Biology subjects

Jirsa, V.

Publications and source records attributed to Jirsa, V..

At least 19 recordsLinked to original sources

bioRxiv2026

Slow Ionic Dynamics Reshape Bistability in a Biophysical Mean-Field Model of Brain Activity

Brain states such as sleep, seizures, and resting wakefulness are often described in terms of transitions between distinct population states. Biophysically inspired mean-field models that couple neuronal firing statistics to slow ionic homeostasis provide a mechanistic bridge between detailed single-neuron physiology and macroscopic brain activity. Here we examine this issue in a biophysical mean-field model of cortical populations driven by potassium exchange and compare its dynamical repertoire with a matched spiking network across six physiological and pathological regimes. The fast subsystem contains a substantial region in which low- and high-activity fixed points coexist, but this conditional bistability is not inherited by the physiological resting branch of the complete slow-fast system. A generic fast-slow calculation identifies how feedback through a dynamical ionic variable changes the stability conditions of the fast subsystem, and an exact equilibrium reduction of the KIonEx-MF equations yields a single equilibrium on the physiological resting branch over the range examined. This resting state is excitable: perturbations generate intermittent synchronized events in both the mean-field and spiking-network descriptions. The results show how slow ionic dynamics can reshape conclusions drawn from a frozen fast subsystem and provide noise-driven excitability as an alternative route to spontaneous population transitions.

neuroscience↗

bioRxiv2026

Structural integrity moderates the cognitive bearing of dopamine clearance in aging

Age-related cognitive decline in humans is well established, yet how neurobiological variations influence brain dynamics, and subsequently behavioral performance, remains insufficiently explored. Microscale accounts of cognitive aging revolve around attenuations in dopamine (DA) receptors and transporters (DAT). In contrast, macroscale accounts associate individual declines in cognitive performance with functional brain networks' reorganization and white-matter tracts' deterioration. We endeavor to bridge this gap with an amortized dopaminergic aging model (ADAM) which assembles aging brain twins by coupling dopaminergic modulation with structural connectomes to constrain emergent whole-brain dynamics. ADAM thereby augments established DAT decline with personalized estimates, through a reusable and generalizable estimator capable of disentangling neuromodulatory from structural contributions to age-related decline within each individual. To that end, we embed structural connectomes into a low-dimensional space exhibiting high reconstruction fidelity and -pseudonymized- individual identifiability. The spectrum of structural embeddings is subsequently updated with fMRI observations to estimate DA clearance timescales in 3,650 adults aged 36 to 95 years across five independent aging cohorts. Our analysis reveals an age-related elevation of DA clearance timescales. Critically, low performers exhibit elevated clearance in adults with marked structural degradation, and only within sliding age windows centred between 56 and 76 years. This supports a contribution of dopamine to aging, and reveals a structure- and age-conditional association between its clearance and cognitive performance. Our findings suggest that structural connectivity gates the macroscale reach of dopaminergic modulation, thus rendering the gain-control framework of neuromodulation mechanistically explicit across individuals and cohorts. ADAM therefore provides a biologically plausible framework that anchors interindividual variability in cognitive aging to perturbations in the dopaminergic system by learning cohort-scale mappings which can be shared across sites, establishing a transferable testbed for precision neuromodulation and future in-silico pharmacological trials.

neuroscience↗

bioRxiv2026

The Internal Structure of Seizures Reflects Animal-Specific Trajectories of Disease Progression and Circadian Modulation in Epileptic Mice

Epileptic seizures arise from the abnormal, sustained recruitment of neuronal populations, yet the temporal organisation of activity within individual seizures remains poorly understood. We hypothesised that seizures follow structured trajectories through a restricted state space rather than random patterns of activity. To test this idea, we analysed continuous long-term EEG recordings from six mice with pilocarpine-induced temporal lobe epilepsy and developed a symbolic framework that transforms seizure activity into sequences of discrete burst states. Across more than one thousand spontaneous seizures, we found that seizures are organised by sparse, animal-specific transition rules linking a small repertoire of recurrent burst motifs. These rules evolve over the course of epilepsy: early seizures display substantial variability in burst composition, and, to a smaller but statistically significant degree, in transition structure, whereas later seizures become more stereotyped in which burst types they use, a pattern suggestive of consolidation of the epileptic network into a stable dynamical regime. Seizure microstructure was further modulated by circadian phase, indicating that biological rhythms influence not only when seizures occur but also how they unfold. Moreover, burst sequences exhibited higher-order temporal dependencies that could not be explained by first-order Markov statistics alone. Together, these results reveal an evolving seizure grammar that links ictal dynamics to disease progression and circadian regulation, establishing symbolic dynamics as a powerful framework for quantifying the internal organisation of seizures.

neuroscience↗

bioRxiv2026

Cortico-subcortical multi-head self-attention as a substrate for cognitive performance

The neocortex is central to mammalian cognition, yet a computational framework that is both biologically constrained and capable of performing complex cognitive tasks remains missing. Here we show that cortico-thalamic circuits are well suited to implement multi-head self- and cross-attention, the mechanism underlying the cognitive abilities of transformer networks. We propose that layer 2/3 pyramidal cells maintain a recurrent key-value memory, while layer 5 pyramidal cells decode the memory retrieved by an incoming query. The computation of keys, values and queries maps onto core and matrix thalamo-cortical projections, distributed across the micro- and macro-columns of a cortical area. One cortical area forms an attention head, and cortex a multi-head self-attention network. The same thalamo-cortical microcircuit also calculates sensory prediction errors guiding gradient-based synaptic plasticity. A reward-prediction error gates via basal ganglia the cortical output and the re-activation of hippocampal memories. The trained network aligns with human intracranial recordings during speech perception. Overall, the suggested cortico-subcortical attention circuit may represent a substrate for the cognitive capacity of mammals.

neuroscience↗

bioRxiv2026

Directed Human Structural Connectome Reveals Hierarchical Organization and Shapes Large-Scale Brain Dynamics

The human structural connectome, most commonly derived from diffusion-weighted imaging (DWI) and tractography, provides a macroscopic description of whole-brain wiring and serves as the structural foundation of network neuroscience, large-scale brain simulations, and personalized digital brain twins. However, tractography-derived connectomes are fundamentally limited by their inability to distinguish afferent from efferent connections, yielding networks that are undirected and therefore blind to the hierarchical organization imposed by the directionality of anatomical connections. In this study, we introduce a directed human structural connectome (DHSC) by transferring tracer-derived projection patterns from macaque to human using cross-species connectivity blueprints. Topological analysis of the DHSC manifests biological plausibility, a small-world network organization, and a directionality-based hierarchy, which offer the hierarchical organization of human brain networks. In the context of brain dynamics, the introduction of directionality reshapes the propagation and persistence of sensory inputs. DHSC also best captures the empirical spatiotemporal dynamics of stimulus-evoked brain activity. The findings demonstrate that anatomical directionality is a critical determinant of large-scale brain organization and dynamics. This provides evidence that directed connectome may offer potential advantages in large-scale simulations of the human brain. The resulting DHSC, along with all related analyses and data are openly available.

neuroscience↗

bioRxiv2026

Circadian Modulation of Excitation-Inhibition Balance Drives Ictal Transitions in a Mechanistic Model of Epileptic Networks

Epileptic seizures emerge from pathological synchronization in neuronal networks and are strongly influenced by circadian rhythms. Here, we developed a computational framework to investigate how circadian modulation of excitation/inhibition (E/I) balance shapes transitions from physiological to pathological activity. The model consists of interacting excitatory and inhibitory populations containing varying proportions of impaired neurons with altered intrinsic excitability. Circadian effects were incorporated through modulation of synaptic time constants, mimicking daily fluctuations in E/I dynamics. Network activity was characterized using firing rates and the Spike Time Tiling Coefficient (STTC), enabling simultaneous assessment of excitability and synchrony. Our results show that seizure-like dynamics arise from nonlinear interactions between network composition, neuronal impairment, and synaptic kinetics. Distinct dynamic regimes emerged, separated by sharp transitions in synchrony and activity patterns. These findings provide a mechanistic link between circadian regulation and seizure susceptibility, supporting the development of chronotherapy approaches for epilepsy.

neuroscience↗

bioRxiv2026

Normative Modeling of Static and Dynamic Functional Connectivity

The transition toward individual-level modeling in functional neuroimaging is severely bottlenecked by methodological heterogeneity, which confounds subject-specific effects with study-dependent variance. Normative modeling leverages a multi-level statistical model to parameterize and account for such variability. We examine whether this framework can harmonize fragmented legacy datasets from seven large cohorts (N = 4705), bypassing the need for intensive reprocessing under an arbitrary unified pipeline. We evaluate the construction of a lifespan normative chart by harmonizing open-access functional MRI data extracted with highly heterogeneous processing pipelines, incorporating study-level random effects within a generalized additive model. From this well-calibrated model we drive normative trajectories of both static and dynamic functional connectivity and quantify their invariance to the choice of metric used to compute the interactions between regions. We observe a key decoupling between static and dynamic connectivity. While static connectivity exhibits a monotonic age-related decline, dynamic connectivity follows a more complex trajectory: decreasing during pediatric stabilization, peaking in mid-adulthood metastability, and ultimately declining into senescent rigidity. Overall, this framework establishes a scalable statistical blueprint for modeling functional brain organization to circumvent massive data homogenization, while preserving sensitivity to both between-subject variability and within-subject temporal dynamics.

neuroscience↗

bioRxiv2025

A deformable attractor manifold organizes human resting-state brain dynamics

Intrinsic brain activity is often described as wandering within a continuous multivariate space, yet the organizing principles that constrain these dynamics remain unclear. Here, we show that spontaneous human brain activity during rest is structured by a deformable attractor manifold. Using large-scale fMRI datasets and a latent dynamical model, we find that cortical activity occupies two reproducible regimes: a low-coherence state with a unimodal latent distribution and a high-coherence state that exhibits bimodality, consistent with transient bistability across association networks. A compact two-parameter energy landscape explains these dynamics, revealing that transitions arise not from switching between discrete states, but from continuous deformation of the manifold that reshapes attractor geometry. Excursions into the bistable regime occur as rapid "jumps", whereas returns follow slow drifts along the manifold, reflecting network-specific timescales. Individuals with greater expression of the bistable regime show higher cognitive fluidity, and manifold parameters differentiate mild cognitive impairment from matched controls. These findings identify an organizing geometric and dynamical principle of resting activity, linking large-scale cortical coordination, cognitive variability, and vulnerability to pathology.

neuroscience↗

bioRxiv2025

Temporal Interference Stimulation Can Enhance or Disrupt Human Memory Encoding as a Function of Brain Location and Frequency

Visual memory relies on synchronized interactions and rhythms between the medial temporal lobes and neocortical brain regions. Non-invasive manipulation of memory-related brain regions, specifically deeper temporal lobe regions, has been limited by the lack of precision of non-invasive neuromodulation - when targeting deeper structures, the cortex is always stimulated, never deeper structures in isolation. Temporal Interference (TI) stimulation, a novel non-invasive technique, uses high-frequency carrier fields to deliver targeted, physiologically relevant neuromodulation via amplitude-modulated envelopes at specific brain regions. Here, we investigate TIs impact on figure memory encoding in 70 healthy participants using the Rey-Osterrieth and Taylor Complex Figure tasks, with TI applied in several brain regions independently and simultaneously - allowing investigation of combinations of medial temporal lobe and neocortical brain regions. Interestingly, higher frequency TI envelopes (130 Hz offset) targeting bilateral hippocampi and temporal cortices significantly impair recall (p = 6.54e-04), while lower frequency TI envelopes (5 Hz offset) targeting only the bilateral hippocampi significantly enhance recall (p = 0.0447). Stimulation using other combinations of medial temporal lobe and neocortical regions showed no effect, underscoring the critical role of frequency and focality of non-invasive brain stimulation and correct target selection. Finally, functional MRI reveals strong differences between the effects of 130 Hz and 5 Hz envelopes, specifically in hippocampal BOLD signals, brain connectivity, default mode, and attentional networks. These findings demonstrate TIs ability to bidirectionally modulate memory encoding through precise frequency and target tuning, offering a powerful tool for cognitive neuroscience and potential therapeutic applications for memory disorders. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/677714v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@8e9db3org.highwire.dtl.DTLVardef@1135a3corg.highwire.dtl.DTLVardef@189345dorg.highwire.dtl.DTLVardef@ccca8f_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical Abstract:C_FLOATNO Temporal Interference stimulation for bidirectional memory modulation assessed via behavioral testing and MRI scanning. C_FIG

neuroscience↗

bioRxiv2025

Next generation neural mass model with dopamine modulation mediated by D1-type receptors

Neuromodulation is a complex process in which chemical substances modulate brain activity, allowing its rich repertoire of behaviors. Among these substances, dopamine has a preponderant role, being involved in several mechanisms. Moreover, dysfunctions in the dopamine connections has been observed in pathology, such as Parkinsons disease and schizophrenia. To investigate the mechanism of neuromodulation, we expand a previously proposed mean-field formalism, that describes the average activity of a neural population, by adding the effect of dopamine modulation. This mean-field reduction allows for a direct comparison with the underlying neural network to test its ability to qualitatively reproduce population behavior. The resulting mathematical framework is able to capture network activity in distinct dynamical regimes and transitions between them. Thus, this approach provides a reliable foundation for the development of personalized medicine tools to study how the effect of dopamine modulation on single brain region affects whole brain behavior.

neuroscience↗

bioRxiv2025

Degeneracy in Astrocytic Potassium Buffering: A Minimal Model Capturing the Interplay Between Local and Long-Range Mechanisms

Maintaining extracellular potassium (K+) homeostasis is critical for neuronal function, and astrocytes achieve this through a combination of local uptake and long-range spatial buffering. While degeneracy--the ability of different mechanisms to achieve the same function--is a fundamental property of biological systems, its role in astrocytic potassium buffering has remained unexplored. We present a minimal mathematical model that identifies essential buffering mechanisms while ensuring tractability and interpretability. Incorporating Kir channels and gap junction coupling, the model reproduces experimentally observed astrocyte membrane dynamics under various pharmacological conditions Parameter exploration reveals two levels of degeneracy. At the single-cell level, multiple parameter configurations yield similar membrane potential dynamics, indicating flexibility in local and spatial buffering contributions. At the functional level, despite variations in astrocyte morphology and buffering efficiency, homeostasis of extracellular K+ is restored, demonstrating homeostatic degeneracy. These findings highlight the robustness of astrocytic potassium regulation, showing that diverse buffering strategies ensure stability. Our work establishes a theoretical framework for understanding how astrocytic heterogeneity contributes to robust ionic homeostasis and offers perspectives for studying pathological conditions where buffering mechanisms are impaired.

neuroscience↗

bioRxiv2025

Developmental and Aging Changes in Brain Network Switching Dynamics Revealed by EEG Phase Synchronization

Adaptive behavior depends on the brains capacity to vary its activity across multiple spatial and temporal scales. Yet, how distinct facets of this variability evolve from childhood to older adulthood remains poorly understood, limiting mechanistic models of neurocognitive aging. Here, we characterize lifespan neural variability using an integrated empirical-computational approach. We analyzed high-density EEG cohort data spanning 111 healthy individuals aged 9-75 years, recorded at rest and during passive and attended auditory oddball stimulation task. We extracted scale-dependent measures of EEG fluctuations amplitude and entropy, together with millisecond-resolved phase-synchrony networks in the 2-20 Hz range. Multi-condition partial least squares decomposition analysis revealed two independent lifespan trajectories. First, slow-frequency power, variance and complexity at longer timescales declined monotonically with age, indicating a progressive dampening of low-frequency fluctuations and large-scale coherence. Second, the temporal organization of phase-synchrony reconfigurations followed an inverted U-trend: young adults exhibited the slowest yet most diverse switching--characterized by low mean but high variance and low kurtosis of jump lengths at 2-6 Hz and the opposite pattern at 8-20 Hz--whereas children and older adults showed faster, more stereotyped dynamics. To mechanistically account for these patterns, we fitted a ten-node phase-oscillator model constrained by the human structural connectome. Only an intermediate, metastable coupling regime reproduced the empirical combination of reduced low-frequency variability and maximally heterogeneous synchrony dynamics observed in young adults, while deviations toward weaker or stronger coupling mimicked the childrens and older adults profiles. Our results demonstrate that development and aging entail changes in the switching dynamics of EEG phase synchronization, by differentially sculpting stationary and transient aspects of neural variability. This establishes time-resolved phase-synchrony metrics as sensitive, mechanistically grounded markers of neurocognitive status across the lifespan. Author summaryBrain activations fluctuate and synchronize according to functionally relevant patterns, both at rest and during task performance. Here we investigated how these fluctuations change from childhood to older adulthood and what do these changes reveal about healthy development and aging. Using electrophysiological brain activity recordings from 111 participants aged 9 to 75 years during rest and a cognitive task, we discovered two key developmental trajectories. First, a linear trend reflecting mainly a gradual weakening of slower brain rhythms with age. Second, an inverted U-shaped trend suggesting that neural flexibility peaks in young adults, who switch between neural synchronization states in a uniquely diverse yet controlled ways, whereas children and older adults showed more rigid and predictable patterns. To explain this, we developed a computational model. The simulated data confirmed that young adults operate in a "sweet spot" of connectivity--balanced enough to support dynamic coordination without becoming unstable or overly rigid--unlike in childhood and older age, where connectivity becomes either too weak or too strong. Our work shows that age fundamentally reshapes how flexibly brain regions communicate. This gives us sensitive markers for tracking brain health across life.

neuroscience↗

bioRxiv2025

Siibra: A software tool suite for realizing a Multilevel Human Brain Atlas from complex data resources

Computational technology opens new possibilities towards understanding the complexity of the human brain, but it requires integrating measurements from different modalities and scales in anatomical context and exposing them in interoperable, actionable form. Especially with growing big data resources, accessing information from different scales and modalities coherently for visual exploration, reproducible analysis and application development remains challenging. We present siibra, a tool suite that connects diverse data from cloud resources to reference atlases and coordinate spaces. It supports different use cases by making contents accessible through a web viewer, Python library and HTTP API. Using siibra we implemented a Multilevel Human Brain Atlas linking macro-anatomical concepts and their inter-subject variability with measurements of the microstructural composition and intrinsic variance of brain regions, building on cytoarchitecture as a reference and supporting MRI-based and microscopic templates. The atlas is integrated with the EBRAINS research infrastructure. All software and content are openly accessible.

bioinformatics↗

bioRxiv2025

The role of symmetry breaking in connectivity and neurodegeneracy in the brain's resting state dynamics

The functioning of brain networks can be broadly categorized as an interplay of two main contributors, namely the neurological processes dictating the local dynamics and the patterns of anatomical connections that enable the interactions between the various local processes, resulting in a global emergent behavior. Using Brain Network Modeling we describe the constraints of the anatomical structure upon the resting state dynamics of the human brain. We identify a low-dimensional representation of the brain states, the Resting State Manifold (RSM), and leveraging network degeneracy and its relationship to structural properties, we identify dynamic patterns supported by the RSM. Noise driven dynamics of the BNM are used to explore the manifold and validate the contours of degeneracy. We demonstrate that the patterns of degeneracy regulate the dynamics of the broader system in the presence of external and/or internal perturbations, revealing the productive relationship between emergent network processes and their constituent network entities.

neuroscience↗

bioRxiv2025

Virtual Brain Inference (VBI): A flexible and integrative toolkit for efficient probabilistic inference on virtual brain models

Network neuroscience has proven essential for understanding the principles and mechanisms underlying complex brain (dys)function and cognition. In this context, whole-brain network modeling-also known as virtual brain modeling-combines computational models of brain dynamics (placed at each network node) with individual brain imaging data (to coordinate and connect the nodes), advancing our understanding of the complex dynamics of the brain and its neurobiological underpinnings. However, there remains a critical need for automated model inversion tools to estimate control (bifurcation) parameters at large scales associated with neuroimaging modalities, given their varying spatio-temporal resolutions. This study aims to address this gap by introducing a flexible and integrative toolkit for efficient Bayesian inference on virtual brain models, called Virtual Brain Inference (VBI). This open-source toolkit provides fast simulations, taxonomy of feature extraction, efficient data storage and loading, and probabilistic machine learning algorithms, enabling biophysically interpretable inference from non-invasive and invasive recordings. Through in-silico testing, we demonstrate the accuracy and reliability of inference for commonly used whole-brain network models and their associated neuroimaging data. VBI shows potential to improve hypothesis evaluation in network neuroscience through uncertainty quantification, and contribute to advances in precision medicine by enhancing the predictive power of virtual brain models.

neuroscience↗

bioRxiv2024

Dynamic Causal Modeling in Probabilistic Programming Languages

Understanding the intricate dynamics of brain activities necessitates models that incorporate causality and nonlinearity. Dynamic Causal Modelling (DCM) presents a statistical framework that embraces causal relationships among brain regions and their responses to experimental manipulations, such as stimulation. In this study, we perform Bayesian inference on a neurobiologically plausible generative model that simulates event-related potentials observed in magne-to/encephalography data. This translates into probabilistic inference of latent and observed states of a system driven by input stimuli, described by a set of nonlinear ordinary differential equations (ODEs) and potentially correlated parameters. We provide a guideline for reliable inference in the presence of multimodality, which arises from parameter degeneracy, ultimately enhancing the predictive accuracy of neural dynamics. Solutions include optimizing the hyperparameters, leveraging initialization with prior information, and employing weighted stacking based on predictive accuracy. Moreover, we implement the inference and conduct comprehensive model comparison in several probabilistic programming languages to streamline the process and benchmark their efficiency. Our investigation shows that model inversion in DCM extends beyond variational approximation frameworks, demonstrating the effectiveness of gradient-based Markov Chain Monte Carlo methods. We illustrate the accuracy and efficiency of posterior estimation using a self-tuning variant of Hamiltonian Monte Carlo and the automatic Laplace approximation, effectively addressing parameter degeneracy challenges. This technical endeavor holds the potential to advance the inversion of state-space ODE models, and contribute to neuro-science research and applications in neuroimaging through automatic DCM.

neuroscience↗

bioRxiv2024

Principles and Operation of Virtual Brain Twins

Current clinical methods often overlook individual variability by relying on population-wide trials, while mechanism-based trials remain underutilized in neuroscience due to the brains complexity. A Virtual Brain Twin (VBT) is a personalized digital replica of an individuals brain, integrating structural and functional brain data into advanced computational models and inference algorithms. By bridging the gap between molecular mechanisms, whole-brain dynamics, and imaging data, VBTs enhance the understanding of (patho)physiological mechanisms, advancing insights into both healthy and disordered brain function. Central to VBT is the network modeling that couples mesoscopic representation of neuronal activity through white matter connectivity, enabling the simulation of brain dynamics at a network level. This transformative approach provides interpretable predictive capabilities, supporting clinicians in personalizing treatments and optimizing interventions. This manuscript outlines the key components of VBT development, covering the conceptual, mathematical, technical, and clinical aspects. We describe the stages of VBT construction-from anatomical coupling and modeling to simulation and Bayesian inference-and demonstrate their applications in resting-state, healthy aging, epilepsy, and multiple sclerosis. Finally, we discuss potential extensions to other neurological disorders, such as Parkinsons disease, and explore future applications in consciousness research and brain-computer interfaces, paving the way for advancements in personalized medicine and brain-machine integration. "The brain is conceived as a self-organizing system operating close to instabilities where its activities are governed by collective variables, the order parameters, that enslave the individual parts, i.e., the neurons." Professor Hermann Haken (1927-2024).

neuroscience↗

bioRxiv2024

Probing other's Presence: Probabilistic Inference Across Brain Scales Reveals Enhanced Excitatory Synaptic Efficacy

The presence of conspecifics is a fundamental and arguably invariant prerequisite of social cognition across numerous animal species. While the influence of social presence on behavior has been among the focal points of investigation in social psychology for over a century, its underlying neural mechanisms remain largely unexplored. Here, we attempt to bridge this gap by investigating how the presence of conspecifics changes synaptic efficacy from measurements across spatiotemporal brain scales, and how such changes could lead to modulations of task performance in monkeys and humans. In monkeys performing an association learning task, social presence increased excitatory synaptic efficacy in attention-oriented regions dorsolateral prefrontal cortex and anterior cingulate cortex. In humans performing a visuomotor task, the presence of conspecifics facilitated performance in one of the subject groups, and this facilitation was linked to enhanced excitatory synaptic efficacy within the dorsal and ventral attention networks. We propose that presence-induced improvements in task performance arise from attentional modulation mediated by changes in excitatory synaptic efficacy across three spatiotemporal brain scales, namely, micro-scale (single neurons), meso-scale (cortical columns) and macro-scale (whole-brain). Our findings from Bayesian learning converge to establish a novel, multi-scale framework for understanding the neural underpinnings of social presence effects. This probabilistic framework offers a fresh perspective on social presence research, and lays the groundwork for future investigations into the complex interplay between social presence, neural dynamics, and behavior.

neuroscience↗