Search

Summary: The cerebral cortex is widely considered part of the neural substrate of consciousness, but direct causal evidence is missing. Here, we tested in mice whether optogenetic activation of cortical neurons in posterior parietal cortex (PtA) or medial prefrontal cortex (mPFC) is sufficient for arousal from three behavioral states characterized by progressively deeper unresponsiveness: sleep, a coma-like state induced by muscimol injection in the midbrain, and deep sevoflurane-dexmedetomidine anesthesia. We find that cortical stimulation always awakens the mice from both NREM sleep and REM sleep, with PtA requiring weaker/shorter light pulses than mPFC. Moreover, in most cases light pulses produce both cortical activation (decrease in low frequencies) and behavioral arousal (recovery of the righting reflex) from brainstem coma, as well as cortical activation from anesthesia. These findings provide evidence that direct activation of cortical neurons is sufficient for behavioral and/or cortical arousal from sleep, brainstem coma, and anesthesia.

This paper presents Integrated Information Theory (IIT) 4.0. IIT aims to account for the properties of experience in physical (operational) terms. It identifies the essential properties of experience (axioms), infers the necessary and sufficient properties that its substrate must satisfy (postulates), and expresses them in mathematical terms. In principle, the postulates can be applied to any system of units in a state to determine whether it is conscious, to what degree, and in what way. IIT offers a parsimonious explanation of empirical evidence, makes testable predictions concerning both the presence and the quality of experience, and permits inferences and extrapolations. IIT 4.0 incorporates several developments of the past ten years, including a more accurate formulation of the axioms as postulates and mathematical expressions, the introduction of a unique measure of intrinsic information that is consistent with the postulates, and an explicit assessment of causal relations. By fully unfolding a system's irreducible cause-effect power, the distinctions and relations specified by a substrate can account for the quality of experience.

Neurophysiological differentiation (ND), a measure of the number of distinct activity states that a neural population visits over a time interval, has been used as a correlate of meaningfulness or subjective perception of visual stimuli. ND has largely been studied in non-invasive human whole-brain recordings where spatial resolution is limited. However, it is likely that perception is supported by discrete neuronal populations rather than the whole brain. Therefore, here we use Neuropixels recordings from the mouse brain to characterize the ND metric across a wide range of temporal scales, within neural populations recorded at single-cell resolution in localized regions. Using the spiking activity of thousands of simultaneously recorded neurons spanning 6 visual cortical areas and the visual thalamus, we show that the ND of stimulus-evoked activity of the entire visual cortex is higher for naturalistic stimuli relative to artificial ones. This finding holds in most individual areas throughout the visual hierarchy. Moreover, for animals performing an image change detection task, ND of the entire visual cortex (though not individual areas) is higher for successful detection compared to failed trials, consistent with the assumed perception of the stimulus. Together, these results suggest that ND computed on cellular-level neural recordings is a useful tool highlighting cell populations that may be involved in subjective perception.

Multiple evidence in rodents shows that the strength of excitatory synapses in the cerebral cortex and hippocampus is greater after wake than after sleep. The widespread synaptic weakening afforded by sleep is believed to keep the cost of synaptic activity under control, promote memory consolidation, and prevent synaptic saturation, thus preserving the brain’s ability to learn day after day. The cerebellum is highly plastic and the Purkinje cells, the sole output neurons of the cerebellar cortex, are endowed with a staggering number of excitatory parallel fiber synapses. However, whether these synapses are affected by sleep and wake is unknown. Here, we used serial block face scanning electron microscopy to obtain the full 3D reconstruction of more than 7000 spines and their parallel fiber synapses in the mouse posterior vermis. This analysis was done in mice whose cortical and hippocampal synapses were previously measured, revealing that average synaptic size was lower after sleep compared to wake with no major changes in synapse number. Here, instead, we find that while the average size of parallel fiber synapses does not change, the number of branched synapses is reduced in half after sleep compared to after wake, corresponding to ~16% of all spines after wake and ~8% after sleep. Branched synapses are harbored by two or more spines sharing the same neck and, as also shown here, are almost always contacted by different parallel fibers. These findings suggest that during wake, coincidences of firing over parallel fibers may translate into the formation of synapses converging on the same branched spine, which may be especially effective in driving Purkinje cells to fire. By contrast, sleep may promote the off-line pruning of branched synapses that were formed due to spurious coincidences.

The synaptic mechanisms of how sleep benefits cognitive functions are not well characterised. Here, the authors show that sleep leads to an overall net decrease in spine-surface GluA1-containing AMPA receptors and that this is correlated with changes in performance after sleep.

Integrated information theory (IIT) starts from consciousness itself and identifies a set of properties (axioms) that are true of every conceivable experience. The axioms are translated into a set of postulates about the substrate of consciousness (called a complex), which are then used to formulate a mathematical framework for assessing both the quality and quantity of experience. The explanatory identity proposed by IIT is that an experience is identical to the cause–effect structure unfolded from a maximally irreducible substrate (a Φ-structure). In this work we introduce a definition for the integrated information of a system (φs) that is based on the existence, intrinsicality, information, and integration postulates of IIT. We explore how notions of determinism, degeneracy, and fault lines in the connectivity impact system-integrated information. We then demonstrate how the proposed measure identifies complexes as systems, the φs of which is greater than the φs of any overlapping candidate systems.

Several new derivatives of adenine, purine, and theophylline containing the (CF3)2CH group connected to a nitrogen atom of the imidazole ring were prepared by the reaction of 2,2,4,4-tetrakis(trifluoromethyl)-1,3-dithietane (1) with the corresponding substrates, resulting in the selective alkylation of one of the nitrogen atoms of the imidazole ring. The reaction proceeds under mild conditions in a polar solvent, giving the alkylated products in 47–78% yield. While for purine and 4- and 5-azabenzimidazole, the reaction led to a mixture of two isomers, the reaction of adenine and the corresponding 2-fluoro derivative was regioselective, resulting in the formation of only one isomer in each case. The alkylation of theophylline led to the formation of a new derivative of caffeine.

Decatenation is a crucial in vivo reaction of DNA topoisomerases in DNA replication and is frequently used in in vitro drug screening. Usually this reaction is monitored using kinetoplast DNA as a substrate, although this assay has several limitations. Here we have engineered a substrate for Tn3 resolvase that generates a singly-linked catenane that can readily be purified from the DNA substrate after restriction enzyme digestion and centrifugation. We show that this catenated substrate can be used with high sensitivity in topoisomerase assays and drug-inhibition assays.

AimsUnderstanding atypical forms of diabetes (AD) may advance precision medicine, but methods to identify such patients are needed. We propose an electronic health record (EHR)-based algorithmic approach to identify patients who may have AD, specifically those with insulin-sufficient, non-metabolic diabetes, in order to improve feasibility of identifying these patients through detailed chart review.MethodsPatients with likely T2D were selected using a validated machine-learning (ML) algorithm applied to EHR data. "Typical" T2D cases were removed by excluding individuals with obesity, evidence of dyslipidemia, antibody-positive diabetes, or cystic fibrosis. To filter out likely type 1 diabetes (T1D) cases, we applied six additional "branch algorithms," relying on various clinical characteristics, which resulted in six overlapping cohorts. Diabetes type was classified by manual chart review as atypical, not atypical, or indeterminate due to missing information.ResultsOf 114,975 biobank participants, the algorithms collectively identified 119 (0.1%) potential AD cases, of which 16 (0.014%) were confirmed after expert review. The branch algorithm that excluded T1D based on outpatient insulin use had the highest percentage yield of AD (13 of 27; 48.2% yield). Together, the 16 AD cases had significantly lower BMI and higher HDL than either unselected T1D or T2D cases identified by ML algorithms (PConclusionOur EHR-based algorithms followed by manual chart review identified collectively 16 individuals with AD, representing 0.22% of biobank enrollees with T2D. With a maximum yield of 48% cases after manual chart review, our algorithms have the potential to drastically improve efficiency of AD identification. Recognizing patients with AD may inform on the heterogeneity of T2D and facilitate enrollment in studies like the Rare and Atypical Diabetes Network (RADIANT).

Summary: Background: The COVID-19 pandemic highlights the need for therapies that improve immune function in older adults, including interferon (IFN)-induced antiviral immunity that declines with age. In a previous phase 2a trial, RTB101 (previously known as BEZ235), an oral mechanistic target of rapamycin (mTOR) inhibitor, was observed to increase IFN-induced antiviral gene expression and decrease the incidence of respiratory tract infections (RTIs) in older adults. Therefore, we aimed to investigate whether oral RTB101 upregulated IFN-induced antiviral responses and decreased the incidence of viral RTIs when given once daily for 16 weeks during winter cold and flu season. Methods: We did a phase 2b and a phase 3 double-blind, randomised, placebo-controlled trial in adults aged at least 65 years enrolled in New Zealand, Australia, and the USA at 54 sites. In the phase 2b trial, patients were aged 65–85 years, with asthma, type 2 diabetes, chronic obstructive pulmonary disease (COPD), congestive heart failure, were current smokers, or had an emergency room or hospitalisation for an RTI within the past 12 months. In the phase 3 trial, patients were aged at least 65 years, did not have COPD, and were not current smokers. In the phase 2b trial, patients were randomly assigned to using a validated automated randomisation system to oral RTB101 5 mg, RTB101 10 mg once daily, or placebo in part 1 and RTB101 10 mg once daily, RTB101 10 mg twice daily, RTB101 10 mg plus everolimus once daily, or matching placebo in part 2. In the phase 3 trial, patients were randomly assigned to RTB101 10mg once daily or matching placebo. The phase 2b primary outcome was the incidence of laboratory-confirmed RTIs during 16 weeks of winter cold and influenza season and the phase 3 primary outcome was the incidence of clinically symptomatic respiratory illness defined as symptoms consistent with an RTI, irrespective of whether an infection was laboratory-confirmed. Patients, investigators, and sponsor were masked to treatment assignments. All patients who received at least part of one dose of study drug were included in the primary and safety analyses. The phase 2b trial was registered with ANZCTR, ACTRN12617000468325, ClinicalTrials.gov, NCT03373903, and the phase 3 trial was registered with ANZCTR, ACTRN12619000628145. Findings: In the phase 2b trial, we recruited 652 participants in total between May 16, 2017, and Jan 10, 2018, 179 participants to part 1 of the study (randomly assigned 1:1:1 to RTB101 5 mg once daily [61 participants], RTB101 10 mg once daily [58 participants], or matching placebo [60 participants]) and 473 patients to part 2 (randomly assigned 1:1:1:1 to RTB101 10 mg once daily [118 participants], RTB101 10 mg twice daily [120 participants], RTB101 10 mg in combination with everolimus 0·1 mg daily [115 participants] or matching placebo [120 participants]). In our first prespecified statistical analysis of the primary efficacy endpoint for part 2 of the phase 2b trial efficacy of RTB101 10 mg in combination with everolimus 0·1 mg once daily compared with placebo did not meet statistical significance but, in our second prespecified analysis, which included data from part 1 and part 2, we found a statistically significant reduction in the proportion of patients who had one or more laboratory-confirmed RTIs in the RTB101 10 mg once daily treatment group (34 [19%] of 176) compared with the pooled placebo group (50 [28%] of 180; odds ratio [OR] 0·601 [90% CI 0·391–0·922]; p=0·02). In the phase 3 trial, we enrolled 1024 patients between May 7, 2018, and July 19, 2019. 513 (50·1%) participants were randomly assigned to RTB101 10 mg once daily and 510 (49·9%) to placebo. In the full analysis set of the phase 3 trial, RTB101 did not reduce the proportion of patients with clinically symptomatic respiratory illness (134 [26%] of 511 patients in the RTB101 treatment group vs 125 [25%] 510 patients in the placebo treatment group; OR 1·07 [90% CI 0·80–1·42]; p=0·65). In both trials, significantly more IFN-induced antiviral genes were upregulated in patients treated with RTB101 as compared with placebo. The study drug was found to be safe and well-tolerated across trials and treatment groups. Only one patient in the placebo group in the phase 3 trial had serious adverse events (nausea, fatigue, hyponatraemia, and arthralgia) which were considered related to study drug treatment. Three patients died in the phase 2b trial and one in the phase 3 trial but no deaths were considered related to study treatment. Interpretation: The combined results indicate that low doses of the mTOR inhibitor RTB101 are well tolerated and upregulate IFN-induced antiviral responses in older adults. Further refinement of clinical trial endpoints and patient populations might be required to identify whether upregulation of IFN responses by mTOR inhibitors consistently decreases the incidence or severity of viral infections in older adults. Funding: resTORbio and the National Institute on Aging.

Summary: The photosynthetic water-oxidation reaction is catalyzed by the oxygen-evolving complex in photosystem II (PSII) that comprises the Mn4CaO5 cluster, with participation of the redox-active tyrosine residue (YZ) and a hydrogen-bonded network of amino acids and water molecules. It has been proposed that the strong hydrogen bond between YZ and D1-His190 likely renders YZ kinetically and thermodynamically competent leading to highly efficient water oxidation. However, a detailed understanding of the proton-coupled electron transfer (PCET) at YZ remains elusive owing to the transient nature of its intermediate states involving YZ⋅. Herein, we employ a combination of high-resolution two-dimensional 14N hyperfine sublevel correlation spectroscopy and density functional theory methods to investigate a bioinspired artificial photosynthetic reaction center that mimics the PCET process involving the YZ residue of PSII. Our results underscore the importance of proximal water molecules and charge delocalization on the electronic structure of the artificial reaction center.

Abstract By identifying endogenous molecules in brain extracellular fluid metabolomics can provide insight into the regulatory mechanisms and functions of sleep. Here we studied how the cortical metabolome changes during sleep, sleep deprivation and spontaneous wakefulness. Mice were implanted with electrodes for chronic sleep/wake recording and with microdialysis probes targeting prefrontal and primary motor cortex. Metabolites were measured using ultra performance liquid chromatography-high resolution mass spectrometry. Sleep/wake changes in metabolites were evaluated using partial least squares discriminant analysis, linear mixed effects model analysis of variance, and machine-learning algorithms. More than 30 known metabolites were reliably detected in most samples. When used by a logistic regression classifier, the profile of these metabolites across sleep, spontaneous wake, and enforced wake was sufficient to assign mice to their correct experimental group (pair-wise) in 80–100% of cases. Eleven of these metabolites showed significantly higher levels in awake than in sleeping mice. Some changes extend previous findings (glutamate, homovanillic acid, lactate, pyruvate, tryptophan, uridine), while others are novel (D-gluconate, N-acetyl-beta-alanine, N-acetylglutamine, orotate, succinate/methylmalonate). The upregulation of the de novo pyrimidine pathway, gluconate shunt and aerobic glycolysis may reflect a wake-dependent need to promote the synthesis of many essential components, from nucleic acids to synaptic membranes.

To establish confidence in the results of computerized physics models, a key regulatory requirement is to develop a scientifically defendable process. The methods employed for confidence, characterization, and consolidation, or C3, are statistically involved and are often accessible only to avid statisticians. This manuscript serves as a pedagogical presentation of the C3 process to all stakeholders—including researchers, industrial practitioners, and regulators—to impart an intuitive understanding of the key concepts and mathematical methods entailed by C3. The primary focus is on calculation of tolerance limits, which is the overall goal of the C3 process. Tolerance limits encode the confidence in the calculation results as communicated to the regulator. Understanding the C3 process is especially critical today, as the nuclear industry is considering more innovative ways to assess new technologies, including new reactor and fuel concepts, via an integrated approach that optimally combines modeling and simulation and minimal targeted validation experiments. This manuscript employs intuitive, analytical, numerical, and visual representations to explain how tolerance limits may be calculated for a wide range of configurations, and it also describes how their values may be interpreted. Various verification tests have been developed to test the calculated tolerance limits and to help delineate their values. The manuscript demonstrates the calculation of tolerance limits for TSURFER, a computer code developed by the Oak Ridge National Laboratory for criticality safety applications. The goal is to evaluate the tolerance limit for TSURFER-determined criticality biases to support the determination of upper, subcritical limits for regulatory purposes.

The Integrated Information Theory (IIT) of consciousness starts from essential phenomenological properties, which are then translated into postulates that any physical system must satisfy in order to specify the physical substrate of consciousness. We recently introduced an information measure (Barbosa et al., 2020) that captures three postulates of IIT—existence, intrinsicality and information—and is unique. Here we show that the new measure also satisfies the remaining postulates of IIT—integration and exclusion—and create the framework that identifies maximally irreducible mechanisms. These mechanisms can then form maximally irreducible systems, which in turn will specify the physical substrate of conscious experience.

Integrated information theory provides a mathematical framework to fully characterize the cause-effect structure of a physical system. Here, we introduce PyPhi, a Python software package that implements this framework for causal analysis and unfolds the full cause-effect structure of discrete dynamical systems of binary elements. The software allows users to easily study these structures, serves as an up-to-date reference implementation of the formalisms of integrated information theory, and has been applied in research on complexity, emergence, and certain biological questions. We first provide an overview of the main algorithm and demonstrate PyPhi's functionality in the course of analyzing an example system, and then describe details of the algorithm's design and implementation. PyPhi can be installed with Python's package manager via the command 'pip install pyphi' on Linux and macOS systems equipped with Python 3.4 or higher. PyPhi is open-source and licensed under the GPLv3; the source code is hosted on GitHub at https://github.com/wmayner/pyphi. Comprehensive and continually-updated documentation is available at https://pyphi.readthedocs.io. The pyphi-users mailing list can be joined at https://groups.google.com/forum/#!forum/pyphi-users. A web-based graphical interface to the software is available at http://integratedinformationtheory.org/calculate.html.

Reductionism assumes that causation in the physical world occurs at the micro level, excluding the emergence of macro-level causation. We challenge this reductionist assumption by employing a principled, well-defined measure of intrinsic cause-effect power-integrated information (Φ), and showing that, according to this measure, it is possible for a macro level to "beat" the micro level. Simple systems were evaluated for Φ across different spatial and temporal scales by systematically considering all possible black boxes. These are macro elements that consist of one or more micro elements over one or more micro updates. Cause-effect power was evaluated based on the inputs and outputs of the black boxes, ignoring the internal micro elements that support their input-output function. We show how black-box elements can have more common inputs and outputs than the corresponding micro elements, revealing the emergence of high-order mechanisms and joint constraints that are not apparent at the micro level. As a consequence, a macro, black-box system can have higher Φ than its micro constituents by having more mechanisms (higher composition) that are more interconnected (higher integration). We also show that, for a given micro system, one can identify local maxima of Φ across several spatiotemporal scales. The framework is demonstrated on a simple biological system, the Boolean network model of the fission-yeast cell-cycle, for which we identify stable local maxima during the course of its simulated biological function. These local maxima correspond to macro levels of organization at which emergent cause-effect properties of physical systems come into focus, and provide a natural vantage point for scientific inquiries.

A set of images can be considered as meaningfully different for an observer if they can be distinguished phenomenally from one another. Each phenomenal difference must be supported by some neurophysiological differences. Differentiation analysis aims to quantify neurophysiological differentiation evoked by a given set of stimuli to assess its meaningfulness to the individual observer. As a proof of concept using high-density EEG, we show increased neurophysiological differentiation for a set of natural, meaningfully different images in contrast to another set of artificially generated, meaninglessly different images in nine participants. Stimulus-evoked neurophysiological differentiation (over 257 channels, 800 ms) was systematically greater for meaningful vs. meaningless stimulus categories both at the group level and for individual subjects. Spatial breakdown showed a central-posterior peak of differentiation, consistent with the visual nature of the stimulus sets. Temporal breakdown revealed an early peak of differentiation around 110 ms, prominent in the central-posterior region; and a later, longer-lasting peak at 300–500 ms that was spatially more distributed. The early peak of differentiation was not accompanied by changes in mean ERP amplitude, whereas the later peak was associated with a higher amplitude ERP for meaningful images. An ERP component similar to visual-awareness-negativity occurred during the nadir of differentiation across all image types. Control stimulus sets and further analysis indicate that changes in neurophysiological differentiation between meaningful and meaningless stimulus sets could not be accounted for by spatial properties of the stimuli or by stimulus novelty and predictability.

Actual causation is concerned with the question: “What caused what?” Consider a transition between two states within a system of interacting elements, such as an artificial neural network, or a biological brain circuit. Which combination of synapses caused the neuron to fire? Which image features caused the classifier to misinterpret the picture? Even detailed knowledge of the system’s causal network, its elements, their states, connectivity, and dynamics does not automatically provide a straightforward answer to the “what caused what?” question. Counterfactual accounts of actual causation, based on graphical models paired with system interventions, have demonstrated initial success in addressing specific problem cases, in line with intuitive causal judgments. Here, we start from a set of basic requirements for causation (realization, composition, information, integration, and exclusion) and develop a rigorous, quantitative account of actual causation, that is generally applicable to discrete dynamical systems. We present a formal framework to evaluate these causal requirements based on system interventions and partitions, which considers all counterfactuals of a state transition. This framework is used to provide a complete causal account of the transition by identifying and quantifying the strength of all actual causes and effects linking the two consecutive system states. Finally, we examine several exemplary cases and paradoxes of causation and show that they can be illuminated by the proposed framework for quantifying actual causation.

Integrated information theory (IIT) proposes a measure of integrated information, termed Phi (Φ), to capture the level of consciousness of a physical system in a given state. Unfortunately, calculating Φ itself is currently possible only for very small model systems and far from computable for the kinds of system typically associated with consciousness (brains). Here, we considered several proposed heuristic measures and computational approximations, some of which can be applied to larger systems, and tested if they correlate well with Φ. While these measures and approximations capture intuitions underlying IIT and some have had success in practical applications, it has not been shown that they actually quantify the type of integrated information specified by the latest version of IIT and, thus, whether they can be used to test the theory. In this study, we evaluated these approximations and heuristic measures considering how well they estimated the Φ values of model systems and not on the basis of practical or clinical considerations. To do this, we simulated networks consisting of 3−6 binary linear threshold nodes randomly connected with excitatory and inhibitory connections. For each system, we then constructed the system’s state transition probability matrix (TPM) and generated observed data over time from all possible initial conditions. We then calculated Φ, approximations to Φ, and measures based on state differentiation, coalition entropy, state uniqueness, and integrated information. Our findings suggest that Φ can be approximated closely in small binary systems by using one or more of the readily available approximations (r > 0.95) but without major reductions in computational demands. Furthermore, the maximum value of Φ across states (a state-independent quantity) correlated strongly with measures of signal complexity (LZ, rs = 0.722), decoder-based integrated information (Φ*, rs = 0.816), and state differentiation (D1, rs = 0.827). These measures could allow for the efficient estimation of a system’s capacity for high Φ or function as accurate predictors of low- (but not high-)Φ systems. While it is uncertain whether the results extend to larger systems or systems with other dynamics, we stress the importance that measures aimed at being practical alternatives to Φ be, at a minimum, rigorously tested in an environment where the ground truth can be established.

Despite the wealth of research on differences between experts and novices with respect to their perceptual-cognitive background (e.g., mental representations, gaze behavior), little is known about the change of these perceptual-cognitive components over the course of motor learning. In the present study, changes in one’s mental representation, quiet eye behavior, and outcome performance were examined over the course of skill acquisition as it related to physical and mental practice. Novices (N = 45) were assigned to one of three conditions: physical practice, physical practice plus mental practice, and no practice. Participants in the practice groups trained on a golf putting task over the course of three days, either by repeatedly executing the putt, or by both executing and imaging the putt. Findings revealed improvements in putting performance across both practice conditions. Regarding the perceptual-cognitive changes, participants practicing mentally and physically revealed longer quiet eye durations as well as more elaborate representation structures in comparison to the control group, while this was not the case for participants who underwent physical practice only. Thus, in the present study, combined mental and physical practice led to both formation of mental representations in long-term memory and longer quiet eye durations. Interestingly, the length of the quiet eye directly related to the degree of elaborateness of the underlying mental representation, supporting the notion that the quiet eye reflects cognitive processing. This study is the first to show that the quiet eye becomes longer in novices practicing a motor action. Moreover, the findings of the present study suggest that perceptual and cognitive adaptations co-occur over the course of motor learning.