Abraham Nunes, Selena Singh.
Abstract
Our objective is to propose a method capable of disentangling the magnitude, the speed, and the duration or decay rate of the time course of response to rapid antidepressant therapies. To this end, we introduce a computational model of the time course of response to a single treatment with a rapid antidepressant. Numerical simulation is used to evaluate whether model parameters can be accurately estimated from observed data.
Introduction
Depression is a major public health problem for which there is growing interest in management using rapid antidepressant therapies. These treatments, such as sleep deprivation ketamine and classical psychedelics are notable for their ability to produce robust antidepressant effects after only a single dose. The central problem with these therapies is currently the short duration of response.
Materials and Methods:
We model the effect of a single administration of a rapid antidepressant at time t as a normalized difference of exponentials, which is commonly used as a model of electrical conductance across postsynaptic membranes after arrival of discrete presynaptic action potentials. Adopting this model for the present study is appropriate given its qualitative properties and relative simplicity.
Discussion:
We have introduced a computational model that can be used to disentangle different components of the time course of rapid antidepressant response: the magnitude, speed, and stability or decay of the effect over time. This model is simple and its parameters are interpretable (magnitude of effect g, decay time a, response time b). Furthermore, these parameters can be accurately recovered from data, and these estimates are robust to noise.
Citation: Nunes A, Singh S (2024) A computational model to characterize the time-course of response to rapid antidepressant therapies. PLoS ONE 19(2): e0297708. https://doi.org/10.1371/journal.pone.0297708
Editor: Souparno Mitra, NYU Grossman School of Medicine: New York University School of Medicine, UNITED STATES
Received: November 20, 2023; Accepted: January 10, 2024; Published: February 2, 2024.
Copyright: © 2024 Nunes, Singh. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting information files.
Funding: Research Nova Scotia (AN) (grant: RNS-NHIG-2021-1931); QEII Foundation (AN). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
