Association between motor cortex grey matter loss and inability to control an ECoG-based implanted Brain-Computer...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Medical Imaging

Background:

• Implantable brain-computer interfaces (iBCIs) offer communication solutions for individuals with amyotrophic lateral sclerosis (ALS).
• ALS causes cortical degeneration, potentially compromising the electrocorticography (ECoG) signals essential for iBCI function.
• The relationship between ALS-induced brain changes and iBCI signal quality requires investigation.

Purpose of the Study:

• To determine if structural and functional magnetic resonance imaging (MRI) metrics correlate with ECoG signal quality in ALS patients.
• To assess the potential of MRI metrics for predicting iBCI effectiveness in individuals with ALS.

Main Methods:

• Structural T1-weighted MRI and functional MRI (fMRI) were performed on six late-stage ALS participants and 76 controls.
• ECoG data from ALS participants were analyzed and benchmarked against epilepsy patient data.
• Grey matter thickness in the sensorimotor cortex and fMRI activation in the motor-hand area were quantified.

Main Results:

• Four ALS participants exhibited significant precentral gyrus thinning (>0.4 mm), while the postcentral gyrus remained unaffected.
• ECoG signal quality demonstrated a significant association with precentral gyrus grey matter thickness.
• fMRI activation in the motor-hand area did not show a significant association with ECoG signal quality.

Conclusions:

• Precentral gyrus grey matter thickness, measurable via presurgical MRI, is a significant predictor of ECoG signal quality in ALS.
• Cortical thickness assessment may aid in selecting suitable candidates for iBCIs, particularly in advanced ALS stages.
• These findings can inform clinical decision-making and optimize patient selection for iBCI implantation.