Phase-coherence classification: A new wavelet-based method to separate local field potentials into local (in)coherent...

Area of Science:

• Neuroscience
• Computational Neuroscience
• Biophysics

Background:

• Local field potentials (LFP) represent integrated neural activity, potentially reflecting diverse network dynamics.
• Understanding the spatial and functional organization of neural networks from LFP is challenging.

Purpose of the Study:

• To introduce a novel wavelet-based phase-coherence classification (PCC) method for LFP analysis.
• To differentiate between volume-conducted, local incoherent, and local coherent neural activity.
• To apply PCC to Parkinson's patient LFP data to study medication and movement effects.

Main Methods:

• Wavelet-based phase-coherence classification (PCC) was developed to decompose LFP signals.
• Synthetic time series were used to optimize PCC parameters.
• PCC was applied to subthalamic nucleus LFP recordings from Parkinson's patients.

Main Results:

• PCC successfully separated local incoherent and coherent signals, outperforming bipolar recordings.
• Medication (apomorphine) reduced low beta band incoherent activity and increased high beta band coherent activity.
• Movement tasks modulated high beta band local coherent activity, increasing during isometric hold and decreasing during phasic movement.

Conclusions:

• PCC provides a method to analyze distinct neural network components within LFP.
• Low and high beta bands are sensitive to medication and movement, reflecting changes in incoherent and local coherent activity, respectively.
• PCC components may represent functionally distinct neural networks in the brain.