Biology subjects

Mediano, P. A. M.

Publications and source records attributed to Mediano, P. A. M..

At least 19 recordsLinked to original sources

bioRxiv2026

Psychedelics restructure information in the human brain

How do psychedelic drugs alter human brain function? Answering this question relies on our ability to understand how information is stored, transferred, and transformed across the cortex. Here, we leverage a precise mathematical framework for multivariate information decomposition to construct the first comprehensive picture of these processes across five compounds: LSD, psilocybin, ketamine, DMT and 5-MeO-DMT (N=80). We provide a formal and empirical explanation of major prior results, revealing sets of competing underlying phenomena masked by extant approaches. We extend this analysis to novel higher-order phenomena, and we demonstrate how all of these observations can emerge from a minimal generative model in which neural populations operate with reduced self-coupling. In sum, our results suggest that the psychedelic brain is one in which cortical populations become alike, but not necessarily together. This has important implications for models of how psychedelics reorganize brain dynamics to alter subjective experience and alleviate mental health conditions.

neuroscience↗

bioRxiv2026

Psychedelics Restructure the Global Workspace of Consciousness

Psychoactive drugs profoundly alter consciousness, but the neural mechanisms by which they do so remain poorly understood. Global workspace theory may provide a framework for investigating these mechanisms. The global workspace, a candidate neural substrate of consciousness, is a set of brain regions that integrate and disseminate information to the rest of the brain. According to a recent model, the integrators, or 'gateways,' are characterized by synergistic interactions with other brain regions, while the disseminators, or 'broadcasters,' exhibit redundant interactions. Here, we measured the effects of various psychoactive drugs, including 'classic' psychedelics like LSD, mescaline, and psilocybin, 'atypical' psychedelics (entactogens) like MDMA, and the stimulant D-amphetamine, on the global workspace. In a secondary analysis of resting-state functional magnetic resonance imaging (fMRI) data on all these drugs, we found that classic psychedelics are associated with significant increases in the number of gateways in the frontoparietal control and dorsal attention networks. Meanwhile, MDMA and amphetamine reduced the number of gateways in the visual and somatomotor networks. The regional topographies of both synergy and redundancy distinguished classic psychedelics from other drugs. Finally, drugs producing similar changes in synergy and redundancy also produced similar profiles of subjective effects. Overall, our findings provide the first direct evidence that psychedelics may alter the synergistic and redundant interactions that characterize the global workspace of consciousness.

neuroscience↗

bioRxiv2026

Synergistic and redundant information dynamics are modulated by Alzheimer's disease and cognitive impairment

BackgroundThe early diagnosis of Alzheimers disease (AD), a cause of progressive cognitive decline, remains challenging. Recent information-theoretic advances allow brain dynamics to be quantified in terms of how regions share and combine information. Integrated Information Decomposition ({Phi}ID) separates redundant (the same content present in multiple regions) from synergistic information (new content that emerges only when regions are considered together). Such information-dynamic measures may provide biomarkers relevant to AD risk and progression. MethodsHere we applied integrated information decomposition ({Phi}ID) to resting-state fMRI from the Alzheimers Disease Neuroimaging Initiative (ADNI), to test whether {Phi}ID measures are diagnostically sensitive and track cognition along the AD spectrum. For each region, we computed total synergy and redundancy and compared values across cognitively normal (CN), mild cognitive impairment (MCI), and AD groups. ResultsCompared to CN, AD patients showed a striking synergy reduction across the entire brain, in concert with widespread redundancy increases, particularly in the executive and default mode networks. Transitions from CN to AD included an intermediate MCI decrease in redundancy, possibly reflecting early disease compensation strategies. This AD informational shift from complex higher level information processing to more robust inefficient forms likely reflects a cognitive shift to simpler, less integrative cognitive processes. Indeed, when re-analysing the data according to a standard cognitive clinical test (the Montreal Cognitive Assessment), we found a synergy-redundancy shift in high versus low performers broadly very similar to the CN to AD shift. ConclusionAD shows a clear information-processing signature: reduced global synergy and increased redundancy, especially in the executive control network. These striking results provide powerful insights into the widespread information processing reconfiguration that occurs in AD, with clear changes already emerging at the earlier MCI stage. Further, these results provide a novel route to support early diagnosis and stratification.

neuroscience↗

bioRxiv2026

Quantifying the Emergence of Population-Level Activity in Neuronal Systems

Collective neural phenomena, such as oscillations and avalanches, are high-level neural signatures observed in aggregated spiking neuronal activity which have been consistently associated with a range of cognitive functions. However, it is often hard to elucidate whether such phenomena are mere epiphenomena or have causal or informational relevance. In this work, we investigate this question by leveraging recent information-theoretic tools to identify emergent phenomena between relevant scales of neural activity. For this, we propose a computational framework combining information-theoretic and network science principles, which we use to investigate emergence in both in-vivo datasets and in-silico simulations. Our approach enables characterisation of emergence phenomena, identifies the relevant scales at which they take place, and elucidates the network-level mechanisms that underpin them. Results show that in-vivo neuronal oscillations show substantial emergent behaviour for smaller prediction delays, while avalanches maintain their emergent nature for larger timescales. These results are supported by in-silico simulations, which show that the emergent signature of oscillations is facilitated by the network structure and interneuronal time-delays. Overall, these results highlight the role of network-level interactions between groups of neuronal assemblies as the key driver of emergent population activity in the brain.

neuroscience↗

bioRxiv2025

Hedonic experiences emerge from an orchestrated balance of synergistic and redundant information processing

Ketamine exerts rapid-acting, pro-hedonic effects, yet its precise mechanism remains elusive. Here, we present behavioral and fMRI data from a randomized, placebo-controlled crossover study in 38 healthy participants investigating ketamines sub-acute effects on multivariate information-processing during music-evoked peak hedonic experiences. Leveraging information-theoretical measures, our findings indicate that hedonic experiences depend on a distinct global (as measured by O-Information) and local (as measured by integrated information) balance between redundant - information shared across nodes - and synergistic - information emerging from joint interactions - processes. As hedonic intensity rises, neural dynamics shift toward greater synergy; with the one exception of a deliberate increase in redundancy particularly for key sensory information to ensure reliable transmission of and access to critical external information for subsequent hedonic processing. In contrast, ketamines sub-acute pro-hedonic effects arise potentially from enhancing redundant dynamics at rest, boosting the brains capability to robustly represent and access critical internal information, and thus, fostering an environment optimized to amplify the phenomenological hedonic experience, while simultaneously allowing for more efficient information integration.

neuroscience↗

bioRxiv2025

Accurate and Interpretable Prediction of Antidepressant Treatment Response from Receptor-informed Neuroimaging

Conventional antidepressants show moderate efficacy in treating major depressive disorder. Psychedelic-assisted therapy holds promise, yet individual responses vary, underscoring the need for predictive tools to guide treatment selection. Here, we present graphTRIP (graph-based Treatment Response Interpretability and Prediction) - a geometric deep learning architecture that enables three advances: 1) accurate prediction of post-treatment depression severity using only pretreatment clinical and neuroimaging data; 2) identification of robust biomarkers; and 3) causal analysis of treatment effects and underlying mechanisms. Trained on data from a clinical trial comparing psilocybin and escitalopram (NCT03429075), graphTRIP achieves strong predictive accuracy (r = 0.72, p = 6.8 x10-8), and shows clear generalization to both an independent dataset and across brain atlases. The model identifies stronger functional connectivity within sensory networks as a robust predictor of poorer response across both treatments. In contrast, causal analysis implicates frontoparietal and default mode networks as key moderators of differential response, with stronger 5-HT1A- and 5-HT2A-related signalling in the frontoparietal network predicting escitalopram response but psilocybin resistance. Overall, this work advances precision medicine and biomarker discovery in depression.

neuroscience↗

bioRxiv2025

An engine for systematic discovery of cause-effect relationships between brain structure and function

Characterising how perturbations of brain architecture influence brain function is essential to understand the origins of brain dysfunction, and devise potential avenues of treatment. Here we introduce a computational engine for systematic causal discovery of the functional consequences of altering network architecture and local biophysics in the brain. We integrate multimodal anatomical and functional neuroimaging to implement over 2, 000 in-silico brains, and provide mechanistic insight into the functional consequences of local lesions, global wiring, and empirically-derived maps of regional cytoarchitecture and chemoarchitecture. We comprehensively assess how each manipulation of brain macrostructure reshapes spatial and temporal signal coordination, information dynamics, and functional hierarchy--as well as spontaneous co-activation of meta-analytic cognitive circuits, and > 6, 000 dimensions of local neural dynamics. Our computational model systematically identifies which features of brain architecture have overlapping or antagonistic causal influence over each dimension of brain function, and how functional properties are traded off against each other across disorders and neuromodulation. We find that regions functional vulnerability to lesions in silico recapitulates their vulnerability to neurodevelopmental and psychiatric in vivo, along a core-periphery organisation. We provide convergent evidence that the brains wiring diagram is finely tuned to favour the hierarchical integration of information. Notably, our model successfully recapitulates known empirical results that have not been modelled before, including desynchronisation and flattening of the brains functional hierarchy induced by psychedelic 5HT2A agonists. To catalyse future discoveries, we make this resource freely available to the neuroscience community through an interactive website (https://systematic-causal-mapping.up.railway.app/), where users can interrogate our systematic database of simulated cause-effect relationships. Altogether, we provide a powerful computational engine to predict the functional consequences of experimental or clinical interventions, and drive neuroscientific hypothesis-generation.

neuroscience↗

bioRxiv2025

Informational Complexity as a Neural Marker of Cognitive Reserve

In Alzheimers disease (AD), a mismatch between neurological damage and cognitive functioning often is attributed to individual differences in cognitive reserve. Understanding the neural mechanism of cognitive reserve could help assessing the therapeutic effectiveness of interventions in AD. To address this, here, 38 elderly participants performed a sustained attention task during high-density EEG while awake and during drowsiness. Operationally, the degree to which performance was impaired under drowsiness signalled the extent of cognitive reserve, with less impairment indicating a higher level of cognitive reserve. Investigating performance variations during the active management of neural challenges offers a novel approach to studying cognitive reserve, capturing dynamics that mirror everyday cognitive demand. We related cognitive reserve to various measures, including informational complexity using the Lempel-Ziv (LZSUM) algorithm. We found a significant interaction effect between arousal and performance, where LZSUM values increased in high performers when drowsy but decreased in low performers. This effect was most pronounced in the frontal and central areas. Our findings suggest LZSUM to be indicative of a compensatory mechanism and thus show potential for LZSUM as a neural marker in assessing cognitive reserve. However, we found no consistent relationship between performance and structural brain measures, and proxies of cognitive reserve. Critically, our findings present a counterexample to the prevailing view that informational complexity purely reflects conscious level. Further research, such as a study with the same paradigm in patients with mild cognitive impairment (MCI) and AD, may lead to additional insights of whether we are truly measuring cognitive reserve.

neuroscience↗

bioRxiv2025

Understanding the high-order network plasticity mechanisms of ultrasound neuromodulation

Transcranial ultrasound stimulation (TUS) is an emerging non-invasive neuromodulation technique, offering a potential alternative to pharmacological treatments for psychiatric and neurological disorders. While functional analysis has been instrumental in characterizing TUS effects, understanding the underlying mechanisms remains a challenge. Here, we developed a whole-brain model to represent functional changes as measured by fMRI, enabling us to investigate how TUS-induced effects propagate throughout the brain with increasing stimulus intensity. We implemented two mechanisms: one based on anatomical distance and another on broadcasting dynamics, to explore plasticity-driven changes in specific brain regions. Finally, we highlighted the role of higher-order functional interactions in localizing spatial effects of off-line TUS at two target areas--the right thalamus and inferior frontal cortex--revealing distinct patterns of functional reorganization. This work lays the foundation for mechanistic insights and predictive models of TUS, advancing its potential clinical applications. Significance StatementTranscranial ultrasound stimulation (TUS) offers a non-invasive approach to modulating brain activity, holding promise for treating psychiatric and neurological disorders. Despite its potential, the mechanisms underlying its effects remain poorly understood. By integrating human fMRI data with whole-brain computational models, we identified how high-order functional interactions localize and propagate TUS-induced effects from local to global brain scales. This work introduces two mechanisms--distance-based propagation and diffusion-like broadcasting--that predict functional plasticity changes, providing a foundation for understanding and optimizing the biological and cognitive outcomes of TUS. Our findings offer critical insights into the dynamics of neuromodulation, bridging experimental results and clinical applications.

neuroscience↗

bioRxiv2024

Competitive interactions shape brain dynamics and computation across species

Adaptive cognition relies on cooperation across anatomically distributed brain circuits. However, specialised neural systems are also in constant competition for limited processing resources. How does the brains network architecture enable it to balance these cooperative and competitive tendencies? Here we use computational whole-brain modelling to examine the dynamical and computational relevance of cooperative and competitive interactions in the mammalian connectome. Across human, macaque, and mouse we show that the architecture of the models that most faithfully reproduce brain activity, consistently combines modular cooperative interactions with diffuse, long-range competitive interactions. The model with competitive interactions consistently outperforms the cooperative-only model, with excellent fit to both spatial and dynamical properties of the living brain, which were not explicitly optimised but rather emerge spontaneously. Competitive interactions in the effective connectivity produce greater levels of synergistic information and local-global hierarchy, and lead to superior computational capacity when used for neuromorphic computing. Altogether, this work provides a mechanistic link between network architecture, dynamical properties, and computation in the mammalian brain.

neuroscience↗

bioRxiv2024

Complex slow waves radically reorganise human brain dynamics under 5-MeO-DMT

5-methoxy-N,N-dimethyltryptamine (5-MeO-DMT) is a psychedelic drug known for its uniquely profound effects on subjective experience, reliably eradicating the perception of time, space, and the self. However, little is known about how this drug alters large-scale brain activity. We collected naturalistic electroencephalography (EEG) data of 29 healthy individuals before and after inhaling a high dose (12mg) of vaporised synthetic 5-MeO-DMT. We replicate work from rodents showing amplified low-frequency oscillations, but extend these findings with novel tools for characterising the organisation and dynamics of complex low-frequency spatiotemporal fields of neural activity. We find that 5-MeO-DMT radically reorganises low-frequency flows of neural activity, causing them to become incoherent, heterogeneous, viscous, fleeting, nonrecurring, and to cease their typical travelling forwards and backwards across the cortex compared to resting state. Further, we find a consequence of this reorganisation in broadband activity, which exhibits slower, more stable, low-dimensional behaviour, with increased energy barriers to rapid global shifts. These findings provide the first detailed empirical account of how 5-MeO-DMT sculpts human brain dynamics, revealing a novel set of cortical slow wave behaviours, with significant implications for extant neuroscientific models of serotonergic psychedelics.

neuroscience↗

bioRxiv2024

Brain signal complexity and aperiodicity predict human corticospinal excitability

BackgroundTranscranial magnetic stimulation (TMS) holds promise for brain modulation with relevant scientific and therapeutic applications, but it is limited by response variability. Targeting state-dependent EEG features such as phase and power shows potential, but uncertainty remains about the suitable brain states. ObjectiveThis study evaluated broadband EEG measures (BEMs), including the aperiodic exponent (AE) and entropy measures (CTW, LZ), as alternatives to band-limited features, such as power and phase, for predicting corticospinal excitability (CSE). MethodsTMS was delivered with randomly applied single pulses targeting the left primary motor cortex in 34 healthy participants while simultaneously recording EEG and EMG signals. Broadband and band-limited EEG features were evaluated for their ability to predict CSE using motor evoked potentials (MEPs) from the right extensor digitorum communis muscle as the outcome measure. ResultsBEMs (AE, CTW) significantly predicted CSE, comparable to beta-band power and phase, the most predictive and spatially specific band-limited markers of motor cortex CSE. Unlike these localized CSE markers at the site of stimulation, BEMs captured more global brain states and greater within-subject variability, indicating sensitivity to dynamic state changes. Notably, CTW was associated with high CSE, while AE was linked to low CSE. ConclusionThis study reveals BEMs as robust predictors of CSE that circumvent challenges of band-limited EEG features, such as narrowband filtering and phase estimation. They may reflect more general markers of brain excitability. With their slower timescale and broader sensitivity, BEMs are promising biomarkers for state-dependent TMS applications, particularly in therapeutic contexts.

neuroscience↗

bioRxiv2023

The entropic heart: Tracking the psychedelic state via heart rate dynamics

A growing body of work shows that autonomic signals provide a privileged evidence-stream to capture various aspects of subjective and neural states. This work investigates the potential for autonomic markers to track the effects of psychedelics -- potent psychoactive drugs with important scientific and clinical value. For this purpose, we introduce a novel Bayesian framework to estimate the entropy of heart rate dynamics under psychedelics. We also calculate Bayesian estimates of mean heart rate and heart rate variability, and investigate how these measures relate to subjective reports and neural effects. Results on datasets covering four drugs -- lysergic acid diethylamide (LSD), dimethyltryptamine (DMT), psilocybin, and sub-anaesthetic doses of the dissociative agent ketamine -- show consistent increases in mean heart rate, high-frequency heart rate variability, and heart rate entropy during the psychedelic experience. Moreover, these effects have predictive power over various dimensions of the psychedelic experience. Changes in heart rate entropy were found to be correlated with increases in brain entropy, while other autonomic markers were not. Overall, our results show that a cost-efficient autonomic measure has the potential to reveal surprising detail about subjective and brain states, opening up a range of new research avenues to explore in both basic and clinical neuroscience.

physiology↗

bioRxiv2023

Transcranial ultrasound stimulation effect in the redundant and synergistic networks consistent across macaques

Low-intensity transcranial ultrasound stimulation (TUS) is a non-invasive technique that safely alters neural activity, reaching deep brain areas with good spatial accuracy. We investigated the effects of TUS at the level of macaque using a recent metric, the synergy minus redundancy rank gradient, that quantifies different kinds of causal neural information processing. We analyzed this high-order quantity on the fMRI data after TUS in two targets: the supplementary motor area (SMA-TUS) and the frontal polar cortex (FPC-TUS). The TUS produced specific changes at the limbic network at FPC-TUS and the motor network at SMA-TUS and altered, in both targets, the sensorimotor, temporal, and frontal networks, consistent across macaques. Moreover, there was a reduction in the structural and functional coupling after both stimulations. Finally, the TUS changed the intrinsic high-order network topology, decreasing the modular organization of the redundancy at SMA-TUS and increasing the synergistic integration at FPC-TUS.

neuroscience↗

bioRxiv2023

The Hierarchy of Psychedelic Effects: Three Systematic Reviews and Meta-Analyses

Serotonergic psychedelics induce altered states of consciousness and have shown potential for treating a variety of neuropsychiatric disorders, including depression and addiction. Yet their modes of action are not fully understood. Here, we provide a novel, synergistic understanding of psychedelics arising from systematic reviews and meta-analyses of three hierarchical levels of analysis: 1) subjective experience (phenomenology), 2) neuroimaging and 3) molecular pharmacology. Phenomenologically, medium and high doses of LSD yield significantly higher ratings of visionary restructuralisation than psilocybin on the 5-dimensional Altered States of Consciousness Scale. Our neuroimaging results reveal that, in general, psychedelics significantly strengthen between-network functional connectivity (FC) while significantly diminishing within-network FC. Pharmacologically, LSD induces significantly more inositol phosphate formation at the 5-HT2A receptor than DMT and psilocin, yet there are no significant between-drug differences in the selectivity of psychedelics for the 5-HT2A, 5-HT2C, or D2 receptors, relative to the 5-HT1A receptor. Our meta-analyses link DMT, LSD, and psilocybin to specific neural fingerprints at each level of analysis. The results show a highly non-linear relationship between these fingerprints. Overall, our analysis highlighted the high heterogeneity and risk of bias in the literature. This suggests an urgent need for standardising experimental procedures and analysis techniques, as well as for more research on the emergence between different levels of psychedelic effects.

neuroscience↗

bioRxiv2023

Brain network dynamics in transitions of consciousness reorganize according to task engagement

Substantial changes in behavior, physiology, and brain function occur when alertness decreases 1- 5. These changes in brain function involve increased synchronization between cortical areas 6,7 as well as alterations in sensory processing pathways and networks connecting the thalamus and cortex 5,8-11. Cognitive tasks engage overlapping functional networks with sensory pathways facilitating information processing 12,13, and thalamocortical and corticocortical networks supporting task performance 14,15. Frontoparietal circuits play a crucial role in cognitive tasks 16 and states of decreased consciousness 17. To develop an integrated framework of consciousness and cognition, it is important to understand how fluctuations in alertness and cognitive processing interact in these shared circuits 18. Our hypothesis is that during periods of low alertness, individuals who actively maintain task engagement would recruit additional frontoparietal and sensory processing networks, while thalamocortical dynamics that typically change during sleep onset would remain unaffected. Our findings demonstrated that as alertness decreased, passively listening to auditory tones led to increased synchronization in the parietal lobe, whereas actively performing an auditory task resulted in increased long-range frontoparietal synchronization. During decreasing alertness, passive listening (but not active task engagement) was associated with widespread increased synchronization between the thalamus and cortex. In contrast, active task engagement (but not passive listening) led to increased synchronization between the auditory cortex and the rest of the brain. These results reveal the functional mechanisms of the brains flexible reorganization during transitions of consciousness when individuals are actively engaged in cognitive processes.

neuroscience↗

bioRxiv2023

Spectrally and temporally resolved estimation of neural signal diversity

Quantifying the complexity of neural activity has provided fundamental insights into cognition, consciousness, and clinical conditions. However, the most widely used approach to estimate the complexity of neural dynamics, Lempel-Ziv complexity (LZ), has fundamental limitations that substantially restrict its domain of applicability. In this article we leverage the information-theoretic foundations of LZ to overcome these limitations by introducing a complexity estimator based on state-space models --which we dub Complexity via State-space Entropy Rate (CSER). While having a performance equivalent to LZ in discriminating states of consciousness, CSER boasts two crucial advantages: 1) CSER offers a principled decomposition into spectral components, which allows us to rigorously investigate the relationship between complexity and spectral power; and 2) CSER provides a temporal resolution two orders of magnitude better than LZ, which allows complexity analyses of e.g. event-locked neural signals. As a proof of principle, we use MEG, EEG and ECoG datasets of humans and monkeys to show that CSER identifies the gamma band as the main driver of complexity changes across states of consciousness; and reveals early entropy increases that precede the standard ERP in an auditory mismatch negativity paradigm by approximately 20ms. Overall, by overcoming the main limitations of LZ and substantially extending its range of applicability, CSER opens the door to novel investigations on the fine-grained spectral and temporal structure of the signal complexity associated with cognitive processes and conscious states.

neuroscience↗

bioRxiv2022

The complexity of event-related MEG signals decreases with maturation in human fetuses and newborns

The complexity of neural activity is a commonly used read-out of healthy functioning in cortical circuits. Prior work has linked neural complexity to the level of maternal care in preterm infants at risk for developing mental disorders, yet the evolution of neural complexity in early human development is largely unknown. We hypothesized that cortical dynamics would evolve to optimize information processing as birth approaches, thereby increasing the complexity of cortical activity. To test this hypothesis, we conducted the first ever study relating prenatal neural complexity to maturation. MEG recordings were obtained from a sample of fetuses and newborns, including longitudinal data before and after birth. Using cortical responses to auditory irregularities, we computed several entropy measures which reflect the complexity of the MEG signal. Despite our hypothesis, neural complexity significantly decreased with maturation in both fetuses and newborns. Furthermore, we found that complexity decreased significantly faster in male fetuses for most entropy measures. Our surprising results lay the groundwork for the first ever mapping of how neural complexity evolves in early human development, with important implications for future efforts to develop predictive biomarkers of psychiatric disorders based on the complexity of perinatal MEG signals.

neuroscience↗