Elsevier

Biological Psychiatry

Volume 92, Issue 3, 1 August 2022, Pages 193-203
Biological Psychiatry

Review
Parsing the Network Mechanisms of Electroconvulsive Therapy

https://doi.org/10.1016/j.biopsych.2021.11.016Get rights and content

Abstract

Electroconvulsive therapy (ECT) is one of the oldest and most effective forms of neurostimulation, wherein electrical current is used to elicit brief, generalized seizures under general anesthesia. When electrodes are positioned to target frontotemporal cortex, ECT is arguably the most effective treatment for severe major depression, with response rates and times superior to other available antidepressant therapies. Neuroimaging research has been pivotal in improving the field’s mechanistic understanding of ECT, with a growing number of magnetic resonance imaging studies demonstrating hippocampal plasticity after ECT, in line with evidence of upregulated neurotrophic processes in the hippocampus in animal models. However, the precise roles of the hippocampus and other brain regions in antidepressant response to ECT remain unclear. Seizure physiology may also play a role in antidepressant response to ECT, as indicated by early positron emission tomography, single-photon emission computed tomography, and electroencephalography research and corroborated by recent magnetic resonance imaging studies. In this review, we discuss the evidence supporting neuroplasticity in the hippocampus and other brain regions during and after ECT, and their associations with antidepressant response. We also offer a mechanistic, circuit-level model that proposes that core mechanisms of antidepressant response to ECT involve thalamocortical and cerebellar networks that are active during seizure generalization and termination over repeated ECT sessions, and their interactions with corticolimbic circuits that are dysfunctional prior to treatment and targeted with the electrical stimulus.

Section snippets

Hippocampal Plasticity After ECT: Epiphenomenon or Core Mechanism?

Increased hippocampal gray matter after ECT is robust and highly replicated across independent MRI studies (15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29) and meta-/mega-analyses (30, 31, 32, 33,35), with larger effect sizes compared with other brain structures (35). Neuroimaging studies have also noted changes in hippocampal function using blood oxygen level–dependent and arterial spin-labeled fMRI, PET, and SPECT (45,47,63,64), as well as changes in metabolites with magnetic

Relevance of Seizure Physiology

ECT and other seizure therapies are unique among brain stimulation treatments, because in addition to the exogenous electrical (or other) stimulus, they activate a kind of endogenous stimulus, a generalized tonic-clonic (convulsive) seizure. Thus, each ECT session involves a cascade of neurofunctional events, beginning with general anesthesia, application of the electrical stimulus, the initiation, generalization, and termination of the seizure, and ending with postictal recovery. Each of these

Network Model of Seizure Therapy

The neurobiology of depression is widely thought to involve circuit-level dysfunction in corticolimbic networks, including structures such as the hippocampus and amygdala (52,53). Based on the neuroimaging evidence discussed here, we propose that seizure therapies improve symptoms by correcting or resetting this dysfunction, through the action of cerebellar-thalamo-cortical circuits on these dysfunctional corticolimbic circuits (Figure 4). During seizure initiation, the electrical stimulus

Conclusions

In this review, we have discussed evidence suggesting that subregional changes within MTL and associated cortical-limbic networks could be core mechanisms of successful antidepressant response to ECT (46, 47, 48,92). We also discussed evidence that networks relevant to seizure physiology could be important to ECT outcomes, particularly networks involving the thalamus and cerebellum (45,56,99,135). Despite the questions that remain, it is notable and potentially significant that such a

Acknowledgments and Disclosures

This work was supported by the National Institutes of Health (Grant Nos. R01MH092301 and U01MH110008 [to KLN and RE], R03MH121769 [to AML], K24MH102743 [to KLN], and K99MH119314 [to BW]), Muriel Harris Chair (Geriatric Psychiatry) (to RE), Brain and Behavior Research Foundation including 2015 & 2020 Young Investigator award (to AML), and 2018 Young Investigator award (to BW).

The authors report no biomedical financial interests or potential conflicts of interest.

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