Broadband Dynamics Rather than Frequency-Specific Rhythms Underlie Prediction Error in the Primate Auditory Cortex.

Detection of statistical irregularities, measured as a prediction error response, is fundamental to the perceptual monitoring of the environment. We studied whether prediction error response is associated with neural oscillations or asynchronous broadband activity. Electrocorticography was conducted in three male monkeys, who passively listened to the auditory roving oddball stimuli. Local field potentials (LFPs) recorded over the auditory cortex underwent spectral principal component analysis, which decoupled broadband and rhythmic components of the LFP signal. We found that the broadband component captured the prediction error response, whereas none of the rhythmic components were associated with statistical irregularities of sounds. The broadband component displayed more stochastic, asymmetrical multifractal properties than the rhythmic components, which revealed more self-similar dynamics. We thus conclude that the prediction error response is captured by neuronal populations generating asynchronous broadband activity, defined by irregular dynamic states, which, unlike oscillatory rhythms, appear to enable the neural representation of auditory prediction error response.SIGNIFICANCE STATEMENT This study aimed to examine the contribution of oscillatory and asynchronous components of auditory local field potentials in the generation of prediction error responses to sensory irregularities, as this has not been directly addressed in the previous studies. Here, we show that mismatch negativity-an auditory prediction error response-is driven by the asynchronous broadband component of potentials recorded in the auditory cortex. This finding highlights the importance of nonoscillatory neural processes in the predictive monitoring of the environment. At a more general level, the study demonstrates that stochastic neural processes, which are often disregarded as neural noise, do have a functional role in the processing of sensory information.

EECoG-Comp: An Open Source Platform for Concurrent EEG/ECoG Comparisons-Applications to Connectivity Studies.

Electrophysiological Source Imaging (ESI) is hampered by lack of "gold standards" for model validation. Concurrent electroencephalography (EEG) and electrocorticography (ECoG) experiments (EECoG) are useful for this purpose, especially primate models due to their flexibility and translational value for human research. Unfortunately, there is only one EECoG experiments in the public domain that we know of: the Multidimensional Recording (MDR) is based on a single monkey ( www.neurotycho.org ). The mining of this type of data is hindered by lack of specialized procedures to deal with: (1) Severe EECoG artifacts due to the experimental produces; (2) Sophisticated forward models that account for surgery induced skull defects and implanted ECoG electrode strips; (3) Reliable statistical procedures to estimate and compare source connectivity (partial correlation). We provide solutions to the processing issues just mentioned with EECoG-Comp: an open source platform ( https://github.com/Vincent-wq/EECoG-Comp ). EECoG lead fields calculated with FEM (Simbio) for MDR data are also provided and were used in other papers of this special issue. As a use case with the MDR, we show: (1) For real MDR data, 4 popular ESI methods (MNE, LCMV, eLORETA and SSBL) showed significant but moderate concordance with a usual standard, the ECoG Laplacian (standard partial [Formula: see text]); (2) In both monkey and human simulations, all ESI methods as well as Laplacian had a significant but poor correspondence with the true source connectivity. These preliminary results may stimulate the development of improved ESI connectivity estimators but require the availability of more EECoG data sets to obtain neurobiologically valid inferences.

Measuring Integrated Information from the Decoding Perspective.

Accumulating evidence indicates that the capacity to integrate information in the brain is a prerequisite for consciousness. Integrated Information Theory (IIT) of consciousness provides a mathematical approach to quantifying the information integrated in a system, called integrated information, Φ. Integrated information is defined theoretically as the amount of information a system generates as a whole, above and beyond the amount of information its parts independently generate. IIT predicts that the amount of integrated information in the brain should reflect levels of consciousness. Empirical evaluation of this theory requires computing integrated information from neural data acquired from experiments, although difficulties with using the original measure Φ precludes such computations. Although some practical measures have been previously proposed, we found that these measures fail to satisfy the theoretical requirements as a measure of integrated information. Measures of integrated information should satisfy the lower and upper bounds as follows: The lower bound of integrated information should be 0 and is equal to 0 when the system does not generate information (no information) or when the system comprises independent parts (no integration). The upper bound of integrated information is the amount of information generated by the whole system. Here we derive the novel practical measure Φ* by introducing a concept of mismatched decoding developed from information theory. We show that Φ* is properly bounded from below and above, as required, as a measure of integrated information. We derive the analytical expression of Φ* under the Gaussian assumption, which makes it readily applicable to experimental data. Our novel measure Φ* can generally be used as a measure of integrated information in research on consciousness, and also as a tool for network analysis on diverse areas of biology.

Loss of Consciousness Is Associated with Stabilization of Cortical Activity.

What aspects of neuronal activity distinguish the conscious from the unconscious brain? This has been a subject of intense interest and debate since the early days of neurophysiology. However, as any practicing anesthesiologist can attest, it is currently not possible to reliably distinguish a conscious state from an unconscious one on the basis of brain activity. Here we approach this problem from the perspective of dynamical systems theory. We argue that the brain, as a dynamical system, is self-regulated at the boundary between stable and unstable regimes, allowing it in particular to maintain high susceptibility to stimuli. To test this hypothesis, we performed stability analysis of high-density electrocorticography recordings covering an entire cerebral hemisphere in monkeys during reversible loss of consciousness. We show that, during loss of consciousness, the number of eigenmodes at the edge of instability decreases smoothly, independently of the type of anesthetic and specific features of brain activity. The eigenmodes drift back toward the unstable line during recovery of consciousness. Furthermore, we show that stability is an emergent phenomenon dependent on the correlations among activity in different cortical regions rather than signals taken in isolation. These findings support the conclusion that dynamics at the edge of instability are essential for maintaining consciousness and provide a novel and principled measure that distinguishes between the conscious and the unconscious brain. SIGNIFICANCE STATEMENT: What distinguishes brain activity during consciousness from that observed during unconsciousness? Answering this question has proven difficult because neither consciousness nor lack thereof have universal signatures in terms of most specific features of brain activity. For instance, different anesthetics induce different patterns of brain activity. We demonstrate that loss of consciousness is universally and reliably associated with stabilization of cortical dynamics regardless of the specific activity characteristics. To give an analogy, our analysis suggests that loss of consciousness is akin to depressing the damper pedal on the piano, which makes the sounds dissipate quicker regardless of the specific melody being played. This approach may prove useful in detecting consciousness on the basis of brain activity under anesthesia and other settings.

Frequency-dependent spatiotemporal profiles of visual responses recorded with subdural ECoG electrodes in awake monkeys: Differences between high- and low-frequency activity.

Electrocorticography (ECoG) constitutes a powerful and promising neural recording modality in humans and animals. ECoG signals are often decomposed into several frequency bands, among which the so-called high-gamma band (80-250Hz) has been proposed to reflect local cortical functions near the cortical surface below the ECoG electrodes. It is typically assumed that the lower the frequency bands, the lower the spatial resolution of the signals; thus, there is not much to gain by analyzing the event-related changes of the ECoG signals in the lower-frequency bands. However, differences across frequency bands have not been systematically investigated. To address this issue, we recorded ECoG activity from two awake monkeys performing a retinotopic mapping task. We characterized the spatiotemporal profiles of the visual responses in the time-frequency domain. We defined the preferred spatial position, receptive field (RF), and response latencies of band-limited power (BLP) (i.e., alpha [3.9-11.7Hz], beta [15.6-23.4Hz], low [30-80Hz] and high [80-250Hz] gamma) for each electrode and compared them across bands and time-domain visual evoked potentials (VEPs). At the population level, we found that the spatial preferences were comparable across bands and VEPs. The high-gamma power showed a smaller RF than the other bands and VEPs. The response latencies for the alpha band were always longer than the latencies for the other bands and fastest in VEPs. Comparing the response profiles in both space and time for each cortical region (V1, V4+, and TEO/TE) revealed regional idiosyncrasies. Although the latencies of visual responses in the beta, low-, and high-gamma bands were almost identical in V1 and V4+, beta and low-gamma BLP occurred about 17ms earlier than high-gamma power in TEO/TE. Furthermore, TEO/TE exhibited a unique pattern in the spatial response profile: the alpha and high-gamma responses tended to prefer the foveal regions, whereas the beta and low-gamma responses preferred the peripheral visual fields with larger RFs. This suggests that neurons in TEO/TE first receive less selective spatial information via beta and low-gamma BLP but later receive more fine-tuned spatial foveal information via high-gamma power. This result is consistent with a hypothesis previously proposed by Nakamura et al. (1993) that states that visual processing in TEO/TE starts with coarse-grained information, which primes subsequent fine-grained information. Collectively, our results demonstrate that ECoG can be a potent tool for investigating the nature of the neural computations in each cortical region that cannot be fully understood by measuring only the spiking activity, through the incorporation of the knowledge of the spatiotemporal characteristics across all frequency bands.

Facilitative effect of repetitive presentation of one stimulus on cortical responses to other stimuli in macaque monkeys--a possible neural mechanism for mismatch negativity.

The event-related potential 'mismatch negativity' (MMN) is an indicator of a perceiver's ability to detect deviations in sensory signal streams. MMN and its homologue in animals, mismatch activity (MMA), are differential neural responses to a repeatedly presented stimulus and a subsequent deviant stimulus (oddball). Because neural mechanisms underlying MMN and MMA remain unclear, there is a controversy as to whether MMN and MMA arise solely from stimulus-specific adaptation (SSA), in which the response to a stimulus cumulatively attenuates with its repetitive presentation. To address this issue, we used electrocorticography and the auditory roving-oddball paradigm in two awake macaque monkeys. We examined the effect of stimulus repetition number on MMA and on responses to repeated stimuli and oddballs across the cerebral cortex in the time-frequency domain. As the repetition number increased, MMA spread across the temporal, frontal and parietal cortices, and each electrode yielded a larger MMA. Surprisingly, this increment in MMA largely depended on response augmentation to the oddball rather than on SSA to the repeated stimulus. Following sufficient repetition, the oddball evoked a spectral power increment in some electrodes on the frontal cortex that had shown no power increase to the stimuli with less or no preceding repetition. We thereby revealed that repetitive presentation of one stimulus not only leads to SSA but also facilitates the cortical response to oddballs involving a wide range of cortical regions. This facilitative effect might underlie the generation of MMN-like scalp potentials in macaques that potentially shares similar neural mechanisms with MMN in humans.

Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding.

Brain-wide interactions generating complex neural dynamics are considered crucial for emergent cognitive functions. However, the irreducible nature of nonlinear and high-dimensional dynamical interactions challenges conventional reductionist approaches. We introduce a model-free method, based on embedding theorems in nonlinear state-space reconstruction, that permits a simultaneous characterization of complexity in local dynamics, directed interactions between brain areas, and how the complexity is produced by the interactions. We demonstrate this method in large-scale electrophysiological recordings from awake and anesthetized monkeys. The cross-embedding method captures structured interaction underlying cortex-wide dynamics that may be missed by conventional correlation-based analysis, demonstrating a critical role of time-series analysis in characterizing brain state. The method reveals a consciousness-related hierarchy of cortical areas, where dynamical complexity increases along with cross-area information flow. These findings demonstrate the advantages of the cross-embedding method in deciphering large-scale and heterogeneous neuronal systems, suggesting a crucial contribution by sensory-frontoparietal interactions to the emergence of complex brain dynamics during consciousness.

Robust Long-Range Coordination of Spontaneous Neural Activity in Waking, Sleep and Anesthesia.

Although the emerging field of functional connectomics relies increasingly on the analysis of spontaneous fMRI signal covariation to infer the spatial fingerprint of the brain's large-scale functional networks, the nature of the underlying neuro-electrical activity remains incompletely understood. In part, this lack in understanding owes to the invasiveness of electrophysiological acquisition, the difficulty in their simultaneous recording over large cortical areas, and the absence of fully established methods for unbiased extraction of network information from these data. Here, we demonstrate a novel, data-driven approach to analyze spontaneous signal variations in electrocorticographic (ECoG) recordings from nearly entire hemispheres of macaque monkeys. Based on both broadband analysis and analysis of specific frequency bands, the ECoG signals were found to co-vary in patterns that resembled the fMRI networks reported in previous studies. The extracted patterns were robust against changes in consciousness associated with sleep and anesthesia, despite profound changes in intrinsic characteristics of the raw signals, including their spectral signatures. These results suggest that the spatial organization of large-scale brain networks results from neural activity with a broadband spectral feature and is a core aspect of the brain's physiology that does not depend on the state of consciousness.

Arousal transitions in sleep, wakefulness, and anesthesia are characterized by an orderly sequence of cortical events.

Many aspects of brain function are influenced by modulatory processes, including arousal. The most abrupt changes in arousal occur at the wake-sleep transition and at the induction of anesthetic conditions. They are accompanied by major electrophysiological changes, including an emergence of low-frequency (sleep-like) activity and a loss of mid-frequency (wake-like) activity that has been linked to feedback processes of the brain. Nevertheless, the causal relationship between these two types of electrophysiological changes, as well as the cortical mechanisms underlying changes in arousal and consciousness, remain poorly understood. To address this, we studied spontaneous electro-cortical activity during arousal changes in macaques. During sleep and at loss of consciousness induced by propofol anesthesia, we identified a prototypical sequence of cortical events in which the loss of mid-frequency activity preceded, by seconds, the increases in low-frequency activity. Furthermore, in visual areas, an influence of mid-frequency change onto high-frequency activity was observed across visual hierarchy. These results are consistent with the notion that drops in arousal and consciousness are facilitated by a release of feedback cortical inhibition.

[Development of the Venture Scientist Mindset among Japanese Basic Researchers].

Japanese basic researchers, known for their dedication to the advancement of science without any expectation of economic benefit, are conventionally regarded as virtuous professionals. However, current social demand requires researchers to adopt a venture mindset, implement their research outcomes for societal benefit, and contribute to society through business. In this paper, I highlight the importance of overcoming the "valley of death" between society and researchers to create useful intersections between science and business, aimed at application of research outcomes to the society and encouraging a lifestyle and challenges as venture scientists who can contribute to the generation of new industries.

A system for recording neural activity chronically and simultaneously from multiple cortical and subcortical regions in nonhuman primates.

A major goal of neuroscience is to understand the functions of networks of neurons in cognition and behavior. Recent work has focused on implanting arrays of ∼100 immovable electrodes or smaller numbers of individually adjustable electrodes, designed to target a few cortical areas. We have developed a recording system that allows the independent movement of hundreds of electrodes chronically implanted in several cortical and subcortical structures. We have tested this system in macaque monkeys, recording simultaneously from up to 127 electrodes in 14 brain regions for up to one year at a time. A key advantage of the system is that it can be used to sample different combinations of sites over prolonged periods, generating multiple snapshots of network activity from a single implant. Used in conjunction with microstimulation and injection methods, this versatile system represents a powerful tool for studying neural network activity in the primate brain.

Neural representation of time in cortico-basal ganglia circuits.

Encoding time is universally required for learning and structuring motor and cognitive actions, but how the brain keeps track of time is still not understood. We searched for time representations in cortico-basal ganglia circuits by recording from thousands of neurons in the prefrontal cortex and striatum of macaque monkeys performing a routine visuomotor task. We found that a subset of neurons exhibited time-stamp encoding strikingly similar to that required by models of reinforcement-based learning: They responded with spike activity peaks that were distributed at different time delays after single task events. Moreover, the temporal evolution of the population activity allowed robust decoding of task time by perceptron models. We suggest that time information can emerge as a byproduct of event coding in cortico-basal ganglia circuits and can serve as a critical infrastructure for behavioral learning and performance.

Global waves synchronize the brain's functional systems with fluctuating arousal.

We propose and empirically support a parsimonious account of intrinsic, brain-wide spatiotemporal organization arising from traveling waves linked to arousal. We hypothesize that these waves are the predominant physiological process reflected in spontaneous functional magnetic resonance imaging (fMRI) signal fluctuations. The correlation structure ("functional connectivity") of these fluctuations recapitulates the large-scale functional organization of the brain. However, a unifying physiological account of this structure has so far been lacking. Here, using fMRI in humans, we show that ongoing arousal fluctuations are associated with global waves of activity that slowly propagate in parallel throughout the neocortex, thalamus, striatum, and cerebellum. We show that these waves can parsimoniously account for many features of spontaneous fMRI signal fluctuations, including topographically organized functional connectivity. Last, we demonstrate similar, cortex-wide propagation of neural activity measured with electrocorticography in macaques. These findings suggest that traveling waves spatiotemporally pattern brain-wide excitability in relation to arousal.

An Open Resource for Non-human Primate Optogenetics.

Optogenetics has revolutionized neuroscience in small laboratory animals, but its effect on animal models more closely related to humans, such as non-human primates (NHPs), has been mixed. To make evidence-based decisions in primate optogenetics, the scientific community would benefit from a centralized database listing all attempts, successful and unsuccessful, of using optogenetics in the primate brain. We contacted members of the community to ask for their contributions to an open science initiative. As of this writing, 45 laboratories around the world contributed more than 1,000 injection experiments, including precise details regarding their methods and outcomes. Of those entries, more than half had not been published. The resource is free for everyone to consult and contribute to on the Open Science Framework website. Here we review some of the insights from this initial release of the database and discuss methodological considerations to improve the success of optogenetic experiments in NHPs.

A network representation of response probability in the striatum.

The striatum of the basal ganglia is considered a key structure in the learning circuitry of the brain. To analyze neural signals that underlie striatal plasticity, we recorded from an identifiable class of striatal interneurons as macaque monkeys underwent training in a range of conditioning and non-associative learning paradigms, and recorded eyeblink electromyographs as the measure of behavioral response. We found that the responses of these striatal interneurons were modifiable under all training conditions and that their population responses were tightly correlated with the probability that a given stimulus would evoke a behavioral response. Such a network signal, proportional to current response probability, could be crucial to the learning and decision functions of the basal ganglia.

Large-Scale Cortical Networks for Hierarchical Prediction and Prediction Error in the Primate Brain.

According to predictive-coding theory, cortical areas continuously generate and update predictions of sensory inputs at different hierarchical levels and emit prediction errors when the predicted and actual inputs differ. However, predictions and prediction errors are simultaneous and interdependent processes, making it difficult to disentangle their constituent neural network organization. Here, we test the theory by using high-density electrocorticography (ECoG) in monkeys during an auditory "local-global" paradigm in which the temporal regularities of the stimuli were controlled at two hierarchical levels. We decomposed the broadband data and identified lower- and higher-level prediction-error signals in early auditory cortex and anterior temporal cortex, respectively, and a prediction-update signal sent from prefrontal cortex back to temporal cortex. The prediction-error and prediction-update signals were transmitted via γ (>40 Hz) and α/ß (<30 Hz) oscillations, respectively. Our findings provide strong support for hierarchical predictive coding and outline how it is dynamically implemented using distinct cortical areas and frequencies.

Prefrontal activity during koh-do incense discrimination.

Whenever we make reasoned decisions we must refer to relevant knowledge obtained through past experience. Our brains test multiple premises and select whichever conclusion serves as the best explanation of the current conditions. In the present study we examined the prefrontal activity of koh-do experts with near infrared spectroscopy while they reasoned about odours during an incense discrimination task. These practitioners of the Japanese incense ceremony have been trained to form and manipulate abstract images of complex olfactory stimuli represented in a multidimensional symbolic space in the mind. In koh-do experts, the right PFC showed a consistent stimulus-non-selective response during discrimination and the left PFC showed phasic stimulus-selective responses modulated by the internal subjective state of the reasoning process. These two dissociated functions appeared to cooperate with each other during reasoning. In contrast, koh-do beginners did not show the organized response pattern found in experts. The results suggest that both PFCs contribute to abductive reasoning, but do so differently through different stages of the process.

A Chronically Implantable Bidirectional Neural Interface for Non-human Primates.

Optogenetics has potential applications in the study of epilepsy and neuroprostheses, and for studies on neural circuit dynamics. However, to achieve translation to clinical usage, optogenetic interfaces that are capable of chronic stimulation and monitoring with minimal brain trauma are required. We aimed to develop a chronically implantable device for photostimulation of the brain of non-human primates. We used a micro-light-emitting diode (LED) array with a flexible polyimide film. The array was combined with a whole-cortex electrocorticographic (ECoG) electrode array for simultaneous photostimulation and recording. Channelrhodopsin-2 (ChR2) was virally transduced into the cerebral cortex of common marmosets, and then the device was epidurally implanted into their brains. We recorded the neural activity during photostimulation of the awake monkeys for 4 months. The neural responses gradually increased after the virus injection for ~8 weeks and remained constant for another 8 weeks. The micro-LED and ECoG arrays allowed semi-invasive simultaneous stimulation and recording during long-term implantation in the brains of non-human primates. The development of this device represents substantial progress in the field of optogenetic applications.

A new method for quantifying the performance of EEG blind source separation algorithms by referencing a simultaneously recorded ECoG signal.

Blind source separation (BSS) algorithms extract neural signals from electroencephalography (EEG) data. However, it is difficult to quantify source separation performance because there is no criterion to dissociate neural signals and noise in EEG signals. This study develops a method for evaluating BSS performance. The idea is neural signals in EEG can be estimated by comparison with simultaneously measured electrocorticography (ECoG). Because the ECoG electrodes cover the majority of the lateral cortical surface and should capture most of the original neural sources in the EEG signals. We measured real EEG and ECoG data and developed an algorithm for evaluating BSS performance. First, EEG signals are separated into EEG components using the BSS algorithm. Second, the EEG components are ranked using the correlation coefficients of the ECoG regression and the components are grouped into subsets based on their ranks. Third, canonical correlation analysis estimates how much information is shared between the subsets of the EEG components and the ECoG signals. We used our algorithm to compare the performance of BSS algorithms (PCA, AMUSE, SOBI, JADE, fastICA) via the EEG and ECoG data of anesthetized nonhuman primates. The results (Best case >JADE = fastICA >AMUSE = SOBI ≥ PCA >random separation) were common to the two subjects. To encourage the further development of better BSS algorithms, our EEG and ECoG data are available on our Web site (http://neurotycho.org/) as a common testing platform.

Extension of corticocortical afferents into the anterior bank of the intraparietal sulcus by tool-use training in adult monkeys.

When humans use a tool, it becomes an extension of the hand physically and perceptually. Common introspection might occur in monkeys trained in tool-use, which should depend on brain operations that constantly update and automatically integrate information about the current intrinsic (somatosensory) and the extrinsic (visual) status of the body parts and the tools. The parietal cortex plays an important role in using tools. Intraparietal neurones of naïve monkeys mostly respond unimodally to somatosensory stimuli; however, after training these neurones become bimodally active and respond to visual stimuli. The response properties of these neurones change to code the body images modified by assimilation of the tool to the hand holding it. In this study, we compared the projection patterns between visually related areas and the intraparietal cortex in trained and naïve monkeys using tracer techniques. Light microscopy analyses revealed the emergence of novel projections from the higher visual centres in the vicinity of the temporo-parietal junction and the ventrolateral prefrontal areas to the intraparietal area in monkeys trained in tool-use, but not in naïve monkeys. Functionally active synapses of intracortical afferents arising from higher visual centres to the intraparietal cortex of the trained monkeys were confirmed by electron microscopy. These results provide the first concrete evidence for the induction of novel neural connections in the adult monkey cerebral cortex, which accompanies a process of demanding behaviour in these animals.