Time-Perception Network and Default Mode Network Are Associated with Temporal Prediction in a Periodic Motion Task

Area of Science:

• Neuroscience
• Cognitive Neuroscience
• Computational Neuroscience

Background:

• Prospective internal model updating is crucial for accurate future predictions.
• Previous research linked internal model updating to frontal and parietal areas.
• Temporal expectations involve a time-perception network and temporal orienting of attention.

Purpose of the Study:

• Investigate how continuous temporal prediction manipulation affects brain areas involved in internal model updating and time perception.
• Examine the neural basis of temporal attention's role in updating internal models.
• Differentiate neural responses to periodic versus non-periodic temporal predictions.

Main Methods:

• Utilized functional magnetic resonance imaging (fMRI) to monitor brain activity.
• Developed an exogenous temporal task combining rhythm cueing and time-to-contact principles.
• Compared brain responses to periodic (simple harmonic oscillation) and non-periodic (variable acceleration) motion patterns.

Main Results:

• Non-periodic motion activated the temporal orienting network (ventral premotor, inferior parietal cortex, cerebellum), presupplementary motor area, and MT+.
• A right-hemisphere dominance was observed, suggesting explicit timing mechanisms.
• Periodic motion, compared to non-periodic, engaged default mode network (DMN) midline areas (DMPFC, ACC, PCC/PC).

Conclusions:

• Continuous temporal prediction manipulation engages specific brain representations for temporal prediction.
• The default mode network appears to play a role in processing expected information and validating internal models.
• Findings suggest task-independent updating of internal models is influenced by temporal predictions.