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Lookup NU author(s): Professor Hamish McAllister-WilliamsORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© The Author(s) 2024. Repetitive transcranial magnetic stimulation (rTMS) is an established non-invasive brain stimulation treatment for major depressive disorder, but there is marked inter-individual variability in response. Using latent class growth analysis with session-by-session patient global impression ratings from the recently completed BRIGhTMIND trial, we identified five distinct classes of improvement trajectory during a 20-session treatment course. This included a substantial class of patients noticing delayed onset of improvement. Contrary to prior expectations, members of a class characterised by early and continued improvement showed greatest inter-session variability in stimulated location. By relating target locations and inter-session variability to a well-studied atlas, we estimated an average of 3.0 brain networks were stimulated across the treatment course in this group, compared to 1.1 in a group that reported symptom worsening (p < 0.001, d = 0.893). If confirmed, this would suggest that deliberate targeting of multiple brain networks could be beneficial to rTMS outcomes.
Author(s): Briley PM, Webster L, Lankappa S, Pszczolkowski S, McAllister-Williams RH, Liddle PF, Auer DP, Morriss R
Publication type: Article
Publication status: Published
Journal: npj Mental Health Research
Year: 2024
Volume: 3
Online publication date: 27/06/2024
Acceptance date: 05/06/2024
Date deposited: 28/05/2025
ISSN (electronic): 2731-4251
Publisher: Springer Nature
URL: https://doi.org/10.1038/s44184-024-00077-8
DOI: 10.1038/s44184-024-00077-8
Data Access Statement: Analysis code and mPGIC values used for deriving response trajectories (alongside fitted latent class models) are available at: https://github.com/pmbriley/trajectories. Demographic, clinical outcome, and treatment variables used in the current study will be made available on the University of Nottingham data repository (https://rdmc.nottingham.ac.uk).
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