Decoding voluntary movements and postural tremor based on thalamic LFPs as a basis for closed-loop stimulation for...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Neuromodulation

Background:

• High-frequency deep brain stimulation (DBS) targeting the motor thalamus is effective for essential tremor (ET).
• Continuous DBS may deliver unnecessary current as ET tremor primarily affects voluntary movements and sustained postures.

Purpose of the Study:

• To decode voluntary movements and postural tremor from motor thalamus local field potentials (LFPs).
• To enable closed-loop DBS for on-demand stimulation without peripheral sensors or additional invasive electrodes.

Main Methods:

• Simultaneous recording of LFPs, EMG, and behavioral data from ET patients during specific movements.
• Utilized a machine learning binary classifier to detect movements and tremor from thalamic LFP features.

Main Results:

• Voluntary movements and postural tremor were decoded with 0.8 average sensitivity and 0.2 false detection rate.
• Beta (13-23 Hz) and theta (4-7 Hz) band oscillations were key for decoding movements and tremor, respectively.
• Incorporating features from multiple frequency bands improved decoding accuracy.

Conclusions:

• Decoding motor thalamus LFPs can differentiate voluntary movements from ET postural tremor.
• This LFP-based decoding approach supports the development of adaptive, on-demand DBS for essential tremor.