Biology subjects

Petkoski, S.

Publications and source records attributed to Petkoski, S..

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

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↗

bioRxiv2026

Digital Twin Brain simulation and manipulation of a functional brain network underlying mental illness

Linking synaptic-level perturbations to distributed brain-network dynamics remains a central challenge for understanding and treating mental illness. Although recent whole-brain models can reproduce individual brain activity patterns, they largely function as descriptive simulators rather than mechanistic, intervention-capable systems. Here we present an intervention-capable digital twin of the human brain, integrating individual neuroanatomy and task-evoked dynamics within a neuronal-scale framework. Individualised digital twin brains recapitulate a participant-specific compact cortico-subcortical network phenotype that captures transdiagnostic psychopathology across population and clinical cohorts. In silico modulation of excitatory and inhibitory synaptic conductance produces bidirectional, heterogeneous network responses across individuals. Population-scale simulations stratify individuals and predict longitudinal symptom trajectories from DTB-derived response profiles. Independent pharmacological functional MRI data further validate the predicted baseline-dependent network responses in vivo. Together, these findings establish digital brain models as experimental platforms for mechanistic perturbation, behavioural prediction and stratification, providing a foundation for precision neuroscience and psychiatry.

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

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

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

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

A neural mass model with neuromodulation

The study of brain activity and its function requires the development of computational models alongside experimental investigations to explore different effects of multiple mechanisms at play in the central nervous system. Chemical neuromodulators, such as dopamine, play central roles in regulating the dynamics of neuronal populations. In this work, we propose a modular framework to capture the effects of neuromodulators at the neural mass level. Using this framework, we formulate a specific model for dopamine dynamics affecting D1-type receptors. We detail the dynamical repertoire associated with dopamine concentration evolution and characterize the transitions across qualitatively different oscillatory states as a function of relevant parameters, along with the variations in frequency of the emergent multi-scale oscillations.

neuroscience↗

bioRxiv2023

Large-scale brain signatures of fluid dynamics and responsiveness linked to consciousness

Signatures of consciousness are found in spectral and temporal properties of neuronal activity. Among these, spatiotemporal complexity after a perturbation has recently emerged as a robust metric to infer levels of consciousness. Perturbation paradigms remain, however, difficult to perform routinely. To discover alternative paradigms and metrics, we systematically explore brain stimulation and resting-state activity in a whole-brain model. We find that perturbational complexity only occurs when the brain model operates within a specific dynamical regime, in which spontaneous activity produces a large degree of functional network reorganizations referred to as being fluid. The regime of high brain fluidity is characterized by a small battery of metrics drawn from dynamical systems theory and predicts the impact of consciousness-altering drugs (Xenon, Propofol, and Ketamine). We validate the predictions in a cohort of 15 subjects at various stages of consciousness and demonstrate their agreement with previously reported perturbational complexity, but in a more accessible paradigm. Beyond the facilitation in clinical use, the metrics highlight complexity properties of brain dynamics in support of the emergence of consciousness.

neuroscience↗

bioRxiv2022

The virtual aging brain: a model-driven explanation for cognitive decline in older subjects

Healthy aging is accompanied by heterogeneous decline of cognitive abilities among individuals, especially during senescence. The mechanisms of this variability are not understood, but have been associated with the reorganization of white matter fiber tracts and the functional co-activations of brain regions. Here, we built a causal inference framework to provide mechanistic insight into the link between structural connectivity and brain function, informed by brain imaging data and network modeling. By applying various degrees of interhemispheric degradation of structural connectivity, we were not only able to reproduce the age-related decline in interhemispheric functional communication and the associated dynamical flexibility, but we obtained an increase of global modulation of structural connectivity over the brain function during senescence. Notably, the increase in modulation between structural connectivity and brian function was higher in magnitude and steeper in its increase in older adults with poor cognitive performance. We independently validated the causal hypothesis of our framework via a Bayesian approach based on deep-learning. The current results might be the first mechanistic demonstration of dedifferentiation and scaffolding during aging leading to cognitive decline demonstrated in a large cohort.

neuroscience↗

bioRxiv2022

The structured flow on the brain's resting state manifold

Spontaneously fluctuating brain activity patterns that emerge at rest have been linked to brains health and cognition. Despite detailed descriptions of the spatio-temporal brain patterns, our understanding of their generative mechanism is still incomplete. Using a combination of computational modeling and dynamical systems analysis we provide a mechanistic description of the formation of a resting state manifold via the network connectivity. We demonstrate that the symmetry breaking by the connectivity creates a characteristic flow on the manifold, which produces the major data features across scales and imaging modalities. These include spontaneous high amplitude co-activations, neuronal cascades, spectral cortical gradients, multistability and characteristic functional connectivity dynamics. When aggregated across cortical hierarchies, these match the profiles from empirical data. The understanding of the brains resting state manifold is fundamental for the construction of task-specific flows and manifolds used in theories of brain function such as predictive coding. In addition, it shifts the focus from the single recordings towards brains capacity to generate certain dynamics characteristic of health and pathology.

neuroscience↗

bioRxiv2022

White-matter degradation and dynamical compensation support age-related functional alterations in human brain

Structural connectivity of the brain at different ages is analyzed using diffusion-weighted Magnetic Resonance Imaging (MRI) data. The largest decrease of the number and average length of streamlines is found for the long inter-hemispheric links, with the strongest impact for frontal regions. From the BOLD functional MRI (fMRI) time series we identify age-related changes of dynamic functional connectivity (dFC) and spatial covariation features of the FC links captured by metaconnectivity (MC). They indicate more constant dFC, but wider range and variance of MC. Finally we applied computational whole-brain network model based on oscillators, which mechanistically expresses the impact of the spatio-temporal structure of the brain (weights and the delays) to the dynamics. With this we tested several hypothesis, which revealed that the spatio-temporal reorganization of the brain with ageing, supports the observed functional fingerprints only if the model accounts for: (i) compensation of the individual brains for the overall loss of structural connectivity, and (ii) decrease of propagation velocity due to the loss of myelination. We also show that having these two conditions, it is sufficient to decompose the time-delays as bimodal distribution that only distinguishes between intra- and inter-hemispheric delays, and that the same working point also captures the static FC the best.

neuroscience↗

bioRxiv2021

Mean field approximation of network of biophysical neurons driven by conductance-based ion exchange dynamics

Whole-brain simulations are a valuable tool for gaining insight into the multiscale processes that regulate brain activity. Due to the complexity of the brain, it is impractical to include all microscopic details in a simulation. Hence, researchers often simulate the brain as a network of coupled neural masses, each described by a mean-field model. These models capture the essential features of neuronal populations while approximating most biophysical details. However, it may be important to include certain parameters that significantly impact brain function. The concentration of ions in the extracellular space is one key factor to consider, as its fluctuations can be associated with healthy and pathological brain states. In this paper, we develop a new mean-field model of a population of Hodgkin-Huxley-type neurons, retaining a microscopic perspective on the ion-exchange mechanisms driving neuronal activity. This allows us to maintain biophysical interpretability while bridging the gap between micro- and macro-scale mechanisms. Our model is able to reproduce a wide range of activity patterns, also observed in large neural network simulations. Specifically, slow-changing ion concentrations modulate the fast neuroelectric activity, a feature of our model that we validated through in vitro experiments. By studying how changes in extracellular ionic conditions can affect whole-brain dynamics, this model serves as a foundation to measure biomarkers of pathological activity and provide potential therapeutic targets in cases of brain dysfunctions like epilepsy.

neuroscience↗

bioRxiv2021

Parameter inference on brain network models with unknown node dynamics and spatial heterogeneity

Model-based data analysis of whole-brain dynamics links the observed data to model parameters in a network of neural masses. In recent years a special focus was placed on the role of regional variance of model parameters for the emergent activity. Such analyses however depend on the properties of the employed neural mass model, which is often obtained through a series of major simplifications or analogies. Here we propose a data-driven approach where the neural mass model needs not to be specified. Building on the recent progresses in identification of dynamical systems with neural networks, we propose a method to infer from the functional data both the neural mass model representing the regional dynamics as well as the region- and subject-specific parameters, while respecting the known network structure. We demonstrate on two synthetic data sets that our method is able to recover the original model parameters, and that the trained generative model produces dynamics resembling the training data both on the regional level and on the whole-brain level. We further apply the method to resting-state fMRI data from Human Connectome Project. We find that to achieve best fit, the model needs two dimensional state space and three regional parameters, one of which is strongly correlated with the map of genetic expression in human brain. The present approach opens a novel way to the analysis of resting-state fMRI with possible applications in understanding the changes of whole-brain dynamics during aging or in neurodegenerative diseases.

neuroscience↗

bioRxiv2021

On the topochronic map of the human brain dynamics

Two structurally connected brain regions are more likely to interact, with the lengths of the structural bundles, their widths, myelination, and the topology of the structural connectome influencing the timing of the interactions. We introduce an in vivo approach for measuring functional delays across the whole brain using magneto/electroencephalography and integrating them with the structural bundles. The resulting topochronic map of the functional delays/velocities shows that larger bundles have faster velocities. We estimated the topochronic map in multiple sclerosis patients, who have damaged myelin sheaths, and controls, demonstrating greater delays in patients across the network and that structurally lesioned tracks were slowed down more than unaffected ones. We provide a novel framework for estimating functional transmission delays in vivo at the single-subject and single-fiber level. One-Sentence SummaryA non-invasive estimation of the individual deterministic spatio-temporal scaffold underlying the evolution of brain dynamics.

neuroscience↗

bioRxiv2020

Renormalization of the brain connectome: Duality of particle and wave

Networks in neuroscience determine how brain function unfolds, and their perturbations lead to psychiatric disorders and brain disease. Brain networks are characterized by their connectomes, which comprise the totality of all connections, and are commonly described by graph theory. This approach is deeply rooted in a particle view of information processing, based on the quantification of informational bits such as firing rates. Oscillations and brain rhythms demand, however, a wave perspective of information processing based on synchronization. We extend traditional graph theory to a dual particle-wave-perspective, integrate time delays due to finite transmission speeds and derive a normalization of the connectome. When applied to the data base of the Human Connectome project, we explain the emergence of frequency-specific network cores including the visual and default mode networks. These findings are robust across human subjects (N=100) and are a fundamental network property within the wave picture. The normalized connectome comprises the particle view in the limit of infinite transmission speeds and opens the applicability of graph theory to a wide range of novel network phenomena, including physiological and pathological brain rhythms. These two perspectives are orthogonal, but not incommensurable, when understood within the novel here proposed generalized framework of structural connectivity. AUTHOR SUMMARYAll networks are composed of nodes and links, forming the structural frame, in which communication occurs. We demonstrate that graph theoretical tools make the implicit assumption of information transmission via exchange of bits, suggesting that the stronger connected nodes are more impactful upon the remainder of the network. This corollary does not extend to communication through oscillations, which is the prominent information carrier in brain networks. We extend traditional network analysis to the oscillatory domain and derive a novel network normalization including descriptive metrics. Along the prototypical example of the brain as a network, we illustrate the consequences of this novel approach and demonstrate that the normalization robustly explains the emergence of the prominent frequency-specific network cores, which cannot be understood within the traditional framework.

neuroscience↗