New Computational Method Successfully Tracks Vital Brain Balance

The brain operates on a delicate see-saw of activity known as the excitation and inhibition (E/I) balance. Maintaining this equilibrium is crucial for healthy neurophysiological functioning, yet tracking how it shifts over time in a living brain has proven difficult with existing tools. While several cutting-edge methods exist, they often struggle to monitor continuous changes.

To address this limitation, scientists developed an enhanced computational technique that employs data assimilation (DA) to analyse electroencephalography (EEG) data. This method involves using neural-mass models to interpret brain signals. Although previous work suggested this approach could estimate sleep-dependent changes, its direct physiological validity had remained unproven.

In this validation study, the team compared their computer-modelled estimates against data collected via transcranial magnetic stimulation combined with EEG (TMS-EEG). The results were significant: the computational estimates strongly correlated with the physical TMS-EEG indices within the dorsolateral prefrontal cortex. These findings confirm that the new computational approach provides valid, neurophysiologically accurate estimations of time-varying E/I balance, offering a powerful tool for future brain research.