Use of the Temperament and Character Inventory to predict response to repetitive transcranial magnetic stimulation for major depression

Abstract

OBJECTIVE: We investigated the utility of the Temperament and Character Inventory (TCI) in predicting antidepressant response to rTMS.

BACKGROUND: Although rTMS of the dorsolateral prefrontal cortex (DLPFC) is an established antidepressant treatment, little is known about predictors of response. The TCI measures multiple personality dimensions (harm avoidance, novelty seeking, reward dependence, persistence, self-directedness, self-transcendence, and cooperativeness), some of which have predicted response to antidepressants and cognitive-behavioral therapy. A previous study suggested a possible association between higher self-directedness and rTMS response specifically in melancholic depression, although this was limited by the fact that melancholic depression is associated with a limited range of TCI profiles.

METHODS: Sixteen patients in a major depressive episode completed a TCI prior to a clinical course of rTMS over the DLPFC. Treatment response was defined as ≥50% decrease in Hamilton Depression Rating Scale (HDRS). Baseline scores on each TCI dimension were compared between responders and non-responders via paired t-test with Bonferroni correction. Temperament/character scores were also subjected to regression analysis against percentage improvement in HDRS.

RESULTS: Ten of the sixteen patients responded to rTMS. T-scores for Persistence were significantly higher in responders (48.3, 95% CI 40.9-55.7) than in non-responders (35.3, 95% CI 29.2-39.9) (p=0.006). Linear regression revealed a correlation between persistence score and percentage improvement in HRDS (R=0.65±0.29).

CONCLUSIONS: Higher persistence predicted antidepressant response to rTMS. This may be explained by rTMS-induced enhancement of cortical excitability, which has been found to be decreased in patients with high persistence. Personality assessment that includes measurement of TCI persistence may be a useful component of precision medicine initiatives in rTMS for depression.

Background

The antidepressant efficacy of recurrent transcranial magnetic stimulation (rTMS) has been supported by a growing number of clinical trials(George , George a, Connolly ), leading to its FDA approval for major depressive disorder in 2008(George 2013). More recent studies have demonstrated that differential treatment parameters are effective for patients with varying degrees of treatment resistance(Pascual-Leone 1996, Fitzgerald , McGirr 2015). When rTMS is effective, its antidepressant results have been demonstrated to persist well beyond the initial treatment course(Mantovani ). However, its utility is somewhat limited by the fact that not all studies have found positive results, although this has been associated with methodological variability; as a result, more recent treatment protocols have found better results than older studies(Gross ).

A major limitation to the widespread use of rTMS is the fact that it is difficult to predict which patients will improve, thereby necessitating significant financial and/or time investment despite uncertainty regarding potential efficacy for any given patient. As a result, such predictive factors have been investigated thoroughly. Baseline clinical characteristics associated with improved response rates include concurrent antidepressant pharmacotherapy(Dumas ), fewer prior treatment failures, shorter duration of the current episode, and lack of a baseline anxiety disorder(Lisanby ). Impaired response is associated with the converses of these factors as well as benzodiazepine or anticonvulsant pharmacotherapy(Dumas ). Response in older patients is improved when using increased doses in order to overcome the higher coil-to-cortex distance caused by cerebral atrophy in these populations(Nahas ).

Several biomarkers have also been identified to predict some degree of treatment response. Algorithms involving various electroencephalographic parameters have been reported for this purpose, although these studies have yet to be replicated in a prospective design(Arns , Khodayari-Rostamabad ). Anterior cingulate cortex activity and prefrontal cortex activity have shown predictive capability in various neuroimaging studies, but these findings are not unique to rTMS and are also found in patients more likely to respond to other treatments(Langguth , Micoulaud-Franchi 2013, Hernández-Ribas , Kito 2012, Micoulaud-Franchi 2013, Hernández-Ribas , Richieri , Weiduschat 2013). Electromyography-based motor cortex excitability has also demonstrated some limited utility in predicting response(Fitzgerald 2004). More recent data have suggested involvement of functional network connectivity, including activity of frontostriatal networks(Salomons 2013, Avissar 2015) and the default mode network(Liston ). While most of these variables are promising tools, none of them have yet been validated to the point of routine clinical utility.

In addition to direct biological measures, response to various treatments has also been predicted by the Temperament and Character Inventory, an objective questionnaire that assesses a seven-factor psychobiologic model of quantifiable personality traits, which have been validated via various genetic and neurobiologic data. Four temperaments - harm avoidance (HA), novelty seeking (NS), reward dependence (RD), and Persistence (P) - are rooted primarily in various neurobiologic data. Three characters - self-directedness (SD), cooperativity (C), and self-transcendence (ST) - develop based on social learning(Farmer 2008). HA, which tends to be associated with baseline anxiety, has been correlated with serotonin transporter polymorphisms(Mandelli ) and has repeatedly been shown to predict response to serotonergic pharmacotherapy(Quilty , Kampman , Joyce , Hirano 2002). Self-directedness has additionally been correlated with response to cognitive-behavioral therapy in major depressive disorder(Johansson ) and various other illnesses(Corchs , Anderson ).

One previous study has also reported improved rTMS response in patients with melancholic treatment-resistant depression who demonstrated high self-directedness as measured by the TCI, but this study was limited by the restricted range of temperaments and characters inherent to melancholic patients and lack of correction for multiple comparisons. Other reports investigating a five-factor personality model previously suggested that extraversion may be associated with improved response to both rTMS(Berlim 2013) and deep TMS(McGirr 2014), although this was somewhat limited by the fact that extraversion is generally associated with improved outcomes independent of TMS treamtent(Spinhoven ). As a result, further characterization of personality profiles to predict rTMS response is warranted. This study aims to investigate the utility of the TCI to predict antidepressant response to rTMS in a general clinical sample of patients receiving rTMS for major depressive episodes.

Methods

Subjects

We used a convenience sample of treatment-seeking patients who presented with a major depressive episode as defined by DSM-IV criteria. All patients signed informed consent as approved by the Human Research Protection Office at Washington University in St. Louis. 22 patients enrolled in the study, of which 16 patients completed the treatment course and the baseline personality assessment.

Instruments

Subjects completed a baseline written version of the TCI, the TCI-R 140, which was scored by the Center for Well-Being at Washington Univerity in St. Louis. The 21-item Hamilton Rating Scale for Depression (HRSD)(HAMILTON ) was used at baseline, at every two-week interval, and at the end of the treatment course in order to measure treatment response.

rTMS treatment

All subjects received a standard clinical protocol with high frequency (10Hz) rTMS over the left dorsolateral prefrontal cortex (DLPFC) at 4000 pulses for up to 20 treatments with a Magpro R30 device (MagVenture, Tonica Elektronik – Denmark). Additional slow (1Hz) rTMS at 600 – 1200 pulses over the right DLPFC was used for augmentation as needed. Investigators providing rTMS treatments were not aware of patients’ TCI profiles until the treatment course was completed.

Statistical analyses

Treatment response was defined as ≥50% decrease in Hamilton Depression Rating Scale (HDRS) scores. Baseline scores on each TCI dimension were compared between responders and nonresponders via unpaired t-test calculated with Microsoft Excel 2010. The threshold for significant p-value was set at 0.007 based on Bonferroni correction due to presence of seven simultaneous comparisons. Temperament/character scores were also subjected to linear regression analysis against percentage improvement in HDRS. Pearson correlation coefficients were compared for each temperament/character score against baseline HDRS score and percentage improvement in HDRS.

Results

Baseline clinical characteristics of patients are summarized in Table 1. Ten of the sixteen patients responded to rTMS. Mean improvement in HRDS was 40% (95% CI 20% - 61%). Mean improvement was 9% in responders (95% CI -12% - 29%) and 77% in responders (95% CI 70% - 85%).

Pearson correlations between baseline personality characteristics and baseline HRSD are summarized in Table 2. Baseline HRSD was not significantly correlated with any individual personality trait; the smallest p-value was 0.015 for self-directedness, which did not meet the Bonferroni threshold for statistical significance (p = 0.007).

Mean T-scores for individual personality characteristics are compared between responders and nonresponders in Table 3. Persistence scores were significantly higher in responders (48, 95% CI 42 - 55) than in non-responders (35, 95% CI 30 - 40) (p = 0.0065). None of the other personality traits predicted rTMS response.

Linear regressions of individual personality T-scores are compared with percentage improvement in HRSD in Figure 2, while the associated Pearson correlation coefficients are summarized in Table 2. The only personality characteristic that was associated with a significant improvement in HRSD was Persistence (R = 0.65, p = 0.0062).

Discussion

Persistence was the only TCI trait that was correlated with rTMS response, while it was not correlated with baseline depressive symptoms. This may be explained by rTMS-induced enhancement of cortical excitability, which has been demonstrated via optical neuroimaging to be inversely proportional to Persistence. While rTMS response has previously been correlated with TCI self-directedness, this prior study investigated only patients with melancholic depression, which tends to be associated with decreased Persistence (figure 1). Therefore, this study was limited by inability to investigate a full distribution of Persistence scores. Of note, prior literature has also suggested lower probability of ECT response in patients with melancholic features, suggesting that Persistence may also predict ECT response. These findings are distinct from the TCI profiles associated with SSRI response (low harm avoidance), suggesting that certain traits may predict which patients will respond to rTMS despite failing SSRIs. Our study is limited by small sample size, lack of randomization, and lack of mechanistic explanation. Future directions will include baseline optical neuroimaging to investigate the relationship between rTMS response, Persistence, and cortical excitability.

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