RESUMO
Perseverative thinking (PT), or repetitive negative thinking, has historically been measured using global self-report scales. New methods of assessment are needed to advance understanding of this inherently temporal process. We developed an intensive longitudinal method for assessing PT. A mixed sample of 77 individuals ranging widely in trait PT, including persons with PT-related disorders (generalized anxiety disorder, major depression) and persons without psychopathology, used a joystick to provide continuous ratings of thought valence and intensity following exposure to scenarios of differing valence. Joystick responses were robustly predicted by trait PT, clinical status, and stimulus valence. Higher trait perseverators exhibited more extreme joystick values overall, greater stability in values following threatening and ambiguous stimuli, weaker stability in values following positive stimuli, and greater inertia in values following ambiguous stimuli. The joystick method is a promising measure with the potential to shed new light on the dynamics and precipitants of perseverative thinking.
RESUMO
Given the high prevalence of treatment-resistant depression and the long delays in finding effective treatments via trial and error, valid biomarkers of treatment outcome with the ability to guide treatment selection represent one of the most important unmet needs in mood disorders. A large body of research has investigated, for this purpose, biomarkers derived from electroencephalography (EEG), using resting state EEG or evoked potentials. Most studies have focused on specific EEG features (or combinations thereof), whereas more recently machine-learning approaches have been used to define the EEG features with the best predictive abilities without a priori hypotheses. While reviewing these different approaches, we have focused on the predictor characteristics and the quality of the supporting evidence.
