Neural activity in relation to empirically derived personality syndromes in depression using a psychodynamic fMRI paradigm

The diagnostic and predictive potential of personality traits and coping styles in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a global public health concern that is notably underdiagnosed and undertreated due to its complexity and subjective diagnostic methods. A holistic diagnostic procedure, which sufficiently considers all possible contributors to MDD symptoms, would improve MDD diagnosis and treatment. This study aims to explore whether personality and coping styles can predict MDD status and differentiate between depressed patients and healthy individuals.

METHODS

Seventy healthy controls (N = 54 females) were matched to 70 MDD patients for age, sex, ethnicity, and years of education. MDD severity was measured using the Hamilton Depression Rating Scale, while personality traits and coping styles were measured by the Ten-Item Personality (TIPI) and Brief COPE questionnaires, respectively. Logistic regression analyses were conducted to investigate the diagnostic and predictive potential of personality and coping styles. Receiver operating characteristic (ROC) analyses were also conducted to examine their discriminative ability to distinguish between depressed and healthy individuals.

RESULTS

Introversion, lack of organisation skills, and neuroticism were statistically significant in predicting MDD status. Dysfunctional coping strategies, such as denial and self-blame, were also shown to significantly predict MDD status. ROC analyses found both the TIPI questionnaire (AUC = 0.90), and dysfunctional coping (as measured by Brief COPE) (AUC = 0.90) to be excellent predictors of MDD.

CONCLUSIONS

Our findings demonstrate the diagnostic and predictive potential of personality and coping styles for MDD in the clinical setting. They also demonstrate the remarkable ability of personality and coping styles to differentiate between depressed patients and healthy controls.

Systematic Review of Functional MRI Applications for Psychiatric Disease Subtyping

Background: Psychiatric disorders have been historically classified using symptom information alone. Recently, there has been a dramatic increase in research interest not only in identifying the mechanisms underlying defined pathologies but also in redefining their etiology. This is particularly relevant for the field of personalized medicine, which searches for data-driven approaches to improve diagnosis, prognosis, and treatment selection for individual patients. Methods: This review aims to provide a high-level overview of the rapidly growing field of functional magnetic resonance imaging (fMRI) from the perspective of unsupervised machine learning applications for disease subtyping. Following the PRISMA guidelines for protocol reproducibility, we searched the PubMed database for articles describing functional MRI applications used to obtain, interpret, or validate psychiatric disease subtypes. We also employed the active learning framework ASReview to prioritize publications in a machine learning-guided way. Results: From the 20 studies that met the inclusion criteria, five used functional MRI data to interpret symptom-derived disease clusters, four used it to interpret clusters derived from biomarker data other than fMRI itself, and 11 applied clustering techniques involving fMRI directly. Major depression disorder and schizophrenia were the two most frequently studied pathologies (35% and 30% of the retrieved studies, respectively), followed by ADHD (15%), psychosis as a whole (10%), autism disorder (5%), and the consequences of early exposure to violence (5%). Conclusions: The increased interest in personalized medicine and data-driven disease subtyping also extends to psychiatric disorders. However, to date, this subfield is at an incipient exploratory stage, and all retrieved studies were mostly proofs of principle where further validation and increased sample sizes are craved for. Whereas results for all explored diseases are inconsistent, we believe this reflects the need for concerted, multisite data collection efforts with a strong focus on measuring the generalizability of results. Finally, whereas functional MRI is the best way of measuring brain function available to date, its low signal-to-noise ratio and elevated monetary cost make it a poor clinical alternative. Even with technology progressing and costs decreasing, this might incentivize the search for more accessible, clinically ready functional proxies in the future.

One look is worth a thousand words: New picture stimuli of interpersonal situations

This paper introduces a picture system that can be used in functional imaging experiments exploring interpersonal relations. This is important for psychotherapy research to understand the neural basis of psychological treatment effects. Pictures have many advantages for the design of functional imaging experiments, but no picture system illustrating interpersonal behavior patterns is, to date, available. We therefore developed, on the basis of a validated card-sorting test, the Interpersonal Relations Picture System. In summary, 43 pictures with 2 or more stick figures in different social situations and 9 control pictures were composed. To test the relation between each picture and the appropriate description, two successive online surveys, including 1058 and 675 individuals respectively, were conducted. Using two question types, the grade expressiveness of each picture was assessed. In total, 24 pictures and 6 control pictures met our criteria for sufficient strength and consistency with the appropriate description. Both measures are correlated with each other in all pictures illustrating interpersonal behavior, but not in the control pictures. Relations to other stimulus types and the applicability of the new picture system in functional neuroimaging methods are discussed. It is concluded that the new system will be helpful in studying the profound effect of relational change in psychotherapy.