Michel J. A. M. van Putten

This study develops a biophysical model of the glutamate-glutamine cycle during ischemia, showing that synaptic failure is due to EAAT dysfunction and sodium gradient collapse, not glutamate release or glutamine synthetase.

The paper identifies burst suppression as a default brain state linked to loss of network complexity, distinguishing between two forms with different clinical implications.

This study examines sex differences in neuronal response to hypoxia, finding that estrogen modulates network activity but does not provide long-term neuroprotection.

This study uses simulation-based inference to automatically identify molecular mechanisms from patient-derived neuronal network activity measured by MEA.

This study shows that NMDA receptor activation is essential for forming long-term memory traces in cultured neural networks, which contribute to predictive capabilities.

The paper investigates fragmented network bursts in patient-derived neurons using a biophysically detailed model and experimental validation, identifying short-term synaptic depression as a key mechanism.

The paper constructs invariant features for EEG/MEG signals using Grassmann manifolds to improve the analysis of functional connectivity in brain activity.

This paper develops a neural mass model to simulate EEG changes during cerebral ischemia, showing synaptic arrest precedes cell swelling and demonstrating the role of thalamocortical projections in EEG patterns.

This paper shows that deep learning models for detecting interictal epileptiform discharges in EEGs perform differently based on the type of EEG data used for training.

The paper uses EEG data to estimate biophysical model parameters in comatose cardiac arrest survivors, finding distinct patterns in good vs poor recovery groups.