A core component of psychological therapy causes adaptive changes in computational learning mechanisms.

BACKGROUND: Cognitive distancing is an emotion regulation strategy commonly used in psychological treatment of various mental health disorders, but its therapeutic mechanisms are unknown. METHODS: 935 participants completed an online reinforcement learning task involving choices between pairs of symbols with differing reward contingencies. Half (49.1%) of the sample was randomised to a cognitive self-distancing intervention and were trained to regulate or 'take a step back' from their emotional response to feedback throughout. Established computational (Q-learning) models were then fit to individuals' choices to derive reinforcement learning parameters capturing clarity of choice values (inverse temperature) and their sensitivity to positive and negative feedback (learning rates). RESULTS: Cognitive distancing improved task performance, including when participants were later tested on novel combinations of symbols without feedback. Group differences in computational model-derived parameters revealed that cognitive distancing resulted in clearer representations of option values (estimated 0.17 higher inverse temperatures). Simultaneously, distancing caused increased sensitivity to negative feedback (estimated 19% higher loss learning rates). Exploratory analyses suggested this resulted from an evolving shift in strategy by distanced participants: initially, choices were more determined by expected value differences between symbols, but as the task progressed, they became more sensitive to negative feedback, with evidence for a difference strongest by the end of training. CONCLUSIONS: Adaptive effects on the computations that underlie learning from reward and loss may explain the therapeutic benefits of cognitive distancing. Over time and with practice, cognitive distancing may improve symptoms of mental health disorders by promoting more effective engagement with negative information.

Markedly increased prevalence of migraine headaches in adolescents with bipolar disorder.

OBJECTIVE: There is substantial evidence of increased prevalence of migraines, and negative psychiatric correlates of migraines, in adults with bipolar disorder (BD). Given the paucity of data on this topic in youth, we investigated the prevalence and correlates of migraine in a large sample of adolescents with BD. METHOD: The study included 165 adolescents with BD-I, -II, or -not otherwise specified (NOS), diagnosed via the KSADS-PL semi-structured interview, and 89 healthy controls (HCs). Non-migraine headache and migraine headache was evaluated using the validated ID-Migraine 3-item screener. RESULTS: Although the prevalence of non-migraine headaches did not differ between adolescents with BD (24.2%) and HCs (32.6%; P = .15), migraine was significantly more prevalent among adolescents with BD (38.2%) compared to HCs (3.4%; adjusted odds ratio 14.76, 95% confidence interval 4.39-49.57; P < .001). Within BD, migraine was associated with female sex, BD-II/-NOS subtype, less severe worst past functioning, higher past depression severity, higher self-reported affective lability, higher body mass index, and less use of lithium and second-generation antipsychotics. DISCUSSION: Migraine is much more prevalent among adolescents with BD compared to HCs; the magnitude of this association exceeds what has been reported in adult samples. Correlates of migraine in youth BD are similar to those found for adults, including the link with the depressive polarity of BD. Future prospective studies are warranted to evaluate temporal associations between migraine and mood symptoms, and to evaluate neurobiological and cardiovascular underpinnings of these associations.

Prevalence of white matter hyperintensities is not elevated in a large sample of adolescents and young adults with bipolar disorder.

OBJECTIVE: The increased prevalence rate of white matter hyperintensities is one of the most consistently reported brain abnormalities in adults with bipolar disorder. However, findings in children and adolescents with bipolar disorder are less consistent. Prior studies have been constrained by small sample sizes and/or poor age- and sex-matching of healthy controls. We examined this topic in the largest sample of adolescents with bipolar disorder to date. METHODS: T2-weighted 3-Tesla magnetic resonance imaging data were acquired for 83 adolescents with bipolar disorder diagnosed via the Kiddie Schedule for Affective Disorders and the Schizophrenia, Present and Lifetime version semi-structured interview and 64 age- and sex-matched healthy controls. All acquired scans were examined by neuroradiologists and the presence or absence of white matter hyperintensities was determined for each participant. RESULTS: The prevalence of white matter hyperintensities did not differ between adolescents with bipolar disorder (13.3%) and controls (21.9%; χ2 = 1.90; p = 0.168). CONCLUSION: In contrast to the study hypothesis, the prevalence of white matter hyperintensities was not higher in adolescents with bipolar disorder than controls. The large sample size and good matching for age and sex bolster the reliability of this negative finding. Future studies are warranted to evaluate the prevalence, incidence, and predictors of white matter hyperintensities in early-onset bipolar disorder prospectively.

Prevalence of white matter hyperintensities is not elevated in a large sample of adolescents and young adults with bipolar disorder

Objective: The increased prevalence rate of white matter hyperintensities is one of the most consistently reported brain abnormalities in adults with bipolar disorder. However, findings in children and adolescents with bipolar disorder are less consistent. Prior studies have been constrained by small sample sizes and/or poor age- and sex-matching of healthy controls. We examined this topic in the largest sample of adolescents with bipolar disorder to date. Methods: T2-weighted 3-Tesla magnetic resonance imaging data were acquired for 83 adolescents with bipolar disorder diagnosed via the Kiddie Schedule for Affective Disorders and the Schizophrenia, Present and Lifetime version semi-structured interview and 64 age- and sex-matched healthy controls. All acquired scans were examined by neuroradiologists and the presence or absence of white matter hyperintensities was determined for each participant. Results: The prevalence of white matter hyperintensities did not differ between adolescents with bipolar disorder (13.3%) and controls (21.9%; χ2 = 1.90; p = 0.168). Conclusion: In contrast to the study hypothesis, the prevalence of white matter hyperintensities was not higher in adolescents with bipolar disorder than controls. The large sample size and good matching for age and sex bolster the reliability of this negative finding. Future studies are warranted to evaluate the prevalence, incidence, and predictors of white matter hyperintensities in early-onset bipolar disorder prospectively.