Symptom cluster response trajectories with dorsolateral prefrontal rTMS for depression: A THREE-D Study

Symptom cluster response trajectories with dorsolateral prefrontal rTMS for depression: A THREE-D Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Symptom cluster response trajectories with dorsolateral prefrontal rTMS for depression: A THREE-D Study Tyler Kaster, Xiao Chen, Jonathan Downar, Fidel Vila-Rodriguez, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6428690/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Using the Hamilton Depression Rating Scale (HDRS) sum score to quantify the efficacy of repetitive transcranial magnetic stimulation (rTMS) in major depressive disorder (MDD) may overlook the heterogeneity of distinct longitudinal symptom cluster response trajectories. We used data from the THREE-D clinical trial (N = 388) comparing two forms of rTMS delivered over 4–6 weeks to the left dorsolateral prefrontal cortex (DLPFC) for treatment-resistant depression (TRD). We examined the four symptom domains measured with the HDRS (anxiety, mood, insomnia, and somatic) and applied group-based multi-trajectory modeling (GBMTM) to identify latent groups based on responses simultaneously occurring within each symptom cluster. We then used multinomial regression to identify patient characteristics associated with each group. We identified four distinct longitudinal symptom cluster response trajectory groups: Optimal response (N = 119; 30.7%); Partial response, high anxiety (N = 128; 33.0%); Partial response, low anxiety (N = 91; 23.5%); Minimal response (N = 50; 12.9%). The optimal response trajectory was characterized by a rapid decrease in all symptom clusters by week 2, while the Minimal response trajectory group showed no or even worsened symptom severity. Two moderate response trajectories showed linear response but differing baseline anxiety severity. The optimal response group was associated with lower symptom scores, and the Minimal response group was associated with younger age, benzodiazepine use, lower baseline anxiety, and higher baseline depression symptoms. This work demonstrates that distinct symptom cluster response trajectories exist amongst individuals with TRD receiving rTMS, with anxiety symptoms being particularly important for identifying those most likely to benefit from treatment. ClinicalTrials.Gov: NCT01887782 Health sciences/Diseases/Psychiatric disorders/Depression Biological sciences/Neuroscience repetitive transcranial magnetic stimulation cluster analysis group-based trajectory modeling multi-trajectory individualized medication depression treatment-resistant depression Figures Figure 1 Introduction Repetitive transcranial magnetic stimulation (rTMS) is a well-established treatment for individuals suffering from treatment-resistant depression (TRD) 1 . rTMS treatment uses magnetic field pulses to focally stimulate regions of the brain implicated in the pathophysiology of depression – most commonly the left dorsolateral prefrontal cortex (DLPFC) – and is typically delivered once daily for 4–6 weeks 2 . While rTMS achieves remission in 20–40% of those with TRD 2 and is superior to medication treatment 3 , there continue to be large numbers of individuals who do not sufficiently benefit from treatment. Various approaches have been taken to optimize treatment response to rTMS, including the use of accelerated protocols 4 , pharmacotherapy augmentation 5 , and imaging-guided target heuristics 6 . Of these various approaches, one of the most promising leverages the spatial resolution of rTMS to actively target specific neuronal circuits thought to be dysfunctional within depression 7 . In this manner, the optimal treatment location is identified through the individual’s presenting symptoms (e.g., anxiosomatic vs. dysphoric) 7 . While this approach has yet to be supported by prospective clinical trials, it demonstrates enormous promise because it does not require clinic operational advancements, new medications, or new technologies. Instead, it simply requires identifying the dominant symptom profile and adjusting to the appropriate treatment location. 7 A major barrier to this personalized treatment approach, however, is the heterogeneous nature of depression, which encompasses over 200 symptom combinations, some of which share no common symptoms 8 . The standard practice of using the sum score of a depression rating scale, such as the Hamilton Depression Rating Scale (HDRS) 9 , exacerbates this problem by implicitly viewing depression as a unidimensional construct, which does not allow for evaluation of the diverse manifestations of symptoms 10 . Rather than conceptualizing depression as a unidimensional construct, viewing depression as a disorder with multiple distinct clusters or dimensions (i.e., multidimensionality 11 ) provides a framework for incorporating the heterogeneity present in different expressions of the illness. Our group has previously conducted work using data from clinical trials in rTMS for TRD and identified four distinct symptom clusters measured in the HDRS: anxiety, mood, insomnia, and somatic clusters 12 . These symptom clusters were identified using hypothesis-driven analytic techniques to evaluate a previously published symptom cluster model 13 , which supports its validity. We found that rTMS delivered had differing impacts on each of these symptom clusters with most patients experiencing greater reductions of mood, insomnia, and somatic symptoms compared to anxiety symptoms 12 . While this was the first study to identify differential symptom cluster response to rTMS treatment, it only examined group-level effect of rTMS on each symptom cluster. Identifying subgroups with distinct co-occurring longitudinal trajectories for each symptom cluster would, therefore, represent an important advance toward understanding the heterogeneity of treatment response that occurs with rTMS treatment in TRD. Our group has also conducted prior work examining longitudinal response trajectories to rTMS using group-based trajectory modeling (GBTM) 14 – 17 . While this work identified distinct longitudinal symptom response trajectories, it used the HDRS sum score to identify these trajectories, which does capture the heterogeneity of how different symptom clusters may respond to rTMS treatment. Recent methodologic advances in GBTM have led to the development of group-based multi -trajectory modeling (GBMTM). This approach can identify latent clusters of individuals following similar trajectories across multiple domains for a single condition of interest 18 , such as distinct symptom clusters in depression. The objective of this work, therefore, was to identify distinct symptom cluster response trajectories occurring amongst individuals with TRD receiving rTMS delivered to the left DLPFC. We sought to identify response trajectories across the four symptom clusters of depression and identify baseline clinical characteristics associated with membership in each response trajectory. Identifying these trajectories will provide knowledge on how depression symptom clusters respond to rTMS treatment and may eventually allow for personalized rTMS treatment protocols based on symptom patterns. Methods Study procedures This was a secondary analysis using data from THREE-D, a multi-center randomized trial of rTMS. THREE-D was conducted in three Canadian academic health centres (Centre for Addiction and Mental Health, Toronto, ON; Toronto Western Hospital, Toronto, ON; University of British Columbia Hospital, Vancouver, BC) and used a non-inferiority design to compare two rTMS protocols applied to the left DLPFC: standard (10 Hz) high frequency left (HFL) or intermittent theta-burst (iTBS) 19 . Local research ethics board approval was obtained for all three study sites, and all participants provided written and informed consent. This trial was registered with ClinicalTrials.gov, number NCT01887782. Treatment was delivered once daily, 5 days per week for 4 weeks (i.e., 20 treatments). Participants who achieved remission (HDRS-17 total score < 8) or had an insufficient response (< 30% reduction in HDRS-17 total from baseline) were considered to have completed the study. Participants who had ≥ 30% reduction in symptoms but did not achieve remission were provided an additional ten treatments over 2 weeks to optimize treatment response and durability for 30 treatments over 6 weeks. Individuals continued their psychotropic medications unchanged for the study duration. The HDRS-17 was administered weekly by trained research staff blinded to treatment allocation. The exclusion criteria included substance abuse, acute suicidality, bipolar disorder, and psychotic disorders. For details regarding the study procedures, refer to the supplementary information and the original publication 19 . rTMS procedure Before treatment, all participants underwent an anatomical MRI. rTMS treatments were guided using MRI-guided neuronavigation to optimize coil positioning. The left DLPFC was targeted using the MNI-152 stereotaxic coordinate (x-38, y + 44, z + 26) 6 . A MagPro X100/R30 stimulator equipped with a B70 fluid-cooled coil (MagVenture, Farum, Denmark) was used for stimulation. The resting motor threshold (RMT) was determined using visual observation. HFL was delivered with the FDA-approved treatment settings (120% RMT, 10Hz, 4 seconds on, 26 seconds off, 3000 pulses/session over 37.5 min) 20 . iTBS was delivered to the same site with the same intensity but used a different stimulation pattern (triplet 50Hz bursts, repeated at 5Hz, 2 seconds on, 8 seconds off, 600 pulses/session over 3 min) 19 . Choice of primary outcome measure The primary outcome measures in this study were symptom cluster scores from one of four domains previously identified (anxiety, mood, somatic, and insomnia) 12 . These symptom clusters were derived from the 17 item HDRS, the most commonly used clinician-rated psychometric measure of depression severity. 9 Of the 17 items, nine are scored between 0 (not present) and four points (severe), while the remaining eight are scored between 0 (not present) and two points (severe) for a total score ranging from 0 to 52. The sum score of each symptom cluster was calculated and rescaled using the proportion of the maximum possible scaling method 21 to allow for comparison among different symptom clusters. For details, refer to the supplementary materials. Statistical analyses Analytic Overview To classify participants into subgroups based on their longitudinal response trajectories, we used an application of finite-mixture modeling known as group-based trajectory modeling (GBTM) implemented via the traj 22 command in Stata (Stata 16.1 (StataCorp, Texas, USA)). We determined the optimal number of response trajectories (i.e., latent longitudinal groups) and the optimal polynomial degree within each response trajectory using the Bayesian information criterion (BIC). The BIC measures improvement in model fit gained by adding additional groups or shape parameters but also penalizes added complexity. The BIC log Bayes factor approximation, defined as 2 \(\:\times\:\varDelta\:\) BIC (with \(\:\varDelta\:\) BIC as the difference between a more complex and less complex model), is an acceptable approximation to the log Bayes factor criterion 23 . When 2 \(\:\times\:\varDelta\:\) BIC was > 10; this was used as evidence favouring the more complex model 22 . For all single trajectory models, we assessed model fit by calculating the average posterior probability of group membership (70% minimum for each group), determining the percentage of the total sample within each trajectory (5% minimum for each group), and calculating the odds of correct classification (> 5 considered adequate). While the THREE-D study consisted of treatment for up to 6 weeks, participant data for weeks 5 and 6 were missing not at random, which may result in biased estimates as the GBTM and GBMTM procedures assume data are missing at random 14 . Because of this pattern of missingness, only participant data up to week 4 was used for determining symptom cluster response trajectories. Multi-trajectory Analyses The first step of identifying the symptom cluster response trajectories was to conduct a separate trajectory analysis for each symptom cluster using GBTM using previously described approaches (see supplementary material). 15 , 17 The number of trajectory groups within the single trajectory analyses informed subsequent multi-trajectory analyses using GBMTM 18 . For the GBMTM analysis, we followed a conceptually similar model development process in which we first identified the optimal number of latent response trajectories and then sought to identify - within the optimal number of response trajectory groups - the ideal polynomial degree for each symptom cluster. However, given the proliferation of trajectories with multiple symptom clusters and limited sample size, we were required to make assumptions in the multi-trajectory analytic procedure and used the single trajectory analytic results to inform the multi-trajectory model development. Specifically, we made the following assumptions in developing the multi-trajectory model: (1) the number of groups in the multi-trajectory analysis would be between the smallest and largest number of trajectories identified in the single trajectory analysis, and (2) the polynomial degrees of the individual clusters within the multi-trajectory model would be similar to the single trajectory model. The outcome variable, the rescaled sum score of each separate symptom cluster, was assumed to follow a censored normal distribution, an assumption we verified through visual inspection of the sum score distribution (Supplemental Fig. 2). Outcome Analyses and Membership Predictors Once the symptom cluster response trajectories had been identified, we conducted a categorical comparison of the remission rates (HDRS score < 8) and response rates (HDRS change from baseline ≥ 50%) among all trajectories throughout four weeks of rTMS treatments. We then conducted a secondary analysis to determine which characteristics were potentially associated with membership in each response trajectory using a multinomial regression analysis weighted by the probability of group membership (to account for the uncertainty of group membership). This was conducted using the multinom function of the nnet package (Version 7.3.19) from R (Version 4.4.1) 24 . We used several a priori characteristics informed by prior work 15 , including age, sex, baseline symptom cluster severity (mood, anxiety, insomnia, and somatic clusters), benzodiazepine use, and Antidepressant Treatment History Form (ATHF) severity 25 . Following our prior studies 15 , 16 , the trajectory with the largest membership was picked as the reference group. Statistical tests were two-tailed, with \(\:\alpha\:\) set to 0.05. All analyses were reported according to the Guidelines for Reporting on Latent Trajectory Studies (GRoLTS) 26 . The analytic plan has been registered with the Open Science Foundation: https://osf.io/3mn8u . Role of funding The study funders for THREE-D (Canadian Institutes for Health Research) as well as the device manufacturer (MagVenture), which provided in-kind equipment support for THREE-D (two coils and two high-performance coolers at each site), had no role in study design, data collection, data analysis, data interpretation or writing of the report. The corresponding author (TSK) and senior author (DMB) had full access to the data and the corresponding author had final responsibility for the decision to submit for publication. Results A total of 414 participants were randomized in the study, of which 26 were excluded due to violating study inclusion criteria (2 prior to receiving treatment and 24 after receiving the allocated intervention). As a result, there were 388 participants included in the analytic cohort. Response trajectories In identifying the response trajectories of each symptom cluster separately using GBTM methods, we found that the optimal number of trajectories for each symptom cluster ranged between 3 and 5 (Supplemental Fig. 1) (BIC values listed in Supplemental Table 1). The polynomial coefficients generally consisted of a combination of cubic, quadratic, and linear for most symptom clusters, except for the insomnia cluster, of which all groups were linear. There was no indication of delayed responses amongst the symptom clusters, and with a single exception (Group 4 in somatic symptom cluster), groups with lower baseline symptoms consistently achieved lower final symptoms after 4 weeks of treatment. All groups demonstrated adequate model fit parameters, except for one group in the somatic symptom cluster, demonstrating group membership slightly below the 5% threshold (Supplemental Tables 2–5). In the multi-trajectory analyses, we anticipated the optimal number of groups would be between 3 and 5. We assumed that for the insomnia indicator, all polynomials would be linear. We also assumed that there would be one or two groups within each indicator demonstrating a non-linear response pattern (i.e., quadratic or cubic polynomial), with the remainder being linear. Given the number of trajectories being concurrently estimated, this process involved subjective decisions and the modification of polynomial degrees in the event of model non-convergence. The optimal fitting model consisted of four response trajectories with the co-occurring symptom cluster trajectories presented in Fig. 1 and BIC values listed in Table 1 . The polynomial coefficients of a combination of linear and quadratic polynomial terms. The “optimal response” group (N = 119; 30.7%) was characterized by a low baseline mood and somatic symptoms with a rapid reduction in all symptoms. The “Partial response, high anxiety” group (N = 128; 33.0%) was characterized by high levels of baseline anxiety and intermediate levels of mood, insomnia, and somatic symptoms, along with a linear, incremental reduction in symptoms. The “Partial response, low anxiety” group (N = 91; 23.5%) was characterized by low levels of insomnia and anxiety with a linear, incremental reduction in symptoms. The “minimum response” group (N = 50; 12.9%), which was the smallest group, was characterized by high levels of insomnia, mood, and somatic symptoms with minimal change in symptoms over the course of treatment. Table 1 BIC scores for each symptom cluster as the number of groups increases Number of Groups BIC 2 × 𝚫BIC 1 -15,960.35 NA 2 -15,505.82 909.06 3 -15,333.41 344.82 4 -15,206.52 253.78 5 -15,280.18 -147.32 The value in the boldface denotes the selected solution. Table 2 Baseline characteristics of participants receiving repetitive transcranial magnetic stimulation for depression by multiple symptom trajectory group Characteristic 1 Total sample, N = 388 Optimal response (N = 119; 30.7%) Partial response, high anxiety (N = 128; 33.0%) Partial response, low anxiety (N = 91; 23.5%) Minimal response (N = 50; 12.9%) F/ 𝛘 2 p Age (years) 42.33 (11.48) 44.08 (10.97) 43.10 (10.38) 40.56 (12.94) 39.42 (11.90) 2.95 0.03 Gender Female 229 (59.02%) 66 (55.46%) 80 (62.50%) 51 (56.04%) 32 (64.00%) 2.11 0.55 Male 159 (40.98%) 53 (44.54%) 48 (37.50%) 40 (43.96%) 18 (36.00%) Education (years) 16.31 (3.05) 16.21 (2.75) 16.59 (3.50) 16.21 (2.46) 16.06 (3.48) 0.54 0.66 Age at depressive symptom onset (years) 20.90 (10.89) 21.16 (11.20) 21.58 (10.81) 20.49 (10.52) 19.30 (11.13) 0.59 0.62 Current episode length 23.42 (27.39) 28.10 (32.85) 20.91 (27.72) 22.82 (22.19) 19.81 (18.66) 1.83 0.14 Handedness Left 39 (10.05%) 16 (13.45%) 13 (10.16%) 6 (6.59%) 4 (8.00%) 6.82 0.34 Right 345 (88.92%) 103 (86.55%) 112 (87.50%) 84 (92.31%) 46 (92.00%) Both 4 (1.03%) 0 (0.00%) 3 (2.34%) 1 (1.10%) 0 (0.00%) Baseline HDRS 23.54 (4.27) 21.21 (3.00) 25.21 (4.02) 22.36 (3.98) 26.92 (4.06) 40.68 < 0.001 Antidepressant treatment 295 (76.03%) 93 (78.15%) 98 (76.56%) 67 (73.63%) 37 (74.00%) 0.72 0.87 Antidepressant combination 84 (21.65%) 24 (20.17%) 32 (25.00%) 20 (21.98%) 8 (16.00%) 1.95 0.58 Antidepressant augmentation 71 (18.30%) 19 (15.97%) 19 (14.84%) 24 (26.37%) 9 (18.00%) 5.43 0.14 Benzodiazepine use 123 (31.70%) 31 (26.05%) 45 (35.16%) 23 (25.27%) 24 (48.00%) 10.33 0.16 History of ECT treatment 18 (4.64%) 0 (0.00%) 5 (3.91%) 6 (6.59%) 7 (14.00%) NA 2 NA 2 Any anxiety comorbidity 207 (53.35%) 52 (43.70%) 84 (65.63%) 45 (49.45%) 26 (52.00%) 12.80 0.005 Number of adequate antidepressant trials None 30 (7.73%) 6 (5.04%) 14 (10.94%) 6 (6.59%) 4 (8.00%) 12.29 0.20 One 173 (44.59%) 59 (49.58%) 56 (43.75%) 36 (39.56%) 22 (44.00%) Three 74 (19.07%) 15 (12.61%) 22 (17.19%) 25 (27.47%) 12 (24.00%) Two 111 (28.61%) 39 (32.77%) 36 (28.13%) 24 (26.37%) 12 (24.00%) 1 All values are n (%) or mean (SD). 2 Statistical testing was not performed due to the small expected cell size (< 5). Response trajectory outcomes We found strong evidence of differences among trajectories regarding response rates by week 2 and remission rates by week 4. Such differences could also be observed by the completion of the treatment (Table 3 ). Strong evidence of differences in both the response and remission rates was observed among four groups ( p s < 0.001). The response/remission rates of the optimal response (N = 119; 30.7%) were higher than the other three trajectories. The Partial response, high anxiety (N = 128; 33.0%), had an intermediate response rate (43%) and a low remission rate (4.1%) by week 4. The remaining two trajectories were characterized by low response (< 30%) and remission rates (< 3%). Table 3 Clinical outcomes for each group Characteristic 1 Total sample, N = 388 Optimal response (N = 119; 30.7%) Partial response, high anxiety (N = 128; 33.0%) Partial response, low anxiety (N = 91; 23.5%) Minimal response (N = 50; 12.9%) 𝛘 2 p 2 Response Week 1 35 (9.3%) 31 (27%) 3 (2.4%) 1 (1.1%) 0 (0%) NA NA Week 2 75 (20%) 60 (52%) 10 (8.3%) 5 (5.6%) 0 (0%) 105.30 < 0.001 Week 3 109 (30%) 74 (65%) 26 (21%) 9 (10%) 0 (0%) 105.34 < 0.001 Week 4 179 (49%) 101 (88%) 52 (43%) 25 (29%) 1 (2.5%) 118.96 < 0.001 Final 181 (50%) 96 (83%) 51 (42%) 34 (40%) 0 (0%) 97.88 < 0.001 Remission Week 1 9 (2.4%) 9 (7.8%) 0 (0%) 0 (0%) 0 (0%) NA NA Week 2 25 (6.7%) 23 (20%) 1 (0.8%) 1 (1.1%) 0 (0%) NA NA Week 3 33 (9.1%) 31 (27%) 1 (0.8%) 1 (1.2%) 0 (0%) NA NA Week 4 49 (14%) 42 (37%) 5 (4.1%) 2 (2.4%) 0 (0%) 76.54 < 0.001 Final 111 (31%) 75 (65%) 21 (17%) 15 (18%) 0 (0%) 98.98 < 0.001 1 All values are n (%) or mean (SD) 2 Statistical testing was not performed due to the small expected cell size (< 5). Multivariable trajectory membership predictors Table 4 presents the results of the multinomial logistic regression model for group membership using the Partial response, high anxiety group as a reference. We found that age, benzodiazepine use, and baseline scores of all symptom clusters (anxiety, depression, insomnia, and somatic) are associated with the multi-trajectory group membership. Lower baseline anxiety (odds ratio [OR] = 0.62, 95%CI = 0.53, 0.74), depression (OR = 0.68, 95% CI = 0.59, 0.79), insomnia (OR = 0.68, 95% CI = 0.57, 0.81), and somatic symptoms (OR = 0.77, 95% CI = 0.63, 0.95) were associated with the membership in the optimal response (N = 119; 30.7%). The membership in the Partial response, low anxiety (N = 91; 23.5%) were only associated with lower baseline anxiety (OR = 0.45, 95% CI = 0.36, 0.55) and lower insomnia (OR = 0.55, 95% CI = 0.45, 0.68). The membership in the Minimal response(N = 50; 12.9%) was associated with younger age (OR = 0.95, 95% CI = 0.92, 0.99), Benzodiazepine use (OR = 2.76, 95% CI = 1.25, 6.09), lower baseline anxiety (OR = 0.73, 95% CI = 0.60, 0.89), and higher depression (OR = 1.33, 95% CI = 1.12, 1.58). Table 4 Characteristics associated with trajectory groups Characteristics Optimal response (N = 119; 30.7%) Partial response, high anxiety (N = 128; 33.0%) Partial response, low anxiety (N = 91; 23.5%) Minimal response (N = 50; 12.9%) Odds Ratio 95% CI Odds Ratio 95% CI Odds Ratio 95% CI Odds Ratio 95% CI Age 1.01 0.98–1.04 1.00 (Reference) 0.99 0.95–1.02 0.95 0.92–0.99 Gender 0.68 0.36–1.28 1.00 (Reference) 0.41 0.41–1.79 1.07 0.48–2.38 Benzodiazepine use 0.70 0.36–1.37 1.00 (Reference) 0.79 0.36–1.74 2.73 1.24–6.05 Number of adequate antidepressant trials 0.96 0.37–2.53 1.00 (Reference) 1.21 0.43–3.36 1.36 0.47–3.91 Anxiety 0.62 0.52–0.74 1.00 (Reference) 0.44 0.35–0.55 0.73 0.60–0.89 Mood 0.68 0.59–0.78 1.00 (Reference) 1.05 0.89–1.23 1.35 1.13–1.61 Insomnia 0.67 0.56–0.80 1.00 (Reference) 0.54 0.44–0.67 1.16 0.92–1.48 Somatic 0.80 0.64–0.98 1.00 (Reference) 1.08 0.86–1.36 1.19 0.94–1.51 Boldface indicates statistical significance at p < 0.05. Discussion In this re-analysis of the largest rTMS trial conducted to date, we identified and characterized several distinct symptom cluster response trajectories amongst individuals with TRD receiving rTMS. Using validated symptom clusters of depression – anxiety, mood, insomnia, and somatic domains – we identified four distinct trajectories of change with each of these domains. The optimal response group comprised approximately one-third (30.7%) of the cohort and had the highest rates of response and remission, 83% and 65%, respectively. In contrast, the Minimal response group did not have a single participant achieve response or remission at the end of the treatment course but was fortunately a minority of the study cohort (12.9%). The bulk of individuals (56.5%) belonged to one of two moderate response groups, which were differentiated by either high or low levels of baseline anxiety and had response and remission rates intermediate between the optimal and Minimal response groups. The two moderate response groups (low anxiety and high anxiety) had remarkably similar responses (40% low anxiety vs 42% high anxiety) and remission rates (18% low anxiety vs 17% high anxiety) at treatment completion. In addition to the baseline symptom severity of each symptom cluster, we also found that age and benzodiazepine use was associated with membership in each of these trajectories. Younger age and benzodiazepine usage were associated with membership in the Minimal response group, even after accounting for each baseline symptom cluster severity (i.e., anxiety and insomnia). This work is an important extension of prior symptom trajectory work both as it pertains to rTMS and depression symptom profiles 15 – 17 . Our prior work highlighted the heterogeneity of response to rTMS when depression is viewed as a unidimensional measure 12 ; however, the current work represents an important advance toward an even more nuanced view of the heterogeneity that exists with longitudinal responses to depression. For example, this work highlights that the core mood symptoms of the HRSD may be particularly important in delineating response trajectories as individuals with higher baseline mood symptoms are more likely to belong to a Minimal response trajectory, while individuals with lower baseline mood symptoms are more likely to belong to an optimal response trajectory. In contrast, somatic symptoms may be the least helpful for delineating response trajectories, as only the optimal response group had lower somatic symptoms. Interestingly, baseline anxiety symptoms are most helpful for delineating between individuals belonging to the moderate response trajectories. In addition to baseline symptoms, the change in various symptom domains may be clinically relevant. The change in anxiety symptoms may also be particularly relevant for distinguishing between individuals in optimal response and minimum response, as those in the optimal response group demonstrated a dramatic reduction in anxiety symptoms after only 1 week of treatment. In this manner, it may be the pattern of symptom reductions amongst symptom clusters – particularly anxiety and mood symptoms – that may alert a clinician to an individual being a member of the optimal response group. Similarly, a lack of change in anxiety and somatic symptom clusters may alert a clinician that an individual belongs to the Minimal response trajectory, which may inform treatment expectations and consideration for an alternate treatment target. Consistent with our prior work, we found several baseline clinical characteristics associated with membership in different symptom cluster response trajectories. Relative to the Partial response, high anxiety group, the optimal response group was associated with lower symptom severity in all symptom clusters. The Minimal response group had significantly higher severity of the mood symptom cluster, and was associated with younger age. Of particular note, it was also associated with benzodiazepine use with quite a large effect size (OR: 2.76) that was significant even after controlling for the anxiety symptom cluster, which supports the notion of benzodiazepines interfering with rTMS treatment effectiveness 15 , 27 . While our study has several strengths, there are also important limitations. First, the dataset used in the current work considered a single rTMS treatment type and location. The findings need to be validated before extending to potential new targets or protocols. Second, our distinct symptom clusters were derived from a single clinician-administered instrument (HDRS); while these were validated in previous analyses, understanding the heterogeneity of TRD would benefit from administering multiple unidimensional assessments. Third, the present work did not incorporate phenotypic information from comorbidities (e.g., features of post-traumatic stress, obsessive-compulsive, personality, or eating disorders 28 – 31 ); such transdiagnostic features are hypothesized to be potentially useful in predicting and tracking rTMS response trajectories 32 , 33 . Finally, this work did not include biomarkers in the current analysis, such as neuroimaging 34 . Identification of distinct imaging biomarkers of specific symptom responses would provide important biological evidence regarding the heterogeneity of depression and also inform future studies designed to personalize rTMS interventions based on markers of symptom profiles 35 . The current work has identified four distinct symptom cluster response trajectories that differ in important baseline characteristics as well as initial responses to treatment. This work has immediate implications for clinicians who may use these results to guide treatment planning and discussion with patients while also providing a novel avenue to explore for developing and generating biomarkers based on response trajectories. The identification of these trajectories may, therefore, facilitate the development of protocols and treatment locations based on clinical and imaging phenotypes that are an important advance toward personalized rTMS treatment and better outcomes for those suffering from depression. Declarations Contribution Concept and design: Kaster, Chen, Blumberger. Acquisition, analysis, or interpretation of data: Kaster, Chen, Downar, Vila-Rodriguez, Baribeau, Thorpe, Daskalakis, Yan, Blumberger. Drafting of the manuscript: Kaster, Chen. Critical revision of the manuscript for important intellectual content: Kaster, Chen, Downar, Vila-Rodriguez, Baribeau, Thorpe, Daskalakis, Yan, Blumberger. Statistical analysis: Kaster, Chen, Baribeau, Thorpe. Administrative, technical, or material support: Downar, Vila-Rodriguez, Blumberger. Supervision: Yan, Blumberger. Data Sharing Deidentified participant data, along with data dictionaries, is available and can be shared with researchers who provide a methodologically sound proposal that includes a protocol and a statistical analysis plan and is not in conflict with the investigators’ publication plan. Proposals should be directed to [email protected] . To gain access, data requestors will need to sign a data access agreement. Declaration of Interest No funding was provided for the analysis or manuscript creation. Tyler S. Kaster is supported by the Canadian Institute for Health Research, the AFP Innovation Fund, and the Patient-Centered Outcomes Research Institute. Xiao Chen has received research support from the National Natural Science Foundation of China and the China Scholarship Council. Daniel M. Blumberger receives research support from CIHR, NIMH, Brain Canada and the Buchan Family Foundation, and Temerty Family through the CAMH Foundation and the Campbell Family Research Institute. He received research support and in-kind equipment support for an investigator-initiated study from Brainsway Ltd. He was the site principal investigator for three sponsor-initiated studies for Brainsway Ltd. He also received in-kind equipment support from Magventure for two investigator-initiated studies. He received medication supplies for an investigator-initiated trial from Indivior. He is a scientific advisor for Sooma Medical. He is the Co-Chair of the Clinical Standards Committee of the Clinical TMS Society (unpaid). Jonathan Downar has received research support from NIH, CIHR, Brain Canada, Ontario Brain Institute, the Klarman Family Foundation, the Arrell Family Foundation, and the Buchan Family Foundation, in-kind equipment support for investigator-initiated trials from MagVenture, is an advisor for BrainCheck, Arc Health Partners and Salience Neuro Health, and is a co-founder of Ampa Health. Fidel Vila-Rodriguez has received research support from CIHR, Brain Canada, Michael Smith Foundation for Health Research, Vancouver Coastal Health Research Institute, and Weston Brain Institute for investigator-initiated research. In-kind equipment support for investigator-initiated trial from MagVenture. He has received honoraria for participation in an advisory board for Allergan. Fidel Vila-Rodriguez is a volunteer director on the board of directors of the British Columbia Schizophrenia Society. Danielle A. Baribeau has received research support from CIHR, Patient-Centered Outcomes Research Institute, the McLaughlin Foundation and the Kimmel Foundation. She is the site principal investigator for a clinical trial by MapLight therapeutics. Kevin E. Thorpe has no disclosures. Zafiris J. Daskalakis has received grants from Brainsway Inc and nonfinancial support from Magventure Inc as well as served on the scientific advisory board for Brainsway Inc. Chao-Gan Yan has received research support from the National Natural Science Foundation of China (grant numbers: 82122035, 81671774, 81630031), Beijing Nova Program of Science and Technology (grant number: Z191100001119104 and 20230484465), and Beijing Natural Science Foundation (J230040). Acknowledgment Xiao Chen is funded by the National Natural Science Foundation of China (No. 32300933) and the China Scholarship Council (CSC, No. 202104910248). Danielle A. Baribeau acknowledges the Glenda MacQueen Memorial Award, the Department of Psychiatry Academic Scholars Award, and the Arthur Family Foundation. References George MS, Taylor JJ, Short EB. The expanding evidence base for rTMS treatment of depression. Current opinion in psychiatry 2013; 26(1): 13–8. Perera T, George MS, Grammer G, Janicak PG, Pascual-Leone A, Wirecki TS. The Clinical TMS Society Consensus Review and Treatment Recommendations for TMS Therapy for Major Depressive Disorder. Brain Stimul 2016; 9(3): 336–46. Dalhuisen I, van Oostrom I, Spijker J, et al. rTMS as a Next Step in Antidepressant Nonresponders: A Randomized Comparison With Current Antidepressant Treatment Approaches. Am J Psychiatry 2024; 181(9): 806–14. Goodman MS, Trevizol AP, Konstantinou GN, et al. Extended course accelerated intermittent theta burst stimulation as a substitute for depressed patients needing electroconvulsive therapy. Neuropsychopharmacology 2024. Wrightson JG, Cole J, Sohn MN, McGirr A. The effects of D-Cycloserine on corticospinal excitability after repeated spaced intermittent theta-burst transcranial magnetic stimulation: A randomized controlled trial in healthy individuals. Neuropsychopharmacology 2023; 48(8): 1217–24. Fox MD, Buckner RL, White MP, Greicius MD, Pascual-Leone A. Efficacy of transcranial magnetic stimulation targets for depression is related to intrinsic functional connectivity with the subgenual cingulate. Biol Psychiatry 2012; 72(7): 595–603. Siddiqi SH, Taylor SF, Cooke D, Pascual-Leone A, George MS, Fox MD. Distinct Symptom-Specific Treatment Targets for Circuit-Based Neuromodulation. Am J Psychiatry 2020; 177(5): 435–46. Fried EI, Nesse RM. Depression sum-scores don't add up: why analyzing specific depression symptoms is essential. BMC Med 2015; 13: 72. Hamilton M. A rating scale for depression. Journal of neurology, neurosurgery, and psychiatry 1960; 23(1): 56–62. Fried EI, van Borkulo CD, Epskamp S, Schoevers RA, Tuerlinckx F, Borsboom D. Measuring depression over time.. . Or not? Lack of unidimensionality and longitudinal measurement invariance in four common rating scales of depression. Psychol Assess 2016; 28(11): 1354–67. Bagby RM, Ryder AG, Schuller DR, Marshall MB. The Hamilton Depression Rating Scale: has the gold standard become a lead weight? Am J Psychiatry 2004; 161(12): 2163–77. Kaster TS, Downar J, Vila-Rodriguez F, et al. Differential symptom cluster responses to repetitive transcranial magnetic stimulation treatment in depression. EClinicalMedicine 2023; 55: 101765. Shafer AB. Meta-analysis of the factor structures of four depression questionnaires: Beck, CES-D, Hamilton, and Zung. J Clin Psychol 2006; 62(1): 123–46. Nagin DS, Odgers CL. Group-based trajectory modeling in clinical research. Annual review of clinical psychology 2010; 6: 109–38. Kaster TS, Downar J, Vila-Rodriguez F, et al. Trajectories of Response to Dorsolateral Prefrontal rTMS in Major Depression: A THREE-D Study. Am J Psychiatry 2019; 176(5): 367–75. Chen X, Blumberger DM, Downar J, et al. Depressive symptom trajectories with prolonged rTMS treatment. Brain Stimul 2024; 17(3): 525–32. Kaster TS, Chen L, Daskalakis ZJ, Hoy KE, Blumberger DM, Fitzgerald PB. Depressive symptom trajectories associated with standard and accelerated rTMS. Brain Stimul 2020; 13(3): 850–7. Nagin DS, Jones BL, Passos VL, Tremblay RE. Group-based multi-trajectory modeling. Statistical methods in medical research 2018; 27(7): 2015–23. Blumberger DM, Vila-Rodriguez F, Thorpe KE, et al. Effectiveness of theta burst versus high-frequency repetitive transcranial magnetic stimulation in patients with depression (THREE-D): a randomised non-inferiority trial. Lancet (London, England) 2018; 391(10131): 1683–92. O'Reardon JP, Solvason HB, Janicak PG, et al. Efficacy and safety of transcranial magnetic stimulation in the acute treatment of major depression: a multisite randomized controlled trial. Biol Psychiatry 2007; 62(11): 1208–16. Little TD. Longitudinal structural equation modeling: Guilford Publications; 2024. Jones BL, Nagin DS. A Note on a Stata Plugin for Estimating Group-based Trajectory Models. Sociological Methods & Research 2013; 42(4): 608–13. Kass RE, Raftery AE. Bayes factors. Journal of the american statistical association 1995; 90(430): 773–95. R Core Team. R: A language and environment for statistical computing. Vienna, Austria: Vienna: R Foundation for Statistical Computing; 2013. Sackeim HA. The definition and meaning of treatment-resistant depression. J Clin Psychiatry 2001; 62 Suppl 16: 10–7. Van De Schoot R, Sijbrandij M, Winter SD, Depaoli S, Vermunt JK. The GRoLTS-checklist: guidelines for reporting on latent trajectory studies. Structural Equation Modeling: A Multidisciplinary Journal 2017; 24(3): 451–67. Hunter AM, Leuchter AF. Benzodiazepine Use and rTMS Outcome. Am J Psychiatry 2020; 177(2): 172. Dunlop K, Woodside B, Lam E, et al. Increases in frontostriatal connectivity are associated with response to dorsomedial repetitive transcranial magnetic stimulation in refractory binge/purge behaviors. Neuroimage Clin 2015; 8: 611–8. Dunlop K, Woodside B, Olmsted M, Colton P, Giacobbe P, Downar J. Reductions in Cortico-Striatal Hyperconnectivity Accompany Successful Treatment of Obsessive-Compulsive Disorder with Dorsomedial Prefrontal rTMS. Neuropsychopharmacology 2016; 41(5): 1395–403. Feffer K, Lee HH, Wu W, et al. Dorsomedial prefrontal rTMS for depression in borderline personality disorder: A pilot randomized crossover trial. J Affect Disord 2022; 301: 273–80. Woodside DB, Colton P, Lam E, Dunlop K, Rzeszutek J, Downar J. Dorsomedial prefrontal cortex repetitive transcranial magnetic stimulation treatment of posttraumatic stress disorder in eating disorders: An open-label case series. Int J Eat Disord 2017; 50(10): 1231–4. Peters SK, Dunlop K, Downar J. Cortico-Striatal-Thalamic Loop Circuits of the Salience Network: A Central Pathway in Psychiatric Disease and Treatment. Frontiers in systems neuroscience 2016; 10: 104. Taylor JJ, Lin C, Talmasov D, et al. A transdiagnostic network for psychiatric illness derived from atrophy and lesions. Nat Hum Behav 2023; 7(3): 420–9. Yan CG, Chen X, Li L, et al. Reduced default mode network functional connectivity in patients with recurrent major depressive disorder. Proc Natl Acad Sci U S A 2019. Siddiqi SH, Fox MD. Targeting Symptom-Specific Networks With Transcranial Magnetic Stimulation. Biol Psychiatry 2024; 95(6): 502–9. Additional Declarations Yes No funding was provided for the analysis or manuscript creation. Tyler S. Kaster is supported by the Canadian Institute for Health Research, the AFP Innovation Fund, and the Patient-Centered Outcomes Research Institute. Xiao Chen has received research support from the National Natural Science Foundation of China and the China Scholarship Council. Daniel M. Blumberger receives research support from CIHR, NIMH, Brain Canada and the Buchan Family Foundation, and Temerty Family through the CAMH Foundation and the Campbell Family Research Institute. He received research support and in-kind equipment support for an investigator-initiated study from Brainsway Ltd. He was the site principal investigator for three sponsor-initiated studies for Brainsway Ltd. He also received in-kind equipment support from Magventure for two investigator-initiated studies. He received medication supplies for an investigator-initiated trial from Indivior. He is a scientific advisor for Sooma Medical. He is the Co-Chair of the Clinical Standards Committee of the Clinical TMS Society (unpaid). Jonathan Downar has received research support from NIH, CIHR, Brain Canada, Ontario Brain Institute, the Klarman Family Foundation, the Arrell Family Foundation, and the Buchan Family Foundation, in-kind equipment support for investigator-initiated trials from MagVenture, is an advisor for BrainCheck, Arc Health Partners and Salience Neuro Health, and is a co-founder of Ampa Health. Fidel Vila-Rodriguez has received research support from CIHR, Brain Canada, Michael Smith Foundation for Health Research, Vancouver Coastal Health Research Institute, and Weston Brain Institute for investigator-initiated research. In-kind equipment support for investigator-initiated trial from MagVenture. He has received honoraria for participation in an advisory board for Allergan. Fidel Vila-Rodriguez is a volunteer director on the board of directors of the British Columbia Schizophrenia Society. Danielle A. Baribeau has received research support from CIHR, Patient-Centered Outcomes Research Institute, the McLaughlin Foundation and the Kimmel Foundation. She is the site principal investigator for a clinical trial by MapLight therapeutics. Kevin E. Thorpe has no disclosures. Zafiris J. Daskalakis has received grants from Brainsway Inc and nonfinancial support from Magventure Inc as well as served on the scientific advisory board for Brainsway Inc. Chao-Gan Yan has received research support from the National Natural Science Foundation of China (grant numbers: 82122035, 81671774, 81630031), Beijing Nova Program of Science and Technology (grant number: Z191100001119104 and 20230484465), and Beijing Natural Science Foundation (J230040). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6428690","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":490489647,"identity":"579c7bc2-60a3-42d9-a09b-996eae442b34","order_by":0,"name":"Tyler Kaster","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYFAC5gYgkQBhfwBiA0IaeBgYoVrYGBgYZ5CshZmHGC32EomNjysY0uTl5zcf+2xTYxdtzsD88ANeWyQSmw3PMOQYbjjGljw751hy7s4GNmMJAlraJBsYKhg3sPEYM+c2MOduOMDDQEhL+0+gFvv5bfyfmS0b6kFamH8QsgUYADmJDcd4mJkZGw6DtLDht+XMw2bJBoO05A3H0owZe44dz91wmM3MAp8W9vbkgx8bKpJt5zcffszwo6Y6d8Px5sc38GlhEEhgQIsLZrzqgYD/ACEVo2AUjIJRMOIBAM/6Rl65rl3FAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-5299-4794","institution":"Centre for Addiction and Mental Health","correspondingAuthor":true,"prefix":"","firstName":"Tyler","middleName":"","lastName":"Kaster","suffix":""},{"id":490489648,"identity":"67522533-e13b-45db-8c0a-4a19ad114464","order_by":1,"name":"Xiao Chen","email":"","orcid":"https://orcid.org/0000-0001-5561-6572","institution":"CAS Key Laboratory of Behavioral Science, Institute of 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Yan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Chao-Gan","middleName":"","lastName":"Yan","suffix":""},{"id":490489655,"identity":"65046ee0-92e9-46b0-9f2c-3e331d3725bc","order_by":8,"name":"Daniel Blumberger","email":"","orcid":"https://orcid.org/0000-0002-8422-5818","institution":"Centre for Addiction and Mental Health","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Blumberger","suffix":""}],"badges":[],"createdAt":"2025-04-11 13:20:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6428690/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6428690/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87828317,"identity":"1cce5364-e36c-4759-8152-1b18e5b08610","added_by":"auto","created_at":"2025-07-29 12:03:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":118711,"visible":true,"origin":"","legend":"\u003cp\u003eSymptom cluster response trajectories over four weeks of rTMS treatment. \u003cbr\u003e\nAbbreviations: repetitive transcranial magnetic stimulation, rTMS.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6428690/v1/264edd902842e6ca9daa51f8.png"},{"id":108180901,"identity":"55c19b49-fc5b-4fec-99d0-bff5bb413d5b","added_by":"auto","created_at":"2026-04-30 08:54:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":588531,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6428690/v1/388de207-8a87-4e32-98f0-7e3edeed7f6d.pdf"},{"id":87829482,"identity":"55733100-1f05-4d46-b456-da9a4c4cea06","added_by":"auto","created_at":"2025-07-29 12:11:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1640361,"visible":true,"origin":"","legend":"Appendices","description":"","filename":"KasterappendiceMPv1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6428690/v1/9f72e43d83a0901cea73cb2a.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e\nNo funding was provided for the analysis or manuscript creation. Tyler S. Kaster is supported by the Canadian Institute for Health Research, the AFP Innovation Fund, and the Patient-Centered Outcomes Research Institute. Xiao Chen has received research support from the National Natural Science Foundation of China and the China Scholarship Council. Daniel M. Blumberger receives research support from CIHR, NIMH, Brain Canada and the Buchan Family Foundation, and Temerty Family through the CAMH Foundation and the Campbell Family Research Institute. He received research support and in-kind equipment support for an investigator-initiated study from Brainsway Ltd. He was the site principal investigator for three sponsor-initiated studies for Brainsway Ltd. He also received in-kind equipment support from Magventure for two investigator-initiated studies. He received medication supplies for an investigator-initiated trial from Indivior. He is a scientific advisor for Sooma Medical. He is the Co-Chair of the Clinical Standards Committee of the Clinical TMS Society (unpaid). Jonathan Downar has received research support from NIH, CIHR, Brain Canada, Ontario Brain Institute, the Klarman Family Foundation, the Arrell Family Foundation, and the Buchan Family Foundation, in-kind equipment support for investigator-initiated trials from MagVenture, is an advisor for BrainCheck, Arc Health Partners and Salience Neuro Health, and is a co-founder of Ampa Health. Fidel Vila-Rodriguez has received research support from CIHR, Brain Canada, Michael Smith Foundation for Health Research, Vancouver Coastal Health Research Institute, and Weston Brain Institute for investigator-initiated research. In-kind equipment support for investigator-initiated trial from MagVenture. He has received honoraria for participation in an advisory board for Allergan. Fidel Vila-Rodriguez is a volunteer director on the board of directors of the British Columbia Schizophrenia Society. Danielle A. Baribeau has received research support from CIHR, Patient-Centered Outcomes Research Institute, the McLaughlin Foundation and the Kimmel Foundation. She is the site principal investigator for a clinical trial by MapLight therapeutics. Kevin E. Thorpe has no disclosures. Zafiris J. Daskalakis has received grants from Brainsway Inc and nonfinancial support from Magventure Inc as well as served on the scientific advisory board for Brainsway Inc. Chao-Gan Yan has received research support from the National Natural Science Foundation of China (grant numbers: 82122035, 81671774, 81630031), Beijing Nova Program of Science and Technology (grant number: Z191100001119104 and 20230484465), and Beijing Natural Science Foundation (J230040).","formattedTitle":"Symptom cluster response trajectories with dorsolateral prefrontal rTMS for depression: A THREE-D Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRepetitive transcranial magnetic stimulation (rTMS) is a well-established treatment for individuals suffering from treatment-resistant depression (TRD) \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. rTMS treatment uses magnetic field pulses to focally stimulate regions of the brain implicated in the pathophysiology of depression \u0026ndash; most commonly the left dorsolateral prefrontal cortex (DLPFC) \u0026ndash; and is typically delivered once daily for 4\u0026ndash;6 weeks \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. While rTMS achieves remission in 20\u0026ndash;40% of those with TRD \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and is superior to medication treatment \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, there continue to be large numbers of individuals who do not sufficiently benefit from treatment.\u003c/p\u003e \u003cp\u003eVarious approaches have been taken to optimize treatment response to rTMS, including the use of accelerated protocols \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, pharmacotherapy augmentation \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, and imaging-guided target heuristics \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Of these various approaches, one of the most promising leverages the spatial resolution of rTMS to actively target specific neuronal circuits thought to be dysfunctional within depression \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In this manner, the optimal treatment location is identified through the individual\u0026rsquo;s presenting symptoms (e.g., anxiosomatic vs. dysphoric)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. While this approach has yet to be supported by prospective clinical trials, it demonstrates enormous promise because it does not require clinic operational advancements, new medications, or new technologies. Instead, it simply requires identifying the dominant symptom profile and adjusting to the appropriate treatment location.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eA major barrier to this personalized treatment approach, however, is the heterogeneous nature of depression, which encompasses over 200 symptom combinations, some of which share no common symptoms \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The standard practice of using the sum score of a depression rating scale, such as the Hamilton Depression Rating Scale (HDRS) \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, exacerbates this problem by implicitly viewing depression as a unidimensional construct, which does not allow for evaluation of the diverse manifestations of symptoms \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Rather than conceptualizing depression as a unidimensional construct, viewing depression as a disorder with multiple distinct clusters or dimensions (i.e., multidimensionality\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e) provides a framework for incorporating the heterogeneity present in different expressions of the illness.\u003c/p\u003e \u003cp\u003eOur group has previously conducted work using data from clinical trials in rTMS for TRD and identified four distinct symptom clusters measured in the HDRS: anxiety, mood, insomnia, and somatic clusters \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. These symptom clusters were identified using hypothesis-driven analytic techniques to evaluate a previously published symptom cluster model \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, which supports its validity. We found that rTMS delivered had differing impacts on each of these symptom clusters with most patients experiencing greater reductions of mood, insomnia, and somatic symptoms compared to anxiety symptoms \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. While this was the first study to identify differential symptom cluster response to rTMS treatment, it only examined group-level effect of rTMS on each symptom cluster. Identifying subgroups with distinct co-occurring longitudinal trajectories for each symptom cluster would, therefore, represent an important advance toward understanding the heterogeneity of treatment response that occurs with rTMS treatment in TRD.\u003c/p\u003e \u003cp\u003eOur group has also conducted prior work examining longitudinal response trajectories to rTMS using group-based trajectory modeling (GBTM) \u003csup\u003e\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. While this work identified distinct longitudinal symptom response trajectories, it used the HDRS sum score to identify these trajectories, which does capture the heterogeneity of how different symptom clusters may respond to rTMS treatment. Recent methodologic advances in GBTM have led to the development of group-based \u003cb\u003emulti\u003c/b\u003e-trajectory modeling (GBMTM). This approach can identify latent clusters of individuals following similar trajectories across multiple domains for a single condition of interest \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, such as distinct symptom clusters in depression.\u003c/p\u003e \u003cp\u003eThe objective of this work, therefore, was to identify distinct symptom cluster response trajectories occurring amongst individuals with TRD receiving rTMS delivered to the left DLPFC. We sought to identify response trajectories across the four symptom clusters of depression and identify baseline clinical characteristics associated with membership in each response trajectory. Identifying these trajectories will provide knowledge on how depression symptom clusters respond to rTMS treatment and may eventually allow for personalized rTMS treatment protocols based on symptom patterns.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy procedures\u003c/h2\u003e \u003cp\u003eThis was a secondary analysis using data from THREE-D, a multi-center randomized trial of rTMS. THREE-D was conducted in three Canadian academic health centres (Centre for Addiction and Mental Health, Toronto, ON; Toronto Western Hospital, Toronto, ON; University of British Columbia Hospital, Vancouver, BC) and used a non-inferiority design to compare two rTMS protocols applied to the left DLPFC: standard (10 Hz) high frequency left (HFL) or intermittent theta-burst (iTBS) \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Local research ethics board approval was obtained for all three study sites, and all participants provided written and informed consent. This trial was registered with ClinicalTrials.gov, number NCT01887782.\u003c/p\u003e \u003cp\u003eTreatment was delivered once daily, 5 days per week for 4 weeks (i.e., 20 treatments). Participants who achieved remission (HDRS-17 total score\u0026thinsp;\u0026lt;\u0026thinsp;8) or had an insufficient response (\u0026lt;\u0026thinsp;30% reduction in HDRS-17 total from baseline) were considered to have completed the study. Participants who had\u0026thinsp;\u0026ge;\u0026thinsp;30% reduction in symptoms but did not achieve remission were provided an additional ten treatments over 2 weeks to optimize treatment response and durability for 30 treatments over 6 weeks. Individuals continued their psychotropic medications unchanged for the study duration. The HDRS-17 was administered weekly by trained research staff blinded to treatment allocation. The exclusion criteria included substance abuse, acute suicidality, bipolar disorder, and psychotic disorders. For details regarding the study procedures, refer to the supplementary information and the original publication \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003erTMS procedure\u003c/h3\u003e\n\u003cp\u003eBefore treatment, all participants underwent an anatomical MRI. rTMS treatments were guided using MRI-guided neuronavigation to optimize coil positioning. The left DLPFC was targeted using the MNI-152 stereotaxic coordinate (x-38, y\u0026thinsp;+\u0026thinsp;44, z\u0026thinsp;+\u0026thinsp;26) \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. A MagPro X100/R30 stimulator equipped with a B70 fluid-cooled coil (MagVenture, Farum, Denmark) was used for stimulation. The resting motor threshold (RMT) was determined using visual observation. HFL was delivered with the FDA-approved treatment settings (120% RMT, 10Hz, 4 seconds on, 26 seconds off, 3000 pulses/session over 37.5 min) \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. iTBS was delivered to the same site with the same intensity but used a different stimulation pattern (triplet 50Hz bursts, repeated at 5Hz, 2 seconds on, 8 seconds off, 600 pulses/session over 3 min) \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eChoice of primary outcome measure\u003c/h3\u003e\n\u003cp\u003eThe primary outcome measures in this study were symptom cluster scores from one of four domains previously identified (anxiety, mood, somatic, and insomnia) \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. These symptom clusters were derived from the 17 item HDRS, the most commonly used clinician-rated psychometric measure of depression severity.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Of the 17 items, nine are scored between 0 (not present) and four points (severe), while the remaining eight are scored between 0 (not present) and two points (severe) for a total score ranging from 0 to 52. The sum score of each symptom cluster was calculated and rescaled using the proportion of the maximum possible scaling method\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e to allow for comparison among different symptom clusters. For details, refer to the supplementary materials.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAnalytic Overview\u003c/h2\u003e \u003cp\u003eTo classify participants into subgroups based on their longitudinal response trajectories, we used an application of finite-mixture modeling known as group-based trajectory modeling (GBTM) implemented via the \u003cem\u003etraj\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e command in Stata (Stata 16.1 (StataCorp, Texas, USA)). We determined the optimal number of response trajectories (i.e., latent longitudinal groups) and the optimal polynomial degree within each response trajectory using the Bayesian information criterion (BIC). The BIC measures improvement in model fit gained by adding additional groups or shape parameters but also penalizes added complexity. The BIC log Bayes factor approximation, defined as 2\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\varDelta\\:\\)\u003c/span\u003e\u003c/span\u003eBIC (with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\)\u003c/span\u003e\u003c/span\u003eBIC as the difference between a more complex and less complex model), is an acceptable approximation to the log Bayes factor criterion \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. When 2\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\varDelta\\:\\)\u003c/span\u003e\u003c/span\u003eBIC was \u0026gt;\u0026thinsp;10; this was used as evidence favouring the more complex model \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. For all single trajectory models, we assessed model fit by calculating the average posterior probability of group membership (70% minimum for each group), determining the percentage of the total sample within each trajectory (5% minimum for each group), and calculating the odds of correct classification (\u0026gt;\u0026thinsp;5 considered adequate). While the THREE-D study consisted of treatment for up to 6 weeks, participant data for weeks 5 and 6 were missing not at random, which may result in biased estimates as the GBTM and GBMTM procedures assume data are missing at random \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Because of this pattern of missingness, only participant data up to week 4 was used for determining symptom cluster response trajectories.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMulti-trajectory Analyses\u003c/h2\u003e \u003cp\u003eThe first step of identifying the symptom cluster response trajectories was to conduct a separate trajectory analysis for each symptom cluster using GBTM using previously described approaches (see supplementary material).\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e The number of trajectory groups within the single trajectory analyses informed subsequent multi-trajectory analyses using GBMTM\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. For the GBMTM analysis, we followed a conceptually similar model development process in which we first identified the optimal number of latent response trajectories and then sought to identify - within the optimal number of response trajectory groups - the ideal polynomial degree for each symptom cluster. However, given the proliferation of trajectories with multiple symptom clusters and limited sample size, we were required to make assumptions in the multi-trajectory analytic procedure and used the single trajectory analytic results to inform the multi-trajectory model development. Specifically, we made the following assumptions in developing the multi-trajectory model: (1) the number of groups in the multi-trajectory analysis would be between the smallest and largest number of trajectories identified in the single trajectory analysis, and (2) the polynomial degrees of the individual clusters within the multi-trajectory model would be similar to the single trajectory model. The outcome variable, the rescaled sum score of each separate symptom cluster, was assumed to follow a censored normal distribution, an assumption we verified through visual inspection of the sum score distribution (Supplemental Fig.\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOutcome Analyses and Membership Predictors\u003c/h3\u003e\n\u003cp\u003eOnce the symptom cluster response trajectories had been identified, we conducted a categorical comparison of the remission rates (HDRS score\u0026thinsp;\u0026lt;\u0026thinsp;8) and response rates (HDRS change from baseline\u0026thinsp;\u0026ge;\u0026thinsp;50%) among all trajectories throughout four weeks of rTMS treatments. We then conducted a secondary analysis to determine which characteristics were potentially associated with membership in each response trajectory using a multinomial regression analysis weighted by the probability of group membership (to account for the uncertainty of group membership). This was conducted using the \u003cem\u003emultinom\u003c/em\u003e function of the \u003cem\u003ennet\u003c/em\u003e package (Version 7.3.19) from R (Version 4.4.1) \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. We used several \u003cem\u003ea priori\u003c/em\u003e characteristics informed by prior work \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, including age, sex, baseline symptom cluster severity (mood, anxiety, insomnia, and somatic clusters), benzodiazepine use, and Antidepressant Treatment History Form (ATHF) severity \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Following our prior studies \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, the trajectory with the largest membership was picked as the reference group. Statistical tests were two-tailed, with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\alpha\\:\\)\u003c/span\u003e\u003c/span\u003e set to 0.05. All analyses were reported according to the Guidelines for Reporting on Latent Trajectory Studies (GRoLTS) \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The analytic plan has been registered with the Open Science Foundation: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/3mn8u\u003c/span\u003e\u003cspan address=\"https://osf.io/3mn8u\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eRole of funding\u003c/h3\u003e\n\u003cp\u003eThe study funders for THREE-D (Canadian Institutes for Health Research) as well as the device manufacturer (MagVenture), which provided in-kind equipment support for THREE-D (two coils and two high-performance coolers at each site), had no role in study design, data collection, data analysis, data interpretation or writing of the report. The corresponding author (TSK) and senior author (DMB) had full access to the data and the corresponding author had final responsibility for the decision to submit for publication.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 414 participants were randomized in the study, of which 26 were excluded due to violating study inclusion criteria (2 prior to receiving treatment and 24 after receiving the allocated intervention). As a result, there were 388 participants included in the analytic cohort.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eResponse trajectories\u003c/h2\u003e \u003cp\u003eIn identifying the response trajectories of each symptom cluster separately using GBTM methods, we found that the optimal number of trajectories for each symptom cluster ranged between 3 and 5 (Supplemental Fig.\u0026nbsp;1) (BIC values listed in Supplemental Table\u0026nbsp;1). The polynomial coefficients generally consisted of a combination of cubic, quadratic, and linear for most symptom clusters, except for the insomnia cluster, of which all groups were linear. There was no indication of delayed responses amongst the symptom clusters, and with a single exception (Group 4 in somatic symptom cluster), groups with lower baseline symptoms consistently achieved lower final symptoms after 4 weeks of treatment. All groups demonstrated adequate model fit parameters, except for one group in the somatic symptom cluster, demonstrating group membership slightly below the 5% threshold (Supplemental Tables\u0026nbsp;2\u0026ndash;5).\u003c/p\u003e \u003cp\u003eIn the multi-trajectory analyses, we anticipated the optimal number of groups would be between 3 and 5. We assumed that for the insomnia indicator, all polynomials would be linear. We also assumed that there would be one or two groups within each indicator demonstrating a non-linear response pattern (i.e., quadratic or cubic polynomial), with the remainder being linear. Given the number of trajectories being concurrently estimated, this process involved subjective decisions and the modification of polynomial degrees in the event of model non-convergence.\u003c/p\u003e \u003cp\u003eThe optimal fitting model consisted of four response trajectories with the co-occurring symptom cluster trajectories presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and BIC values listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The polynomial coefficients of a combination of linear and quadratic polynomial terms. The \u0026ldquo;optimal response\u0026rdquo; group (N\u0026thinsp;=\u0026thinsp;119; 30.7%) was characterized by a low baseline mood and somatic symptoms with a rapid reduction in all symptoms. The \u0026ldquo;Partial response, high anxiety\u0026rdquo; group (N\u0026thinsp;=\u0026thinsp;128; 33.0%) was characterized by high levels of baseline anxiety and intermediate levels of mood, insomnia, and somatic symptoms, along with a linear, incremental reduction in symptoms. The \u0026ldquo;Partial response, low anxiety\u0026rdquo; group (N\u0026thinsp;=\u0026thinsp;91; 23.5%) was characterized by low levels of insomnia and anxiety with a linear, incremental reduction in symptoms. The \u0026ldquo;minimum response\u0026rdquo; group (N\u0026thinsp;=\u0026thinsp;50; 12.9%), which was the smallest group, was characterized by high levels of insomnia, mood, and somatic symptoms with minimal change in symptoms over the course of treatment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBIC scores for each symptom cluster as the number of groups increases\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Groups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 \u0026times; \u0026#120491;BIC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-15,960.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-15,505.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e909.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-15,333.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e344.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-15,206.52\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e253.78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-15,280.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-147.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eThe value in the boldface denotes the selected solution.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of participants receiving repetitive transcranial magnetic stimulation for depression by multiple symptom trajectory group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal sample, N\u0026thinsp;=\u0026thinsp;388\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOptimal response (N\u0026thinsp;=\u0026thinsp;119; 30.7%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePartial response, high anxiety (N\u0026thinsp;=\u0026thinsp;128; 33.0%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePartial response, low anxiety (N\u0026thinsp;=\u0026thinsp;91; 23.5%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMinimal response (N\u0026thinsp;=\u0026thinsp;50; 12.9%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF/ \u003cem\u003e\u0026#120536;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.33 (11.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.08 (10.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.10 (10.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.56 (12.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.42 (11.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e229 (59.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (55.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (62.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51 (56.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32 (64.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159 (40.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (44.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (37.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 (43.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18 (36.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.31 (3.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.21 (2.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.59 (3.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.21 (2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.06 (3.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at depressive symptom onset (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.90 (10.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.16 (11.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.58 (10.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.49 (10.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.30 (11.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent episode length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.42 (27.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.10 (32.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.91 (27.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.82 (22.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.81 (18.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHandedness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (10.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (13.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (10.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (6.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (8.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e6.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e345 (88.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103 (86.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112 (87.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84 (92.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46 (92.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (2.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline HDRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.54 (4.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.21 (3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.21 (4.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.36 (3.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.92 (4.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntidepressant treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e295 (76.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (78.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98 (76.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67 (73.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37 (74.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntidepressant combination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84 (21.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (20.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (25.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (21.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8 (16.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntidepressant augmentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (18.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (15.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (14.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (26.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (18.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenzodiazepine use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123 (31.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (26.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (35.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (25.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24 (48.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of ECT treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (4.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (3.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (6.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (14.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny anxiety comorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e207 (53.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (43.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (65.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45 (49.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26 (52.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of adequate antidepressant trials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (7.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (5.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (10.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (6.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (8.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e12.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOne\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173 (44.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (49.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (43.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36 (39.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22 (44.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 (19.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (12.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (17.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (27.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 (24.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTwo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111 (28.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (32.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (28.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (26.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 (24.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003eAll values are n (%) or mean (SD).\u003c/p\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eStatistical testing was not performed due to the small expected cell size (\u0026lt;\u0026thinsp;5).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eResponse trajectory outcomes\u003c/h2\u003e \u003cp\u003eWe found strong evidence of differences among trajectories regarding response rates by week 2 and remission rates by week 4. Such differences could also be observed by the completion of the treatment (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Strong evidence of differences in both the response and remission rates was observed among four groups (\u003cem\u003ep\u003c/em\u003es\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The response/remission rates of the optimal response (N\u0026thinsp;=\u0026thinsp;119; 30.7%) were higher than the other three trajectories. The Partial response, high anxiety (N\u0026thinsp;=\u0026thinsp;128; 33.0%), had an intermediate response rate (43%) and a low remission rate (4.1%) by week 4. The remaining two trajectories were characterized by low response (\u0026lt;\u0026thinsp;30%) and remission rates (\u0026lt;\u0026thinsp;3%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical outcomes for each group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal sample, N\u0026thinsp;=\u0026thinsp;388\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOptimal response (N\u0026thinsp;=\u0026thinsp;119; 30.7%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePartial response, high anxiety (N\u0026thinsp;=\u0026thinsp;128; 33.0%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePartial response, low anxiety (N\u0026thinsp;=\u0026thinsp;91; 23.5%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMinimal response (N\u0026thinsp;=\u0026thinsp;50; 12.9%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026#120536;\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeek 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeek 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e105.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeek 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e105.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeek 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e179 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e118.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e181 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96 (83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e97.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeek 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeek 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeek 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeek 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eAll values are n (%) or mean (SD)\u003c/p\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eStatistical testing was not performed due to the small expected cell size (\u0026lt;\u0026thinsp;5).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable trajectory membership predictors\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the results of the multinomial logistic regression model for group membership using the Partial response, high anxiety group as a reference. We found that age, benzodiazepine use, and baseline scores of all symptom clusters (anxiety, depression, insomnia, and somatic) are associated with the multi-trajectory group membership. Lower baseline anxiety (odds ratio [OR]\u0026thinsp;=\u0026thinsp;0.62, 95%CI\u0026thinsp;=\u0026thinsp;0.53, 0.74), depression (OR\u0026thinsp;=\u0026thinsp;0.68, 95% CI\u0026thinsp;=\u0026thinsp;0.59, 0.79), insomnia (OR\u0026thinsp;=\u0026thinsp;0.68, 95% CI\u0026thinsp;=\u0026thinsp;0.57, 0.81), and somatic symptoms (OR\u0026thinsp;=\u0026thinsp;0.77, 95% CI\u0026thinsp;=\u0026thinsp;0.63, 0.95) were associated with the membership in the optimal response (N\u0026thinsp;=\u0026thinsp;119; 30.7%). The membership in the Partial response, low anxiety (N\u0026thinsp;=\u0026thinsp;91; 23.5%) were only associated with lower baseline anxiety (OR\u0026thinsp;=\u0026thinsp;0.45, 95% CI\u0026thinsp;=\u0026thinsp;0.36, 0.55) and lower insomnia (OR\u0026thinsp;=\u0026thinsp;0.55, 95% CI\u0026thinsp;=\u0026thinsp;0.45, 0.68). The membership in the Minimal response(N\u0026thinsp;=\u0026thinsp;50; 12.9%) was associated with younger age (OR\u0026thinsp;=\u0026thinsp;0.95, 95% CI\u0026thinsp;=\u0026thinsp;0.92, 0.99), Benzodiazepine use (OR\u0026thinsp;=\u0026thinsp;2.76, 95% CI\u0026thinsp;=\u0026thinsp;1.25, 6.09), lower baseline anxiety (OR\u0026thinsp;=\u0026thinsp;0.73, 95% CI\u0026thinsp;=\u0026thinsp;0.60, 0.89), and higher depression (OR\u0026thinsp;=\u0026thinsp;1.33, 95% CI\u0026thinsp;=\u0026thinsp;1.12, 1.58).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics associated with trajectory groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOptimal response (N\u0026thinsp;=\u0026thinsp;119; 30.7%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePartial response, high anxiety (N\u0026thinsp;=\u0026thinsp;128; 33.0%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ePartial response, low anxiety (N\u0026thinsp;=\u0026thinsp;91; 23.5%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eMinimal response (N\u0026thinsp;=\u0026thinsp;50; 12.9%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026ndash;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.95\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.92\u0026ndash;0.99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.36\u0026ndash;1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.41\u0026ndash;1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.48\u0026ndash;2.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenzodiazepine use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.36\u0026ndash;1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.36\u0026ndash;1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2.73\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.24\u0026ndash;6.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of adequate antidepressant trials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.37\u0026ndash;2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.43\u0026ndash;3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.47\u0026ndash;3.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.62\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.52\u0026ndash;0.74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.35\u0026ndash;0.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.73\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.60\u0026ndash;0.89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.68\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.59\u0026ndash;0.78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.89\u0026ndash;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.13\u0026ndash;1.61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsomnia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.56\u0026ndash;0.80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.44\u0026ndash;0.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.92\u0026ndash;1.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSomatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.64\u0026ndash;0.98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.86\u0026ndash;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.94\u0026ndash;1.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eBoldface indicates statistical significance at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this re-analysis of the largest rTMS trial conducted to date, we identified and characterized several distinct symptom cluster response trajectories amongst individuals with TRD receiving rTMS. Using validated symptom clusters of depression \u0026ndash; anxiety, mood, insomnia, and somatic domains \u0026ndash; we identified four distinct trajectories of change with each of these domains. The optimal response group comprised approximately one-third (30.7%) of the cohort and had the highest rates of response and remission, 83% and 65%, respectively. In contrast, the Minimal response group did not have a single participant achieve response or remission at the end of the treatment course but was fortunately a minority of the study cohort (12.9%). The bulk of individuals (56.5%) belonged to one of two moderate response groups, which were differentiated by either high or low levels of baseline anxiety and had response and remission rates intermediate between the optimal and Minimal response groups. The two moderate response groups (low anxiety and high anxiety) had remarkably similar responses (40% low anxiety vs 42% high anxiety) and remission rates (18% low anxiety vs 17% high anxiety) at treatment completion. In addition to the baseline symptom severity of each symptom cluster, we also found that age and benzodiazepine use was associated with membership in each of these trajectories. Younger age and benzodiazepine usage were associated with membership in the Minimal response group, even after accounting for each baseline symptom cluster severity (i.e., anxiety and insomnia).\u003c/p\u003e \u003cp\u003eThis work is an important extension of prior symptom trajectory work both as it pertains to rTMS and depression symptom profiles \u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Our prior work highlighted the heterogeneity of response to rTMS when depression is viewed as a unidimensional measure \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e; however, the current work represents an important advance toward an even more nuanced view of the heterogeneity that exists with longitudinal responses to depression. For example, this work highlights that the core mood symptoms of the HRSD may be particularly important in delineating response trajectories as individuals with higher baseline mood symptoms are more likely to belong to a Minimal response trajectory, while individuals with lower baseline mood symptoms are more likely to belong to an optimal response trajectory. In contrast, somatic symptoms may be the least helpful for delineating response trajectories, as only the optimal response group had lower somatic symptoms. Interestingly, baseline anxiety symptoms are most helpful for delineating between individuals belonging to the moderate response trajectories.\u003c/p\u003e \u003cp\u003eIn addition to baseline symptoms, the change in various symptom domains may be clinically relevant. The change in anxiety symptoms may also be particularly relevant for distinguishing between individuals in optimal response and minimum response, as those in the optimal response group demonstrated a dramatic reduction in anxiety symptoms after only 1 week of treatment. In this manner, it may be the pattern of symptom reductions amongst symptom clusters \u0026ndash; particularly anxiety and mood symptoms \u0026ndash; that may alert a clinician to an individual being a member of the optimal response group. Similarly, a lack of change in anxiety and somatic symptom clusters may alert a clinician that an individual belongs to the Minimal response trajectory, which may inform treatment expectations and consideration for an alternate treatment target.\u003c/p\u003e \u003cp\u003eConsistent with our prior work, we found several baseline clinical characteristics associated with membership in different symptom cluster response trajectories. Relative to the Partial response, high anxiety group, the optimal response group was associated with lower symptom severity in all symptom clusters. The Minimal response group had significantly higher severity of the mood symptom cluster, and was associated with younger age. Of particular note, it was also associated with benzodiazepine use with quite a large effect size (OR: 2.76) that was significant even after controlling for the anxiety symptom cluster, which supports the notion of benzodiazepines interfering with rTMS treatment effectiveness \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile our study has several strengths, there are also important limitations. First, the dataset used in the current work considered a single rTMS treatment type and location. The findings need to be validated before extending to potential new targets or protocols. Second, our distinct symptom clusters were derived from a single clinician-administered instrument (HDRS); while these were validated in previous analyses, understanding the heterogeneity of TRD would benefit from administering multiple unidimensional assessments. Third, the present work did not incorporate phenotypic information from comorbidities (e.g., features of post-traumatic stress, obsessive-compulsive, personality, or eating disorders \u003csup\u003e\u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e); such transdiagnostic features are hypothesized to be potentially useful in predicting and tracking rTMS response trajectories \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Finally, this work did not include biomarkers in the current analysis, such as neuroimaging \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Identification of distinct imaging biomarkers of specific symptom responses would provide important biological evidence regarding the heterogeneity of depression and also inform future studies designed to personalize rTMS interventions based on markers of symptom profiles \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe current work has identified four distinct symptom cluster response trajectories that differ in important baseline characteristics as well as initial responses to treatment. This work has immediate implications for clinicians who may use these results to guide treatment planning and discussion with patients while also providing a novel avenue to explore for developing and generating biomarkers based on response trajectories. The identification of these trajectories may, therefore, facilitate the development of protocols and treatment locations based on clinical and imaging phenotypes that are an important advance toward personalized rTMS treatment and better outcomes for those suffering from depression.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eContribution\u003c/h2\u003e \u003cp\u003eConcept and design: Kaster, Chen, Blumberger. Acquisition, analysis, or interpretation of data: Kaster, Chen, Downar, Vila-Rodriguez, Baribeau, Thorpe, Daskalakis, Yan, Blumberger. Drafting of the manuscript: Kaster, Chen. Critical revision of the manuscript for important intellectual content: Kaster, Chen, Downar, Vila-Rodriguez, Baribeau, Thorpe, Daskalakis, Yan, Blumberger. Statistical analysis: Kaster, Chen, Baribeau, Thorpe. Administrative, technical, or material support: Downar, Vila-Rodriguez, Blumberger. Supervision: Yan, Blumberger.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eData Sharing\u003c/h2\u003e \u003cp\u003eDeidentified participant data, along with data dictionaries, is available and can be shared with researchers who provide a methodologically sound proposal that includes a protocol and a statistical analysis plan and is not in conflict with the investigators\u0026rsquo; publication plan. Proposals should be directed to [email protected]. To gain access, data requestors will need to sign a data access agreement.\u003c/p\u003e \u003c/div\u003e\u003cp\u003e \u003ch2\u003eDeclaration of Interest\u003c/h2\u003e \u003cp\u003eNo funding was provided for the analysis or manuscript creation. Tyler S. Kaster is supported by the Canadian Institute for Health Research, the AFP Innovation Fund, and the Patient-Centered Outcomes Research Institute. Xiao Chen has received research support from the National Natural Science Foundation of China and the China Scholarship Council. Daniel M. Blumberger receives research support from CIHR, NIMH, Brain Canada and the Buchan Family Foundation, and Temerty Family through the CAMH Foundation and the Campbell Family Research Institute. He received research support and in-kind equipment support for an investigator-initiated study from Brainsway Ltd. He was the site principal investigator for three sponsor-initiated studies for Brainsway Ltd. He also received in-kind equipment support from Magventure for two investigator-initiated studies. He received medication supplies for an investigator-initiated trial from Indivior. He is a scientific advisor for Sooma Medical. He is the Co-Chair of the Clinical Standards Committee of the Clinical TMS Society (unpaid). Jonathan Downar has received research support from NIH, CIHR, Brain Canada, Ontario Brain Institute, the Klarman Family Foundation, the Arrell Family Foundation, and the Buchan Family Foundation, in-kind equipment support for investigator-initiated trials from MagVenture, is an advisor for BrainCheck, Arc Health Partners and Salience Neuro Health, and is a co-founder of Ampa Health. Fidel Vila-Rodriguez has received research support from CIHR, Brain Canada, Michael Smith Foundation for Health Research, Vancouver Coastal Health Research Institute, and Weston Brain Institute for investigator-initiated research. In-kind equipment support for investigator-initiated trial from MagVenture. He has received honoraria for participation in an advisory board for Allergan. Fidel Vila-Rodriguez is a volunteer director on the board of directors of the British Columbia Schizophrenia Society. Danielle A. Baribeau has received research support from CIHR, Patient-Centered Outcomes Research Institute, the McLaughlin Foundation and the Kimmel Foundation. She is the site principal investigator for a clinical trial by MapLight therapeutics. Kevin E. Thorpe has no disclosures. Zafiris J. Daskalakis has received grants from Brainsway Inc and nonfinancial support from Magventure Inc as well as served on the scientific advisory board for Brainsway Inc. Chao-Gan Yan has received research support from the National Natural Science Foundation of China (grant numbers: 82122035, 81671774, 81630031), Beijing Nova Program of Science and Technology (grant number: Z191100001119104 and 20230484465), and Beijing Natural Science Foundation (J230040).\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAcknowledgment\u003c/h2\u003e \u003cp\u003eXiao Chen is funded by the National Natural Science Foundation of China (No. 32300933) and the China Scholarship Council (CSC, No. 202104910248). Danielle A. Baribeau acknowledges the Glenda MacQueen Memorial Award, the Department of Psychiatry Academic Scholars Award, and the Arthur Family Foundation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGeorge MS, Taylor JJ, Short EB. The expanding evidence base for rTMS treatment of depression. 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A Note on a Stata Plugin for Estimating Group-based Trajectory Models. Sociological Methods \u0026amp; Research 2013; 42(4): 608\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKass RE, Raftery AE. Bayes factors. Journal of the american statistical association 1995; 90(430): 773\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team. R: A language and environment for statistical computing. Vienna, Austria: Vienna: R Foundation for Statistical Computing; 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSackeim HA. The definition and meaning of treatment-resistant depression. J Clin Psychiatry 2001; 62 Suppl 16: 10\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan De Schoot R, Sijbrandij M, Winter SD, Depaoli S, Vermunt JK. The GRoLTS-checklist: guidelines for reporting on latent trajectory studies. Structural Equation Modeling: A Multidisciplinary Journal 2017; 24(3): 451\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHunter AM, Leuchter AF. Benzodiazepine Use and rTMS Outcome. Am J Psychiatry 2020; 177(2): 172.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDunlop K, Woodside B, Lam E, et al. Increases in frontostriatal connectivity are associated with response to dorsomedial repetitive transcranial magnetic stimulation in refractory binge/purge behaviors. Neuroimage Clin 2015; 8: 611\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDunlop K, Woodside B, Olmsted M, Colton P, Giacobbe P, Downar J. Reductions in Cortico-Striatal Hyperconnectivity Accompany Successful Treatment of Obsessive-Compulsive Disorder with Dorsomedial Prefrontal rTMS. Neuropsychopharmacology 2016; 41(5): 1395\u0026ndash;403.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeffer K, Lee HH, Wu W, et al. Dorsomedial prefrontal rTMS for depression in borderline personality disorder: A pilot randomized crossover trial. J Affect Disord 2022; 301: 273\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoodside DB, Colton P, Lam E, Dunlop K, Rzeszutek J, Downar J. Dorsomedial prefrontal cortex repetitive transcranial magnetic stimulation treatment of posttraumatic stress disorder in eating disorders: An open-label case series. Int J Eat Disord 2017; 50(10): 1231\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeters SK, Dunlop K, Downar J. Cortico-Striatal-Thalamic Loop Circuits of the Salience Network: A Central Pathway in Psychiatric Disease and Treatment. Frontiers in systems neuroscience 2016; 10: 104.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaylor JJ, Lin C, Talmasov D, et al. A transdiagnostic network for psychiatric illness derived from atrophy and lesions. Nat Hum Behav 2023; 7(3): 420\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan CG, Chen X, Li L, et al. Reduced default mode network functional connectivity in patients with recurrent major depressive disorder. Proc Natl Acad Sci U S A 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiddiqi SH, Fox MD. Targeting Symptom-Specific Networks With Transcranial Magnetic Stimulation. Biol Psychiatry 2024; 95(6): 502\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"repetitive transcranial magnetic stimulation, cluster analysis, group-based trajectory modeling, multi-trajectory, individualized medication, depression, treatment-resistant depression","lastPublishedDoi":"10.21203/rs.3.rs-6428690/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6428690/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUsing the Hamilton Depression Rating Scale (HDRS) sum score to quantify the efficacy of repetitive transcranial magnetic stimulation (rTMS) in major depressive disorder (MDD) may overlook the heterogeneity of distinct longitudinal symptom cluster response trajectories. We used data from the THREE-D clinical trial (N = 388) comparing two forms of rTMS delivered over 4–6 weeks to the left dorsolateral prefrontal cortex (DLPFC) for treatment-resistant depression (TRD). We examined the four symptom domains measured with the HDRS (anxiety, mood, insomnia, and somatic) and applied group-based multi-trajectory modeling (GBMTM) to identify latent groups based on responses simultaneously occurring within each symptom cluster. We then used multinomial regression to identify patient characteristics associated with each group. We identified four distinct longitudinal symptom cluster response trajectory groups: Optimal response (N = 119; 30.7%); Partial response, high anxiety (N = 128; 33.0%); Partial response, low anxiety (N = 91; 23.5%); Minimal response (N = 50; 12.9%). The optimal response trajectory was characterized by a rapid decrease in all symptom clusters by week 2, while the Minimal response trajectory group showed no or even worsened symptom severity. Two moderate response trajectories showed linear response but differing baseline anxiety severity. The optimal response group was associated with lower symptom scores, and the Minimal response group was associated with younger age, benzodiazepine use, lower baseline anxiety, and higher baseline depression symptoms. This work demonstrates that distinct symptom cluster response trajectories exist amongst individuals with TRD receiving rTMS, with anxiety symptoms being particularly important for identifying those most likely to benefit from treatment.\u003c/p\u003e\n\u003cp\u003eClinicalTrials.Gov: NCT01887782\u003c/p\u003e","manuscriptTitle":"Symptom cluster response trajectories with dorsolateral prefrontal rTMS for depression: A THREE-D Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-29 12:03:27","doi":"10.21203/rs.3.rs-6428690/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"86014b7b-b571-4adf-9e36-91174969acf6","owner":[],"postedDate":"July 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52081780,"name":"Health sciences/Diseases/Psychiatric disorders/Depression"},{"id":52081781,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-04-27T09:28:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-29 12:03:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6428690","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6428690","identity":"rs-6428690","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}