Objective: : This study aims to examine the clinical characteristics, cognitive functions, and levels of insight, which are thought to be related to disability in schizophrenia patients, and to determine which variable will guide the clinician to predict the disability.
Methods: : Participants were 102 individuals with schizophrenia aged 18-60. All participants completed the social functioning scale and the Beck cognitive insight scale. To determine the severity of disability, World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) scale was conducted. Positive and negative syndrome scale, Calgary depression scale for schizophrenia, trail making tests and Stroop test were performed.
Results: : The regression analysis indicated that high income, increased education level, and fewer hospitalization variables had significant negative effects ( < 0.05) on the WHODAS overall score, explaining 20.8% of the variance. The duration of trail-making test form A, PANSS total score, and Stroop 3 duration variables had significant positive effects ( < 0.05) on the WHODAS score, explaining 49.3% of the total variance. Increased levels of education, higher income, and higher cognitive insight were found to be associated with less disability. Increased severity of disease and some deterioration in the mental field were found to be related to high disability.
Conclusion: : In this research, the predictors of disability in individuals with schizophrenia, level of education, and income are among the predictors of disability, and disease severity seems to be more related to the impairment of cognitive functions. Interventions and treatments that support the psychosocial functionality should be planned rather than symptom-oriented treatment approaches.
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http://dx.doi.org/10.9758/cpn.23.1126 | DOI Listing |
Sci Rep
December 2024
Harman International, HarmanX Neurosense, 30001 Cabot Dr, Novi, MI, 48377, USA.
Cognitive load (CL) is one of the leading factors moderating states and performance among drivers. Heavily increased CL may contribute to the development of mental stress. Averaged heart rate (HR) and heart rate variability (HRV) indices are shown to reflect CL levels in different tasks.
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December 2024
Creative Robotics Lab, UNSW, Sydney, 2021, Australia.
Unlike the conventional, embodied, and embrained whole-body movements in the sagittal forward and vertical axes, movements in the lateral/transversal axis cannot be unequivocally grounded, embodied, or embrained. When considering motor imagery for left and right directions, it is assumed that participants have underdeveloped representations due to a lack of familiarity with moving along the lateral axis. In the current study, a 32 electroencephalography (EEG) system was used to identify the oscillatory neural signature linked with lateral axis motor imagery.
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December 2024
BAOBAB Unit, NeuroSpin center, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.
Decoding states of consciousness from brain activity is a central challenge in neuroscience. Dynamic functional connectivity (dFC) allows the study of short-term temporal changes in functional connectivity (FC) between distributed brain areas. By clustering dFC matrices from resting-state fMRI, we previously described "brain patterns" that underlie different functional configurations of the brain at rest.
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December 2024
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, China.
The unknown boundary issue, between superior computational capability of deep neural networks (DNNs) and human cognitive ability, has becoming crucial and foundational theoretical problem in AI evolution. Undoubtedly, DNN-empowered AI capability is increasingly surpassing human intelligence in handling general intelligent tasks. However, the absence of DNN's interpretability and recurrent erratic behavior remain incontrovertible facts.
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December 2024
Computational Neuroscience Unit, Intelligent Systems Labs, Faculty of Engineering, University of Bristol, Bristol, UK.
The brain must maintain a stable world model while rapidly adapting to the environment, but the underlying mechanisms are not known. Here, we posit that cortico-cerebellar loops play a key role in this process. We introduce a computational model of cerebellar networks that learn to drive cortical networks with task-outcome predictions.
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