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

Abstract

AbstractBackgroundImprovements 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.ObjectiveHowever, 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.MethodsWe 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 (~1 A) current and when dynamic impedance is measured, as well as prior to stimulation when low (~1 mA) 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 (σDS).ResultsWe demonstrate that variation in these scalp parameters 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 demonstrate 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.ConclusionsOur predictions that variation in individual skin properties, rather than other aspects of anatomy, largely govern the relationship between static impedance, dynamic impedance, and current delivery to the brain, are themselves subject to assumptions about tissue properties. Broadly, our novel pipeline for tES models is important in ongoing efforts to optimize devices, personalize interventions, and explain clinical findings.