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  • Floros, Orestis, et al. (författare)
  • Vulnerability in Executive Functions to Sleep Deprivation Is Predicted by Subclinical Attention-Deficit/Hyperactivity Disorder Symptoms
  • 2021
  • Ingår i: Biological Psychiatry. - : Elsevier. - 2451-9022. ; 6:3, s. 290-298
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: Sleep loss results in state instability of cognitive functioning. It is not known whether this effect is more expressed when there is an increased cognitive demand. Moreover, while vulnerability to sleep loss varies substantially among individuals, it is not known why some people are more affected than others. We hypothesized that top-down regulation was specifically affected by sleep loss and that subclinical inattention and emotional instability traits, related to attention-deficit/hyperactivity disorder symptoms, predict this vulnerability in executive function and emotion regulation, respectively.Methods: Healthy subjects (ages 17–45 years) rated trait inattention and emotional instability before being randomized to either a night of normal sleep (n = 86) or total sleep deprivation (n = 87). Thereafter, they performed a neutral and emotional computerized Stroop task, involving words and faces. Performance was characterized primarily by cognitive conflict reaction time and reaction time variability (RTV), mirroring conflict cost in top-down regulation.Results: Sleep loss led to increased cognitive conflict RTV. Moreover, a higher level of inattention predicted increased cognitive conflict RTV in the neutral Stroop task after sleep deprivation (r = .30, p = .0055) but not after normal sleep (r = .055, p = .65; interaction effect β = 6.19, p = .065). This association remained after controlling for cognitive conflict reaction time and emotional instability, suggesting domain specificity. Correspondingly, emotional instability predicted cognitive conflict RTV for the emotional Stroop task only after sleep deprivation, although this effect was nonsignificant after correcting for multiple comparisons.Conclusions: Our findings suggest that sleep deprivation affects cognitive conflict variability and that less stable performance in executive functioning may surface after sleep loss in vulnerable individuals characterized by subclinical symptoms of inattention.
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  • Lindner, Philip, et al. (författare)
  • Associations of Psychopathic Traits With Local and Global Brain Network Topology in Young Adult Women
  • 2018
  • Ingår i: Biological Psychiatry. - : Elsevier. - 2451-9022. ; 3:12, s. 1003-1012
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: Psychopathic traits vary dimensionally in the population and are associated with multiple negative outcomes. The impaired integration theory (IIT) proposes that psychopathic traits are associated with abnormal neural network topology, such that disturbed integration of neural networks results in a self-perpetuating impairment in rapid integration and learning from multiple components of information. The IIT is based on findings from male offenders presenting high scores on all psychopathic traits. The present study investigated whether IIT predictions of topology abnormalities were associated with psychopathic traits, measured dimensionally, in young adult women with subsyndromal scores.Methods: Seventy-three women, with an average age of 25 years, were assessed using the Psychopathy Checklist–Revised and completed resting-state magnetic resonance imaging. Preprocessed time series from 90 anatomical regions were extracted to form connectivity matrices and used to calculate network topology based on graph theory. Correlations between total psychopathy and factor scores with both the raw connectivity matrix and global and local graph theory measures were computed.Results: Total psychopathy scores and behavioral factor scores were related to connectivity between several pairs of regions, primarily limbic/paralimbic. Psychopathic traits were not associated with global topology measures. Topology abnormalities, robust across network formation thresholds, were found in nodes of the default mode network and in hubs connecting several resting-state networks.Conclusions: IIT predictions of abnormal topology of hubs and default mode network nodes with dimensionally measured psychopathic traits were confirmed in a sample of young women. Regional abnormalities, accompanied by preserved global topology, may underlie context-specific abnormal information processing and integration.
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  • Tønnesen, Siren, et al. (författare)
  • Brain Age Prediction Reveals Aberrant Brain White Matter in Schizophrenia and Bipolar Disorder : A Multisample Diffusion Tensor Imaging Study
  • 2020
  • Ingår i: Biological Psychiatry. - : Elsevier BV. - 2451-9022. ; 5:12, s. 1095-1103
  • Tidskriftsartikel (refereegranskat)abstract
    • BACKGROUND: Schizophrenia (SZ) and bipolar disorder (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, patients with BD, and healthy control (HC) subjects across 10 cohorts.METHODS: We trained 6 cross-validated models using different combinations of DTI data from 927 HC subjects (18-94 years of age) and applied the models to the test sets including 648 patients with SZ (18-66 years of age), 185 patients with BD (18-64 years of age), and 990 HC subjects (17-68 years of age), estimating the brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results.RESULTS: Tenfold cross-validation revealed high accuracy for all models. Compared with HC subjects, the model including all feature sets significantly overestimated the age of patients with SZ (Cohen's d = -0.29) and patients with BD (Cohen's d = 0.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy-based models showed larger group differences than the models based on other DTI-derived metrics.CONCLUSIONS: Brain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners.
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