LUISA DE SIQUEIRA ROTENBERG
Projetos de Pesquisa
Unidades Organizacionais
LIM/21 - Laboratório de Neuroimagem em Psiquiatria, Hospital das Clínicas, Faculdade de Medicina
3 resultados
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- The longitudinal trajectory of emotional cognition in subgroups of recently diagnosed patients with bipolar disorder(2023) ROTENBERG, Luisa de Siqueira; KJAERSTAD, Hanne Lie; VARO, Cristina; VINBERG, Maj; KESSING, Lars Vedel; LAFER, Beny; MISKOWIAK, Kamilla WoznicaAlthough cross-sectional studies show heterogeneity in emotional cognition in bipolar disorder (BD), the temporal course within subgroups is unclear. In this prospective, longitudinal study we assessed the trajectories of emotional cognition subgroups within a 16-month follow-up pe-riod in recently diagnosed BD patients compared to healthy controls (HC). Recently diagnosed BD patients and HC underwent comprehensive emotional and non-emotional testing at base-line and again at follow-up. We employed hierarchical cluster analysis at baseline to identify homogenous emotional cognition subgroups of patients, and changes across the subgroups of BD and HC were assessed with linear mixed-model analyses. We found two emotional cogni-tion subgroups: subgroup 1 (65%, n = 179), showing heightened negative emotional reactivity in neutral and negative social scenarios and faster recognition of emotional facial expressions than HC (ps< 0.001, n = 190), and subgroup 2 (35%, n = 96) showing blunted reactivity in pos-itive social scenarios, impaired emotion regulation, poorer recognition of positive and slower recognition of all facial expressions than HC (ps <=.03). Subgroup 1 exhibited normalization of the initial emotional cognition abnormalities in follow-up. In contrast, subgroup 2 showed a lack of improvement in reactivity positively-valenced emotional information. Patients in sub-group 2 presented more and longer mixed episodes during the follow-up time and were more often prescribed lithium. One third of patients display blunted emotional reactivity, impaired emotion regulation abilities and facial expression recognition difficulties also show persistent impairments and poorer course of illness. This subgroup may indicate a need for earlier and more targeted therapeutic interventions. (c) 2023 The Author(s).
- Exploring machine learning to predict depressive relapses of bipolar disorder patients(2021) ROTENBERG, Luisa de Siqueira; BORGES-JUNIOR, Renato Gomes; LAFER, Beny; SALVINI, Rogerio; DIAS, Rodrigo da SilvaBackground: Bipolar disorder (BD) is a chronic mood disorder characterized by recurrent episodes of mania or hypomania and depression, expressed by changes in energy levels and behavior. However, most of relapse studies use evidence-based approaches with statistical methods. With the advance of the precision medicine this study aims to use machine learning (ML) approaches as a possible predictor in depressive relapses in BD. Method: Four accepted and well used ML algorithms (Support Vector Machines, Random Forests, Naive Bayes, and Multilayer Perceptron) were applied to the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) dataset in a cohort of 800 patients (507 patients presented depressive relapse and 293 did not), who became euthymic during the study and were followed for one year. Results: The ML algorithms presented reasonable performance in the prediction task, ranging from 61 to 80% in the F-measure. The Random Forest algorithm obtained a higher average of performance (Relapse Group 68%; No Relapse Group 74%). The three most important mood symptoms observed in the relapse visit (Random Forest) were: interest; depression mood and energy. Limitations: Social and psychological parameters such as marital status, social support system, personality traits, might be an important predictor in depressive relapses, although we did not compute this data in our study. Conclusions: Our findings indicate that applying precision medicine models by means of machine learning in BD studies could be feasible as a sensible approach to better support medical decision-making in the BD treatment and prevention of future relapses.
- Impaired social cognition in bipolar disorder: A meta-analysis of Theory of Mind in euthymic patients(2020) ROTENBERG, Luisa de Siqueira; BERALDI, Gabriel Henrique; BELIZARIO, Gabriel Okawa; LAFER, BenyObjective: To conduct a meta-analysis of Theory of Mind studies exclusively in euthymic patients with bipolar disorder. Method: After the exclusion of studies evaluating symptomatic patients during acute episodes, we performed a meta-analysis including a total of 30 studies, comparing 1294 euthymic bipolar disorder patients and 1116 healthy controls. Results: Patients with bipolar disorder presented a significant impairment in Theory of Mind performance when compared to controls (Hedge's g = -0.589, 95% confidence interval: -0.764 to -0.414, Z = -6.594, p < 0.001). When compared to controls, Theory of Mind was impaired in patients with both bipolar disorder I (Hedge's g = -0.663, 95% confidence interval: -0.954 to -0.372, Z = -4.462, p < 0.001) and bipolar disorder II (Hedge's g = -1.165, 95% confidence interval: -1.915 to -0.415, Z = -3.044, p = 0.002). Theory of Mind impairments were also significantly more severe in verbal tasks (Hedge's g = -1.077, 95% confidence interval: -1.610 to -0.544, Z = -3.961 p < 0.001) than visual tasks (Hedge's g =-0.614, 95% confidence interval: -0.844 to -0.384, Z = -5.231, p < 0.001) when compared to controls. Conclusion: The results obtained confirm that Theory of Mind is impaired in remitted bipolar disorder patients, being a potential endophenotype for bipolar disorder. Moreover, we found higher deficits in verbal Theory of Mind, compared with visual Theory of Mind. Since most studies were cross-sectional, there is a need for longitudinal studies to evaluate whether the deficits detected in Theory of Mind are progressive over the course of the illness.