Petra Ritter

The Cerebellar Engine: Multiscale Digital Brain Co-simulations Reveal How Cerebellar Spiking Architecture Shapes Cortical Coherence

In a mouse whole-brain co-simulation with a spiking olivocerebellar network, cutting mossy-fiber input to cerebellar nuclei lowers M1-S1 gamma coherence, while removing Purkinje inhibition raises it.

Dopamine Depletion Drives Whole-Brain Oscillatory Disruptions via Cortico-Subcortical Resonance: A Multiscale Model of Parkinson's Disease in Mice

This study shows that dopamine depletion in subcortical circuits drives widespread beta-band oscillatory disruptions in the mouse brain through cortico-subcortical resonance.

The Virtual Child Brain: Modeling Neuromaturational Trajectories

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

Brain network modeling with The Virtual Brain derives pharmacodynamics of ketamine

This paper uses computational modeling to explore how ketamine's dose-dependent NMDAR antagonism affects brain dynamics across its full dose spectrum.

An Interactive Brain Atlas of Knowledge

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

The Virtual Brain links transcranial magnetic stimulation evoked potentials and inhibitory neurotransmitter changes in major depressive disorder

The paper uses computational modeling to show that GABAergic deficits in simulations increase TEP amplitude, aligning with MDD characteristics.

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

This paper integrates the Larter-Breakspear model into a mouse brain connectome to study chaotic dynamics and ion-channel effects on network behavior.

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

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

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

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

Using an Ordinary Differential Equation model to separate Rest and Task signals in fMRI

This study uses an ODE model to separate rest and task signals in fMRI data, showing task-specific dynamics are subsets of rest dynamics and improving reaction time predictions.

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

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.

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

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.

Whole-brain dynamics and hormonal shifts throughout women's lifespan: From reproductive stages to menopausal transition and beyond

This study examines brain dynamics across women's reproductive stages using fMRI and machine learning, finding that brain dynamics decline at menopause and that combining brain dynamics with hormone levels improves stage classification.

Off-Equilibrium Fluctuation-Dissipation Theorem Paves the Way in Alzheimer's Disease Research

This study uses off-equilibrium fluctuation-dissipation theorem to analyze brain dynamics in Alzheimer's disease, finding increased deviations in MCI and decreased deviations in AD, suggesting potential biomarkers for early detection.

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

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

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

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

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

This paper develops a multiscale closed-loop model of Alzheimer's disease that links proteinopathy to electrophysiological changes, revealing causal relationships between Aβ and hp-tau depositions and neural activity alterations.

Altered intrinsic ignition dynamics linked to Amyloid-β and tau pathology in Alzheimer's disease

This study uses intrinsic ignition analysis on rs-fMRI data to show reduced dynamical complexity and node-metastability in Alzheimer's disease, linked to Amyloid-β and tau pathology.

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

This study models the differential effects of Amyloid-Beta and Tau on neuronal activity in Alzheimer's Disease, finding A{beta} dominance in early stages and Tau dominance in later stages.

Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias

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.

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

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.

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

The paper analyzes age-related changes in brain structure and function using MRI data, finding that compensation mechanisms and reduced propagation velocity explain functional alterations.

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

This paper demonstrates a multiscale co-simulation approach combining a spiking basal ganglia model with a whole-brain mean-field model to predict the effects of virtual deep brain stimulation in Parkinson's disease.

Overcoming the Reproducibility Crisis - Results of the first Community Survey of the German National Research Data Infrastructure for Neuroscience.

A survey of the German neuroscience community identifies key barriers to reproducible research and outlines strategies for improving research data management.

Brain simulation augments machine-learning-based classification of dementia

The paper combines brain simulations with machine learning to improve dementia classification by integrating PET and MRI data with simulated electrophysiological features.

Virtual connectomic datasets in Alzheimer's Disease and aging using whole-brain network dynamics modelling

The paper introduces virtual connectomic datasets by simulating missing connectivity features from existing neuroimaging data, enabling machine learning classification comparable to empirical data.

Effect of aging-related network changes on unimanual sensorimotor learning - a simultaneous EEG-fMRI study

This study investigates how aging-related network changes affect unimanual motor learning using simultaneous EEG-fMRI, finding reduced beta power and altered inhibitory interactions in older adults.

Dynamic Functional Connectivity between Order and Randomness and its Evolution across the Human Adult Lifespan

The paper analyzes dynamic functional connectivity (dFC) in fMRI data from healthy elderly, finding that dFC slows down and becomes less complex with aging, correlating with cognitive performance.

Fifty shades of The Virtual Brain: Converging optimal working points yield biologically plausibleelectrophysiological and imaging features

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.

Modeling brain dynamics after tumor resection using The Virtual Brain

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