Recovery from depression often demonstrates a nonlinear pattern of treatment response, where the largest reduction in symptoms is observed early followed by smaller improvements. This study investigated whether this exponential pattern could model the antidepressant response to repetitive transcranial magnetic stimulation (TMS). Symptom ratings from 97 patients treated with TMS for depression were collected at baseline and after every five sessions. A nonlinear mixed-effects model was constructed using an exponential decay function. This model was also applied to group-level data from several published clinical trials of TMS for treatment-resistant depression. These nonlinear models were compared to corresponding linear models. In our clinical sample, response to TMS was well modeled with the exponential decay function, yielding significant estimates for all parameters and demonstrating superior fit compared to a linear model. Similarly, when applied to multiple studies comparing TMS modalities as well as to previously identified treatment response trajectories, the exponential decay models yielded consistently better fits compared to linear models. These results demonstrate that the antidepressant response to TMS follows a nonlinear pattern of improvement that is well modeled with an exponential decay function. This modeling offers a simple and useful framework to inform clinical decisions and future studies.

No abstract

The diagnostic categories in psychiatry often encompass heterogeneous symptom profiles associated with differences in the underlying etiology, pathogenesis and prognosis. Prior work demonstrated that some of this heterogeneity can be quantified though dimensional analysis of the Depression Anxiety Stress Scale (DASS), yielding unique transdiagnostic symptom subtypes. This study investigated whether classifying patients according to these symptom profiles would have prognostic value for the treatment response to therapeutic transcranial magnetic stimulation (TMS) in comorbid major depressive disorder (MDD) and posttraumatic stress disorder (PTSD). A linear discriminant model was constructed using a simulation dataset to classify 35 participants into one of the following six pre-defined symptom profiles: Normative Mood, Tension, Anxious Arousal, Generalized Anxiety, Anhedonia and Melancholia. Clinical outcomes with TMS across MDD and PTSD were assessed. All six symptom profiles were present. After TMS, participants with anxious arousal were less likely to achieve MDD remission compared to other subtypes (FET, odds ratio 0.16, p = 0.034), exhibited poorer PTSD symptom reduction (21% vs. 46%; t (33) = 2.025, p = 0.051) and were less likely to complete TMS (FET, odds ratio 0.066, p = 0.011). These results offer preliminary evidence that classifying individuals according to these transdiagnostic symptom profiles may offer a simple method to inform TMS treatment decisions.

Recovery from depression often demonstrates a nonlinear pattern of treatment response, where the largest reduction in symptoms is observed early followed by smaller improvements. This study investigated whether this exponential pattern could model the antidepressant response to repetitive transcranial magnetic stimulation (TMS). Symptom ratings from 97 patients treated with TMS for depression were collected at baseline and after every five sessions. A nonlinear mixed-effects model was constructed using an exponential decay function. This model was also applied to group-level data from several published clinical trials of TMS for treatment-resistant depression. These nonlinear models were compared to corresponding linear models. In our clinical sample, response to TMS was well modeled with the exponential decay function, yielding significant estimates for all parameters and demonstrating superior fit compared to a linear model. Similarly, when applied to multiple studies comparing TMS modalities as well as to previously identified treatment response trajectories, the exponential decay models yielded consistently better fits compared to linear models. These results demonstrate that the antidepressant response to TMS follows a nonlinear pattern of improvement that is well modeled with an exponential decay function. This modeling offers a simple and useful framework to inform clinical decisions and future studies.