Adaptive current-flow models of ECT: Explaining individual static impedance, dynamic impedance, and brain current...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Computational Modeling

Background:

• Electroconvulsive therapy (ECT) outcomes are linked to device electrical output and electrode placement.
• Understanding the relationship between stimulus properties, head anatomy, and brain current delivery is crucial for ECT optimization.
• Patient-specific impedance is a key factor influencing ECT efficacy.

Purpose of the Study:

• To develop a computational framework to investigate the relationship between physical properties, head anatomy, and patient-specific impedance in ECT.
• To simulate transcranial electrical stimulation (tES) using adaptive models that account for dynamic changes in tissue conductivity.
• To explore how scalp properties influence current delivery to the brain during ECT.

Main Methods:

• Developed anatomical MRI-derived models for transcranial electrical stimulation (tES).
• Incorporated adaptive tissue conductivity changes due to electrical current flow in scalp models.
• Modeled superficial scalp with adaptive conductivity and deep scalp with fixed conductivity.

Main Results:

• Variation in scalp parameters explains clinical data on static and dynamic impedance, their correlation, and seizure threshold.
• Adaptive transcranial electrical stimulation (tES) models revealed that current flow alters local tissue conductivity, impacting brain current delivery.
• Fixed tissue conductivity models do not fully account for these dynamic changes.

Conclusions:

• Individual skin properties significantly influence the relationship between impedance and ECT current delivery.
• Novel modeling pipeline enables exploration of adaptive scalp conductivity's impact on transcutaneous electrical stimulation (tES).
• Findings depend on assumptions about tissue properties, warranting further investigation.