Biology subjects

Deco, G.

Publications and source records attributed to Deco, G..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

The cost of cognition: Measuring the energy consumption of non-equilibrium computation

In biological systems, survival is predicated on an animal being able to perform computations quickly on a minimal energy budget. What is the energy consumption of non-equilibrium brain computation, i.e., what is the cost of cognition? Previous literature has estimated the metabolic cost using neuroimaging measures of glucose consumption but complementary to these findings, here we directly estimate the computational costs by combining the new field of stochastic thermodynamics with whole-brain modelling. We developed the COCO (COst of COgnition) framework using an analytical expression quantifying the links between energy cost, non-equilibrium and information processing for any given brain state measured with neuroimaging. Importantly, this key relationship also holds at the level of individual brain regions. We used this to quantify the benefits of information processing on the highly anatomically, interconnected hierarchical systems of the brain. Crucially, in empirical neuroimaging data we demonstrate that the human brain uses significantly less energy overall than other mammals (including non-human primates and mice), suggestive of an evolutionary optimisation of the effectiveness of computation. Focusing on the cost of cognition, using large-scale human neuroimaging data of 970 healthy human participants, we show that the resting state uses significantly less energy that seven different cognitive tasks. Furthermore, different kinds of tasks require different amounts of non-equilibrium, information processing and energy consumption. We found that tasks requiring more distributed computation also use more energy. Overall, these results directly quantify the cost of cognition, i.e., the non-equilibrium and energetic demands of information processing, allowing a deeper understanding of how the brain compute in a way that is far more energy efficient than current generations of digital computers and artificial intelligence.

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bioRxiv2025

Evolution's boldest trick: Neurotransmission modulated whole-brain computation captures full task repertoire

The perhaps most important unsolved problem in neuroscience is how the brain survives in a complex world by performing a rich repertoire of computation on a minimal energy budget. The brain is much better at adapting to the multiplicity of stimuli and outcomes than current generations of computers, artificial neural deep learning and reservoir model architectures. Yet, at first glance the brain appears to use a fixed anatomical architecture to perform the necessary huge variety of computations. But evolutions boldest trick is that in fact the brains effective connectivity is constantly being updated through neuromodulation to allow the rich repertoire of computation. Inspired by this, we created a whole-brain model using empirical neurotransmitter maps modulating the underlying local regional dynamics. This NEMO (neurotransmission modulated) whole-brain model is able to flexibly compute the full task repertoire and associated functional connectivity of the neuroimaging data from 971 healthy participants. For each individual we defined a measure of brain computability as the fitting of the NEMO whole-brain model to all tasks performed by the individual. Importantly, brain computability correlates with both behavioural performance on individual tasks and with a general behavioural measure of intelligence. Overall, our proposed unifying NEMO framework offers a natural way to sculpt different brain dynamics in a fixed brain architecture to compute the rich repertoire of tasks required for surviving and thriving.

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bioRxiv2025

Optimizing Biophysical Large-Scale Brain Circuit Models With Deep Neural Networks

Biophysical modeling provides mechanistic insights into brain function, spanning single-neuron dynamics to large-scale circuit models. These models are governed by biologically meaningful parameters, many of which can be experimentally measured. Some parameters are unknown, and optimizing them improves fit to experimental data, enhancing biological plausibility. However, existing methods require repeated, computationally expensive numerical integration of differential equations, limiting scalability to population-level datasets. Here, we introduce DELSSOME (DEep Learning for Surrogate Statistics Optimization in MEan field modeling), a framework that bypasses numerical integration by directly predicting whether parameter sets produce realistic brain dynamics. Across three large-scale circuit models, DELSSOME achieves a 1500-8000x speedup over numerical integration in predicting model realism. When embedded within an evolutionary optimization strategy, DELSSOME enables 50-100x faster parameter estimation without sacrificing agreement with numerical integration. Because of computational constraints, most studies simulate large-scale circuit models only at the group level. DELSSOME enables efficient individual-level optimization of the feedback inhibition control model. By collating 12,005 individuals across 14 datasets, we derive - for the first time - normative trajectories of cortical E/I ratio across the lifespan, revealing new insights into sex differences and network-specific patterns. This acceleration enables population-scale mechanistic modeling and unlocks new opportunities for understanding brain function.

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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.

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bioRxiv2025

Fast Interneuron Dysfunction in Laminar Neural Mass Model Reproduces Alzheimer's Oscillatory Biomarkers

Alzheimers disease (AD) is characterized by a progressive cognitive decline underpinned by disruptions in neural circuit dynamics. Early-stage AD is associated with cortical hyperexcitability, whereas later stages exhibit oscillatory slowing and hypoactivity, a progression observable in electrophysiological spectral characteristics. While previous studies have linked these changes to the dysfunction of fast-spiking parvalbumin-positive (PV) interneurons and neuronal loss associated with amyloid-beta (A{beta}) and hyperphos-phorylated tau (hp-{tau}) pathology, the precise mechanistic relationship between cellular and altered electrophysiology remains unclear. To study this relationship, we employed a Laminar Neural Mass Model that integrates excitatory and inhibitory neural populations within a biophysically informed columnar framework. The connectivity constant from PV cells to pyramidal neurons was gradually reduced to simulate the progressive neurotoxic effects of A{beta} oligomers. Other model parameters were systematically varied to compare with existing modeling literature and also to simulate the effects of hp-{tau}. All model predictions were compared to empirical M/EEG findings in the literature. Our simulations of PV interneuron dysfunction successfully reproduced the biphasic electrophysiological progression observed in AD: an early phase of hyperexcitability with increased gamma and alpha power, followed by oscillatory slowing and reduced spectral power. Alternative mechanisms and model parameters, such as increased excitatory drive, failed to replicate the observed biomarker trajectory. Additionally, to reconcile the hypoactivity and decreased firing rates observed in advanced AD stages, we combined the PV dysfunction model with a disruption of the pyramidal cell populations that reflects the neurotoxicity induced by hp-{tau}. Although this additional mechanism is not necessary to reproduce oscillatory changes in the isolated neural mass, it is crucial for aligning the model with evidence of reduced firing rates, metabolic activity, and cell loss and will enhance its applicability in future whole-brain modeling studies. These results support the hypothesis that at the local level, PV interneuron dysfunction is a primary driver of cortical electrophysiological alterations, while pyramidal neuron loss underlies later-stage severe hypoactivity. Our model provides a mechanistic framework for interpreting excitation-inhibition imbalance across AD progression, demonstrating the value of biophysically constrained models for interpreting electrophysiological biomarkers. Author summaryAlzheimers disease (AD) is not just a disorder of memory--it is a disease of brain networks. Before widespread neuronal loss occurs, the brain enters a state of hyperexcitability, which may contribute to disease progression. This hyperactivity is thought to arise from the selective dysfunction of inhibitory interneurons, particularly parvalbumin-positive (PV) interneurons, which play a crucial role in maintaining balanced brain activity. However, as the disease advances, a dramatic shift occurs, with neurons becoming progressively less active, leading to network breakdown. Understanding how this transition unfolds is essential for identifying new targets for early intervention. In this study, we developed a computational model of AD that represents the effects of amyloid-beta (A{beta}) oligomers and hyperphosphorylated tau (hp-{tau}) on neural circuits in a biologically meaningful way. Our model reproduces key features of M/EEG biomarkers, demonstrating that PV interneuron dysfunction leads to early hyperexcitability and the electrophysiological oscillatory changes characteristic of AD, while pyramidal cell pathology is necessary to drive later hypoactivity and network failure. By bridging molecular pathology mechanisms with mesoscale neural activity, our model provides a powerful tool for studying AD-related circuit dysfunction. It also highlights the importance of PV interneurons as a potential therapeutic target, paving the way for biologically informed interventions that could alter the course of the disease.

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bioRxiv2025

Psychedelics Align Brain Activity with Context

Psychedelics can profoundly alter consciousness by reorganising brain connectivity; however, their effects are context-sensitive. To understand how this reorganisation depends on context, we collected and comprehensively analysed the largest psychedelic neuroimaging dataset to date. Sixty-two adults were scanned with functional MRI and EEG during rest and naturalistic stimuli (meditation, music, and movie), before and after ingesting 19 mg of psilocybin (functional MRI {approx}80 min post-dose; EEG {approx}150 min post-dose). Half the participants ranked the experience among the most meaningful of their lives. Under psilocybin, functional MRI and EEG signals recorded during eyes-closed conditions became similar to those recorded during an eyes-open condition. Global functional connectivity increased in associative regions and decreased in sensory areas. Using machine learning to represent neural activity as low-dimensional trajectories, we found that psilocybin reorganised these into structured, context-sensitive patterns of brain activity that reflected both experimental condition and the quality of subjective experience, revealing an organisation that was missed by time-averaged connectivity measures. Under psilocybin, brain networks that ordinarily segregate internal and external processing coherently integrated and aligned neural dynamics with context. This context-alignment manifested as distinct and cohesive neural trajectories in participants reporting positively felt self- and boundary-dissolving effects, corresponding to the felt experience of being part of the environment, which we refer to as embeddedness--the subjective experience of being continuous with, rather than separate from, the surrounding environment. The strength of this context-alignment was associated with next-day mindset change, bridging the neural, experiential, and therapeutic dimensions of the psychedelic state. These findings show that the organisation of brain activity covaries with the experiential coherence of the psychedelic state, and provide a systems-level framework for how context-sensitive brain dynamics link neurobiology to subjective experience and behavioural change.

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bioRxiv2025

Impact of meningioma and glioma on whole-brain dynamics

Brain tumors, particularly meningiomas and gliomas, can profoundly affect neural function, yet their impact on brain dynamics remains incompletely understood. This study investigates alterations in normal brain function among meningioma and glioma patients by assessing dynamical complexity through the Intrinsic Ignition Framework. We analyzed resting-state fMRI data from 34 participants to quantify brain dynamics using intrinsic ignition and metastability metrics. Our results revealed distinct patterns of disruption: glioma patients showed significant reductions in both metrics compared to controls, indicating widespread network disturbances. In contrast, meningioma patients exhibited significant changes predominantly in regions with substantial tumor involvement. Resting-state network analysis demonstrated strong metastability and metastability/ignition correlations between regions in controls, which were slightly weakened in meningioma patients and severely disrupted in glioma patients. These findings highlight the differential impacts of gliomas and meningiomas on brain function, offering insights into their distinct pathophysiological mechanisms. Furthermore, these results show that brain dynamics metrics can be effective biomarkers for identifying disruptions in brain information transmission caused by tumors.

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bioRxiv2025

Perturbing whole-brain models of brain hierarchy: an application for depression following pharmacological treatment

Neural representation can extend beyond localised activity to encompass global patterns, where information is distributed across brain networks in a hierarchical manner. Recent research suggests that the hierarchy of causal influences shaping these patterns can serve as a signature of distinct brain states, with implications for neuropsychiatric disorders. Here, we first delve into how whole-brain models, guided by the Thermodynamics of Mind framework, can estimate the brain hierarchy of specific brain states, and how perturbations of such models can study the in-silico transitions to other states represented by static functional connectivity. We then show an application for major depressive disorder, where different brain hierarchical reconfigurations have been found following psilocybin and escitalopram treatments. We build whole-brain models of depressed patients before and after psilocybin and escitalopram interventions, and we carry a dynamic sensitivity analysis to explore the susceptibility of brain states and their drivability to healthier states. We show that susceptibility is on average reduced by escitalopram and increased by psilocybin, and that both treatments succeed in promoting healthier transitions. These results align with the post-treatment window of plasticity opened by serotonergic psychedelics, as well as with the similar clinical efficacy of both drugs observed in clinical trials. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=193 HEIGHT=200 SRC="FIGDIR/small/631011v1_ufig1.gif" ALT="Figure 1"> View larger version (56K): org.highwire.dtl.DTLVardef@7f1b46org.highwire.dtl.DTLVardef@d7a883org.highwire.dtl.DTLVardef@783e39org.highwire.dtl.DTLVardef@3d334d_HPS_FORMAT_FIGEXP M_FIG C_FIG We apply whole-brain models of brain hierarchy based on the Thermodynamics of Mind framework to investigate state transitions in depression. Dynamic sensitivity analysis explores how psilocybin and escitalopram affect susceptibility and drivability to healthier states. Results show that psilocybin increases susceptibility, while escitalopram reduces it, with both enabling optimal transitions. This pipeline demonstrates the promise of in-silico approaches to inform neurostimulation protocols, potentially enhancing or complementing antidepressant therapies.

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bioRxiv2024

Collapse of directed functional hierarchy under classical serotonergic psychedelics

Despite divergent behavioral and phenomenological profiles, both psychedelic states and reduced states of consciousness have been associated with a flattening of the brains functional hierarchy. To address this apparent paradox, we developed a more specific definition of hierarchy based on the proximity of the brain to thermodynamic equilibrium and then applied it to investigate the changes to the functional hierarchy elicited by three classical serotonergic psychedelics - psilocybin, lysergic acid diethylamide, and dimethyltryptamine. We found that all three psychedelics consistently induced a global reduction in the functional hierarchy. In contrast to the flattening of the functional hierarchy observed during loss of consciousness, psychedelics displaced the brain towards equilibrium while simultaneously increasing the complexity of neural activity, indicating a unique mechanism linked to specific changes in the configuration and differentiation of resting-state networks. This work showcases how metrics based on statistical mechanics can be used for the specific characterization of different global brain states, contributing to the understanding of consciousness as a collective process emerging from complex neural interactions.

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bioRxiv2024

Nonequilibrium dynamics elicited as the origin of perturbative complexity

Assessing the level of consciousness someone is in, is not a trivial question and physicians have to rely on behavioural evaluations instead of quantifiable metrics. Many studies have empirically investigated measures related to the complexity elicited after the brain is stimulated to quantify and assess the level of consciousness across different states. Here we hypothesized that the level of non-equilibrium dynamics of the unperturbed brain already contains the information needed to know how the system will react to an external stimulus. We created personalized whole-brain models fitted to resting state fMRI data recorded in participants in different states of reduced consciousness (such as deep sleep and disorders of consciousness) to infer the effective connections underlying their brain dynamics. We then measured the out-of-equilibrium nature of the unperturbed brain by evaluating the level of asymmetry of the inferred connectivity, the time irreversibility in each model and compared this with the elicited complexity generated after in silico perturbations. Crucially, we found that states of reduced consciousness had a lower level of asymmetry in their effective connectivities compared to control subjects, as well as a lower level of irreversibility in their simulated dynamics, and a lower complexity. We demonstrated that the asymmetry in the underlying connections drives the nonequilibrium state of the system and in turn the differences in complexity as a response to the external stimuli.

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bioRxiv2024

Long-term effects of psilocybin on dynamic and effectivity connectivity of fronto-striatal-thalamic circuits

Psilocybin has been shown to induce fast and sustained improvements in mental well-being across various populations, yet its long-term mechanisms of action are not fully understood. Initial evidence suggests that longitudinal functional and structural brain changes implicate fronto-striatal-thalamic (FST) circuitry, a broad system involved in goal-directed behavior and motivational states. Here, we apply empirical methods and computational modeling to resting-state fMRI data from a within-subject longitudinal psilocybin trial in psychedelic-naive healthy volunteers. We first show increases in FST dynamic activity four weeks after a full dose of psilocybin. We then proceed to mechanistically account for these increased dynamics, by showing that reduced structural constraints underlie increased FST dynamic activity post psilocybin. Further, we show that these reduced structural constraints come along with increased bottom-up and reduced top-down modulation of FST circuits. While cortical reductions in top-down modulation are linked to regional 5-HT2A receptor availability, increased information outflow via subcortical and limbic regions relate to local D2 receptor availability. Together, these findings show that increased FST flexibility weeks after psilocybin administration is linked to serotonergic-mediated decreases in top-down information flow and dopaminergic-mediated increases in bottom-up information flow. This long-term functional re-organization of FST circuits may represent a common mechanism underling the potential clinical efficacy of psilocybin across various neuropsychiatric disorders including substance abuse, major depression, and anorexia. Significance StatementFronto-striatal-thalamic systems, which underlie motivation and reward, go through profound functional and structural changes following psilocybin administration. We leveraged longitudinal fMRI data from a within-subject psilocybin trial in psychedelic-naive healthy participants to show that psilocybin increases fronto-striatal-thalamic dynamic activity as well as flexibility four weeks after dosing. Computational modeling revealed that this increased flexibility is mechanistically caused by reduced structural constraints on functional dynamics. Further long-term changes included increased bottom-up and reduced top-down information flow mediated by the serotonergic and dopaminergic systems. This long-term functional re-organization of fronto-striatal-thalamic circuits may reflect a common mechanism underlying clinical symptoms improvements across diagnostic groups, such as increased openness, improved well-being, and reductions in anhedonia, apathy, and substance craving.

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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.

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bioRxiv2024

Off-Equilibrium Fluctuation-Dissipation Theorem Paves the Way in Alzheimer's Disease Research

INTRODUCTIONAlzheimers disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline. Although traditional methods have provided insights into brain dynamics in AD, they have limitations in capturing non-equilibrium dynamics across disease stages. Recent studies suggest that dynamic functional connectivity in resting-state networks (RSNs) may serve as a biomarker for AD, but the role of deviations from dynamical equilibrium remains underexplored. OBJECTIVEThis study applies the off-equilibrium fluctuation-dissipation theorem (FDT)1 to analyze brain dynamics in AD, aiming to compare deviations from equilibrium in healthy controls, patients with mild cognitive impairment (MCI), and those with AD. The goal is to identify potential biomarkers for early AD detection and understand disease progressions mechanisms. METHODSWe employed a model-free approach based on FDT to analyze functional magnetic resonance imaging (fMRI) data, including healthy controls, MCI patients, and AD patients. Deviations from equilibrium in resting-state brain activity were quantified using fMRI scans. In addition, we performed model-based simulations incorporating Amyloid-Beta (A{beta}), tau burdens, and Generative Effective Connectivity (GEC) for each subject. RESULTSOur findings show that deviations from equilibrium increase during the MCI stage, indicating hyperexcitability, followed by a significant decline in later stages of AD, reflecting neuronal damage. Model-based simulations incorporating A{beta} and tau burdens closely replicated these dynamics, especially in AD patients, highlighting their role in disease progression. Healthy controls exhibited lower deviations, while AD patients showed the most significant disruptions in brain dynamics. DISCUSSIONThe study demonstrates that the off-equilibrium FDT framework can accurately characterize brain dynamics in AD, providing a potential biomarker for early detection. The increase in non-equilibrium deviations during the MCI stage followed by their decline in AD offers a mechanistic explanation for disease progression. Future research should explore how combining this framework with other dynamic brain measures could further refine diagnostic tools and therapeutic strategies for AD and other neurodegenerative diseases.

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bioRxiv2024

Modeling disorders of consciousness at the patient level reveals the network's influence on the diagnosis vs the local node parameters role in prognosis

The study of disorders of consciousness (DoC) is very complex because patients suffer from a wide variety of lesions, affected brain mechanisms, different severity of symptoms, and are unable to communicate. Combining neuroimaging data and mathematical modeling can help us quantify and better describe some of these alterations. The goal of this study is to provide a new analysis and modeling pipeline for fMRI data leading to new diagnosis and prognosis biomarkers at the individual patient level. To do so, we project patients fMRI data into a low-dimension latent-space. We define the latent spaces dimension as the smallest dimension able to maintain the complexity, non-linearities, and information carried by the data, according to different criteria that we detail in the first part. This dimensionality reduction procedure then allows us to build biologically inspired latent whole-brain models that can be calibrated at the single-patient level. In particular, we propose a new model inspired by the regulation of neuronal activity by astrocytes in the brain. This modeling procedure leads to two types of model-based biomarkers (MBBs) that provide novel insight at different levels: (1) the connectivity matrices bring us information about the severity of the patients diagnosis, and, (2) the local node parameters correlate to the patients etiology, age and prognosis. Altogether, this study offers a new data processing framework for resting-state fMRI which provides crucial information regarding DoC patients diagnosis and prognosis. Finally, this analysis pipeline could be applied to other neurological conditions.

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bioRxiv2024

Reconfiguration of Functional Brain Hierarchy in Schizophrenia

The multidimensional nature of schizophrenia requires a comprehensive exploration of the functional and structural brain networks. While prior research has provided valuable insights into these aspects, our study goes a step further to investigate the reconfiguration of the hierarchy of brain dynamics, which can help understand how brain regions interact and coordinate in schizophrenia. We applied an innovative thermodynamic framework, which allows for a quantification of the degree of functional hierarchical organization by analysing resting state fMRI-data. Our findings reveal increased hierarchical organization at the whole-brain level and within specific resting-state networks in individuals with schizophrenia, which correlated with negative symptoms, positive formal thought disorder and apathy. Moreover, using a machine learning approach, we showed that hierarchy measures allow a robust diagnostic separation between healthy controls and schizophrenia patients. Thus, our findings provide new insights into the nature of functional connectivity anomalies in schizophrenia, suggesting that they could be caused by the breakdown of the functional orchestration of brain dynamics.

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bioRxiv2024

A virtual clinical trial of psychedelics to treat patients with disorders of consciousness

Disorders of consciousness (DoC), including the unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS), have limited treatment options. Recent research suggests that psychedelic drugs, known for their complexity-enhancing properties, could be promising treatments for DoC. This study uses whole-brain computational models to explore this potential. We created individualised models for DoC patients, optimised with empirical fMRI and diffusion-weighted imaging (DWI) data, and simulated the administration of LSD and psilocybin. We used an in-silico perturbation protocol to distinguish between different states of consciousness, including DoC, anaesthesia, and the psychedelic state, and assess the dynamical stability of the brains of DoC patients pre- and post-psychedelic simulation. Our findings indicate that LSD and psilocybin shift DoC patients brains closer to criticality, with a greater effect in MCS patients. In UWS patients, the treatment response correlates with structural connectivity, while in MCS patients, it aligns with baseline functional connectivity. This virtual clinical trial lays a computational foundation for using psychedelics in DoC treatment and highlights the future role of computational modelling in drug discovery and personalised medicine.

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bioRxiv2024

Emergence and long-term maintenance of modularity in plastic networks of spiking neurons

The modular and hierarchical organization of the brain is believed to support the coexistence of segregated (specialization) and integrated (binding) information processes. A relevant question is yet to understand how such architecture naturally emerges and is sustained over time, given the plastic nature of the brains wiring. Following evidences that the sensory cortices organize into assemblies under selective stimuli, it has been shown that stable neuronal assemblies can emerge due to targeted stimulation, embedding various forms of synaptic plasticity in presence of homeostatic and/or control mechanisms. Here, we show that simple spike-timing-dependent plasticity (STDP) rules, based only on pre- and post-synaptic spike times, can also lead to the stable encoding of memories in the absence of any control mechanism. We develop a model of spiking neurons, trained by stimuli targeting different sub-populations. The model satisfies some biologically plausible features: (i) it contains excitatory and inhibitory neurons with Hebbian and anti-Hebbian STDP; (ii) neither the neuronal activity nor the synaptic weights are frozen after the learning phase. Instead, the neurons are allowed to fire spontaneously while synaptic plasticity remains active. We find that only the combination of two inhibitory STDP sub-populations allows for the formation of stable modules in the network, with each sub-population playing a distinctive role. The Hebbian sub-population controls for the firing activity, while the anti-Hebbian neurons promote pattern selectivity. After the learning phase, the network settles into an asynchronous irregular resting-state. This post-learning activity is associated with spontaneous memory recalls which turn out to be fundamental for the long-term consolidation of the learned memories. Due to its simplicity, the introduced model can represent a test-bed for further investigations on the role played by STDP on memory storing and maintenance. Author summaryOne of the most remarkable qualities of the brain is its capacity to learn and adapt. How the learning process imprints and maintains memories, by shaping the architecture of connectivity among neurons in a constantly changing and dynamic environment, is a major question of neuroscience. Here, we explore the idea that the segregation of inputs received by a neural network, with inputs targeting distinct populations, is a key factor for shaping the architecture of the network. We find that the presence of inhibitory neurons is necessary for the emergence and the long-term maintenance of modularity in spiking neural networks with plasticity. In particular, we show that two different inhibitory sub-populations, one subject to Hebbian and the other to anti-Hebbian plasticity, are required to promote the formation of feedback and feed-forward inhibition circuits controlling memory consolidation. On one side, these inhibitory circuits favour long-term memory consolidation by inducing spontaneous memory recalls in the asynchronous irregular resting phase. On another side, the number of inhibitory neurons control the maximal memory capacity of the considered model.

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bioRxiv2024

Threat-Related Corticocortical Connectivity Elicited by Rapid Auditory Looms

While sounds of approaching objects are generally more salient than those of receding ones, the traditional association of this auditory looming bias with threat perception is subject to debate. Differences between looming and receding sounds may also be learned through non-threatening multisensory information, or influenced by confounding stimulus characteristics. To investigate, we analyzed corticocortical connectivity patterns from electroencephalography, examining the preferential processing of looming sounds under different attentional states. To simulate rapid distance changes we used complementary distance cues, previously studied in the looming bias literature. Notably, despite the absence of conscious threat perception, we observed crucial involvement of frontal cortical regions typically associated with threat and fear responses. Our findings suggest an underlying bias towards the ventral what stream over the dorsal where stream in auditory information processing, even when the participants task was solely focused on the discrimination of movement direction. These results support the idea, that the perceptual bias towards looming sounds reflects an auditory threat detection mechanism, while offering insights into the neural function involved in processing ecologically relevant environmental cues.

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