Background: The study is aimed to identify brain functional connectomes predictive of depressed and elevated mood symptomatology in individuals with bipolar disorder (BD) using the machine learning approach Connectome-based Predictive Modeling (CPM).
Methods: Functional magnetic resonance imaging data were obtained from 81 adults with BD while they performed an emotion processing task. CPM with 5000 permutations of leave-one-out cross-validation was applied to identify functional connectomes predictive of depressed and elevated mood symptom scores on the Hamilton Depression and Young Mania rating scales.
Bipolar disorder (BD) and exposure to childhood maltreatment (CM), which is present at high rates in BD, are both associated with hippocampus and prefrontal cortex structural alterations thought to contribute to clinical features. Gender-related differences are implicated in BD for CM exposure, brain structure and clinical features. However, relationships among these factors in BD are understudied.
View Article and Find Full Text PDFObjectives: Identifying hubs of brain dysfunction in adolescents and young adults with Bipolar I Disorder (BD ) could provide targets for early detection, prevention, and treatment. Previous neuroimaging studies across mood states of BD are scarce and often examined limited brain regions potentially prohibiting detection of other important regions. We used a data-driven whole-brain Intrinsic Connectivity Distribution (ICD) approach to investigate dysconnectivity hubs across mood states in BD .
View Article and Find Full Text PDFIntroduction: Despite extensive studies on the relationship between diabetes mellitus (DM) and depression, the associations of depressive symptom severity with prevalence, awareness, treatment, and control of diabetes remain unclear. We aimed to investigate changes in these outcomes of diabetes as depressive symptoms aggravate.
Research Design And Methods: We conducted a cross-sectional analysis of 14 328 participants in the 2011-2016 National Health and Nutrition Examination Survey.
Background: The effect of antipsychotics adherence on the risk of cardiovascular disease (CVD) among schizophrenia patients has not been studied. While antipsychotic adherence is favorable for all-cause mortality, its association with CVD incidence is unclear due to the potential risk of CVD caused by antipsychotics.
Methods: Using the Korean National Health Insurance Service Database, we constructed a case-cohort of 80,581 newly-diagnosed schizophrenia patients between 2004 and 2013 from a cohort of all Koreans 20-40 years old.
Background: Whether depression before diagnosis of dyslipidemia is associated with higher cardiovascular disease (CVD) risk among newly diagnosed dyslipidemia patients is yet unclear.
Methods: The study population consisted of 72,235 newly diagnosed dyslipidemia patients during 2003 to 2012 from the National Health Insurance Service-Health Screening Cohort of South Korea. Newly diagnosed dyslipidemia patients were then detected for pre-existing depression within 3 years before dyslipidemia diagnosis.