Parietofrontal integrity determines neural modulation associated with grasping imagery after stroke

Area of Science:

• Neuroscience
• Biomedical Engineering
• Rehabilitation Medicine

Background:

• Chronic stroke survivors can regain some motor control via brain-computer interfaces (BCIs).
• Volitional modulation of sensorimotor rhythms (SMRs) is a key BCI skill for hand grasping.
• The neural basis linking lesion pathology to SMR modulation skill remains unclear.

Purpose of the Study:

• Investigate how individual lesion pathology affects functional and structural brain networks.
• Determine the relationship between network integrity and SMR modulation skill in chronic stroke.
• Identify predictors of BCI control success for hand grasping.

Main Methods:

• Magnetoencephalography (MEG) for functional network analysis during SMR training.
• Structural MRI (T1-weighted, diffusion-weighted) for structural network modeling.
• Graph theory analysis to assess network properties (e.g., cost-efficiency, centrality).
• Correlation analysis between network metrics, lesion characteristics, and BCI skill.

Main Results:

• Inter-lesion variability differentially impacted functional and structural network integrity.
• Higher MEG global cost-efficiency correlated with greater SMR modulation skill.
• Impaired ipsilesional primary motor cortex nodal betweenness centrality (due to lesions) correlated with reduced skill.
• White matter microstructure integrity in contralesional parietofrontal pathways predicted BCI control success.

Conclusions:

• Volitional SMR modulation for BCI hand control relies on intact ipsilesional and contralesional parietofrontal pathways.
• Structural and functional network integrity are crucial for learning and executing BCI-driven grasping.
• Extant structural network integrity may predict therapeutic response to SMR-based interventions.