The project has made strong progress in developing and evaluating a new explanation for how different brain areas coordinate their activity over time.
Computational modelling work examined multiple candidate mechanisms for coordination between sensory and frontal brain regions while maintaining sensitivity to external input. This work identified that even though frontal cortex is the main driver for determining the content of information during inter-area communication, anticipation requires that frontal cortex does not drive the neuronal temporal coordination. Modelling work showed that one viable solution allowing such coordination is a connection scheme relying on inhibitory feedback loops in frontal areas, so-called anticipated synchronization. This coordination allows for efficient coordination without disrupting sensory processing. Besides the theoretical implications, the work also identified and developed suitable analytical approaches able to quantify anticipatory communication in the brain. Currently, we are applying these methods to human intracranial EEG data verifying the existence of anticipatory synchronization in the brain.
In parallel, behavioural and neurophysiological experiments were conducted to investigate the role of anticipatory synchronization during sensory processes requiring continuous monitoring and evaluation of incoming input. While some experimental paradigms required refinement, this led to improved designs better suited to studying anticipatory mechanisms in the brain.
The final goal of the project is to use transcranial electrical stimulation to induce anticipatory synchronization by implementing a feedback loop in an online, closed-loop setting. Achieving this requires two key capabilities: (1) the ability to record and analyse EEG data in real time, and (2) reliable removal of stimulation-related artifacts from EEG signals recorded during electrical stimulation. The first objective has been successfully achieved. We implemented a closed-loop EEG framework and used it in an initial study to test whether aligning stimulation or sensory input with specific phases of ongoing brain activity can improve memory performance. The results demonstrate the potential of precisely timed, phase-specific interventions. The second objective is currently underway. A complete combined brain stimulation–EEG dataset has been collected, and we are now verifying whether our newly developed artifact-removal methods enable reliable analysis of EEG data recorded during stimulation.