Investigation and Modulation of Temporal Prediction Using Non-Invasive and Invasive Brain Stimulation

Abstract

The human ability to estimate time plays a central role in cognitive and motor processes. However, the underlying neural mechanisms and their modifiability through external influences are not yet fully understood. This dissertation investigated temporal prediction from three different methodological perspectives: (1) using magnetoencephalography (MEG) to examine oscillatory mechanisms specific to crossmodal temporal prediction, (2) through non-invasive brain stimulation to modulate temporal prediction, and (3) via invasive brain stimulation to directly influence neural networks. In the first study, previously established neural correlates of time estimation were examined using MEG to ensure the reproducibility of earlier findings and to further characterize the underlying oscillatory mechanisms of crossmodal time estimation. The results showed that delta-band phase alignment increased during temporal prediction and correlated with behavioral performance. Building on these insights, the second study investigated whether non-invasive brain stimulation, such as transcranial alternating current stimulation (tACS), can influence subjective temporal prediction. This approach showed that behavioral performance was modulated in a phase-dependent, sinusoidal manner, supporting the functional role of low-frequency delta phase in temporal prediction. However, the tACS-induced effects did not extend to a crossmodal task, suggesting increased cognitive demands and different network requirements for multimodal integration. Finally, the third study employed invasive brain stimulation in patients with Parkinson's disease (PD) to determine whether a direct modulation of the basal ganglia induces even more targeted effects on temporal prediction. We found that subthalamic deep brain stimulation (DBS) enhances temporal prediction performance in patients with PD by modulating oscillatory mechanisms, particularly through beta power suppression. Together, these studies underscore the importance of oscillatory phase dynamics, particularly delta phase alignment, in temporal prediction. They also demonstrate that these mechanisms can be modulated through external stimulation, offering both theoretical advances and translational potential. The integration of MEG, tACS, and DBS within a single research program provides a comprehensive framework for probing the neural, oscillatory structure of non-rhythmic unimodal and crossmodal temporal prediction.