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Updated: Jan 26, 2026

MRI-guided dmPFC-rTMS as a Treatment for Treatment-resistant Major Depressive Disorder
Published on: August 11, 2015
Temperament and character influence on depression treatment outcome
Martina Balestri1, Stefano Porcelli1, Daniel Souery2
1Department of Biomedical and NeuroMotor Sciences, University of Bologna, Italy.
Background:
personality features have been repeatedly associated with depression treatment outcome in Major Depressive Disorder (MDD), however conclusive results are still lacking. Moreover, as for Bipolar Disorder (BD), results are only few and preliminary.
Aim:
the aim of the present study was to perform an exploratory investigation of the influence of personality traits as assessed by the Temperament and Character Inventory (TCI), on principal depression treatment outcomes (non remission, non response and resistance).
Methods:
743 mood disorders patients (455 MDD (61.24%) and 288 BD (38.76%)) were recruited in the context of 6 European studies. Generalized logit models were performed to test the effects of TCI dimensions on treatment outcomes, considering possible confounders such as age, gender and education. Positive results were controlled for comorbidities (anxiety and substance use disorders) as well.
Results:
MDD Non-Remitters showed high Harm Avoidance (HA) and Self Transcendence (ST) (p = 0.0004, d = 0.40; p = 0.007, d = 0.36 respectively) and low Persistence (P) and Self Directedness (SD) (p = 0.05; d = 0.18; p = 0.002, d = 0.40, respectively); MDD Non-Responders showed a slightly different profile with high HA and low Reward Dependence (RD) and SD; finally, MDD Resistants showed low RD, P and Cooperativeness (C). In BD patients, only higher HA in non response was observed.
Limitations:
the retrospective cross-sectional design, the TCI assessment regardless of the mood state and the small number of bipolar patients represent the main limitations.
Conclusion:
specific TCI personality traits are associated with depression treatment outcome in MDD patients. The inclusion of such personality traits, together with other socio-demographic and clinical predictors, could ameliorate the accuracy of the prediction models available to date.
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