Weighted Uncertainty Relations.
Ontology highlight
ABSTRACT:
SUBMITTER: Xiao Y
PROVIDER: S-EPMC4794717 | biostudies-other | 2016 Mar
REPOSITORIES: biostudies-other
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Project description:BackgroundIntolerance of uncertainty (IU), perceived attentional control (AC), and poor cognitive control abilities are risk factors for anxiety; however, few studies have examined their interactive effects in relation to anxiety. A more complete understanding of interplay between IU, perceived AC, and cognitive control could inform intervention efforts.MethodsThe current study examined the direct and interactive effects of IU and AC on anxiety in a sample of 280 community outpatients (M age = 36.01 years, SD = 16.17). Perceived AC was measured using self-report and cognitive control abilities were measured using a Go/No-Go task.ResultsFindings indicated a significant IU by perceived AC interaction predicting worry and GAD diagnoses. There was a positive relation between IU and worry/GAD diagnoses that was strongest among those with high perceived AC. Perceived AC was unrelated to cognitive control abilities, and cognitive control abilities did not interact significantly with IU. Cognitive control abilities were related to worry symptoms but not to GAD diagnoses.ConclusionsThese results indicate that at high levels of perceived AC, individuals with elevated IU report higher levels of worry, potentially due to the conscious use of worry as an emotion regulation strategy. Clinical implications and future directions are discussed.
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Project description:BackgroundIntolerance of uncertainty (IU) is considered as a specific risk factor in the development and maintenance of generalized anxiety disorder (GAD). Yet, researches have investigated the relations between IU and GAD (or worry) using total scores on self-report measures. This ignores that there are different components exist in IU and the heterogeneity of GAD symptoms. In the present study, we explored the relations among different components of IU and symptoms of GAD.MethodsA dimensional approach which take individual differences into consideration in different components of IU along a full range of normal to abnormal symptom severity levels of GAD were used in this study. Components of IU were measured by 12-item Intolerance of Uncertainty Scale and symptoms of GAD were measured by Generalized Anxiety Disorder 7-Item Questionnaire. Regularized partial-correlation network was estimated using cross-sectional data from 624 university students.ResultsFour strongest edges are between components of IU, like "Unforeseen events upset me greatly" and "It frustrates me not having all the information I need". Two strongest edges are between symptoms of GAD, like "Being so restless that it is hard to sit still" and "Feeling afraid as if something awful might happen". Symptom "Worrying too much about different things" and component "It frustrates me not having all the information I need" have the highest expected influences in the present network. In the community of IU, component "It frustrates me not having all the information I need" has the highest bridge expected influence. And in the community of GAD, symptoms "Worrying too much about different things" and "Not being able to stop or control worrying" have the highest bridge expected influence.ConclusionsThis study reveals potential pathways between different components of IU and various symptoms of GAD. Understanding how putative risk factors such as different components of IU are related to symptoms of GAD may provide some references for related preventions and interventions, such as targeting component "It frustrates me not having all the information I need" may be more effective at reducing symptoms of GAD than targeting other components of IU.
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Project description:The aim of this study was to assess the uncertainty in estimation of MR tracer kinetic parameters and water exchange rates in T1-weighted dynamic contrast enhanced (DCE) MRI.Simulated DCE-MRI data were used to assess four kinetic models; general kinetic model with a vascular compartment (GKM2), GKM2 combined with water exchange (SSM2), adiabatic approximation of the tissue homogeneity model (ATH), and ATH combined with water exchange (ATHX).In GKM2 and SSM2, increase in transfer constant (K(trans)) led to underestimation of vascular volume fraction (vb), and increase in vb led to overestimation of K(trans). Such coupling between K(trans) and vb was not observed in ATH and ATHX. The precision of estimated intracellular water lifetime (?i) was substantially improved in both SSM2 and ATHX when K(trans)?>?0.3 min(-1). K(trans) and vb from ATHX model had significantly smaller errors than those from ATH model (P?<?0.05).The results of this study demonstrated the feasibility of measuring ?i from DCE-MRI data albeit low precision. While the inclusion of water exchange improved the accuracy of K(trans), vb, and the interstitial volume fraction estimation (ve), it lowered the precision of other kinetic model parameters within the conditions investigated in this study.
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Project description:BackgroundThe relations between depression and intolerance of uncertainty (IU) have been extensively investigated during the COVID-19 pandemic. However, there is a lack of understanding on how each component of IU may differentially affect depression symptoms and vice versa. The current study used a network approach to reveal the component-to-symptom interplay between IU and depression and identify intervention targets for depression during the COVID-19 pandemic.MethodsA total of 624 college students participated in the current study. An IU-Depression network was estimated using items from the 12-item Intolerance of Uncertainty Scale and the Patient Health Questionnaire-9. We examined the network structure, node centrality, and node bridge centrality to identify component-to-symptom pathways, central nodes, and bridge nodes within the IU-Depression network.ResultsSeveral distinct pathways (e.g., "Frustration when facing uncertainty" and "Feelings of worthlessness") emerged between IU and Depression. "Fatigue" and "Frustration when facing uncertainty" were identified as the central nodes in the estimated network. "Frustration when facing uncertainty," "Psychomotor agitation/retardation," and "Depressed or sad mood" were identified as bridging nodes between the IU and Depression communities.ConclusionBy delineating specific pathways between IU and depression and highlighting the influential role of "Frustration when facing uncertainty" in maintaining the IU-Depression co-occurrence, current findings may inform targeted prevention and interventions for depression during the COVID-19 pandemic.
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