Back

Effect of interhemispheric zero-phase entrainment of the intrinsic mu-rhythm on behavioral and neural markers of predictive coding

Heise, K.-F.; Albouy, G.; Dolfen, N.; Peeters, R.; Mantini, D.; Swinnen, S. P.

2024-05-08 neuroscience
10.1101/2024.05.07.592996 bioRxiv
Show abstract

Goal-directed behavior requires the integration of information from the outside world and internal (somatosensory) sources about our own actions. Expectations (or internal models) are generated from prior knowledge and constantly updated based on sensory feedback. This optimized information integration (predictive coding) results in a global behavioral advantage of anticipated action in the presence of uncertainty. Our goal was to probe the effect of phase entrainment of the sensorimotor mu-rhythm on visuomotor integration. Participants received transcranial alternating current stimulation over bilateral motor cortices (M1) while performing a visually-guided force adjustment task during functional magnetic resonance imaging. Inter-hemispheric zero-phase entrainment resulted in effector-specific modulation of performance precision and effector-generic minimization of force signal complexity paralleled by BOLD activation changes in bilateral caudate and increased functional connectivity between the right M1 and contralateral putamen, inferior parietal, and medial temporal regions. While effector-specific changes in performance precision were associated with contralateral caudate and hippocampal activation decreases, only the global reduction in force signal complexity was associated with increased functional M1 connectivity with bilateral striatal regions. We propose that zero-phase synchronization represents a neural mode of optimized information integration related to internal model updating within the recursive perception-action continuum associated with predictive coding.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.