Year: 2023
Type: article
Abstract: Background: Computational models of current flow during Electroconvulsive Therapy (ECT) rely on the quasi-static assumption, yet tissue impedance during ECT may be frequency specific and change adaptively to local electric field intensity. Objectives: We systematically consider the application of the quasi-static assumption to ECT under conditions where 1) static impedance is measured before ECT and 2) during ECT when dynamic impedance is measured. We propose an update to ECT modeling accounting for frequency-dependent impedance. Methods: The frequency content on an ECT device output is analyzed. The ECT electrode-body impedance under low-current conditions is measured with an impedance analyzer. A framework for ECT modeling under quasi-static conditions based on a single device-specific frequency (e.g., 1 kHz) is proposed. Results: Impedance using ECT electrodes under low-current is frequency dependent and subject specific, and can be approximated at >100 Hz with a subject specific lumped parameter circuit model but at <100 Hz increased non-linearly. Combined with prior evidence suggesting that conductivity does not vary significantly across ECT output frequencies at high-currents, we adapt our previously developed adaptive pipeline for individualized ECT modeling around a 1 kHz frequency. Conclusions: By considering ECT modeling to occur at a single representative frequency, existing ECT adaptive and non-adaptive modeling are rational under the quasi-static assumption. Research Category and Technology and Methods Basic Research: 2. Electroconvulsive Therapy (ECT) Keywords: Quasi-static assumption, electroconvulsive therapy, computational modeling
Source: Brain stimulation
Authors: Gozde Unal, Cynthia Poon, Mohamad FallahRad, Myesha Thahsin, Miklos Argyelan, Marom Bikson
Institutions: City College of New York, Northwell Health
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,830
OpenAlex ID: W4320898572