The Role of Top-Down Modulation in Shaping Sensory Processing Across Brain States: Implications for Consciousness

An integrative, multiscale view on neural theories of consciousness

How is conscious experience related to material brain processes? A variety of theories aiming to answer this age-old question have emerged from the recent surge in consciousness research, and some are now hotly debated. Although most researchers have so far focused on the development and validation of their preferred theory in relative isolation, this article, written by a group of scientists representing different theories, takes an alternative approach. Noting that various theories often try to explain different aspects or mechanistic levels of consciousness, we argue that the theories do not necessarily contradict each other. Instead, several of them may converge on fundamental neuronal mechanisms and be partly compatible and complementary, so that multiple theories can simultaneously contribute to our understanding. Here, we consider unifying, integration-oriented approaches that have so far been largely neglected, seeking to combine valuable elements from various theories.

Association of bidirectional network cores in the brain with conscious perception and cognition

The brain comprises a complex network of interacting regions. To understand the roles and mechanisms of this complex network, its structural features related to specific cognitive functions need to be elucidated. Among such relationships, recent developments in neuroscience highlight the link between network bidirectionality and conscious perception. Given the essential roles of both feedforward and feedback signals in conscious perception, it is surmised that subnetworks with bidirectional interactions are critical. However, the link between such subnetworks and conscious perception remains unclear due to the network's complexity. In this study, we propose a framework for extracting subnetworks with strong bidirectional interactions-termed the "cores" of a network-from brain activity. We applied this framework to resting-state and task-based fMRI data to identify regions forming strongly bidirectional cores. We then explored the association of these cores with conscious perception and cognitive functions. The central cores predominantly included cerebral cortical regions, which are crucial for conscious perception, rather than subcortical regions. Furthermore, the cores were composed of previously reported regions in which electrical stimulation altered conscious perception. These results suggest a link between the bidirectional cores and conscious perception. A meta-analysis and comparison of the core structure with a cortical functional connectivity gradient suggested that the central cores were related to lower-order sensorimotor functions. An ablation study emphasized the importance of incorporating bidirectionality, not merely interaction strength for these outcomes. The proposed framework provides novel insight into the roles of network cores with strong bidirectional interactions in conscious perception and lower-order sensorimotor functions.

The claustrum and consciousness: An update

The seminal paper of Crick and Koch (2005) proposed that the claustrum, an enigmatic and thin grey matter structure that lies beside the insular cortex, may be involved in the processing of consciousness. As a result, this otherwise obscure structure has received ever-increasing interest in the search for neural correlates of consciousness. Here we review theories of consciousness and discuss the possible relationship between the claustrum and consciousness. We review relevant experimental evidence collected since the Crick and Koch (2005) paper and consider whether these findings support or contradict their hypothesis. We also explore how future experimental work can be designed to clarify how consciousness emerges from neural activity and to understand the role of the claustrum in consciousness.

Bidirectionally connected cores in a mouse connectome: towards extracting the brain subnetworks essential for consciousness

Where in the brain consciousness resides remains unclear. It has been suggested that the subnetworks supporting consciousness should be bidirectionally (recurrently) connected because both feed-forward and feedback processing are necessary for conscious experience. Accordingly, evaluating which subnetworks are bidirectionally connected and the strength of these connections would likely aid the identification of regions essential to consciousness. Here, we propose a method for hierarchically decomposing a network into cores with different strengths of bidirectional connection, as a means of revealing the structure of the complex brain network. We applied the method to a whole-brain mouse connectome. We found that cores with strong bidirectional connections consisted of regions presumably essential to consciousness (e.g. the isocortical and thalamic regions, and claustrum) and did not include regions presumably irrelevant to consciousness (e.g. cerebellum). Contrarily, we could not find such correspondence between cores and consciousness when we applied other simple methods that ignored bidirectionality. These findings suggest that our method provides a novel insight into the relation between bidirectional brain network structures and consciousness.

Awareness and consciousness in humans and animals – neural and behavioral correlates in an evolutionary perspective

Awareness or consciousness in the context of stimulus perception can directly be assessed in well controlled test situations with humans via the persons’ reports about their subjective experiences with the stimuli. Since we have no direct access to subjective experiences in animals, their possible awareness or consciousness in stimulus perception tasks has often been inferred from behavior and cognitive abilities previously observed in aware and conscious humans. Here, we analyze published human data primarily on event-related potentials and brain-wave generation during perception and responding to sensory stimuli and extract neural markers (mainly latencies of evoked-potential peaks and of gamma-wave occurrence) indicating that a person became aware or conscious of the perceived stimulus. These neural correlates of consciousness were then applied to sets of corresponding data from various animals including several species of mammals, and one species each of birds, fish, cephalopods, and insects. We found that the neural markers from studies in humans could also successfully be applied to the mammal and bird data suggesting that species in these animal groups can become subjectively aware of and conscious about perceived stimuli. Fish, cephalopod and insect data remained inconclusive. In an evolutionary perspective we have to consider that both awareness of and consciousness about perceived stimuli appear as evolved, attention-dependent options added to the ongoing neural activities of stimulus processing and action generation. Since gamma-wave generation for functional coupling of brain areas in aware/conscious states is energetically highly cost-intensive, it remains to be shown which animal species under which conditions of lifestyle and ecological niche may achieve significant advantages in reproductive fitness by drawing upon these options. Hence, we started our discussion about awareness and consciousness in animals with the question in how far these expressions of brain activity are necessary attributes for perceiving stimuli and responding in an adaptive way.

Bipolar reasoning in feedback pathways

Instead of the conventional 0 and 1 values, bipolar reasoning uses -1, 0, +1 to describe double-sided judgements in which neutral elements are halfway between positive and negative evaluations (e.g., "uncertain" lies between "impossible" and "totally sure"). We discuss the state-of-the-art in bipolar logics and recall two medieval forerunners, i.e., William of Ockham and Nicholas of Autrecourt, who embodied a bipolar mode of thought that is eminently modern. Starting from the trivial observation that "once a wheat sheaf is sealed and tied up, the packed down straws display the same orientation", we work up a new theory of the bipolar nature of networks, suggesting that orthodromic (i.e., feedforward, bottom-up) projections might be functionally coupled with antidromic (i.e., feedback, top-down) projections via the mathematical apparatus of presheaves/globular sets. When an entrained oscillation such as a neuronal spike propagates from A to B, changes in B might lead to changes in A, providing unexpected antidromic effects. Our account points towards the methodological feasibility of novel neural networks in which message feedback is guaranteed by backpropagation mechanisms endowed in the same feedforward circuits. Bottom-up/top-down transmission at various coarse-grained network levels provides fresh insights in far-flung scientific fields such as object persistence, memory reinforcement, visual recognition, Bayesian inferential circuits and multidimensional activity of the brain. Implying that axonal stimulation by external sources might backpropagate and modify neuronal electric oscillations, our theory also suggests testable previsions concerning the optimal location of transcranial magnetic stimulation's coils in patients affected by drug-resistant epilepsy.

Consumer Consciousness in Multisensory Extended Reality

The reality-virtuality continuum encompasses a multitude of objects, events and environments ranging from real-world multisensory inputs to interactive multisensory virtual simulators, in which sensory integration can involve very different combinations of both physical and digital inputs. These different ways of stimulating the senses can affect the consumer's consciousness, potentially altering their judgements and behaviours. In this perspective paper, we explore how technologies such as Augmented Reality (AR) and Virtual Reality (VR) can, by generating and modifying the human sensorium, act on consumer consciousness. We discuss the potential impact of this altered consciousness for consumer behaviour while, at the same time, considering how it may pave the way for further research.

Intrinsic neural timescales: temporal integration and segregation

We are continuously bombarded by external inputs of various timescales from the environment. How does the brain process this multitude of timescales? Recent resting state studies show a hierarchy of intrinsic neural timescales (INT) with a shorter duration in unimodal regions (e.g., visual cortex and auditory cortex) and a longer duration in transmodal regions (e.g., default mode network). This unimodal-transmodal hierarchy is present across acquisition modalities [electroencephalogram (EEG)/magnetoencephalogram (MEG) and fMRI] and can be found in different species and during a variety of different task states. Together, this suggests that the hierarchy of INT is central to the temporal integration (combining successive stimuli) and segregation (separating successive stimuli) of external inputs from the environment, leading to temporal segmentation and prediction in perception and cognition.

Higher Order Visual Areas Enhance Stimulus Responsiveness in Mouse Primary Visual Cortex

Over the past few years, the various areas that surround the primary visual cortex (V1) in the mouse have been associated with many functions, ranging from higher order visual processing to decision-making. Recently, some studies have shown that higher order visual areas influence the activity of the primary visual cortex, refining its processing capabilities. Here, we studied how in vivo optogenetic inactivation of two higher order visual areas with different functional properties affects responses evoked by moving bars in the primary visual cortex. In contrast with the prevailing view, our results demonstrate that distinct higher order visual areas similarly modulate early visual processing. In particular, these areas enhance stimulus responsiveness in the primary visual cortex, by more strongly amplifying weaker compared with stronger sensory-evoked responses (for instance specifically amplifying responses to stimuli not moving along the direction preferred by individual neurons) and by facilitating responses to stimuli entering the receptive field of single neurons. Such enhancement, however, comes at the expense of orientation and direction selectivity, which increased when the selected higher order visual areas were inactivated. Thus, feedback from higher order visual areas selectively amplifies weak sensory-evoked V1 responses, which may enable more robust processing of visual stimuli.

Automatic Sensory Predictions: A Review of Predictive Mechanisms in the Brain and Their Link to Conscious Processing

The human brain has the astonishing capacity of integrating streams of sensory information from the environment and forming predictions about future events in an automatic way. Despite being initially developed for visual processing, the bulk of predictive coding research has subsequently focused on auditory processing, with the famous mismatch negativity signal as possibly the most studied signature of a surprise or prediction error (PE) signal. Auditory PEs are present during various consciousness states. Intriguingly, their presence and characteristics have been linked with residual levels of consciousness and return of awareness. In this review we first give an overview of the neural substrates of predictive processes in the auditory modality and their relation to consciousness. Then, we focus on different states of consciousness - wakefulness, sleep, anesthesia, coma, meditation, and hypnosis - and on what mysteries predictive processing has been able to disclose about brain functioning in such states. We review studies investigating how the neural signatures of auditory predictions are modulated by states of reduced or lacking consciousness. As a future outlook, we propose the combination of electrophysiological and computational techniques that will allow investigation of which facets of sensory predictive processes are maintained when consciousness fades away.

The posterior auditory field is the chief generator of prediction error signals in the auditory cortex

The auditory cortex (AC) encompasses distinct fields subserving partly different aspects of sound processing. One essential function of the AC is the detection of unpredicted sounds, as revealed by differential neural activity to predictable and unpredictable sounds. According to the predictive coding framework, this effect can be explained by repetition suppression and/or prediction error signaling. The present study investigates functional specialization of the rat AC fields in repetition suppression and prediction error by combining a tone frequency oddball paradigm (involving high-probable standard and low-probable deviant tones) with two different control sequences (many-standards and cascade). Tones in the control sequences were comparable to deviant events with respect to neural adaptation but were not violating a regularity. Therefore, a difference in the neural activity between deviant and control tones indicates a prediction error effect, whereas a difference between control and standard tones indicates a repetition suppression effect. Single-unit recordings revealed by far the largest prediction error effects for the posterior auditory field, while the primary auditory cortex, the anterior auditory field, the ventral auditory field, and the suprarhinal auditory field were dominated by repetition suppression effects. Statistically significant repetition suppression effects occurred in all AC fields, whereas prediction error effects were less robust in the primary auditory cortex and the anterior auditory field. Results indicate that the non-lemniscal, posterior auditory field is more engaged in context-dependent processing underlying deviance-detection than the other AC fields, which are more sensitive to stimulus-dependent effects underlying differential degrees of neural adaptation.

Partial information decomposition reveals that synergistic neural integration is greater downstream of recurrent information flow in organotypic cortical cultures

The directionality of network information flow dictates how networks process information. A central component of information processing in both biological and artificial neural networks is their ability to perform synergistic integration–a type of computation. We established previously that synergistic integration varies directly with the strength of feedforward information flow. However, the relationships between both recurrent and feedback information flow and synergistic integration remain unknown. To address this, we analyzed the spiking activity of hundreds of neurons in organotypic cultures of mouse cortex. We asked how empirically observed synergistic integration–determined from partial information decomposition–varied with local functional network structure that was categorized into motifs with varying recurrent and feedback information flow. We found that synergistic integration was elevated in motifs with greater recurrent information flow beyond that expected from the local feedforward information flow. Feedback information flow was interrelated with feedforward information flow and was associated with decreased synergistic integration. Our results indicate that synergistic integration is distinctly influenced by the directionality of local information flow.

Networks compute information. That is, they modify inputs to generate distinct outputs. These computations are an important component of network information processing. Knowing how the routing of information in a network influences computation is therefore crucial. Here we asked how a key form of computation—synergistic integration—is related to the direction of local information flow in networks of spiking cortical neurons. Specifically, we asked how information flow between input neurons (i.e., recurrent information flow) and information flow from output neurons to input neurons (i.e., feedback information flow) was related to the amount of synergistic integration performed by output neurons. We found that greater synergistic integration occurred where there was more recurrent information flow. And, lesser synergistic integration occurred where there was more feedback information flow relative to feedforward information flow. These results show that computation, in the form of synergistic integration, is distinctly influenced by the directionality of local information flow. Such work is valuable for predicting where and how network computation occurs and for designing networks with desired computational abilities.

Top-Down Inference in the Auditory System: Potential Roles for Corticofugal Projections

It has become widely accepted that humans use contextual information to infer the meaning of ambiguous acoustic signals. In speech, for example, high-level semantic, syntactic, or lexical information shape our understanding of a phoneme buried in noise. Most current theories to explain this phenomenon rely on hierarchical predictive coding models involving a set of Bayesian priors emanating from high-level brain regions (e.g., prefrontal cortex) that are used to influence processing at lower-levels of the cortical sensory hierarchy (e.g., auditory cortex). As such, virtually all proposed models to explain top-down facilitation are focused on intracortical connections, and consequently, subcortical nuclei have scarcely been discussed in this context. However, subcortical auditory nuclei receive massive, heterogeneous, and cascading descending projections at every level of the sensory hierarchy, and activation of these systems has been shown to improve speech recognition. It is not yet clear whether or how top-down modulation to resolve ambiguous sounds calls upon these corticofugal projections. Here, we review the literature on top-down modulation in the auditory system, primarily focused on humans and cortical imaging/recording methods, and attempt to relate these findings to a growing animal literature, which has primarily been focused on corticofugal projections. We argue that corticofugal pathways contain the requisite circuitry to implement predictive coding mechanisms to facilitate perception of complex sounds and that top-down modulation at early (i.e., subcortical) stages of processing complement modulation at later (i.e., cortical) stages of processing. Finally, we suggest experimental approaches for future studies on this topic.

Prediction error signaling explains neuronal mismatch responses in the medial prefrontal cortex

The mismatch negativity (MMN) is a key biomarker of automatic deviance detection thought to emerge from 2 cortical sources. First, the auditory cortex (AC) encodes spectral regularities and reports frequency-specific deviances. Then, more abstract representations in the prefrontal cortex (PFC) allow to detect contextual changes of potential behavioral relevance. However, the precise location and time asynchronies between neuronal correlates underlying this frontotemporal network remain unclear and elusive. Our study presented auditory oddball paradigms along with "no-repetition" controls to record mismatch responses in neuronal spiking activity and local field potentials at the rat medial PFC. Whereas mismatch responses in the auditory system are mainly induced by stimulus-dependent effects, we found that auditory responsiveness in the PFC was driven by unpredictability, yielding context-dependent, comparatively delayed, more robust and longer-lasting mismatch responses mostly comprised of prediction error signaling activity. This characteristically different composition discarded that mismatch responses in the PFC could be simply inherited or amplified downstream from the auditory system. Conversely, it is more plausible for the PFC to exert top-down influences on the AC, since the PFC exhibited flexible and potent predictive processing, capable of suppressing redundant input more efficiently than the AC. Remarkably, the time course of the mismatch responses we observed in the spiking activity and local field potentials of the AC and the PFC combined coincided with the time course of the large-scale MMN-like signals reported in the rat brain, thereby linking the microscopic, mesoscopic, and macroscopic levels of automatic deviance detection.

The (Un)Conscious Mouse as a Model for Human Brain Functions: Key Principles of Anesthesia and Their Impact on Translational Neuroimaging

In recent years, technical and procedural advances have brought functional magnetic resonance imaging (fMRI) to the field of murine neuroscience. Due to its unique capacity to measure functional activity non-invasively, across the entire brain, fMRI allows for the direct comparison of large-scale murine and human brain functions. This opens an avenue for bidirectional translational strategies to address fundamental questions ranging from neurological disorders to the nature of consciousness. The key challenges of murine fMRI are: (1) to generate and maintain functional brain states that approximate those of calm and relaxed human volunteers, while (2) preserving neurovascular coupling and physiological baseline conditions. Low-dose anesthetic protocols are commonly applied in murine functional brain studies to prevent stress and facilitate a calm and relaxed condition among animals. Yet, current mono-anesthesia has been shown to impair neural transmission and hemodynamic integrity. By linking the current state of murine electrophysiology, Ca2+ imaging and fMRI of anesthetic effects to findings from human studies, this systematic review proposes general principles to design, apply and monitor anesthetic protocols in a more sophisticated way. The further development of balanced multimodal anesthesia, combining two or more drugs with complementary modes of action helps to shape and maintain specific brain states and relevant aspects of murine physiology. Functional connectivity and its dynamic repertoire as assessed by fMRI can be used to make inferences about cortical states and provide additional information about whole-brain functional dynamics. Based on this, a simple and comprehensive functional neurosignature pattern can be determined for use in defining brain states and anesthetic depth in rest and in response to stimuli. Such a signature can be evaluated and shared between labs to indicate the brain state of a mouse during experiments, an important step toward translating findings across species.

Peri-Personal Space Tracing by Hand-Blink Reflex Modulation in Patients with Chronic Disorders of Consciousness

The assessment of awareness in patients with chronic Disorders of Consciousness (DoC), including Unresponsive Wakefulness Syndrome (UWS) and Minimally Conscious State (MCS), is challenging. The level of awareness impairment may depend on the degree of deterioration of the large-scale cortical-thalamo-cortical networks induced by brain injury. Electrophysiological approaches may shed light on awareness presence in patients with DoC by estimating cortical functions related to the cortical-thalamo-cortical networks including, for example, the cortico-subcortical processes generating motor responses to the perturbation of the peri-personal space (PPS). We measured the amplitude, latency, and duration of the hand-blink reflex (HBR) responses by recording electromyography (EMG) signals from both the orbicularis oculi muscles while electrically stimulating the median nerve at the wrist. Such a BR is thought to be mediated by a neural circuit at the brainstem level. Despite its defensive-response nature, HBR can be modulated by the distance between the stimulated hand and the face. This suggests a functional top-down control of HBR as reflected by HBR features changes (latency, amplitude, and magnitude). We therefore estimated HBR responses in a sample of patients with DoC (8 MCS and 12 UWS, compared to 15 healthy controls -HC) while performing a motor task targeting the PPS. This consisted of passive movements in which the hand of the subject was positioned at different distances from the participant's face. We aimed at demonstrating a residual top-down modulation of HBR properties, which could be useful to differentiate patients with DoC and, potentially, demonstrate awareness preservation. We found a decrease in latency, and an increase in duration and magnitude of HBR responses, which were all inversely related to the hand-to-face distance in HC and patients with MCS, but not in individuals with UWS. Our data suggest that only patients with MCS have preserved, residual, top-down modulation of the processes related to the PPS from higher-order cortical areas to sensory-motor integration network. Although the sample size was relatively small, being thus our data preliminary, HBR assessment seems a rapid, easy, and first-level tool to differentiate patients with MCS from those with UWS. We may also hypothesize that such a HBR modulation suggests awareness preservation.

Auditory Mismatch Negativity Under Predictive Coding Framework and Its Role in Psychotic Disorders

Traditional neuroscience sees sensory perception as a simple feedforward process. This view is challenged by the predictive coding model in recent years due to the robust evidence researchers had found on how our prediction could influence perception. In the first half of this article, we reviewed the concept of predictive brain and some empirical evidence of sensory prediction in visual and auditory processing. The predictive function along the auditory pathway was mainly studied by mismatch negativity (MMN)-a brain response to an unexpected disruption of regularity. We summarized a range of MMN paradigms and discussed how they could contribute to the theoretical development of the predictive coding neural network by the mechanism of adaptation and deviance detection. Such methodological and conceptual evolution sharpen MMN as a tool to better understand the structural and functional brain abnormality for neuropsychiatric disorder such as schizophrenia.