Paul Triebkorn

This paper develops a normative modeling framework to analyze static and dynamic functional connectivity across lifespan, revealing distinct age-related trajectories for each.

This paper identifies a deformable attractor manifold that organizes human resting-state brain dynamics, showing it relates to cognitive variability and pathology.

This paper introduces Virtual Brain Twins (VBTs) as personalized digital brain models that integrate structural and functional data to improve understanding of brain function and support personalized treatment.

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

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.

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.

The paper uses computational modeling to link molecular pathways with neural dynamics in Alzheimer’s disease, showing how Abeta burden affects brain activity and potential reversibility with memantine.