Neural Phenomena Research Articles

Theories of consciousness: A concise overview

Consciousness or conscious experience is a mental phenomenon that is familiar to all of us, but the way in which it is produced escapes us to a large extent. Each person has a vague idea of what it means to be conscious, but consciousness is rather hard to define, albeit easy to identify. It is that function of the brain that makes us conscious of external or internal stimuli and of our thoughts regarding these subjective experiences. Conscious experience is a first-person perspective of mental states and events tracking as they unfold. It includes mental phenomena such as a perception, emotion, memory, idea, continuous temporal sequence of events. A mental process and its adjoining neurophysiological phenomena represent two aspects of the same event. We have direct access to the mental aspect, while we can observe the neurophysiological aspect only when we study the event as a biological process. The psychological study of consciousness describes the special properties of this brain function, its origin and utility in the global economy of an animal organism. The neurobiological study aims to find the neural correlates of consciousness, aims to establish causal relations between the neural phenomena and the different conscious states. Lastly, the formulation of an explanatory theory can provide a satisfactory understanding of the phenomenon. This review aims to bring some clarification in the field of consciousness, selecting the hypotheses which mostly fulfill the requirements, in order to be confirmed as explanatory theories. A valuable test for confirming an explanatory hypothesis is its predictive power. Using this criterion we have evaluated comparatively, some of the proposed explaining hypotheses.

Post-task responses following working memory and movement are driven by transient spectral bursts with similar characteristics.

The post-movement beta rebound has been studied extensively using magnetoencephalography (MEG) and is reliably modulated by various task parameters as well as illness. Our recent study showed that rebounds, which we generalise as "post-task responses" (PTRs), are a ubiquitous phenomenon in the brain, occurring across the cortex in theta, alpha, and beta bands. Currently, it is unknown whether PTRs following working memory are driven by transient bursts, which are moments of short-lived high amplitude activity, similar to those that drive the post-movement beta rebound. Here, we use three-state univariate hidden Markov models (HMMs), which can identify bursts without a priori knowledge of frequency content or response timings, to compare bursts that drive PTRs in working memory and visuomotor MEG datasets. Our results show that PTRs across working memory and visuomotor tasks are driven by pan-spectral transient bursts. These bursts have very similar spectral content variation over the cortex, correlating strongly between the two tasks in the alpha (R2 = .89) and beta (R2 = .53) bands. Bursts also have similar variation in duration over the cortex (e.g., long duration bursts occur in the motor cortex for both tasks), strongly correlating over cortical regions between tasks (R2 = .56), with a mean over all regions of around 300 ms in both datasets. Finally, we demonstrate the ability of HMMs to isolate signals of interest in MEG data, such that the HMM probability timecourse correlates more strongly with reaction times than frequency filtered power envelopes from the same brain regions. Overall, we show that induced PTRs across different tasks are driven by bursts with similar characteristics, which can be identified using HMMs. Given the similarity between bursts across tasks, we suggest that PTRs across the cortex may be driven by a common underlying neural phenomenon.

Vast Parameter Space Exploration of the Virtual Brain: A Modular Framework for Accelerating the Multi-Scale Simulation of Human Brain Dynamics

Global neural dynamics emerge from multi-scale brain structures, with nodes dynamically communicating to form transient ensembles that may represent neural information. Neural activity can be measured empirically at scales spanning proteins and subcellular domains to neuronal assemblies or whole-brain networks connected through tracts, but it has remained challenging to bridge knowledge between empirically tractable scales. Multi-scale models of brain function have begun to directly link the emergence of global brain dynamics in conscious and unconscious brain states with microscopic changes at the level of cells. In particular, adaptive exponential integrate-and-fire (AdEx) mean-field models representing statistical properties of local populations of neurons have been connected following human tractography data to represent multi-scale neural phenomena in simulations using The Virtual Brain (TVB). While mean-field models can be run on personal computers for short simulations, or in parallel on high-performance computing (HPC) architectures for longer simulations and parameter scans, the computational burden remains red heavy and vast areas of the parameter space remain unexplored. In this work, we report that our HPC framework, a modular set of methods used here to implement the TVB-AdEx model for the graphics processing unit (GPU) and analyze emergent dynamics, notably accelerates simulations and substantially reduces computational resource requirements. The framework preserves the stability and robustness of the TVB-AdEx model, thus facilitating a finer-resolution exploration of vast parameter spaces as well as longer simulations that were previously near impossible to perform. Comparing our GPU implementations of the TVB-AdEx framework with previous implementations using central processing units (CPUs), we first show correspondence of the resulting simulated time-series data from GPU and CPU instantiations. Next, the similarity of parameter combinations, giving rise to patterns of functional connectivity, between brain regions is demonstrated. By varying global coupling together with spike-frequency adaptation, we next replicate previous results indicating inter-dependence of these parameters in inducing transitions between dynamics associated with conscious and unconscious brain states. Upon further exploring parameter space, we report a nonlinear interplay between the spike-frequency adaptation and subthreshold adaptation, as well as previously unappreciated interactions between the global coupling, adaptation, and propagation velocity of action potentials along the human connectome. Given that simulation and analysis toolkits are made public as open-source packages, this framework serves as a template onto which other models can be easily scripted. Further, personalized data-sets can be used for for the creation of red virtual brain twins toward facilitating more precise approaches to the study of epilepsy, sleep, anesthesia, and disorders of consciousness. These results thus represent potentially impactful, publicly available methods for simulating and analyzing human brain states.

Blink-Related Oscillations Provide Naturalistic Assessments of Brain Function and Cognitive Workload within Complex Real-World Multitasking Environments.

There is a significant need to monitor human cognitive performance in complex environments, with one example being pilot performance. However, existing assessments largely focus on subjective experiences (e.g., questionnaires) and the evaluation of behavior (e.g., aircraft handling) as surrogates for cognition or utilize brainwave measures which require artificial setups (e.g., simultaneous auditory stimuli) that intrude on the primary tasks. Blink-related oscillations (BROs) are a recently discovered neural phenomenon associated with spontaneous blinking that can be captured without artificial setups and are also modulated by cognitive loading and the external sensory environment-making them ideal for brain function assessment within complex operational settings. Electroencephalography (EEG) data were recorded from eight adult participants (five F, M = 21.1 years) while they completed the Multi-Attribute Task Battery under three different cognitive loading conditions. BRO responses in time and frequency domains were derived from the EEG data, and comparisons of BRO responses across cognitive loading conditions were undertaken. Simultaneously, assessments of blink behavior were also undertaken. Blink behavior assessments revealed decreasing blink rate with increasing cognitive load (p < 0.001). Prototypical BRO responses were successfully captured in all participants (p < 0.001). BRO responses reflected differences in task-induced cognitive loading in both time and frequency domains (p < 0.05). Additionally, reduced pre-blink theta band desynchronization with increasing cognitive load was also observed (p < 0.05). This study confirms the ability of BRO responses to capture cognitive loading effects as well as preparatory pre-blink cognitive processes in anticipation of the upcoming blink during a complex multitasking situation. These successful results suggest that blink-related neural processing could be a potential avenue for cognitive state evaluation in operational settings-both specialized environments such as cockpits, space exploration, military units, etc. and everyday situations such as driving, athletics, human-machine interactions, etc.-where human cognition needs to be seamlessly monitored and optimized.

Transcutaneous Electrical Nerve Stimulation Provides Early Recovery from Arthrogenic Muscle Inhibition Post-Anterior Cruciate Ligament Reconstruction

Abstract Background: Quadriceps weakness that ensues anterior cruciate ligament (ACL) reconstruction, a common procedure done in sportspersons, is a hindrance in the active rehabilitation and delays return to sport. This weakness of quadriceps muscle is a complex neural phenomenon ascribed to arthrogenic muscle inhibition (AMI). Purpose: The purpose of the study was to assess the effectiveness of transcutaneous electrical nerve stimulation (TENS) in AMI post-ACL reconstruction (ACLR). Methods: A prospective randomized study involving 60 patients with isolated ACL injury above 18 years of age were included. Patients with osteoarthritis knee, multiligament injury, previous knee surgery, and inflammatory knee pathology were excluded from the study. Patients were divided into Groups A and B (n = 30, each). Group A received TENS therapy with exercises and ice packs, whereas Group B received only exercise and ice packs post-ACLR. All patients were assessed subjectively using Visual Analog Scale score for pain, Lysholm, and IKDC score for functional outcome pre and postoperatively on day 2, 1 month, 3 months, 6 months, and 1 year. Objective assessment was done by measuring thigh girth (10 cm above knee joint line) and isometric quadriceps strength (using David Biofeedback Strength Evaluation Machine) pre and postoperatively at day 2, 1 week, 2 weeks, 1 month, 3 months, 6 months, and 1 year. Results: Pain decreased in both groups at 1 month, 3 months, and 6 months, but there was significantly lower pain in Group A in comparison to Group B at 1 month (P = 0.003), 3 months (P = 0.001), and 6 months (P ≤ 0.0001). There was no pain at 1 year in both the groups. Lysholm score improved in both groups, but there was statistically significantly better Lysholm score in Group A in comparison to Group B at each follow-up. IKDC score improved in both groups, but the improvement in Group A was significantly higher than Group B at each follow-up. No significant difference in mean thigh girth was observed. Mean quadriceps strength was similar in both groups except at 6 months where Group A was better than Group B (&lt;0.001). Conclusion: Addition of TENS in ACLR rehabilitation decreases pain and provides better clinical outcome.

Decoding neural activity to assess individual latent state in ecologically valid contexts.

Objective.Currently, there exists very few ways to isolate cognitive processes, historically defined via highly controlled laboratory studies, in more ecologically valid contexts. Specifically, it remains unclear as to what extent patterns of neural activity observed under such constraints actually manifest outside the laboratory in a manner that can be used to make accurate inferences about latent states, associated cognitive processes, or proximal behavior. Improving our understanding of when and how specific patterns of neural activity manifest in ecologically valid scenarios would provide validation for laboratory-based approaches that study similar neural phenomena in isolation and meaningful insight into the latent states that occur during complex tasks.Approach.Domain generalization methods, borrowed from the work of the brain-computer interface community, have the potential to capture high-dimensional patterns of neural activity in a way that can be reliably applied across experimental datasets in order to address this specific challenge. We previously used such an approach to decode phasic neural responses associated with visual target discrimination. Here, we extend that work to more tonic phenomena such as internal latent states. We use data from two highly controlled laboratory paradigms to train two separate domain-generalized models. We apply the trained models to an ecologically valid paradigm in which participants performed multiple, concurrent driving-related tasks while perched atop a six-degrees-of-freedom ride-motion simulator.Main Results.Using the pretrained models, we estimate latent state and the associated patterns of neural activity. As the patterns of neural activity become more similar to those patterns observed in the training data, we find changes in behavior and task performance that are consistent with the observations from the original, laboratory-based paradigms.Significance.These results lend ecological validity to the original, highly controlled, experimental designs and provide a methodology for understanding the relationship between neural activity and behavior during complex tasks.

Computational analysis of crosstalk between transcriptional regulators and RNA-binding proteins suggests mutual regulation of polycomb proteins and SRSF1 influencing adult hippocampal neurogenesis

BackgroundAdult hippocampal neurogenesis (AHN) is a clinically significant neural phenomenon. Understanding its molecular regulation would be important. In this regard, most studies have focused on transcriptional regulators (TRs), epigenetic modifiers, or non-coding RNAs. RNA-binding proteins (RBPs) have emerged as dominant molecular regulators. It would be significant to understand the potential cross-talk between RBPs and TRs, which could influence AHN.MethodsThe present study employed computational analyses to identify RBPs and TRs regulating AHN, followed by the analysis of their interaction networks and detection of hub proteins. Next, the potential mutual regulation of hub TRs and RBPs was analyzed. Additionally, hippocampal genes differentially expressed upon exercise were analyzed for potential regulation by the identified TRs and RBPs.Results105 TRs and 26 RBPs were found to influence AHN, which could also form interactive networks. Polycomb complex proteins were among the TR network hubs, while HNRNP and SRSF family members were among the hub RBPs. Further, the polycomb complex proteins and SRSF1 could have a mutual regulatory relationship, suggesting a cross-talk between epigenetic/transcriptional and post-transcriptional regulatory pathways. A number of exercise-induced hippocampal genes were also found to be potential targets of the identified TRs and RBPs.ConclusionSRSF1 may influence post-transcriptional stability, localization, and alternative splicing patterns of polycomb complex transcripts, and the polycomb proteins may in turn epigenetically influence the SRSF1. Further experimental validation of these regulatory loops/networks could provide novel insights into the molecular regulation of AHN, and unravel new targets for disease-treatment.

Enhancing Bayesian Approaches in the Cognitive and Neural Sciences via Complex Dynamical Systems Theory

In the cognitive and neural sciences, Bayesianism refers to a collection of concepts and methods stemming from various implementations of Bayes’ theorem, which is a formal way to calculate the conditional probability of a hypothesis being true based on prior expectations and updating priors in the face of errors. Bayes’ theorem has been fruitfully applied to describe and explain a wide range of cognitive and neural phenomena (e.g., visual perception and neural population activity) and is at the core of various theories (e.g., predictive processing). Despite these successes, we claim that Bayesianism has two interrelated shortcomings: its calculations and models are predominantly linear and noise is assumed to be random and unstructured versus deterministic. We outline ways that Bayesianism can address those shortcomings: first, by making more central the nonlinearities characteristic of biological cognitive systems, and second, by treating noise not as random and unstructured dynamics, but as the kind of structured nonlinearities of complex dynamical systems (e.g., chaos and fractals). We provide bistable visual percepts as an example of a real-world phenomenon that demonstrates the fruitfulness of integrating complex dynamical systems theory in Bayesian treatments of perception. Doing so facilitates a Bayesianism that is more capable of explaining a number of currently out-of-reach natural phenomena on their own, biologically realistic terms.

EPViz: A flexible and lightweight visualizer to facilitate predictive modeling for multi-channel EEG.

Scalp Electroencephalography (EEG) is one of the most popular noninvasive modalities for studying real-time neural phenomena. While traditional EEG studies have focused on identifying group-level statistical effects, the rise of machine learning has prompted a shift in computational neuroscience towards spatio-temporal predictive analyses. We introduce a novel open-source viewer, the EEG Prediction Visualizer (EPViz), to aid researchers in developing, validating, and reporting their predictive modeling outputs. EPViz is a lightweight and standalone software package developed in Python. Beyond viewing and manipulating the EEG data, EPViz allows researchers to load a PyTorch deep learning model, apply it to EEG features, and overlay the output channel-wise or subject-level temporal predictions on top of the original time series. These results can be saved as high-resolution images for use in manuscripts and presentations. EPViz also provides valuable tools for clinician-scientists, including spectrum visualization, computation of basic data statistics, and annotation editing. Finally, we have included a built-in EDF anonymization module to facilitate sharing of clinical data. Taken together, EPViz fills a much needed gap in EEG visualization. Our user-friendly interface and rich collection of features may also help to promote collaboration between engineers and clinicians.

Animo nullius: on AI's origin story and a data colonial doctrine of discovery

AbstractThis paper traces elements of the theoretical origins of artificial intelligence to capitalism, not neurophysiology. It considers efforts in the twentieth and twenty-first centuries to formalize a science of mental behaviour using the dynamics of social rather than neural phenomena. I first revisit early American theorists’ controversial ambivalence toward neurophysiology, showing how this group benefited from post-war corporate and military investments in commercial and imperial expansion, which sustained and expanded their influence over the emerging field. I then trace the lasting effect of the founders’ early rhetoric through AI's institutionalization after 1960, arguing that from the 2010s technology corporations set out to veil their enclosure of the data commons via appeal to a curious precedent: the scientific pedigree of AI. By relating the field to the history of capitalism, and specifically the rise of assetization in modern technoscience, I invite reflection on AI's origin story and on broader parallels between historical colonialism and data colonialism. I offer a heuristic – animo nullius, for ‘no persons’ mind’ – as an attempt to name rhetorical manoeuvres that leverage the authority of mind-as-computer metaphors in order to naturalize acts of seizure.

Mechanisms of microglia-mediated synapse turnover and synaptogenesis.

Microglia shape synaptic connections among neurons of the central nervous system (CNS) during development and adulthood. In this review, the actions by which they facilitate pruning, refinement, and new synaptic development throughout the lifespan are considered, along with the molecular mechanisms by which neurons and microglia communicate to guide these actions. Microglia survey neuronal activity and selectively modify synaptic connections at the level of individual dendrites and synapses. This is important given that microglia are necessary for a healthy nervous system capable of learning and other neural phenomena based on synaptic modifications and can also cause pathological synaptic disfunctions in immunologically driven neurodegenerative diseases. Understanding how microglia directly shape synaptic connections between neurons yields a more complete understanding of normal neuroplasticity and provides new routes for understanding disease states.

Unusual Mathematical Approaches Untangle Nervous Dynamics.

The massive amount of available neurodata suggests the existence of a mathematical backbone underlying neuronal oscillatory activities. For example, geometric constraints are powerful enough to define cellular distribution and drive the embryonal development of the central nervous system. We aim to elucidate whether underrated notions from geometry, topology, group theory and category theory can assess neuronal issues and provide experimentally testable hypotheses. The Monge’s theorem might contribute to our visual ability of depth perception and the brain connectome can be tackled in terms of tunnelling nanotubes. The multisynaptic ascending fibers connecting the peripheral receptors to the neocortical areas can be assessed in terms of knot theory/braid groups. Presheaves from category theory permit the tackling of nervous phase spaces in terms of the theory of infinity categories, highlighting an approach based on equivalence rather than equality. Further, the physical concepts of soft-matter polymers and nematic colloids might shed new light on neurulation in mammalian embryos. Hidden, unexpected multidisciplinary relationships can be found when mathematics copes with neural phenomena, leading to novel answers for everlasting neuroscientific questions. For instance, our framework leads to the conjecture that the development of the nervous system might be correlated with the occurrence of local thermal changes in embryo–fetal tissues.

A biologically inspired spiking neural P system in selective visual attention for efficient feature extraction from human motion.

Nowadays, human action recognition has become an essential task in health care and other fields. During the last decade, several authors have developed algorithms for human activity detection and recognition by exploiting at the maximum the high-performance computing devices to improve the quality and efficiency of their results. However, in real-time and practical human action recognition applications, the simulation of these algorithms exceed the capacity of current computer systems by considering several factors, such as camera movement, complex scene and occlusion. One potential solution to decrease the computational complexity in the human action detection and recognition can be found in the nature of the human visual perception. Specifically, this process is called selective visual attention. Inspired by this neural phenomena, we propose for the first time a spiking neural P system for efficient feature extraction from human motion. Specifically, we propose this neural structure to carry out a pre-processing stage since many studies have revealed that an analysis of visual information of the human brain proceeds in a sequence of operations, in which each one is applied to a specific location or locations. In this way, this specialized processing have allowed to focus the recognition of the objects in a simpler manner. To create a compact and high speed spiking neural P system, we use their cutting-edge variants, such as rules on the synapses, communication on request and astrocyte-like control. Our results have demonstrated that the use of the proposed neural P system increases significantly the performance of low-computational complexity neural classifiers up to more 97% in the human action recognition.

A generalized cortical activity pattern at internally generated mental context boundaries during unguided narrative recall.

Current theory and empirical studies suggest that humans segment continuous experiences into events based on the mismatch between predicted and actual sensory inputs; detection of these 'event boundaries' evokes transient neural responses. However, boundaries can also occur at transitions between internal mental states, without relevant external input changes. To what extent do such 'internal boundaries' share neural response properties with externally driven boundaries? We conducted an fMRI experiment where subjects watched a series of short movies and then verbally recalled the movies, unprompted, in the order of their choosing. During recall, transitions between movies thus constituted major boundaries between internal mental contexts, generated purely by subjects' unguided thoughts. Following the offset of each recalled movie, we observed stereotyped spatial activation patterns in the default mode network, especially the posterior medial cortex, consistent across different movie contents and even across the different tasks of movie watching and recall. Surprisingly, the between-movie boundary patterns did not resemble patterns at boundaries between events within a movie. Thus, major transitions between mental contexts elicit neural phenomena shared across internal and external modes and distinct from within-context event boundary detection, potentially reflecting a cognitive state related to the flushing and reconfiguration of situation models.

Neural Phenomenon in Musicality: The Interpretation of Dual-Processing Modes in Melodic Perception.

The confluence of creativity in music performance finds itself in performance practices and cultural motifs, the communication of the human body along with the instrument it interacts with, and individual performers’ perceptual, motor, and cognitive abilities that contribute to varied musical interpretations of the same piece or melodic line. The musical and artistic execution of a player, as well as the product of this phenomena can become determinant causes in a creative mental state. With advances in neurocognitive measures, the state of one’s artistic intuition and execution has been a growing interest in understanding the creative thought process of human behavior, particularly in improvising artists. This article discusses the implementation on the concurrence of spontaneous (Type-1) and controlled (Type-2) processing modes that may be apparent in the perception of non-improvising artists on how melodic lines are perceived in music performance. Elucidating the cortical-subcortical activity in the dual-process model may extend to non-improvising musicians explored in the paradigm of neural correlates. These interactions may open new possibilities for expanding the repertoire of executive functions, creativity, and the coordinated activity of cortical-subcortical regions that regulate the free flow of artistic ideas and expressive spontaneity in future neuromusical research.

Monofractal analysis of functional magnetic resonance imaging: An introductory review

The following review will aid readers in providing an overview of scale‐free dynamics and monofractal analysis, as well as its applications and potential in functional magnetic resonance imaging (fMRI) neuroscience and clinical research. Like natural phenomena such as the growth of a tree or crashing ocean waves, the brain expresses scale‐invariant, or fractal, patterns in neural signals that can be measured. While neural phenomena may represent both monofractal and multifractal processes and can be quantified with many different interrelated parameters, this review will focus on monofractal analysis using the Hurst exponent (H). Monofractal analysis of fMRI data is an advanced analysis technique that measures the complexity of brain signaling by quantifying its degree of scale‐invariance. As such, the H value of the blood oxygenation level‐dependent (BOLD) signal specifies how the degree of correlation in the signal may mediate brain functions. This review presents a brief overview of the theory of fMRI monofractal analysis followed by notable findings in the field. Through highlighting the advantages and challenges of the technique, the article provides insight into how to best conduct fMRI fractal analysis and properly interpret the findings with physiological relevance. Furthermore, we identify the future directions necessary for its progression towards impactful functional neuroscience discoveries and widespread clinical use. Ultimately, this presenting review aims to build a foundation of knowledge among readers to facilitate greater understanding, discussion, and use of this unique yet powerful imaging analysis technique.

Modulation of cortical oscillations by periodic electrical stimulation is frequency-dependent

The brain oscillations play a pivotal role in transmitting and processing the information. Periodic electrical stimulation has been proposed to shape the oscillations. However, how the periodic electrical stimulation modulates the oscillatory activities remains poorly understood. To address this question, a modified cortical network by adding the brain state information (eye–open and eye–closed states) and synaptic plasticity is employed. Constraining on eye–open state and without considering the synaptic plasticity, the numerical simulation results have shown that the network responses are strong frequency–dependent and the underlying mechanism of the network oscillations has been recognized as three typical neural phenomena, that is resonance, entrainment, and nonlinear oscillation, induced by periodic stimulation with different frequencies and amplitudes. We further prove the frequency dependency of network responses through theoretical analysis. Moreover, the impacts of the brain state have been explored. It is shown that the brain state has little impact on the network oscillatory response during stimulus and generates only a very short–lasting effect. However, the introduce of the plasticity in the cortical network makes the periodic stimulation generate obvious post—stimulus effects. Even though the stimulation is removed, the cortical network can still maintain the resonance, entrainment, and nonlinear oscillatory activities. The frequency–dependent network response mechanism provides a profound understanding of neural information communication theory induced by the periodic stimulus.

Person identity‐specific adaptation effects in the ventral occipito‐temporal cortex

Identifying the faces of familiar persons requires the ability to assign several different images of a face to a common identity. Previous research showed that the occipito-temporal cortex, including the fusiform and the occipital face areas, is sensitive to personal identity. Still, the viewpoint, facial expression and image-independence of this information are currently under heavy debate. Here we adapted a rapid serial visual stimulation paradigm Johnston et al. (2016, https://doi.org/10.1016/j.cortex.2016.10.002) and presented highly variable ambient-face images of famous persons to measure functional magnetic resonance imaging (fMRI) adaptation. fMRI adaptation is considered as the neuroimaging manifestation of repetition suppression, a neural phenomenon currently explained as a correlate of reduced predictive error responses for expected stimuli. We revisited the question of image-invariant identity-specific encoding mechanisms of the occipito-temporal cortex, using fMRI adaptation with a particular interest in predictive mechanisms. Participants were presented with trials containing eight different images of a famous person, images of eight different famous persons or seven different images of a particular famous person followed by an identity change to violate potential expectation effects about person identity. We found an image-independent adaptation effect of identity for famous faces in the fusiform face area. However, in contrast to previous electrophysiological studies, using similar paradigms, no release of the adaptation effect was observed when identity-specific expectations were violated. Our results support recent multivariate pattern analysis studies, showing image-independent identity encoding in the core face-processing areas of the occipito-temporal cortex. These results are discussed in the frame of recent identity-processing models and predictive mechanisms.

Time-Resolved Smoothness of Distributed Brain Activity Tracks Conscious States and Unifies Emergent Neural Phenomena

Time-Resolved Smoothness of Distributed Brain Activity Tracks Conscious States and Unifies Emergent Neural Phenomena

Sex and Gender Science: The World Writes on the Body.

Sex and Gender Science seeks to better acknowledge that the body cannot be removed from the world it inhabits. We believe that to best answer any neuroscience question, the biological and the social need to be addressed through both objective means to learn, "how it is like" and subjective means to learn, "what it is like." We call bringing the biological and social together, "Situated Neuroscience" and the mixing of approaches to do so, Very Mixed Methods. Taken together, they constitute an approach to Sex and Gender Science. In this chapter, we describe neural phenomena for which considering sex and gender together produces a fuller knowledge base: sleep, pain, memory, and concussion. For these brain phenomena examples, studying only quantitative measures does not reveal the full impact of these lived experiences on the brain but studying only the qualitative would not reveal how the brain responds. We discuss how Sex and Gender Science allows us to begin to bring together biology and its social context and acknowledge where context can contribute to resolving ignorance to offer more expansive, complementary, and interrelating pictures of an intricate neuro-landscape.