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Modeling the antidepressant treatment response to transcranial magnetic stimulation using an exponential decay
Yosef A Berlow1,2, Amin Zandvakili3,4, McKenna C Brennan4
1Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA. yosef_berlow@brown.edu.
Scientific Reports
|May 2, 2023
Summary
Antidepressant treatment response to repetitive transcranial magnetic stimulation (TMS) follows a nonlinear pattern. An exponential decay model accurately describes this improvement, offering a framework for clinical decisions.
Area of Science:
- Neuroscience
- Psychiatry
- Medical Statistics
Background:
- Depression recovery often shows nonlinear patterns, with initial rapid symptom reduction.
- Repetitive transcranial magnetic stimulation (TMS) is a treatment for depression.
- Understanding the temporal dynamics of TMS response can optimize treatment.
Purpose of the Study:
- To investigate if an exponential decay function can model the antidepressant response to TMS.
- To compare nonlinear exponential models with linear models for TMS treatment response.
- To assess the generalizability of the exponential model across different TMS studies.
Main Methods:
- Collected symptom ratings from 97 patients undergoing TMS for depression at baseline and every five sessions.
- Developed a nonlinear mixed-effects model using an exponential decay function.
- Applied the model to patient data and group-level data from published TMS trials for treatment-resistant depression.
Main Results:
- The exponential decay function provided a superior fit to patient symptom data compared to a linear model.
- Nonlinear exponential models consistently demonstrated better fits than linear models when applied to multiple published TMS studies.
- All parameters in the exponential decay model were significant, indicating robust modeling of TMS response.
Conclusions:
- Antidepressant response to TMS is characterized by a nonlinear pattern, well-described by an exponential decay function.
- This exponential modeling provides a simple, effective framework for understanding TMS treatment trajectories.
- The findings can inform clinical decision-making and guide future research in TMS for depression.
