Electric field strength induced by electroconvulsive therapy is associated with clinical outcome

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Highlights

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Finite element modelling is used to estimate the electric field induced by ECT.
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The electric field induced by ECT is highly variable between patients.
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The electric field in certain brain regions is associated with less optimal ECT-outcome.
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White matter distribution in temporal lobes seems important for this relationship.

Abstract

The clinical effect of electroconvulsive therapy (ECT) is mediated by eliciting a generalized seizure, which is achieved by applying electrical current to the head via scalp electrodes. The anatomy of the head influences the distribution of current flow in each brain region. Here, we investigated whether individual differences in simulated local electrical field strength are associated with ECT efficacy. We modeled the electric field of 67 depressed patients receiving ECT. Patient’s T1 magnetic resonance images were segmented, conductivities were assigned to each tissue and the finite element method was used to solve for the electric field induced by the electrodes. We investigated the correlation between modelled electric field and ECT outcome using voxel-wise general linear models. The difference between bilateral (BL) and right unilateral (RUL) electrode placement was striking. Even within electrode configuration, there was substantial variability between patients. For the modeled BL placement, stronger electric field strengths appeared in the left hemisphere and part of the right temporal lobe. Importantly, a stronger electric field in the temporal lobes was associated with less optimal ECT response in patients treated with BL-ECT. No significant differences in electric field distributions were found between responders and non-responders to RUL-ECT. These results suggest that overstimulation of the temporal lobes during BL stimulation has negative consequences on treatment outcome. If replicated, individualized pre-ECT computer-modelled electric field distributions may inform the development of patient-specific ECT protocols.

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Most cited references 47

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A reproducible evaluation of ANTs similarity metric performance in brain image registration.

Brian Avants, Nicholas Tustison, Gang Song … (2011)

The United States National Institutes of Health (NIH) commit significant support to open-source data and software resources in order to foment reproducibility in the biomedical imaging sciences. Here, we report and evaluate a recent product of this commitment: Advanced Neuroimaging Tools (ANTs), which is approaching its 2.0 release. The ANTs open source software library consists of a suite of state-of-the-art image registration, segmentation and template building tools for quantitative morphometric analysis. In this work, we use ANTs to quantify, for the first time, the impact of similarity metrics on the affine and deformable components of a template-based normalization study. We detail the ANTs implementation of three similarity metrics: squared intensity difference, a new and faster cross-correlation, and voxel-wise mutual information. We then use two-fold cross-validation to compare their performance on openly available, manually labeled, T1-weighted MRI brain image data of 40 subjects (UCLA's LPBA40 dataset). We report evaluation results on cortical and whole brain labels for both the affine and deformable components of the registration. Results indicate that the best ANTs methods are competitive with existing brain extraction results (Jaccard=0.958) and cortical labeling approaches. Mutual information affine mapping combined with cross-correlation diffeomorphic mapping gave the best cortical labeling results (Jaccard=0.669±0.022). Furthermore, our two-fold cross-validation allows us to quantify the similarity of templates derived from different subgroups. Our open code, data and evaluation scripts set performance benchmark parameters for this state-of-the-art toolkit. This is the first study to use a consistent transformation framework to provide a reproducible evaluation of the isolated effect of the similarity metric on optimal template construction and brain labeling. Copyright © 2010 Elsevier Inc. All rights reserved.

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Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain.

B. Avants, C. Epstein, M. Grossman … (2008)

One of the most challenging problems in modern neuroimaging is detailed characterization of neurodegeneration. Quantifying spatial and longitudinal atrophy patterns is an important component of this process. These spatiotemporal signals will aid in discriminating between related diseases, such as frontotemporal dementia (FTD) and Alzheimer's disease (AD), which manifest themselves in the same at-risk population. Here, we develop a novel symmetric image normalization method (SyN) for maximizing the cross-correlation within the space of diffeomorphic maps and provide the Euler-Lagrange equations necessary for this optimization. We then turn to a careful evaluation of our method. Our evaluation uses gold standard, human cortical segmentation to contrast SyN's performance with a related elastic method and with the standard ITK implementation of Thirion's Demons algorithm. The new method compares favorably with both approaches, in particular when the distance between the template brain and the target brain is large. We then report the correlation of volumes gained by algorithmic cortical labelings of FTD and control subjects with those gained by the manual rater. This comparison shows that, of the three methods tested, SyN's volume measurements are the most strongly correlated with volume measurements gained by expert labeling. This study indicates that SyN, with cross-correlation, is a reliable method for normalizing and making anatomical measurements in volumetric MRI of patients and at-risk elderly individuals.

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Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference.

Stephen Smith, Thomas Nichols (2009)

Many image enhancement and thresholding techniques make use of spatial neighbourhood information to boost belief in extended areas of signal. The most common such approach in neuroimaging is cluster-based thresholding, which is often more sensitive than voxel-wise thresholding. However, a limitation is the need to define the initial cluster-forming threshold. This threshold is arbitrary, and yet its exact choice can have a large impact on the results, particularly at the lower (e.g., t, z < 4) cluster-forming thresholds frequently used. Furthermore, the amount of spatial pre-smoothing is also arbitrary (given that the expected signal extent is very rarely known in advance of the analysis). In the light of such problems, we propose a new method which attempts to keep the sensitivity benefits of cluster-based thresholding (and indeed the general concept of "clusters" of signal), while avoiding (or at least minimising) these problems. The method takes a raw statistic image and produces an output image in which the voxel-wise values represent the amount of cluster-like local spatial support. The method is thus referred to as "threshold-free cluster enhancement" (TFCE). We present the TFCE approach and discuss in detail ROC-based optimisation and comparisons with cluster-based and voxel-based thresholding. We find that TFCE gives generally better sensitivity than other methods over a wide range of test signal shapes and SNR values. We also show an example on a real imaging dataset, suggesting that TFCE does indeed provide not just improved sensitivity, but richer and more interpretable output than cluster-based thresholding.

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Author and article information

Contributors

Guido A. van Wingen

Journal

Journal ID (nlm-ta): Neuroimage Clin

Journal ID (iso-abbrev): Neuroimage Clin

Title: NeuroImage : Clinical

Publisher: Elsevier

ISSN (Electronic): 2213-1582

Publication date PMC-release: 09 February 2021

Publication date Collection: 2021

Publication date (Electronic): 09 February 2021

Volume: 30

Electronic Location Identifier: 102581

Affiliations

[a ]Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, Amsterdam, the Netherlands

[b ]Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, the Netherlands

[c ]Noninvasive Neuromodulation Unit, Experimental Therapeutics & Pathophysiology Branch, National Institute of Mental Health, United States

[d ]Department of Psychiatry, Rijnstate Hospital, Arnhem, the Netherlands

[e ]The Netherlands Institute for Neuroscience, an Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, the Netherlands

Author notes

[* ]Corresponding author at: Department of Psychiatry, Amsterdam UMC, location AMC, PO Box 22660, 1100 DD Amsterdam, The Netherlands. guidovanwingen@ 123456gmail.com

Article

Publisher Item ID: S2213-1582(21)00025-5 Publisher ID: 102581

DOI: 10.1016/j.nicl.2021.102581

PMC ID: 7895836

PubMed ID: 33588322

SO-VID: d1845633-b801-4622-8c40-99cce7433006

License:

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

History

Date received : 19 June 2020

Date revision received : 26 January 2021

Date accepted : 27 January 2021

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Electric field strength induced by electroconvulsive therapy is associated with clinical outcome

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Most cited references 47

A reproducible evaluation of ANTs similarity metric performance in brain image registration.

Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain.

Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference.

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Cited by 11

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