Related Experiment Video
Updated: Jun 5, 2026

08:50
Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation
Published on: August 20, 2019
Primary Somatosensory to Motor Cortex Microstructural Connectivity Predicts the Mu-Rhythm Phase Effect on
Juliana R Hougland1,2, Timo Roine3,4, Andreas Jooß1,2
1Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany.
Human Brain Mapping
|June 4, 2026
Summary
Sensorimotor mu-rhythm phase affects brain signal transmission. Higher microstructural connectivity between the primary somatosensory cortex (S1) and motor cortex (M1) strengthens this effect.
Area of Science:
- Neuroscience
- Motor Control
- Brain Connectivity
Background:
- Sensorimotor rhythms, specifically the mu-rhythm, are known to influence corticospinal excitability.
- Individual variability exists in how mu-rhythm phase impacts neural activity.
- This variability may be linked to the physical connections within the brain.
Purpose of the Study:
- To investigate the relationship between sensorimotor mu-rhythm phase and corticospinal excitability.
- To explore whether microstructural connectivity between the primary somatosensory cortex (S1) and motor cortex (M1) influences this relationship.
Main Methods:
- Electrophysiological recordings to measure mu-rhythm phase.
- Transcranial magnetic stimulation (TMS) to assess corticospinal excitability.
- Diffusion tensor imaging (DTI) to quantify S1-M1 microstructural connectivity.
Main Results:
- Mu-rhythm phase significantly modulates corticospinal excitability.
- A positive correlation was found between S1-M1 microstructural connectivity and the magnitude of the mu-phase effect.
- Individuals with higher S1-M1 connectivity exhibited a stronger influence of mu-phase on corticospinal excitability.
Conclusions:
- Microstructural integrity of the S1-M1 pathway is a key factor in modulating corticospinal excitability by sensorimotor mu-rhythm.
- Understanding individual differences in brain connectivity can help predict responses to interventions targeting sensorimotor pathways.

