Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks.

Striking progress has been made in understanding cognition by analyzing how the brain is engaged in different modes of information processing. For instance, so-called synergistic information (information encoded by a set of neurons but not by any subset) plays a key role in areas of the human brain linked with complex cognition. However, two questions remain unanswered: (a) how and why a cognitive system can become highly synergistic; and (b) how informational states map onto artificial neural networks in various learning modes. Here we employ an information-decomposition framework to investigate neural networks performing cognitive tasks. Our results show that synergy increases as networks learn multiple diverse tasks, and that in tasks requiring integration of multiple sources, performance critically relies on synergistic neurons. Overall, our results suggest that synergy is used to combine information from multiple modalities-and more generally for flexible and efficient learning. These findings reveal new ways of investigating how and why learning systems employ specific information-processing strategies, and support the principle that the capacity for general-purpose learning critically relies on the system's information dynamics.

A role for the serotonin 2A receptor in the expansion and functioning of human transmodal cortex.

Integrating independent but converging lines of research on brain function and neurodevelopment across scales, this article proposes that serotonin 2A receptor (5-HT2AR) signalling is an evolutionary and developmental driver and potent modulator of the macroscale functional organization of the human cerebral cortex. A wealth of evidence indicates that the anatomical and functional organization of the cortex follows a unimodal-to-transmodal gradient. Situated at the apex of this processing hierarchy-where it plays a central role in the integrative processes underpinning complex, human-defining cognition-the transmodal cortex has disproportionately expanded across human development and evolution. Notably, the adult human transmodal cortex is especially rich in 5-HT2AR expression and recent evidence suggests that, during early brain development, 5-HT2AR signalling on neural progenitor cells stimulates their proliferation-a critical process for evolutionarily-relevant cortical expansion. Drawing on multimodal neuroimaging and cross-species investigations, we argue that, by contributing to the expansion of the human cortex and being prevalent at the apex of its hierarchy in the adult brain, 5-HT2AR signalling plays a major role in both human cortical expansion and functioning. Owing to its unique excitatory and downstream cellular effects, neuronal 5-HT2AR agonism promotes neuroplasticity, learning and cognitive and psychological flexibility in a context-(hyper)sensitive manner with therapeutic potential. Overall, we delineate a dual role of 5-HT2ARs in enabling both the expansion and modulation of the human transmodal cortex.

Emergence of Group B Streptococcus Disease in Pigs and Porcupines, Italy.

We describe group B Streptococcus linked to disease in farmed pigs and wild porcupines in Italy. Occurrence in pigs was attributed to transmission from nonpasteurized bovine milk whey. Antimicrobial-resistance profiles in isolates from porcupines suggest no common source of infection. Our findings expand the known host range for group B Streptococcus disease.

Streptococcus suis serotype 9 in Italy: genomic insights into high-risk clones with emerging resistance to penicillin.

BACKGROUND: Streptococcus suis is an important pig pathogen and an emerging zoonotic agent. In a previous study, we described a high proportion of penicillin-resistant serotype 9 S. suis (SS9) isolates on pig farms in Italy. OBJECTIVES: We hypothesized that resistance to penicillin emerged in some SS9 lineages characterized by substitutions at the PBPs, contributing to the successful spread of these lineages in the last 20 years. METHODS: Sixty-six SS9 isolates from cases of streptococcosis in pigs were investigated for susceptibility to penicillin, ceftiofur and ampicillin. The isolates were characterized for ST, virulence profile, and antimicrobial resistance genes through WGS. Multiple linear regression models were employed to investigate the associations between STs, year of isolation, substitutions at the PBPs and an increase in MIC values to ß-lactams. RESULTS: MIC values to penicillin increased by 4% each year in the study period. Higher MIC values for penicillin were also positively associated with ST123, ST1540 and ST1953 compared with ST16. The PBP sequences presented a mosaic organization of blocks. Within the same ST, substitutions at the PBPs were generally more frequent in recent isolates. Resistance to penicillin was driven by substitutions at PBP2b, including K479T, D512E and K513E, and PBP2x, including T551S, while reduced susceptibility to ceftiofur and ampicillin were largely dependent on substitutions at PBP2x. CONCLUSIONS: Here, we identify the STs and substitutions at the PBPs responsible for increased resistance of SS9 to penicillin on Italian pig farms. Our data highlight the need for monitoring the evolution of S. suis in the coming years.

Acute thalamic connectivity precedes chronic post-concussive symptoms in mild traumatic brain injury.

Chronic post-concussive symptoms are common after mild traumatic brain injury (mTBI) and are difficult to predict or treat. Thalamic functional integrity is particularly vulnerable in mTBI and may be related to long-term outcomes but requires further investigation. We compared structural MRI and resting state functional MRI in 108 patients with a Glasgow Coma Scale (GCS) of 13-15 and normal CT, and 76 controls. We examined whether acute changes in thalamic functional connectivity were early markers for persistent symptoms and explored neurochemical associations of our findings using PET data. Of the mTBI cohort, 47% showed incomplete recovery 6 months post-injury. Despite the absence of structural changes, we found acute thalamic hyperconnectivity in mTBI, with specific vulnerabilities of individual thalamic nuclei. Acute fMRI markers differentiated those with chronic post-concussive symptoms, with time- and outcome-dependent relationships in a sub-cohort followed longitudinally. Moreover, emotional and cognitive symptoms were associated with changes in thalamic functional connectivity to known serotonergic and noradrenergic targets, respectively. Our findings suggest that chronic symptoms can have a basis in early thalamic pathophysiology. This may aid identification of patients at risk of chronic post-concussive symptoms following mTBI, provide a basis for development of new therapies and facilitate precision medicine application of these therapies.

Dopaminergic brainstem disconnection is common to pharmacological and pathological consciousness perturbation.

Clinical research into consciousness has long focused on cortical macroscopic networks and their disruption in pathological or pharmacological consciousness perturbation. Despite demonstrating diagnostic utility in disorders of consciousness (DoC) and monitoring anesthetic depth, these cortico-centric approaches have been unable to characterize which neurochemical systems may underpin consciousness alterations. Instead, preclinical experiments have long implicated the dopaminergic ventral tegmental area (VTA) in the brainstem. Despite dopaminergic agonist efficacy in DoC patients equally pointing to dopamine, the VTA has not been studied in human perturbed consciousness. To bridge this translational gap between preclinical subcortical and clinical cortico-centric perspectives, we assessed functional connectivity changes of a histologically characterized VTA using functional MRI recordings of pharmacologically (propofol sedation) and pathologically perturbed consciousness (DoC patients). Both cohorts demonstrated VTA disconnection from the precuneus and posterior cingulate (PCu/PCC), a main default mode network node widely implicated in consciousness. Strikingly, the stronger VTA-PCu/PCC connectivity was, the more the PCu/PCC functional connectome resembled its awake configuration, suggesting a possible neuromodulatory relationship. VTA-PCu/PCC connectivity increased toward healthy control levels only in DoC patients who behaviorally improved at follow-up assessment. To test whether VTA-PCu/PCC connectivity can be affected by a dopaminergic agonist, we demonstrated in a separate set of traumatic brain injury patients without DoC that methylphenidate significantly increased this connectivity. Together, our results characterize an in vivo dopaminergic connectivity deficit common to reversible and chronic consciousness perturbation. This noninvasive assessment of the dopaminergic system bridges preclinical and clinical work, associating dopaminergic VTA function with macroscopic network alterations, thereby elucidating a critical aspect of brainstem-cortical interplay for consciousness.

Reduced emergent character of neural dynamics in patients with a disrupted connectome.

High-level brain functions are widely believed to emerge from the orchestrated activity of multiple neural systems. However, lacking a formal definition and practical quantification of emergence for experimental data, neuroscientists have been unable to empirically test this long-standing conjecture. Here we investigate this fundamental question by leveraging a recently proposed framework known as "Integrated Information Decomposition," which establishes a principled information-theoretic approach to operationalise and quantify emergence in dynamical systems - including the human brain. By analysing functional MRI data, our results show that the emergent and hierarchical character of neural dynamics is significantly diminished in chronically unresponsive patients suffering from severe brain injury. At a functional level, we demonstrate that emergence capacity is positively correlated with the extent of hierarchical organisation in brain activity. Furthermore, by combining computational approaches from network control theory and whole-brain biophysical modelling, we show that the reduced capacity for emergent and hierarchical dynamics in severely brain-injured patients can be mechanistically explained by disruptions in the patients' structural connectome. Overall, our results suggest that chronic unresponsiveness resulting from severe brain injury may be related to structural impairment of the fundamental neural infrastructures required for brain dynamics to support emergence.

Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness.

Disorders of consciousness are complex conditions characterised by persistent loss of responsiveness due to brain injury. They present diagnostic challenges and limited options for treatment, and highlight the urgent need for a more thorough understanding of how human consciousness arises from coordinated neural activity. The increasing availability of multimodal neuroimaging data has given rise to a wide range of clinically- and scientifically-motivated modelling efforts, seeking to improve data-driven stratification of patients, to identify causal mechanisms for patient pathophysiology and loss of consciousness more broadly, and to develop simulations as a means of testing in silico potential treatment avenues to restore consciousness. As a dedicated Working Group of clinicians and neuroscientists of the international Curing Coma Campaign, here we provide our framework and vision to understand the diverse statistical and generative computational modelling approaches that are being employed in this fast-growing field. We identify the gaps that exist between the current state-of-the-art in statistical and biophysical computational modelling in human neuroscience, and the aspirational goal of a mature field of modelling disorders of consciousness; which might drive improved treatments and outcomes in the clinic. Finally, we make several recommendations for how the field as a whole can work together to address these challenges.

Metastability, fractal scaling, and synergistic information processing: What phase relationships reveal about intrinsic brain activity.

Dynamic functional connectivity (dFC) in resting-state fMRI holds promise to deliver candidate biomarkers for clinical applications. However, the reliability and interpretability of dFC metrics remain contested. Despite a myriad of methodologies and resulting measures, few studies have combined metrics derived from different conceptualizations of brain functioning within the same analysis - perhaps missing an opportunity for improved interpretability. Using a complexity-science approach, we assessed the reliability and interrelationships of a battery of phase-based dFC metrics including tools originating from dynamical systems, stochastic processes, and information dynamics approaches. Our analysis revealed novel relationships between these metrics, which allowed us to build a predictive model for integrated information using metrics from dynamical systems and information theory. Furthermore, global metastability - a metric reflecting simultaneous tendencies for coupling and decoupling - was found to be the most representative and stable metric in brain parcellations that included cerebellar regions. Additionally, spatiotemporal patterns of phase-locking were found to change in a slow, non-random, continuous manner over time. Taken together, our findings show that the majority of characteristics of resting-state fMRI dynamics reflect an interrelated dynamical and informational complexity profile, which is unique to each acquisition. This finding challenges the interpretation of results from cross-sectional designs for brain neuromarker discovery, suggesting that individual life-trajectories may be more informative than sample means.

Network dynamics scale with levels of awareness.

Small world topologies are thought to provide a valuable insight into human brain organisation and consciousness. However, functional magnetic resonance imaging studies in consciousness have not yielded consistent results. Given the importance of dynamics for both consciousness and cognition, here we investigate how the diversity of small world dynamics (quantified by sample entropy; dSW-E1) scales with decreasing levels of awareness (i.e., sedation and disorders of consciousness). Paying particular attention to result reproducibility, we show that dSW-E is a consistent predictor of levels of awareness even when controlling for the underlying functional connectivity dynamics. We find that dSW-E of subcortical, and cortical areas are predictive, with the former showing higher and more robust effect sizes across analyses. We find that the network dynamics of intermodular communication in the cerebellum also have unique predictive power for levels of awareness. Consequently, we propose that the dynamic reorganisation of the functional information architecture, in particular of the subcortex, is a characteristic that emerges with awareness and has explanatory power beyond that of the complexity of dynamic functional connectivity.

New Sequence Types and Antimicrobial Drug-Resistant Strains of Streptococcus suis in Diseased Pigs, Italy, 2017-2019.

Streptococcus suis is a pathogen associated with severe diseases in pigs and humans. Human infections have a zoonotic origin in pigs. To assess circulating strains, we characterized the serotypes, sequence types, and antimicrobial susceptibility of 78 S. suis isolates from diseased farmed pigs in Italy during 2017-2019. Almost 60% of infections were caused by serotypes 1/2 and 9. All but 1 of the serotype 2 and 1/2 isolates were confined to a single cluster, and serotype 9 isolates were distributed along the phylogenetic tree. Besides sequence type (ST) 1, the serotype 2 cluster included ST7, which caused severe human infections in China in 1998 and 2005. A large proportion of serotype 9 isolates, assigned to ST123, were resistant to penicillin. The emergence of this clone threatens the successful treatment of S. suis infection. Characterizing S. suis isolates from pigs will promote earlier detection of emerging clones.

Greater than the parts: a review of the information decomposition approach to causal emergence.

Emergence is a profound subject that straddles many scientific disciplines, including the formation of galaxies and how consciousness arises from the collective activity of neurons. Despite the broad interest that exists on this concept, the study of emergence has suffered from a lack of formalisms that could be used to guide discussions and advance theories. Here, we summarize, elaborate on, and extend a recent formal theory of causal emergence based on information decomposition, which is quantifiable and amenable to empirical testing. This theory relates emergence with information about a system's temporal evolution that cannot be obtained from the parts of the system separately. This article provides an accessible but rigorous introduction to the framework, discussing the merits of the approach in various scenarios of interest. We also discuss several interpretation issues and potential misunderstandings, while highlighting the distinctive benefits of this formalism. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.

Complementary Inhibitory Weight Profiles Emerge from Plasticity and Allow Flexible Switching of Receptive Fields.

Cortical areas comprise multiple types of inhibitory interneurons, with stereotypical connectivity motifs that may follow specific plasticity rules. Yet, their combined effect on postsynaptic dynamics has been largely unexplored. Here, we analyze the response of a single postsynaptic model neuron receiving tuned excitatory connections alongside inhibition from two plastic populations. Synapses from each inhibitory population change according to distinct plasticity rules. We tested different combinations of three rules: Hebbian, anti-Hebbian, and homeostatic scaling. Depending on the inhibitory plasticity rule, synapses become unspecific (flat), anticorrelated to, or correlated with excitatory synapses. Crucially, the neuron's receptive field (i.e., its response to presynaptic stimuli) depends on the modulatory state of inhibition. When both inhibitory populations are active, inhibition balances excitation, resulting in uncorrelated postsynaptic responses regardless of the inhibitory tuning profiles. Modulating the activity of a given inhibitory population produces strong correlations to either preferred or nonpreferred inputs, in line with recent experimental findings that show dramatic context-dependent changes of neurons' receptive fields. We thus confirm that a neuron's receptive field does not follow directly from the weight profiles of its presynaptic afferents. Our results show how plasticity rules in various cell types can interact to shape cortical circuit motifs and their dynamics.SIGNIFICANCE STATEMENT Neurons in sensory areas of the cortex are known to respond to specific features of a given input (e.g., specific sound frequencies), but recent experimental studies show that such responses (i.e., their receptive fields) depend on context. Inspired by the cortical connectivity, we built models of excitatory and inhibitory inputs onto a single neuron, to study how receptive fields may change on short and long time scales. We show how various synaptic plasticity rules allow for the emergence of diverse connectivity profiles and, moreover, how their dynamic interaction creates a mechanism by which postsynaptic responses can quickly change. Our work emphasizes multiple roles of inhibition in cortical processing and provides a first mechanistic model for flexible receptive fields.

LSD alters dynamic integration and segregation in the human brain.

Investigating changes in brain function induced by mind-altering substances such as LSD is a powerful method for interrogating and understanding how mind interfaces with brain, by connecting novel psychological phenomena with their neurobiological correlates. LSD is known to increase measures of brain complexity, potentially reflecting a neurobiological correlate of the especially rich phenomenological content of psychedelic-induced experiences. Yet although the subjective stream of consciousness is a constant ebb and flow, no studies to date have investigated how LSD influences the dynamics of functional connectivity in the human brain. Focusing on the two fundamental network properties of integration and segregation, here we combined graph theory and dynamic functional connectivity from resting-state functional MRI to examine time-resolved effects of LSD on brain networks properties and subjective experiences. Our main finding is that the effects of LSD on brain function and subjective experience are non-uniform in time: LSD makes globally segregated sub-states of dynamic functional connectivity more complex, and weakens the relationship between functional and anatomical connectivity. On a regional level, LSD reduces functional connectivity of the anterior medial prefrontal cortex, specifically during states of high segregation. Time-specific effects were correlated with different aspects of subjective experiences; in particular, ego dissolution was predicted by increased small-world organisation during a state of high global integration. These results reveal a more nuanced, temporally-specific picture of altered brain connectivity and complexity under psychedelics than has previously been reported.

Brain network integration dynamics are associated with loss and recovery of consciousness induced by sevoflurane.

The dynamic interplay of integration and segregation in the brain is at the core of leading theoretical accounts of consciousness. The human brain dynamically alternates between a sub-state where integration predominates, and a predominantly segregated sub-state, with different roles in supporting cognition and behaviour. Here, we combine graph theory and dynamic functional connectivity to compare resting-state functional MRI data from healthy volunteers before, during, and after loss of responsiveness induced with different concentrations of the inhalational anaesthetic, sevoflurane. We show that dynamic states characterised by high brain integration are especially vulnerable to general anaesthesia, exhibiting attenuated complexity and diminished small-world character. Crucially, these effects are reversed upon recovery, demonstrating their association with consciousness. Higher doses of sevoflurane (3% vol and burst-suppression) also compromise the temporal balance of integration and segregation in the human brain. Additionally, we demonstrate that reduced anticorrelations between the brain's default mode and executive control networks dynamically reconfigure depending on the brain's state of integration or segregation. Taken together, our results demonstrate that the integrated sub-state of brain connectivity is especially vulnerable to anaesthesia, in terms of both its complexity and information capacity, whose breakdown represents a generalisable biomarker of loss of consciousness and its recovery.

Mechanisms Underlying Disorders of Consciousness: Bridging Gaps to Move Toward an Integrated Translational Science.

AIM: In order to successfully detect, classify, prognosticate, and develop targeted therapies for patients with disorders of consciousness (DOC), it is crucial to improve our mechanistic understanding of how severe brain injuries result in these disorders. METHODS: To address this need, the Curing Coma Campaign convened a Mechanisms Sub-Group of the Coma Science Work Group (CSWG), aiming to identify the most pressing knowledge gaps and the most promising approaches to bridge them. RESULTS: We identified a key conceptual gap in the need to differentiate the neural mechanisms of consciousness per se, from those underpinning connectedness to the environment and behavioral responsiveness. Further, we characterised three fundamental gaps in DOC research: (1) a lack of mechanistic integration between structural brain damage and abnormal brain function in DOC; (2) a lack of translational bridges between micro- and macro-scale neural phenomena; and (3) an incomplete exploration of possible synergies between data-driven and theory-driven approaches. CONCLUSION: In this white paper, we discuss research priorities that would enable us to begin to close these knowledge gaps. We propose that a fundamental step towards this goal will be to combine translational, multi-scale, and multimodal data, with new biomarkers, theory-driven approaches, and computational models, to produce an integrated account of neural mechanisms in DOC. Importantly, we envision that reciprocal interaction between domains will establish a "virtuous cycle," leading towards a critical vantage point of integrated knowledge that will enable the advancement of the scientific understanding of DOC and consequently, an improvement of clinical practice.

Novel plasmid-mediated colistin resistance mcr-4 gene in Salmonella and Escherichia coli, Italy 2013, Spain and Belgium, 2015 to 2016.

A novel mcr colistin resistance gene was identified in a strain of Salmonella enterica, monophasic variant of serovar Typhimurium (4,5,12:i:- ), isolated from a pig at slaughter in Italy in 2013, and in Escherichia coli strains collected during routine diagnostic of post-weaning diarrhoea in pigs from Spain and Belgium in 2015 and 2016. Immediate implementation of mcr-screening including this novel gene variant is required for Salmonella and E. coli from humans and food-producing animals in Europe.