Biology subjects

Schirner, M.

Publications and source records attributed to Schirner, M..

16 recordsLinked to original sources

bioRxiv2026

The Virtual Child Brain: Modeling Neuromaturational Trajectories

IntroductionUnderstanding the mechanisms of human neuromaturation constitutes one of the fundamental questions of neuroscience. While it is well described that large-scale brain maturation is initiated within sensorimotor brain regions and progresses to associative cortex, the underlying developmental neurobiology remains to be fully characterized. Animal models have indicated that cortical inhibitory upregulation might be a driver of neurodevelopment. To investigate the hypothesis that cortical inhibitory upregulation plays a similar role in human neuromaturation, we developed a The Virtual Brain (TVB) based computational model (TVB-Child) to explore potential mechanisms of human neurodevelopment. Material and methodWe created neurodevelopmental dynamic brain network models capturing neurobiological maturation by using the large-scale brain simulator TVB and fitting brain network models to developmental functional MRI (fMRI) from the Human Connectome Project-Development (HCP-D) data set with 640 subjects with an age range of 6-21 years. Age-dependent trajectories in the fMRI data set were first analyzed by combined group-ICA/Dual Regression extracting subject-specific resting-state networks (RSN). Maturational topographical and topological redistribution of these networks were analyzed by linear and non-linear regression of RSN size and degree and strength centrality. Brain network models were fitted to the fMRI functional connectivity obtained from the HCP-D data set. Hypothesizing that cortical inhibition is a driver of neuromaturation, we analyzed spatiotemporal inhibition parameter gradients in the dynamic brain network model for the hypothesized significant correlations with fMRI RSN maturational trajectories. ResultsWhile during development frontoparietal (FP) and default mode network (DMN) grew and exhibited an increase in both degree and strength centrality, becoming dominant network hubs, the attention network underwent network pruning with a decrease in size and node degree. The primary sensory network changed little. For the fitted brain network models, we obtained a high degree of reproduction with correlation coefficients between empirical and simulated functional connectivities ranging between 0.80 and 0.95. Values of the feed forward inhibition model parameter [Formula] representing the strength of regional feedforward inhibitory input exhibited the most significant increase with age within the FP and DMN networks. A less pronounced, but significant, age-dependent increase of the inhibitory parameter values were seen in attention networks and no change within primary sensory networks. ConclusionOur study shows that high order (FP, DMN), attention and primary sensory networks exhibit distinct topographical and topological maturation trajectories. Moreover, brain network modeling revealed RSN-specific age-dependent inhibition trajectories, indicating that the model is able to reproduce and thus support candidate mechanisms of neurodevelopment. Graphical abstractOverview of the study combining empirical and modeling analyses of neuromaturation in 640 study participants from the HCP-D data set, age range 6-21 years old. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=149 SRC="FIGDIR/small/737052v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@1764432org.highwire.dtl.DTLVardef@17740ceorg.highwire.dtl.DTLVardef@3fa9a6org.highwire.dtl.DTLVardef@19afc80_HPS_FORMAT_FIGEXP M_FIG C_FIG

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

The Virtual Brain Ontology: A Digital Knowledge Framework for Reproducible Brain Network Modeling

Computational models of brain network dynamics offer mechanistic insights into brain function and disease, and are utilized for hypothesis generation, data interpretation, and the creation of personalized digital brain twins. However, results remain difficult to reproduce and compare because equations, parameters, networks, and numerical settings are reported inconsistently across the literature, and shared code is often not fully documented, standardized, or executable. We introduce The Virtual Brain Ontology (TVB-O), a semantic knowledge base, minimal metadata standard, and Python toolbox that simplifies the description, execution, and sharing of network simulations. TVB-O offers 1) a common vocabulary and ontology for core concepts and axioms representing current domain knowledge for simulating brain network dynamics, 2) a minimal, human- and machine-readable metadata specification for the information needed to reproduce an experiment, 3) a curated database of published models, brain networks, and study configurations, and 4) software that generates executable code for various simulation platforms and programming languages, including The Virtual Brain, Jax, or Julia. FAIR metadata and provenance-aware reports can be exported from TVB-Os model specification. It hereby enables a flexible framework for adopting new models and enhances reproducibility, comparability, and portability across simulators, while making assumptions explicit and linking models to biomedical knowledge and observation pathways. By reducing technical barriers and standardizing workflows, TVB-O broadens access to computational neuroscience and establishes a foundation for transparent, shareable "digital brain twins" that integrate with clinical pipelines and large-scale data resources.

neuroscience↗

bioRxiv2025

Fast and Easy Whole-Brain Network Model Parameter Estimation with Automatic Differentiation

Personalized brain modeling at clinically relevant scales requires integrating biophysical models with empirical neuroimaging data, yet high-dimensional parameter estimation in whole-brain network models remains computationally prohibitive. We present TVB-Optim, an open-source Python library providing a general and extensible framework for gradient-based optimization of brain network models build on JAX. Leveraging automatic differentiation, we demonstrate direct optimization of thousands of parameters, from global coupling (N=2) to regional dynamics (N=168) to full structural connectivity matrices (N=14,028), across functional MRI and magnetoencephalography data. Forward simulations achieve 10x CPU speedup over reference implementations and scale efficiently to GPU and multi-device configurations. We establish best practices through documented workflows that combine coarse parameter space exploration with gradient refinement, often yielding superior solutions faster than gradient methods alone. By bridging mechanistic neuroscience with modern machine learning infrastructure, TVB-Optim enables large-scale personalized brain models, bringing computational neuroscience closer to clinical translation.

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.

neuroscience↗

bioRxiv2024

Connectomes, simultaneous EEG-fMRI resting-state data and brain simulation results from 50 healthy subjects

We present raw and processed multimodal empirical data as well as simulation results from a study with The Virtual Brain (TVB). Simultaneous electroencephalography (EEG) - functional magnetic resonance imaging (fMRI) resting-state data, diffusion-weighted MRI, and structural MRI were acquired for 50 healthy adult subjects (18 - 80 years of age) at the Charite University Medicine, Berlin, Germany. We constructed personalized models from this multimodal data with TVB by optimizing parameters on an individual basis that predict multiple empirical features in fMRI and EEG, e.g. dynamic functional connectivity and bimodality in the alpha band power. We annotated this large comprehensive empirical and simulated dataset according to the openMINDS metadata framework and structured it following Brain Imaging Data Structure (BIDS) standards for EEG and MRI as well as the BIDS Extension Proposal for computational modeling data. This dataset provides ready-to-use data for future research at various levels of processing including the thereof inferred brain simulation results for a large dataset of healthy subjects with a wide age range.

neuroscience↗

bioRxiv2024

Multiscale Detrended Cross-Correlation Coefficient: Estimating Coupling in Nonstationary Neurophysiological Signals

The brain consists of a vastly interconnected network of regions, the connectome. By estimating the statistical interdependence of neurophysiological time series, we can measure the functional connectivity (FC) of this connectome. Pearsons correlation (rP) is a common metric of coupling in FC studies. Yet rP does not account properly for the non-stationarity of the signals recorded in neuroimaging. In this study, we introduced a novel estimator of coupled dynamics termed multiscale detrended cross-correlation coefficient (MDC3). Firstly, we showed that MDC3 had higher accuracy compared to rP using simulated time series with known coupling, as well as simulated functional magnetic resonance imaging (fMRI) signals with known underlying structural connectivity. Next, we computed functional brain networks based on empirical magnetoencephalography (MEG) and fMRI. We found that by using MDC3 we could construct networks of healthy populations with significantly different properties compared to rP networks. Based on our results, we believe that MDC3 is a valid alternative to rP that should be incorporated in future FC studies. Author SummaryThe brain consists of a vastly interconnected network of regions. To estimate the connection strength of such networks the coupling between different brain regions should be calculated. This can be achieved by using a series of statistical methods that capture the connection strength between signals originating across the brain, one of them being Pearsons correlation (rP). Despite its benefits, rP is not suitable for realistic estimation of brain network architecture. In this study, we introduced a novel estimator called multiscale detrended cross-correlation coefficient (MDC3). Firstly, we showed that MDC3 was more accurate than rP using simulated signals with known connection strength, as well as simulated brain activity emerging from realistic brain simulations. Next, we constructed brain networks based on real-life brain activity, recorded using two different methodologies. We found that by using MDC3 we could construct networks of healthy populations with significantly different properties compared to rP networks. Based on our results, we believe that MDC3 is a valid alternative to rP that should be incorporated in future studies of brain networks.

neuroscience↗

bioRxiv2023

Lesion aware automated processing pipeline for multimodal neuroimaging stroke data and The Virtual Brain (TVB)

BackgroundProcessing stroke magnetic resonance imaging (MRI) brain data can be susceptible to lesion-based abnormalities. In this study we developed and validated the Lesion Aware automated Processing Pipeline (LeAPP) that incorporates mitigation measures, improving volumetric and connectomics outputs compared to current standards in automated MRI processing pipelines. MethodsBuilding upon the Human Connectome Project (HCP) minimal processing pipeline, we introduced correction measures, such as cost-function masking and virtual brain transplant, and extended functional and diffusion processing to match acquisition protocols often found in a clinical context. A total of 51 participants (36 stroke patients (65.7{+/-}12.96 years, 18 female) and 15 healthy controls (69.2{+/-}7.4 years, 7 female)) were processed across four time points for patients (3-5, 30-40, 85-95, 340-380 days after stroke onset) and one time point for controls. Artificially lesioned brains (N=82), derived from healthy brains and informed by real stroke lesions were created, thus generating ground-truth data for validation. The processing pipeline and validation framework are available as containerized open-source software. Reconstruction quality has been quantified on whole brain level and for lesion affected and unaffected regions-of-interest (ROIs) using metrics like dice score, volume difference and center-of-gravity distance. Global and local level connectome reconstruction was assessed using node strength, node centrality and clustering coefficient. ResultsThe new pipeline LeAPP provides close reconstructions of the ground truth. Deviations in reconstructed averaged whole brain node strength and all ROI based volume and connectome metrics were significantly reduced compared to the HCP pipeline without stroke specific mitigation measures. ConclusionsLeAPP improves reconstruction quality of multimodal MRI processing for brain parcellation and structural connectome estimation significantly over the non-adapted HCP in the presence of lesions and provides a robust framework for diffusion and functional image processing of clinical stroke data. This novel open-source automated processing pipeline contributes to a development towards reproducible research. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=190 HEIGHT=200 SRC="FIGDIR/small/555078v1_ufig1.gif" ALT="Figure 1"> View larger version (85K): org.highwire.dtl.DTLVardef@ca58aborg.highwire.dtl.DTLVardef@8342f4org.highwire.dtl.DTLVardef@1a0d4b9org.highwire.dtl.DTLVardef@1edf1cf_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2023

Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias

IntroductionNeural circuit alterations lay at the core of brain physiopathology, and yet are hard to unveil in living subjects. Virtual brain modelling (TVB), by exploiting structural and functional MRI, yields mesoscopic parameters of connectivity and synaptic transmission. MethodsWe used TVB to simulate brain networks, which are key for human brain function, in Alzheimers disease (AD) and Frontotemporal Dementia (FTD) patients, whose connectivity and synaptic parameters remain largely unknown; we then compared them to healthy controls, to reveal novel in vivo pathological hallmarks. ResultsThe pattern of simulated parameter differed between AD and FTD, shedding light on disease-specific alterations in brain networks. Individual subjects displayed subtle differences in network parameter patterns that significantly correlated with their individual neuropsychological, clinical, and pharmacological profiles. DiscussionThese TVB simulations, by informing about a new personalized set of networks parameters, open new perspectives for understanding dementias mechanisms and design personalized therapeutic approaches.

neuroscience↗

bioRxiv2021

A DEEP LEARNING APPROACH TO ESTIMATING INITIAL CONDITIONS OF BRAIN NETWORK MODELS IN REFERENCE TO MEASURED FMRI DATA

1.Brain Network Models (BNMs) are a family of dynamical systems that simulate whole brain activity using neural mass models to represent local activity in different brain regions that influence each other via a global structural network. Research has been interested in using these network models to explain measured whole brain activity measured via resting state functional magnetic resonance imaging (rs-fMRI). Properties computed over longer periods of simulated and measured data such as average functional connectivity (FC), have shown to be comparable with similar properties estimated from measured rs-fMRI data. While this shows that these network models have similar properties over the dynamical landscape, it is unclear how well simulated trajectories compare with empirical trajectories on a timepoint-by-timepoint basis. Previous studies have shown that BNMs are able to produce relevant features at shorter timescales, but analysis of short-term trajectories or transient dynamics as defined by synchronized predictions from BNM made at the same timescale as the collected data has not yet been conducted. Relevant neural processes exist in the time frame of measurements and are often used in task fMRI studies to understand neural responses to behavioral cues. Therefore, it is important to investigate how much of these dynamics are captured by our current brain simulations. To test the nature of BNMs short term trajectories against observed data, we utilize a deep learning technique known as Neural ODE that based on an observed sequence of fMRI measurements, estimates the initial conditions such that the BNMs simulation is synchronized to produce the closest trajectory relative to the observed data. We test to see if the parameterization of a specific well studied BNM, the Firing Rate Model, calculated by maximizing its accuracy in reproducing observed short term trajectories matches with the parameterized model that produces the best average long-term metrics. Our results show that such an agreement between parameterization using long and short simulation analysis exists if also considering other factors such as the sensitivity in accuracy with relative to changes in structural connectivity. Therefore, we conclude that there is evidence that by solving for initial conditions, BNMs can be simulated in a meaningful way when comparing against measured data trajectories, although future studies are necessary to establish how BNM activity relate to behavioral variables or to faster neural processes during this time period.

neuroscience↗

bioRxiv2019

Modeling brain dynamics after tumor resection using The Virtual Brain

Brain tumor patients scheduled for tumor resection often face significant uncertainty, as the outcome of neurosurgery is difficult to predict at the individual patient level. Recently, computational modeling of brain activity using so-called brain network models has been introduced as a promising tool for this purpose. However, brain network models first have to be validated, before they can be used to predict brain dynamics. In prior work, we optimized individual brain network model parameters to maximize the fit with empirical brain activity. In this study, we extend this line of research by examining the stability of fitted parameters before and after tumor resection, and compare it with baseline parameter variability using data from healthy control subjects. Based on these findings, we perform the first \"virtual neurosurgery\" analyses to evaluate the potential of brain network modeling in predicting brain dynamics after tumor resection.\n\nWe find that brain network model parameters are relatively stable over time in brain tumor patients who underwent tumor resection, compared with baseline variability in healthy control subjects. In addition, we identify several robust associations between individually optimized model parameters, structural network topology and cognitive performance from pre-to post-operative assessment. Concerning the virtual neurosurgery analyses, we obtain promising results in some patients, whereas the predictive accuracy of the currently applied model is poor in others. These findings reveal interesting avenues for future research, as well as important limitations that warrant further investigation.

neuroscience↗

bioRxiv2018

Differentiation of Alzheimer’s disease based on local and global parameters in personalized Virtual Brain models

Alzheimers disease (AD) is marked by cognitive dysfunction emerging from neuropathological processes impacting brain function. AD affects brain dynamics at the local level, such as changes in the balance of inhibitory and excitatory neuronal populations, as well as long-range changes to the global network. Individual differences in these changes as they relate to behaviour are poorly understood. Here, we use a multi-scale neurophysiological model, \"The Virtual Brain (TVB)\", based on empirical multi-modal neuroimaging data, to study how local and global dynamics correlate with individual differences in cognition. In particular, we modeled individual resting-state functional activity of 124 individuals across the behavioral spectrum from healthy aging, to amnesic Mild Cognitive Impairment (MCI), to AD. The model parameters required to accurately simulate empirical functional brain imaging data correlated significantly with cognition, and exceeded the predictive capacity of empirical connectomes.

neuroscience↗

bioRxiv2018

Subject-specificity of the correlation between large-scale structural and functional connectivity

Structural connectivity (SC), the physical pathways connecting regions in the brain, and functional connectivity (FC), the temporal co-activations, are known to be tightly linked. However, the nature of this relationship is still not understood. In the present study, we examined this relation more closely in six separate human neuroimaging datasets with different acquisition and preprocessing methods. We show that using simple linear associations, the relation between an individuals SC and FC is not subject-specific for five of the datasets. Subject-specificity of SC-FC fit is achieved only for one of the six datasets, the multi-modal Glasser HCP parcellated dataset. We show that subject-specificity of SC-FC correspondence is limited across datasets due to relatively small variability between subjects in SC compared to the larger variability in FC.

neuroscience↗

bioRxiv2018

Modeling brain dynamics in brain tumor patients using The Virtual Brain

Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, non-invasive neuroimaging techniques such as functional MRI and diffusion weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex non-linear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics.\n\nAs a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong-Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed.\n\nResults showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance.

bioinformatics↗

bioRxiv2017

Inferring multi-scale neural mechanisms with brain network modelling

The neurophysiological processes underlying non-invasive brain activity measurements are not well understood. Here, we developed a novel connectome-based brain network model that integrates individual structural and functional data with neural population dynamics to support multi-scale neurophysiological inference. Simulated populations were linked by structural connectivity and, as a novelty, driven by electroencephalography (EEG) source activity. Simulations not only predicted subjects individual resting-state functional magnetic resonance imaging (fMRI) time series and spatial network topologies over 20 minutes of activity, but more importantly, they also revealed precise neurophysiological mechanisms that underlie and link six empirical observations from different scales and modalities: (1) slow resting-state fMRI oscillations, (2) spatial topologies of functional connectivity networks, (3) excitation-inhibition balance, (4, 5) pulsed inhibition on short and long time scales, and (6) fMRI power-law scaling. These findings underscore the potential of this new modelling framework for general inference and integration of neurophysiological knowledge to complement empirical studies.

bioinformatics↗

bioRxiv2016

Bridging multiple scales in the human brain using computational modelling

Brain dynamics span multiple spatial and temporal scales, from fast spiking neurons to slow fluctuations over distributed areas. No single experimental method links data across scales. Here, we bridge this gap using The Virtual Brain connectome-based modelling platform to integrate multimodal data with biophysical models and support neurophysiological inference. Simulated cell populations were linked with subject-specific white-matter connectivity estimates and driven by electroencephalography-derived electric source activity. The models were fit to subject-specific resting-state functional magnetic resonance imaging data, and overfitting was excluded using 5-fold cross-validation. Further evaluation of the models show how balancing excitation with feedback inhibition generates an inverse relationship between -rhythms and population firing on a faster time scale and resting-state network oscillations on a slower time scale. Lastly, large-scale interactions in the model lead to the emergence of scale-free power-law spectra. Our novel findings underscore the integrative role for computational modelling to complement empirical studies.

neuroscience↗