Biology subjects

Stefanovski, L.

Publications and source records attributed to Stefanovski, L..

10 recordsLinked to original sources

bioRxiv2026

Brain network modeling with The Virtual Brain derives pharmacodynamics of ketamine

Ketamine, an N-Methyl-D-aspartate receptor (NMDAR) antagonist, is used clinically as an anesthetic and antidepressant, and is also known for its psychotomimetic effects. Its impact on brain dynamics and behavior varies significantly with dosage likely via a dose-dependent modulation of the NMDARergic transmission. Currently, it is unclear how molecular changes at the microscopic level of NMDAR antagonism lead to large-scale changes in brain dynamics. We implement a dose-dependent NMDAR antagonism based on ketamines disinhibition theory into a biophysically grounded mean-field model within The Virtual Brain (TVB) framework to replicate ketamines key signatures across its dose spectrum. Our results imply that in low doses ketamine preferentially impairs excito-inhibitory neurotransmission while in higher doses antagonism on excito-excitatory connections plays a role. These findings highlight the utility of computational modeling for disentangling dose-specific mechanisms of action and provide a framework for exploring NMDAR-related interventions. Author summaryKetamine is a dissociative anesthetic at high doses, but at lower, sub-anesthetic doses, it has garnered significant interest for its rapid-acting antidepressant and anxiolytic effects. Despite its growing clinical use in psychiatric conditions, the precise neural mechanisms underlying ketamines dose-dependent effects remain incompletely understood. Ketamine primarily acts as a non-competitive antagonist of the NMDAR, which is expressed on both excitatory and inhibitory neurons throughout the cortex. One of the leading hypotheses explaining its antidepressant effects is the disinhibition theory which proposes that low doses of ketamine preferentially block NMDARs on inhibitory interneurons, resulting in increased cortical excitability. At high doses ketamine exerts anesthetic effects potentially through more widespread NMDAR antagonism including on excitatory neurons. In this study, we used a computational model to explore how selective NMDAR antagonism at different doses affects large-scale brain dynamics. A key novelty of our work is the integration of ketamines full dose spectrum within a single computational modeling framework, allowing us to relate distinct neural effects from disinhibition to anesthesia to experimental findings. This modeling approach contributes to a deeper understanding of how ketamine modulates cortical activity across different contexts.

neuroscience↗

bioRxiv2025

An Interactive Brain Atlas of Knowledge

Biomedical knowledge about the brain increases every day, with a rapidly growing number of scientific publications, datasets, and software tools. While this informational plethora is not merely comprehensible by human beings, recent developments in information science and computational linguistics aim to make this knowledge programmatically accessible by literature mining. However, integrating these semantic methods into neuroimaging standards remains insufficient, hindering researchers from unraveling their full potential. Therefore, we developed the semantic meta-analysis platform The Virtual Brain adapter of semantics (TVBase) that projects biomedical knowledge preserved in over 36 million scientific articles onto a 3D standardized brain. The literature-mining platform SCAIView was used to extract ontologically defined biomedical entities and their associations with brain anatomy from the PubMed database. By querying a specific concept, the association strength with each anatomical term was calculated using entropy. To project the data onto a standardized brain, we created a unique transformation matrix that links over 800 anatomical terms to voxel coordinates of a parcellated standard brain. This novel method of knowledge projection extracts region-specific information about biomedical concepts from the literature to support translational multi-scale approaches to computational neuroscience. The multi-purpose software framework TVBase is openly available as a Python library. It aims for hypothesis-free neuroimaging pattern interpretation, hypothesis generation, and applications in personalized medicine.

neuroscience↗

bioRxiv2025

From Ions to Chaos: exploring a whole-brain modelling framework in the mouse

Computational neuroscience offers powerful tools for understanding brain function in both healthy and diseased states. However, detailed data on microscale mechanisms--such as those involved in genetics and pharmacology--are often derived from mouse experiments and further lack integration into whole-brain models. In this work, we bridge microscale ion-channel dynamics and macroscale network behavior by employing the Larter-Breakspear neural mass model on The Virtual Brain platform with a mouse brain connectome. Our simulations reproduced key findings from prior computational studies with the Larter-Breakspear model, demonstrating its cross-species translation to the mouse. This included the emergence of chaotic dynamics and network synchrony as a function of coupling and delay parameters. Furthermore, we demonstrate that Calcium, Sodium, and Potassium-related parameters each critically shape the global dynamic regimes and that the response to external stimuli is highly sensitive to calcium concentrations. This work represents the first integration of the Larter-Breakspear model into a mouse-scale connectome, validating previous results in a new context. In addition, it establishes a framework for a detailed exploration of synchrony, ion dependency, and stimulus reactivity. By linking molecular simulations, genetic data, and mouse electrophysiology, this approach holds promise for mechanistic insights into neuropsychiatric disorders, such as schizophrenia and epilepsy.

neuroscience↗

bioRxiv2024

Biological Database Mining for LLM-Driven Alzheimer's Disease Drug Repurposing

BACKGROUNDThis study presents a software pipeline that leverages LLMs to apply knowledge stored in natural language (such as in pharmacological texts) and ontologies in a transparent Drug Repurposing (DR) information structure. METHODSAlzheimers Disease (AD) related entries in Gene Ontology and DrugBank were integrated into a Knowledge Graph database to inform LLM prompts. 16,581 drugs were screened for their DR potential by the LLM Llama3:8b. The vector embedding representation of the drugs in the LLM was investigated to asses if LLMs store pharmacological information in alignment with domain expert understanding of pharmacological groups. By measuring the semantic similarity of drugs quantitatively, the performance of the DR pipeline was examined. A manual hallucination check was performed to assess the impact of the ontology-database combination on LLM-hallucination performance. The results were compared against registered clinical trials (RCTs) and proposed medications in meta-analyses to evaluate their predictive value. RESULTSThe embedding analysis showed that the vector representations of drugs in the LLM show clusters in alignment with pharmacological groups. The ontologically enhanced prompt was closer to the expert domain proposals than a zero-shot control prompt without that knowledge. The results of the ontology-based prompt showed fewer hallucinations in their responses compared to the zero-shot control prompting. CONCLUSIONSOntology-augmented LLM interaction leads to fewer hallucinations and output closer to expert assessment in comparison with a zero-shot control. We propose retrospective analyses, considering the high-rated drugs and their effect on AD patients as a starting point for further (prospective) research.

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bioRxiv2023

A multiscale closed-loop neurotoxicity model of Alzheimer's disease progression explains functional connectivity alterations

While the accumulation of amyloid-beta (A{beta}) and hyperphosphorylated-tau (hp-tau) as two classical histopathological biomarkers are crucial in Alzheimers disease (AD), their detailed interaction with the electrophysiological changes at the meso- and macroscale are not yet fully understood. We developed a mechanistic mequltiscale model of AD progression, linking proteinopathy to its effects on neural activity and vice-versa. We integrated a heterodimer model of prion-like protein propagation, and a network of Jansen-Rit electrical oscillators whose model parameters varied due to neurotoxicity. Changes in inhibition guided the electrophysiological alterations found in AD, and both A{beta} and hp-tau-related inhibition changes were able to produce similar effects independently. Additionally, we found a causal disconnection between cellular hyperactivity and interregional hypersynchrony. Finally, we demonstrated that early A{beta} and hp-tau depositions location determine the spatiotemporal profile of the proteinopathy. The presented model combines the molecular effects of bothA{beta} and hp-tau together with a mechanistic protein propagation model and network effects within a unique closed-loop model. This holds the potential to enlighten the interplay between AD mechanisms on various scales, aiming to develop and test novel hypotheses on the contribution of different AD-related variables to the disease evolution. Significance StatementThis research presents a groundbreaking closed-loop model of AD mechanisms, bridging the gap between protein distribution and neural activity. Contrary to prior assumptions, the study reveals that interregional hyper-synchrony and cellular hyperactivity are not directly linked. Notably, the model identifies neural inhibition as a potential causal factor in neurophysiological AD alterations and posits early depositions of A{beta} as a determinant of the spatiotemporal profile of proteinopathy. The significance of this mechanistic disease framework lies in its potential to produce insights into AD evolution and to guide novel treatment strategies. It underscores the importance of further experiments and modelling efforts to refine our understanding of AD, offering hope for more effective treatments and personalized care in the fight against dementia.

neuroscience↗

bioRxiv2022

Whole-brain modeling of the differential influences of Amyloid-Beta and Tau in Alzheimer's Disease

BackgroundAlzheimers Disease is a neurodegenerative condition associated with the accumulation of two misfolded proteins, amyloid-beta (A{beta}) and tau. We study their effect on neuronal activity, with the aim of assessing their individual and combined impact. MethodsWe use a whole-brain dynamic model to find the optimal parameters that best describe the effects of A{beta} and tau on the excitation-inhibition balance of the local nodes. ResultsWe found a clear dominance of A{beta} over tau in the early disease stages (MCI), while tau dominates over A{beta} in the latest stages (AD). We identify crucial roles for A{beta} and tau in complex neuronal dynamics and demonstrate the viability of using regional distributions to define models of large-scale brain function in AD. ConclusionsOur study provides further insight into the dynamics and complex interplay between these two proteins, opening the path for further investigations on biomarkers and candidate therapeutic targets in-silico.

neuroscience↗

bioRxiv2021

Virtual deep brain stimulation: Multiscale co-simulation of a spiking basal ganglia model and a whole-brain mean-field model with The Virtual Brain

Deep brain stimulation (DBS) has been successfully applied in various neurodegenerative diseases as an effective symptomatic treatment. However, its mechanisms of action within the brain network are still poorly understood. Many virtual DBS models analyze a subnetwork around the basal ganglia and its dynamics as a spiking network with their details validated by experimental data. However, connectomic evidence shows widespread effects of DBS affecting many different cortical and subcortical areas. From a clinical perspective, various effects of DBS besides the motoric impact have been demonstrated. The neuroinformatics platform The Virtual Brain (TVB) offers a modeling framework allowing us to virtually perform stimulation, including DBS, and forecast the outcome from a dynamic systems perspective prior to invasive surgery with DBS lead placement. For an accurate prediction of the effects of DBS, we implement a detailed spiking model of the basal ganglia, which we combine with TVB via our previously developed co-simulation environment. This multiscale co-simulation approach builds on the extensive previous literature of spiking models of the basal ganglia while simultaneously offering a whole-brain perspective on widespread effects of the stimulation going beyond the motor circuit. In the first demonstration of our model, we show that virtual DBS can move the firing rates of a Parkinsons disease patients thalamus - basal ganglia network towards the healthy regime while, at the same time, altering the activity in distributed cortical regions with a pronounced effect in frontal regions. Thus, we provide proof of concept for virtual DBS in a co-simulation environment with TVB. The developed modeling approach has the potential to optimize DBS lead placement and configuration and forecast the success of DBS treatment for individual patients. Highlights- We implement and validate a co-simulation approach of a spiking network model for subcortical regions in and around the basal ganglia and interface it with mean-field network models for each cortical region. - Our simulations are based on a normative connectome including detailed tracts between the cortex and the basal ganglia regions combined with subject-specific optimized weights for a healthy control and a patient with Parkinsons disease. - We provide proof of concept by demonstrating that the implemented model shows biologically plausible dynamics during resting state including decreased thalamic activity in the virtual patient and during virtual deep brain stimulation including normalized thalamic activity and distributed altered cortical activity predominantly in frontal regions. - The presented co-simulation model can be used to tailor deep brain stimulation for individual patients.

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bioRxiv2021

Multi-scale brain simulation with integrated positron emission tomography yields hidden local field potential activity that augments machine-learning classification of Alzheimer's disease

INTRODUCTIONComputational brain network modeling using The Virtual Brain (TVB) simulation platform acts synergistically with machine learning and multi-modal neuroimaging to reveal mechanisms and improve diagnostics in Alzheimers disease. METHODSWe enhance large-scale whole-brain simulation in TVB with a cause-and-effect model linking local Amyloid {beta} PET with altered excitability. We use PET and MRI data from 33 participants of Alzheimers Disease Neuroimaging Initiative (ADNI3) combined with frequency compositions of TVB-simulated local field potentials (LFP) for machine-learning classification. RESULTSThe combination of empirical neuroimaging features and simulated LFPs significantly outperformed the classification accuracy of empirical data alone by about 10% (weighted F1-score empirical 64.34% vs. combined 74.28%). Informative features showed high biological plausibility regarding the Alzheimers-typical spatial distribution. DISCUSSIONThe cause-and-effect implementation of local hyperexcitation caused by Amyloid {beta} can improve the machine-learning-driven classification of Alzheimers and demonstrates TVBs ability to decode information in empirical data employing connectivity-based brain simulation. RESEARCH IN CONTEXTO_LISYSTEMATIC REVIEW. Machine-learning has been proven to augment diagnostics of dementia in several ways. Imaging-based approaches enable early diagnostic predictions. However, individual projections of long-term outcome as well as differential diagnosis remain difficult, as the mechanisms behind the used classifying features often remain unclear. Mechanistic whole-brain models in synergy with powerful machine learning aim to close this gap. C_LIO_LIINTERPRETATION. Our work demonstrates that multi-scale brain simulations considering Amyloid {beta} distributions and cause-and-effect regulatory cascades reveal hidden electrophysiological processes that are not readily accessible through measurements in humans. We demonstrate that these simulation-inferred features hold the potential to improve diagnostic classification of Alzheimers disease. C_LIO_LIFUTURE DIRECTIONS. The simulation-based classification model needs to be tested for clinical usability in a larger cohort with an independent test set, either with another imaging database or a prospective study to assess its capability for long-term disease trajectories. C_LI

neuroscience↗

bioRxiv2020

Identifying optimal working points of individual Virtual Brains: A large-scale brain network modelling study

Brain network modeling studies are often limited with respect to the number of data features fitted, although capturing multiple empirical features is important to validate the models overall biological plausibility. Here we construct personalized models from multimodal data of 50 healthy individuals (18-80 years) with The Virtual Brain and demonstrate that an individuals brain has its own converging optimal working point in the parameter space that predicts multiple empirical features in functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). We further show that bimodality in the alpha band power - as an explored novel feature - arises as a function of global coupling and exhibits inter-regional differences depending on the degree. Reliable inter-individual differences with respect to these optimal working points were found that seem to be driven by the individual structural rather than by the functional connectivity. Our results provide the groundwork for future multimodal brain modeling studies.

neuroscience↗

bioRxiv2019

Linking molecular pathways and large-scale computational modeling to assess candidate disease mechanisms and pharmacodynamics in Alzheimer's disease

IntroductionWhile the prevalence of neurodegenerative diseases associated with dementia such as Alzheimers disease (AD) increases, our knowledge on the underlying mechanisms, outcome predictors, or therapeutic targets is limited. In this work, we demonstrate how computational multi-scale brain modelling links phenomena of different scales and therefore identifies potential disease mechanisms leading the way to improved diagnostics and treatment. MethodsThe Virtual Brain (TVB; thevirtualbrain.org) neuroinformatics platform allows standardized large-scale structural connectivity-based simulations of whole brain dynamics. We provide proof of concept for a novel approach that quantitatively links the effects of altered molecular pathways onto neuronal population dynamics. As a novelty, we connect chemical compounds measured with positron emission tomography (PET) with neural function in TVB addressing the phenomenon of hyperexcitability in AD related to the protein amyloid beta (Abeta). We construct personalized virtual brains based on individual PET derived distributions of Abeta in patients with mild cognitive impairment (MCI, N=8) and Alzheimers Disease (AD, N=10) and in age-matched healthy controls (HC, N=15) using data from ADNI-3 data base (http://adni.lni.usc.edu). In the personalized virtual brains, individual Abeta burden modulates regional inhibition, leading to disinhibition and hyperexcitation with high Abeta loads. We analyze simulated regional neural activity and electroencephalograms (EEG). ResultsKnown empirical alterations of EEG in patients with AD compared to HCs were reproduced by simulations. The virtual AD group showed slower frequencies in simulated local field potentials and EEG compared to MCI and HC groups. The heterogeneity of the Abeta load is crucial for the virtual EEG slowing which is absent for control models with homogeneous Abeta distributions. Slowing phenomena primarily affect the network hubs, independent of the spatial distribution of Abeta. Modeling the N-methyl-D-aspartate (NMDA) receptor antagonism of memantine in local population models, reveals potential functional reversibility of the observed large-scale alterations (reflected by EEG slowing) in virtual AD brains. DiscussionWe demonstrate how TVB enables the simulation of systems effects caused by pathogenetic molecular candidate mechanisms in human virtual brains.

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