13 research outputs found
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Geometric structures induced by deformations of the legendre transform
The recent link discovered between generalized Legendre transforms and non-dually flat statistical manifolds suggests a fundamental reason behind the ubiquity of RĂ©nyiâs divergence and entropy in a wide range of physical phenomena. However, these early findings still provide little intuition on the nature of this relationship and its implications for physical systems. Here we shed new light on the Legendre transform by revealing the consequences of its deformation via symplectic geometry and complexification. These findings reveal a novel common framework that leads to a principled and unified understanding of physical systems that are not well-described by classic information-theoretic quantities.</p
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Dynamical noise can enhance high-order statistical structure in complex systems
Recent research has provided a wealth of evidence highlighting the pivotal role of high-order interdependencies in supporting the information-processing capabilities of distributed complex systems. These findings may suggest that high-order interdependencies constitute a powerful resource that is, however, challenging to harness and can be readily disrupted. In this paper, we contest this perspective by demonstrating that high-order interdependencies can not only exhibit robustness to stochastic perturbations, but can in fact be enhanced by them. Using elementary cellular automata as a general testbed, our results unveil the capacity of dynamical noise to enhance the statistical regularities between agents and, intriguingly, even alter the prevailing character of their interdependencies. Furthermore, our results show that these effects are related to the high-order structure of the local rules, which affect the systemâs susceptibility to noise and characteristic time scales. These results deepen our understanding of how high-order interdependencies may spontaneously emerge within distributed systems interacting with stochastic environments, thus providing an initial step toward elucidating their origin and function in complex systems like the human brain.</p
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Nested selves: selfâorganization and shared Markov blankets in prenatal development in humans
The immune system is a central component of organismic function in humans. This paper addresses selfâorganization of biological systems in relation toâand nested withinâother biological systems in pregnancy. Pregnancy constitutes a fundamental state for human embodiment and a key step in the evolution and conservation of our species. While not all humans can be pregnant, our initial state of emerging and growing within another person's body is universal. Hence, the pregnant state does not concern some individuals but all individuals. Indeed, the hierarchical relationship in pregnancy reflects an even earlier autopoietic process in the embryo by which the number of individuals in a single blastoderm is dynamically determined by cellâ interactions. The relationship and the interactions between the two selfâorganizing systems during pregnancy may play a pivotal role in understanding the nature of biological selfâorganization per se in humans. Specifically, we consider the role of the immune system in biological selfâorganization in addition to neural/brain systems that furnish us with a sense of self. We examine the complex case of pregnancy, whereby two immune systems need to negotiate the exchange of resources and information in order to maintain viable selfâregulation of nested systems. We conclude with a proposal for the mechanismsâthat scaffold the complex relationship between two selfâorganising systems in pregnancyâthrough the lens of the Active Inference, with a focus on shared Markov blankets.</p
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Pattern breaking: a complex systems approach to psychedelic medicine
Recent research has demonstrated the potential of psychedelic therapy for mental health care. However, the psychological experience underlying its therapeutic effects remains poorly understood. This paper proposes a framework that suggests psychedelics act as destabilizers, both psychologically and neurophysiologically. Drawing on the âentropic brainâ hypothesis and the âRElaxed Beliefs Under pSychedelicsâ model, this paper focuses on the richness of psychological experience. Through a complex systems theory perspective, we suggest that psychedelics destabilize fixed points or attractors, breaking reinforced patterns of thinking and behaving. Our approach explains how psychedelic-induced increases in brain entropy destabilize neurophysiological set points and lead to new conceptualizations of psychedelic psychotherapy. These insights have important implications for risk mitigation and treatment optimization in psychedelic medicine, both during the peak psychedelic experience and during the subacute period of potential recovery
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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.</p
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Assessing expectancy and suggestibility in a trial of escitalopram v. psilocybin for depression
Background To investigate the association between pre-trial expectancy, suggestibility, and response to treatment in a trial of escitalopram and investigational drug, COMP360, psilocybin, in the treatment of major depressive disorder (ClinicalTrials.gov registration: NCT03429075).Methods We used data (n = 55) from our recent double-blind, parallel-group, randomized head-to-head comparison trial of escitalopram and investigational drug, COMP360, psilocybin. Mixed linear models were used to investigate the association between pre-treatment efficacy-related expectations, as well as baseline trait suggestibility and absorption, and therapeutic response to both escitalopram and COMP360 psilocybin.Results Patients had significantly higher expectancy for psilocybin relative to escitalopram; however, expectancy for escitalopram was associated with improved therapeutic outcomes to escitalopram, expectancy for psilocybin was not predictive of response to psilocybin. Separately, we found that pre-treatment trait suggestibility was associated with therapeutic response in the psilocybin arm, but not in the escitalopram arm.Conclusions Overall, our results suggest that psychedelic therapy may be less vulnerable to expectancy biases than previously suspected. The relationship between baseline trait suggestibility and response to psilocybin therapy implies that highly suggestible individuals may be primed for response to this treatment.</p
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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.</p
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Effects of discontinuation of serotonergic antidepressants prior to psilocybin therapy versus escitalopram for major depression
Background: There is growing evidence for the therapeutic effects of the psychedelic drug psilocybin for major depression. However, due to the lack of safety data on combining psilocybin with selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs) and concerns that there may be a negative interaction on efficacy, participants enrolling in psychedelic trials are usually required to discontinue SNRI/SNRIs prior to enrolling.Aims: Using data from a recent clinical trial examining the comparative efficacy the psychedelic drug psilocybin (P) combined with approximately 20 h of psychological support to a 6-week (daily) course of the SSRI escitalopram plus matched psychological support for major depressive disorder, we explored the effects of discontinuing SSRI/SNRIs prior to study enrolment on study outcomes.Methods: Exploratory post hoc analyses using linear mixed effects model were performed to investigate the discontinuation effect on various validated depression symptom severity scales and well-being. The impact of SSRI/SNRIs discontinuation on the acute psychedelic experience was also explored.Results/outcomes: In the psilocybin group, there was a reduced treatment effect on all outcome measures for SSRI/SNRIs discontinuers compared with unmedicated patients at trial entry. However, no effects of discontinuation on measures of the acute psychedelic experience were found.Conclusion: Discontinuation of SSRI/SNRIs before psilocybin might diminish response to treatment; however, as we did not test SSRI/SNRI continuation in our trial, we cannot infer such causation. Moreover, the exploratory nature of the analyses makes them hypothesis generating, and not confirmatory. A controlled trial of SSRI/SNRI discontinuation versus continuation prior to psilocybin is urgently required.</p
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High-order interdependencies in the aging brain
Background: Brain interdependencies can be studied from either a structural/anatomical perspective ("structural connectivity") or by considering statistical interdependencies ("functional connectivity" [FC]). Interestingly, while structural connectivity is by definition pairwise (white-matter fibers project from one region to another), FC is not. However, most FC analyses only focus on pairwise statistics and they neglect higher order interactions. A promising tool to study high-order interdependencies is the recently proposed O-Information, which can quantify the intrinsic statistical synergy and the redundancy in groups of three or more interacting variables. Methods: We analyzed functional magnetic resonance imaging (fMRI) data obtained at rest from 164 healthy subjects with ages ranging in 10 to 80 years and used O-Information to investigate how high-order statistical interdependencies are affected by age. Results: Older participants (from 60 to 80 years old) exhibited a higher predominance of redundant dependencies compared with younger participants, an effect that seems to be pervasive as it is evident for all orders of interaction. In addition, while there is strong heterogeneity across brain regions, we found a "redundancy core" constituted by the prefrontal and motor cortices in which redundancy was evident at all the interaction orders studied. Discussion: High-order interdependencies in fMRI data reveal a dominant redundancy in functions such as working memory, executive, and motor functions. Our methodology can be used for a broad range of applications, and the corresponding code is freely available. Impact statement Past research has showcased multiple changes to the brain's structural and functional properties caused by aging. Here we expand prior work through recent advancements in multivariate information theory, which provide richer and more theoretically principled analyses than existing alternatives. We show that the brains of older participants contain more redundant information at multiple spatial scales-that is, activation in different brain regions is less diverse, compared with younger participants-and identify a "redundancy core" constituted by prefrontal and motor cortices, which might explained impaired performance in the old population in functions such as working memory and executive control
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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