Computational Model of Electroconvulsive Therapy Considering Electric Field Dependent Skin Conductivity

<p>Improvements in electroconvulsive therapy (ECT) outcomes have followed refinement in device electrical output and electrode montage. The physical properties of the ECT stimulus, together with those of the patient’s head, determine the impedances measured by the device and govern current delivery to the brain and ECT outcomes. However, the precise relations among physical properties of the stimulus, patient head anatomy, and patient-specific impedance to the passage of current are long-standing questions in ECT research and practice. In this thesis, we develop a computational framework based on diverse clinical data sets. We developed anatomical MRI-derived models of transcranial electrical stimulation (tES) that included changes in tissue conductivity due to local electrical current flow. These “adaptive” models simulate ECT both during therapeutic stimulation using high current and when dynamic impedance is measured, as well as prior to stimulation when low current is used to measure static impedance. We modeled two scalp layers: a superficial scalp layer with adaptive conductivity that increases with electric field up to a subject-specific maximum, and a deep scalp layer with a subject-specific fixed conductivity. We demonstrated that variation in these scalp parameters may explain clinical data on subject-specific static impedance and dynamic impedance, their imperfect correlation across subjects, their relationships to seizure threshold, and the role of head anatomy. Adaptive tES models demonstrated that current flow changes local tissue conductivity which in turn shapes current delivery to the brain in a manner not accounted for in fixed tissue conductivity models. Our predictions that variation in individual skin properties, rather than other aspects of anatomy, largely govern the relationship between static impedance, dynamic impedance, and ECT current delivery to the brain, themselves depend on assumptions about tissue properties. Broadly, our novel modeling pipeline opens the door to explore how adaptive-scalp conductivity may impact transcutaneous electrical stimulation (tES). Lastly, we incorporate the (device specific) role of frequency with a single overall assumption allowing quasi-static stimulations of ECT: appropriately parametrizing effective resistivity at single representative frequency (e.g., at 1 kHz), including subject-specific and adaptive skin resistivities. We only stipulate that our functions for (adaptive) resistivity at 1 kHz explain local tissue resistivity as they impact the static and dynamic impedance measures by specific ECT devices (e.g., Thymatron).</p>

Name thesis:degree_name

Doctor of Philosophy (Ph.D.)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Biomedical Engineering

Year dc:date.available

2022

Author dc:creator

• Unal, Gozde

Contributors dc:contributor

• Marom Bikson

• Electroconvulsive Therapy
• Computational Modeling
• Transcranial Electrical Stimulation
• Biomedical Engineering and Bioengineering

Repository record dc:identifier

https://academicworks.cuny.edu/cc_etds_theses/1062

OAI identifier oai:identifier

oai:academicworks.cuny.edu:cc_etds_theses-2083