Publications
79 results found
Luppi AI, Girn M, Rosas FE, et al., 2024, A role for the serotonin 2A receptor in the expansion and functioning of human transmodal cortex, Brain: a journal of neurology, Vol: 147, Pages: 56-80, ISSN: 0006-8950
Integrating independent but converging lines of research on brain function and neurodevelopment across scales, this article proposes that serotonin 2A receptor (5-HT2AR) signaling 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 signaling 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 signaling plays a major role in both human cortical expansion and functioning. Due 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.
Luppi AI, Cabral J, Cofre R, et al., 2023, Computational modelling in disorders of consciousness: closing the gap towards personalised models for restoring consciousness, NeuroImage, Vol: 275, ISSN: 1053-8119
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.
Morales PA, Korbel J, Rosas FE, 2023, Thermodynamics of exponential Kolmogorov-Nagumo averages, New Journal of Physics, Vol: 25, ISSN: 1367-2630
This paper investigates generalized thermodynamic relationships in physical systems where relevant macroscopic variables are determined by the exponential Kolmogorov–Nagumo average. We show that while the thermodynamic entropy of such systems is naturally described by Rényi's entropy with parameter γ, an ordinary Boltzmann distribution still describes their statistics under equilibrium thermodynamics. Our results show that systems described by exponential Kolmogorov–Nagumo averages can be interpreted as systems originally in thermal equilibrium with a heat reservoir with inverse temperature β that are suddenly quenched to another heat reservoir with inverse temperature $\beta^{^{\prime}} = (1-\gamma)\beta$. Furthermore, we show the connection with multifractal thermodynamics. For the non-equilibrium case, we show that the dynamics of systems described by exponential Kolmogorov–Nagumo averages still observe a second law of thermodynamics and the H-theorem. We further discuss the applications of stochastic thermodynamics in those systems—namely, the validity of fluctuation theorems—and the connection with thermodynamic length.
Girn M, Rosas FE, Daws RE, et al., 2023, A complex systems perspective on psychedelic brain action, TRENDS IN COGNITIVE SCIENCES, Vol: 27, Pages: 433-445, ISSN: 1364-6613
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- Citations: 3
Luppi AI, Mediano PAM, Rosas FE, et al., 2023, Reduced emergent character of neural dynamics in patients with a disrupted connectome, NeuroImage, Vol: 269, Pages: 1-17, ISSN: 1053-8119
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.
Morales PA, Korbel J, Rosas FE, 2023, Geometric Structures Induced by Deformations of the Legendre Transform, ENTROPY, Vol: 25
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- Citations: 1
Hancock F, Rosas FE, McCutcheon RAE, et al., 2023, Metastability as a candidate neuromechanistic biomarker of schizophrenia pathology, PLoS One, Vol: 18, ISSN: 1932-6203
The disconnection hypothesis of schizophrenia proposes that symptoms of the disorder arise as a result of aberrant functional integration between segregated areas of the brain. The concept of metastability characterizes the coexistence of competing tendencies for functional integration and functional segregation in the brain, and is therefore well suited for the study of schizophrenia. In this study, we investigate metastability as a candidate neuromechanistic biomarker of schizophrenia pathology, including a demonstration of reliability and face validity. Group-level discrimination, individual-level classification, pathophysiological relevance, and explanatory power were assessed using two independent case-control studies of schizophrenia, the Human Connectome Project Early Psychosis (HCPEP) study (controls n = 53, non-affective psychosis n = 82) and the Cobre study (controls n = 71, cases n = 59). In this work we extend Leading Eigenvector Dynamic Analysis (LEiDA) to capture specific features of dynamic functional connectivity and then implement a novel approach to estimate metastability. We used non-parametric testing to evaluate group-level differences and a naïve Bayes classifier to discriminate cases from controls. Our results show that our new approach is capable of discriminating cases from controls with elevated effect sizes relative to published literature, reflected in an up to 76% area under the curve (AUC) in out-of-sample classification analyses. Additionally, our new metric showed explanatory power of between 81–92% for measures of integration and segregation. Furthermore, our analyses demonstrated that patients with early psychosis exhibit intermittent disconnectivity of subcortical regions with frontal cortex and cerebellar regions, introducing new insights about the mechanistic bases of these conditions. Overall, these findings demonstrate reliability and face validity of metastability as a candidate neuromechanistic biomarker of schizop
Timmermann Slater CB, Roseman L, Haridas S, et al., 2023, Human brain effects of DMT assessed via EEG-fMRI, Proceedings of the National Academy of Sciences of USA, Vol: 120, Pages: 1-12, ISSN: 0027-8424
Psychedelics have attracted medical interest, but their effects on human brain function are incompletely understood. In a comprehensive, within-subjects, placebo-controlled design, we acquired multimodal neuroimaging [i.e., EEG-fMRI (electroencephalography-functional MRI)] data to assess the effects of intravenous (IV) N,N-Dimethyltryptamine (DMT) on brain function in 20 healthy volunteers. Simultaneous EEG-fMRI was acquired prior to, during, and after a bolus IV administration of 20 mg DMT, and, separately, placebo. At dosages consistent with the present study, DMT, a serotonin 2A receptor (5-HT2AR) agonist, induces a deeply immersive and radically altered state of consciousness. DMT is thus a useful research tool for probing the neural correlates of conscious experience. Here, fMRI results revealed robust increases in global functional connectivity (GFC), network disintegration and desegregation, and a compression of the principal cortical gradient under DMT. GFC × subjective intensity maps correlated with independent positron emission tomography (PET)-derived 5-HT2AR maps, and both overlapped with meta-analytical data implying human-specific psychological functions. Changes in major EEG-measured neurophysiological properties correlated with specific changes in various fMRI metrics, enriching our understanding of the neural basis of DMT’s effects. The present findings advance on previous work by confirming a predominant action of DMT—and likely other 5-HT2AR agonist psychedelics—on the brain’s transmodal association pole, i.e., the neurodevelopmentally and evolutionarily recent cortex that is associated with species-specific psychological advancements, and high expression of 5-HT2A receptors.
Carhart-Harris RL, Chandaria S, Erritzoe DE, et al., 2023, Canalization and plasticity in psychopathology, NEUROPHARMACOLOGY, Vol: 226, ISSN: 0028-3908
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- Citations: 2
Ruffini G, Damiani G, Lozano-Soldevilla D, et al., 2023, LSD-induced increase of Ising temperature and algorithmic complexity of brain dynamics, PLoS Computational Biology, Vol: 19, Pages: 1-29, ISSN: 1553-734X
A topic of growing interest in computational neuroscience is the discovery of fundamental principles underlying global dynamics and the self-organization of the brain. In particular, the notion that the brain operates near criticality has gained considerable support, and recent work has shown that the dynamics of different brain states may be modeled by pairwise maximum entropy Ising models at various distances from a phase transition, i.e., from criticality. Here we aim to characterize two brain states (psychedelics-induced and placebo) as captured by functional magnetic resonance imaging (fMRI), with features derived from the Ising spin model formalism (system temperature, critical point, susceptibility) and from algorithmic complexity. We hypothesized, along the lines of the entropic brain hypothesis, that psychedelics drive brain dynamics into a more disordered state at a higher Ising temperature and increased complexity. We analyze resting state blood-oxygen-level-dependent (BOLD) fMRI data collected in an earlier study from fifteen subjects in a control condition (placebo) and during ingestion of lysergic acid diethylamide (LSD). Working with the automated anatomical labeling (AAL) brain parcellation, we first create "archetype" Ising models representative of the entire dataset (global) and of the data in each condition. Remarkably, we find that such archetypes exhibit a strong correlation with an average structural connectome template obtained from dMRI (r = 0.6). We compare the archetypes from the two conditions and find that the Ising connectivity in the LSD condition is lower than in the placebo one, especially in homotopic links (interhemispheric connectivity), reflecting a significant decrease of homotopic functional connectivity in the LSD condition. The global archetype is then personalized for each individual and condition by adjusting the system temperature. The resulting temperatures are all near but above the critical point of the model i
Scagliarini T, Nuzzi D, Antonacci Y, et al., 2023, Gradients of O-information: Low-order descriptors of high-order dependencies, Physical Review Research, Vol: 5, Pages: 1-8, ISSN: 2643-1564
O-information is an information-theoretic metric that captures the overall balance between redundant and synergistic information shared by groups of three or more variables. To complement the global assessment provided by this metric, here we propose the gradients of the O-information as low-order descriptors that can characterize how high-order effects are localized across a system of interest. We illustrate the capabilities of the proposed framework by revealing the role of specific spins in Ising models with frustration, in Ising models with three-spin interactions, and in a linear vectorial autoregressive process. We also provide an example of practical data analysis on U.S. macroeconomic data. Our theoretical and empirical analyses demonstrate the potential of these gradients to highlight the contribution of variables in forming high-order informational circuits.
Zeifman R, Spriggs M, Kettner H, et al., 2023, From Relaxed Beliefs Under Psychedelics (REBUS) to Revised Beliefs After Psychedelics (REBAS), Scientific Reports, ISSN: 2045-2322
Objectives: This was a cross sectional study aimed at describing chest x-ray findings among children hospitalised with clinically diagnosed severe pneumonia and hypoxaemia (SpO2<92%) in three tertiary facilities in Uganda.Methods: We studied chest x-rays of 375 children aged 28 days to 12 years enrolled into the Children’s Oxygen Administration Strategies Trial (COAST)(ISRCTN15622505). Radiologists blinded to the clinical findings reported chest x-rays using the standardized World Health Organization methodology for paediatric chest Xray reporting. We summarised clinical data and chest x-ray findings using descriptive statistics. Chi-square and proportion tests were used to compare proportions and quantile regression compared medians. Results: We found 172, (45.8%) children had radiological pneumonia, 136 (36.3%) normal chest radiographs while 123 (32.8%) non-pneumonia findings, the major one being cardiovascular abnormalities,106 (28.3%); 56 (14.9%) chest radiographs had both pneumonia and other abnormalities. There was no difference in the prevalence of radiological pneumonia, cardiovascular abnormalities, and mortality between the group with severe hypoxaemia (SpO2<80%) and that with mild hypoxaemia (SpO280 to <92%), (95% CI: -13.2,7.1, -6.1,15.9) and -37.2, 20.4) respectively. Conclusion: This study highlights a relatively high prevalence of cardiovascular abnormalities in children who fulfill the WHO clinical criteria for severe pneumonia and have hypoxaemia. We recommend that chest x-ray examinations be routinely done for all children in this population because information concerning cardiovascular and respiratory systems can be obtained in one sitting and guide management better. We hope that these findings can prompt discussions into refining the clinical criteria used to classify and manage pneumonia in children in limited resource settings.
Hipolito I, Mago J, Rosas FE, et al., 2023, Pattern breaking: a complex systems approach to psychedelic medicine, Neuroscience of Consciousness, Vol: 2023, ISSN: 2057-2107
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 ‘entropic brain’ hypothesis and the ‘RElaxed Beliefs Under pSychedelics’ 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.
Herzog R, Rosas FE, Whelan R, et al., 2022, Genuine high-order interactions in brain networks and neurodegeneration, Neurobiology of Disease, Vol: 175, Pages: 1-15, ISSN: 0969-9961
Brain functional networks have been traditionally studied considering only interactions between pairs of regions, neglecting the richer information encoded in higher orders of interactions. In consequence, most of the connectivity studies in neurodegeneration and dementia use standard pairwise metrics. Here, we developed a genuine high-order functional connectivity (HOFC) approach that captures interactions between 3 or more regions across spatiotemporal scales, delivering a more biologically plausible characterization of the pathophysiology of neurodegeneration. We applied HOFC to multimodal (electroencephalography [EEG], and functional magnetic resonance imaging [fMRI]) data from patients diagnosed with behavioral variant of frontotemporal dementia (bvFTD), Alzheimer's disease (AD), and healthy controls. HOFC revealed large effect sizes, which, in comparison to standard pairwise metrics, provided a more accurate and parsimonious characterization of neurodegeneration. The multimodal characterization of neurodegeneration revealed hypo and hyperconnectivity on medium to large-scale brain networks, with a larger contribution of the former. Regions as the amygdala, the insula, and frontal gyrus were associated with both effects, suggesting potential compensatory processes in hub regions. fMRI revealed hypoconnectivity in AD between regions of the default mode, salience, visual, and auditory networks, while in bvFTD between regions of the default mode, salience, and somatomotor networks. EEG revealed hypoconnectivity in the γ band between frontal, limbic, and sensory regions in AD, and in the δ band between frontal, temporal, parietal and posterior areas in bvFTD, suggesting additional pathophysiological processes that fMRI alone can not capture. Classification accuracy was comparable with standard biomarkers and robust against confounders such as sample size, age, education, and motor artifacts (from fMRI and EEG). We conclude that high-order interactions p
Spriggs MJ, Giribaldi B, Lyons T, et al., 2022, Body mass index (BMI) does not predict responses to psilocybin, Journal of Clinical Psychopharmacology, Vol: 37, Pages: 107-116, ISSN: 0271-0749
Background:Psilocybin is a serotonin type 2A (5-HT2A) receptor agonist and naturally occurring psychedelic. 5-HT2A receptor density is known to be associated with body mass index (BMI), however, the impact of this on psilocybin therapy has not been explored. While body weight-adjusted dosing is widely used, this imposes a practical and financial strain on the scalability of psychedelic therapy. This gap between evidence and practice is caused by the absence of studies clarifying the relationship between BMI, the acute psychedelic experience and long-term psychological outcomes.Method:Data were pooled across three studies using a fixed 25 mg dose of psilocybin delivered in a therapeutic context to assess whether BMI predicts characteristics of the acute experience and changes in well-being 2 weeks later. Supplementing frequentist analysis with Bayes Factors has enabled for conclusions to be drawn regarding the null hypothesis.Results:Results support the null hypothesis that BMI does not predict overall intensity of the altered state, mystical experiences, perceptual changes or emotional breakthroughs during the acute experience. There was weak evidence for greater ‘dread of ego dissolution’ in participants with lower BMI, however, further analysis suggested BMI did not meaningfully add to the combination of the other covariates (age, sex and study). While mystical-type experiences and emotional breakthroughs were strong predictors of improvements in well-being, BMI was not.Conclusions:These findings have important implications for our understanding of pharmacological and extra-pharmacological contributors to psychedelic-assisted therapy and for the standardization of a fixed therapeutic dose in psychedelic-assisted therapy.
Rajpal H, Martinez Mediano PA, Rosas De Andraca FE, et al., 2022, Psychedelics and schizophrenia: Distinct alterations to Bayesian inference, NeuroImage, Vol: 263, ISSN: 1053-8119
Schizophrenia and states induced by certain psychotomimetic drugs may share some physiological and phenomenological properties, but they differ in fundamental ways: one is a crippling chronic mental disease, while the others are temporary, pharmacologically-induced states presently being explored as treatments for mental illnesses. Building towards a deeper understanding of these different alterations of normal consciousness, here we compare the changes in neural dynamics induced by LSD and ketamine (in healthy volunteers) against those associated with schizophrenia, as observed in resting-state M/EEG recordings. While both conditions exhibit increased neural signal diversity, our findings reveal that this is accompanied by an increased transfer entropy from the front to the back of the brain in schizophrenia, versus an overall reduction under the two drugs. Furthermore, we show that these effects can be reproduced via different alterations of standard Bayesian inference applied on a computational model based on the predictive processing framework. In particular, the effects observed under the drugs are modelled as a reduction of the precision of the priors, while the effects of schizophrenia correspond to an increased precision of sensory information. These findings shed new light on the similarities and differences between schizophrenia and two psychotomimetic drug states, and have potential implications for the study of consciousness and future mental health treatments.
Hancock F, Cabral J, Luppi AI, et al., 2022, Metastability, fractal scaling, and synergistic information processing: what phase relationships reveal about intrinsic brain activity, NeuroImage, Vol: 259, Pages: 1-16, ISSN: 1053-8119
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.
Wang Z, Chen J, Rosas FE, et al., 2022, A hypergraph-based framework for personalized recommendations via user preference and dynamics clustering, Expert Systems with Applications, Vol: 204, Pages: 117552-117552, ISSN: 0957-4174
The ever-increasing number of users and items continuously imposes new challenges to existent clustering-based recommendation algorithms. To better simulate the interactions between users and items in the recommendation system, in this paper, we propose a collaborative filtering recommendation algorithm based on dynamics clustering and similarity measurement in hypergraphs (Hg-PDC). The main idea of Hg-PDC is to discover several interest communities by aggregating users with high attention, and make recommendations within each community, thereby improving the recommendation performance and reducing the time cost. Firstly, we introduce a hypergraph model to capture complex relations beyond pairwise relations, while preserving attention relations in the network. In addition, we construct a novel hypergraph model, which defines a user and his evaluated items to form a hyperedge. Secondly, an extended game dynamics clustering method is proposed for the constructed hypergraph to aggregate users with high attention into the same interest community. Here, we combine the payoff function in game theory with the traditional dynamics clustering method. Finally, we apply the dynamics clustering results and a new similarity measurement strategy with user preferences to recommend items for target users. The effectiveness of Hg-PDC is verified by experiments on six real datasets. Experimental results illustrate that our algorithm outperforms state-of-the-art algorithms in prediction errors and recommendation performance.
Virgo N, Rosas FE, Biehl M, 2022, Embracing sensorimotor history: Time-synchronous and time-unrolled Markov blankets in the free-energy principle., Behavioral and Brain Sciences, Vol: 45, Pages: e215-e215, ISSN: 0140-525X
The free-energy principle (FEP) builds on an assumption that sensor-motor loops exhibit Markov blankets in stationary state. We argue that there is rarely reason to assume a system's internal and external states are conditionally independent given the sensorimotor states, and often reason to assume otherwise. However, under mild assumptions internal and external states are conditionally independent given the sensorimotor history.
Gatica M, E Rosas F, A M Mediano P, et al., 2022, High-order functional redundancy in ageing explained via alterations in the connectome in a whole-brain model, PLoS Computational Biology, Vol: 18, Pages: 1-21, ISSN: 1553-734X
The human brain generates a rich repertoire of spatio-temporal activity patterns, which support a wide variety of motor and cognitive functions. These patterns of activity change with age in a multi-factorial manner. One of these factors is the variations in the brain's connectomics that occurs along the lifespan. However, the precise relationship between high-order functional interactions and connnectomics, as well as their variations with age are largely unknown, in part due to the absence of mechanistic models that can efficiently map brain connnectomics to functional connectivity in aging. To investigate this issue, we have built a neurobiologically-realistic whole-brain computational model using both anatomical and functional MRI data from 161 participants ranging from 10 to 80 years old. We show that the differences in high-order functional interactions between age groups can be largely explained by variations in the connectome. Based on this finding, we propose a simple neurodegeneration model that is representative of normal physiological aging. As such, when applied to connectomes of young participant it reproduces the age-variations that occur in the high-order structure of the functional data. Overall, these results begin to disentangle the mechanisms by which structural changes in the connectome lead to functional differences in the ageing brain. Our model can also serve as a starting point for modeling more complex forms of pathological ageing or cognitive deficits.
Mediano PAM, Rosas FE, Bor D, et al., 2022, The strength of weak integrated information theory, Trends in Cognitive Sciences, Vol: 26, Pages: 646-655, ISSN: 1364-6613
The integrated information theory of consciousness (IIT) is divisive: while some believe it provides an unprecedentedly powerful approach to address the ‘hard problem’, others dismiss it on grounds that it is untestable. We argue that the appeal and applicability of IIT can be greatly widened if we distinguish two flavours of the theory: strong IIT, which identifies consciousness with specific properties associated with maxima of integrated information; and weak IIT, which tests pragmatic hypotheses relating aspects of consciousness to broader measures of information dynamics. We review challenges for strong IIT, explain how existing empirical findings are well explained by weak IIT without needing to commit to the entirety of strong IIT, and discuss the outlook for both flavours of IIT.
Mediano PAM, Rosas FE, Luppi AI, et al., 2022, Greater than the parts: a review of the information decomposition approach to causal emergence., Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol: 380, Pages: 20210246-20210246, ISSN: 1364-503X
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'.
Nayak SM, Bari BA, Yaden DB, et al., 2022, A Bayesian Reanalysis of a Trial of Psilocybin versus Escitalopram for Depression
<p>Objectives: To perform a Bayesian reanalysis of a recent trial of psilocybin (COMP360) versus escitalopram for Major Depressive Disorder (MDD) in order to provide a more informative interpretation of the indeterminate outcome of a previous frequentist analysis.Design: Reanalysis of a two-arm double-blind placebo controlled trial.Participants: Fifty-nine patients with MDD.Interventions: Two doses of psilocybin 25mg and daily oral placebo versus daily escitalopram and 2 doses of psilocybin 1mg, with psychological support for both groups.Outcome measures: Quick Inventory of Depressive Symptomatology–Self-Report (QIDS SR-16), and three other depression scales as secondary outcomes: HAMD-17, MADRS, and BDI-1A.Results: Using Bayes factors and ‘skeptical priors’ which bias estimates towards zero, for the hypothesis that psilocybin is superior by any margin, we found indeterminate evidence for QIDS SR-16, strong evidence for BDI-1A and MADRS, and extremely strong evidence for HAMD-17. For the stronger hypothesis that psilocybin is superior by a ‘clinically meaningful amount’ (using literature defined values of the minimally clinically important difference), we found moderate evidence against it for QIDS SR-16, indeterminate evidence for BDI-1A and MADRS, and moderate evidence supporting it for HAMD-17. Furthermore, across the board we found extremely strong evidence for psilocybin’s non-inferiority versus escitalopram. These findings were robust to prior sensitivity analysis.Conclusions: This Bayesian reanalysis supports the following inferences: 1) that psilocybin did indeed outperform escitalopram in this trial, but not to an extent that was clinically meaningful—-and 2) that psilocybin is almost certainly non-inferior to escitalopram. The present results provide a more precise and nuanced interpretation to previously reported results from this trial, and support the need for further research into the relative efficacy of
McCulloch DE-W, Knudsen GM, Barrett FS, et al., 2022, Psychedelic resting-state neuroimaging: a review and perspective on balancing replication and novel analyses, Neuroscience & Biobehavioral Reviews, Vol: 138, Pages: 104689-104689, ISSN: 0149-7634
Clinical research into serotonergic psychedelics is expanding rapidly, showing promising efficacy across myriad disorders. Resting-state functional magnetic resonance imaging (rs-fMRI) is a commonly used strategy to identify psychedelic-induced changes in neural pathways in clinical and healthy populations. Here we, a large group of psychedelic imaging researchers, review the 42 research articles published to date, based on the 17 unique studies evaluating psychedelic effects on rs-fMRI, focusing on methodological variation. Prominently, we observe that nearly all studies vary in data processing and analysis methodology, two datasets are the foundation of over half of the published literature, and there is lexical ambiguity in common outcome metric terminology. We offer guidelines for future studies that encourage coherence in the field. Psychedelic rs-fMRI will benefit from the development of novel methods that expand our understanding of the brain mechanisms mediating its intriguing effects; yet, this field is at a crossroads where we must also consider the critical importance of consistency and replicability to effectively converge on stable representations of the neural effects of psychedelics.
Hancock F, Rosas FE, Mediano PAM, et al., 2022, May the 4C's be with you: an overview of complexity-inspired frameworks for analysing resting-state neuroimaging data, Journal of the Royal Society Interface, Vol: 19, Pages: 1-14, ISSN: 1742-5662
Competing and complementary models of resting-state brain dynamics contribute to our phenomenological and mechanistic understanding of whole-brain coordination and communication, and provide potential evidence for differential brain functioning associated with normal and pathological behaviour. These neuroscientific theories stem from the perspectives of physics, engineering, mathematics and psychology and create a complicated landscape of domain-specific terminology and meaning, which, when used outside of that domain, may lead to incorrect assumptions and conclusions within the neuroscience community. Here, we review and clarify the key concepts of connectivity, computation, criticality and coherence—the 4C's—and outline a potential role for metastability as a common denominator across these propositions. We analyse and synthesize whole-brain neuroimaging research, examined through functional magnetic imaging, to demonstrate that complexity science offers a principled and integrated approach to describe, and potentially understand, macroscale spontaneous brain functioning.
Luppi A, Rosas F, Noonan M, et al., 2022, Oxygen and the Spark of Human Brain Evolution: a Complex Systems Account
<jats:p>Scientific theories on the functioning and dysfunction of the human brain require a good understanding of both its development &mdash; before and after birth, and through maturation to adulthood &mdash; and its evolution from the ancestral primate brain. Adopting a complex-systems approach, here we propose that the apparent uniqueness of humans&rsquo; cognitive capacities might best be understood as emerging from multiple nested &ldquo;virtuous cycles.&rdquo; In particular, we propose that the intimate link that exists between oxygen metabolic loops, cortical expansion, and ultimately cognitive and social demands is a key driver of genetic developmental programs for the human brain. Overall, our proposed evolutionary model makes explicit mechanistic links between metabolism, molecular and cellular brain heterogeneity, and behaviour that may in time provide a clearer understanding of brain developmental trajectories and their disorders.</jats:p>
Luppi AI, Mediano PAM, Rosas FE, et al., 2022, A synergistic core for human brain evolution and cognition., Nat Neurosci, Vol: 25, Pages: 771-782
How does the organization of neural information processing enable humans' sophisticated cognition? Here we decompose functional interactions between brain regions into synergistic and redundant components, revealing their distinct information-processing roles. Combining functional and structural neuroimaging with meta-analytic results, we demonstrate that redundant interactions are predominantly associated with structurally coupled, modular sensorimotor processing. Synergistic interactions instead support integrative processes and complex cognition across higher-order brain networks. The human brain leverages synergistic information to a greater extent than nonhuman primates, with high-synergy association cortices exhibiting the highest degree of evolutionary cortical expansion. Synaptic density mapping from positron emission tomography and convergent molecular and metabolic evidence demonstrate that synergistic interactions are supported by receptor diversity and human-accelerated genes underpinning synaptic function. This information-resolved approach provides analytic tools to disentangle information integration from coupling, enabling richer, more accurate interpretations of functional connectivity, and illuminating how the human neurocognitive architecture navigates the trade-off between robustness and integration.
Rosas FE, Mediano PAM, Luppi AI, et al., 2022, Disentangling high-order mechanisms and high-order behaviours in complex systems, NATURE PHYSICS, Vol: 18, Pages: 476-477, ISSN: 1745-2473
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- Citations: 14
Luppi AI, Mediano PAM, Rosas FE, et al., 2022, Whole-brain modelling identifies distinct but convergent paths to unconsciousness in anaesthesia and disorders of consciousness, Communications Biology, Vol: 5, ISSN: 2399-3642
The human brain entertains rich spatiotemporal dynamics, which are drastically reconfigured when consciousness is lost due to anaesthesia or disorders of consciousness (DOC). Here, we sought to identify the neurobiological mechanisms that explain how transient pharmacological intervention and chronic neuroanatomical injury can lead to common reconfigurations of neural activity. We developed and systematically perturbed a neurobiologically realistic model of whole-brain haemodynamic signals. By incorporating PET data about the cortical distribution of GABA receptors, our computational model reveals a key role of spatially-specific local inhibition for reproducing the functional MRI activity observed during anaesthesia with the GABA-ergic agent propofol. Additionally, incorporating diffusion MRI data obtained from DOC patients reveals that the dynamics that characterise loss of consciousness can also emerge from randomised neuroanatomical connectivity. Our results generalise between anaesthesia and DOC datasets, demonstrating how increased inhibition and connectome perturbation represent distinct neurobiological paths towards the characteristic activity of the unconscious brain.
Hancock F, Rosas F, Mediano P, et al., 2022, May the 4c’s Be with You: An Overview of Complexity-Inspired Frameworks for Analyzing Resting-State Neuroimaging Data
<jats:p>Competing and complementary models of resting-state brain dynamics contribute to our phenomenological and mechanistic understanding of whole-brain coordination and communication, and provide potential evidence for differential brain functioning associated with normal and pathological behavior. These neuroscientific theories stem from the perspectives of physics, engineering, mathematics, and psychology and create a complicated landscape of domain-specific terminology and meaning, which, when used outside of that domain, may lead to incorrect assumptions and conclusions within the neuroscience community. Here we review and clarify the key concepts of Connectivity, Computation, Criticality, and Coherence &mdash; the 4C&rsquo;s &mdash; and outline a potential role for metastability as a common denominator across these propositions. We analyze and synthesize whole-brain neuroimaging research, examined through functional magnetic imaging (fMRI), to demonstrate that complexity science offers a principled and integrated approach to describe, and potentially understand, macroscale spontaneous brain functioning. </jats:p>
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