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.

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.

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.

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.

Ongoing neural oscillations influence behavior and sensory representations by suppressing neuronal excitability.

The ability to process and respond to external input is critical for adaptive behavior. Why, then, do neural and behavioral responses vary across repeated presentations of the same sensory input? Ongoing fluctuations of neuronal excitability are currently hypothesized to underlie the trial-by-trial variability in sensory processing. To test this, we capitalized on intracranial electrophysiology in neurosurgical patients performing an auditory discrimination task with visual cues: specifically, we examined the interaction between prestimulus alpha oscillations, excitability, task performance, and decoded neural stimulus representations. We found that strong prestimulus oscillations in the alpha+ band (i.e., alpha and neighboring frequencies), rather than the aperiodic signal, correlated with a low excitability state, indexed by reduced broadband high-frequency activity. This state was related to slower reaction times and reduced neural stimulus encoding strength. We propose that the alpha+ rhythm modulates excitability, thereby resulting in variability in behavior and sensory representations despite identical input.

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.

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.

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.

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.

A challenge for predictive coding: Representational or experiential diversity?

To become a unifying theory of brain function, predictive processing (PP) must accommodate its rich representational diversity. Gilead et al. claim such diversity requires a multi-process theory, and thus is out of reach for PP, which postulates a universal canonical computation. We contend this argument and instead propose that PP fails to account for the experiential level of representations.

Not All Predictions Are Equal: "What" and "When" Predictions Modulate Activity in Auditory Cortex through Different Mechanisms.

Using predictions based on environmental regularities is fundamental for adaptive behavior. While it is widely accepted that predictions across different stimulus attributes (e.g., time and content) facilitate sensory processing, it is unknown whether predictions across these attributes rely on the same neural mechanism. Here, to elucidate the neural mechanisms of predictions, we combine invasive electrophysiological recordings (human electrocorticography in 4 females and 2 males) with computational modeling while manipulating predictions about content ("what") and time ("when"). We found that "when" predictions increased evoked activity over motor and prefrontal regions both at early (∼180 ms) and late (430-450 ms) latencies. "What" predictability, however, increased evoked activity only over prefrontal areas late in time (420-460 ms). Beyond these dissociable influences, we found that "what" and "when" predictability interactively modulated the amplitude of early (165 ms) evoked responses in the superior temporal gyrus. We modeled the observed neural responses using biophysically realistic neural mass models, to better understand whether "what" and "when" predictions tap into similar or different neurophysiological mechanisms. Our modeling results suggest that "what" and "when" predictability rely on complementary neural processes: "what" predictions increased short-term plasticity in auditory areas, whereas "when" predictability increased synaptic gain in motor areas. Thus, content and temporal predictions engage complementary neural mechanisms in different regions, suggesting domain-specific prediction signaling along the cortical hierarchy. Encoding predictions through different mechanisms may endow the brain with the flexibility to efficiently signal different sources of predictions, weight them by their reliability, and allow for their encoding without mutual interference.SIGNIFICANCE STATEMENT Predictions of different stimulus features facilitate sensory processing. However, it is unclear whether predictions of different attributes rely on similar or different neural mechanisms. By combining invasive electrophysiological recordings of cortical activity with experimental manipulations of participants' predictions about content and time of acoustic events, we found that the two types of predictions had dissociable influences on cortical activity, both in terms of the regions involved and the timing of the observed effects. Further, our biophysical modeling analysis suggests that predictability of content and time rely on complementary neural processes: short-term plasticity in auditory areas and synaptic gain in motor areas, respectively. This suggests that predictions of different features are encoded with complementary neural mechanisms in different brain regions.

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).

Subjective and objective learning effects dissociate in space and in time.

Perceptual learning not only improves sensitivity, but it also changes our subjective experience. However, the question of how these two learning effects relate is largely unexplored. Here we investigate how subjects learn to see initially indiscriminable metacontrast-masked shapes. We find that sensitivity and subjective awareness increase with training. However, sensitivity and subjective awareness dissociate in space: Learning effects on performance are lost when the task is performed at an untrained location in another quadrant, whereas learning effects on subjective awareness are maintained. This finding indicates that improvements in shape sensitivity involve visual areas up to V4, whereas changes in subjective awareness involve other brain regions. Furthermore, subjective awareness dissociates from sensitivity in time: In an early phase of perceptual learning, subjects perform above chance on trials that they rate as subjectively invisible. Later, this phenomenon disappears. Subjective awareness is thus neither necessary nor sufficient for achieving above-chance objective performance.

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.

Local category-specific gamma band responses in the visual cortex do not reflect conscious perception.

Which neural processes underlie our conscious experience? One theoretical view argues that the neural correlates of consciousness (NCC) reside in local activity in sensory cortices. Accordingly, local category-specific gamma band responses in visual cortex correlate with conscious perception. However, as most studies manipulated conscious perception by altering the amount of sensory evidence, it is possible that they reflect prerequisites or consequences of consciousness rather than the actual NCC. Here we directly address this issue by developing a new experimental paradigm in which conscious perception is modulated either by sensory evidence or by previous exposure of the images while recording intracranial EEG from the higher-order visual cortex of human epilepsy patients. A clear prediction is that neural processes directly reflecting conscious perception should be present regardless of how it comes about. In contrast, we observed that although subjective reports were modulated both by sensory evidence and by previous exposure, gamma band responses solely reflected sensory evidence. This result contradicts the proposal that local gamma band responses in the higher-order visual cortex reflect conscious perception.

Interaction between bottom-up saliency and top-down control: how saliency maps are created in the human brain.

Whether an object captures our attention depends on its bottom-up salience, that is, how different it is compared with its neighbors, and top-down control, that is, our current inner goals. At which neuronal stage they interact to guide behavior is still unknown. In a functional magnetic resonance imaging study, we found evidence for a hierarchy of saliency maps in human early visual cortex (V1 to hV4) and identified where bottom-up saliency interacts with top-down control: V1 represented pure bottom-up signals, V2 was only responsive to top-down modulations, and in hV4 bottom-up saliency and top-down control converged. Two distinct cerebral networks exerted top-down control: distractor suppression engaged the left intraparietal sulcus, while target enhancement involved the frontal eye field and lateral occipital cortex. Hence, attentional selection is implemented in integrated maps in visual cortex, which provide precise topographic information about target-distractor locations thus allowing for successful visual search.

"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.

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.