Michael Schirner

The paper develops a computational model to study human brain maturation by analyzing functional MRI data and simulating neural network dynamics.

The paper develops TVBase, a platform that projects biomedical knowledge onto a 3D brain atlas to support neuroimaging analysis and hypothesis generation.

The paper introduces TVB-O, a framework to improve reproducibility and comparability of brain network simulations by standardizing metadata and enabling executable code generation.

TVB-Optim enables efficient parameter estimation for whole-brain network models using automatic differentiation, achieving faster and more scalable optimization than traditional methods.

This paper develops an LLM-driven pipeline for Alzheimer's drug repurposing by integrating ontologies and drug databases, showing improved alignment with expert knowledge and reduced hallucinations.

This paper introduces MDC3, a novel estimator for coupling in neurophysiological signals, showing it outperforms Pearson correlation in accuracy for both simulated and real data.

The paper develops and validates LeAPP, an automated processing pipeline for stroke MRI data that improves volumetric and connectome reconstructions compared to standard pipelines.

The paper presents multimodal empirical data and simulation results from 50 healthy subjects using The Virtual Brain, annotated with openMINDS and BIDS standards.

The paper uses virtual brain simulations to compare network-specific parameters in Alzheimer's disease and Frontotemporal Dementia patients with healthy controls, revealing disease-specific alterations.

This paper uses Neural ODE to estimate initial conditions for brain network models to synchronize simulations with fMRI data, showing agreement between short-term and long-term parameterizations.

The paper demonstrates that each individual's brain has a converging optimal working point in The Virtual Brain model that predicts fMRI and EEG features, with inter-individual differences driven by structural connectivity.

This study evaluates brain network models to predict brain dynamics after tumor resection by comparing parameter stability in patients and healthy controls.

The paper constructs a macaque connectome for use in TheVirtualBrain simulations to model brain network dynamics and validate against empirical resting-state fMRI data.

The paper uses The Virtual Brain to model brain dynamics in brain tumor patients and controls, finding that individualized model parameters improve functional connectivity prediction and correlate with structural network topology and cognitive performance.

The paper examines the subject-specificity of the correlation between structural and functional connectivity across six human neuroimaging datasets, finding limited subject-specificity except for one dataset.

This paper develops a brain network model that integrates structural and functional data with neural dynamics to infer multi-scale neurophysiological mechanisms from EEG and fMRI data.

This study uses personalized Virtual Brain models to show that local and global brain dynamics correlate with cognitive differences in Alzheimer’s disease and related conditions.