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, n