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Electroconvulsive therapy (ECT), or shock therapy, remains a critical biomedical intervention for severe, treatment-resistant depression. While its origins can be traced back to Hippocrates' observations that malaria-induced convulsions alleviated mental illness, modern ECT has evolved significantly from its earlier, more primitive applications. First introduced in 1938 by Ugo Cerletti and his colleagues, ECT involves inducing controlled seizures using electrical currents. In its early...

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Updated: Jul 17, 2025

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

Optimization of ictal aborting stimulation using the dynamotype taxonomy.

Matthew P Szuromi^1,2, Viktor K Jirsa³, William C Stacey^4,5,6

¹Department of Biomedical Engineering and Biointerfaces Institute, University of Michigan, Ann Arbor, USA.

Journal of Computational Neuroscience

|September 4, 2023

Summary

This summary is machine-generated.

Electrical stimulation can stop epileptic seizures, but success varies. Understanding seizure dynamics and dynamotype can optimize stimulation timing and effectiveness, especially for seizures with a baseline shift.

Keywords:

Bifurcation Dynamics Epilepsy Seizure Stimulation

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Published on: October 22, 2015

Area of Science:

Neuroscience
Computational modeling
Epilepsy research

Background:

Electrical stimulation is used to terminate epileptic seizures, but its efficacy is inconsistent.
Current stimulation methods are often empirical, lacking consideration for seizure-specific dynamical properties.

Purpose of the Study:

To investigate how the dynamical properties of seizures influence the success of electrical stimulation for seizure termination.
To identify key factors determining the efficacy of aborting stimulation based on seizure characteristics.

Main Methods:

Utilized a validated computational model of seizure dynamics to simulate seizure progression and response to electrical stimulation.
Analyzed different bursting classes (dynamotypes) and their state space structures, including the presence of a DC (baseline) shift.
Examined the impact of stimulation timing and phase on termination probability.

Main Results:

Different seizure dynamotypes exhibit varying responses to electrical stimulation, with some being more amenable to termination than others.
Seizures with a DC (baseline) shift are significantly more likely to be terminated due to their state space structure.
Termination probability is dependent on the seizure's dynamotype, presence of a DC shift, and the timing of stimulation application.

Conclusions:

The effectiveness of electrical stimulation to terminate seizures can be predicted by considering seizure dynamotype, DC shift, and stimulation timing.
A computational approach offers a method to personalize seizure termination strategies for improved clinical outcomes.
Optimizing aborting stimulation requires accounting for the unique dynamical properties of individual seizures.