Reconstruction of post-synaptic potentials by reverse modeling of local field potentials

Area of Science:

• Neuroscience
• Computational Neuroscience
• Electrophysiology

Background:

• Local field potentials (LFPs) are crucial for studying brain activity.
• LFPs reflect combined excitatory and inhibitory neuronal processes.
• Estimating individual excitatory (ePSP) and inhibitory (iPSP) components from LFP is challenging.

Purpose of the Study:

• To develop and validate a method for estimating ePSP and iPSP components from single extracellular LFP recordings.
• To enable dynamical tracking of local brain excitability changes.

Main Methods:

• A model-based reverse engineering approach using a neural mass model.
• Inputting measured LFP signals into the model to estimate synaptic activity.
• Validation with simulated data and evaluation on in vivo and in clinico data.

Main Results:

• The method reliably estimates average excitatory and inhibitory postsynaptic potentials from LFPs.
• Demonstrated successful application in various in vivo (rat, mouse) and in clinico (human) seizure models.
• The method enables monitoring of the excitation/inhibition ratio.

Conclusions:

• The proposed reverse engineering method accurately quantifies excitatory and inhibitory synaptic activity from LFPs.
• This technique offers a valuable tool for neuroscience research, particularly for monitoring brain excitability dynamics.
• Potential applications include understanding neurological disorders and brain function.