Altered structural connectivity and functional brain dynamics in individuals with heavy alcohol use elucidated via network control theory.

BACKGROUND: Heavy alcohol use (HAU) and its associated conditions, such as alcohol use disorder (AUD), impact millions of individuals worldwide. While our understanding of the neurobiological correlates of alcohol use has evolved substantially, we still lack models incorporating whole-brain neuroanatomical, functional, and pharmacological information under one framework. METHODS: Here, we utilize diffusion and functional magnetic resonance imaging to investigate alterations to brain dynamics in N = 130 individuals with a high amount of current alcohol use. We compared these alcohol using individuals to N = 308 individuals with minimal use of any substances. RESULTS: We find that individuals with HAU had less dynamic and complex brain activity, and through leveraging network control theory, had increased control energy to complete transitions between activation states. Further, using separately acquired positron emission tomography (PET) data, we deploy an in silico evaluation demonstrating that decreased D2 receptor levels, as found previously in individuals with AUD, may relate to our observed findings. CONCLUSIONS: This work demonstrates that whole-brain, multimodal imaging information can be combined under a network control framework to identify and evaluate neurobiological correlates and mechanisms of heavy alcohol use.

Time-resolved coupling between connectome harmonics and subjective experience under the psychedelic DMT.

Exploring the intricate relationship between brain's structure and function, and how this affects subjective experience is a fundamental pursuit in neuroscience. Psychedelic substances offer a unique insight into the influences of specific neurotransmitter systems on perception, cognition and consciousness. Specifically, their impact on brain function propagates across the structural connectome - a network of white matter pathways linking different regions. To comprehensively grasp the effects of psychedelic compounds on brain function, we used a theoretically rigorous framework known as connectome harmonic decomposition. This framework provides a robust method to characterize how brain function intricately depends on the organized network structure of the human connectome. We show that the connectome harmonic repertoire under DMT is reshaped in line with other reported psychedelic compounds - psilocybin, LSD and ketamine. Furthermore, we show that the repertoire entropy of connectome harmonics increases under DMT, as with those other psychedelics. Importantly, we demonstrate for the first time that measures of energy spectrum difference and repertoire entropy of connectome harmonics indexes the intensity of subjective experience of the participants in a time-resolved manner reflecting close coupling between connectome harmonics and subjective experience.

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.

Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics.

Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics.

Unravelling consciousness and brain function through the lens of time, space, and information.

Disentangling how cognitive functions emerge from the interplay of brain dynamics and network architecture is among the major challenges that neuroscientists face. Pharmacological and pathological perturbations of consciousness provide a lens to investigate these complex challenges. Here, we review how recent advances about consciousness and the brain's functional organisation have been driven by a common denominator: decomposing brain function into fundamental constituents of time, space, and information. Whereas unconsciousness increases structure-function coupling across scales, psychedelics may decouple brain function from structure. Convergent effects also emerge: anaesthetics, psychedelics, and disorders of consciousness can exhibit similar reconfigurations of the brain's unimodal-transmodal functional axis. Decomposition approaches reveal the potential to translate discoveries across species, with computational modelling providing a path towards mechanistic integration.

Quantifying synergy and redundancy between networks.

Understanding how different networks relate to each other is key for understanding complex systems. We introduce an intuitive yet powerful framework to disentangle different ways in which networks can be (dis)similar and complementary to each other. We decompose the shortest paths between nodes as uniquely contributed by one source network, or redundantly by either, or synergistically by both together. Our approach considers the networks' full topology, providing insights at multiple levels of resolution: from global statistics to individual paths. Our framework is widely applicable across scientific domains, from public transport to brain networks. In humans and 124 other species, we demonstrate the prevalence of unique contributions by long-range white-matter fibers in structural brain networks. Across species, efficient communication also relies on significantly greater synergy between long-range and short-range fibers than expected by chance. Our framework could find applications for designing network systems or evaluating existing ones.

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.

Local orchestration of distributed functional patterns supporting loss and restoration of consciousness in the primate brain.

A central challenge of neuroscience is to elucidate how brain function supports consciousness. Here, we combine the specificity of focal deep brain stimulation with fMRI coverage of the entire cortex, in awake and anaesthetised non-human primates. During propofol, sevoflurane, or ketamine anaesthesia, and subsequent restoration of responsiveness by electrical stimulation of the central thalamus, we investigate how loss of consciousness impacts distributed patterns of structure-function organisation across scales. We report that distributed brain activity under anaesthesia is increasingly constrained by brain structure across scales, coinciding with anaesthetic-induced collapse of multiple dimensions of hierarchical cortical organisation. These distributed signatures are observed across different anaesthetics, and they are reversed by electrical stimulation of the central thalamus, coinciding with recovery of behavioural markers of arousal. No such effects were observed upon stimulating the ventral lateral thalamus, demonstrating specificity. Overall, we identify consistent distributed signatures of consciousness that are orchestrated by specific thalamic nuclei.

Controlling the human connectome with spatially diffuse input signals.

The human brain is never at "rest"; its activity is constantly fluctuating over time, transitioning from one brain state-a whole-brain pattern of activity-to another. Network control theory offers a framework for understanding the effort - energy - associated with these transitions. One branch of control theory that is especially useful in this context is "optimal control", in which input signals are used to selectively drive the brain into a target state. Typically, these inputs are introduced independently to the nodes of the network (each input signal is associated with exactly one node). Though convenient, this input strategy ignores the continuity of cerebral cortex - geometrically, each region is connected to its spatial neighbors, allowing control signals, both exogenous and endogenous, to spread from their foci to nearby regions. Additionally, the spatial specificity of brain stimulation techniques is limited, such that the effects of a perturbation are measurable in tissue surrounding the stimulation site. Here, we adapt the network control model so that input signals have a spatial extent that decays exponentially from the input site. We show that this more realistic strategy takes advantage of spatial dependencies in structural connectivity and activity to reduce the energy (effort) associated with brain state transitions. We further leverage these dependencies to explore near-optimal control strategies such that, on a per-transition basis, the number of input signals required for a given control task is reduced, in some cases by two orders of magnitude. This approximation yields network-wide maps of input site density, which we compare to an existing database of functional, metabolic, genetic, and neurochemical maps, finding a close correspondence. Ultimately, not only do we propose a more efficient framework that is also more adherent to well-established brain organizational principles, but we also posit neurobiologically grounded bases for optimal control.

Systemic Candida Infection and Pulmonary Aspergillosis in an Alpaca (Vicugna pacos): A Case Report.

This study reports a peculiar case of systemic candidiasis infection associated with pulmonary aspergillosis in an apparently immunocompetent alpaca. A captive 7-year-old female alpaca exhibited respiratory symptoms, underwent treatment with benzylpenicillin and dexamethasone, and succumbed to the infection 40 days later. During the post-mortem examination, subcutaneous emphysema, widespread pneumonia with multiple suppurative foci, scattered necro-suppurative lesions throughout the renal and hepatic parenchyma were evident. Histopathological analysis of the collected tissues revealed multifocal mild lymphoplasmacytic chronic interstitial nephritis, necro-suppurative pneumonia with the presence of fungal hyphae, multifocal foci of mineralization, and fibrosis in the liver. Fungal cultures confirmed the growth of Aspergillus fumigatus from the lungs, and Candida albicans from the liver, kidney, and heart. The only recognizable risk factor for candidiasis and pulmonary aspergillosis in this case was prior corticosteroid and antibiotic therapy. Nevertheless, it is crucial to consider systemic candidosis and pulmonary aspergillosis as potential differential diagnoses in respiratory infections among camelids. Prolonged treatment with glucocorticoids and antibiotics should be avoided as it could represent a risk factor for the onset of pathologies caused by opportunistic fungi such as Candida spp. and Aspergillus spp.

First Report of Caseous Lymphadenitis by Corynebacterium pseudotubercolosis and Pulmonary Verminosis in a Roe Deer (Capreolus capreolus Linnaeus, 1758) in Italy.

Caseous lymphadenitis is a chronic debilitating disease typical of small ruminants, but it is also noted in several other domestic and wild species. In this report, we present the first documented case in Italy of pseudotuberculosis in a roe deer (Capreolus capreolus, Linnaeus 1758) found dead in the mountains of Forlì-Cesena province, Emilia Romagna region. The carcass underwent necropsy according to standard protocols, revealing generalized lymphadenopathy and severe apostematous pneumonia with multifocal and encapsulated abscesses. Corynebacterium pseudotuberculosis was isolated from the lung parenchyma, lymph nodes and abscesses. Additionally, severe parasitic bronchopneumonia of the caudal lobes and gastrointestinal strongyle infestation were detected. To our knowledge, this is the first documented case of CLA referable to C. pseudotubercolosis in a roe deer in Italy.

In vitro susceptibility of Brachyspira hyodysenteriae strains isolated in pigs in northern Italy between 2005 and 2022.

Pleuromutilins (tiamulin and valnemulin) are often used to treat swine dysentery due to recurrent resistance to macrolides and lincosamides. Recently, reduced susceptibility of B. hyodysenteriae to pleuromutilin has been reported. 536 strains of B. hyodysenteriae were isolated from symptomatic pigs weighing 30-150 kg in northern Italy between 2005 and 2022. B. hyodysenteriae was isolated by standard methods and confirmed by PCR. The minimum inhibitory concentration (MIC) to doxycycline, lincomycin, tiamulin, tylosin, tylvalosine and valnemulin was evaluated according to CLSI procedures and MIC data were reported as MIC 50 and MIC 90. The temporal trend of the MIC values was evaluated by dividing the data into two groups (2005-2013 and 2014-2022). Comparison of the distribution in frequency classes in the two periods was performed using Pearson's chi-squared test (p < 0.01). MIC 50 was close to the highest values tested for lincomycin and tylosin, while MIC 90 was close to the highest values tested for all antibiotics. 71.7% of the strains were susceptible to tylvalosin, while 75%-80.4% had reduced susceptibility to valnemulin and tiamulin, respectively. The difference in the distribution of MIC classes was statistically significant in the two periods for doxycycline, tiamulin, tylvalosin and valnemulin, and more MIC classes above the epidemiological cut-off were observed in 2014-2022 compared with 2005-2013. The evaluation of the trends during the period considered shows a decreasing rate of wild-type strains with MIC values below the epidemiological cut-off over time and confirms the presence of resistant strains in northern Italy.

Bayesian at heart: Towards autonomic outflow estimation via generative state-space modelling of heart rate dynamics.

Recent research is revealing how cognitive processes are supported by a complex interplay between the brain and the rest of the body, which can be investigated by the analysis of physiological features such as breathing rhythms, heart rate, and skin conductance. Heart rate dynamics are of particular interest as they provide a way to track the sympathetic and parasympathetic outflow from the autonomic nervous system, which is known to play a key role in modulating attention, memory, decision-making, and emotional processing. However, extracting useful information from heartbeats about the autonomic outflow is still challenging due to the noisy estimates that result from standard signal-processing methods. To advance this state of affairs, we propose a novel approach in how to conceptualise and model heart rate: instead of being a mere summary of the observed inter-beat intervals, we introduce a modelling framework that views heart rate as a hidden stochastic process that drives the observed heartbeats. Moreover, by leveraging the rich literature of state-space modelling and Bayesian inference, our proposed framework delivers a description of heart rate dynamics that is not a point estimate but a posterior distribution of a generative model. We illustrate the capabilities of our method by showing that it recapitulates linear properties of conventional heart rate estimators, while exhibiting a better discriminative power for metrics of dynamical complexity compared across different physiological states.

Information decomposition and the informational architecture of the brain.

To explain how the brain orchestrates information-processing for cognition, we must understand information itself. Importantly, information is not a monolithic entity. Information decomposition techniques provide a way to split information into its constituent elements: unique, redundant, and synergistic information. We review how disentangling synergistic and redundant interactions is redefining our understanding of integrative brain function and its neural organisation. To explain how the brain navigates the trade-offs between redundancy and synergy, we review converging evidence integrating the structural, molecular, and functional underpinnings of synergy and redundancy; their roles in cognition and computation; and how they might arise over evolution and development. Overall, disentangling synergistic and redundant information provides a guiding principle for understanding the informational architecture of the brain and cognition.

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.

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.

Altered structural connectivity and functional brain dynamics in individuals with heavy alcohol use.

Heavy alcohol use and its associated conditions, such as alcohol use disorder (AUD), impact millions of individuals worldwide. While our understanding of the neurobiological correlates of AUD has evolved substantially, we still lack models incorporating whole-brain neuroanatomical, functional, and pharmacological information under one framework. Here, we utilize diffusion and functional magnetic resonance imaging to investigate alterations to brain dynamics in N = 130 individuals with a high amount of current alcohol use. We compared these alcohol using individuals to N = 308 individuals with minimal use of any substances. We find that individuals with heavy alcohol use had less dynamic and complex brain activity, and through leveraging network control theory, had increased control energy to complete transitions between activation states. Further, using separately acquired positron emission tomography (PET) data, we deploy an in silico evaluation demonstrating that decreased D2 receptor levels, as found previously in individuals with AUD, may relate to our observed findings. This work demonstrates that whole-brain, multimodal imaging information can be combined under a network control framework to identify and evaluate neurobiological correlates and mechanisms of AUD.

General anaesthesia reduces the uniqueness of brain connectivity across individuals and across species.

The human brain is characterised by idiosyncratic patterns of spontaneous thought, rendering each brain uniquely identifiable from its neural activity. However, deep general anaesthesia suppresses subjective experience. Does it also suppress what makes each brain unique? Here we used functional MRI under the effects of the general anaesthetics sevoflurane and propofol to determine whether anaesthetic-induced unconsciousness diminishes the uniqueness of the human brain: both with respect to the brains of other individuals, and the brains of another species. We report that under anaesthesia individual brains become less self-similar and less distinguishable from each other. Loss of distinctiveness is highly organised: it co-localises with the archetypal sensory-association axis, correlating with genetic and morphometric markers of phylogenetic differences between humans and other primates. This effect is more evident at greater anaesthetic depths, reproducible across sevoflurane and propofol, and reversed upon recovery. Providing convergent evidence, we show that under anaesthesia the functional connectivity of the human brain becomes more similar to the macaque brain. Finally, anaesthesia diminishes the match between spontaneous brain activity and meta-analytic brain patterns aggregated from the NeuroSynth engine. Collectively, the present results reveal that anaesthetised human brains are not only less distinguishable from each other, but also less distinguishable from the brains of other primates, with specifically human-expanded regions being the most affected by anaesthesia.