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Consciousness science is experiencing a coming-of-age moment. Following three decades of sustained efforts by a relatively small group of consciousness researchers, the field has seen exponential growth over the past 5 years. It is increasingly recognized that although the investigation of subjective experiences is a difficult task, modern neuroscience need not and cannot shy away from the challenge of peeling away the mysteries of conscious experiences. In June 2023, with the joint support of the U.S. National Institutes of Health and the U.S. National Science Foundation, a 3-day workshop was held at the Bethesda, MD, campus of the National Institutes of Health, convening experts whose work focuses primarily on problems of consciousness, or an adjacent field, to discuss the current state of consciousness science and consider the most fruitful avenues for future research. This Special Focus features empirical and theoretical contributions from some of the invited speakers at the workshop. Here, I will cover the scope of the workshop, the content of this Special Focus, and advocate for stronger bridges between consciousness science and other subdisciplines of cognitive neuroscience. [ABSTRACT FROM AUTHOR]

Consciousness science has matured over the past three decades and is currently on the cusp of explosive growth, with the potential to transform medicine and technology. The global community recently met to synthesize the current state of knowledge and define the most exciting approaches to advance the field. [ABSTRACT FROM AUTHOR]

Recent neuroscientific findings challenge the widely held assumption that similar neural mechanisms underlie different types of conscious awareness, such as seeing, feeling, knowing, and willing. These data show that, even within visual awareness, the brain regions involved, the latency of neural activity, and the information flow patterns underlying conscious perception vary depending on both external and internal factors. Different types of conscious awareness – such as perception and volition – likely have different neural processing architecture. Thus, a pluralistic approach to investigating the neurobiological underpinnings of consciousness may be more fruitful. A new framework, the JDT, is proposed which has the potential to accommodate different brain circuit-level mechanisms underlying different aspects of conscious awareness. Theories of consciousness are often based on the assumption that a single, unified neurobiological account will explain different types of conscious awareness. However, recent findings show that, even within a single modality such as conscious visual perception, the anatomical location, timing, and information flow of neural activity related to conscious awareness vary depending on both external and internal factors. This suggests that the search for generic neural correlates of consciousness may not be fruitful. I argue that consciousness science requires a more pluralistic approach and propose a new framework: joint determinant theory (JDT). This theory may be capable of accommodating different brain circuit mechanisms for conscious contents as varied as percepts, wills, memories, emotions, and thoughts, as well as their integrated experience. [ABSTRACT FROM AUTHOR]

Humans spend hours each day spontaneously engaging with visual content, free from specific tasks and at their own pace. Currently, the brain mechanisms determining the duration of self-paced perceptual behavior remain largely unknown. Here, participants viewed naturalistic images under task-free settings and self-paced each image's viewing duration while undergoing EEG and pupillometry recordings. Across two independent data sets, we observed large inter- and intra-individual variability in viewing duration. However, beyond an image's presentation order and category, specific image content had no consistent effects on spontaneous viewing duration across participants. Overall, longer viewing durations were associated with sustained enhanced posterior positivity and anterior negativity in the ERPs. Individual-specific variations in the spontaneous viewing duration were consistently correlated with evoked EEG activity amplitudes and pupil size changes. By contrast, presentation order was selectively correlated with baseline alpha power and baseline pupil size. Critically, spontaneous viewing duration was strongly predicted by the temporal stability in neural activity patterns starting as early as 350 msec after image onset, suggesting that early neural stability is a key predictor for sustained perceptual engagement. Interestingly, neither bottom–up nor top–down predictions about image category influenced spontaneous viewing duration. Overall, these results suggest that individual-specific factors can influence perceptual processing at a surprisingly early time point and influence the multifaceted ebb and flow of spontaneous human perceptual behavior in naturalistic settings. [ABSTRACT FROM AUTHOR]

Prior knowledge profoundly influences perceptual processing. Previous studies have revealed consistent suppression of predicted stimulus information in sensory areas, but how prior knowledge modulates processing higher up in the cortical hierarchy remains poorly understood. In addition, the mechanism leading to suppression of predicted sensory information remains unclear, and studies thus far have revealed a mixed pattern of results in support of either the "sharpening" or "dampening" model. Here, using 7T fMRI in humans (both sexes), we observed that prior knowledge acquired from fast, one-shot perceptual learning sharpens neural representation throughout the ventral visual stream, generating suppressed sensory responses. In contrast, the frontoparietal and default mode networks exhibit similar sharpening of content-specific neural representation, but in the context of unchanged and enhanced activity magnitudes, respectively: a pattern we refer to as "selective enhancement." Together, these results reveal a heretofore unknown macroscopic gradient of prior knowledge's sharpening effect on neural representations across the cortical hierarchy. [ABSTRACT FROM AUTHOR]

How a stimulus or a task alters the spontaneous dynamics of the brain remains a fundamental open question in neuroscience. One of the most robust hallmarks of task/stimulus-driven brain dynamics is the decrease of variability with respect to the spontaneous level, an effect seen across multiple experimental conditions and in brain signals observed at different spatiotemporal scales. Recently, it was observed that the trial-to-trial variability and temporal variance of functional magnetic resonance imaging (fMRI) signals decrease in the task-driven activity. Here we examined the dynamics of a large-scale model of the human cortex to provide a mechanistic understanding of these observations. The model allows computing the statistics of synaptic activity in the spontaneous condition and in putative tasks determined by external inputs to a given subset of brain regions. We demonstrated that external inputs decrease the variance, increase the covariances, and decrease the autocovariance of synaptic activity as a consequence of single node and large-scale network dynamics. Altogether, these changes in network statistics imply a reduction of entropy, meaning that the spontaneous synaptic activity outlines a larger multidimensional activity space than does the task-driven activity. We tested this model’s prediction on fMRI signals from healthy humans acquired during rest and task conditions and found a significant decrease of entropy in the stimulus-driven activity. Altogether, our study proposes a mechanism for increasing the information capacity of brain networks by enlarging the volume of possible activity configurations at rest and reliably settling into a confined stimulus-driven state to allow better transmission of stimulus-related information. [ABSTRACT FROM AUTHOR]

While there is a wealth of knowledge about core object recognition--our ability to recognize clear, high-contrast object images--how the brain accomplishes object recognition tasks under increased uncertainty remains poorly understood. We investigated the spatiotemporal neural dynamics underlying object recognition under increased uncertainty by combining MEG and 7 Tesla (7T) fMRI in humans during a threshold-level object recognition task. We observed an early, parallel rise of recognition-related signals across ventral visual and frontoparietal regions that preceded the emergence of category-related information. Recognition-related signals in ventral visual regions were best explained by a two-state representational format whereby brain activity bifurcated for recognized and unrecognized images. By contrast, recognition-related signals in frontoparietal regions exhibited a reduced representational space for recognized images, yet with sharper category information. These results provide a spatiotemporally resolved view of neural activity supporting object recognition under uncertainty, revealing a pattern distinct from that underlying core object recognition. [ABSTRACT FROM AUTHOR]

While neuroscientific research on perceptual awareness has traditionally focused on the spatial and temporal localizations of neural activity underlying conscious processing, recent development suggests that the dynamic characteristics of spatiotemporally distributed neural activity contain important clues about the neural computational mechanisms underlying conscious processing. Here, we summarize recent progress. [ABSTRACT FROM AUTHOR]

Brain activity observed at many spatiotemporal scales exhibits a 1/ f -like power spectrum, including neuronal membrane potentials, neural field potentials, noninvasive electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) signals. A 1/ f -like power spectrum is indicative of arrhythmic brain activity that does not contain a predominant temporal scale (hence, ‘scale-free’). This characteristic of scale-free brain activity distinguishes it from brain oscillations. Although scale-free brain activity and brain oscillations coexist, our understanding of the former remains limited. Recent research has shed light on the spatiotemporal organization, functional significance, and potential generative mechanisms of scale-free brain activity, as well as its developmental and clinical relevance. A deeper understanding of this prevalent brain signal should provide new insights into, and analytical tools for, cognitive neuroscience. [ABSTRACT FROM AUTHOR]

It is well known that even under identical task conditions, there is a tremendous amount of trial-to-trial variability in both brain activity and behavioral output. Thus far the vast majority of event-related potential (ERP) studies investigating the relationship between trial-to-trial fluctuations in brain activity and behavioral performance have only tested a monotonic relationship between them. However, it was recently found that across-trial variability can correlate with behavioral performance independent of trial-averaged activity. This finding predicts a U- or inverted-U- shaped relationship between trial-to-trial brain activity and behavioral output, depending on whether larger brain variability is associated with better or worse behavior, respectively. Using a visual stimulus detection task, we provide evidence from human electrocorticography (ECoG) for an inverted-U brain-behavior relationship: When the raw fluctuation in broadband ECoG activity is closer to the across-trial mean, hit rate is higher and reaction times faster. Importantly, we show that this relationship is present not only in the post-stimulus task-evoked brain activity, but also in the pre-stimulus spontaneous brain activity, suggesting anticipatory brain dynamics. Our findings are consistent with the presence of stochastic noise in the brain. They further support attractor network theories, which postulate that the brain settles into a more confined state space under task performance, and proximity to the targeted trajectory is associated with better performance. [ABSTRACT FROM AUTHOR]

The spatiotemporal structure of functional magnetic resonance imaging (fMRI) signals has provided a valuable window into the network underpinnings of human brain function and dysfunction. Although some cross-regional temporal correlation patterns (functional connectivity; FC) exhibit a high degree of stability across individuals and species, there is growing acknowledgment that measures of FC can exhibit marked changes over a range of temporal scales. Further, FC can co-vary with experimental task demands and ongoing neural processes linked to arousal, consciousness and perception, cognitive and affective state, and brain-body interactions. The increased recognition that such interrelated neural processes modulate FC measurements has raised both challenges and new opportunities in using FC to investigate brain function. Here, we review recent advances in the quantification of neural effects that shape fMRI FC and discuss the broad implications of these findings in the design and analysis of fMRI studies. We also discuss how a more complete understanding of the neural factors that shape FC measurements can resolve apparent inconsistencies in the literature and lead to more interpretable conclusions from fMRI studies. [ABSTRACT FROM AUTHOR]

A widely held assumption is that spontaneous and task-evoked brain activity sum linearly, such that the recorded brain response in each single trial is the algebraic sum of the constantly changing ongoing activity and the stereotypical evoked activity. Using functional magnetic resonance imaging signals acquired from normal humans, we show that this assumption is invalid. Across widespread cortices, evoked activity interacts negatively with ongoing activity, such that higher prestimulus baseline results in less activation or more deactivation. As a consequence of this negative interaction, trial-to-trial variability of cortical activity decreases following stimulus onset. We further show that variability reduction follows overlapping but distinct spatial pattern from that of task-activation/deactivation and it contains behaviorally relevant information. These results favor an alternative perspective to the traditional dichotomous framework of ongoing and evoked activity. That is, to view the brain as a nonlinear dynamical system whose trajectory is tighter when performing a task. Further, incoming sensory stimuli modulate the brain's activity in a manner that depends on its initial state. We propose that across-trial variability may provide a new approach to brain mapping in the context of cognitive experiments. [ABSTRACT FROM AUTHOR]

It has been shown recently that a significant portion of brain electrical field potentials consists of scale-free dynamics. These scale-free brain dynamics contain complex spatiotemporal structures and are modulated by task performance. Here we show that the fMRI signal recorded from the human brain is also scale free; its power-law exponent differentiates between brain networks and correlates with fMRI signal variance and brain glucose metabolism. Importantly, in parallel to brain electrical field potentials, the variance and power-law exponent of the fMRI signal decrease during task activation, suggesting that the signal contains more long-range memory during rest and conversely is more efficient at online information processing during task. Remarkably, similar changes also occurred in task-deactivated brain regions, revealing the presence of an optimal dynamic range in the fMRI signal. The scale-free properties of the fMRI signal and brain electrical field potentials bespeak their respective stationarity and nonstationarity. This suggests that neurovascular coupling mechanism is likely to contain a transformation from nonstationarity to stationarity. In summary, our results demonstrate the functional relevance of scale-free properties of the fMRI signal and impose constraints on future models of neurovascular coupling. [ABSTRACT FROM AUTHOR]

Summary: Scale-free dynamics, with a power spectrum following P ∝f −β, are an intrinsic feature of many complex processes in nature. In neural systems, scale-free activity is often neglected in electrophysiological research. Here, we investigate scale-free dynamics in human brain and show that it contains extensive nested frequencies, with the phase of lower frequencies modulating the amplitude of higher frequencies in an upward progression across the frequency spectrum. The functional significance of scale-free brain activity is indicated by task performance modulation and regional variation, with β being larger in default network and visual cortex and smaller in hippocampus and cerebellum. The precise patterns of nested frequencies in the brain differ from other scale-free dynamics in nature, such as earth seismic waves and stock market fluctuations, suggesting system-specific generative mechanisms. Our findings reveal robust temporal structures and behavioral significance of scale-free brain activity and should motivate future study on its physiological mechanisms and cognitive implications. [Copyright &y& Elsevier]

As functional magnetic resonance imaging (fMRI) has become a driving force in cognitive neuroscience, it is crucial to understand the neural basis of the fMRI signal. Here, we discuss a novel neurophysiological correlate of the fMRI signal, the slow cortical potential (SCP), which also seems to modulate the power of higher-frequency activity, the more established neurophysiological correlate of the fMRI signal. We further propose a hypothesis for the involvement of the SCP in the emergence of consciousness, and review existing data that lend support to our proposal. This hypothesis, unlike several previous theories of consciousness, is firmly rooted in physiology and as such is entirely amenable to empirical testing. [Copyright &y& Elsevier]

Spontaneous fluctuations in the blood-oxygen-level-dependent (BOLD) signals demonstrate consistent temporal correlations within large-scale brain networks associated with different functions. The neurophysiological correlates of this phenomenon remain elusive. Here, we show in humans that the slow cortical potentials recorded by electrocorticography demonstrate a correlation structure similar to that of spontaneous BOLD fluctuations across wakefulness, slow-wave sleep, and rapid-eye-movement sleep. Gamma frequency power also showed a similar correlation structure but only during wakefulness and rapid-eye-movement sleep. Our results provide an important bridge between the large-scale brain networks readily revealed by spontaneous BOLD signals and their underlying neurophysiology. [ABSTRACT FROM AUTHOR]

Summary: Spatial neglect is a syndrome following stroke manifesting attentional deficits in perceiving and responding to stimuli in the contralesional field. We examined brain network integrity in patients with neglect by measuring coherent fluctuations of fMRI signals (functional connectivity). Connectivity in two largely separate attention networks located in dorsal and ventral frontoparietal areas was assessed at both acute and chronic stages of recovery. Connectivity in the ventral network, part of which directly lesioned, was diffusely disrupted and showed no recovery. In the structurally intact dorsal network, interhemispheric connectivity in posterior parietal cortex was acutely disrupted but fully recovered. This acute disruption, and disrupted connectivity in specific pathways in the ventral network, strongly correlated with impaired attentional processing across subjects. Lastly, disconnection of the white matter tracts connecting frontal and parietal cortices was associated with more severe neglect and more disrupted functional connectivity. These findings support a network view in understanding neglect. [Copyright &y& Elsevier]

Ambiguous images elicit bistable perception, wherein periods of momentary perceptual stability are interrupted by sudden perceptual switches. When intermittently presented, ambiguous images trigger a perceptual memory trace in the intervening blank periods. Understanding the neural bases of perceptual stability and perceptual memory during bistable perception may hold clues for explaining the apparent stability of visual experience in the natural world, where ambiguous and fleeting images are prevalent. Motivated by recent work showing the involvement of the right inferior frontal gyrus (rIFG) in bistable perception, we conducted a transcranial direct-current stimulation (tDCS) study with a double-blind, within-subject cross-over design to test a potential causal role of rIFG in these processes. Subjects viewed ambiguous images presented continuously or intermittently while under EEG recording. We did not find any significant tDCS effect on perceptual behavior. However, the fluctuations of oscillatory power in the alpha and beta bands predicted perceptual stability, with higher power corresponding to longer percept durations. In addition, higher alpha and beta power predicted enhanced perceptual memory during intermittent viewing. These results reveal a unified neurophysiological mechanism sustaining perceptual stability and perceptual memory when the visual system is faced with ambiguous input. [ABSTRACT FROM AUTHOR]

Arousal levels perpetually rise and fall spontaneously. How markers of arousal-pupil size and frequency content of brain activity-relate to each other and influence behavior in humans is poorly understood. We simultaneously monitored magnetoencephalography and pupil in healthy volunteers at rest and during a visual perceptual decision-making task. Spontaneously varying pupil size correlates with power of brain activity in most frequency bands across large-scale resting state cortical networks. Pupil size recorded at prestimulus baseline correlates with subsequent shifts in detection bias (c) and sensitivity (d'). When dissociated from pupil-linked state, prestimulus spectral power of resting state networks still predicts perceptual behavior. Fast spontaneous pupil constriction and dilation correlate with large-scale brain activity as well but not perceptual behavior. Our results illuminate the relation between central and peripheral arousal markers and their respective roles in human perceptual decision-making. [ABSTRACT FROM AUTHOR]

Neural activity and behavior are both notoriously variable, with responses differing widely between repeated presentation of identical stimuli or trials. Recent results in humans and animals reveal that these variations are not random in their nature, but may in fact be due in large part to rapid shifts in neural, cognitive, and behavioral states. Here we review recent advances in the understanding of rapid variations in the waking state, how variations are generated, and how they modulate neural and behavioral responses in both mice and humans. We propose that the brain has an identifiable set of states through which it wanders continuously in a nonrandom fashion, owing to the activity of both ascending modulatory and fast-acting corticocortical and subcortical-cortical neural pathways. These state variations provide the backdrop upon which the brain operates, and understanding them is critical to making progress in revealing the neural mechanisms underlying cognition and behavior. [ABSTRACT FROM AUTHOR]

The vast majority of experiments examining perception and behavior are conducted using experimental paradigms that adhere to a rigid trial structure: each trial consists of a brief and discrete series of events and is regarded as independent from all other trials. The assumptions underlying this structure ignore the reality that natural behavior is rarely discrete, brain activity follows multiple time courses that do not necessarily conform to the trial structure, and the natural environment has statistical structure and dynamics that exhibit long-range temporal correlation. Modern advances in statistical modeling and analysis offer tools that make it feasible for experiments to move beyond rigid independent and identically distributed trial structures. Here we review literature that serves as evidence for the feasibility and advantages of moving beyond trial-based paradigms to understand the neural basis of perception and cognition. Furthermore, we propose a synthesis of these efforts, integrating the characterization of natural stimulus properties with measurements of continuous neural activity and behavioral outputs within the framework of sensory-cognitive-motor loops. Such a framework provides a basis for the study of natural statistics, naturalistic tasks, and/or slow fluctuations in brain activity, which should provide starting points for important generalizations of analytical tools in neuroscience and subsequent progress in understanding the neural basis of perception and cognition. [ABSTRACT FROM AUTHOR]

Recent research has identified late-latency, long-lasting neural activity as a robust correlate of conscious perception. Yet, the dynamical nature of this activity is poorly understood, and the mechanisms governing its presence or absence and the associated conscious perception remain elusive. We applied dynamic-pattern analysis to whole-brain slow (< 5 Hz) cortical dynamics recorded by magnetoencephalography (MEG) in human subjects performing a threshold-level visual perception task. Up to 1 second before stimulus onset, brain activity pattern across widespread cortices significantly predicted whether a threshold-level visual stimulus was later consciously perceived. This initial state of brain activity interacts nonlinearly with stimulus input to shape the evolving cortical activity trajectory, with seen and unseen trials following well separated trajectories. We observed that cortical activity trajectories during conscious perception are fast evolving and robust to small variations in the initial state. In addition, spontaneous brain activity pattern prior to stimulus onset also influences unconscious perceptual making in unseen trials. Together, these results suggest that brain dynamics underlying conscious visual perception belongs to the class of initial-state-dependent, robust, transient neural dynamics. [ABSTRACT FROM AUTHOR]

Humans seem to decide for themselves what to do, and when to do it. This distinctive capacity may emerge from an ability, shared with other animals, to make decisions for action that are related to future goals, or at least free from the constraints of immediate environmental inputs. Studying such volitional acts proves a major challenge for neuroscience. This review highlights key mechanisms in the generation of voluntary, as opposed to stimulus-driven actions, and highlights three issues. The first part focuses on the apparent spontaneity of voluntary action. The second part focuses on one of the most distinctive, but elusive, features of volition, namely, its link to conscious experience, and reviews stimulation and patient studies of the cortical basis of conscious volition down to the single-neuron level. Finally, we consider the goal-directedness of voluntary action, and discuss how internal generation of action can be linked to goals and reasons. [ABSTRACT FROM AUTHOR]

Abstract: Studies employing functional connectivity-type analyses have established that spontaneous fluctuations in functional magnetic resonance imaging (fMRI) signals are organized within large-scale brain networks. Meanwhile, fMRI signals have been shown to exhibit 1/f-type power spectra — a hallmark of scale-free dynamics. We studied the interplay between functional connectivity and scale-free dynamics in fMRI signals, utilizing the fractal connectivity framework — a multivariate extension of the univariate fractional Gaussian noise model, which relies on a wavelet formulation for robust parameter estimation. We applied this framework to fMRI data acquired from healthy young adults at rest and while performing a visual detection task. First, we found that scale-invariance existed beyond univariate dynamics, being present also in bivariate cross-temporal dynamics. Second, we observed that frequencies within the scale-free range do not contribute evenly to inter-regional connectivity, with a systematically stronger contribution of the lowest frequencies, both at rest and during task. Third, in addition to a decrease of the Hurst exponent and inter-regional correlations, task performance modified cross-temporal dynamics, inducing a larger contribution of the highest frequencies within the scale-free range to global correlation. Lastly, we found that across individuals, a weaker task modulation of the frequency contribution to inter-regional connectivity was associated with better task performance manifesting as shorter and less variable reaction times. These findings bring together two related fields that have hitherto been studied separately — resting-state networks and scale-free dynamics, and show that scale-free dynamics of human brain activity manifest in cross-regional interactions as well. [Copyright &y& Elsevier]

Despite intense recent research, the neural correlates of conscious visual perception remain elusive. The most established paradigm for studying brain mechanisms underlying conscious perception is to keep the physical sensory inputs constant and identify brain activities that correlate with the changing content of conscious awareness. However, such a contrast based on conscious content alone would not only reveal brain activities directly contributing to conscious perception, but also include brain activities that precede or follow it. To address this issue, we devised a paradigm whereby we collected, trial-by-trial, measures of objective performance, subjective awareness, and the confidence level of subjective awareness. Using magnetoencephalography recordings in healthy human volunteers, we dissociated brain activities underlying these different cognitive phenomena. Our results provide strong evidence that widely distributed slow cortical potentials (SCPs) correlate with subjective awareness, even after the effects of objective performance and confidence were both removed. The SCP correlate of conscious perception manifests strongly in its waveform, phase, and power. In contrast, objective performance and confidence were both contributed by relatively transient brain activity. These results shed new light on the brain mechanisms of conscious, unconscious, and metacognitive processing. [ABSTRACT FROM AUTHOR]

Unilateral spatial neglect (neglect) is a syndrome characterized by perceptual deficits that prevent patients from attending and responding to the side of space and of the body opposite a damaged hemisphere (contralesional side). Neglect also involves motor deficits: patients may be slower to initiate a motor response to targets appearing in the left hemispace, even when using their unaffected arm (directional hypokinesia). Although this impairment is well known, its anatomical correlate has not been established. We tested 52 patients with neglect after right hemisphere stroke, and conducted an anatomical analysis on 29 of them to find the anatomical correlate of directional hypokinesia. We found that patients with directional hypokinesia had a lesion involving the ventral lateral putamen, the claustrum, and the white matter underneath the frontal lobe. Most importantly, none of the patients without directional hypokinesia had a lesion in the same region. The localization of neglect's motor deficits to the basal ganglia establishes interesting homologies with animal data; it also suggests that a relative depletion of dopamine in the nigrostriatal pathway on the same side of the lesion may be an important pathophysiological mechanism potentially amenable to intervention. [ABSTRACT FROM AUTHOR]

A degraded, black-and-white image of an object, which appears meaningless on first presentation, is easily identified after a single exposure to the original, intact image. This striking example of perceptual learning reflects a rapid (one-trial) change in performance, but the kind of learning that is involved is not known. We asked whether this learning depends on conscious (hippocampus-dependent) memory for the images that have been presented or on an unconscious (hippocampus-independent) change in the perception of images, independently of the ability to remember them. We tested five memory-impaired patients with hippocampal lesions or larger medial temporal lobe (MTL) lesions. In comparison to volunteers, the patients were fully intact at perceptual learning, and their improvement persisted without decrement from 1 d to more than 5 mo. Yet, the patients were impaired at remembering the test format and, even after 1 d, were impaired at remembering the images themselves. To compare perceptual learning and remembering directly, at 7 d after seeing degraded images and their solutions, patients and volunteers took either a naming test or a recognition memory test with these images. The patients improved as much as the volunteers at identifying the degraded images but were severely impaired at remembering them. Notably, the patient with the most severe memory impairment and the largest MTL lesions performed worse than the other patients on the memory tests but was the best at perceptual learning. The findings show that one-trial, long-lasting perceptual learning relies on hippocampus-independent (nondeclarative) memory, independent of any requirement to consciously remember. [ABSTRACT FROM AUTHOR]

Many large-scale functional connectivity studies have emphasized the importance of communication through increased inter-region correlations during task states. In contrast, local circuit studies have demonstrated that task states primarily reduce correlations among pairs of neurons, likely enhancing their information coding by suppressing shared spontaneous activity. Here we sought to adjudicate between these conflicting perspectives, assessing whether co-active brain regions during task states tend to increase or decrease their correlations. We found that variability and correlations primarily decrease across a variety of cortical regions in two highly distinct data sets: non-human primate spiking data and human functional magnetic resonance imaging data. Moreover, this observed variability and correlation reduction was accompanied by an overall increase in dimensionality (reflecting less information redundancy) during task states, suggesting that decreased correlations increased information coding capacity. We further found in both spiking and neural mass computational models that task-evoked activity increased the stability around a stable attractor, globally quenching neural variability and correlations. Together, our results provide an integrative mechanistic account that encompasses measures of large-scale neural activity, variability, and correlations during resting and task states. Author summary: Statistical estimates of correlated neural activity and variability are widely used to characterize neural systems during different states. However, there is a conceptual gap between the use and interpretation of these measures between the human neuroimaging and non-human primate electrophysiology literature. For example, in the human neuroimaging literature, "functional connectivity" is often used to refer to correlated activity, while in the non-human primate electrophysiology literature, the equivalent term is "noise correlation". In an effort to unify these two perspectives under a single theoretical framework, we provide empirical evidence from human functional magnetic resonance imaging and non-human primate mean-field spike rate data that functional connectivity and noise correlations reveal similar statistical patterns during task states. In short, we found that task states primarily quench neural variability and correlations in both data sets. To provide a theoretically rigorous account capable of explaining this phenomena across both data sets, we use mean-field dynamical systems modeling to demonstrate the deterministic relationship between task-evoked activity, neural variability and correlations. Together, we provide an integrative account, showing that task-evoked activity quenches neural variability and correlations in large-scale neural systems. [ABSTRACT FROM AUTHOR]

Cortical responses to sensory inputs vary across repeated presentations of identical stimuli, but how this trial-to-trial variability impacts detection of sensory inputs is not fully understood. Using multi-channel local field potential (LFP) recordings in primary somatosensory cortex (S1) of the awake mouse, we optimized a data-driven cortical state classifier to predict single-trial sensory-evoked responses, based on features of the spontaneous, ongoing LFP recorded across cortical layers. Our findings show that, by utilizing an ongoing prediction of the sensory response generated by this state classifier, an ideal observer improves overall detection accuracy and generates robust detection of sensory inputs across various states of ongoing cortical activity in the awake brain, which could have implications for variability in the performance of detection tasks across brain states. [ABSTRACT FROM AUTHOR]

Conscious intention is a fundamental aspect of the human experience. Despite long-standing interest in the basis and implications of intention, its underlying neurobiological mechanisms remain poorly understood. Using high-definition transcranial DC stimulation (tDCS), we observed that enhancing spontaneous neuronal excitability in both the angular gyrus and the primary motor cortex caused the reported time of conscious movement intention to be~60 -70 ms earlier. Slow brain waves recorded~2-3 s before movement onset, as well as hundreds of milliseconds after movement onset, independently correlated with the modulation of conscious intention by brain stimulation. These brain activities together accounted for 81% of interindividual variability in the modulation of movement intention by brain stimulation. A computational model using coupled leaky integrator units with biophysically plausible assumptions about the effect of tDCS captured the effects of stimulation on both neural activity and behavior. These results reveal a temporally extended brain process underlying conscious movement intention that spans seconds around movement commencement. [ABSTRACT FROM AUTHOR]

An abstract of the article "Brain mechanisms for simple perception and bistable perception," by Megan Wang, Daniel Arteaga, and Biyu J. He is presented.

Slow (-0.1 Hz), spontaneous fluctuations in the functional magnetic resonance imaging blood oxygen level-dependent (BOLD) signal have been shown to exhibit phase coherence within functionally related areas of the brain. Surprisingly, this phenomenon appears to transcend levels of consciousness. The genesis of coherent BOLD fluctuations remains to be fully explained. We present a resting state functional connectivity study of a 6-year-old child with a radiologically normal brain imaged both before and after complete section of the corpus callosum for the treatment of intractable epilepsy. Postoperatively, there was a striking loss of interhemispheric BOLD correlations with preserved intrahemispheric correlations. These unique data provide important insights into the relationship between connectional anatomy and functional organization of the human brain. Such observations have the potential to increase our understanding of large-scale brain systems in health and disease as well as improve the treatment of neurologic disorders. [ABSTRACT FROM AUTHOR]