Personality Disorders in Functional and Idiopathic Dystonia

Area of Science:

• Neurology
• Psychiatry
• Personality Disorders

Background:

• Differentiating functional dystonia (FD) from idiopathic dystonia (ID) is challenging due to similar clinical presentations and non-motor symptoms.
• Personality abnormalities are noted in functional neurological disorders but are understudied in dystonia differential diagnosis.
• Comparative data on personality in FD versus ID, especially in the Russian population, is lacking.

Purpose of the Study:

• To investigate and compare personality characteristics between patients with functional dystonia (FD) and idiopathic dystonia (ID).
• To explore the potential of personality traits as adjunctive markers for differentiating FD from ID.

Main Methods:

• A cohort of 178 patients with idiopathic dystonia (ID) and 32 patients with functional dystonia (FD) were assessed.
• Psychiatric clinical interviews were conducted.
• The Structured Clinical Interview for DSM-IV Personality Disorders-II (SCID-II-PD) and the Five-Factor Personality Questionnaire (5-PFQ) were used to evaluate personality disorders (PD) and traits.

Main Results:

• FD patients showed a higher prevalence of dependent, paranoid, passive-aggressive, borderline, schizoid, schizotypal, obsessive-compulsive, and avoidant personality disorders compared to ID patients (p < 0.001 to p < 0.049).
• FD patients exhibited a predominance of Cluster A personality traits, characterized by greater introversion, detachment, irresponsibility, impulsivity, emotional restraint, and practicality.
• Significant differences in both categorical and dimensional personality characteristics were observed between FD and ID groups.

Conclusions:

• Patients with FD possess distinct categorical and dimensional personality profiles compared to those with ID.
• The prevalence of Cluster A personality pathology and specific personality patterns in FD may serve as potential adjunctive markers for clinical differentiation.
• Further external validation is necessary to integrate these findings into diagnostic algorithms for dystonia.