title: Computational models of Bitemporal, Bifrontal and Right Unilateral ECT predict differential stimulation of brain regions associated with efficacy and cognitive side effects.
abstract: Extensive clinical research has shown that the efficacy and cognitive outcomes of electroconvulsive therapy (ECT) are determined, in part, by the type of electrode placement used. Bitemporal ECT (BT, stimulating electrodes placed bilaterally in the frontotemporal region) is the form of ECT with relatively potent clinical and cognitive side effects. However, the reasons for this are poorly understood.
  
  
  
  This study used computational modelling to examine regional differences in brain excitation between BT, Bifrontal (BF) and Right Unilateral (RUL) ECT, currently the most clinically-used ECT placements. Specifically, by comparing similarities and differences in current distribution patterns between BT ECT and the other two placements, the study aimed to create an explanatory model of critical brain sites that mediate antidepressant efficacy and sites associated with cognitive, particularly memory, adverse effects.
  
  
  
  High resolution finite element human head models were generated from MRI scans of three subjects. The models were used to compare differences in activation between the three ECT placements, using subtraction maps.
  
  
  
  In this exploratory study on three realistic head models, Bitemporal ECT resulted in greater direct stimulation of deep midline structures and also left temporal and inferior frontal regions. Interpreted in light of existing knowledge on depressive pathophysiology and cognitive neuroanatomy, it is suggested that the former sites are related to efficacy and the latter to cognitive deficits. We hereby propose an approach using binarised subtraction models that can be used to optimise, and even individualise, ECT therapies.
authors.affiliation: Department of Electrical and Computer Engineering, Technische Universität München, 80333 München, Germany; Graduate School of Biomedical Engineering, Faculty of Engineering, University of New South Wales (UNSW), NSW 2052, Australia.
authors.forename: S
authors.initials: S
authors.lastname: Bai
authors.affiliation: School of Psychiatry, UNSW, NSW 2052, Australia; Black Dog Institute, NSW 2031, Australia.
authors.forename: V
authors.initials: V
authors.lastname: Gálvez
authors.affiliation: Graduate School of Biomedical Engineering, Faculty of Engineering, University of New South Wales (UNSW), NSW 2052, Australia.
authors.forename: S
authors.initials: S
authors.lastname: Dokos
authors.affiliation: School of Psychiatry, UNSW, NSW 2052, Australia; Black Dog Institute, NSW 2031, Australia.
authors.forename: D
authors.initials: D
authors.lastname: Martin
authors.affiliation: Department of Biomedical Engineering, The City College of The City University of New York, New York, USA.
authors.forename: M
authors.initials: M
authors.lastname: Bikson
authors.affiliation: School of Psychiatry, UNSW, NSW 2052, Australia; Black Dog Institute, NSW 2031, Australia; Department of Psychiatry, St George Hospital, NSW 2217, Australia. Electronic address: colleen.loo@unsw.edu.au.
authors.forename: C
authors.initials: C
authors.lastname: Loo
mesh_terms: D003071:Cognition / Q000502:physiology*; D060825:Cognitive Dysfunction / Q000175:diagnosis / Q000209:etiology / Q000503:physiopathology; D003198:Computer Simulation; D046690:Deep Brain Stimulation / Q000009:adverse effects* / Q000379:methods; D004565:Electroconvulsive Therapy / Q000009:adverse effects* / Q000379:methods; D020342:Finite Element Analysis; D005625:Frontal Lobe / Q000000981:diagnostic imaging / Q000503:physiopathology; D006801:Humans; D008279:Magnetic Resonance Imaging; D013702:Temporal Lobe / Q000000981:diagnostic imaging / Q000503:physiopathology; D016896:Treatment Outcome
keywords: Brain stimulation; Cognition; Computational model; Depression; Efficacy; Electroconvulsive therapy (ECT)