with the predictive coding framework.

Motion along the mental number line reveals shared representations for numerosity and space

Perception of number and space are tightly intertwined. It has been proposed that this is due to ‘cortical recycling’, where numerosity processing takes over circuits originally processing space. Do such ‘recycled’ circuits retain their original functionality? Here, we investigate interactions between numerosity and motion direction, two functions that both localize to parietal cortex. We describe a new phenomenon in which visual motion direction adapts nonsymbolic numerosity perception, giving rise to a repulsive aftereffect: motion to the left adapts small numbers, leading to overestimation of numerosity, while motion to the right adapts large numbers, resulting in underestimation. The reference frame of this effect is spatiotopic. Together with the tuning properties of the effect this suggests that motion direction-numerosity cross-adaptation may occur in a homolog of area LIP. ‘Cortical recycling’ thus expands but does not obliterate the functions originally performed by the recycled circuit, allowing for shared computations across domains.

DOI:http://dx.doi.org/10.7554/eLife.10806.001

Our sense of number is thought to have emerged from the circuits of cortical neurons in the brain that originally represent space, a process known as 'cortical recycling'. Accordingly, our perception of space and number are tightly intertwined: for example, people think about numbers on a mental number line, where smaller numbers are mapped to the left, and larger numbers are mapped to the right. Also, damage to a brain region called the parietal cortex disrupts both space and number processing.

If number processing recycles the neurons that encode space, which form does this appropriation take? Recycling could preserve the original behavior of the neurons (processing space), thus enriching the neurons’ functional repertoire with a new capacity (processing number). Alternatively, the newly developed role could replace the original one, such that space and number cohabitate the same brain area but use separate neurons.

To disentangle these hypotheses, Schwiedrzik et al. used a technique called 'perceptual adaptation'. Here, continuously showing someone a particular feature eventually exhausts the neurons that respond to that feature. Neurons that respond to the opposite feature are however less exhausted and dominate perception. Consequently, people perceive the opposite of what they are adapted to. For example, after continuously seeing dots moving to the right, people perceive stationary dots as moving to the left. Similarly, after being exposed to large numbers of dots they underestimate how many dots they see.

If the same neurons process numbers and space, then adapting to movement in a particular direction should influence number perception. During Schwiedrzik et al.’s experiments, volunteers watched moving dots on a computer screen. After seeing dots move to the right, they underestimated the number of dots that then appeared on the screen. This is likely to be because larger numbers are mentally mapped to the right, and seeing rightward motion for a long time exhausted these neurons. This means that neurons that respond to smaller numbers (mentally mapped to the left) were more active when the new dots were presented, leading the volunteers to underestimate how many dots they saw. Adapting to leftward motion led to the opposite effect, with volunteers overestimating the number of dots. Thus, motion can literally move us up and down the number line.

These results indicate that the same neurons encode both space and numbers. Cortical recycling does not erase the neurons’ original behavior: instead, neurons may carry out the same computations when processing numbers or space. This would allow the brain to add new functionality without sacrificing any of the computational resources for processing space.

DOI:http://dx.doi.org/10.7554/eLife.10806.002

Cortical tracking of hierarchical linguistic structures in connected speech

The most critical attribute of human language is its unbounded combinatorial nature: smaller elements can be combined into larger structures on the basis of a grammatical system, resulting in a hierarchy of linguistic units, such as words, phrases and sentences. Mentally parsing and representing such structures, however, poses challenges for speech comprehension. In speech, hierarchical linguistic structures do not have boundaries that are clearly defined by acoustic cues and must therefore be internally and incrementally constructed during comprehension. We found that, during listening to connected speech, cortical activity of different timescales concurrently tracked the time course of abstract linguistic structures at different hierarchical levels, such as words, phrases and sentences. Notably, the neural tracking of hierarchical linguistic structures was dissociated from the encoding of acoustic cues and from the predictability of incoming words. Our results indicate that a hierarchy of neural processing timescales underlies grammar-based internal construction of hierarchical linguistic structure.

How previous experience shapes perception in different sensory modalities

What has transpired immediately before has a strong influence on how sensory stimuli are processed and perceived. In particular, temporal context can have contrastive effects, repelling perception away from the interpretation of the context stimulus, and attractive effects (TCEs), whereby perception repeats upon successive presentations of the same stimulus. For decades, scientists have documented contrastive and attractive temporal context effects mostly with simple visual stimuli. But both types of effects also occur in other modalities, e.g., audition and touch, and for stimuli of varying complexity, raising the possibility that context effects reflect general computational principles of sensory systems. Neuroimaging shows that contrastive and attractive context effects arise from neural processes in different areas of the cerebral cortex, suggesting two separate operations with distinct functional roles. Bayesian models can provide a functional account of both context effects, whereby prior experience adjusts sensory systems to optimize perception of future stimuli.

Expecting to See a Letter: Alpha Oscillations as Carriers of Top-Down Sensory Predictions

Predictions strongly influence perception. However, the neurophysiological processes that implement predictions remain underexplored. It has been proposed that high- and low-frequency neuronal oscillations act as carriers of sensory evidence and top-down predictions, respectively (von Stein and Sarnthein 2000; Bastos et al. 2012). However, evidence for the latter hypothesis remains scarce. In particular, it remains to be shown whether slow prestimulus alpha oscillations in task-relevant brain regions are stronger in the presence of predictions, whether they influence early categorization processes, and whether this interplay indeed boosts perception. Here, we directly address these questions by manipulating subjects' prior expectations about the identity of visually presented letters while collecting magnetoencephalographic recordings. We find that predictions lead to increased prestimulus alpha oscillations in a multisensory network representing grapheme/phoneme associations. Furthermore, alpha power interacts with stimulus degradation and top-down expectations to predict visibility ratings, and correlates with the amplitude of early sensory components (P1/N1m complex), suggesting a role in the selective amplification of predicted information. Our results thus indicate that low-frequency alpha oscillations can serve as a mechanism to carry and test sensory predictions about letters.

Untangling perceptual memory: hysteresis and adaptation map into separate cortical networks

Perception is an active inferential process in which prior knowledge is combined with sensory input, the result of which determines the contents of awareness. Accordingly, previous experience is known to help the brain "decide" what to perceive. However, a critical aspect that has not been addressed is that previous experience can exert 2 opposing effects on perception: An attractive effect, sensitizing the brain to perceive the same again (hysteresis), or a repulsive effect, making it more likely to perceive something else (adaptation). We used functional magnetic resonance imaging and modeling to elucidate how the brain entertains these 2 opposing processes, and what determines the direction of such experience-dependent perceptual effects. We found that although affecting our perception concurrently, hysteresis and adaptation map into distinct cortical networks: a widespread network of higher-order visual and fronto-parietal areas was involved in perceptual stabilization, while adaptation was confined to early visual areas. This areal and hierarchical segregation may explain how the brain maintains the balance between exploiting redundancies and staying sensitive to new information. We provide a Bayesian model that accounts for the coexistence of hysteresis and adaptation by separating their causes into 2 distinct terms: Hysteresis alters the prior, whereas adaptation changes the sensory evidence (the likelihood function).

Meditation increases the depth of information processing and improves the allocation of attention in space

During meditation, practitioners are required to center their attention on a specific object for extended periods of time. When their thoughts get diverted, they learn to quickly disengage from the distracter. We hypothesized that learning to respond to the dual demand of engaging attention on specific objects and disengaging quickly from distracters enhances the efficiency by which meditation practitioners can allocate attention. We tested this hypothesis in a global-to-local task while measuring electroencephalographic activity from a group of eight highly trained Buddhist monks and nuns and a group of eight age and education matched controls with no previous meditation experience. Specifically, we investigated the effect of attentional training on the global precedence effect, i.e., faster detection of targets on a global than on a local level. We expected to find a reduced global precedence effect in meditation practitioners but not in controls, reflecting that meditators can more quickly disengage their attention from the dominant global level. Analysis of reaction times confirmed this prediction. To investigate the underlying changes in brain activity and their time course, we analyzed event-related potentials. Meditators showed an enhanced ability to select the respective target level, as reflected by enhanced processing of target level information. In contrast with control group, which showed a local target selection effect only in the P1 and a global target selection effect in the P3 component, meditators showed effects of local information processing in the P1, N2, and P3 and of global processing for the N1, N2, and P3. Thus, meditators seem to display enhanced depth of processing. In addition, meditation altered the uptake of information such that meditators selected target level information earlier in the processing sequence than controls. In a longitudinal experiment, we could replicate the behavioral effects, suggesting that meditation modulates attention already after a 4-day meditation retreat. Together, these results suggest that practicing meditation enhances the speed with which attention can be allocated and relocated, thus increasing the depth of information processing and reducing response latency.

Brain Oscillations during Spoken Sentence Processing

Spoken sentence comprehension relies on rapid and effortless temporal integration of speech units displayed at different rates. Temporal integration refers to how chunks of information perceived at different time scales are linked together by the listener in mapping speech sounds onto meaning. The neural implementation of this integration remains unclear. This study explores the role of short and long windows of integration in accessing meaning from long samples of speech. In a cross-linguistic study, we explore the time course of oscillatory brain activity between 1 and 100 Hz, recorded using EEG, during the processing of native and foreign languages. We compare oscillatory responses in a group of Italian and Spanish native speakers while they attentively listen to Italian, Japanese, and Spanish utterances, played either forward or backward. The results show that both groups of participants display a significant increase in gamma band power (55–75 Hz) only when they listen to their native language played forward. The increase in gamma power starts around 1000 msec after the onset of the utterance and decreases by its end, resembling the time course of access to meaning during speech perception. In contrast, changes in low-frequency power show similar patterns for both native and foreign languages. We propose that gamma band power reflects a temporal binding phenomenon concerning the coordination of neural assemblies involved in accessing meaning of long samples of speech.